mirror of
https://asciireactor.com/otho/industry-website.git
synced 2024-11-22 09:35:07 +00:00
126 lines
4.2 KiB
Python
126 lines
4.2 KiB
Python
|
# -*- coding: utf-8 -*-
|
|||
|
|
from __future__ import unicode_literals
|
|||
|
|
import sys
|
|||
|
|
import numpy as np
|
|||
|
|
import getopt
|
|||
|
|
sys.path.insert(1, "/home/caes/science/clag/")
|
|||
|
|
import clag
|
|||
|
|
|
|||
|
|
|
|||
|
|
# For jupyter notebook
|
|||
|
|
# %pylab inline
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
opts, args = getopt.getopt(sys.argv[1:], "")
|
|||
|
|
except getopt.GetoptError:
|
|||
|
|
print 'analyze_lightcure.py <reference curve> <compared curve>'
|
|||
|
|
sys.exit(2)
|
|||
|
|
|
|||
|
|
# Time resolution determined from inspection and testing. This script
|
|||
|
|
# does not expect evenly spaced data in time.
|
|||
|
|
dt = 0.1
|
|||
|
|
|
|||
|
|
### Get the psd for the #fqL = np.hstack((np.array(0.5*f1),np.logspace(np.log10(0.9*f1),
|
|||
|
|
# first light curve ###
|
|||
|
|
|
|||
|
|
|
|||
|
|
# These bin values determined summer 2016 for STORM III optical/UV lightcurves
|
|||
|
|
fqL = np.array([0.0049999999, 0.018619375, 0.044733049, 0.069336227,
|
|||
|
|
0.10747115, 0.16658029, 0.25819945, 0.40020915,
|
|||
|
|
0.62032418])
|
|||
|
|
|
|||
|
|
#A general rules for fqL is as follows:
|
|||
|
|
#
|
|||
|
|
# define f1, f2 as the two extreme frequencies allowed by the data. i.e.
|
|||
|
|
# f1=1/T with T being the length of observation in time units, and
|
|||
|
|
# f2=0.5/Δt
|
|||
|
|
#
|
|||
|
|
# The frequency limits where the psd/lag can be constrained are about
|
|||
|
|
# ~0.9f1−0.5f2. The 0.9 factor doesn't depend on the data much, but
|
|||
|
|
# values in the range ~[0.9-1.1] are ok. The factor in front of f2
|
|||
|
|
# depends on the quality of the data, for low qualily data, use ~0.1--
|
|||
|
|
# 0.2, and for high quality data, increase it up to 0.9−−1.
|
|||
|
|
#
|
|||
|
|
# Always include two dummy bins at the low and high frequencies and
|
|||
|
|
# ignore them. The first and last bins are generally biased, So I suggest
|
|||
|
|
# using the first bin as 0.5f1−0.9f1 (or whatever value you use instead
|
|||
|
|
# of 0.9f1, see second point above), and the last bin should be
|
|||
|
|
# 0.5f2−2∗f2 (or whatever value instead of 0.5f2, see second point
|
|||
|
|
# above). So the frequency bins should be something like:
|
|||
|
|
# [0.5f1,0.9f1,...,0.5f2,2f2], the bins in between can be devided as
|
|||
|
|
# desired.
|
|||
|
|
#
|
|||
|
|
#fqd is the bin center
|
|||
|
|
#
|
|||
|
|
# If lightcurves need to be split:
|
|||
|
|
# seg_length = 256;
|
|||
|
|
# fqL = np.logspace(np.log10(1.1/seg_length),np.log10(.5/dt),7)
|
|||
|
|
# fqL = np.concatenate(([0.5/seg_length], fqL))
|
|||
|
|
#
|
|||
|
|
|
|||
|
|
#f1 = 1/175.
|
|||
|
|
#f2 = 0.5/dt
|
|||
|
|
#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)))
|
|||
|
|
fqL = np.logspace(np.log10(0.0049999999),np.log10(0.62032418),9)
|
|||
|
|
nfq = len(fqL) - 1
|
|||
|
|
fqd = 10**(np.log10( (fqL[:-1]*fqL[1:]) )/2.)
