kiwiheretic/npn-bc547-transistor.ipynb

## npn-bc547-transistor.ipynb
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy import Matrix, symbols, sin, cos, trigsimp, init_printing, I\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "init_printing()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![electronic-circuit1.png](http://files.kiwiheretic.xyz/electronic-circuit1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In my case R1 is 1K, R2 is 1K, R3 is 220 Ohms.  The cap is about 100 microfarad.  The transistor is an NPN bc547.\n",
    "\n",
    "I used Arduino to generate a voltage range from 0 to 5V on the PWM output pin.  Then read the voltages marked in blue using Arduino analog input pins."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>$V_{cap}$</th>\n",
       "      <th>$V_c$</th>\n",
       "      <th>$V_b$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      $V_{cap}$  $V_c$  $V_b$\n",
       "V                            \n",
       "0.00        0.0    5.0    0.0\n",
       "0.02        0.0    5.0    0.0\n",
       "0.04        0.0    5.0    0.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.read_csv(\"npn-data1.csv\", index_col=0, header=None, names=['V','$V_{cap}$','$V_c$','$V_b$'])\n",
    "df1.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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bV9reC9wEHHXAdxGZB8wDyMnJ6fNEAfX19UEzyUBPpVeVcRKw6p1XqUmb0OvtB2KZj1e4lTncygt9L3Nqaip1dd72evv444+58sorDxoSuaWlhenTp/PMM88wbty4I27X0dHRo9ibm5uP7/Ogqq7cgAuBP/r/LgRe6m6badOmaV8tXLiwz9t6pnSd6k9TVNc816fNB2SZj1O4lTncyqva9zKvX7++fwPpg46ODk1NTdWOjo4Dy/7whz/o3Llzj7ldbW1tj/Z/pDICy7WHednNGv5M4CIROR+IA1JE5B+q+hUXjzmwHGjSsRO3xoSCsJ3iUFVvAW4BEJFC4IeW7A+RkAER0VBf6nUkxoSUu5fezcaqjf26z3EZ47j51Ju7XS+Ypzi0fvheEnFq+XV20taYUNE5xWF9fT0PPvggt956KwDXXHONx5EFaHhkVS0CigJxrAEnKdt66RjTz3pSE3fLkaY4bGpqYuPGjdx+++1s3LiRp556ChEJeGxWw/da8mBL+MaEkIKCApYuXcpjjz3GjTfeCMCqVav44he/yO23305qaio1NTWexGYJ32tJOZbwjQkhR5ricOnSpUyePBmAxsZG0tLSPInNZrzyWlIONOyDjnaItLfDmIHuSFMcrlu3jtLSUp5++mmuvvpqjyKzhO+9hExAobkGEjO9jsYY44KHH37Y6xAAa9LxXny6c99U7W0cxpiQZwnfa5bwjTEBYgnfa5bwjTEBYgnfa/H+s/WW8I0xLrOE7zWr4RvTb5yxxEJTf5TNEr7X4lIBsYRvzHGKi4ujsrIyJJO+qlJZWUlcXNxx7ce6ZXotIhLiUizhG3OccnNzKSkpoaKiwutQeq25ubnbZB4XF0dubu5xHccSfjCIT7eEb8xxio6OJj8/3+sw+qSoqIgpU6a4fhxr0gkGlvCNMQFgCT8YWMI3xgSAJfxgYAnfGBMAlvCDgSV8Y0wAWMIPBvHp0LwffD6vIzHGhDBL+MEgPh3UBy21XkdijAlhlvCDgV1ta4wJAEv4wcASvjEmACzhB4PYZOfemnSMMS6yhB8MouOd+7Zmb+MwxoQ0S/jBIMqf8NubvI3DGBPSLOEHgwM1fEv4xhj3WMIPBtEJzn1bo7dxGGNCmiX8YBDtHxbV2vCNMS6yhB8MDtTwrUnHGOMeS/jBIDIaJNJO2hpjXGUJP1hEJ1gN3xjjKkv4wSI63k7aGmNc5VrCF5E4EVkqIh+KyDoRucOtY4WE6Dg7aWuMcZWbc9q2AJ9W1XoRiQaWiMgCVX3fxWMOXNEJVsM3xrjKtYSvqgrU+x9G+2/q1vEGvOh4aLcavjHGPa624YtIpIisBsqBN1T1AzePN6BFxdtJW2OMq8SpiLt8EJE04HngO6q69pDn5gHzAHJycqbNnz+/T8eor68nKSnpeEP1zOQPbyeqvZ6V0+7p8TYDvcx9EW5lDrfygpW5t+bMmbNCVaf3ZN1um3REJBe4DDgTGAo0AWuBl4EFqtrtvHyqul9EioBz/dt2fe4h4CGA6dOna2FhYU/iPkxRURF93TYolA6Dqu29KsOAL3MfhFuZw628YGV20zGbdETkceAxoBW4G7gcuBZ4Eyd5LxGRWUfZNstfs0dE4oGzgI39F3qIsW6ZxhiXdVfD/+2hTTB+a4F/iUgMMOIo2w4B/ioikThfLE+r6kt9DzXE2UlbY4zLjpnwj5TsRSQdGK6qH6lqK7D1KNt+BEzplyjDQZTV8I0JR8X7Glhd3k5hAI7Vo26Z/vb3i/zrrwYqRGSRqn7fxdjCS7T10jEm1Pl8yoqd1fx3ayUNre28s2UfG/bWEh8F113qIzrS3cEPetoPP1VVa0XkGuBxVf2piHzkZmBhJzoBOlrB1wERkV5HY4zpR6t2VvOvlbt5bV0p5XUtAERFCCcNT+MnFxSQWlfserKHnif8KBEZAnwJuNXFeMLXgTHxmyA2vLqkGRNqWtt9vPvxPpZtr2JXdRMvfriH+OhICsdmce7EwXx6XDbJcdEH1i8q2hmQuHqa8O8AXgOWqOoyERkFbHEvrDDUOSZ+e7MlfGMGqLrmNn73xmaeXVFCXXM7URFCfHQk82aN4obPnEBirJuj2XSvp0ffq6qTOx+o6jYR+Z1LMYWnqM4avp24NWYgKd7XwAsf7uFv7xVT09RGu0/5/MnDuHDyEGaOGURcdPA00fY04d8PTO3BMtNXByYyt66ZxgS7LWV1LFhbyoK1pWzYWwvA6aMzmZSbyrkTBjNlRLrHER7ZMRO+iJwGnA5kiUjXHjkpQPB8bYUCm8jcmKC2sbSWlz/ayytr9vJxRQMiMG1EOj+5oIBzJw4mNz3B6xC71V0NPwZI8q+X3GV5LfBFt4IKS11P2hpjPOfzKR+W7GfD3jpW76rm6eUlRAjMyM/kf0/P47MTBpOdEud1mL3S3YVXi4BFIvIXVd0RoJjC04GTtpbwjfFSXXMbDyzcygur97C3xmlijRCYN2sU35g1isykWI8j7LvumnRexD+GvYgc9ryqXuROWGEoymr4xnihrcPHB9uqWLB2Lyt37qestpnqxlY+My6bH54zlhmjMkiOiyY1Prr7nQW57pp0ej5Wrzk+B9rwLeEb46am1g4WrN3L+9sqqW9p578fV7K/sY2EmEhOyctgTHYSX52Zx9QgPfF6PHrSpAOAf6C0E/0PN6lqm5uBhZ0DvXQs4RvjhqbWDt7eWM7dr25kZ1UjGYkxJMdFUXhiFudNGsLsE7OCqgulG3o6lk4h8FegGBBguIjMVdXF7oUWZizhG9PvPthWyfxlu6hpauO9jytpausgLzOBv199KjNHDyIi4vCm6lDW0374vwXOUdVNACJyIvAUMM2twMJOZ5NOa/2x1zPGHFVjazt1zc6gZAvW7OWtjeVkJMYwKCmGS6cN47yJQ5iRn0FUAMatCUY9TfjRnckeQFU3i8jAP4MRTGISnKTfWOl1JMYMOKrKCx/u4cZnPqK1w5mEb2hqHN+eM4Zr54wmIcbbIQ2CRU9fheUi8