{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "conservative-essence",
   "metadata": {},
   "source": [
    "This is an attempt to collect data points for the BC547 transistor in the common emmitter configuration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "super-portrait",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sympy import symbols\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bearing-criticism",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import optimize\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "scheduled-attention",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "attachments": {
    "bc547-common-emitter-2.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "id": "useful-evans",
   "metadata": {},
   "source": [
    "![bc547-common-emitter-2.png](attachment:bc547-common-emitter-2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bridal-meaning",
   "metadata": {},
   "source": [
    "In this scenario we have a variable resistor pot VR1 (variable resistor 1) and a switch SW1 (which is a push down switch non-toggle switch).  R2 is a 1K resistor inline with the collector and supply. We measure the voltage across the variable terminal of R1, to it's middle terminal, with the switch open.  This allows us to calculate the selected impedance of VR1 with the switch open.  Then we close the switch and measure the voltage drop  at the middle terminal with the switch closed.  This in theory allows us to calculate the impedance of the base-emitter junction at the specified base voltage and therefore the base current.  VR1 is a 1K pot with the middle pin being some value between 0 and 1K ohms depending upon the knob position."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blank-default",
   "metadata": {},
   "source": [
    "Going to model this against the Eber Molls equations ( or a simplified version of them from [Wikipedia](https://en.wikipedia.org/wiki/Bipolar_junction_transistor#Ebers%E2%80%93Moll_model) ).\n",
    "\n",
    "$$ I_E = I_{ES} \\left( e^{\\frac{V_{BE}}{V_T}} \\right) - 1 $$\n",
    "$$ I_C = \\alpha_F I_E $$\n",
    "$$ I_B = ( 1 - \\alpha_F ) I_E $$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bronze-setup",
   "metadata": {},
   "source": [
    "Then I obtained by measurement the voltage at the middle pin of the variable resistor with the switch open, then the switch closed and finally the voltage at the collector of the transistor with the switch closed.  With my oscillopscope I measured the supply voltage at 4.96 volts but I take it to be 5 Volts for simplicity.  These measurements I put into an array called 'data'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "widespread-telephone",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotRawData(data, prefix=None):\n",
    "    fig, (ax1,ax2, ax3) = plt.subplots(3,1, figsize=(6,12))\n",
    "\n",
    "\n",
    "    ax1.set_title('$V(open)$ vs $V(closed)$')\n",
    "\n",
    "    ax1.scatter(data[:,0], data[:,1])\n",
    "    ax1.grid()\n",
    "\n",
    "\n",
    "    ax1.set_xlabel('$V_{open}$')\n",
    "    ax1.set_ylabel('$V_{closed}$')\n",
    "\n",
    "    ax2.set_title('$V(open)$ vs $V(collector)$')\n",
    "    ax2.grid()\n",
    "\n",
    "    ax2.scatter(data[:,0], data[:,2])\n",
    "\n",
    "\n",
    "    ax3.set_title('$V(closed)$ vs $V(Collector)$')\n",
    "\n",
    "    ax3.scatter(data[:,1], data[:,2])\n",
    "    ax3.grid()\n",
    "\n",
    "\n",
    "    ax3.set_xlabel('$V_{open}$')\n",
    "    ax3.set_ylabel('$V_{closed}$')\n",
    "\n",
    "\n",
    "    plt.tight_layout(pad=0.9)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "quality-peace",
   "metadata": {},
   "outputs": [],
   "source": [
    "def annotate(df, rcoll, extraR1a=0):\n",
    "    supplyV = df.loc[:,'Vcoll'].max()\n",
    "    maxvalue = 1e7\n",
    "    #print (supplyV)\n",
    "    \n",
    "    df['R1b'] = (df['Vro'])/supplyV*(1000+extraR1a)\n",
    "    df['R1a'] = (extraR1a+1000-df['R1b'])\n",
    "    df['Rce'] = df.loc[:,'Vcoll']/supplyV*rcoll/(1-df.loc[:,'Vcoll']/supplyV)\n",
    "    df['Rce'].replace(np.inf, maxvalue, inplace=True)\n",
    "\n",
    "    # Compute the current going through R1_a as (Vcc - Vbe)/R1a\n",
    "    # Then Reff is VBE/(current through R1_a), because Reff is R1b in parallel with \n",
    "    # base-emitter resistance\n",
    "    # Reff is the effective resistance to ground from the centre pot with switch closed\n",
    "    # Reff = R1_a * V_R1(closed) / (Vcc - V_R1(closed))\n",
    "    df['Reff'] = df['R1a']*(df.loc[:,'Vrc'])/(supplyV- (df.loc[:,'Vrc']))\n",
    "    df[abs(df['Reff'])>maxvalue]=maxvalue\n",
    "    # Rbe is resistance calculcated for the base-emitter Junction\n",
    "    df['Rbe'] = df['R1b']*df['Reff']/(df['R1b']-df['Reff'])\n",
    "    df['Rbe'].replace(np.inf, maxvalue, inplace=True)\n",
    "    # Ib is the base current calculated as base-emitter voltage divided by base-emitter resistance\n",
    "    Ib = df.loc[:,'Vrc']/df['Rbe']\n",
    "    # Ic is the collector current calculated accross resistor R2\n",
    "    Ic = (supplyV - df.loc[:,'Vcoll'])/rcoll\n",
    "    # Ie is the emmitter current which equals Ib + Ic\n",
    "    Ie = Ib + Ic\n",
    "    df['Ie']=Ie\n",
    "    df['Ie (milliAmps)'] = Ie*1000\n",
    "    df['Ib (microAmps)'] = Ib*1000000\n",
    "    df['Ic (milliAmps)'] = Ic*1000\n",
    "    df[ df < 0] = 0\n",
    "    df.replace(np.inf, np.nan, inplace=True)\n",
    "    df.dropna(axis=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "seasonal-cookbook",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2k2a = np.array([[4.5,.912,.03],[4.36,.90,.03],[4.29, .90,.03],[4.2,.89, .03],\n",
    "                     [4.07, .88, .02],[3.72,.87,.02],[3.21,.84, .02], [2.7,.82,.01],\n",
    "                    [2.3,.81,.01], [1.97, .81, .01],[1.3, .77, .01],[1.46, .77,.01],\n",
    "                    [1.15, .756, .01], [1.05, .748, .01],[.91, .731, .01],[.72, .69, .03],\n",
    "                    [.69, .67, .05],[.59, .59, 4.51],[.61,.61,4.09],[.70, .68, .04],\n",
    "                    [.73, .69,.03],[.65, .64, 1.69],[.66,.65,.10], [.64, .64, 1.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vanilla-cache",
   "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>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.07</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.20</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.29</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.36</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.50</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vro    Vrc  Vcoll\n",
       "19  4.07  0.880   0.02\n",
       "20  4.20  0.890   0.03\n",
       "21  4.29  0.900   0.03\n",
       "22  4.36  0.900   0.03\n",
       "23  4.50  0.912   0.03"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2k2asorted = data2k2a[data2k2a[:,0].argsort()]\n",
