2017-06-22 12:11:27 +00:00
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#!/usr/bin/env python
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import numpy as np
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from scipy.stats import norm
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from scipy.stats import lognorm
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import sys
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import getopt
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sys.path.insert(1,"/usr/local/science/clag-agn/data/")
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import clag
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import matplotlib
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# %pylab inline
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#ref_file="data/lc/1367A.lc"
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#echo_file="data/lc/2246A.lc"
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ref_file = str(sys.argv[1])
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echo_file = str(sys.argv[2])
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dt = 0.01
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t1, l1, l1e = np.loadtxt(ref_file).T
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# errorbar(t1, l1, yerr=l1e, fmt='o')
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2017-06-22 13:28:23 +00:00
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#A
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2017-06-22 19:24:09 +00:00
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fqL = [0.0049999999] + np.logspace(np.log10(0.049999999),np.log10(0.340002000),7)
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2017-06-22 12:11:27 +00:00
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2017-06-22 13:28:23 +00:00
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#B
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#fqL = np.array([0.0049999999, 0.018619375, 0.044733049,
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# 0.069336227, 0.10747115, 0.16658029,
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# 0.25819945, 0.40020915])
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#C
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#fqL = np.array([0.0049999999, 0.018619375, 0.044733049,
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# 0.069336227, 0.10747115, 0.16658029,
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# 0.25819945, 0.40020915])
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2017-06-22 12:11:27 +00:00
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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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P1 = clag.clag('psd10r', [t1], [l1], [l1e], dt, fqL)
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p1 = np.ones(nfq)
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p1, p1e = clag.optimize(P1, p1)
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p1, p1e = clag.errors(P1, p1, p1e)
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# xscale('log'); ylim(-4,2)
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# errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color="black")
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ref_psd = p1
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ref_psd_err = p1e
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t2, l2, l2e = np.loadtxt(echo_file).T
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# errorbar(t1, l1, yerr=l1e, fmt='o', color="green")
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# errorbar(t2, l2, yerr=l2e, fmt='o', color="black")
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P2 = clag.clag('psd10r', [t2], [l2], [l2e], dt, fqL)
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p2 = np.ones(nfq)
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p2, p2e = clag.optimize(P2, p2)
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p2, p2e = clag.errors(P2, p2, p2e)
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# xscale('log'); ylim(-6,2)
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# errorbar(fqd, p1, yerr=p1e, fmt='o', ms=10, color="green")
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# errorbar(fqd, p2, yerr=p2e, fmt='o', ms=10, color="black")
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echo_psd = p2
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echo_psd_err = p2e
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Cx = clag.clag('cxd10r', [[t1,t2]], [[l1,l2]], [[l1e,l2e]], dt, fqL, p1, p2)
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p = np.concatenate( ((p1+p2)*0.5-0.3,p1*0+0.1) ) # a good starting point generally
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p, pe = clag.optimize(Cx, p)
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2017-06-22 19:19:38 +00:00
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p, pe = clag.errors(Cx, p, pe)
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2017-06-22 12:11:27 +00:00
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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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cross_spectrm = cx
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cross_spectrm_err = cxe
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# xscale('log'); ylim(-2,1)
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# errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color="black")
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s, loc, scale = lognorm.fit(lag,loc=.01)
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# xscale('log'); ylim(-4,1.5)
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# errorbar(fqd, lag, yerr=lage, fmt='o', ms=10,color="black")
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##plot(fqd,norm.pdf(fqd,mu,sigma))
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#plot(fqd,lognorm.pdf(fqd,s,loc,scale))
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# mu,sigma
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#plot(ifft(lag))
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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",[cross_spectrm,cross_spectrm_err])
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np.savetxt("lag.out",[lag,lage])
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