ijwd//jK4EV7oQUxhKzoL7c6yiMGRA2ldaxYkc1qfHR3PP6Jrbva2DSsFS+OC2Xk4ancVJu6hF7F4aznib8bwHXAdfjtOEvBv7oVlBhKykbGizhG3M0HT5l6fYqXvpoD/9ctot2nwIwOiuRX186mfMnDyHJ4wHKgllPX5nzgQdV1QZMc1NiNlQXex2FMUGjvqWdtzaU8eraUlbsqKahpZ2G1g5ioyL4wtRh/M9peZRUN1E4NvR72PSHnib8i4B7RWQxMB94TVXb3QsrTCVlQclSr6MwxlMNbcrr60oprW3mvje3UNnQSlZyLGeeMIjk2ChmjMqkcGzWgXb5icNSPY544OhRwlfVr/pP0p4HXAH8UUTeUNVrXI0u3CRmOSdtbdYrE4ZeX1fK39/fwX+3NtKhzinCybmpPHjlVE7JyyAyzLpQuqHHjV2q2iYiC3CGWogHLgYs4fenxGxQHzRWObV9Y0JYaU0zy4qrqGtu57V1pSzaXMHwjHg+mxfN1z47nbSEGPIHJVqi70c9vfDqXOAyYA5QBDyCM92h6U+dSb6h3BK+CTnldc1kJcVS1dDKHS+u54UP9xx4Ljs5lpvPHcc1Z+bz7juLmZ6X4WGkoaunNfz/xWm7/4aqtrgXTphLzHbu68shZ4K3sRjTT6oaWrnzpfU8v2o3Z4wZxLo9NTS0dHBt4WjOnzSElLhoctPjw+6qVy90N1qmqOOy7tbp/9DCUJI/4TdUeBuHMceppLqRqoZWlhVX88eFW6lpauPzJw/l1XWlFAxJ4deXTuaEnOTud2T6VXc1/IUi8hzwH1U9MK26fyC1M4C5wELgL65FGE4S/c04dvGVGaCKNpXzzPISXlm7l85q4NQRafzikkkUDEmhoaWd+OhIq817pLuEfy7wNeApEckH9gNxONMbvg78XlVXuxtiGIlLhcgYu/jKDBhvbSjjjfVlqMKemibe2bKPzMQYvjYzn1PznaGGR2clHVg/0S6K8lR3wyM341xR+0d/t8xBQJOq7g9EcGFHxKnlN+zzOhJjjqi13UddcxtvbSznxQ/38M6WfaTGRxMfHUlUpHDjZ8cyb9YoosN0cLJg16tumcBeF2MxYOPpmKBUVtvMXQs28vyq3QeWDUuL56Zzx/L1My3BDxT2+yrYJGVDfZnXUZgwpqpsLK3jw137yR+UyP8t3sbCTeVERQj/e3oeQ9PiOG3UICYOS7HByQYYS/jBJjEbStd6HYUJUyXVjdz6/FoWbf6kp1hSbBTfmTOGS6bmkj8o0cPozPHqrlvmA8CTqvrfAMVjkrKcbpmqTpu+MS7aW9PESx/upc3no665nb/9txgFbjlvHIVjs1m/t4YZ+ZkMTYv3OlTTD7qr4W8BfisiQ4B/Ak/1tFeOiAwH/gYMBnzAQ6p63/EEGxYSs8HXBk3VkGBXGxp3+HzKk0t3cteCjdS3fDIO4uwTs/jFJRPJTXdmYBs72PrKh5LueuncB9wnIiNxhlZ4XETicKY3nK+qm4+xeTvwA1VdKSLJwAr/gGvr+yv4kNT14itL+KafqCr/XLaLPy/6mOY2H20dPiobWpk5JpOff34SQ1LjEIHYKBu0L5T1dLTMHcDdwN0iMgV4DPgpTn/8o22zF3+vHlWtE5ENwDDAEv6xJA5y7hsqIGust7GYAW3VzmpGZiZSXtfMnS+t592tlUwdkcYJ2U6t/dT8DL4wdZideA0j0pNREfx98DsHUPsMsAineeffPTqISB7OLFkTVbX2kOfmAfMAcnJyps2fP78X4X+ivr6epKSk7lcMcon1Ozhl+fWsG38jFdlnHHPdUClzb4RbmftS3tpWpWhXG//a0obgDG8bFwmXjYthVm4UEUGe4MPtPYbjK/OcOXNWqOr0nqzb3Unbs4HLgQuApTgDqM1T1YaeBiMiScBzwHcPTfYAqvoQ8BDA9OnTtbCwsKe7PkhRURF93TaoNOyD5dczYWQWzCg85qohU+ZeCLcy96S8pTXNPLN8F8t3VFPX3MbqXfvxKZx5wiCmj8wgIzGa8yYNYVBSbGCCPk7h9h5D4MrcXZPOj4EngR+qalVvd+7/ZfAc8ISq/qsP8YWf+AyQSLv4yhyVqrKvvpXMxJiDTrwWDEkhOTaKawvHcN6kwYwfYv3kzcG6O2k7p687FueT9iiwwebC7YWICKcd38bTMYfoPPH6p0Ufs6