{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "53a7a462-ee69-4ef6-929f-a80534b9a65b", "metadata": { "tags": [] }, "outputs": [], "source": [ "import numpy as np\n", "from math import exp\n", "import matplotlib.pyplot as plt\n", "from scipy.integrate import solve_ivp\n", "from scipy.interpolate import interp1d\n", "from scipy.optimize import root_scalar\n", "from scipy.stats import linregress\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "a5b7ec98", "metadata": {}, "outputs": [], "source": [ "from sympy import symbols, exp as sexp, diff, Derivative, Eq, solve, lambdify, solveset" ] }, { "attachments": { "diode-cap.png": { "image/png": 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" } }, "cell_type": "markdown", "id": "9a5dc2ed", "metadata": {}, "source": [ "![diode-cap.png](attachment:diode-cap.png)" ] }, { "cell_type": "markdown", "id": "ae0f8575", "metadata": {}, "source": [ "The object was to measure the voltage at the base of the transmitter (and later on at the base of R2) to determine the characteristics of the transistor. Specifically to estimate the thermal coefficient $V_T$ and the scale current $I_{ES}$. The capacitor was to produce a comparatively slowly changing transistor base voltage with hopefully enough time for me to make measurements using an Arduino. (This has since become slightly more complicated because the Arduino analog inputs have a time lag of 100 $\\mu S$ which may add some measurement bias)." ] }, { "cell_type": "code", "execution_count": 3, "id": "17ce4b69", "metadata": {}, "outputs": [], "source": [ "vcc, r, r1, r2, c, issym, vt, vin, vout, vbe, vc, t = symbols(\"V_{CC} R R1 R2 C I_{ES} V_T V_{in} V_{out} V_{BE} V_C t\")" ] }, { "cell_type": "code", "execution_count": 4, "id": "771d4afc", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - C \\frac{d}{d t} V_{C} - I_{ES} \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right) + \\frac{- V_{C} + V_{in}}{R_{1}} = 0$" ], "text/plain": [ "Eq(-C*Derivative(V_C, t) - I_{ES}*(exp(V_C/V_T) - 1) + (-V_C + V_{in})/R1, 0)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn1= Eq((vin - vc)/r1 - c * Derivative(vc,t) - issym*(sexp(vc/vt)-1),0)\n", "eqn1" ] }, { "cell_type": "markdown", "id": "09b9a562", "metadata": {}, "source": [ "The above is a differential equation for the R1, C and base-emitter junction. $V_C$ is the voltage across the capacitor. Vin is a choice of 0 or 5 Volts as it is connected to one of the outputs of the Arduino digital pins. By turning it on or off I was hoping to sweep through a range of voltages controlled by the time constand of R1 $\\cdot$ C. In my case R1 = 6K8 and C = $100 \\mu F$.\n", "\n", "It's basically a non linear differential equation which is impossible to solve using analytical methods. Hence the attempt to solve it below using numerical methods." ] }, { "cell_type": "markdown", "id": "29143ef7", "metadata": {}, "source": [ "Now I try to build my theoretical model using the equation above and some scientific libraries to integrate the equation to find what the solution would look like over time.\n", "I am basically plotting the voltage across the capacitor as function of time." ] }, { "cell_type": "code", "execution_count": 5, "id": "4e3423e0", "metadata": {}, "outputs": [], "source": [ "vtval = 0.0321850033399526\n", "isval = 1.602564611659814e-12\n", "\n", "def model(t, y, vin, isval, vt):\n", " vcc=vin\n", " r=6800\n", " c=100e-6\n", " \n", " vc = y\n", " try:\n", " vcp = (vcc-vc)/(r*c) - isval * (exp(vc/vt)-1) /(c)\n", " except OverflowError:\n", " #print (vc)\n", " return 1e20\n", " #print (vcp)\n", " return vcp\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 6, "id": "168c3e34-94e1-4399-a682-5e4793d1d94c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "plt\n" ] }, { "cell_type": "code", "execution_count": 7, "id": "6b52e0bf", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Charging of $V_C$ over time(t)')" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for vtvalue in (0.030, 0.0322, 0.034):\n", " teval = np.linspace(0, .4,100)\n", " res1 = solve_ivp(model, (0,.4), (0,),args=(5, 1.6e-12, vtvalue), t_eval = teval, first_step=0.001)\n", "\n", " ax.plot(res1.t,res1.y[0], label=\"$V_T$ = {}\".format(vtvalue))\n", "\n", "ax.grid()\n", "ax.set_xlabel(\"x (secs)\")\n", "ax.set_ylabel(\"Vc (cap)\")\n", "ax.legend()\n", "ax.set_title(\"Charging of $V_C$ over time(t)\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "555f6e1b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Charging of $V_C$ over time(t)')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "for isvalue in (1.6e-11, 1.6e-12, 1.6e-13):\n", " teval = np.linspace(0, .4,100)\n", " res1 = solve_ivp(model, (0,.4), (0,),args=(5, isvalue, 0.0322), t_eval = teval, first_step=0.001)\n", "\n", " ax.plot(res1.t,res1.y[0], label=\"$I_{{ES}}$ = {}\".format(isvalue))\n", "\n", "ax.grid()\n", "ax.set_xlabel(\"x (secs)\")\n", "ax.set_ylabel(\"Vc (cap)\")\n", "ax.legend()\n", "ax.set_title(\"Charging of $V_C$ over time(t)\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "5b459d43", "metadata": {}, "outputs": [], "source": [ "teval = np.linspace(0, 1,100)\n", "res2 = solve_ivp(model, (0,1), (.7,),args=(0,isval, vtval), t_eval = teval, first_step=0.001)" ] }, { "cell_type": "code", "execution_count": 10, "id": "a829b00b", "metadata": {}, "outputs": [], "source": [ "x = res2.t\n", "y = res2.y[0]" ] }, { "cell_type": "code", "execution_count": 11, "id": "c46ad600", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0.5, 1.0, 'Discharging of C over time(t)')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.plot(x,y)\n", "ax.grid()\n", "ax.set_xlabel(\"x (secs)\")\n", "ax.set_ylabel(\"Vc (cap)\")\n", "ax.set_title(\"Discharging of C over time(t)\")" ] }, { "cell_type": "markdown", "id": "7fae56b9", "metadata": {}, "source": [ "The results are unsurprising as the capacitor charges up to ( or discharges from ) 0.7 volts approximately. Anything higher and the transistor conducts so the results are at least realistic." ] }, { "cell_type": "markdown", "id": "551150e0", "metadata": {}, "source": [ "Now I look at the results obtained from direct measurement via the Arduino analog input