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@jradavenport
Created September 8, 2017 17:05
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some fake flares
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm\n",
"import matplotlib\n",
"\n",
"matplotlib.rcParams.update({'font.size':18})\n",
"matplotlib.rcParams.update({'font.family':'serif'})"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def aflare1(t, tpeak, fwhm, ampl):\n",
" '''\n",
" The Analytic Flare Model evaluated for a single-peak (classical).\n",
" Reference Davenport et al. (2014) http://arxiv.org/abs/1411.3723\n",
"\n",
" Use this function for fitting classical flares with most curve_fit\n",
" tools.\n",
"\n",
" Note: this model assumes the flux before the flare is zero centered\n",
"\n",
" Parameters\n",
" ----------\n",
" t : 1-d array\n",
" The time array to evaluate the flare over\n",
" tpeak : float\n",
" The time of the flare peak\n",
" fwhm : float\n",
" The \"Full Width at Half Maximum\", timescale of the flare\n",
" ampl : float\n",
" The amplitude of the flare\n",
"\n",
" Returns\n",
" -------\n",
" flare : 1-d array\n",
" The flux of the flare model evaluated at each time\n",
" '''\n",
" _fr = [1.00000, 1.94053, -0.175084, -2.24588, -1.12498]\n",
" _fd = [0.689008, -1.60053, 0.302963, -0.278318]\n",
"\n",
" flare = np.piecewise(t, [(t<= tpeak) * (t-tpeak)/fwhm > -1.,\n",
" (t > tpeak)],\n",
" [lambda x: (_fr[0]+ # 0th order\n",
" _fr[1]*((x-tpeak)/fwhm)+ # 1st order\n",
" _fr[2]*((x-tpeak)/fwhm)**2.+ # 2nd order\n",
" _fr[3]*((x-tpeak)/fwhm)**3.+ # 3rd order\n",
" _fr[4]*((x-tpeak)/fwhm)**4. ),# 4th order\n",
" lambda x: (_fd[0]*np.exp( ((x-tpeak)/fwhm)*_fd[1] ) +\n",
" _fd[2]*np.exp( ((x-tpeak)/fwhm)*_fd[3] ))]\n",
" ) * np.abs(ampl) # amplitude\n",
"\n",
" return flare"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dt = 2./60./24. # 2 min\n",
"time = np.arange(0, 0.5, dt)\n",
"flux0 = np.zeros_like(time)"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"p1 = [0.25, 2./60./24., 0.0005]\n",
"p2 = [0.12, 10./60./24., 0.007]\n",
"p3 = [0.1, 30./60./24., 0.02]\n",
"\n",
"flare1 = aflare1(time, *p1)\n",
"flare2 = aflare1(time, *p2)\n",
"flare3 = aflare1(time, *p3) + flare2 + flare1"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [],
"source": [
"ED1 = np.trapz(flare1, time*86400.)\n",
"ED2 = np.trapz(flare2, time*86400.)\n",
"ED3 = np.trapz(flare3, time*86400.)"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [
{
"data": {
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3fGvxrJdnTL8RSsBzi0hBZw9FZG+gNdQGiMgUEVksImUiskFEpolI0IuvRKRE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\ncQqBBzzHcRynEHjAcxzHcQrB/wE7meu6mQunsgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x111e1c1d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(time, flux0 + flare1, label='ED = '+format(ED1, '5.2f') + ' sec')\n",
"plt.plot(time, flux0 + flare2, label='ED = '+format(ED2, '5.2f') + ' sec')\n",
"plt.plot(time, flux0 + flare3, label='ED = '+format(ED3, '5.2f') + ' sec')\n",
"plt.legend(fontsize=10)\n",
"plt.xlabel('Time [days]')\n",
"plt.ylabel('Relative Flux')\n",
"plt.xlim(0,0.4);\n",
"plt.savefig('jrad_flares.png', dpi=150, bbox_inches='tight', pad_inches=0.25)"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [],
"source": [
"np.savetxt(\"flare1.csv\", np.asarray([time,flare1]).T, delimiter=',', header='time, flux')\n",
"np.savetxt(\"flare2.csv\", np.asarray([time,flare2]).T, delimiter=',', header='time, flux')\n",
"np.savetxt(\"flare3.csv\", np.asarray([time,flare3]).T, delimiter=',', header='time, flux')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python [Root]",
"language": "python",
"name": "Python [Root]"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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