    "df2k2a = pd.DataFrame(list(zip(*data2k2asorted))).T.astype(float)\n",
    "df2k2a.columns = ('Vro', 'Vrc', 'Vcoll')\n",
    "df2k2a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "mathematical-ownership",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRawData(data2k2asorted)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "mental-strength",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2k2b = np.array([[.624,.622,3.56],[.631, .630, 3.06], [.629,.628, 3.19],[.634, .629, .96],\n",
    "                    [.660, .655, .28],[.64,.637,.81],[.624,.621,3.63],[.613, .613, 3.98],\n",
    "                    [.602,.601,4.38],[5.99,5.99, 4.44],[.627,.626,3.42],[.656,.651,.51],\n",
    "                    [.685,.662,.09],[.667,.659, .13],[.658, .656, .20],[.632,.627,.91]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bright-battlefield",
   "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>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.660</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.685</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Vro    Vrc  Vcoll\n",
       "10  0.656  0.651   0.51\n",
       "11  0.658  0.656   0.20\n",
       "12  0.660  0.655   0.28\n",
       "13  0.667  0.659   0.13\n",
       "14  0.685  0.662   0.09"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2k2bsorted = data2k2b[data2k2b[:,0].argsort()][:-1]\n",
    "df2k2b = pd.DataFrame(list(zip(*data2k2bsorted))).T.astype(float)\n",
    "df2k2b.columns = ('Vro', 'Vrc', 'Vcoll')\n",
    "df2k2b.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "steady-movie",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x864 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plotRawData(data2k2bsorted[:-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usual-rolling",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    }
   },
   "source": [
    "Now given we have a 1K pot I calculate the resistances of VR1<sub>a</sub> (the part of resistance connected to supply and the middle pin) and VR1<sub>b</sub> (the part of the variable resistance connected to ground and the middle pin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "parliamentary-quantity",
   "metadata": {},
   "outputs": [],
   "source": [
    "def computeGain(df):\n",
    "    ib = df['Ib (microAmps)'].head(10)\n",
    "    ie = df['Ie (milliAmps)'].head(10)\n",
    "    a = np.dot(ib, ie)/np.dot(ib, ib)\n",
    "    return a*1000\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "hispanic-charleston",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotIbvsIeData(df, xlim=None, ylim=None):\n",
    "    \n",
    "    ax = df.plot('Ib (microAmps)', 'Ie (milliAmps)', kind='scatter', xlim=xlim, ylim=ylim)\n",
    "    ax.set_title('$I_e$ vs $I_b$ with an npn BC547')\n",
    "    ax.grid()\n",
    "    #ax.plot(x,y)\n",
    "    ax.set_ylabel('$I_e$ (mA)')\n",
    "    ax.set_xlabel('$I_b$ ($\\mu$A)')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "athletic-furniture",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotIbvsRbeData(df, xlim=None, ylim=None):\n",
    "    ax = df.plot('Ib (microAmps)', 'Rbe', kind='scatter', xlim=xlim, ylim=ylim)\n",
    "    ax.grid()\n",
    "    ax.set_title('$R_{be}$ vs $I_b$ with an npn BC547')\n",
    "\n",
    "    ax.set_ylabel('$R_{be}$ ($\\Omega$)')\n",
    "    ax.set_xlabel('$I_b$ ($\\mu$A)')\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "charged-statistics",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotIbvsRceData(df, xlim=None, ylim=None):\n",
    "    ax = df.plot('Ib (microAmps)', 'Rce', kind='scatter', xlim=xlim, ylim=ylim)\n",
    "    ax.set_title('$R_{ce}$ vs $I_b$ with an npn BC547')\n",
    "\n",
    "    ax.grid()\n",
    "    #ax.plot(x,y)\n",
    "    ax.set_ylabel('CE resistance ($\\Omega$)')\n",
    "    ax.set_xlabel('$I_b$ ($\\mu$A)')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "damaged-tuner",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plotVbevsIeData(df, xlim=None, ylim=None):\n",
    "    ax = df.plot('Vrc', 'Ie (milliAmps)', kind='scatter', xlim=xlim, ylim=ylim)\n",
    "    ax.set_title('$V_{be}$ vs $I_e$ with an npn BC547')\n",
    "\n",
    "    ax.grid()\n",
    "    #ax.plot(x,y)\n",
    "    ax.set_ylabel('$I_E$ (mA)')\n",
    "    ax.set_xlabel('$V_{BE}$')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "korean-progressive",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotate(df2k2a,2200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "entire-dallas",
   "metadata": {},
   "outputs": [],
   "source": [
    "annotate(df2k2b, 2200, 5000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "operating-payday",
   "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>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "      <th>R1b</th>\n",
       "      <th>R1a</th>\n",
       "      <th>Rce</th>\n",
       "      <th>Reff</th>\n",
       "      <th>Rbe</th>\n",
       "      <th>Ie</th>\n",
       "      <th>Ie (milliAmps)</th>\n",
       "      <th>Ib (microAmps)</th>\n",
       "      <th>Ic (milliAmps)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.07</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.02</td>\n",
       "      <td>902.439024</td>\n",
       "      <td>97.560976</td>\n",
       "      <td>9.799555</td>\n",
       "      <td>23.651146</td>\n",
       "      <td>24.287678</td>\n",
       "      <td>0.038273</td>\n",
       "      <td>38.273274</td>\n",
       "      <td>3.623236e+04</td>\n",
       "      <td>2.040909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.20</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.03</td>\n",
       "      <td>931.263858</td>\n",
       "      <td>68.736142</td>\n",
       "      <td>14.732143</td>\n",
       "      <td>16.899217</td>\n",
       "      <td>17.211547</td>\n",
       "      <td>0.053746</td>\n",
       "      <td>53.745834</td>\n",
       "      <td>5.170947e+04</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.29</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "      <td>951.219512</td>\n",
       "      <td>48.780488</td>\n",
       "      <td>14.732143</td>\n",
       "      <td>12.161340</td>\n",
       "      <td>12.318837</td>\n",
       "      <td>0.075095</td>\n",
       "      <td>75.095210</td>\n",
       "      <td>7.305885e+04</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.36</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "      <td>966.740576</td>\n",
       "      <td>33.259424</td>\n",
       "      <td>14.732143</td>\n",
       "      <td>8.291823</td>\n",
       "      <td>8.363558</td>\n",
       "      <td>0.109646</td>\n",
       "      <td>109.646067</td>\n",
       "      <td>1.076097e+05</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.50</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.03</td>\n",