pin." ] }, { "cell_type": "code", "execution_count": 12, "id": "ea25cc0b", "metadata": {}, "outputs": [], "source": [ "measurements = np.array([[ 0, 19, 0.09], \n", "[ 4, 24, 0.12], \n", "[ 8, 31, 0.15], \n", "[ 12, 36, 0.18], \n", "[ 16, 42, 0.21], \n", "[ 20, 49, 0.24], \n", "[ 24, 53, 0.26], \n", "[ 28, 58, 0.28], \n", "[ 32, 64, 0.31], \n", "[ 36, 69, 0.34], \n", "[ 40, 76, 0.37], \n", "[ 44, 80, 0.39], \n", "[ 48, 86, 0.42], \n", "[ 52, 92, 0.45], \n", "[ 56, 96, 0.47], \n", "[ 60, 101, 0.49], \n", "[ 65, 106, 0.52], \n", "[ 69, 112, 0.55], \n", "[ 73, 115, 0.56], \n", "[ 77, 119, 0.58], \n", "[ 81, 123, 0.60], \n", "[ 85, 125, 0.61], \n", "[ 89, 127, 0.62], \n", "[ 93, 129, 0.63], \n", "[ 97, 129, 0.63], \n", "[ 101, 130, 0.63], \n", "[ 105, 130, 0.63], \n", "[ 109, 131, 0.64], \n", "[ 113, 131, 0.64], \n", "[ 117, 131, 0.64], \n", "[ 121, 131, 0.64],\n", "])" ] }, { "cell_type": "code", "execution_count": 13, "id": "c3d98428", "metadata": {}, "outputs": [], "source": [ "dfmeasure = pd.DataFrame(measurements)\n", "dfmeasure.columns = ['time', 'ADC', 'volts']\n", "# ADC is the measure obtained from Arduino analogRead which returns a value from 0 to 1023.\n", "# I divide that by 1024 and multiply by 5 to get a voltage." ] }, { "cell_type": "code", "execution_count": 14, "id": "79933ebd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Arduino measure charging of cap across transistor base')\n", "\n", "\n", "ax1 = dfmeasure.plot('time', 'volts', kind='scatter', marker=\".\", ax=ax)\n", "ax1.set_xlabel('Time (mSec)')\n", "ax1.set_ylabel('voltage $V_C$')\n", "\n", "\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 15, "id": "f95dd834", "metadata": {}, "outputs": [], "source": [ "maxidx = np.argmax(res1.t>.120)" ] }, { "cell_type": "code", "execution_count": 16, "id": "538ff5b3", "metadata": { "scrolled": true }, "outputs": [], "source": [ "teval = np.linspace(0.001, .4,100)\n", "res1 = solve_ivp(model, (0.001,.4), (0.09,),args=(5,isval, vtval), t_eval = teval, first_step=0.001)" ] }, { "cell_type": "code", "execution_count": 17, "id": "17d13ab0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Arduino measure charging of cap across transistor base')\n", "\n", "\n", "ax1 = dfmeasure.plot('time', 'volts', kind='scatter', marker=\".\", ax=ax, label=\"measured\")\n", "ax1.set_xlabel('Time (mSec)')\n", "ax1.set_ylabel('voltage $V_C$')\n", "\n", "x = (res1.t*1000)[0:maxidx]\n", "y = res1.y[0][0:maxidx]\n", "ax1.plot(x,y, label='predicted')\n", "ax1.grid()\n", "ax1.legend()" ] }, { "cell_type": "code", "execution_count": 18, "id": "31f79b18", "metadata": {}, "outputs": [], "source": [ "f=interp1d(dfmeasure['time'],dfmeasure['volts'], kind='linear', bounds_error=False, fill_value='extrapolate')" ] }, { "cell_type": "code", "execution_count": 19, "id": "15e7fa23", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Arduino measure charging of cap across transistor base')\n", "\n", "\n", "\n", "ax.set_xlabel('Time (mSec)')\n", "ax.set_ylabel('voltage $V_C$')\n", "\n", "x = (res1.t*1000)[0:maxidx]\n", "y = res1.y[0][0:maxidx]\n", "fy = f(x)\n", "ax.plot(x,y, label='theoretical')\n", "ax.scatter(x, fy, label='experimental')\n", "ax.grid()\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 20, "id": "250d9f4a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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YgVdeeaUuy3fQMrPcZsxrZ8qtC+hYnb03sH15B1NuXQBQMnA5wNEjAlanetbFHTHMLLfLZy1cH7A6daxey+WzFm6UtzPAtS/vIHg7wM2Yl+cdlGYZBy0zy+355R0Vp+cJcNZ9pk6dyhVXXMH555/PXXdlb0C599572Xvvvdl///3p6OjgnHPOYe+99+bLX/5ynUv7NjcPmlluw4a00l4iQA0b0rpRWp4AZ93vwgsvXP/5xhtvZMqUKXzsYx8DYNq0aSxdupQ///nP9SreRnymZWa5nTNhDK39WjZIa+3XwjkTxmyUt1Qg21S6ZU2qh106m1GTb+OwS2dXrSn14osv5p3vfCeHH344CxdmZ7qnnXYat9xyC1dffTU333wz5513HqeccgrHHXccK1eu5MADD+SnP/1pVZZfDT7TMrPcOjtRVNK54pwJYzbotAHlA5zl7+RSqblz5zJ9+nTmz5/PmjVrOOCAAzjwwAPXjz/jjDO47777OPbYYznhhBMAGDhwIPPnz2fFihVbUKPqctAys80yaezwig6ieQKcbfoa4Jass3vvvZcPf/jDbL311gAcd9xxW1TOenHQMrOaqzTAma8BdsXXtMzMGkitrgEeccQRzJgxg46ODlasWMEvfvGLLZpfvThomZk1kDydXPI44IADOOmkk9hvv/045phjOOigg7ZofvXi5kEzswZSy2uA5557Lueee27Z8dddd90GwytXrtziZVabg5aZWYPxNcDy3DxoZmZNw0HLzKwbRES9i1A19ayLg5aZWY0NGDCAJUuW9IjA1fk+rQEDBtRl+b6mZWZWYyNGjGDx4sV1ewfVllq1atUGQarzzcX14KBlZlZj/fr1q8tbfqulra2NsWPH1rsYgJsHzcysiThomZlZ03DQMjOzptFQQUvS0ZIWSlokaXKJ8VtJuimNf0DSyJS+g6RfS1op6aqiadrSPOenv527qTpmZlZlDdMRQ1IL8C3gg8Bi4EFJMyPi0YJspwPLImJPSScDlwEnAauA84B90l+xUyJiTk0rYGZmNdcwQQt4D7AoIp4CkDQdmAgUBq2JwNT0+RbgKkmKiDeA+yTt2Y3lNbMqmzGv3e/dsk1So9zsJukE4OiIOCMNfxw4OCLOLsjzcMqzOA0/mfK8moZPA8YVTdMG7ACsBX4KXBQlKi3pLOAsgKFDhx44ffr0XOVfuXIlAwcOzDVNI3N9GltPrM+alq1oX9bBuoKvZx+J4du1MqS130bTLO9YzUuvreKttevo39KHoYMHlMxXDz1x+1RSnyOPPHJuRIyrZVka6UyrVk6JiHZJg8iC1seBG4ozRcQ0YBrAuHHjYvz48bkW0tbWRt5pGpnr09h6Yn3OvX8d7ctbNho3fEgLv508foO0GfPamXL3AjpW96Hz0nxrv7VccvxeDXFm1hO3T6PUp5E6YrQDuxYMj0hpJfNI6gsMBpZsaqYR0Z7+rwB+TNYMaWYNJs8bezf1Snrr2RopaD0IjJY0SlJ/4GRgZlGemcCp6fMJwOxSTX2dJPWVtGP63A84Fni46iU3sy2W5429fiV979UwQSsi1gBnA7OAx4CbI+IRSRdKOi5luwbYQdIi4F+B9d3iJT0D/CdwmqTFkvYCtgJmSXoImE92pvb9bqqSmeWQ5429tXolvTW+hrqmFRG3A7cXpZ1f8HkVcGKZaUeWme2B1SqfmdVOnjf2njNhDFNuXbBBE2E1Xklvja+hgpaZ9W6VvrG3lq+kt8bmoGVmTcmvpO+dGuaalpmZWVcctMzMrGk4aJmZWdNw0DIzs6bhoGVmZk3DQcvMzJqGg5aZmTUNBy0zM2saDlpmZtY0HLTMzKxpOGiZmVnTcNAyM7Om4aBlZmZNw0HLzMyahoOWmZk1DQctMzNrGg5aZmbWNBy0zMysaThomZlZ0+hb7wKYmdXSjHntXD5rIc8v72DYkFbOmTCGSWOH17tYtpka6kxL0tGSFkpaJGlyifFbSbopjX9A0siUvoOkX0taKemqomkOlLQgTfMNSeqm6phZnc2Y186UWxfQvryDANqXdzDl1gXMmNde76LZZmqYoCWpBfgWcAywF/BRSXsVZTsdWBYRewJXApel9FXAecAXSsz6O8CZwOj0d3T1S29mjejyWQvpWL12g7SO1Wu5fNbCOpXItlTDBC3gPcCiiHgqIt4CpgMTi/JMBK5Pn28BjpKkiHgjIu4jC17rSdoF2DYi7o+IAG4AJtWyEmbWOJ5f3pEr3RpfI13TGg48VzC8GDi4XJ6IWCPpNWAH4NVNzHNx0TxLNmZLOgs4C2Do0KG0tbXlKvzKlStzT9PIXJ/G5vpUZvL+63hr7bqN0vu39Knp+vP2qZ1GClp1FRHTgGkA48aNi/Hjx+eavq2tjbzTNDLXp7HlrU+jd0ao1fZZnq5pFTYRtvZr4ZLj92V8Devf2/e3WmqkoNUO7FowPCKllcqzWFJfYDCwpIt5juhinmY92oyiA3dnZwSgoQJXLXTWr5EDtuXTSEHrQWC0pFFkgeVk4O+K8swETgV+D5wAzE7XqkqKiBckvS7pEOAB4BPAN2tReLNGtanOCL3h4D1p7PBeUc/eomGCVrpGdTYwC2gBro2IRyRdCMyJiJnANcAPJS0ClpIFNgAkPQNsC/SXNAn4UEQ8CvwjcB3QCtyR/sx6DXdGsJ6kYYIWQETcDtxelHZ+wedVwIllph1ZJn0OsE/1SmnWXIYNaaW9RIAaNqS1DqUx2zKN1OXdzGrgnAljaO3XskFaa78Wzpkwpk4lMtt8DXWmZWb5VNIr0J0RrCdx0DJrUnl6BbozgvUUbh40a1J+RJH1Rg5