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@iamareebjamal
Last active August 31, 2018 20:02
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Least Square Non Linear
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{
"cells": [
{
"cell_type": "code",
"execution_count": 208,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"P = np.array([16.2, 28.6, 43.5, 77.4, 102.5, 112.6, 122.3])\n",
"Vo= np.array([221, 251, 335, 461, 540, 664, 695])"
]
},
{
"cell_type": "code",
"execution_count": 209,
"metadata": {},
"outputs": [],
"source": [
"def func_orig(x, Vo):\n",
" return (810/x[1])*np.log(1 + x[1]/x[0]*Vo**2)\n",
"\n",
"def func(x, Vo, P):\n",
" return func_orig(x, Vo) - P"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [],
"source": [
"x0 = np.array([10, 10])"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1.48808954e+06 8.84646332e+00]\n"
]
}
],
"source": [
"from scipy.optimize import least_squares\n",
"\n",
"result = least_squares(func, x0, args=(Vo, P))\n",
"x = result.x\n",
"print(x)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7fc665c36390>"
]
},
"execution_count": 213,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib.pyplot as plt\n",
"\n",
"plt.plot(Vo, P, 'o',label='Actual Points')\n",
"plt.legend()\n",
"V1 = np.linspace(-800, 800, 1000)\n",
"plt.plot(V1, func2(x, V1), label='Calculated Function')\n",
"plt.legend()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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