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@embray
Created October 3, 2014 23:41
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{
"metadata": {
"name": "",
"signature": "sha256:6fa840a8518b575ae61b3452c3a9acc9e02cbc23af82f1cc2537ae06f2e9fe54"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"import numpy as np\n",
"from astropy.modeling.models import Gaussian1D\n",
"from astropy.modeling.fitting import SLSQPLSQFitter"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"orig_1 = Gaussian1D(1, 0, 0.2)\n",
"orig_2 = Gaussian1D(2.5, 0.5, 0.1)\n",
"\n",
"x = np.linspace(-1, 1, 500)\n",
"y_orig = orig_1(x) + orig_2(x)\n",
"y = y_orig + np.random.normal(0., 0.2, x.shape)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(x, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
"[<matplotlib.lines.Line2D at 0x3888a90>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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r9mxOF1F2C39bG58bG/ksImynTZ51lrf7LS5mr2ChtJQVLa++2kxzlzUYGzN3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\noiiKoiiKoiiKoiiKoiiKomQn/x/H/tUfMDdmpwAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x1a337d0>"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"GG = Gaussian1D + Gaussian1D\n",
"\n",
"gg_init = GG(1, 0, 0.1, 1, 0, 0.1)\n",
"fitter = SLSQPLSQFitter()\n",
"gg_fit = fitter(gg_init, x, y)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Optimization terminated successfully. (Exit mode 0)\n",
" Current function value: 20.8200294269\n",
" Iterations: 32\n",
" Function evaluations: 299\n",
" Gradient evaluations: 32\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gg_fit"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"<CompoundModel0(amplitude_0=1.0202880768396347, mean_0=0.011598375431720322, stddev_0=0.20225000431697765, amplitude_1=2.4507042716877523, mean_1=0.5030996478012479, stddev_1=0.10038272281282644)>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(x, y, color='gray')\n",
"plt.plot(x, gg_fit(x), lw=3, color='red')\n",
"plt.plot(x, y_orig, lw=2, color='green', linestyle='--')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"[<matplotlib.lines.Line2D at 0x44d16d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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cbWX27NloNBo0yclMrTTzWaKTrVkw7OBBnLNntynzam+lMxZwhZfuaTZP7eLF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\n",
"text": [
"<matplotlib.figure.Figure at 0x442ef90>"
]
}
],
"prompt_number": 11
}
],
"metadata": {}
}
]
}
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