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Created May 30, 2017 05:47
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scipy interpolate sample
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1\"><a href=\"#scipy.interpolate-sample\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>scipy.interpolate sample</a></div><div class=\"lev2\"><a href=\"#一次元の補間\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>一次元の補間</a></div><div class=\"lev3\"><a href=\"#線形補間\"><span class=\"toc-item-num\">1.1.1&nbsp;&nbsp;</span>線形補間</a></div><div class=\"lev3\"><a href=\"#最近傍補間\"><span class=\"toc-item-num\">1.1.2&nbsp;&nbsp;</span>最近傍補間</a></div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# scipy.interpolate sample\n",
"\n",
"## 一次元の補間"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x11572e208>]"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x114d7ba90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import scipy.interpolate\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"%matplotlib inline \n",
"\n",
"# raw data\n",
"measured_time = np.linspace(0, 1, 10)\n",
"noise = (np.random.random(10)*2 - 1) * 1e-1\n",
"measures = np.sin(2 * np.pi * measured_time) + noise\n",
"plt.plot(measured_time,measures,\"xk\", label=\"raw\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 線形補間"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x115cc1198>]"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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4YzLbkCFujehBg3wnMXsbNuzXDuesqBbGTArRFoYcYHz4+XigS4KPNyZzNG7s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\neu0ri2sAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115cc11d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# linear interpolation\n",
"interp = scipy.interpolate.interp1d(measured_time, measures)\n",
"x = np.linspace(0,1,100)\n",
"y = interp(x)\n",
"plt.plot(measured_time,measures,\"xk\", label=\"raw\")\n",
"plt.plot(x,y,\"-r\", label=\"linear\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 最近傍補間"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x1157c6748>]"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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w5LO7nzfWMTN7ycwWuvtuM1sIvDzOpS4EfuruLxVce+izmX0V+Ndx6rEeWA/Q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\nW50tr8V0AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115993cf8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# nearest interpolation\n",
"nearest_interp = scipy.interpolate.interp1d(measured_time, measures, kind=\"nearest\")\n",
"y = nearest_interp(x)\n",
"plt.plot(x,y, label=\"nearest\")\n",
"plt.plot(measured_time,measures,\"xk\", label=\"raw\")\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x115e3fe80>]"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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0Z9PLr7Jr9yAv//I7vHLfzcw+aQn7vesjocVQtOqiRbz3qDeEft7JYmbr3L19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\nQtB1AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x115e3fb38>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# zero spline interpolation\n",
"interp = scipy.interpolate.interp1d(measured_time, measures, kind=\"zero\")\n",
"y = interp(x)\n",
"plt.plot(x,y, label=\"zero\")\n",
"plt.plot(measured_time,measures,\"xk\", label=\"raw\")"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x115340048>"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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FYHtRUFrJ8yv30zmsEXf2jTQ6ziVJAQhhkDYhvvSIbMyi7eloLReD7cE/v0/i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\nRK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x1135d3c18>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# linear spline interpolation\n",
"interp = scipy.interpolate.interp1d(measured_time, measures, kind=\"slinear\")\n",
"y = interp(x)\n",
"#plt.plot(x,y, label=\"slinear\")\n",
"\n",
"# quadratic spline interpolation\n",
"interp = scipy.interpolate.interp1d(measured_time, measures, kind=\"quadratic\")\n",
"y = interp(x)\n",
"plt.plot(x,y, label=\"quadratic\")\n",
"\n",
"\n",
"# cubic interpolation\n",
"cubic_interp = scipy.interpolate.interp1d(measured_time, measures, kind=\"cubic\")\n",
"yc = cubic_interp(x)\n",
"#plt.plot(x,yc,\"-b\", label=\"cubic\")\n",
"\n",
"plt.legend()"
]
}
],
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"language": "python",
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"toc": {
"toc_cell": true,
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},
"nbformat": 4,
"nbformat_minor": 0
}
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