       "      <td>997.782705</td>\n",
       "      <td>2.217295</td>\n",
       "      <td>14.732143</td>\n",
       "      <td>0.562027</td>\n",
       "      <td>0.562344</td>\n",
       "      <td>1.623820</td>\n",
       "      <td>1623.820337</td>\n",
       "      <td>1.621784e+06</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vro    Vrc  Vcoll         R1b        R1a        Rce       Reff  \\\n",
       "19  4.07  0.880   0.02  902.439024  97.560976   9.799555  23.651146   \n",
       "20  4.20  0.890   0.03  931.263858  68.736142  14.732143  16.899217   \n",
       "21  4.29  0.900   0.03  951.219512  48.780488  14.732143  12.161340   \n",
       "22  4.36  0.900   0.03  966.740576  33.259424  14.732143   8.291823   \n",
       "23  4.50  0.912   0.03  997.782705   2.217295  14.732143   0.562027   \n",
       "\n",
       "          Rbe        Ie  Ie (milliAmps)  Ib (microAmps)  Ic (milliAmps)  \n",
       "19  24.287678  0.038273       38.273274    3.623236e+04        2.040909  \n",
       "20  17.211547  0.053746       53.745834    5.170947e+04        2.036364  \n",
       "21  12.318837  0.075095       75.095210    7.305885e+04        2.036364  \n",
       "22   8.363558  0.109646      109.646067    1.076097e+05        2.036364  \n",
       "23   0.562344  1.623820     1623.820337    1.621784e+06        2.036364  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2k2a.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "suspected-departure",
   "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>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "      <th>R1b</th>\n",
       "      <th>R1a</th>\n",
       "      <th>Rce</th>\n",
       "      <th>Reff</th>\n",
       "      <th>Rbe</th>\n",
       "      <th>Ie</th>\n",
       "      <th>Ie (milliAmps)</th>\n",
       "      <th>Ib (microAmps)</th>\n",
       "      <th>Ic (milliAmps)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.51</td>\n",
       "      <td>898.630137</td>\n",
       "      <td>5101.369863</td>\n",
       "      <td>289.922481</td>\n",
       "      <td>890.585085</td>\n",
       "      <td>99478.109964</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>1.765635</td>\n",
       "      <td>6.544153</td>\n",
       "      <td>1.759091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.20</td>\n",
       "      <td>901.369863</td>\n",
       "      <td>5098.630137</td>\n",
       "      <td>105.263158</td>\n",
       "      <td>898.147521</td>\n",
       "      <td>251234.417965</td>\n",
       "      <td>0.001903</td>\n",
       "      <td>1.902611</td>\n",
       "      <td>2.611107</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.660</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.28</td>\n",
       "      <td>904.109589</td>\n",
       "      <td>5095.890411</td>\n",
       "      <td>150.243902</td>\n",
       "      <td>896.055898</td>\n",
       "      <td>100591.480578</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>1.870148</td>\n",
       "      <td>6.511486</td>\n",
       "      <td>1.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.13</td>\n",
       "      <td>913.698630</td>\n",
       "      <td>5086.301370</td>\n",
       "      <td>67.294118</td>\n",
       "      <td>900.798872</td>\n",
       "      <td>63804.195948</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>1.942147</td>\n",
       "      <td>10.328474</td>\n",
       "      <td>1.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.685</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.09</td>\n",
       "      <td>938.356164</td>\n",
       "      <td>5061.643836</td>\n",
       "      <td>46.153846</td>\n",
       "      <td>901.239435</td>\n",
       "      <td>22784.431508</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>1.979055</td>\n",
       "      <td>29.054927</td>\n",
       "      <td>1.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Vro    Vrc  Vcoll         R1b          R1a         Rce        Reff  \\\n",
       "10  0.656  0.651   0.51  898.630137  5101.369863  289.922481  890.585085   \n",
       "11  0.658  0.656   0.20  901.369863  5098.630137  105.263158  898.147521   \n",
       "12  0.660  0.655   0.28  904.109589  5095.890411  150.243902  896.055898   \n",
       "13  0.667  0.659   0.13  913.698630  5086.301370   67.294118  900.798872   \n",
       "14  0.685  0.662   0.09  938.356164  5061.643836   46.153846  901.239435   \n",
       "\n",
       "              Rbe        Ie  Ie (milliAmps)  Ib (microAmps)  Ic (milliAmps)  \n",
       "10   99478.109964  0.001766        1.765635        6.544153        1.759091  \n",
       "11  251234.417965  0.001903        1.902611        2.611107        1.900000  \n",
       "12  100591.480578  0.001870        1.870148        6.511486        1.863636  \n",
       "13   63804.195948  0.001942        1.942147       10.328474        1.931818  \n",
       "14   22784.431508  0.001979        1.979055       29.054927        1.950000  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2k2b.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "bb1e37ea",
   "metadata": {},
   "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": [
    "plotIbvsIeData(df2k2a[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1e06f6cb",
   "metadata": {},
   "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": [
    "plotIbvsIeData(df2k2b[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d114a2a",
   "metadata": {},
   "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": [
    "plotVbevsIeData(df2k2a[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3df3ecfe",
   "metadata": {},
   "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": [
    "plotVbevsIeData(df2k2b[:-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "acting-enclosure",
   "metadata": {},
   "outputs": [],
   "source": [
    "df2k2 = df2k2a.append(df2k2b).sort_values('Vro')\n",
    "# Remove an outlier\n",
    "df2k2 = df2k2[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "aboriginal-sussex",
   "metadata": {},
   "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": [
    "plotIbvsIeData(df2k2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "editorial-mainland",
   "metadata": {},
   "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": [
    "plotVbevsIeData(df2k2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "stuck-broadway",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function scipy.optimize.minpack.curve_fit(f, xdata, ydata, p0=None, sigma=None, absolute_sigma=False, check_finite=True, bounds=(-inf, inf), method=None, jac=None, **kwargs)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "optimize.curve_fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "confirmed-passing",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fn(x, Is, vt ):\n",
