mirror of
https://asciireactor.com/otho/psdlag-agn.git
synced 2024-11-22 01:15:06 +00:00
125 lines
2.8 KiB
Python
125 lines
2.8 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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import numpy as np
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import clag
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import sys
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# For jupyter notebook
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# %pylab inline
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try:
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opts,args = getopt.getopt(args, "")
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except getopt.GetoptError:
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print 'analyze_lightcure.py <reference curve> <compared curve>'
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sys.exit(2)
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## load the first light curve
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lc1 = np.loadtxt(args[0])
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# works if first two entries represent minimum spacing, from example
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# dt = lc1[1,0] - lc1[0, 0]
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# Time resolution determined from inspection and testing.
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dt = 0.01
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_ = plot(lc1[:,0], lc1[:,1])
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_ = plot(lc1[:,0], lc1[:,3])
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# Split the light curve into segments #
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seg_length = 256
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index = np.arange(len(lc1)).reshape((-1, seg_length))
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lc1_time = [lc1[i, 0] for i in index]
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lc1_strength = [lc1[i, 1] for i in index]
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lc1_strength_err = [lc1[i, 2] for i in index]
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# This would work if both curves are in same file
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# Lc2 = [lc1[i, 3] for i in index]
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#Lc2e = [lc1[i, 4] for i in index]
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#### Get the psd for the first light curve ####
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# These bin values determined summer 2015 for STORM III optical/UV lightcurves
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fqL = [0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, 0.25819945, 0.40020915, 0.62032418]
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# using utilities to set up frequency bins #
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# fqL = np.logspace(np.log10(1.1/seg_length),np.log10(.5/dt),7)
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# fqL = np.concatenate(([0.5/seg_length], fqL))
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nfq = len(fqL) - 1
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## initialize the psd class for multiple light curves ##
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P1 = clag.clag('psd', lc1_time, lc1_strength, lc1_strength_err, dt, fqL)
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## initial parameters, start with ones
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inpars = np.ones(nfq)
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## print the loglikelihood for the input values ##
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print P1.logLikelihood(inpars)
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## Now do the fitting and find the best fit psd values at the given frequency bins ##
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psd1, psd1e = clag.optimize(P1, inpars)
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## plot ##
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fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)
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loglog(fqd, 0.1*fqd**(-1.5), label='input psd')
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errorbar(fqd[1:-1], psd1[1:-1], yerr=psd1e[1:-1], fmt='o', ms=10, label='fit')
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## Now do the second light curve
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P2 = clag.clag('psd', lc1_time, Lc2, Lc2e, dt, fqL)
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psd2, psd2e = clag.optimize(P2, inpars)
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### Now the cross spectrum ###
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### We also give it the calculated psd values as input ###
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Cx = clag.clag('cxd',
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[list(i) for i in zip(lc1_time,lc1_time)],
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[list(i) for i in zip(lc1_strength,Lc2)],
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[list(i) for i in zip(lc1_strength_err,Lc2e)],
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dt, fqL, psd1, psd2)
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inpars = np.concatenate( (0.3*(psd1*psd2)**0.5, psd1*0+1.) )
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p, pe = clag.optimize(Cx, inpars)
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phi, phie = p[nfq:], pe[nfq:]
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lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)
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cx, cxe = p[:nfq], pe[:nfq]
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## plot ##
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semilogx(fqd, fqd*0+1.0, label='input phase lag')
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ylim([0.8, 1.2])
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errorbar(fqd[1:-1], phi[1:-1], yerr=phie[1:-1], fmt='o', ms=10, label='fit')
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