print 'Day %d' % day
t0 = ( day - 1 )*dayinsecs
t = t0 + options.stime * np.arange( N )
orfpaths = [ orfdir + '/%s/d%03d.pkl' % ( IJ , day ) for IJ in IJs ]
Npd_before_today = (day - 1) * Nseg * 2 * Nvar * freqdict['Nf']
s = 1 ; tails = [ None ]*Nvar ; TSs = [ [] for v in range( Nvar ) ]
while s <= Nseg :
print 'Segments (%d|%d)' % ( s , s+1 )
Npd_before_segl = Npd_before_today + (s-1)*2*Nvar*freqdict['Nf']
Npd_before_segr = Npd_before_today + s*2*Nvar*freqdict['Nf']
tl , tsl = tsim.simulate_AETnoise_from_arbitrary_SpH(duration,options.stime,
t0 + (s-1)*duration/2,
options.GWSpectralSlope, options.H0,
Ppath, options.lmax,
options.seed,
Npd_before_segl,
Nvar,
options.compute_ORF_SpHs,
*orfpaths )
tr , tsr = tsim.simulate_AETnoise_from_arbitrary_SpH(duration,options.stime,
t0 + s*duration/2,
options.GWSpectralSlope, options.H0,
Ppath, options.lmax,
options.seed,
Npd_before_segr,
Nvar,
options.compute_ORF_SpHs,
*orfpaths )
for v in range( Nvar ) :
ts , tail = mufls.window_and_join( tsl[v] , tsr[v] , tails[v] )
parser.error('You must specify ORFDIR, PPATH and TSDIR!')
else:
orfdir, Ppath, tsdir = args[:3]
IJs = ['AA', 'AE', 'AT', 'EE', 'ET', 'TT']
duration = 86400.
Nvar = 3
freqdict = mufls.get_freqs_from_duration_and_stime(options.stime, duration)
for day in options.days:
t0 = (day - 1) * duration
N_previous_draws = (day - 1) * freqdict['Nf'] * 2 * Nvar
orfpaths = [orfdir + '/%s/d%03d.pkl' % (IJ, day) for IJ in IJs]
t, n = tsim.simulate_AETnoise_from_arbitrary_SpH(
duration, options.stime, t0, options.GWSpectralSlope, Ppath,
options.lmax, options.seed, N_previous_draws, Nvar,
options.compute_ORF_SpHs, *orfpaths)
tscale = {'Cadence1': options.stime, 'Offset1': t0}
tsdict = {
't': AS.Coarsable(t, **tscale),
'1': AS.Coarsable(n[0], **tscale),
'2': AS.Coarsable(n[1], **tscale),
'3': AS.Coarsable(n[2], **tscale)
}
if tsdir == '':
pass
elif tsdir not in glob.glob(tsdir):
os.system('mkdir -p %s' % tsdir)
print 'saving time-series to disk...'
file = open(tsdir + '/d%03d.pkl' % day, 'wb')
cpkl.dump(tsdict, file, -1)
t0 = (day - 1) * dayinsecs
t = t0 + options.stime * np.arange(N)
orfpaths = [orfdir + '/%s/d%03d.pkl' % (IJ, day) for IJ in IJs]
Npd_before_today = (day - 1) * Nseg * 2 * Nvar * freqdict['Nf']
s = 1
tails = [None] * Nvar
TSs = [[] for v in range(Nvar)]
while s <= Nseg:
print 'Segments (%d|%d)' % (s, s + 1)
Npd_before_segl = Npd_before_today + (s -
1) * 2 * Nvar * freqdict['Nf']
Npd_before_segr = Npd_before_today + s * 2 * Nvar * freqdict['Nf']
tl, tsl = tsim.simulate_AETnoise_from_arbitrary_SpH(
duration, options.stime, t0 + (s - 1) * duration / 2,
options.GWSpectralSlope, options.H0, Ppath, options.lmax,
options.seed, Npd_before_segl, Nvar, options.compute_ORF_SpHs,
*orfpaths)
tr, tsr = tsim.simulate_AETnoise_from_arbitrary_SpH(
duration, options.stime, t0 + s * duration / 2,
options.GWSpectralSlope, options.H0, Ppath, options.lmax,
options.seed, Npd_before_segr, Nvar, options.compute_ORF_SpHs,
*orfpaths)
for v in range(Nvar):
ts, tail = mufls.window_and_join(tsl[v], tsr[v], tails[v])
TSs[v] += list(ts)
tails[v] = np.copy(tail)
s += 2
TSs = np.array(TSs)[:, :N]
else :
orfdir , Ppath , tsdir = args[ :3 ]
IJs = [ 'AA' , 'AE' , 'AT' , 'EE' , 'ET' , 'TT' ]
duration = 86400.
Nvar = 3
freqdict = mufls.get_freqs_from_duration_and_stime( options.stime , duration )
for day in options.days :
t0 = ( day - 1 )*duration
N_previous_draws = (day - 1) * freqdict['Nf'] * 2 * Nvar
orfpaths = [ orfdir + '/%s/d%03d.pkl' % ( IJ , day ) for IJ in IJs ]
t , n = tsim.simulate_AETnoise_from_arbitrary_SpH( duration , options.stime , t0 ,
options.GWSpectralSlope , Ppath , options.lmax ,
options.seed , N_previous_draws ,
Nvar , options.compute_ORF_SpHs ,
*orfpaths )
tscale = { 'Cadence1':options.stime , 'Offset1':t0 }
tsdict = { 't':AS.Coarsable( t , **tscale ) ,
'1':AS.Coarsable( n[0] , **tscale ) ,
'2':AS.Coarsable( n[1] , **tscale ) ,
'3':AS.Coarsable( n[2] , **tscale ) }
if tsdir == '' :
pass
elif tsdir not in glob.glob( tsdir ) :
os.system( 'mkdir -p %s' % tsdir )
print 'saving time-series to disk...'
file = open( tsdir+'/d%03d.pkl' % day , 'wb' )
cpkl.dump( tsdict , file , -1 ) ; file.close()