    "    return Is*(np.exp(x/vt)-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "copyrighted-physics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Vro', 'Vrc', 'Vcoll', 'R1b', 'R1a', 'Rce', 'Reff', 'Rbe', 'Ie',\n",
       "       'Ie (milliAmps)', 'Ib (microAmps)', 'Ic (milliAmps)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2k2.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "secure-perth",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.37986205e-18, 2.39589154e-02])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popt,_ = optimize.curve_fit(fn, df2k2['Vrc'].to_numpy(), df2k2['Ie'].to_numpy(), p0=(1e-12, 0.0258))\n",
    "is1, vt = popt\n",
    "popt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "norman-dinner",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '$y=Is ( e^{vbe/vt} -1)$')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+AHfvAGZGVZSIiITb0dHDocQQ77v3ca783CNs2rYnsmPlGxBD6SuOHECPvRARKb7OxCDPdHTjQO/gMAPJFLdvbI1sJJFvQDxgZl8leJjeB4EfAf8YSUUiIhKqvat/zPOJqmMx2rv6IzleXrMc7v55M3sH0AOcD/yVuz8cSUUiIhKqqaGeVM4T7ZKpFE0N9ZEcL+9p8HQgKBREREpkZl1w93RVLPi60WQqxdrrlzF3Rm0kxztuQJhZL2OfwArBU1jd3WdFUpWIiIzR2RfMNdz+zvO5/Jy5NDXURxYOMEFAuLuuVBIRKRMHe4Ob5BbPm84lZ8yJ/Hi6k1pEpEIcTF+tNG9mdKOGTAoIEZEKcaA3CIj5EZ5WyqSAEBGpEAdGRxAKCBERyXQwMciM2irqa+JFOZ4CQkSkQhzoHWTejOif4jpKASEiUiEOJgaZX6QJalBAiIhUjIOJoaLNP4ACQkSkYhxMDCogREQk29BwisNHkgoIERHJNnqTnOYgREQky76eAQBeM1sBISIiGV7tDgKicVZd0Y6pgBARqQBHRxAKCBERybSvZ4CaeIzTputGORERybCve4DTZ9Vilvulo9FRQIiIVIB93QNFPb0ECggRkYrwas8AjbOnUECY2TVm9ryZtZnZnSHrzcy+lF7famYrctbHzexpM/telHWKiJQzd2dfzxQaQZhZHLgHWAUsBW4ys6U5zVYBS9KvNcBXctZ/DNgZVY0iIpWgp3+YgWRq6gQEcBnQ5u4vuvsQcD+wOqfNamCDBx4D5pjZAgAzawJ+C7g3whpFRMre6CWuxT7FVBXhvhcBuzPetwOX59FmEbAX+CJwOzDzeAcxszUEow8aGxtpaWmZVLGJRGLS20416ots6o9s6o9jitUX2/cPA7Cn7VlaunZFfrxRUQZE2LVYnk8bM3sXsN/dnzSz5uMdxN3XA+sBVq5c6c3Nx20+rpaWFia77VSjvsim/sim/jimGH3xnW17+PLD2wH44tND3H3DhVy3fFGkxxwV5SmmduCMjPdNQEeeba4ErjOzXxOcmnq7mX09ulJFRMpPZ2KQOza2MjwSfLYeHHZu39hKZ/rBfVGLMiC2AEvM7GwzqwFuBDbltNkE3Jy+mukKoNvd97r7X7p7k7svTm/3iLu/L8JaRUTKTntXP9Wx7D/T1bEY7V39RTl+ZKeY3H3YzG4DHgLiwH3uvsPMbk2vXwdsBq4F2oAjwC1R1SMiUmmaGupJplJZy5KpFE0N9UU5fpRzELj7ZoIQyFy2LuNnBz48wT5agJYIyhMRKWtzZ9Sy9vplfPT+bVTFjKq4sfb6Zcwt0pcG6U5qEZEyturiBcQM3rNiET+/4+1Fm6AGBYSISFnb1z1AyuH1ZzUUbeQwSgEhIlLGdncdAaCpYVrRj62AEBEpY3vSVywtmlOcielMCggRkTLW3tWPGSyYU9zHbIACQkSkrL1y6AgLZtVRWxUv+rEVECIiZeylg30snje9JMdWQIiIlLGXOxUQIiKSo/tIkq4jSRbPLf4VTKCAEBEpW7/u7ANg8VyNIEREJMPRgNApJhERyfTrg0cwgzNP0ykmERHJ8HJnHwtm1VFXXfxLXEEBISJStl442MfZ80tzegkUECIiZcndaXu1lyWnzyxZDQoIEZEy1NE9QN/QCEsaZ5SsBgWEiEgZ2vVqL4BGECIikq3t1QQAS07XCEJERDLserWXeTNqaZheU7IaFBAiImXoV/sTnFfC+QdQQIiIlJ1Uymnbnyjp6SVQQIiIlJ1XDh0hMTjM0oWzSlqHAkJEpMw809ENwEULZ5e0DgWEiEiZ2dHRQ1XMSnoPBCggRETKzo6OHs6ZP53n9vbSmRgsWR1VJTuyiIiM4e489XIX/UPDvO/ex0mmUqy9fhnXLV9U9Fo0ghARKSPP7e0hMTjMiEPv4DADyRS3b2wtyUgi0oAws2vM7HkzazOzO0PWm5l9Kb2+1cxWpJefYWY/NrOdZrbDzD4WZZ0iIuXiJ7sOjllWHYvR3tVf9FoiCwgziwP3AKuApcBNZrY0p9kqYEn6tQb4Snr5MPBxd78QuAL4cMi2IiJTzp7DY4MgmUrR1FBf9FqiHEFcBrS5+4vuPgTcD6zOabMa2OCBx4A5ZrbA3fe6+1MA7t4L7ASKfwJORKTIdu7tYfHcadRVx5hZW0VddYy11y9j7ozaotcS5ST1ImB3xvt24PI82iwC9o4uMLPFwKXA42EHMbM1BKMPGhsbaWlpmVSxiURi0ttONeqLbOqPbOqPYwrdF8MpZ9vuI1x9ZhWfen0dQyMpauIx4od/RUvLrwp2nHxFGRAWssxPpI2ZzQA2An/i7j1hB3H39cB6gJUrV3pzc/Okim1paWGy20416ots6o9s6o9jCt0XT7/SxfAPf8F/u3IZV1+8oGD7nawoTzG1A2dkvG8COvJtY2bVBOHwr+7+rQjrFBEpC1t/3QXAirMaSlxJIMqA2AIsMbOzzawGuBHYlNNmE3Bz+mqmK4Bud99rZgZ8Ddjp7n8XYY0iImXjFy8c5Jz502mcVVfqUoAIA8Ldh4HbgIcIJpkfcPcdZnarmd2abrYZeBFoA/4R+FB6+ZXA+4G3m9m29OvaqGoVESm15EiKJ146xJWvnVfqUo6K9E5qd99MEAKZy9Zl/OzAh0O2+xnh8xMiIlNSa/th+oZGeNNr55a6lKN0J7WISBn4eVsnZvBGBYSIiGRqeX4/Fy+azZxppfuK0VwKCBGRIupMDLJ99+GsZysdTAzy9O7D/MaFjSWsbCw9zVVEpEi+s20Pd2xspToWy3pK6yPP7ccdrr7w9FKXmEUjCBGRIuhMDHLHxlYGkqkxT2n90bOvsnB2HUsXlPYrRnMpIEREiqC9q5/qWPaf3OpYjF2v9vKTXQd4x9JGglvAyocCQkSkCJoa6kmmUlnLkqkUu17tZXA4VZIvBJqIAkJEpAjmzqhl7fXLxjyl9T+fO0BTQz0rzpxT6hLH0CS1iEiRXLd8EVeeO4/2rv5gRDHi/OkD2/njq84pu9NLoIAQESm4zsTg0RDI/R6HuTNqjy77u4d3kXLnd99wRthuSk4BISJSQONdyporOZLiG0+8wlvPm89Zc6eXoNKJaQ5CRKRAjncpa66HduzjQO8gN7/xrBJUmh8FhIhIAXQmBvnxc/upimXPJVTHYrR3ZX/PtLuz7icvcNbcabz1vPK6OS6TTjGJiJyk0dNKcTP6hkay1iVTKZoa6rOW/Wjnfp7Z08PdNywjHiu/yelRCggRkZOQeVop0/TaOCMpZ+31y7Imqt2dL/5oF2fNnca7Ly2/ex8yKSBERE7C6B3SAxwLiOk1cf76ty/ibRecPuYqpo1P7WFHRw9f+J1LqIqX91n+8q5ORKTMNTXUMzCcfVppaCQVGg7dR5L87eadrDhzTtmPHkAjCBGRkxZ8Oeb470d9dvNOuo4MseEDlxEr47mHURpBiIhkCPu+huNp7+qnvjr7s3Z9ddWYK5e+u72Df9+6mz9+62u5aOHsgtUbJY0gROSUlXvH83e27eH2B1uJx4yRlHP3DeE3uWUa7yF8mVcute1P8Ilv/5JLz5zDn73jvEh+lygoIETklNOZGORfH3+Fe37cRk08uOP5U7+1lM98dwfJkWOnhz7+ze1cee68MXMJmUYfwnd7zt3To9vs7x3gD+57gtqqOF++6VKqy3xiOpMCQkSmrLBnIgWjhO0MDgdBMDgcfPr/zHefIZk910xyxNnR0c1VE9zMlvsQvtFj7ese4P1fe5yuI0P8+5o30tQwrcC/YbQUECIyJYU9E+nKc+dxx8bWo+GQqcpiJEmF7Cm/yeTMh/ABPL+vlz/asIVDiSG+9gdv4OKmyph3yKSAEJGiGv1UP70mTkf3AD39Q8yqr2bh7Hr6hkaOfgLP/PTf1TfEtt2HWX7GHM5tnJnXMUZvXhu9P+H2ja2sf//rx9yzMCqFEzfIOMNEVQwuWnhiXwPq7mx8ag+f/I9fMqO2mn/74BVccsacE9pHuVBAiEhBdSYG2dHRAzgLZ9fz3L4eDiaGePO589ixt4fbH2zF3RkaGfspvq46OD//3tc38cCT7VTHYiQGh8lsefMbz+RvVl983BrCbl4Lvu7TxkwoA9RWxbj7hmUA/MWD24lbjBFPcfcNlxx3/iHXCwcSfGbTDn76q4Nccc5pfOmmSzl9Zl3e25cbBYSIFMx3tu3h4w9sYzjsTA0QM0iF3yIAcPRxFRseeyV4H/JJf8Ojr3DzFYuPO5IY78qiixbOyppQHhoZ4ba3LeH3Lj/zaBCEzSVM5NmOHr76Xy/w3e0dTK+p4q+vu4j3XXFWWT9nKR8KCBEpiK0vdfJn/76NkIHBUccLhxOxbffh4wbE8a4sGm9COXPbiYLB3XnpYB8/eCnJ2u0/5dm9PUyvifPBt5zDH73lHObPzH/UUc4iDQgzuwb4eyAO3Ovud+Wst/T6a4EjwB+6+1P5bCsi0cu9CqgzMUh/cuToTWSjp5K+17qXB7a2F62u5Xmc0z9eEOQTApkO9Q3x3L4edu7tZUdHN4+90ElH9wAAlzTV8+nfXsq7L13EnGk1J/y7lLPIAsLM4sA9wDuAdmCLmW1y92czmq0ClqRflwNfAS7Pc1sRiVDuVUCj8wIfvTDJRz77I1JeuBHBqKNzECubeGDr+HMQ+UxUQxAEc6bVkBxJ0d2fJDmSYmg4RXIkePUPpegZSNI7kKSnf5iegSQ9A8Mc6B1kb3c/ew8P0NHdT+/A8NF9zptRy2VnN/Ch186j5tALvPfaNxeyC8qKjffMkJPesdkbgc+4+zvT7/8SwN3/NqPNV4EWd/9G+v3zQDOweKJtw6xcudK3bt16wrW+68s/5dDhXqZND//av3z6aMIWEzTI57/CRHXkt4+J6nAG+geoqx9/Ym3CfRTgn1Qh+jyfOnyCvbjD0NAQNTXjfzIsRB0T7SW/32WiOvLvU3fo7k+GtqmLOwMjkzu3XhUzhnNSpTpuVMdjmAUFmBlGcFWRO5gF9aRSTixmxM3y+rc+nEqRHHFGTjDFzOC0aTUsmFPHgtn1LJxdR1PDNC5YMJMLXjMr6/RRS0sLzc3NJ7T/cmNmT7r7yrB1UZ5iWgTsznjfTjBKmKjNojy3BcDM1gBrABobG2lpaTnhQmemBqiuT1EV6x+3TSGmmibahxXgIHntYoJGI/EUVdXhfxxO6DgTlTHBTopxjHwMJ1NUV48ct82Eh8mjjgn/fUy8iwkb5dsdIymnpz8VelfA9Co4EnIfwfGOOa0mTk1VjHjMSDlH/2hXxWzc/0Y2plo7zruQ7S1OdSy4VDUeg+qYETeOLquKGdUxmF5t1FfBtGpjWpVRVwUxM2AESASvFIzsgR17so+RSCQm9TenUkQZEGH//XL/VY3XJp9tg4Xu64H1EIwgJpPmzc1T45NAoagvsp2K/dGZGOTKzz0y5ktwAD5+8TBf+GV+fzqq48YXfueSCZ9nVKmm+r+NKAOiHTgj430T0JFnm5o8thWRiIRdBTQ6LxA3oypG1hxEddz4i988n5qqGLVVMWbWVTGrvpqLFs4+oclgKS9RBsQWYImZnQ3sAW4Efi+nzSbgNjO7n+AUUre77zWzA3lsKyIRCrsK6GNXn8cTj/6Mxz8RTMyOXsWkIJiaIgsIdx82s9uAhwguVb3P3XeY2a3p9euAzQSXuLYRXOZ6y/G2japWEQmXezno3Bm11FfHjy676rz5pSpNiiDS+yDcfTNBCGQuW5fxswMfzndbEREpnsp5MLmIiBSVAkJEREIpIEREJJQCQkREQkX2qI1SSF8e+/IkN58HHCxgOZVMfZFN/ZFN/XHMVOiLs9w99HK0KRUQJ8PMto73PJJTjfoim/ojm/rjmKneFzrFJCIioRQQIiISSgFxzPpSF1BG1BfZ1B/Z1B/HTOm+0ByEiIiE0ghCRERCKSBERCTUKRUQZnaNmT1vZm1mdmfIejOzL6XXt5rZilLUWSx59Mfvp/uh1cx+YWaXlKLOYpmoPzLavcHMRszshmLWV0z59IWZNZvZNjPbYWY/KXaNxZTH/yuzzey7ZrY93R+