{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "793b4985",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from scipy.optimize import curve_fit"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "144062d7",
   "metadata": {},
   "source": [
    "This is a test circuit to try and calibrate the arudino analog input with a smoother emitter follower voltage buffer.  The circuit has two 10K resistors and a 4.7 microFarad capacitors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "74b9371f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dp1 = pd.read_csv('datasets/calibrate_IN4001.csv', names=['p3', 'a0', 'a1'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4ac192ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>p3</th>\n",
       "      <th>a0</th>\n",
       "      <th>a1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   p3  a0  a1\n",
       "0   0   0   0\n",
       "1   1   0   0\n",
       "2   2   7   8\n",
       "3   3   7   7\n",
       "4   4   0   0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a79657c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "dp1['Vin'] = dp1.p3 * 5 / 255\n",
    "dp1['Va0'] = dp1.a0 * 5 / 1023\n",
    "dp1['Va1'] = dp1.a1 * 5 / 1023"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "30b89a7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Vin</th>\n",
       "      <th>Va0</th>\n",
       "      <th>Va1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>4.921569</td>\n",
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       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>4.960784</td>\n",
       "      <td>4.271750</td>\n",
       "      <td>0.537634</td>\n",
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       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>4.980392</td>\n",
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      ],
      "text/plain": [
       "          Vin       Va0       Va1\n",
       "251  4.921569  4.232649  0.537634\n",
       "252  4.941176  4.247312  0.537634\n",
       "253  4.960784  4.271750  0.537634\n",
       "254  4.980392  4.286413  0.537634\n",
       "255  5.000000  4.305963  0.537634"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp2 = dp1.loc[:,'Vin':'Va1']\n",
    "dp2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fc3e71e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(dp2.Vin, dp2.Va0, label='$V_R + V_D$')\n",
    "ax.plot(dp2.Vin, dp2.Va1, label='$V_D$')\n",
    "\n",
    "ax.grid()\n",
    "plt.legend()\n",
    "plt.title(\"Voltage across diode\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8337323a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vin</th>\n",
       "      <th>Va0</th>\n",
       "      <th>Va1</th>\n",
       "      <th>Id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>251</th>\n",
       "      <td>4.921569</td>\n",
       "      <td>4.232649</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>0.000370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>252</th>\n",
       "      <td>4.941176</td>\n",
       "      <td>4.247312</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>0.000371</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>253</th>\n",
       "      <td>4.960784</td>\n",
       "      <td>4.271750</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>0.000373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>254</th>\n",
       "      <td>4.980392</td>\n",
       "      <td>4.286413</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>0.000375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.305963</td>\n",
       "      <td>0.537634</td>\n",
       "      <td>0.000377</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Vin       Va0       Va1        Id\n",
       "251  4.921569  4.232649  0.537634  0.000370\n",
       "252  4.941176  4.247312  0.537634  0.000371\n",
       "253  4.960784  4.271750  0.537634  0.000373\n",
       "254  4.980392  4.286413  0.537634  0.000375\n",
       "255  5.000000  4.305963  0.537634  0.000377"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp2['Id'] = (dp2.Va0 - dp2.Va1)/10000\n",
    "dp2.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4510faf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      0.000000e+00\n",
       "1      0.000000e+00\n",
       "2     -4.887586e-07\n",
       "3      0.000000e+00\n",
       "4      0.000000e+00\n",
       "           ...     \n",
       "251    3.695015e-04\n",
       "252    3.709677e-04\n",
       "253    3.734115e-04\n",
       "254    3.748778e-04\n",
       "255    3.768328e-04\n",
       "Name: Id, Length: 256, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dp2.Id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b484fb1b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(dp2.Va1, dp2.Id*1000)\n",
    "ax.grid()\n",
    "plt.title(\"V-I of diode\")\n",
    "plt.xlabel ('volts')\n",
    "plt.ylabel('mA')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "eaddb369",
   "metadata": {},
   "outputs": [],
   "source": [
    "def f(x, a,b):\n",
    "    y = a*(np.exp(b*x)-1)\n",
    "    return y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf4db354",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.16074402e-07, 1.61169647e-06])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(np.array([0.2, 0.4]), 1e-6, 2.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "debe61d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "popt, pcov = curve_fit(f, dp2.Va1, dp2.Id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "339ac841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.10076765e-09, 2.24282822e+01])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "20a9afd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.04458656224148477"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/popt[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e531f999",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.90340285e-20, -6.26250318e-11],\n",
       "       [-6.26250318e-11,  5.68605673e-02]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pcov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "818c62f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00015581589671406038"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f(0.5, *popt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "367b15f3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(dp2.Va1, dp2.Id*1000, label='actual')\n",
    "ax.plot(dp2.Va1, f(dp2.Va1, *popt)*1000, label='predicted')\n",
    "ax.grid()\n",
    "plt.title(\"V-I of IN4148 diode\")\n",
    "plt.xlabel ('volts')\n",
    "plt.ylabel('mA')\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67188974",
   "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}