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@FZambia
Last active August 29, 2015 14:04
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scipy curve fit ipython notebook
{
"metadata": {
"name": "",
"signature": "sha256:afb8fe099dd7002cd0ade007e898b737fa62de1797f3754a2e9fe7df02a1fded"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib\n",
"from matplotlib import rcParams\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize import curve_fit\n",
"import numpy as np\n",
"matplotlib.rcParams['figure.figsize'] = (13,5)\n",
"rcParams['savefig.dpi'] = 100"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 81
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"x = np.linspace(0,2*np.pi,50)\n",
"y = np.sin(x)\n",
"plt.plot(x,y)\n",
"\n",
"noise = np.random.randn(y.size)\n",
"noisy_flux = y + noise\n",
"plt.plot(x, noisy_flux, 'ko') # no clear this time\n",
"\n",
"plt.show()\n",
"\n",
"print np.random.randn(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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jjijO0EFiCgGAwgkEbCva/fdLL70kfeUrVjkWCtnv0fvus2a8AABgcHhOj0Ij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\nCd93n/TyyxY+NDVJU6dKhx8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"text": [
"<matplotlib.figure.Figure at 0x10f2607d0>"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
"[-0.2110824 -0.18586515 2.21866535 1.55195327 0.14804412 -1.40925137\n",
" -0.06166137 -1.42590628 0.64844885 1.00065205]\n"
]
}
],
"prompt_number": 82
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sinfunc(x,a,b):\n",
" return a*np.sin(x-b)\n",
"\n",
"fitpars, covmat = curve_fit(sinfunc, x, noisy_flux)\n",
"# The diagonals of the covariance matrix are variances\n",
"# variance = standard deviation squared, so we'll take the square roots to get the standard devations!\n",
"# You can get the diagonals of a 2D array easily:\n",
"variances = covmat.diagonal()\n",
"std_devs = np.sqrt(variances)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 83
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fitted_y = sinfunc(x, *fitpars)\n",
"\n",
"plt.plot(\n",
" x, noisy_flux, 'ko',\n",
" x, sinfunc(x, *fitpars), 'b',\n",
" #x, sinfunc(x, *(fitpars+std_devs)), 'r', # ?\n",
" #x, sinfunc(x, *(fitpars-std_devs)), 'g' # ?\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 88,
"text": [
"[<matplotlib.lines.Line2D at 0x110055ed0>,\n",
" <matplotlib.lines.Line2D at 0x110061150>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x10f4f6110>"
]
}
],
"prompt_number": 88
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def sqr(x, a, b):\n",
" return a*x**2 + b\n",
"\n",
"x = np.linspace(0, 3)\n",
"y = sqr(x, 1, 0)\n",
"noise = np.random.randn(y.size)\n",
"y = y + noise\n",
"\n",
"fitpars, covmat = curve_fit(sqr, x, y)\n",
"perr = np.sqrt(np.diag(covmat))\n",
"\n",
"print \"parameter standard deviation errors:\", perr\n",
"\n",
"plt.plot(\n",
" x, y, 'ro',\n",
" x, sqr(x, *fitpars),\n",
" #x, sqr(x, *(fitpars + perr)), # ?\n",
" #x, sqr(x, *(fitpars - perr)), # ?\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"parameter standard deviation errors: [ 0.05229061 0.21366982]\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 87,
"text": [
"[<matplotlib.lines.Line2D at 0x10fce2690>,\n",
" <matplotlib.lines.Line2D at 0x10fce28d0>]"
]
},
{
"metadata": {},
"output_type": "display_data",
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CkiRJJa2trY3JkyYxPZEgRmh42Ak0x+PUNTbS0NRkKNFLZyc88kgIIf7wB/jg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\n",
"text": [
"<matplotlib.figure.Figure at 0x10fa13510>"
]
}
],
"prompt_number": 87
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"z = np.polyfit(x, y, 2)\n",
"p = np.poly1d(z)\n",
"\n",
"plt.plot(\n",
" x, y, 'ro',\n",
" x, p(x)\n",
")"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 94,
"text": [
"[<matplotlib.lines.Line2D at 0x110faa2d0>,\n",
" <matplotlib.lines.Line2D at 0x110faa510>]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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uvBFeegm23TZURpxxBuy7LwzorXFDhjkBRcotBhKSJEkqek1NTcSiURLd3qRG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\n0dwMixb1HDosWhT6PkBoKLnLLiF0qK4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WrvuBTwA/R2hzD9zgemOn4tRq+xzD2Kn4/A3w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\n",
"text": [
"<matplotlib.figure.Figure at 0x110a66fd0>"
]
}
],
"prompt_number": 94
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
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