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https://asciireactor.com/otho/psdlag-agn.git
synced 2024-11-24 03:05:06 +00:00
changed freq bins
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9fe84e07b3
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@ -3,15 +3,15 @@ from __future__ import unicode_literals
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import numpy as np
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import sys
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import getopt
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sys.path.insert(1,"/usr/local/science/clag/")
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import clag
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sys.path.insert(1, "/usr/local/science/clag/")
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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(sys.argv[1:], "")
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opts, args = getopt.getopt(sys.argv[1:], "")
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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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@ -20,10 +20,14 @@ except getopt.GetoptError:
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# does not expect evenly spaced data in time.
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dt = 0.1
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#### Get the psd for the first light curve ####
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### Get the psd for the #fqL = np.hstack((np.array(0.5*f1),np.logspace(np.log10(0.9*f1),
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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,
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0.10747115, 0.16658029, 0.25819945, 0.40020915,
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0.62032418])
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#A general rules for fqL is as follows:
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#
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@ -54,15 +58,18 @@ fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227, 0.10747115,
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# fqL = np.concatenate(([0.5/seg_length], fqL))
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#
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f1 = 1/175.
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f2 = 0.5/dt
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fqL = np.hstack((np.array(0.5*f1),np.logspace(np.log10(0.9*f1),np.log10(0.3*f2),9),np.array(2*f2)))
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#f1 = 1/175.
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#f2 = 0.5/dt
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#fqL = np.hstack((np.array(0.5*f1),np.logspace(np.log10(0.9*f1),
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# np.log10(0.3*f2),9),np.array(2*f2)))
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fqL = np.logspace(np.log10(0.0049999999),np.log10(0.62032418),9)
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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).T
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lc1_time, lc1_strength, lc1_strength_err =
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np.loadtxt(args[0],skiprows=1).T
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# for pylab: errorbar(t1,l1,yerr=l1e,fmt='o')
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@ -71,7 +78,9 @@ initial_args = np.ones(nfq)
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## initialize the psd class for multiple light curves ##
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P1 = clag.clag('psd10r', [lc1_time], [lc1_strength], [lc1_strength_err], dt, fqL)
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P1 = clag.clag('psd10r',
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[lc1_time], [lc1_strength], [lc1_strength_err],
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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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