OykdT4aGqa2pg5JpNffH4SedZH3nTDzQuvZgJXAWtEpLMr549V9RUXjxkaErOh3oZIDncdPmVvvY/NZXU0tLTzm9c28d+PnROvX5o+nPV7a5l9Yhb/37Rcq8mbHnEt4avqEsA+hX2RlGU1/DD39PJd/PrVTeyrb4EliwHnitdfXjKJy04ZbsMLmz6xoRWCUWIW7NvqdRQmQDp8yqNLtvHoku2MG5xCekI0/169h1PzMrg4T5k6eSIAp+Slk50S53G0ZiCzhB+MknKcAdRseIWQ19ru44b5q1iwtpQZ+Rnsqm5k7e42vjB1GHd9YTL/XbKYwslDvA7ThAhL+MEoKQc6WqC5BuLTvI7G9KMtZXXc++YWFm+pYHRWEvsbWymubOQnFxRw9Rn51hZvXGUJPxglD3bu68ss4Q9w2/c18M6WCnw+Ze2eWl5YvYf4mEjOGT+YrRX15KYncPO54zhvktXijfss4QejzvF06stseIUB7PF3t/OrBRtpbfcBkBwXxaXThvGDc8YOmIugTGixhB+Mkvw1/DqbCGWgaO/w8eiS7Ty7ooQIEXLT43lrYzlnFWRz24XjSY6LJjkuymaGMp6yhB+MknOc+/pSb+MwR9XhUzaX1TE6K4kNe2u57T9r+aikhlPzM0iIiWTJ1n18efpwfvmFSURaF0oTJCzhB6PYFIiKs6kOg9TmsjpuevYjVu/aT0xkBK0dPgYlxfDgFVM5f9JgRIQOn1qiN0HHEn4wEnF66liTTlBoae9g5Y791Le08+7WfTzxwQ6SYqO49fwCdu9v4oScJC6cNJTUhOgD21iyN8HIEn6wSsqxJh2PtXX4eGjxNv5U9PGBWaFiIiP43OSh3HpBAZl24tUMMJbwg1VyDuzb4nUUYWVHZQO/eW0Tja0d+FT5qKSGqoZWzhkFAEyWAAAVFklEQVSfw5dPGU5GYgyjs5NIiYvufmfGBCFL+MEqaTBsf8frKELa/sZW6lvayUmJY3NZHV/7yzIaWzoOjDp5xphBfGHqMArHZnscqTH9wxJ+sErKgeb90N4CUdZ00J9a2jv43RubeXjxNnwKKXFRNLZ2kJ4Yw7PfOt0m7jYhyxJ+sErKcu4bKiA119tYBrjmtg6e/GAnZbXNDM9I4I31ZSzaXMGXpucyZUQ6y4urSYyN5HtnnUh6YozX4RrjGkv4wSrGP79la6O3cQxwtc1tfPXxZazYUX2gC6UI/PrSyXzplOEAXH7qCI+jNCYwLOEHq+gE577NEn5vrNpZzf8t2saKndVMHpbKxtI6ymqbeeCKKVwwaQiby+ppbG1nyoh0r0M1JuAs4Qer6Hjn3hJ+jzS2tvPb1zfz2LvbyUyMYcaoTNaU1DA6O4m7L53MGScMArD2eRPWLOEHqxj//KSW8I+oqbWDe9/czN/e20Fbhw+fKj6Fr3xqBDefO45k6zppzGEs4Qerzhq+teEDzonXBWv3snpnG8Xvbucv/y2muLKRi04ayvAM57UqHJvNKXkZHkdqTPCyhB+sDrThN3kbRxBYVlzFzc9+xLZ9Dc6C9esZkZHAk9fM4PQxg7wNzpgBxBJ+sDqQ8Bu8jSPAGlvbefzdYt7eWE5DSzs+VbaU1zMsLZ7Hv3oKNdvXcvrpp5ORGEOUDTVsTK9Ywg9WMeFRw1dVtpbXU93Yhqry29c3s7S4ipOHpzEy03kNzh6fw7WFY0iMjaJob4RN5G1MH1nCD1adNfwQbsNfuLGcO19ez7aKT37FREYID1wxhQsnD/UwMmNCkyX8YBUZDRHRIdVLp7mtg1U799PhU55dsYt/r97DCdlJ/PKSSQdq80PT4sn3j2VjjOlflvCDWXRCyCT8Q0+8RkUIN3zmBK6dM5rYqEiPozMmPFjCD2YxAzPh17e08/yq3bz/cSXtPh9NbT7e2VLBsLR4HrhiCtnJcQxNiyM3PcHrUI0JK5bwg1l0/IBpw69pbKOlvYPG1g6ue3Il6/bUMiwtnuQ45yN29cx8vnf2iSTG2kfOGK/Yf18wi04M+l46q3ZWc/sL6/iwpObAstioCB6dO51Pj8tGxKb6MyZYWMIPZtHxQdcPv6KuhbrmNt79uJLd1U38/b1i0hJi+MHZJ5KR5AwtPHVEOgVDUrwN1BhzGEv4wSwmIWhq+LuqGrn3zS08t7LkwLIIgYIhKTw69xQGp1rfeGOCnWsJX0QeAy4EylV1olvHCWnRCdBQ6cmha5ra+POij3l1bSn76lqoa2knKkL4+pn5nJiTzJQRaYzJtpEnjRlI3Kzh/wV4APibi8cIbQFu0lFVNpfVs2DtXp78YCf76luYdWIWhWOzGJIax3kThzA8w3rWGDNQuZbwVXWxiOS5tf+wEO1+k05ZbTP3v72FN9eXs7+pleY2Z0aoGfkZPDr3FCblprp6fGNM4FgbfjCLTnClW+aKHVV8XN7A5rI6/rl8Fy3tPs4qyCY3PYERGQmcMyGH7GRrkzcm1Iiqurdzp4b/0rHa8EVkHjAPICcnZ9r8+fP7dKz6+nqSkpL6tG2wyt/2N4bv+g+LZz93xOd7W2ZV5bktbby0rQ2ASIHJWZFcNjaGnMSBMfJkKL7PxxJu5QUrc2/NmTNnhapO78m6ntfwVfUh4CGA6dOna2FhYZ/2U1RURF+3DVqyFHY+R+GZM52xdQ7RXZkbWtr563vFFG2qoKm1g5b2DjaXtXHZKcP5VuFoMpNiSRpgF0KF5Pt8DOFWXrAyu2lg/beHm64TmUf2vC29tKaZ3fub+MXL61m5cz+Tc1PJSo4F4OKTh3Ft4Wi7IMqYMORmt8yngEJgkIiUAD9V1UfdOl5I6jrNYdyxE/7bG8u4/+2t1DS1HRhuOCpC+NOVUzlv0hC3IzXGDABu9tK53K19h41jTGS+payODZUdrH17CwvWlrJuTy2jshI5MTuZS6fmMn5ICiMyExidFV5tocaYo7MmnWDWWcPvkvCrG1q58+X1/Gvlbv+SzUwdkcZPPzeeK2eMJCZqYJx8NcYEniX8YBbdWcNvYkdlAwvWlvLIO9vY39jGdXNGk1i/my+cNdOGNTDG9Igl/CC2vdZHPnD935bwQl0FANNHpvOzr01k/NAUiopKLdkHQFtHG9H+XlLtvnbqW+sDduyGjgb2N+/v9/368LGmYg3VLdXMGDyD+Kh42rWd5aXLKakvOep2CVEJnJR9Euv2raO2tRaAtNg0CjILWF2+mqb2479QcFvNNrau2Xrc+/FCm6+NFaUrEBFyEnJYXbGadl/7YetFR0QzNWcqxTXFlDWWEdkaSSGFrsdnCT8IlVQ38uraUt5862PmAyflxDB5VgGfnTC410MbtHW0sb5qPXkpeaTGplLZVMmGqg24ef3FoXzqY1X5Kopri0mKTiIjLoOddTuJj4rntKGnkRydzPKy5eyu393tvioqKvj3wn8HIGrH9prtFNcWc3LWySTHJLOqfNWBRBcw/wzs4YLCSq8D6LsxaWPo0A7W7lvLjCEzSIg6/H+2rrWOBdsXMCJ5BFOyp1BbEZjPlCX8IOHzKX99r5jnV+3mI//Y8mcMHUFNTSQXjdgN05KBBlaXb2ZZ6TIa2hp4Y+8b3PnMncfcb11rHU3tTURJFBlxGexr3odPfe4X6BCREkleSh4763bSoR3kp+RT3VLNS9teApwaz8iUkd12F21ob6CpLnAjiGYlZHHGsDNYVb6KPQ17mJ07mwmDJgTs+Fu3bGXMCWNc2XdeSh6Z8ZmsLl9Nh3YAMC5jHBMyJxz1fShvLGd1+WomDJrAsKRhAOyq3cWGqg1My5lGZnzmcce1ePFiZs2addz78YIgxETG9Hq7oqKi/g/mCCzhB4Ha5jZ+/tJ6nl6+i3EjavjSbCErvYYV+xZxRuUw2PsSPPPSQdsIQl5sHmcMO+OY+46NjGVq9lQ2VW+iqrmKnIQcTh18ap8+lMdjRPII0uLSqGmpwac+0uPS8amPLdVbaOloIT81n+SY7kffDLeLcorKiigsKHT1GOMyxvV43eHJwxmePPygZWPSxzAmvf++lKIlmtjI2H7bn/mEJXwPNba285tXN/LkR4sh8SMGT9zE7o597C4HymFC5gS+nXMG6etegDO+D2nDSY1N5bShp5EUncTiRYspPL2wR8c6N/9cV8vSU6mxn1xPECERjM0Y62E0xoQXS/ge+WDnDq578X6a4t4lZng90RExnDL0dM4eeTZj0saQGZdJTmIONFXD0megqhxm/MDrsI0xA5gl/ACrb27jloX3UFQ2H5J8TM2YyRUTL2ZW7iwSO7thdhWfDhM+D6uegDm3QpxNHWiM6RtL+AH0wHuv8PDqJ/AlfERsy3TuOecGCvNP7n7DT10La56BVf+A0651P1BjTEiyhB8Amyr28M0FP2GfLkPi4jk/9yp+WfhDIiN7eFXssKkw4jR47wE45WqIshNaxpjes+vwXaSq3LP4Rb744iVUdKzklNQreO/Kxdz9mZt6nuw7zb4ZanfDir+6E6wxJuRZDd8la0tLuHHBX9jFc8RKNvfM+g1zRk/u+w5HFcLImbDkd3Dq18GGNzbG9JIl/H5W1dDMHUWP8lb540hEC7lx43nioj+TGZ9+fDsWgYmXwsvfd2r6qbn9E7AxJmxYwu9H/1j2Eb9a/hMiEraTwnjunv0Tzsyb1H8HyB7v3JdvsIRvjOk1S/j9oK29gzvffJXndt1DZHwd14z7Ed8+5XIiIvr5FEm2/4rI8vVwwtn9u29jTMizhH8c9jXU8Z0Fv2Fd7UI0cj/RMfH8+aw/86lhPZpPuPfi0yF5iFPDN8aYXrKE30f/2vA2d7x3B77IKtIiT+b8/M9y3acuIiXG5QujsgucGr4xxvSSJfw+uGPxH3h2+8Pgy+KmKX/gqilzAnfw7PGw7BHwdQTumMaYkGAJvxcq6mu55qU72NbyOlFN0/jHxfcwYcigwAaRXQDtzdasY4zpNUv4PdDQ0sp3/vMky2ofQ6P2c0Lc+fzlSz8jNd6DK17HnAUxSfDm7TDsusAf3xgzYFnC78Y7O5dx/dvfp132kxAzhO+d/ACXTfZwcobkwTDnx/DajxkSMQYIYHOSMWZAs4R/DL9+52n+vvVufO0pfGn0Lfxo9qXBMTHDqd+ArW8xdvOfYNkJzvg6xhjTDUv4R7CxooRvvnIblSwnxjeC+z5zP2eMGuV1WJ+IjILLnqTyTxeQ+fL3nRO4M+Z5HZUxJshZwu+iurGeu955ild2P4xKG6dlXMV9599AQnQQ1OoPFR3H2ok/YnbZY7DgJhh0Aoy25h1jzNFZwgca21q4/pV7eb/qaSSilXjGcE/hz5mVH7jJqvtCI6Lh0kfg4U/Ds1+Fz/0BCj5nA6sZY44o7BP+a1tWccs7t9IWuYuMiKnMnXAFc6ecRVRkpNeh9UxMIlz2JDwzF56+ClJHQNoISM+Dk74M+R6eYDbGBJWwTvgLt63hh+9cCwhX5d/GTbO+5HVIfZM5Gr6+ED56Gja94syDu+FFWP0POPE8J/l3ShkK4y86eJkxJiyEZcJvbGvhOy//ng+q5yPEcn/hoxSOmuh1WMcnMhqmXOncANpbYMm9sPQh2PFf/0oKLbVOH/4hJ0FEFMQmwag5kJBx8P6i4yF/NiQG+MIyY4xrwirhV9TXcsOrv2NtzUI0qoqsyOn8+bxfMDZrqNeh9b+oWCi82bl1Vb3DmSpx3xbnce0eeOO2o+9HjtC0JQLDpkPOeOAo5wvi02D0pyE2uXdxx6VB+sjebWOM6ZGwSPibKvbw88WP8uH+1/FF1pAaOZEvjvku35t5idehBV76SDj/NwcvqyuDjpaDlzXsg20LobXx8H10tMC2RbD+haMfp3k/vPPbvsWYMfqoXxTT6upgUw++RCJjYMSnoGobRMU5f0d4cF4mPQ9GnuH8AusLVecWKHbCP6S5mvBF5FzgPiASeERV73LzeIfaU7eHX787n7f2zEcjmkmMOIHvTvk5l580O5BhBL/knMOXpY1wJk/vq6b9sGsp+Np6t131DtjxLnQcebvWlihIyux+P837nV8yyUOdsYfWPtu7OIJEIcCiAB4wewIMm8KBX27tzbB9sfMFHJfm/DLMP9MZ3mPHu84XxMiZTvNgPxm7dy/UDMz3q69GV9RAYaHrx3Et4YtIJPAgcDZQAiwTkRdU1fWxfTeUl/CHpU/w7r75qLSTwFjunnX7wG+nH0ji0+DEc/q27WnXHvWpNUVFFPb0H6O10TkXoT5oqOhbLMdDFfashNI1fd7F9uJi8vPy+i+mY+loc873bH37k2USASNOg+YaaKmDgguddTpaYcjJoB3Or71+lNHSAg3hNQR4mgbmWh83a/inAltVdRuAiMwHLgb6/Z0874nvUN9YR/yu+exvK6UxYhsiSkzrSfzv2G9z3ZkziIiwn6phJybBuZdIZwwiL6RcAOMu6PPmO4qKyA9AzS+YvNebL/UQsaKoyPk15zJRl9oHReSLwLmqeo3/8VXADFX99iHrzQPmAeTk5EybP39+r491w9Y78dGGIERoPCMjJzInfQonpQ1FQrhNsr6+nqSkJK/DCKhwK3O4lReszL01Z86cFarao2n23KzhHynTHvbtoqoPAQ8BTJ8+Xfvyzf5hYSFFYVgrsDKHvnArL1iZ3dTPs2wfpAQY3uVxLrDHxeMZY4w5BjcT/jLgBBHJF5EY4DLgGP34jDHGuMm1Jh1VbReRbwOv4XTLfExV17l1PGOMMcfmaj98VX0FeMXNYxhjjOkZN5t0jDHGBBFL+MYYEyYs4RtjTJiwhG+MMWHCtStt+0JEKoAdfdx8ELCvH8MZCKzMoS/cygtW5t4aqapZPVkxqBL+8RCR5T29vDhUWJlDX7iVF6zMbrImHWOMCROW8I0xJkyEUsJ/yOsAPGBlDn3hVl6wMrsmZNrwjTHGHFso1fCNMcYcgyV8Y4wJEwM+4YvIuSKySUS2isiPvI4nEETkMREpF5G1XscSCCIyXEQWisgGEVknIjd4HZPbRCRORJaKyIf+Mt/hdUyBIiKRIrJKRF7yOpZAEJFiEVkjIqtFZLmrxxrIbfj+idI302WidODyQEyU7iURmQXUA39T1ZCfmV1EhgBDVHWliCQDK4DPh/L7LM7cnImqWi8i0cAS4AZVfd/j0FwnIt8HpgMpqnqh1/G4TUSKgemq6vrFZgO9hn9gonRVbQU6J0oPaaq6GKjyOo5AUdW9qrrS/3cdsAEY5m1U7lJHvf9htP82cGtnPSQiucAFwCNexxKKBnrCHwbs6vK4hBBPBOFORPKAKcAH3kbiPn/TxmqgHHhDVUO+zMC9wE2Az+tAAkiB10VkhYjMc/NAAz3h92iidBMaRCQJeA74rqrWeh2P21S1Q1VPxpkP+lQRCenmOxG5EChX1RVexxJgM1V1KnAecJ2/ydYVAz3h20TpYcLfjv0c8ISq/svreAJJVfcDRcC5HofitpnARf427fnAp0XkH96G5D5V3eO/Lweex2mqdsVAT/g2UXoY8J/AfBTYoKq/8zqeQBCRLBFJ8/8dD5wFbPQ2Knep6i2qmquqeTj/y2+r6lc8DstVIpLo74iAiCQC5wCu9b4b0AlfVduBzonSNwBPh8NE6SLyFPAeMFZESkTkaq9jctlM4CqcGt9q/+18r4Ny2RBgoYh8hFOxeUNVw6KbYpjJAZaIyIfAUuBlVX3VrYMN6G6Zxhhjem5A1/CNMcb0nCV8Y4wJE5bwjTEmTFjCN8aYMGEJ3xhjwoQlfGOMCROW8I3pQkTGiMiaQ5bFish2ERnvVVzG9AdL+MYcbBswXES6/m/MAxaF8nDMJjxEeR2AMcFEVX0ishPIA7b5hzX4AVDoZVzG9Aer4RtzuA3AOP/f1wEvqGqxd+EY0z8s4RtzuA044xQl4ST8XwCIyJ2eRmXMcbKEb8zhOmv4N+AMx1wmIoOBKBHJ9c+v+z0R+ae3YRrTO5bwjTncBpwxyb8G/Ma/bAqwGjgJ+Leq/h5o9yY8Y/rGEr4xh9sETAIeUtUa/7KT+SThv+ZfZkPNmgHFeukYcwhVbeHw/40xwBb//WYRGQSUBjo2Y46HjYdvjDFhwpp0jDEmTFjCN8aYMGEJ3xhjwoQlfGOMCROW8I0xJkxYwjfGmDBhCd8YY8KEJXxjjAkTlvCNMSZM/D8fH9vapwirSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df1.plot(title=\"NPN Transistor - Common Emmitter\")\n",
    "ax.set_ylabel(\"V (volts)\")\n",
    "ax.set_xlabel(\"$V_{in}$\")\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['$I_b$'] = (df1['$V_{cap}$']-df1['$V_b$'])/1000\n",
    "df1['$I_c$'] = (5 - df1['$V_c$'])/220\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>$V_{cap}$</th>\n",
       "      <th>$V_c$</th>\n",
       "      <th>$V_b$</th>\n",
       "      <th>$I_b$</th>\n",
       "      <th>$I_c$</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>V</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4.92</th>\n",