3lKLOgnP3U+JF8NjwF4BzCL6QaDuwNKfNtcD3Cb7R7grg8VLXXeL+eBPQkP551aneHxntHiF40vANpa67hP825gDPAmem359e6rpL3B+fAD6X/nk+cAioKXXtJ/s6lUYQlwFt7v6iuw8B9wOrc9qsBjZ44DFgjpktKHahRTJhf7j7L9y9K/32MYJv9puq8vn3AfARYCOwv5jFFVk+ffF7wLfc/RUAdz/V+8OBmWZmwAyCgBgubpmFdyoFxCJgd8b79vSyE20zVZzo7/oBgtHVVDVhf5jZIuDdwDqmtnz+bZwHNJhZi5k9aWY3F6264sunP/4vcCHBVyP/EviYu4/9Qu8KE+kXBpUZC1mWe41vPm2mirx/VzN7G0FAvDnSikorn/74InCHu48EHxSnrHz6ogp4PXA1UA88amaPufuuqIsrgXz6453ANuDtwGuBh83sp+7eE3FtkTqVAqIdOCPjfRNB2p9om6kir9/VzJYB9wKr3L2zSLWVQj79sRK4Px0O84BrzWzY3f+jKBUWT77/rxx09z6gz8z+C7gEmIoBkU9/3ALc5cEkRJuZvQRcADxRnBKjcSqdYtoCLDGzs82sBrgR2JTTZhNwc/pqpiuAbnffW+xCi2TC/jCzM4FvAe+fop8MM03YH+5+trsvdvfFwIPAh6ZgOEB+/698B3iLmVWZ2TTgcmBnkesslnz64xWC0RRm1gicD7xY1CojcMqMINx92MxuAx4iuCrhPnffYWa3ptevI7gy5VqgDThC8KlgSsqzP/4KmAv8Q/pT87BP0SdX5tkfp4R8+sLdd5rZD4BWIAXc6+7PlK7q6OT5b+N/Av9sZr8kOCV1h7tX+mPA9agNEREJdyqdYhIRkROggBARkVAKCBERCaWAEBGRUAoIEREJpYAQiUD6SaffK3UdIidDASEiIqEUECInycw+Z2Yfynj/GYLnFM0ys2+b2bNmts7MYun1v2lmj5rZU2b2TTObUaLSRY5LASFy8u4Hfjfj/XuBAwSPif44cDHBA9zeY2bzgE8Cv+HuK4CtwJ8Vt1yR/Jwyj9oQiYq7P21mp5vZQoIvi+kieDbPE+7+IoCZfYPgabgDwFLg5+nHl9QAj5akcJEJKCBECuNB4AbgNQQjChj7SGgneE7Pw+5+UxFrE5kUnWISKYz7CZ7yeQNBWABcln4CaIzgFNTPCL6Z70ozOxfAzKaZ2XmlKFhkIgoIkQJw9x3ATGBPxiPiHwXuAp4BXgK+7e4HgD8EvmFmrQSBcUHxKxaZmJ7mKiIioTSCEBGRUAoIEREJpYAQEZFQCggREQmlgBARkVAKCBERCaWAEBGRUP8fkVhwxub2cFsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "x = np.linspace(0,.9,1000 )\n",
    "y = is1*(np.exp(x/vt)-1)\n",
    "ax.plot(x,y)\n",
    "df2k2.plot('Vrc', 'Ie', kind='scatter', ax=ax)\n",
    "ax.grid()\n",
    "ax.set_xlabel(\"vbe\")\n",
    "ax.set_ylabel(\"Ie\")\n",
    "ax.set_title(\"$y=Is ( e^{vbe/vt} -1)$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "deluxe-dispute",
   "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>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "      <th>R1b</th>\n",
       "      <th>R1a</th>\n",
       "      <th>Rce</th>\n",
       "      <th>Reff</th>\n",
       "      <th>Rbe</th>\n",
       "      <th>Ie</th>\n",
       "      <th>Ie (milliAmps)</th>\n",
       "      <th>Ib (microAmps)</th>\n",
       "      <th>Ic (milliAmps)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.59</td>\n",
       "      <td>0.590</td>\n",
       "      <td>4.51</td>\n",
       "      <td>130.820399</td>\n",
       "      <td>869.179601</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>130.820399</td>\n",
       "      <td>6.021445e+17</td>\n",
       "      <td>9.798312e-19</td>\n",
       "      <td>9.798312e-16</td>\n",
       "      <td>9.798312e-13</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.61</td>\n",
       "      <td>0.610</td>\n",
       "      <td>4.09</td>\n",
       "      <td>135.254989</td>\n",
       "      <td>864.745011</td>\n",
       "      <td>2.142381e+04</td>\n",
       "      <td>135.254989</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>1.909701e-04</td>\n",
       "      <td>1.909701e-01</td>\n",
       "      <td>6.100000e-02</td>\n",
       "      <td>0.190909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.50</td>\n",
       "      <td>141.906874</td>\n",
       "      <td>858.093126</td>\n",
       "      <td>1.096346e+03</td>\n",
       "      <td>141.906874</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.368182e-03</td>\n",
       "      <td>1.368182e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.368182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.640</td>\n",
       "      <td>1.69</td>\n",
       "      <td>144.124169</td>\n",
       "      <td>855.875831</td>\n",
       "      <td>1.318440e+03</td>\n",
       "      <td>141.540189</td>\n",
       "      <td>7.894553e+03</td>\n",
       "      <td>1.362887e-03</td>\n",
       "      <td>1.362887e+00</td>\n",
       "      <td>8.106855e+01</td>\n",
       "      <td>1.281818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.66</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.10</td>\n",
       "      <td>146.341463</td>\n",
       "      <td>853.658537</td>\n",
       "      <td>4.988662e+01</td>\n",
       "      <td>143.750790</td>\n",
       "      <td>8.120167e+03</td>\n",
       "      <td>2.084593e-03</td>\n",
       "      <td>2.084593e+00</td>\n",
       "      <td>8.004762e+01</td>\n",
       "      <td>2.004545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.69</td>\n",
       "      <td>0.670</td>\n",
       "      <td>0.05</td>\n",
       "      <td>152.993348</td>\n",
       "      <td>847.006652</td>\n",
       "      <td>2.466368e+01</td>\n",
       "      <td>147.785015</td>\n",
       "      <td>4.341144e+03</td>\n",
       "      <td>2.181610e-03</td>\n",
       "      <td>2.181610e+00</td>\n",
       "      <td>1.543372e+02</td>\n",
       "      <td>2.027273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.70</td>\n",
       "      <td>0.680</td>\n",
       "      <td>0.04</td>\n",
       "      <td>155.210643</td>\n",
       "      <td>844.789357</td>\n",
       "      <td>1.968680e+01</td>\n",
       "      <td>149.988711</td>\n",
       "      <td>4.458090e+03</td>\n",
       "      <td>2.184350e-03</td>\n",
       "      <td>2.184350e+00</td>\n",
       "      <td>1.525317e+02</td>\n",
       "      <td>2.031818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.72</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.03</td>\n",
       "      <td>159.645233</td>\n",
       "      <td>840.354767</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>151.791830</td>\n",
       "      <td>3.085649e+03</td>\n",
       "      <td>2.259980e-03</td>\n",
       "      <td>2.259980e+00</td>\n",
       "      <td>2.236159e+02</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.73</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.03</td>\n",
       "      <td>161.862528</td>\n",
       "      <td>838.137472</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>151.391324</td>\n",
       "      <td>2.340188e+03</td>\n",
       "      <td>2.331212e-03</td>\n",
       "      <td>2.331212e+00</td>\n",
       "      <td>2.948482e+02</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.91</td>\n",
       "      <td>0.731</td>\n",
       "      <td>0.01</td>\n",
       "      <td>201.773836</td>\n",
       "      <td>798.226164</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>154.406808</td>\n",
       "      <td>6.577414e+02</td>\n",
       "      <td>3.156834e-03</td>\n",
       "      <td>3.156834e+00</td>\n",
       "      <td>1.111379e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.05</td>\n",
       "      <td>0.748</td>\n",
       "      <td>0.01</td>\n",
       "      <td>232.815965</td>\n",
       "      <td>767.184035</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>152.539516</td>\n",
       "      <td>4.423917e+02</td>\n",
       "      <td>3.736264e-03</td>\n",
       "      <td>3.736264e+00</td>\n",