aZk3KvQKtN3LQMmtS5Xr/uVeg9WQOWmZNyr0CrTdyRwyzJuVegdYbOWiZNTH3CrTexs2DZmbWNHymZWZWQ43+Wphm46BlZlYjvfm1MLXi5kEzsxrxDeDV5zMtM9uAm7OqxzeAV5+Dlpmt5+asylQa2P1amOpz86CZrefmrK51Bvb25R0Ebwf2GfPaN8rrG8Crz0HLzNZzc1bX8gT2SWOHc8nx+zJ8SCsChg9p5ZLj9/VZ6xbI3Two6RLgvIhYI6kPMDAiXq9+0cysu7k5q2t5A7tvAK+uzTnTGhgRawAiYh1wZXWLZGb14uasrvlBxfW1OUErioZXVKMgZlZ/bs7qWq0C+4x57Rx26WxGTb6Nwy6dXfIamW1e78E/SroC+C9gLbBzVUtkZnXl5qxNq8WDit1rs3JdBi1J746IxzqHI+I6SYcAXwa2Ai6qYfnMzBpOtQP7pjp3OGhtqJLmwdsk/UDSbp0JEXF/RHw6Ij4ZEY9WqzCSjpa0UNIiSZNLjN9K0k1p/AOSRhaMm5LSF0qaUJD+jKQFkuZLmlOtspqZVYt7bVaukqD1LuCPwG8kfV3STrUoiKQW4FvAMcBewEcl7VWU7XRgWUTsSdYB5LI07V7AycDewNHAt9P8Oh0ZEftHxLhalN3MeoZ6XVdy547KdRm0IuKtiPgm8G7gOeAPkr4iadsql+U9wKKIeCoi3gKmAxOL8kwErk+fbwGOkqSUPj0i3oyIp4FFaX5mZhXJc9NwtbnXZuUUUdwZsIsJpG2AzwGfBL4XEVdUpSDSCcDREXFGGv44cHBEnF2Q5+GUZ3EafhI4GJgK3B8RP0rp1wB3RMQtkp4GlpH1evxeREwrs/yzgLMAhg4deuD06dNzlX/lypUMHDgw1zSNzPVpbK5P9S18cQVvrV23UXr/lj6MecegXPPanPos71jNS6+t4q216+jf0oehgwcwpLVfrnnUSqX1OfLII+fWukWr4t6D6frRu4AxwG5kXd3/A6hK0KqhwyOiXdLOwJ2SHo+Ie4ozpWA2DWDcuHExfvz4XAtpa2sj7zSNzPVpbK5P9X1y8m1EicYnAU9fOj7XvBqhPtXUSPXpsnlQ0kOSlgA/A04FBgN3A58AqvnTqB3YtWB4REormUdS31SWJZuaNiI6/7+c6uBmQzPbiK8rNYdKOmJMAnaMiLER8dGIuDAifhIRD6drT9XyIDBa0ihJ/ck6VswsyjOTLHACnADMjqx9cyZwcupdOAoYTXbtbRtJg2B9s+aHgIerWGYz6yF8Xak5dNk8GBFPdUdB0rMMzwZmAS3AtRHxiKQLgTkRMRO4BvihpEXAUrLARsp3M/AosAb4TESslTQU+FnWV4O+wI8j4lfdUR8zay61uGnYqq+h3qcVEbcDtxelnV/weRVwYplpLwYuLkp7Ctiv+iU1s57ITwNpfA0VtMzMbw4225SKH5irzMcknZ+Gd5PkTg1mVVTPe4XMmkGep7x/GzgU+GgaXkH2BAszqxK/Odhs0/I0Dx4cEQdImgcQEctSLz8zqxI/g85s0/Kcaa1Oz/MLgPQMwo1vHzezzeZ7hcw2LU/Q+gbZzbk7S7oYuI/siRhmViW+V8hs0ypuHoyIGyXNBY4ie7LJpML3bJnZlvO9QmablqvLe0Q8Djxeo7KYGb5XyGxT8jww919LJL8GzI2I+VUrkZmZWRl5rmmNAz4NDE9/f0/2wsXvS/piDcpmZma2gTzNgyOAAyJiJYCkC4DbgCOAucBXq188MzOzt+U509oZeLNgeDUwNCI6itLNzMxqIs+Z1o3AA5J+nob/FvhxeuXHo1UvmZmZWZE8Xd6/IukO4LCU9OmImJM+n1L1kpmZmRXJ+5T3J8maFAcAW0s6otSr683MzGohT5f3M4DPkXXImA8cAvweeH9NSmZmZlYkT0eMzwEHAc9GxJHAWGB5LQplZmZWSp6gtSq9ORhJW6WnY/iBaGZm1m3yXNNaLGkIMAO4U9Iy4NlaFMrMzKyUPL0HP5w+TpX0a2AwcEdNSmVmZlZCno4Yl0XElwAi4jedacCXalQ264FmzGv3E8zNbLPluab1wRJpx1SrINbzzZjXzpRbF9C+vIMA2pd3MOXWBcyY117voplZk+gyaEn6B0kLgHdJekjSgvT3DLCgmoWRdLSkhZIWSZpcYvxWkm5K4x+QNLJg3JSUvlDShErnad3n8lkL6Vi9doO0jtVruXzWwjqVyMyaTSXNgzcCtwOXAJPJXgAZwIqIWFatgkhqAb5Fdka3GHhQ0syIKHxE1OnAsojYU9LJwGXASZL2Ak4G9gaGAXdJemeapqt5Wjd5fnlHrvR6cjOmWWOqJGi1kwUpAccWpEtSRMS2VSrLe4BFEfFUmvl0YCIbPtdwIjA1fb4FuEqSUvr0iHgTeFrSojQ/KpindZNhQ1ppLxGghg1prUNpyutsxuw8K+xsxgQcuMzqrMvmwYgYFBHbFvzftnC4imUZDjxXMLw4pZXMExFryF5CucMmpq1kntZNzpkwhtZ+LRuktfZr4ZwJjXW7n5sxzRpX3mcP9liSzgLOAhg6dChtbW25pl+5cmXuaRpZLeozBLjkvS289Npq3lq7jv4tfRg6uD9DXnuCtrYnqrqsYnnqc/KuK2DXUmNWNMw29v7W2Fyf2skVtCTtB7wvDd4bEX+qYlna2fBQMSKllcqzWFJfsnvFlnQxbVfzBCAipgHTAMaNGxfjx4/PVfi2tjbyTtPIenN9zr10dslmzOFDWvmnUyqbR6315u3TDFyf2qm4y7ukz5F1ytg5/f1I0j9VsSwPAqMljZLUn6xjxcyiPDOBU9PnE4DZEREp/eTUu3AUMBr4Q4XzNNtAszRjmvVGec60TgcOjog3YP2Nxb8HvlmNgkTEGklnA7OAFuDaiHhE0oXAnIiYCVwD/DB1tFhKFoRI+W4m62CxBvhMRKxN5dxontUor/VcnZ0t3HvQrPHkCVoCCq9Or01pVRMRt5N1ry9MO7/g8yrgxDLTXgxcXMk8zboyaexwBymzBpQnaF0LPCDpZ2TBaiLZmY+ZmVm3yHumdTbZe7QAPhkR86pfJDMzs9LyPHtwEPBdsua5dcDzNSmRmZlZGRUHrYj494jYG/gMsAvwG0l31axkZmZmRTbn5uKXgRfJ7o/aubrFMcv42X9mVkqe92n9I/ARYCfgJ8CZfvCs1YKf/Wdm5eQ509oV+OeImF+jspgBm372n4OWWe9WcdCKiCm1LIhZp2Z6hYlZPfTm5nM/MNcaTrO8wsSsHmrVfN4sgTBPl3ezbuFn/5mVl+fVOTPmtXPYpbMZNfk2Drt0NjPmlXxe+PpA2L68g+DtQFgufz35TMsaTk999l+z/JK1xlZp83meM7Jmuo7soGUNqac9+889Iq1aKm0+zxOImuk6spsHzbZApc0vfhuyVUulzed5AlG568WNeB3ZQctsM+W5DtBMv2StsU0aO5xLjt+X4UNaEdnLSS85ft+Nzp7yBKJmuo7s5kGzzZSn+cU9Iq2aKmk+P2fCmA2apKF8IGqm68gOWmabKc/ZU54DiFk15A1EzXId2UHLeo1q997Lc/bUTL9kredolkCUh4OW9QrLO1Yz5e7q9t7Le/bUEw8gZt3NHTGsV3jptVVV771X6QVxM6sen2nZFmuGm2bfWruOUr/RtrT3ns+ezLqXg5ZtkUa4abaSoNm/pXSjgnvvmTUXNw/aFqn3TbOV3is1dPCAprkPxczKc9CyLVLvm2YrDZpDWvv5+pNZD9AQzYOStgduAkYCzwAfiYhlJfKdCnw5DV4UEden9AOB64BW4HbgcxERkqYCZwKvpGn+LSJur1lFeqF63zSbJ2j6+pNZ82uUM63JwN0RMRq4Ow1vIAW2C4CDgfcAF0jaLo3+DllwGp3+ji6Y9MqI2D/9OWBVWb0f/9JMz0wzsy3XKEFrInB9+nw9MKlEngnAnRGxNJ2F3QkcLWkXYNuIuD8iArihzPRWA/Xu9l3voGlm3UvZcb7OhZCWR8SQ9FnAss7hgjxfAAZExEVp+DygA2gDLo2ID6T09wFfiohjU/PgacDrwBzg86WaHdN0ZwFnAQwdOvTA6dOn56rDypUrGThwYK5pGlkz1Wd5x2peem0Vb61dR/+WPgwdPIAhrf02yNNM9amE69PYemt9jjzyyLkRMa6WZem2a1qS7gLeUWLUuYUD6VpUtSLpd4CvAJH+fw34VKmMETENmAYwbty4GD9+fK4FtbW1kXeaRub6NDbXp7G5PrXTbUGr80yoFEkvSdolIl5IzX0vl8jWDowvGB5BdpbVnj4XprenZb5UsIzvA7/c3PKbmVn9Nco1rZnAqenzqcDPS+SZBXxI0napA8aHgFkR8QLwuqRDUtPiJzqnTwGw04eBh2tVATMzq72G6PIOXArcLOl04FngIwCSxgGfjogzImKppK8AD6ZpLoyIpenzP/J2l/c70h/AVyXtT9Y8+Azw97WvipmZ1UpDBK2IWAIcVSJ9DnBGwfC1wLVl8u1TIv3j1S2pmZnVU6M0D5qZmXXJQcvMzJqGg5aZmTUNBy0zM2saDlpmZtY0HLTMzKxpNESX955gecdqDrt0dkO/ct7MrNk5aFXBjHnttC/roH159rTxerxy3sysN3DzYBVcPmsh64qelt+dr5w3M+stHLSqoN6vnDcz6y0ctKrAb881M+seDlpVcM6EMfSRNkjz23PNzKrPQasKJo0dzvDtWuv2ynkzs97CvQerZEhrP347eXy9i2Fm1qM5aNXBjHntXD5roe/pMjPLyUGrm82Y186UWxfQsXot4Hu6zMzy8DWtbnb5rIXrA1Yn39NlZlYZB61u5nu6zMw2n4NWN/M9XWZmm89Bq5udM2EMrf1aNkjzPV1mZpVxR4xu1tnZwr0Hzczyc9Cqg0ljhztImZlthoZoHpS0vaQ7JT2R/m9XJt+pKc8Tkk4tSL9Y0nOSVhbl30rSTZIWSXpA0sgaV6WqZsxr57BLZzNq8m0cdulsZsxrr3eRzMzqqiGCFjAZuDsiRgN3p+ENSNoeuAA4GHgPcEFBcPtFSit2OrAsIvYErgQuq0HZa6Lzfq725R0Eb9/P5cBlZr1ZowSticD16fP1wKQSeSYAd0bE0ohYBtwJHA0QEfdHxAtdzPcW4Cip6Mm2Dcr3c5mZbUxR9PLCuhRCWh4RQ9JnkZ0dDSnK8wVgQERclIbPAzoi4oqCPCsjYmDB8MPA0RGxOA0/CRwcEa+WKMNZwFkAQ4cOPXD69Om56rBy5UoGDhzYdcYKLWh/rey4fYcPrtpyyql2ferN9Wlsrk9jq7Q+Rx555NyIGFfLsnRbRwxJdwHvKDHq3MKBiAhJ3R5JI2IaMA1g3LhxMX78+FzTt7W1kXeaTTn30tm0l7jhePiQVv7plOotp5xq16feXJ/G5vo0tkaqT7c1D0bEByJinxJ/PwdekrQLQPr/colZtAO7FgyPSGmbsn4aSX2BwcCSLa1Ld/D9XGZmG2uUa1ozgc7egKcCPy+RZxbwIUnbpQ4YH0pplc73BGB2NEJ7aAUmjR3OJcfv63d0mZkVaJT7tC4FbpZ0OvAs8BEASeOAT0fEGRGxVNJXgAfTNBdGxNKU76vA3wFbS1oMXB0RU4FrgB9KWgQsBU7uzkptKd/PZWa2oYYIWhGxBDiqRPoc4IyC4WuBa0vk+yLwxRLpq4ATq1pYMzOrm0ZpHjQzM+tSQ5xp2Zbz25DNrDdw0OoB/DZkM+st3DzYA/jpGWbWWzho9QB+G7KZ9RYOWj2A34ZsZr2Fg1YPUKunZ/jVKGbWaNwRoweoxduQl3esZsrd7txhZo3FQauHqPbTM156bRUdqzc8Ee/s3OGgZWb14uZBK+mttetKprtzh5nVk4OWldS/pfSu4c4dZlZPDlpW0tDBA/xqFDNrOA5avUylPQKHtPbzq1HMrOG4I0YvkvdxT341ipk1Gp9p9SJ+3JOZNTsHrV7Ej3sys2bnoNWL+HFPZtbsHLR6kVo97snMrLu4I0YvUovHPZmZdScHrV7GPQLNrJm5edDMzJqGg5aZmTUNBy0zM2saDlpmZtY0HLTMzKxpKCLqXYaGI+kV4Nmck+0IvFqD4tSL69PYXJ/G1lvrs3tE7FTLgjhoVYmkORExrt7lqBbXp7G5Po3N9akdNw+amVnTcNAyM7Om4aBVPdPqXYAqc30am+vT2FyfGvE1LTMzaxo+0zIzs6bhoGVmZk3DQWsLSTpa0kJJiyRNrnd58pK0q6RfS3pU0iOSPpfSt5d0p6Qn0v/t6l3WPCS1SJon6ZdpeJSkB9J2uklS/3qXsVKShki6RdLjkh6TdGgzbx9J/5L2tYcl/bekAc22fSRdK+llSQ8XpJXcJsp8I9XtIUkH1K/kpZWpz+Vpn3tI0s8kDSkYNyXVZ6GkCd1ZVgetLSCpBfgWcAywF/BRSXvVt1S5rQE+HxF7AYcAn0l1mAzcHRGjgbvTcDP5HPBYwfBlwJURsSewDDi9LqXaPF8HfhUR7wL2I6tXU24fScOBzwLjImIfoAU4mebbPtcBRxelldsmxwCj099ZwHe6qYx5XMfG9bkT2Cci/hL4X2AKQDo+nAzsnab5djoWdgsHrS3zHmBRRDwVEW8B04GJdS5TLhHxQkT8MX1eQXZAHE5Wj+tTtuuBSXUp4GaQNAL4G+DqNCzg/cAtKUvT1EfSYOAI4BqAiHgrIpbTxNuH7D1+rZL6AlsDL9Bk2yci7gGWFiWX2yYTgRsicz8wRNIu3VLQCpWqT0T8T0SsSYP3AyPS54nA9Ih4MyKeBhaRHQu7hYPWlhkOPFcwvDilNSVJI4GxwAPA0Ih4IY16ERhar3Jthv8CvgisS8M7AMsLvoDNtJ1GAa8AP0jNnVdL2oYm3T4R0Q5cAfwfWbB6DZhL826fQuW2SU84TnwKuCN9rmt9HLQMAEkDgZ8C/xwRrxeOi+y+iKa4N0LSscDLETG33mWpkr7AAcB3ImIs8AZFTYFNtn22I/ulPgoYBmzDxs1STa+ZtklXJJ1LdhnhxnqXBRy0tlQ7sGvB8IiU1lQk9SMLWDdGxK0p+aXOJoz0/+V6lS+nw4DjJD1D1lz7frJrQkNScxQ013ZaDCyOiAfS8C1kQaxZt88HgKcj4pWIWA3cSrbNmnX7FCq3TZr2OCHpNOBY4JR4+6beutbHQWvLPAiMTj2f+pNdnJxZ5zLlkq73XAM8FhH/WTBqJnBq+nwq8PPuLtvmiIgpETEiIkaSbY/ZEXEK8GvghJStmerzIvCcpDEp6SjgUZp0+5A1Cx4iaeu073XWpym3T5Fy22Qm8InUi/AQ4LWCZsSGJelosmb24yLizwWjZgInS9pK0iiyDiZ/6LaCRYT/tuAP+GuynjVPAufWuzybUf7DyZoxHgLmp7+/JrsOdDfwBHAXsH29y7oZdRsP/DJ9/guyL9Yi4CfAVvUuX4567A/MSdtoBrBdM28f4N+Bx4GHgR8CWzXb9gH+m+ya3Gqys+HTy20TQGS9jJ8EFpD1nKx7HSqozyKya1edx4XvFuQ/N9VnIXBMd5bVj3EyM7Om4eZBMzNrGg5aZmbWNBy0zMysaThomZlZ03DQMjOzpuGgZZaTpB0kzU9/L0pqT59XSvp2jZb5z5I+kXOac9PT1B9K5Tt4M5a7k6Rf5Z3OrFb6dp3FzApFxBKye6eQNBVYGRFX1Gp56UkRnyJ7Ekal0xxK9iSDAyLiTUk7Arlf9xERr0h6QdJhEfHbvNObVZvPtMyqRNJ4vf3+rqmSrpd0r6RnJR0v6auSFkj6VXp0FpIOlPQbSXMlzSrz9O/3A3+M9EBZSW2SrpQ0R9n7tQ6SdGt6j9NFaZpdgFcj4k2AiHg1Ip7f1DIl7SnpLkl/kvRHSXukec0ATqnRajPLxUHLrHb2IAs4xwE/An4dEfsCHcDfpMD1TeCEiDgQuBa4uMR8DiN7EnqhtyJiHPBdsscFfQbYBzhN0g7A/wC7SvpfSd+W9Few/jmT5ZZ5I/CtiNgPeC/ZExIgexrH+7ZsVZhVh5sHzWrnjohYLWkB2csOO68NLQBGAmPIAs2d2WP4aOHtQFFoFzZ8oSW8/YzLBcAjkZ5lJ+kpYNeImC/pQLJgcyRwk7I3a88ptUxJg4DhEfEzgIhYVbCsl8meyG5Wdw5aZrXT2TS3TtLqePuZaevIvnsiCziHdjGfDmBAqXmneb1ZkN45byJiLdAGtKXAeSrZGdtGy0xBq5wBqQxmdefmQbP6WQjslDpNIKmfpL1L5HsM2DPPjCWNkTS6IGl/4Nlyy4zsrdWLJU1K6VtJ2jpN+06yh9ua1Z2DllmdRMRbZK/juEzSn8iepP3eElnvAI7IOfuBwPWSHpX0ELAXMLWLZX4c+GzK/zvgHSn9SOC2nMs3qwk/5d2sCUj6GfDFiHiiDsu+B5gYEcu6e9lmxRy0zJpAegnk0Ii4p5uXuxNwWETM6M7lmpXjoGVmZk3D17TMzKxpOGiZmVnTcNAyM7Om4aBlZmZNw0HLzMyaxv8HZHGrdgTn8d0AAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Measuring the differential errors of predicted vs experimental')\n", "\n", "\n", "\n", "ax.set_xlabel('Time (mSec)')\n", "ax.set_ylabel('voltage $V_C$')\n", "\n", "x = (res1.t*1000)[0:maxidx]\n", "y = res1.y[0][0:maxidx]\n", "fy = f(x)\n", "\n", "ax.scatter(x, y-fy, label='diff')\n", "ax.grid()\n", "ax.legend()" ] }, { "cell_type": "code", "execution_count": 21, "id": "6a662408", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle \\frac{I_{ES} R_{1} \\left(1 - e^{\\frac{V_{C}}{V_{T}}}\\right) - V_{C} + V_{in}}{C R_{1}}$" ], "text/plain": [ "(I_{ES}*R1*(1 - exp(V_C/V_T)) - V_C + V_{in})/(C*R1)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn2 = solve(eqn1, Derivative(vc,t))[0]\n", "eqn2" ] }, { "cell_type": "code", "execution_count": 22, "id": "5e0e027c", "metadata": {}, "outputs": [], "source": [ "e2 = eqn2.subs([(issym, 1.6e-12),(r1, 6800),(c,100e-6), (vin,5), (vt, .032)])\n", "f2 = lambdify([vc],e2)" ] }, { "cell_type": "code", "execution_count": 23, "id": "63dfe9ab", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - 1.47058823529412 V_{C} - 1.6 \\cdot 10^{-8} e^{31.25 V_{C}} + 7.35294119247059$" ], "text/plain": [ "-1.47058823529412*V_C - 1.6e-8*exp(31.25*V_C) + 7.35294119247059" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "e2" ] }, { "cell_type": "code", "execution_count": 24, "id": "4c3f5307", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "7.35294117647059" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f2(0)" ] }, { "cell_type": "code", "execution_count": 25, "id": "d0df8aef", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('$\\\\frac{d}{dt} V_C$')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('dVc/dt')\n", "ax1.set_xlabel('$V_C$')\n", "\n", "x = np.linspace(0, .6,100)\n", "y = f2(x)\n", "ax1.plot(x,y)\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 26, "id": "afe5b396", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('$\\\\frac{d}{dt} V_C$')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('dVc/dt')\n", "ax1.set_xlabel('$V_C$')\n", "\n", "x = np.linspace(0, .65,100)\n", "y = f2(x)\n", "ax1.plot(x,y)\n", "ax1.set_ylim(0,7)\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 27, "id": "32668090", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - \\frac{V_{C}}{I_{ES} \\left(1 - e^{\\frac{V_{C}}{V_{T}}}\\right)}$" ], "text/plain": [ "-V_C/(I_{ES}*(1 - exp(V_C/V_T)))" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "symid = -eqn2.args[2].args[2]/r1\n", "symrd = vc/symid\n", "symrd" ] }, { "cell_type": "code", "execution_count": 28, "id": "51c634c0", "metadata": {}, "outputs": [], "source": [ "rdf=symrd.subs([(issym, 1.6e-12), (vt,0.0321)])" ] }, { "cell_type": "code", "execution_count": 29, "id": "9143c2ce", "metadata": {}, "outputs": [], "source": [ "f3 = lambdify([vc],rdf)" ] }, { "cell_type": "code", "execution_count": 30, "id": "7c9162d5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Resistance of diode')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('Ohms')\n", "ax1.set_xlabel('$V_D$')\n", "\n", "x = np.linspace(0.01, .6,100)\n", "y = f3(x)\n", "ax1.plot(x,y)\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 31, "id": "ea43e0f4", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Resistance of diode')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('Ohms')\n", "ax1.set_xlabel('$V_D$')\n", "\n", "x = np.linspace(0.01, 1,100)\n", "y = f3(x)\n", "ax1.semilogy(x,y)\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 32, "id": "98ef5be4-8f42-4932-bdd1-5eb379d4405e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Resistance of diode')\n", "\n", "res = linregress(x, np.log(y))\n", "slope = res.slope\n", "intercept = res.intercept\n", "def f4(x):\n", " y = np.exp(slope*x+intercept)\n", " return y\n", " \n", "ax1 = ax\n", "ax1.set_ylabel('Ohms')\n", "ax1.set_xlabel('$V_D$')\n", "\n", "x = np.linspace(0.01, 1,100)\n", "y = f3(x)\n", "ax1.semilogy(x,y, 'r-')\n", "ax1.grid()\n", "ax1.semilogy(x,f4(x),'b-')" ] }, { "cell_type": "code", "execution_count": 33, "id": "5a33a4c5-3684-4221-8aad-bb8573250bd0", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=-28.500520562252703, intercept=24.891568553601804, rvalue=-0.9993844511758472, pvalue=2.105053863075327e-144, stderr=0.10106169422624751, intercept_stderr=0.058785425358614585)" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "res" ] }, { "cell_type": "code", "execution_count": 34, "id": "89f377e3", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - \\frac{V_{C}}{I_{ES} \\left(1 - e^{\\frac{V_{C}}{V_{T}}}\\right) \\left(R - \\frac{V_{C}}{I_{ES} \\left(1 - e^{\\frac{V_{C}}{V_{T}}}\\right)}\\right)} = \\frac{V_{C}}{V_{CC}}$" ], "text/plain": [ "Eq(-V_C/(I_{ES}*(1 - exp(V_C/V_T))*(R - V_C/(I_{ES}*(1 - exp(V_C/V_T))))), V_C/V_{CC})" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn3 = Eq(symrd/(r + symrd), vc/vcc); eqn3" ] }, { "cell_type": "code", "execution_count": 35, "id": "ab257ccb", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - \\frac{V_{C} - V_{CC}}{I_{ES} \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right)}$" ], "text/plain": [ "-(V_C - V_{CC})/(I_{ES}*(exp(V_C/V_T) - 1))" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn4 = solve(eqn3, r)[0]; eqn4" ] }, { "cell_type": "code", "execution_count": 36, "id": "8f8beae3", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - \\frac{625000000000.0 \\left(V_{C} - 5\\right)}{e^{31.1526479750779 V_{C}} - 1}$" ], "text/plain": [ "-625000000000.0*(V_C - 5)/(exp(31.1526479750779*V_C) - 1)" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rdf4=eqn4.subs([(issym, 1.6e-12), (vt,0.0321), (vcc,5)]); rdf4" ] }, { "cell_type": "code", "execution_count": 37, "id": "72e54f68", "metadata": {}, "outputs": [], "source": [ "f4 = lambdify([vc],rdf4)" ] }, { "cell_type": "code", "execution_count": 38, "id": "5f9e8181", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Required Inline Impedance for Voltage')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('Required Inline Impedance ($\\\\Omega$)')\n", "ax1.set_xlabel('$V_D$')\n", "\n", "x = np.linspace(0.01, 1,100)\n", "y = f4(x)\n", "ax1.semilogy(x,y)\n", "ax1.grid()" ] }, { "cell_type": "code", "execution_count": 39, "id": "fc6ba93a", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle I_{ES} \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right)$" ], "text/plain": [ "I_{ES}*(exp(V_C/V_T) - 1)" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqndiode = -eqn1.args[0].args[2]; eqndiode" ] }, { "cell_type": "code", "execution_count": 40, "id": "2ea011f8", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - I_{ES} R \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right) - V_{C} + V_{CC}$" ], "text/plain": [ "-I_{ES}*R*(exp(V_C/V_T) - 1) - V_C + V_{CC}" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn6 = vcc-r*eqndiode-vc; eqn6" ] }, { "cell_type": "code", "execution_count": 41, "id": "bcf14d31", "metadata": {}, "outputs": [], "source": [ "f5def = (eqn6-vc).subs([(issym, isval),(vcc,5),])\n", "f5 = lambdify([vc, vt,r],f5def)" ] }, { "cell_type": "code", "execution_count": 42, "id": "e0266705", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-26.802408272819907" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f5(0.7, vtval, 6800)" ] }, { "cell_type": "code", "execution_count": 43, "id": "418a25d6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ " converged: True\n", " flag: 'converged'\n", " function_calls: 15\n", " iterations: 14\n", " root: 0.6931488934999699" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "root_scalar(f5, args=(vtval,1000), bracket=(0,1))" ] }, { "cell_type": "code", "execution_count": 44, "id": "5112c836", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.set_title('Calculation of saturation voltage')\n", "ax1 = ax\n", "ax1.set_ylabel('Saturation voltage (volts)')\n", "ax1.set_xlabel('$V_T$')\n", "x = np.linspace(0.02, 0.05, 100)\n", "\n", "for rval in (500, 2000,6000,10000):\n", " y = list(map(lambda v: root_scalar(f5, args=(v,rval), bracket=(0,5)).root, x))\n", " ax1.plot(x,y, label='R = {}'.format(rval))\n", "ax1.grid()\n", "ax1.legend()" ] }, { "cell_type": "code", "execution_count": null, "id": "7784a133-dc44-437d-9156-2c8da58779b6", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "id": "c884cae2-d146-4b83-af2c-1cdd9fcad225", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinregressResult(slope=19.362505778884223, intercept=0.012455723437958088, rvalue=0.9999871327357865, pvalue=1.0480505272651102e-226, stderr=0.009922264979279526, intercept_stderr=0.0003579607711959949)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x = np.linspace(0.02, 0.05, 100)\n", "y = list(map(lambda v: root_scalar(f5, args=(v,6000), bracket=(0,1)).root, x))\n", "fig, ax = plt.subplots()\n", "\n", "ax.set_title('Calculation of saturation voltage')\n", "\n", "\n", "ax1 = ax\n", "ax1.set_ylabel('Saturation voltage (volts)')\n", "ax1.set_xlabel('$V_T$')\n", "res = linregress(x,y)\n", "slope = res.slope\n", "intercept = res.intercept\n", "def g(x):\n", " y = np.array(x)*slope + intercept\n", " return y\n", "\n", "ax1.plot(x,y,'b-')\n", "ax1.plot(x,g(x),'r--')\n", "ax1.grid()\n", "res" ] }, { "cell_type": "code", "execution_count": 46, "id": "c2b622f0-56f0-446e-ae84-3f536613271f", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - I_{ES} R \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right) - V_{C} + V_{CC}$" ], "text/plain": [ "-I_{ES}*R*(exp(V_C/V_T) - 1) - V_C + V_{CC}" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "eqn6" ] }, { "cell_type": "code", "execution_count": 47, "id": "f63a8efe-e19e-4a75-be06-4341ee55a50c", "metadata": {}, "outputs": [ { "data": { "text/latex": [ "$\\displaystyle - 6800 I_{ES} \\left(e^{\\frac{V_{C}}{V_{T}}} - 1\\right) - V_{C} + 5$" ], "text/plain": [ "-6800*I_{ES}*(exp(V_C/V_T) - 1) - V_C + 5" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f6def = eqn6.subs([(r,6800),(vcc,5),])\n", "f6 = lambdify([issym , vc, vt],f6def)\n", "f6def" ] }, { "cell_type": "code", "execution_count": 48, "id": "6d9a4063-2c14-4eb6-9dfd-ee9c92b85820", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-351.2689813249657" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f6(1.6e-8, 0.60, 0.04)" ] }, { "cell_type": "code", "execution_count": 49, "id": "9b411121-082b-4131-99d7-80dc45d3260c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ " converged: True\n", " flag: 'converged'\n", " function_calls: 3\n", " iterations: 2\n", " root: 1.785233779718639e-12" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "vcval = 0.65\n", "vtval = 0.033\n", "root_scalar(f6, args=(vcval, vtval), bracket=[1.6e-30,1.6e-8], rtol=.01)\n", "\n", "#y = list(map(lambda v: root_scalar(f6, args=(v, isval), bracket=[.1,.8], rtol=.1).root, vtval))\n" ] }, { "cell_type": "code", "execution_count": 50, "id": "2068821f-afd3-42d6-b394-e303ba0b0ce3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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dOXMpRaC6MJvGshwu2VBFQ2kODSU5rCh1WhA2MD2LkZNuGByEkwcibh90xhym79+QW+EEQc0m2HCV02ooqueZN46y7d0ftHBYBBYexngQCiuH+0Y4cHyYAz1Dp4VE5E5x6alCfXEOjWW5bGsuoaEkh4ayXBrc/R4y0uJzZrdlLTA62ZV08sDkpfcgnDwEp/qmPj+ryAmHinWw5hIobnQCotjtWsrImfFlRjpaLTgWiYWHMa5wWDncP8qB40O0HXcCos0Ni0MnhhmLCIhMd/yhsSyXlrUVbgsil4bSHGqKsu1QGtOpwmD31GA42ea0HE4egIGuqc9Py5oMg/q3OOFQ3DC5LMGnsS61/rF+uga7Ji9DXRwePEznYCflgXJakvHwJCLSDNwMFKrq1bMtM8YLVaV74BRtbkCMB8WOQ8Mc/8XPprQgMtNSaCjNobksl3etq6ChNJfGUicwqgqybPxhusDo6S2HE22Tt4MjEU8WZ7pqSROsvMgNhUYnGIobnW6nFGuhgXtukohw6BzspGvIvXaXDQYGp6yTnZZNdW411XnVlGppXMoV1/AQkfuA9wLdqvqmiOWXAt8BUoF7VPW22bahqvuBG0TkkWjLjInUOzw2ERBtx4fYHxEUwxFjEBmpKawozaEiJ4XLN62gsSyXptJcC4jZDJ9wWgwn2tzrA5P3p7ce0nOdIChphlXvmuxaKmmyQelpBsYG6BzsdC4Dk+EwHhBDgaEpz89Jy6Emr4bavFrOqzyPmtyaifvVedUUZxZP7LfT2toalzLHu+XxAHAn8L3xBSKSCtwFvBvoAJ4TkUdxguTWaet/TFW741xGs0yNBkIc7Bmm7fgg+44NTQmLyFlMKQJ1xTk0l+eytamEprJcmspyaSzNnehiam1tpaXlLB9rkyDCYXdaaxuc2B8REu716LSxh7wqJwya3+G2HJqc+8WNkFt+xu/r4NVwYHii1dAx2DHZghjsomOwg4GxgSnPz0nLoTa/ltrcWrZUbpkSDHV5dRRkFPi+U2dcw0NVnxKRxmmLtwJ73dYDIvIwcKWq3orTSjFmgqpyuG+U/ceG2H98kP3Hhth3bJC240N09o6gEftBVORn0lSWyyUbqmguc1oPzeW51Nsg9VShIPS1U3ziJXhurxMMJ/Y7l5MHps5cSklzBqaLm6Buc0Q4uAExy8B0sgmEAxwZOuKEw0DHRAtiPCxOjE49PElWahY1eU5r4Zzyc6jLq3MCwg2MwsxC38NhLqKRn754vIATHj8Z77YSkauBS1X14+79PwK2qeqNs6xfCtyC01K5R1VvnWnZDOttB7YDVFZWnvfwww/Puw6Dg4Pk5SXPTkF+1Hc0qBwZCnNkSDk8FOawe/vIcJjIY/RlpUJVbgpVueJc5zi3K3NTyE6b/4ftTPsbSzhI1ugxske6yB457F4fIXvkMFmjR0nRyTc1lJLBSHY1o1lVjGRXMZJd7V6qOJVZjqacOSdZmu/fWVUZDA/SE+zhePA4PcGeicvxwHF6Q72EmRwvSyGFkrQSStJKKE0rPe2Sn5K/ZOGwkP/bF1100QuqunmmxxJ+wFxVe4BPzrVshvXuBu4G2Lx5s7a0tMy7DE6XxvzXX27iVV9V5Wj/KfYdG3Qu3U530/5jg3T1Tf7aFYH64hyaa3K5uCyP5nKnBbGyPI+K/My4fOiW5d84FHCmt57YDz374MS+ydu9hyAiIMjIc8YeqrZOtBxeah9g0zs/SGpeJXkpKZw50Tm7aH/nU6FTTjfSQAftA+10DHZMtCI6BjoYDk49flVZdhl1+XW8Nf+t1ObVUpdXR21eLbX5tVTmVJKWkhhfr/H6v+1H7TqB+oj7de4yc4YYC4Y52DPE3m43JNyupn3dg1N2mMvPTKO5PJdtzaWsdMOhuTyPhtIcO6XouHDI2QmuZzwY9k4GRe+hqYfVyMh3gqFmI7zpg05YlK50rmcYf+gbaIWC6qWsja9UlYHQAL8/9nsnHKaFRPdwN8pkT0x2WrYTCvl1bK3aSl2+Gw7uJSc9ubvs/AiP54DVItKEExrXAh/2oRxmgQZGA+w75oTERFB0D3LwxDChiIMy1RZl01yey4c217OyIm8iKOLVilh2VGHgiBMMJ/ZNBkTPPmeQOvKgfOm5UNoMVefAhvdDycrJkLABaoLhIEeGjtA+0D4lIMZDYigw5EzTcVVkV1CbX8u26m3U5ddRl1dHfX49dfl1lGaV2v/PKOI9VfchnJPnlolIB/BlVb1XRG4EHseZYXWfqu6IZznMwvQMnmKPGxCRlyP9k11N6alCY2kua6vyufzsalZV5LGqIo+mslw7UdC40b7JYDi+x73t3o+cipma6QRC2Wpn7+nSVW4LYiXkVyV9QJwKnZoSCof6D9E+2E57fztdg10EdbI1lp6STm1eLfX59ZxbeS6njp7iok0XUZ9fT21eLVlpNl14vuI92+q6WZY/BjwWz9c2sRnfeW7P0UF+fiDAE//nVfYeHWRP98DkiYNwThq0siKPt60sZaUbEKsq8mgoySEt1WY0ERxzZiz1RITDcfd6KGLWuaQ4+zqUrYaGt00GROkq52RAZ9Ag9XwMB4adYBg45IRDxO3p3Ut56XnU59ezrmQd72l8D/X59U7rIa+OipwKUiPey9bWVlrqW3yo0ZnHfhImmfGpr3u6B9lzdIA9bkDs6R5kYHTyF1th9mFWV+Rx6ZuqWFmex+rKfFZX5FFdmGVNeVUYOua2HvZMtiKO73GCI3KgOrfcCYQ1lzhBUbLSuS5uhLRMv2qQEAbHBp1AcEMhMiSOjxyf8tySrBLq8+vZUrWFFQUrWJG/YiIkijKL7P+kDyw8zlDjM5veODrAG25IvNE9wN6jgwxEnESoNDeD1ZV5XLWxllUVeayuzOPY3le54j0t9oEMjjljDsffcILh+B7nds+eqTvLpWY6rYbKDc44RNlqtyWxCrKLfCt+IhgKDHGw/yCH+g851+NBMXDotH0fKrIrqC+o54LaC1hR4ITDeEjkZSTDXLDlxcLjDHB88BRvHBlg99EB3jg6OBEYkS2JiZDYVMuaysmWRGne6b9+W9sluYJj5CQFfbvgpY6IoHjD2XkushWRXwNlq+BNV7sBsdq5X1if1N1MI8GRKeEQGRY9oz1TnluRU0FDQQMX1TvjDg0FDRMtiGSfvbTcWHgsI30jAfYcdUMiIiwiD8VRlJPOmsp8rtxYw5rKfFZX5LOmcuaQSCrhMPS74XDsjYiQ2A1DxzgX4CUgNcNpMVSc5bYi1jj3y1ZDZr7PlfBPIBSgfbCdg31OQBzoP8Chfue6e3jqEYTKsstYkb+CC+suZEXBChoKGiZaEBYQZw4LjwQ0Ggixt9tpQew+MhkWkTvS5WWmsaYyj/ecVcmaynz3kkd5sk9/DQWc/SGO7XaC4dgbzvXxPRCI2MkruxjK1rpjEWv5/ZExzml5vzMWkaStiLCG6R7upq2vjYP9BznYf5AD/Qc42H+QzsFOwjq5B3VRZhErClawrWobDQUNNBQ20JDfwIqCFeSm5/pYC7NULDx8FA4r7SeH2XXECYldR/rZfWSAAz2T+0lkpKawsiKPrU0lrK0qYG1VHmsq86ktyk7ukAiMOIPUx3a7l13O9Yl9U3ecK6iD8jVw7tuc67K1Tmsit2zKlNcTra3OuEUS6B/r52DfQZ4dfJbfv/j7iZA41H+I0dDkD5TstGwaCxrZULqBy5ouo7Gg0QmKggYKM+18GsnOwmOJ9A6PTQmJ8duRhwdfUZIzsZ/E2qp81lXl01iam9xTYMeGTg+IY7ucWU3j0zUlxd0vYi2suxzK1zkBkcRdTYFwgM6BzolWxIH+A7T1tXGg/8CUgerUE6nU5tXSWNjItuptNBY0OpfCRsqzy5P7B4qJysJjkQVDYdqOD7HzyAA7D/ez67ATFIcjupyKctJZV5XPNZvrWVeVz9oqp9spqXemOzXodjPthu6dblDscg7BMS4l3QmEmo1wzh9C+VonKEpXJu20175TfbT1tTmX/jYO9Dkh0THQMWVnuZKsEhoLGmmpb5loQXTv7uaD7/wg6al23nQTuyT+tlq4k0Nj7Dzcz+tuQOw83M+e7sGJ05Wmpwory/N4S3PpREisry5I7sNyjA07IdG9C47tnLyODInUDGcmU90W2PSRyZAoaYIk/KILhUN0DXbR1t82GRQztCLSU9JZkb+CVUWruLjh4okWRGNB44zdTK37Wy04zLxZeHgQVmX/sUFeP9zvhEVXPzsPD0w5PEdZXibrq/P547c1sr46n3VVBawsz0ve80gETzmD1Md2QffrTkh0vz61u2l6SFSsc0KiuAlSk++/5khwZKLl0Nbfxv7e/bT1t3Gw7yBj4ckZdeOtiIvqL6KpsInGgkaaCpuoyatJmCO5mjOf/U+L4o2jA3z+h79nR+cwY4//CoC0lPHWRAnrqwsmLuX5ydltQjjk7A/R/brT3TR+3bN3ch+JlDRnumv1m+HN17khsd4Zp0jCkOgd7WV/3/4plwN9B+ga7Jo47EaKpFCXV0djYSPn15xPU2GTcylooiiryN8KGIOFR1RF2elkpqXwjro03rPlLNZXF7C6Mo/MtCScyjl+5NfuHXD0dTckXnfGKCbOPCfOVNeK9bD+fc51xXqndZGW4Wfpl5yqcmzkGPt69zkB0TsZFJFdTVmpWTQWNnJO+TlcteqqiZBoKGggMzVJf5CYZcHCI4qKgiwe3v5W52Bqm+vnXmExjQ3D/l9OnXa6RMqO7YAX26G/E/o63JbFDhg5OfmkvConGLZ83NmhbrzLKWNp5/gfHznOS90vLXg7O4Z2EDgYmPuJEVSViX+qdA93s693H/v69rG/dz+DgcGJ5+Zn5LOycCUt9S00FzbTVNhEc2EzNXk1pEiSdm2aZc3CI1G98iD812d8eek3AewAEMirgKIGOOtKqNjgBEblBsgp8aVs093+wu08uu/RxdlY68I3UZpVysqilfxB8x+wsmglzYXNNBc2U5ZdlryTJMwZycIjUZ0acK4/8d+wxOcceO6559hyfotzLKcE724aCgyxIn8Ft190+4K28/xzz7N5y4ynao5Kxv+JUJpVauMRJmlYeCSqkNtdVXXOkk9PHco75oxdLAPBcJDc9FzWFK9Z0Ha6MroWvA1jkol1tiaqsNv/blMvowqGgzY91RgfWHgkqlDACQ7rJ4/KwsMYf1h4JKpwwDkch4kqEA5YeBjjAwuPRBUKOntgm6iCGiTdQtaYJWfhkajCgaTc+zpW1m1ljD8sPBJVaMy6rTwIhAOkiYWHMUvNwiNRhYJJeQTZWFnLwxh/WHgkqnDApul6YOFhjD8sPBJVKGAtDw8sPIzxh4VHogrbbCsvgmGbbWWMHyw8ElXIuq28sJaHMf6w8EhUoTHrtvIgEA5Yy8MYH1h4JKpw0KbqemAtD2P8YeGRqEK2k6AXFh7G+CPhw0NEmkXkXhF5JGLZehH5RxF5RET+1M/yxY0d22pOqkpQLTyM8UNcw0NE7hORbhF5bdryS0Vkt4jsFZEvRNuGqu5X1RumLdupqp8ErgHOX/ySJwCbqjunoDrnPLE9zI1ZevFueTwAXBq5QERSgbuAy4CzgOtE5CwROVtEfjLtUjHbhkXkCuC/gMfiV3wfhW0P87kE3fO7p9v7ZMyS8/STTUQ2AxcANcAI8Brwc1U9GW09VX1KRBqnLd4K7FXV/e62HwauVNVbgfd6LbiqPgo8KiL/BTw4Q5m3A9sBKisraW1t9brp0wwODi5o/fnYOtDLoJ7k9SV+XfCnvvMxHB4G4MD+A7Qeb13QtpZLnReT1Tk5xKvOUcNDRD4K/DnQBrwA7AaygLcDn3e7o/5KVQ/F8Jq1QHvE/Q5gW5QylAK3AJtE5IuqequItAAfADKZpeWhqncDdwNs3rxZW1paYijiVK2trSxk/Xl5OZ2c6loqlvp18am+83Bi9AR8H9atXkfL+pYFbWu51HkxWZ2TQ7zqPFfLIwc4X1VHZnpQRDYCq4FYwiMmqtoDfHLaslagNV6vmRBsqu6cxrutbMDcmKUX9VOnqnfN9piIbFHV5+bxmp1AfcT9OneZiWRTdec0MeZhIWvMkotpwNwd2P66iOwF/mGer/kcsFpEmkQkA7gWeHSe2zpz2VTdOVnLwxj/zPmpcwe8r3MvAaAB2KyqBzys+xDQApSJSAfwZVW9V0RuBB4HUoH7VHXHfCtwxrLT0M7JWh7G+GeuAfOngQLgYeCDqrpHRNq8BAeAql43y/LHOFOn2C6W0Jh1W80hEA4A1vIwxg9zdVsdBfKBSqDcXaZxLZFxWLfVnKzbyhj/RA0PVb0KOBtnmu5XRKQNKBaRrUtQtuQVDoOGbSfBOVjLwxj/zPmpU9U+4H7gfneP72uA20VkharWR1/bzIv7pWjn84jOWh7G+Cem2Vaq2q2qd6rq+Tg7Cpp4CLnhYS2PqOzYVsb4J2p4iMg/icjZszx8XEQ+JiLXx6FcyW285WGzraKyY1sZ45+5frLdBfyVGyCvAcdwDk+yGmcW1n3Av8W1hMko5HwpWrdVdNZtZYx/5trD/GXgGhHJAzYD1TgHRtypqrvjX7wkFRpzru0XdVQTA+bWbWXMkvP0qVPVQc70Y0klkokBcwuPaGwnQWP8k/BnEkxK491W1vKIyrqtjPGPhUcisqm6nlh4GOMfT+EhIh/ysswsEpuq64ntJGiMf7y2PL7ocZlZDDZV1xMb8zDGP3MdGPEy4HKgVkT+V8RDBUAwngVLaiHrtvLCWh7G+GeuT10X8DxwBc7xrcYNAH8Rr0IlPeu28sTGPIzxz1z7ebwCvCIiD6pqYInKZGyqricWHsb4x+unbquIfAXnRFBpgACqqs3xKlhSs6m6ntixrYzxj9dP3b043VQvAKH4FccANlXXo2A4SJqkISJ+F8WYpOP126lPVX8a15KYSSGbbeVFMBy0gyIa4xOv4fFLEfl74EfAqfGFqvpiXEqV7GzA3JNAOGBdVsb4xOsnb5t7vTlimQLvXNziGMC6rTwKhoM2WG6MT7weGPGieBfERLCWhycWHsb4x+vhSSpF5F4R+al7/ywRuSG+RUtiNlXXk0A4YOFhjE+8Hp7kAeBxoMa9/wbw6TiUx4BN1fXIWh7G+MdreJSp6g+AMICqBrEpu/ETtm4rL4LhoB3XyhifeA2PIREpxRkkR0TeAvTFrVTJbvxMgvbFGJV1WxnjH6+fvL8EHgVWishvgHLg6riVKtlZt5Un1m1ljH/m/OSJSCrwDveyFufQJLvtWFdxFA4AAimpfpckoVl4GOOfObutVDUEXKeqQVXdoaqvWXDEWShgrQ4Pghq0nQSN8YnXT95vRORO4PvA0PhC28M8TsJBG+/wIBgOkpFih3Axxg9ew2Oje/21iGVLsoe5iDQDNwOFqnq1u6wF+DqwA3hYVVvjXY4lZS0PT4LhIDnpOX4Xw5ikNGe3lTvm8aiqXjTtMmdwiMh9ItItIq9NW36piOwWkb0i8oVo21DV/ao6fYdEBQaBLKBjrnIsO2ELDy+C4SDpYu+TMX7wPOYxz+0/AFwaucANo7uAy4CzgOvcPdbPFpGfTLtUzLLdX6vqZcDnga/Os2yJKzRm3VYe2FRdY/wT1zEPVX1KRBqnLd4K7FXV/QAi8jBwpareCrzXS2FUNezePAlkzvQcEdkObAeorKyktbXVy6ZnNDg4uKD1Y7Wuq4PCQJBnlvA1Iy11feerf7CfE2MnFqWsy6XOi8nqnBziVWc/xjxqgfaI+x1MHrX3NO7OibcAm0Tki6p6q4h8ALgEKALunGk9Vb0buBtg8+bN2tLSMo+iOlpbW1nI+jE7/j0I5C/ta0ZY8vrO020/vI2aihpaLmhZ8LaWS50Xk9U5OcSrzgl/VF1V7QE+OW3Zj3DOLXJmsgFzT2w/D2P84+mTJyJ/PdNyVf3aTMvn0AnUR9yvc5eZcTZV1xMLD2P84/nYVhGXEM5gd+M8X/M5YLWINIlIBnAtzqFPzDhreXgSVDswojF+8dpt9a3I+yLyTZxDtEclIg8BLUCZiHQAX1bVe0XkRnf9VOA+Vd0Ra8HPaKExCw8PAiGbbWWMX+b7ycvB6W6KSlVnnOKrqo8Bj83ztc981m3liXVbGeMfr2Mer+Iejh2ntVDO1JlXZjGFApBhe07PxY5tZYx/vH7yIve/CAJH3RNCmXgIB6zlMYewhglr2MY8jPFJ1PAQkS04ZxH86bTll4lIt6q+ENfSJatQ0MY85hAMO79drNvKGH/MNdvqG8DrMyx/Hfj7xS+OAezYVh6Mh4e1PIzxx1zhka+qB6cvdJeVxadIxo5tNbeAe553a3kY44+5wqM4ymM2ohsv1m01JwsPY/w1V3j8QkRuEREZXyCOrwH/Hd+iJbFwAOxLMSob8zDGX3N98j4D3APsFZGX3WVvBp4HPh7HciU328N8ThYexvgr6idPVYdwzrfRDGxwF+8YP5y6iRObqjsnCw9j/OX18CT7AQuMpWJjHnOy2VbG+MvrgRHNUrKpunMKqrU8jPGThUeiUbWpuh4EQs5sK2t5GOMPC49EEw4519byiGqi5WHHtjLGF/MODxH5yWIWxLjc/Rdsqm50NmBujL8W0vL4xKKVwkxyu2Os5RGd7SRojL88hYeI5IpISsT9FKAvbqVKZu4valIz/C1HgrPZVsb4y2vL40mmHo4kB/jF4hfHTLQ87Bd1VNZtZYy/vIZHlqoOjt9xb9uxreIhNOZcW7dVVNZtZYy/vIbHkIicO35HRM4DRuJTpCQ3MWBu4RGNtTyM8ZfXT96ngX8XkS5AgCrgD+NVqKQWGh/zsPCIxsLDGH95PTzJcyKyDljrLtqtqoH4FSuJ2VRdT2zA3Bh/Re22EpEtIlIF4IbFucAtwLdEpGQJypd8bKquJ9byMMZfc415fBcYAxCRC4HbgO/hTNO9O75FS1I2VdeT8T3MreVhjD/m+tmWqqon3Nt/CNytqj8Efhhxfg+zmMZnW9kv6qjGj21lLQ9j/DFXyyNVZOLgQe9i6tkD7VMbD9Zt5YkdVdcYf831yXsI+JWIHMeZmvtrABFZhe1hHh82VdeTif087MCIxvhirjMJ3iIiTwLVwBOqqu5DKcCfx7twSWliqq59KUYTDAcRhNSUVL+LYkxSmvMbSlV/N8OyN+JTHGMtD2+C4aB1WRnjIzufR6KZGPOw2VbRBMNBm2lljI8S/qebiDQDNwOFqnq1u+wC4Hqc8p+lqm/zsYiLywbMPQmEA9byMMZHcW15iMh9ItItIq9NW36piOwWkb0i8oVo21DV/ap6w7Rlv1bVTwI/Af558UvuI9vD3BPrtjLGX/H+9D0A3ImzYyEAIpIK3AW8G+gAnhORR4FU4NZp639MVbujbP/DwA1RHl9+rOXhiYWHMf6K66dPVZ8SkcZpi7cCe1V1P4CIPAxcqaq3Au/1um0RWQH0qerALI9vB7YDVFZW0traGnsFXIODgwtaPxY1na+zBvjN754lkFG0JK853VLWd746jncQPBVctHIuhzovNqtzcohXnf346VYLtEfc7wC2zfZkESnFOZ7WJhH5ohsy4LQ47p9tPVW9G/cQKps3b9aWlpZ5F7i1tZWFrB+Tp1+HPXD+Be+A7OKlec1plrS+8/TYrx6j+0T3opVzOdR5sS3XOgcCATo6OhgdHY153cLCQrKysuJQqsTlpc5ZWVnU1dWRnu69xyPh2/2q2gN8coblX/ahOPEXttlWXgTVZlslq46ODvLz82lsbEREYlp3YGCA/Pz8OJUsMc1VZ1Wlp6eHjo4OmpqaPG/Xj6m6nUB9xP06d5mBiNPQ2hdjNDbbKnmNjo5SWloac3CYmYkIpaWlMbfk/AiP54DVItIkIhnAtcCjPpQjMdmAuSeBcMAOTZLELDgW13zez3hP1X0IeBpYKyIdInKDqgaBG4HHgZ3AD1R1RzzLsayEAyCpYB+OqGy2lTH+ivdsq+tmWf4Y8Fg8X3vZCgWs1eGBhYcx/rLDkySacNDGOzyw8DB+2rdvH2efffaUZadOnaKpqYkdOxa3I+VnP/sZa9euZdWqVdx2222zPq+3t5err76adevWsX79ep5++ulFLcd09ulLNKGAHVHXAwsP46empiY6OjoIh8OkpDi/we+++24uvPBCNmzYsGivEwqF+NSnPsXPf/5z6urq2LJlC1dccQVnnXXWac+96aabuPTSS3nkkUcYGxtjeHh40coxE/v0JZpwwKbpemAHRjR+SklJYcWKFRw4cIDm5mZGRkb41re+teg74z377LOsWrWK5uZmAK699lr+4z/+47Tw6Ovr46mnnuKBBx4AICMjg4yM+H6PWHgkmlDAuq08sKm6BuCr/7mD17v6PT8/FAqRmhr9HDBn1RTw5ffN3XpYv349u3btorm5mbvuuov3ve99NDY2eirHBRdcwMDA6QfH+OY3v8nFF188cb+zs5P6+sk9G+rq6njmmWdOW6+trY3y8nI++tGP8sorr3Deeefxne98h9zcXE/lmQ/79CUa67byxLqtjN/Wr1/P7t27ufDCC7nzzjsnvtTvv/9+XnzxRVSV3NxcvvGNb5y27q9//etFLUswGOTFF1/kjjvuYNu2bdx0003cdtttfP3rX1/U14lkn75EE7aWhxfWbWUATy2ESIu5h/n69et58skn+c53vsP1119PZWUlTz/9NK+++ip33HEHAGNjYzOu67XlUVtbS3v75NGcOjo6qK2tPW29uro66urq2LbNOdLT1VdfHXVwfTFYeCQam6rribU8jN/Wr1/Prbfeyi9+8QteeOEFAB544AFuvvnmiefMNu7gteWxZcsW9uzZQ1tbG7W1tTz88MM8+OCDpz2vqqqK+vp6du/ezdq1a3nyySdnHFRfTDZVN9GEg3YuDw+CGrQ9zI2v1qxZw6uvvsr27dspKioCnEOnpKVN/r8MhUILeo20tDTuvPNOLrnkEtavX88111wzZTbX5ZdfTldXFwB33HEH119/Peeccw4vv/wyX/rSlxb02nOWLa5bN7EL2WwrLwLhAOnWQjM+yszMJBgMTln2uc99js985jOUl5czMDDA7bffPhEs83X55Zdz+eWXz/jYY49N7mu9ceNGnn/++QW9ViwsPBJNaMy6rTwIhq3lYRLPhg0beOihh/wuxpKwbqtEY91WnthUXWP8ZeGRaGzAfE6qagPmxvjMwiPR2FTdOYXUGYS08DDGPxYeiSYUtJbHHIJhZ5DSwsMY/1h4JJqwdVvNZTw8bCdBY/xj4ZFoQmPWbTWHgHued2t5GOMfC49EY91Wc7KWhzH+s/BINOGATdWdg415GOM/C49EY1N152ThYYz/LDwSjZ2Gdk4Bdcc8bA9z45PldBraN73pTZx99tls3LiRzZs3L1q57NOXaKzlMaeJMQ97n4xPlttpaH/5y19SVla2aOUCC4/EY1N15zTRbWUtD+MTOw2thUdiUbVuKw9sqq6Z8NMvwJFXPT89OxSc+0ydVWfDZXOfSGm5nIZWRHjPe96DiPAnf/InbN++3VMZ52KfvkQScr4U7TS00dmAuUkEy+U0tI8//jhr166lu7ubd7/73axbt44LL7xwwa9pn75E4v6itpZHdBYeZoKHFkKkkSQ8DW1NTQ0AFRUVvP/97+fZZ5+18DjjTLQ8LDyisZ0ETSJYDqehHRoamjhv+9DQEE888QR//dd/PY/ans7CI5G4X4rW8ojOWh4mEYyfhvaWW26Jehra1NTUeb9G5GloQ6EQH/vYx047De0999xDTU3NxGlox8bGaG5u5v777+fo0aNceeWVpKSkEAwG+fCHP8yll1467/JMKduibMUsDmt5eGItD5MIlsNpaIuLi/ntb3+7aF11kSw8EknI7R+18IjKZluZRGWnoU0gItIsIveKyCMRy84SkR+IyD+IyNV+lm9RWbeVJxYexvgvruEhIveJSLeIvDZt+aUisltE9orIF6JtQ1X3q+oN0xZfBtyhqn8KfGSRi+0fm6rriY15GOO/eH/6HgDuBL43vkBEUoG7gHcDHcBzIvIokArcOm39j6lq9wzb/RfgyyJyBVAah3L7w6bqehJU28PcGL/F9dOnqk+JSOO0xVuBvaq6H0BEHgauVNVbgfd63G438Ck3iH60iEX2lw2Ye2ItD2P858enrxZoj7jfAWyb7ckiUgrcAmwSkS+q6q1uIH0JyAX+fpb1tgPbASorKxd0zJnBwcFFP2bNTAr6dnEu8MqOnZw8nB3315vNUtV3vnb17wLgmaefISclZ1G2meh1joflWufCwsIZd7DzIhQKzXvd5cprnUdHR2P6/5DwP91UtQf45LRlB3CDIcp6dwN3A2zevFlbWlrmXYbW1lYWsr5nB9LgJXjzxvOg+R3xf71ZLFl952n/a/vhBWi5oIWc9MUJj0Svczws1zrv3Llz3lNPBxZxD/Plwmuds7Ky2LRpk+ft+jHbqhOoj7hf5y4z1m3lie3nYYz//AiP54DVItIkIhnAtcCjPpQj8dhUXU9szMMY/8V7qu5DwNPAWhHpEJEbVDUI3Ag8DuwEfqCqi3vqreXKpup6EgwHSZVURMTvohiTtOIaHqp6napWq2q6qtap6r3u8sdUdY2qrlTVW+JZhmXFpup6EgwHrdVhfJVIp6HdvXs3GzdunLgUFBTw7W9/e1HLMBP7BCaSiZZHfM8AttwFwgEb7zC+SqTT0K5du5aXX3554vm1tbW8//3vX7QyzCbhD0+SVMbHPKzbKipreRi/RZ6GFpg4De1Xv/rVRX2dyNPQZmRkTJyGdjZPPvkkK1eupKGhYVHLMRP7BCaS8QMj2q/qqALhgIWHAeAbz36DXSd2eX6+l0OkrytZx+e3fn7ObSXSaWjHPfzww1x33XWeyrBQ9glMJDZV1xNreZhEkEinoQXnrIWPPvoot946/ShP8WGfwERiU3U9CWrQjmtlADy1ECIt5k6CiXQaWoCf/vSnnHvuuVRWVi6kWp7ZJzCR2FRdT6zlYRJBIp2GFuChhx5asi4rsAHzxGJTdT2x8DCJYPw0tNu3b496GtqFiDwN7fr167nmmmtOOw1tV1cXQ0ND/PznP+cDH/jAgl4vprIt2SuZuYXGZ1vZVN1oguGgTdU1vku009D29PQs6HViZeERxeGuF7jjl/8vI6MjPNG5BEe5He2FslL47V8B/u09feT4EZ749RO+vf5cXu95nercar+LYcxpkuk0tBYeUYyc6uXFU8dQFDk1GP8XFCC/CLpfiv9rRTF6apSu7i5fyxBNRmoGb615q9/FMCapWXhE0dz0Ln7W9OqyPXT1fCVbfY0xsbMBc2OMMTGz8DDGLDuq6ncRzijzeT8tPIwxy0pWVhY9PT0WIItEVenp6SErKyum9WzMwxizrNTV1dHR0cGxY8diXnd0dDTmL8nlzkuds7KyqKuri2m7Fh7GmGUlPT2dpqamea3b2toa03m6zwTxqrN1WxljjImZhYcxxpiYWXgYY4yJmSTDjAUROQYcXMAmyoDji1Sc5SDZ6gtW52RhdY5Ng6qWz/RAUoTHQonI86q62e9yLJVkqy9YnZOF1XnxWLeVMcaYmFl4GGOMiZmFhzd3+12AJZZs9QWrc7KwOi8SG/MwxhgTM2t5GGOMiZmFhzHGmJglXXiIyKUisltE9orIF2Z4PFNEvu8+/oyINLrLt4rIy+7lFRF5v9dt+m2x6ywi9SLySxF5XUR2iMhNS1ylOcXj7+w+nioiL4nIT5aoKp7F6f92kYg8IiK7RGSniCTUKRzjVOe/cP9fvyYiD4lIwhxJcb71jXh8hYgMishnvW5zVqqaNBcgFdgHNAMZwCvAWdOe82fAP7q3rwW+797OAdLc29VAN86BJefc5hlY52rgXHd5PvDGmV7niPX+EngQ+Inf9VyKOgP/DHzcvZ0BFPld1zj/364F2oBs97EfAH/sd10XWt+Ixx8B/h34rNdtznZJtpbHVmCvqu5X1THgYeDKac+5EucDA84b/S4REVUdVtWguzwLGJ9p4GWbflr0OqvqYVV90b09AOzE+dAlinj8nRGROuAPgHviWvr5WfQ6i0ghcCFwL4Cqjqlqb3yrEZO4/J1xQiRbRNJwQqYrbjWIzbzrCyAiV+EE444YtzmjZAuPWqA94n4Hp3/pTTzH/c/VB5QCiMg2EdkBvAp80n3cyzb9FI86T3CbxZuAZ+JR+HmKV52/DXwOCMet5PMXjzo3AceA+92uuntEJDe+1YjJotdZVTuBbwKHgMNAn6o+EddaeDfv+opIHvB54Kvz2OaMki08FkRVn1HVDcAW4IuJ1BcaL9Hq7P6H/CHwaVXt96uMi22mOovIe4FuVX3B5+LFxSx/5zTgXOAfVHUTMAQk3JjefM3ydy7G+eXdBNQAuSLy//hZzkXyFeB2VR1crA0mW3h0AvUR9+vcZTM+x222FgI9kU9Q1Z3AIPAmj9v0UzzqjIik4wTHv6nqj+JS8vmLR53PB64QkQM4Tft3isi/xqPw8xSPOncAHao63qp8BCdMEkU86nwx0Kaqx1Q1APwIeFtcSh+7hdR3G/B37v/fTwNfEpEbPW5zZn4PAi3lBeeX1H6cXxXjg0Mbpj3nU0wdcPqBe7uJyQG2Bpx+0DIv2zwD6yzA94Bv+12/parztHVbSLwB87jUGfg1sNa9/RXg7/2uazzrjPMluwNnrENwxg/+3O+6LrS+057zFSYHzOf9/eX7G+LDH+BynNlB+4Cb3WVfA65wb2fhzEbYCzwLNLvL/8j9T/Uy8CJwVbRtJtJlsesMvB1ngPH37mMvA5f7Xc94/50jtt1CgoVHHP9vbwSed//WPwaK/a7nEtT5q8Au4DXgX4BMv+u50PpO28ZXcMNjtm16udjhSYwxxsQs2cY8jDHGLAILD2OMMTGz8DDGGBMzCw9jjDExs/AwxhgTMwsPY4wxMbPwMGaJiMhKEXl12rJMEWkTkQ1+lcuY+bDwMGbptAF1IhL5udsOPKWqO2ZZx5iElOZ3AYxJFqoaFpFDQCOwX0Sygc/g7LFuzLJiLQ9jltZOYJ17+1PAf6rqAf+KY8z8WMvDmKW1E1grIk8BNwLbROSjOAfkuwR4HHhJVb/rYxmNmZMd28qYJSQi1wDvwjnZUI6q3uwuL8E5Yu0NfpbPGK+s5WHM0toJfBHnvBHnRSw/DzgjTzRlzkw25mHM0noDOBu4W6eeD9zCwywr1vIwZgmp6ilm/ty9GfjOEhfHmHmzMQ9jjDExs24rY4wxMbPwMMYYEzMLD2OMMTGz8DDGGBMzCw9jjDExs/AwxhgTMwsPY4wxMbPwMMYYEzMLD2OMMTH7/wGNsJUpFdTN+QAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax = plt.subplots()\n", "ax.set_title('Calculation of scale current (A)')\n", "ax1 = ax\n", "ax1.set_ylabel('Sc. Current (A)')\n", "ax1.set_xlabel('$V_T$')\n", "\n", "for vcval in (0.6, 0.65, 0.7): \n", " vtval = np.linspace(0.030,0.04,100)\n", " \n", " y = np.array(list(map(lambda v: root_scalar(f6, args=(vcval, v), bracket=[1.6e-20,1.6e-8], rtol=.1).root, vtval)))\n", " ax1.semilogy(vtval,y.tolist(), label='$V_C$ = {}'.format(vcval))\n", "ax1.grid()\n", "ax1.legend()" ] }, { "cell_type": "code", "execution_count": 51, "id": "efe1373b-37ed-4ff5-8759-13974018f655", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.6 0.7\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax2 = plt.subplots(1,1)\n", "ax2.set_title('Calculation of $V_T$ at minimal scale current')\n", "#ax1.set_ylabel('Sc. Current (A)')\n", "ax2.set_ylabel('$V_T$')\n", "#ax1.set_xlabel('Saturation Voltage')\n", "ax2.set_xlabel('Saturation Voltage (V)')\n", "\n", "minsc = []\n", "iarr = []\n", "for vcval in np.linspace(0.6, 0.7,100): \n", " vtval = np.linspace(0.025,0.045,500)\n", " \n", " y = np.array(list(map(lambda v: root_scalar(f6, args=(vcval, v), bracket=[1.6e-20,1.6e-8], rtol=.1).root, vtval)))\n", " idx = np.argmax(y>1.6e-20)\n", " \n", " vt = vtval[idx]\n", " sc = y[idx]\n", " iarr.append(idx)\n", " minsc.append((vcval, vt,sc))\n", " #ax1.semilogy(vtval,y.tolist(), label='$V_C$ = {}'.format(vcval))#\n", "#ax1.grid()\n", "ax2.grid()\n", "values = np.array(minsc).T\n", "\n", "f=interp1d(values[0],values[1], kind='linear')\n", "print (values[0][0], values[0][-1])\n", "x = np.linspace(0.6, 0.7, 1000)\n", "#ax1.legend()\n", "ax2.plot(values[0], values[1])\n", "#ax2.plot(x, f(x))\n" ] }, { "cell_type": "code", "execution_count": 66, "id": "9d3de261-d62d-4bff-8365-e9b94b5a0520", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.7, 0.03341683366733467, 1.0000000168e-12)" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "minsc[-1]" ] }, { "cell_type": "code", "execution_count": 62, "id": "11557e8e-3792-4983-b5d1-7849d3c11c77", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "fig, ax2 = plt.subplots(1,1)\n", "ax2.semilogy(values[1], values[2])" ] }, { "cell_type": "code", "execution_count": null, "id": "23db87c2-5630-4c1c-9156-0efaef4c1c09", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }