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fixing up python script for clag
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@ -16,122 +16,97 @@ 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_table = np.loadtxt(args[0],skiprows=1)
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# works if first two entries represent minimum spacing, from example
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# dt = lc1_table[1,0] - lc1_table[0, 0]
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# Time resolution determined from inspection and testing. This script
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# does not expect evenly spaced data in time.
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dt = 0.1
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# _ = plot(lc1_table[:,0], lc1_table[:,1])
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# _ = plot(lc1_table[:,0], lc1_table[:,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(data)).reshape((-1, seg_length))
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# For now, instead of splitting up the curves, the program will assume
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# that the data list is shorter than 256 elemements. so,
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index = np.arange(len(lc1_table)).reshape(-1,len(lc1_table))
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lc1_time = [lc1_table[i, 0] for i in index]
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lc1_strength = [lc1_table[i, 1] for i in index]
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lc1_strength_err = [lc1_table[i, 2] for i in index]
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dt = 0.01
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#### Get the psd for the first light curve ####
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# These bin values determined summer 2016 for STORM III optical/UV lightcurves
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# fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115, 0.16658029, 0.25819945, 0.40020915, 0.62032418])
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fqL = np.array([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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seg_length = 256;
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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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#A general rules for fqL is as follows:
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#
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# define f1, f2 as the two extreme frequencies allowed by the data. i.e.
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# f1=1/T with T being the length of observation in time units, and
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# f2=0.5/Δt
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#
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# The frequency limits where the psd/lag can be constrained are about
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# ~0.9f1−0.5f2. The 0.9 factor doesn't depend on the data much, but
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# values in the range ~[0.9-1.1] are ok. The factor in front of f2
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# depends on the quality of the data, for low qualily data, use ~0.1--
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# 0.2, and for high quality data, increase it up to 0.9−−1.
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#
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# Always include two dummy bins at the low and high frequencies and
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# ignore them. The first and last bins are generally biased, So I suggest
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# using the first bin as 0.5f1−0.9f1 (or whatever value you use instead
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# of 0.9f1, see second point above), and the last bin should be
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# 0.5f2−2∗f2 (or whatever value instead of 0.5f2, see second point
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# above). So the frequency bins should be something like:
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# [0.5f1,0.9f1,...,0.5f2,2f2], the bins in between can be devided as
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# desired.
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#
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#fqd is the bin center
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#
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# If lightcurves need to be split:
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# seg_length = 256;
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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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#
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nfq = len(fqL) - 1
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fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)
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## load the first light curve
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lc1_time, lc1_strength , lc1_strength_err = np.loadtxt(args[0],skiprows=1)
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# for pylab: errorbar(t1,l1,yerr=l1e,fmt='o')
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# Used throughout
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initial_args = np.ones(nfq)
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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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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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ref_psd, ref_psd_err = clag.optimize(P1, inpars)
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P1 = clag.clag('psd10r', [lc1_time], [lc1_strength], [lc1_strength_err], dt, fqL)
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ref_psd, ref_psd_err = clag.optimize(P1, initial_args)
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ref_psd, ref_psd_err = clag.errors(P1, ref_psd, ref_psd_err)
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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], ref_psd[1:-1], yerr=ref_psd_err[1:-1], fmt='o', ms=10, label='fit')
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# load second lightcurve
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# This would work if both curves are in same file
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# lc2_strength = [lc1_table[i, 3] for i in index]
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# lc2_strength_err = [lc1_table[i, 4] for i in index]
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# But, they aren't, so,
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#xscale('log'); ylim(-4,2)
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#errorbar(fqd, ref_psd, yerr=ref_psd_err, fmt='o', ms=10)
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# Load second light curve
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lc2_table = np.loadtxt(args[1],skiprows=1)
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index = np.arange(len(lc2_table)).reshape(-1,len(lc2_table))
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lc2_time = [lc2_table[i, 0] for i in index]
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lc2_strength = [lc2_table[i, 1] for i in index]
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lc2_strength_err = [lc2_table[i, 2] for i in index]
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## Now do the second light curve
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P2 = clag.clag('psd', lc2_time, lc2_strength, lc2_strength_err, dt, fqL)
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echo_psd, echo_psd_err = clag.optimize(P2, inpars)
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lc2_time, lc2_strength, lc2_strength_err = np.loadtxt(args[1],skiprows=1)
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P2 = clag.clag('psd10r', [lc2_time], [lc2_strength], [lc2_strength_err], dt, fqL)
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echo_psd, echo_psd_err = clag.optimize(P2, initial_args)
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echo_psd, echo_psd_err = clag.errors(P2, echo_psd, echo_psd_err)
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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_strength)],
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[list(i) for i in zip(lc1_strength_err,lc2_strength_err)],
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Cx = clag.clag('cxd10r',
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[[lc1_time,lc1_time]],
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[[lc1_strength,lc2_strengt]],
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[[lc1_strength_err,lc2_strength_err]],
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dt, fqL, ref_psd, echo_psd)
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inpars = np.concatenate( (0.3*(ref_psd*echo_psd)**0.5, ref_psd*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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#Cx_vals = np.concatenate( (0.3*(ref_psd*echo_psd)**0.5, ref_psd*0+1.) )
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Cx_vals = np.concatenate( ((ref_psd+echo_psd)*0.5-0.3,ref_psd*0+0.1) )
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Cx_vals, Cx_err = clag.optimize(Cx, Cx_vals)
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#?????? %autoreload
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Cx_vals, Cx_err = clas.errors(Cx,Cx_vals,Cx_err)
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phi, phie = Cx_vals[nfq:], Cx_err[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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cross_spectrm, cross_spectrm_err = Cx_vals[:nfq], Cx_err[:nfq]
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np.savetxt("freq.out",fqL.reshape((-1,len(fqL))))
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np.savetxt("ref_psd.out",[ref_psd,ref_psd_err])
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np.savetxt("echo_psd.out",[echo_psd,echo_psd_err])
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np.savetxt("crsspctrm.out",[cx,cxe])
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np.savetxt("crsspctrm.out",[cross_spectrm,cross_spectrm_err])
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np.savetxt("timelag.out",[lag,lage])
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