{ "cells": [ { "cell_type": "code", "execution_count": 120, "id": "793b4985", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from scipy.optimize import curve_fit\n", "from IPython.display import display" ] }, { "cell_type": "markdown", "id": "144062d7", "metadata": {}, "source": [ "This is a test circuit to try and calibrate the arudino analog input with a smoother emitter follower voltage buffer. The circuit has two 10K resistors and a 4.7 microFarad capacitors." ] }, { "cell_type": "code", "execution_count": 96, "id": "74b9371f", "metadata": {}, "outputs": [], "source": [ "dp1 = pd.read_csv('datasets/bc547-1k-common-emitter-2.csv', names=['p3', 'a0', 'a1', 'a2'])\n" ] }, { "cell_type": "code", "execution_count": 97, "id": "4ac192ec", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " p3 a0 a1 a2\n", "0 0 119 102 1023\n", "1 1 117 101 1023\n", "2 2 117 102 1023\n", "3 3 118 101 1023\n", "4 4 116 101 1023" ] }, "execution_count": 97, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp1.head()" ] }, { "cell_type": "code", "execution_count": 98, "id": "a79657c0", "metadata": {}, "outputs": [], "source": [ "dp1['Vin'] = dp1.p3 * 5 / 255\n", "dp1['Va0'] = dp1.a0 * 5 / 1023\n", "dp1['Va1'] = dp1.a1 * 5 / 1023\n", "dp1['Va2'] = dp1.a2 * 5 / 1023" ] }, { "cell_type": "code", "execution_count": 99, "id": "e1c452b8", "metadata": { "scrolled": false }, "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", "ax.plot(dp1.Vin, dp1.Va0, label='Va0')\n", "ax.plot(dp1.Vin, dp1.Va1, label='Va1')\n", "ax.plot(dp1.Vin, dp1.Va2, label='Va2')\n", "\n", "ax.grid()\n", "plt.xlabel(\"volts\")\n", "plt.ylabel(\"volts\")\n", "plt.legend()\n", "plt.title(\"Common Emitter Circuit\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 100, "id": "5a95abe2", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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p3a0a1a2VinVa0Va1Va2
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12912912110310232.5294120.5913980.5034215.000000
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13213212010310232.5882350.5865100.5034215.000000
\n", "
" ], "text/plain": [ " p3 a0 a1 a2 Vin Va0 Va1 Va2\n", "128 128 121 103 1023 2.509804 0.591398 0.503421 5.000000\n", "129 129 121 103 1023 2.529412 0.591398 0.503421 5.000000\n", "130 130 117 103 1023 2.549020 0.571848 0.503421 5.000000\n", "131 131 122 105 1022 2.568627 0.596285 0.513196 4.995112\n", "132 132 120 103 1023 2.588235 0.586510 0.503421 5.000000" ] }, "execution_count": 100, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dp2 = dp1[dp1['Vin']> 2.5].copy()\n", "dp2.head()" ] }, { "cell_type": "code", "execution_count": 101, "id": "c78d60bd", "metadata": {}, "outputs": [], "source": [ "dp2['Ib'] = (dp2['Va0'] - dp2['Va1']) / 10 # in milliamps\n", "dp2['Ic'] = (5 - dp2['Va2']) # 1k resitor converted to milliamps" ] }, { "cell_type": "code", "execution_count": 104, "id": "d66d39e2", "metadata": {}, "outputs": [], "source": [ "dp3 = dp2[(dp2.Ic < 4.5) & (dp2.Ic > 0.4) ].copy()" ] }, { "cell_type": "code", "execution_count": 105, "id": "fc3e71e5", "metadata": { "scrolled": false }, "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", "ax.scatter(dp3.Ib, dp3.Ic, label='$I_C$')\n", "\n", "ax.grid()\n", "plt.xlabel(\"$I_B$ mA\")\n", "plt.ylabel(\"$I_C$ mA\")\n", "plt.legend()\n", "plt.title(\"Common Emitter Circuit\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 121, "id": "67188974", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Gain is [330.53037231]'" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# See https://stackoverflow.com/questions/9990789/how-to-force-zero-interception-in-linear-regression on\n", "# how this least squares cell works\n", "x = dp3.Ib.to_numpy()\n", "x = x[:,np.newaxis]\n", "a, _, _, _ = np.linalg.lstsq(x, dp3.Ic, rcond=None)\n", "display(\"Gain is {}\".format(a))" ] }, { "cell_type": "code", "execution_count": 117, "id": "05156063", "metadata": { "scrolled": true }, "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", "ax.scatter(dp3.Ib, dp3.Ic, label='$I_C$')\n", "ax.plot(dp3.Ib, dp3.Ib*a)\n", "ax.grid()\n", "plt.xlabel(\"$I_B$ mA\")\n", "plt.ylabel(\"$I_C$ mA\")\n", "plt.legend()\n", "plt.title(\"Common Emitter Circuit\")\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": null, "id": "03da307e", "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 }