|
|||
|
|
|
|||
|
|
|
|||
|
|
## load the first light curve
|
|||
|
|
lc1_time, lc1_strength, lc1_strength_err = np.loadtxt(args[0],
|
|||
|
|
skiprows=1).T
|
|||
|
|
|
|||
|
|
# for pylab: errorbar(t1,l1,yerr=l1e,fmt='o')
|
|||
|
|
|
|||
|
|
# Used throughout
|
|||
|
|
|
|||
|
|
|
|||
|
|
## initialize the psd class for multiple light curves ##
|
|||
|
|
P1 = clag.clag('psd10r',
|
|||
|
|
[lc1_time], [lc1_strength], [lc1_strength_err],
|
|||
|
|
dt, fqL)
|
|||
|
|
ref_psd = np.ones(nfq)
|
|||
|
|
ref_psd, ref_psd_err = clag.optimize(P1, ref_psd)
|
|||
|
|
ref_psd, ref_psd_err = clag.errors(P1, ref_psd, ref_psd_err)
|
|||
|
|
|
|||
|
|
## plot ##
|
|||
|
|
#xscale('log'); ylim(-4,2)
|
|||
|
|
#errorbar(fqd, ref_psd, yerr=ref_psd_err, fmt='o', ms=10)
|
|||
|
|
|
|||
|
|
# Load second light curve
|
|||
|
|
lc2_time, lc2_strength, lc2_strength_err = np.loadtxt(args[1],skiprows=1).T
|
|||
|
|
P2 = clag.clag('psd10r', [lc2_time], [lc2_strength], [lc2_strength_err], dt, fqL)
|
|||
|
|
echo_psd = np.ones(nfq)
|
|||
|
|
echo_psd, echo_psd_err = clag.optimize(P2, echo_psd)
|
|||
|
|
echo_psd, echo_psd_err = clag.errors(P2, echo_psd, echo_psd_err)
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
### Now the cross spectrum ###
|
|||
|
|
### We also give it the calculated psd values as input ###
|
|||
|
|
Cx = clag.clag('cxd10r',
|
|||
|
|
[[lc1_time,lc2_time]],
|
|||
|
|
[[lc1_strength,lc2_strength]],
|
|||
|
|
[[lc1_strength_err,lc2_strength_err]],
|
|||
|
|
dt, fqL, ref_psd, echo_psd)
|
|||
|
|
|
|||
|
|
#Cx_vals = np.concatenate( (0.3*(ref_psd*echo_psd)**0.5, ref_psd*0+1.) )
|
|||
|
|
Cx_vals = np.concatenate( ((ref_psd+echo_psd)*0.5-0.3,ref_psd*0+0.1) )
|
|||
|
|
Cx_vals, Cx_err = clag.optimize(Cx, Cx_vals)
|
|||
|
|
|
|||
|
|
#?????? %autoreload
|
|||
|
|
Cx_vals, Cx_err = clag.errors(Cx,Cx_vals,Cx_err)
|
|||
|
|
|
|||
|
|
phi, phie = Cx_vals[nfq:], Cx_err[nfq:]
|
|||
|
|
lag, lage = phi/(2*np.pi*fqd), phie/(2*np.pi*fqd)
|
|||
|
|
cross_spectrm, cross_spectrm_err = Cx_vals[:nfq], Cx_err[:nfq]
|
|||
|
|
|
|||
|
|
np.savetxt("freq.out",fqL.reshape((-1,len(fqL))))
|
|||
|
|
np.savetxt("ref_psd.out",[ref_psd,ref_psd_err])
|
|||
|
|
np.savetxt("echo_psd.out",[echo_psd,echo_psd_err])
|
|||
|
|
np.savetxt("crsspctrm.out",[cross_spectrm,cross_spectrm_err])
|
|||
|
|
np.savetxt("timelag.out",[lag,lage])
|
|||
|
|
|
|||
|
|
|