       "      <td>1.690809e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.15</td>\n",
       "      <td>0.756</td>\n",
       "      <td>0.01</td>\n",
       "      <td>254.988914</td>\n",
       "      <td>745.011086</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>150.034199</td>\n",
       "      <td>3.645101e+02</td>\n",
       "      <td>4.119471e-03</td>\n",
       "      <td>4.119471e+00</td>\n",
       "      <td>2.074016e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.30</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.01</td>\n",
       "      <td>288.248337</td>\n",
       "      <td>711.751663</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>146.537107</td>\n",
       "      <td>2.980644e+02</td>\n",
       "      <td>4.628789e-03</td>\n",
       "      <td>4.628789e+00</td>\n",
       "      <td>2.583334e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1.46</td>\n",
       "      <td>0.770</td>\n",
       "      <td>0.01</td>\n",
       "      <td>323.725055</td>\n",
       "      <td>676.274945</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>139.233077</td>\n",
       "      <td>2.443100e+02</td>\n",
       "      <td>5.197188e-03</td>\n",
       "      <td>5.197188e+00</td>\n",
       "      <td>3.151733e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1.97</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.01</td>\n",
       "      <td>436.807095</td>\n",
       "      <td>563.192905</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>123.293582</td>\n",
       "      <td>1.717805e+02</td>\n",
       "      <td>6.760774e-03</td>\n",
       "      <td>6.760774e+00</td>\n",
       "      <td>4.715320e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2.30</td>\n",
       "      <td>0.810</td>\n",
       "      <td>0.01</td>\n",
       "      <td>509.977827</td>\n",
       "      <td>490.022173</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>107.275124</td>\n",
       "      <td>1.358519e+02</td>\n",
       "      <td>8.007829e-03</td>\n",
       "      <td>8.007829e+00</td>\n",
       "      <td>5.962374e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2.70</td>\n",
       "      <td>0.820</td>\n",
       "      <td>0.01</td>\n",
       "      <td>598.669623</td>\n",
       "      <td>401.330377</td>\n",
       "      <td>4.888889e+00</td>\n",
       "      <td>89.184528</td>\n",
       "      <td>1.047961e+02</td>\n",
       "      <td>9.870171e-03</td>\n",
       "      <td>9.870171e+00</td>\n",
       "      <td>7.824716e+03</td>\n",
       "      <td>2.045455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.21</td>\n",
       "      <td>0.840</td>\n",
       "      <td>0.02</td>\n",
       "      <td>711.751663</td>\n",
       "      <td>288.248337</td>\n",
       "      <td>9.799555e+00</td>\n",
       "      <td>65.975096</td>\n",
       "      <td>7.271537e+01</td>\n",
       "      <td>1.359280e-02</td>\n",
       "      <td>1.359280e+01</td>\n",
       "      <td>1.155189e+04</td>\n",
       "      <td>2.040909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3.72</td>\n",
       "      <td>0.870</td>\n",
       "      <td>0.02</td>\n",
       "      <td>824.833703</td>\n",
       "      <td>175.166297</td>\n",
       "      <td>9.799555e+00</td>\n",
       "      <td>41.866670</td>\n",
       "      <td>4.410536e+01</td>\n",
       "      <td>2.176640e-02</td>\n",
       "      <td>2.176640e+01</td>\n",
       "      <td>1.972550e+04</td>\n",
       "      <td>2.040909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4.07</td>\n",
       "      <td>0.880</td>\n",
       "      <td>0.02</td>\n",
       "      <td>902.439024</td>\n",
       "      <td>97.560976</td>\n",
       "      <td>9.799555e+00</td>\n",
       "      <td>23.651146</td>\n",
       "      <td>2.428768e+01</td>\n",
       "      <td>3.827327e-02</td>\n",
       "      <td>3.827327e+01</td>\n",
       "      <td>3.623236e+04</td>\n",
       "      <td>2.040909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>4.20</td>\n",
       "      <td>0.890</td>\n",
       "      <td>0.03</td>\n",
       "      <td>931.263858</td>\n",
       "      <td>68.736142</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>16.899217</td>\n",
       "      <td>1.721155e+01</td>\n",
       "      <td>5.374583e-02</td>\n",
       "      <td>5.374583e+01</td>\n",
       "      <td>5.170947e+04</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>4.29</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "      <td>951.219512</td>\n",
       "      <td>48.780488</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>12.161340</td>\n",
       "      <td>1.231884e+01</td>\n",
       "      <td>7.509521e-02</td>\n",
       "      <td>7.509521e+01</td>\n",
       "      <td>7.305885e+04</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>4.36</td>\n",
       "      <td>0.900</td>\n",
       "      <td>0.03</td>\n",
       "      <td>966.740576</td>\n",
       "      <td>33.259424</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>8.291823</td>\n",
       "      <td>8.363558e+00</td>\n",
       "      <td>1.096461e-01</td>\n",
       "      <td>1.096461e+02</td>\n",
       "      <td>1.076097e+05</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.50</td>\n",
       "      <td>0.912</td>\n",
       "      <td>0.03</td>\n",
       "      <td>997.782705</td>\n",
       "      <td>2.217295</td>\n",
       "      <td>1.473214e+01</td>\n",
       "      <td>0.562027</td>\n",
       "      <td>5.623437e-01</td>\n",
       "      <td>1.623820e+00</td>\n",
       "      <td>1.623820e+03</td>\n",
       "      <td>1.621784e+06</td>\n",
       "      <td>2.036364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Vro    Vrc  Vcoll         R1b         R1a           Rce        Reff  \\\n",
       "0   0.59  0.590   4.51  130.820399  869.179601  1.000000e+07  130.820399   \n",
       "1   0.61  0.610   4.09  135.254989  864.745011  2.142381e+04  135.254989   \n",
       "2   0.64  0.640   1.50  141.906874  858.093126  1.096346e+03  141.906874   \n",
       "3   0.65  0.640   1.69  144.124169  855.875831  1.318440e+03  141.540189   \n",
       "4   0.66  0.650   0.10  146.341463  853.658537  4.988662e+01  143.750790   \n",
       "5   0.69  0.670   0.05  152.993348  847.006652  2.466368e+01  147.785015   \n",
       "6   0.70  0.680   0.04  155.210643  844.789357  1.968680e+01  149.988711   \n",
       "7   0.72  0.690   0.03  159.645233  840.354767  1.473214e+01  151.791830   \n",
       "8   0.73  0.690   0.03  161.862528  838.137472  1.473214e+01  151.391324   \n",
       "9   0.91  0.731   0.01  201.773836  798.226164  4.888889e+00  154.406808   \n",
       "10  1.05  0.748   0.01  232.815965  767.184035  4.888889e+00  152.539516   \n",
       "11  1.15  0.756   0.01  254.988914  745.011086  4.888889e+00  150.034199   \n",
       "12  1.30  0.770   0.01  288.248337  711.751663  4.888889e+00  146.537107   \n",
