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@PavelEprines
Last active February 13, 2019 15:29
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"import scipy.interpolate as sc\n"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from scipy.interpolate import lagrange as L"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['arr_0', 'arr_1']"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = np.load('pashe1.npz')\n",
"array.files"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"x = array['arr_0']\n",
"y = array['arr_1']"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-10 -9 -8 -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7\n",
" 8 9 10]\n",
"[1.10132329e+04 4.05154203e+03 1.49047916e+03 5.48317035e+02\n",
" 2.01715636e+02 7.42099485e+01 2.73082328e+01 1.00676620e+01\n",
" 3.76219569e+00 1.54308063e+00 1.00000000e+00 1.54308063e+00\n",
" 3.76219569e+00 1.00676620e+01 2.73082328e+01 7.42099485e+01\n",
" 2.01715636e+02 5.48317035e+02 1.49047916e+03 4.05154203e+03\n",
" 1.10132329e+04]\n"
]
}
],
"source": [
"print(x)\n",
"print(y)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"f = L(x, y)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"import numpy.polynomial.polynomial as npp"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"array([ 9.39330685e-19, -5.01715536e-27, 6.09211990e-17, -9.37848331e-24,\n",
" 5.64903584e-14, -5.88270172e-21, 1.10144364e-11, -2.14233161e-19,\n",
" 2.10198191e-09, 1.36219839e-17, 2.75306847e-07, 1.87024875e-16,\n",
" 2.48044059e-05, 3.55076211e-16, 1.38887344e-03, 2.60295258e-15,\n",
" 4.16667030e-02, 7.94503352e-16, 4.99999977e-01, 1.43982049e-15,\n",
" 1.00000000e+00])"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"npp.Polynomial(f).coef"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"plt.plot(x, y)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f265c8b6588>]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"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": [
"w = np.arange(-10, 10, 0.0001)\n",
"plt.plot(w, f(w))"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"# Как видно в нуле разложение в нуле 1 + x^2/2 + 0.04*x^4 и тд, что похоже на cosh, проверим"
]
},
{
"cell_type": "code",
"execution_count": 49,
"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": [
"plt.plot(w, f(w))\n",
"plt.plot(w, np.cosh(w))\n",
"plt.show()"
]
},
{
"cell_type": "raw",
"metadata": {},
"source": [
"Полное совпадение. Ура, все ок."
]
}
],
"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": "3.7.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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