{
"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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"\n",
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" \n",
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" | \n",
" p3 | \n",
" a0 | \n",
" a1 | \n",
" a2 | \n",
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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": {
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\n",
"text/plain": [
""
]
},
"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": [
"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" p3 | \n",
" a0 | \n",
" a1 | \n",
" a2 | \n",
" Vin | \n",
" Va0 | \n",
" Va1 | \n",
" Va2 | \n",
"
\n",
" \n",
" \n",
" \n",
" 128 | \n",
" 128 | \n",
" 121 | \n",
" 103 | \n",
" 1023 | \n",
" 2.509804 | \n",
" 0.591398 | \n",
" 0.503421 | \n",
" 5.000000 | \n",
"
\n",
" \n",
" 129 | \n",
" 129 | \n",
" 121 | \n",
" 103 | \n",
" 1023 | \n",
" 2.529412 | \n",
" 0.591398 | \n",
" 0.503421 | \n",
" 5.000000 | \n",
"
\n",
" \n",
" 130 | \n",
" 130 | \n",
" 117 | \n",
" 103 | \n",
" 1023 | \n",
" 2.549020 | \n",
" 0.571848 | \n",
" 0.503421 | \n",
" 5.000000 | \n",
"
\n",
" \n",
" 131 | \n",
" 131 | \n",
" 122 | \n",
" 105 | \n",
" 1022 | \n",
" 2.568627 | \n",
" 0.596285 | \n",
" 0.513196 | \n",
" 4.995112 | \n",
"
\n",
" \n",
" 132 | \n",
" 132 | \n",
" 120 | \n",
" 103 | \n",
" 1023 | \n",
" 2.588235 | \n",
" 0.586510 | \n",
" 0.503421 | \n",
" 5.000000 | \n",
"
\n",
" \n",
"
\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",
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