       "13  1.46  0.770   0.01  323.725055  676.274945  4.888889e+00  139.233077   \n",
       "14  1.97  0.810   0.01  436.807095  563.192905  4.888889e+00  123.293582   \n",
       "15  2.30  0.810   0.01  509.977827  490.022173  4.888889e+00  107.275124   \n",
       "16  2.70  0.820   0.01  598.669623  401.330377  4.888889e+00   89.184528   \n",
       "17  3.21  0.840   0.02  711.751663  288.248337  9.799555e+00   65.975096   \n",
       "18  3.72  0.870   0.02  824.833703  175.166297  9.799555e+00   41.866670   \n",
       "19  4.07  0.880   0.02  902.439024   97.560976  9.799555e+00   23.651146   \n",
       "20  4.20  0.890   0.03  931.263858   68.736142  1.473214e+01   16.899217   \n",
       "21  4.29  0.900   0.03  951.219512   48.780488  1.473214e+01   12.161340   \n",
       "22  4.36  0.900   0.03  966.740576   33.259424  1.473214e+01    8.291823   \n",
       "23  4.50  0.912   0.03  997.782705    2.217295  1.473214e+01    0.562027   \n",
       "\n",
       "             Rbe            Ie  Ie (milliAmps)  Ib (microAmps)  Ic (milliAmps)  \n",
       "0   6.021445e+17  9.798312e-19    9.798312e-16    9.798312e-13        0.000000  \n",
       "1   1.000000e+07  1.909701e-04    1.909701e-01    6.100000e-02        0.190909  \n",
       "2   0.000000e+00  1.368182e-03    1.368182e+00    0.000000e+00        1.368182  \n",
       "3   7.894553e+03  1.362887e-03    1.362887e+00    8.106855e+01        1.281818  \n",
       "4   8.120167e+03  2.084593e-03    2.084593e+00    8.004762e+01        2.004545  \n",
       "5   4.341144e+03  2.181610e-03    2.181610e+00    1.543372e+02        2.027273  \n",
       "6   4.458090e+03  2.184350e-03    2.184350e+00    1.525317e+02        2.031818  \n",
       "7   3.085649e+03  2.259980e-03    2.259980e+00    2.236159e+02        2.036364  \n",
       "8   2.340188e+03  2.331212e-03    2.331212e+00    2.948482e+02        2.036364  \n",
       "9   6.577414e+02  3.156834e-03    3.156834e+00    1.111379e+03        2.045455  \n",
       "10  4.423917e+02  3.736264e-03    3.736264e+00    1.690809e+03        2.045455  \n",
       "11  3.645101e+02  4.119471e-03    4.119471e+00    2.074016e+03        2.045455  \n",
       "12  2.980644e+02  4.628789e-03    4.628789e+00    2.583334e+03        2.045455  \n",
       "13  2.443100e+02  5.197188e-03    5.197188e+00    3.151733e+03        2.045455  \n",
       "14  1.717805e+02  6.760774e-03    6.760774e+00    4.715320e+03        2.045455  \n",
       "15  1.358519e+02  8.007829e-03    8.007829e+00    5.962374e+03        2.045455  \n",
       "16  1.047961e+02  9.870171e-03    9.870171e+00    7.824716e+03        2.045455  \n",
       "17  7.271537e+01  1.359280e-02    1.359280e+01    1.155189e+04        2.040909  \n",
       "18  4.410536e+01  2.176640e-02    2.176640e+01    1.972550e+04        2.040909  \n",
       "19  2.428768e+01  3.827327e-02    3.827327e+01    3.623236e+04        2.040909  \n",
       "20  1.721155e+01  5.374583e-02    5.374583e+01    5.170947e+04        2.036364  \n",
       "21  1.231884e+01  7.509521e-02    7.509521e+01    7.305885e+04        2.036364  \n",
       "22  8.363558e+00  1.096461e-01    1.096461e+02    1.076097e+05        2.036364  \n",
       "23  5.623437e-01  1.623820e+00    1.623820e+03    1.621784e+06        2.036364  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2k2a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c0bc0f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Vro</th>\n",
       "      <th>Vrc</th>\n",
       "      <th>Vcoll</th>\n",
       "      <th>R1b</th>\n",
       "      <th>R1a</th>\n",
       "      <th>Rce</th>\n",
       "      <th>Reff</th>\n",
       "      <th>Rbe</th>\n",
       "      <th>Ie</th>\n",
       "      <th>Ie (milliAmps)</th>\n",
       "      <th>Ib (microAmps)</th>\n",
       "      <th>Ic (milliAmps)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.602</td>\n",
       "      <td>0.601</td>\n",
       "      <td>4.38</td>\n",
       "      <td>824.657534</td>\n",
       "      <td>5175.342466</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>823.069813</td>\n",
       "      <td>4.274998e+05</td>\n",
       "      <td>0.000001</td>\n",
       "      <td>0.001406</td>\n",
       "      <td>1.405849</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.613</td>\n",
       "      <td>0.613</td>\n",
       "      <td>3.98</td>\n",
       "      <td>839.726027</td>\n",
       "      <td>5160.273973</td>\n",
       "      <td>2.189000e+04</td>\n",
       "      <td>839.726027</td>\n",
       "      <td>1.000000e+07</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.181879</td>\n",
       "      <td>0.061300</td>\n",
       "      <td>0.181818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.624</td>\n",
       "      <td>0.622</td>\n",
       "      <td>3.56</td>\n",
       "      <td>854.794521</td>\n",
       "      <td>5145.205479</td>\n",
       "      <td>9.551220e+03</td>\n",
       "      <td>851.601333</td>\n",
       "      <td>2.279678e+05</td>\n",
       "      <td>0.000375</td>\n",
       "      <td>0.375456</td>\n",
       "      <td>2.728455</td>\n",
       "      <td>0.372727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.624</td>\n",
       "      <td>0.621</td>\n",
       "      <td>3.63</td>\n",
       "      <td>854.794521</td>\n",
       "      <td>5145.205479</td>\n",
       "      <td>1.064800e+04</td>\n",
       "      <td>850.006013</td>\n",
       "      <td>1.517342e+05</td>\n",
       "      <td>0.000345</td>\n",
       "      <td>0.345002</td>\n",
       "      <td>4.092682</td>\n",
       "      <td>0.340909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.627</td>\n",
       "      <td>0.626</td>\n",
       "      <td>3.42</td>\n",
       "      <td>858.904110</td>\n",
       "      <td>5141.095890</td>\n",
       "      <td>7.837500e+03</td>\n",
       "      <td>857.305814</td>\n",
       "      <td>4.607056e+05</td>\n",
       "      <td>0.000438</td>\n",
       "      <td>0.437722</td>\n",
       "      <td>1.358785</td>\n",
       "      <td>0.436364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.629</td>\n",
       "      <td>0.628</td>\n",
       "      <td>3.19</td>\n",
       "      <td>861.643836</td>\n",
       "      <td>5138.356164</td>\n",
       "      <td>5.897479e+03</td>\n",
       "      <td>860.044688</td>\n",
       "      <td>4.634046e+05</td>\n",
       "      <td>0.000542</td>\n",
       "      <td>0.542264</td>\n",
       "      <td>1.355187</td>\n",
       "      <td>0.540909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.631</td>\n",
       "      <td>0.630</td>\n",
       "      <td>3.06</td>\n",
       "      <td>864.383562</td>\n",
       "      <td>5135.616438</td>\n",
       "      <td>5.100000e+03</td>\n",
       "      <td>862.783562</td>\n",
       "      <td>4.661100e+05</td>\n",
       "      <td>0.000601</td>\n",
       "      <td>0.601352</td>\n",
       "      <td>1.351612</td>\n",
       "      <td>0.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.632</td>\n",
       "      <td>0.627</td>\n",
       "      <td>0.91</td>\n",
       "      <td>865.753425</td>\n",
       "      <td>5134.246575</td>\n",
       "      <td>5.769452e+02</td>\n",
       "      <td>857.759820</td>\n",
       "      <td>9.290032e+04</td>\n",
       "      <td>0.001584</td>\n",
       "      <td>1.584022</td>\n",
       "      <td>6.749169</td>\n",
       "      <td>1.577273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.634</td>\n",
       "      <td>0.629</td>\n",
       "      <td>0.96</td>\n",
       "      <td>868.493151</td>\n",
       "      <td>5131.506849</td>\n",
       "      <td>6.175439e+02</td>\n",
       "      <td>860.495283</td>\n",
       "      <td>9.344169e+04</td>\n",
       "      <td>0.001561</td>\n",
       "      <td>1.561277</td>\n",
       "      <td>6.731470</td>\n",
       "      <td>1.554545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.640</td>\n",
       "      <td>0.637</td>\n",
       "      <td>0.81</td>\n",
       "      <td>876.712329</td>\n",
       "      <td>5123.287671</td>\n",
       "      <td>4.991597e+02</td>\n",
       "      <td>871.903352</td>\n",
       "      <td>1.589545e+05</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>1.626735</td>\n",
       "      <td>4.007436</td>\n",
       "      <td>1.622727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.656</td>\n",
       "      <td>0.651</td>\n",
       "      <td>0.51</td>\n",
       "      <td>898.630137</td>\n",
       "      <td>5101.369863</td>\n",
       "      <td>2.899225e+02</td>\n",
       "      <td>890.585085</td>\n",
       "      <td>9.947811e+04</td>\n",
       "      <td>0.001766</td>\n",
       "      <td>1.765635</td>\n",
       "      <td>6.544153</td>\n",
       "      <td>1.759091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.658</td>\n",
       "      <td>0.656</td>\n",
       "      <td>0.20</td>\n",
       "      <td>901.369863</td>\n",
       "      <td>5098.630137</td>\n",
       "      <td>1.052632e+02</td>\n",
       "      <td>898.147521</td>\n",
       "      <td>2.512344e+05</td>\n",
       "      <td>0.001903</td>\n",
       "      <td>1.902611</td>\n",
       "      <td>2.611107</td>\n",
       "      <td>1.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.660</td>\n",
       "      <td>0.655</td>\n",
       "      <td>0.28</td>\n",
       "      <td>904.109589</td>\n",
       "      <td>5095.890411</td>\n",
       "      <td>1.502439e+02</td>\n",
       "      <td>896.055898</td>\n",
       "      <td>1.005915e+05</td>\n",
       "      <td>0.001870</td>\n",
       "      <td>1.870148</td>\n",
       "      <td>6.511486</td>\n",
       "      <td>1.863636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.659</td>\n",
       "      <td>0.13</td>\n",
       "      <td>913.698630</td>\n",
       "      <td>5086.301370</td>\n",
       "      <td>6.729412e+01</td>\n",
       "      <td>900.798872</td>\n",
       "      <td>6.380420e+04</td>\n",
       "      <td>0.001942</td>\n",
       "      <td>1.942147</td>\n",
       "      <td>10.328474</td>\n",
       "      <td>1.931818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.685</td>\n",
       "      <td>0.662</td>\n",
       "      <td>0.09</td>\n",
       "      <td>938.356164</td>\n",
       "      <td>5061.643836</td>\n",
       "      <td>4.615385e+01</td>\n",
       "      <td>901.239435</td>\n",
       "      <td>2.278443e+04</td>\n",
       "      <td>0.001979</td>\n",
       "      <td>1.979055</td>\n",
       "      <td>29.054927</td>\n",
       "      <td>1.950000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Vro    Vrc  Vcoll         R1b          R1a           Rce        Reff  \\\n",
       "0   0.602  0.601   4.38  824.657534  5175.342466  1.000000e+07  823.069813   \n",
       "1   0.613  0.613   3.98  839.726027  5160.273973  2.189000e+04  839.726027   \n",
       "2   0.624  0.622   3.56  854.794521  5145.205479  9.551220e+03  851.601333   \n",
       "3   0.624  0.621   3.63  854.794521  5145.205479  1.064800e+04  850.006013   \n",
       "4   0.627  0.626   3.42  858.904110  5141.095890  7.837500e+03  857.305814   \n",
       "5   0.629  0.628   3.19  861.643836  5138.356164  5.897479e+03  860.044688   \n",
       "6   0.631  0.630   3.06  864.383562  5135.616438  5.100000e+03  862.783562   \n",
       "7   0.632  0.627   0.91  865.753425  5134.246575  5.769452e+02  857.759820   \n",
       "8   0.634  0.629   0.96  868.493151  5131.506849  6.175439e+02  860.495283   \n",
       "9   0.640  0.637   0.81  876.712329  5123.287671  4.991597e+02  871.903352   \n",
       "10  0.656  0.651   0.51  898.630137  5101.369863  2.899225e+02  890.585085   \n",
       "11  0.658  0.656   0.20  901.369863  5098.630137  1.052632e+02  898.147521   \n",
       "12  0.660  0.655   0.28  904.109589  5095.890411  1.502439e+02  896.055898   \n",
       "13  0.667  0.659   0.13  913.698630  5086.301370  6.729412e+01  900.798872   \n",
       "14  0.685  0.662   0.09  938.356164  5061.643836  4.615385e+01  901.239435   \n",
       "\n",
       "             Rbe        Ie  Ie (milliAmps)  Ib (microAmps)  Ic (milliAmps)  \n",
       "0   4.274998e+05  0.000001        0.001406        1.405849        0.000000  \n",
       "1   1.000000e+07  0.000182        0.181879        0.061300        0.181818  \n",
       "2   2.279678e+05  0.000375        0.375456        2.728455        0.372727  \n",
       "3   1.517342e+05  0.000345        0.345002        4.092682        0.340909  \n",
       "4   4.607056e+05  0.000438        0.437722        1.358785        0.436364  \n",
       "5   4.634046e+05  0.000542        0.542264        1.355187        0.540909  \n",
       "6   4.661100e+05  0.000601        0.601352        1.351612        0.600000  \n",
       "7   9.290032e+04  0.001584        1.584022        6.749169        1.577273  \n",
       "8   9.344169e+04  0.001561        1.561277        6.731470        1.554545  \n",
       "9   1.589545e+05  0.001627        1.626735        4.007436        1.622727  \n",
       "10  9.947811e+04  0.001766        1.765635        6.544153        1.759091  \n",
       "11  2.512344e+05  0.001903        1.902611        2.611107        1.900000  \n",
       "12  1.005915e+05  0.001870        1.870148        6.511486        1.863636  \n",
       "13  6.380420e+04  0.001942        1.942147       10.328474        1.931818  \n",
       "14  2.278443e+04  0.001979        1.979055       29.054927        1.950000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2k2b "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aaa02c1f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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