mirror of
https://asciireactor.com/otho/phy-4660.git
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302 lines
107 KiB
Plaintext
302 lines
107 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Populating the interactive namespace from numpy and matplotlib\n"
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]
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}
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],
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"source": [
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"import numpy as np\n",
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"%pylab inline\n",
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"\n",
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"from scipy.stats import norm\n",
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"from scipy.stats import lognorm\n",
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"from scipy.optimize import curve_fit\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 65,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[<matplotlib.lines.Line2D at 0x7fa4e0d4fc90>]"
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]
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},
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"execution_count": 65,
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"metadata": {},
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"output_type": "execute_result"
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},
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{
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"data": {
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"image/png": 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LF//8i3vfS5Mm6d/FSJ4sObNencV7i9+j3cJ2nL12lm4Vu2mOiIiIkykwPKU5u+aw/ug6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\nkrr/AxFNOGf2kxxVAAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa4e0e32350>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"# Define model function to be used to fit to the data above:\n",
|
|
"def tophat_time(x, *p):\n",
|
|
" mean, width = p\n",
|
|
" if x>(mean+width): y=0\n",
|
|
" if x<(mean-width): y=0\n",
|
|
" if x==(mean+width) | x==(mean-width): y=5\n",
|
|
" return y\n",
|
|
"\n",
|
|
"def tophat_freq(f, *pars):\n",
|
|
" A,T,t0 = pars\n",
|
|
" #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
|
|
" return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
|
|
"\n",
|
|
"\n",
|
|
"\n",
|
|
"# p0 is the initial guess for the fitting coefficients\n",
|
|
"p0 = [3, 3, 3]\n",
|
|
"\n",
|
|
"\n",
|
|
"#x=np.linspace(1,50,200)\n",
|
|
"\n",
|
|
"# freq bins we agreed on\n",
|
|
"x=array([ 0.00964867, 0.02886003, 0.0556922 , 0.08632291, 0.13380051,\n",
|
|
" 0.20739079, 0.32145572, 0.49825637])\n",
|
|
"\n",
|
|
"# from 3471A\n",
|
|
"lag = array([ 1.25569486, 2.37000041, 1.7802513 , 1.66775218, 0.49069246,\n",
|
|
" 0.21781609, -0.44057362, 0.01545348])\n",
|
|
"coeff, var_matrix = curve_fit(tophat_freq, x, lag, p0=p0)\n",
|
|
"\n",
|
|
"# Get the fitted curve\n",
|
|
"\n",
|
|
"\n",
|
|
"plot(tophat_freq(x, *coeff))\n",
|
|
"plot(lag)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 63,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7fa4e0df9250>]"
|
|
]
|
|
},
|
|
"execution_count": 63,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
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"image/png": 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pVAwCKYwaBXV1Dg9IkqqHQSCljnkCra2xK5EkafcZBFJKEnjlFfj972NXIknS7jMIpDRp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\nkiRJKq//B0U68uCZHjX/AAAAAElFTkSuQmCC\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa4e0fdc4d0>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"time_fit = ifft(fit)\n",
|
|
"plot(time_fit)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 30,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7fa4e18f6a50>]"
|
|
]
|
|
},
|
|
"execution_count": 30,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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kzwtSbtRMfPhu795R/UhciPgU70nDd6G4hqNcaqaUIxIKEX+4jhljwqK9vXBC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\nAAAAAAAAAAAAAAAAAAAAYBZxbYxNwe9BwQAAAABJRU5ErkJggg==\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa4e1874e90>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"xscale('log')\n",
|
|
"plot(np.logspace(0.01,10,200),sinc(pi*x))\n",
|
|
"#len(sinc(pi*x))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 49,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"[<matplotlib.lines.Line2D at 0x7fa4e16203d0>]"
|
|
]
|
|
},
|
|
"execution_count": 49,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
},
|
|
{
|
|
"data": {
|
|
"image/png": 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w8WE7JUHDLlpeG7qWLf2dD3ucj4YGuQn26iXzALIOt4hxh13atDHl9+41F0BR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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa4e15acf50>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"plot(np.fft.fft(ifft(sinc(pi*x))))\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 54,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'A' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m----------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-54-e6c186659f7e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msinc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'A' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"A*T*sinc(pi*f*T)*cos(2*pi*f*t0)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {
|
|
"collapsed": false
|
|
},
|
|
"outputs": [
|
|
{
|
|
"ename": "NameError",
|
|
"evalue": "name 'np' is not defined",
|
|
"output_type": "error",
|
|
"traceback": [
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
|
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
|
|
"\u001b[0;32m<ipython-input-1-d7dd796a61c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mA\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0msinc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mT\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mcos\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mpi\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfqd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfqd\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 18\u001b[0m \u001b[0;31m# p0 is the initial guess for the fitting coefficients\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
|
|
"\u001b[0;31mNameError\u001b[0m: name 'np' is not defined"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"from scipy.optimize import curve_fit\n",
|
|
"\n",
|
|
"# Define model function to be used to fit to the data above:\n",
|
|
"def tophat_time(x, *p):\n",
|
|
" mean, width = p\n",
|
|
" if x>(mean+width): y=0\n",
|
|
" if x<(mean-width): y=0\n",
|
|
" if x==(mean+width) | x==(mean-width): y=5\n",
|
|
" return y\n",
|
|
"\n",
|
|
"def tophat_freq(f, *pars):\n",
|
|
" A,T,t0 = pars\n",
|
|
" #return A*T*sinc(pi*f*T)*exp(-i*2*pi*f*t0)\n",
|
|
" return A*T*sinc(pi*f*T)*cos(2*pi*f*t0)\n",
|
|
"\n",
|
|
"x=np.logspace(fqd[0],fqd[-1],200)\n",
|
|
"\n",
|
|
"# p0 is the initial guess for the fitting coefficients\n",
|
|
"p0 = [3, 3, 3]\n",
|
|
"coeff, var_matrix = curve_fit(tophat_freq, fqd, lag, p0)\n",
|
|
"fit = tophat_freq(fqd, *coeff)\n",
|
|
"\n",
|
|
"\n",
|
|
"mpl.rcParams['xtick.labelsize']=12\n",
|
|
"mpl.rcParams['ytick.labelsize']=12\n",
|
|
"xscale('log'); xlim(.009,.6)\n",
|
|
"xlabel(\"Fourier Frequency (days$^{-1}$)\",fontsize=20)\n",
|
|
"ylabel(\"Time Lag (days)\",fontsize=20)\n",
|
|
"\n",
|
|
"\n",
|
|
"errorbar(fqd, lag, yerr=lage, fmt='o', ms=5,color=\"black\")\n",
|
|
"plot(fqd,fit)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {
|
|
"collapsed": true
|
|
},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 2",
|
|
"language": "python",
|
|
"name": "python2"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 2
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython2",
|
|
"version": "2.7.6"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|