       "      <td>2.82</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00203</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.94</th>\n",
       "      <td>2.83</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00204</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.96</th>\n",
       "      <td>2.83</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00205</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.98</th>\n",
       "      <td>2.84</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00205</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00</th>\n",
       "      <td>2.85</td>\n",
       "      <td>0.05</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.00206</td>\n",
       "      <td>0.0225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      $V_{cap}$  $V_c$  $V_b$    $I_b$   $I_c$\n",
       "V                                             \n",
       "4.92       2.82   0.05   0.79  0.00203  0.0225\n",
       "4.94       2.83   0.05   0.79  0.00204  0.0225\n",
       "4.96       2.83   0.05   0.78  0.00205  0.0225\n",
       "4.98       2.84   0.05   0.79  0.00205  0.0225\n",
       "5.00       2.85   0.05   0.79  0.00206  0.0225"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc928b97358>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "df1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1['hfe'] = df1['$I_c$']/df1['$I_b$']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.set_index('$I_b$').drop_duplicates(keep='first')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$I_c$')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = df2['$I_c$'][df2.index < 0.0005 ].plot(title = \"npn current gain\")\n",
    "ax.set_ylabel('$I_c$')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$h_{fe}$')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = df1['hfe'].plot()\n",
    "ax.grid()\n",
    "ax.set_ylabel(\"$h_{fe}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'$h_{fe}$')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "ax = df2['hfe'].plot()\n",
    "ax.grid()\n",
    "ax.set_ylabel(\"$h_{fe}$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}