def get_comatrix( f , tditype , tdigen , whichtdis ) :
"""
Returns the covariance matrix of a set of TDI observables
INPUT:
f --- frequencies (numpy array)
tditype --- type of TDI observables ('Michelson','Sagnac')
tdigen --- generation of TDI observables ('G0','G1','Gm','G2')
whichtdis --- set of TDI observables ('123','AET')
OUTPUT:
comatrix --- covariance matrix ( Nvar x Nvar x Nf numpy array)
"""
if whichtdis == '123' :
pairs = [ ('1','1') , ('1','2') , ('1','3') ,
('2','1') , ('2','2') , ('2','3') ,
('3','1') , ('3','2') , ('3','3') ]
cIJs = [ mlisar.get_tdiNSD( tditype , tdigen , pair[0] , pair[1] , f ) for pair in pairs ]
elif whichtdis == 'AET' :
pairs = [ ('A','A') , ('A','E') , ('A','T') ,
('E','A') , ('E','E') , ('E','T') ,
('T','A') , ('T','E') , ('T','T') ]
cIJs = [ mlisar.get_tdiNSD( tditype , tdigen , pair[0] , pair[1] , f ) for pair in pairs ]
comatrix = np.array( [ [ cIJs[0] , cIJs[1] , cIJs[2] ] ,
[ cIJs[3] , cIJs[4] , cIJs[5] ] ,
[ cIJs[6] , cIJs[7] , cIJs[8] ] ] )
return comatrix
def get_comatrix(f, tditype, tdigen, whichtdis):
"""
Returns the covariance matrix of a set of TDI observables
INPUT:
f --- frequencies (numpy array)
tditype --- type of TDI observables ('Michelson','Sagnac')
tdigen --- generation of TDI observables ('G0','G1','Gm','G2')
whichtdis --- set of TDI observables ('123','AET')
OUTPUT:
comatrix --- covariance matrix ( Nvar x Nvar x Nf numpy array)
"""
if whichtdis == '123':
pairs = [('1', '1'), ('1', '2'), ('1', '3'), ('2', '1'), ('2', '2'),
('2', '3'), ('3', '1'), ('3', '2'), ('3', '3')]
cIJs = [
mlisar.get_tdiNSD(tditype, tdigen, pair[0], pair[1], f)
for pair in pairs
]
elif whichtdis == 'AET':
pairs = [('A', 'A'), ('A', 'E'), ('A', 'T'), ('E', 'A'), ('E', 'E'),
('E', 'T'), ('T', 'A'), ('T', 'E'), ('T', 'T')]
cIJs = [
mlisar.get_tdiNSD(tditype, tdigen, pair[0], pair[1], f)
for pair in pairs
]
comatrix = np.array([[cIJs[0], cIJs[1], cIJs[2]],
[cIJs[3], cIJs[4], cIJs[5]],
[cIJs[6], cIJs[7], cIJs[8]]])
return comatrix
)
else:
csddir = args[0]
if options.whichtdi == 'ordinary':
IJs = ['12', '13', '23']
elif options.whichtdi == 'optimal':
IJs = ['AE', 'AT', 'ET']
if csddir not in glob.glob(csddir):
os.system('mkdir %s' % csddir)
fdata = options.f0 + options.df * np.arange(options.Nf)
fscale = {'Offset1': options.f0, 'Cadence1': options.df}
f = AS.Coarsable(fdata, **fscale)
print "Calculating expected cross NSD..."
PIJdatas = [
mlisar.get_tdiNSD(options.tditype, options.tdigen, IJ[0], IJ[1], fdata)
for IJ in IJs
]
PIJs = [AS.Coarsable(PIJdata, **fscale) for PIJdata in PIJdatas]
P12, P13, P23 = PIJs
print 'done'
psddict = {'f': f, 'AE': P12, 'AT': P13, 'ET': P23}
file = open(csddir + 'd001.pkl', 'wb')
cpkl.dump(psddict, file, -1)
file.close()
def get_PnTnT(f):
return 16e39 * mlisar.get_tdiNSD(tditype, tdigen, 'T', 'T', f)
def get_PnEnE(f):
return 16e39 * mlisar.get_tdiNSD(tditype, tdigen, 'E', 'E', f)
def get_PnAnA(f):
return 16e39 * mlisar.get_tdiNSD(tditype, tdigen, 'A', 'A', f)
def get_PnTnT( f ) :
return 16e39 * mlisar.get_tdiNSD( tditype , tdigen , 'T' , 'T' , f )
def get_PnEnE( f ) :
return 16e39 * mlisar.get_tdiNSD( tditype , tdigen , 'E' , 'E' , f )
def get_PnAnA( f ) :
return 16e39 * mlisar.get_tdiNSD( tditype , tdigen , 'A' , 'A' , f )
( options , args ) = parser.parse_args()
if len( args ) < 1 :
parser.error( "You must specify CSDDIR! Type './x_simulate_expected_signal_CSD.py -h' for help " )
else :
csddir = args[ 0 ]
if options.whichtdi == 'ordinary' :
IJs = [ '12' , '13' , '23' ]
elif options.whichtdi == 'optimal' :
IJs = [ 'AE' , 'AT' , 'ET' ]
if csddir not in glob.glob( csddir ) :
os.system( 'mkdir %s' % csddir )
fdata = options.f0 + options.df * np.arange( options.Nf )
fscale = { 'Offset1':options.f0 , 'Cadence1':options.df }
f = AS.Coarsable( fdata , **fscale )
print "Calculating expected cross NSD..."
PIJdatas = [ mlisar.get_tdiNSD( options.tditype , options.tdigen , IJ[0] , IJ[1] , fdata ) for IJ in IJs ]
PIJs = [ AS.Coarsable( PIJdata , **fscale ) for PIJdata in PIJdatas ]
P12 , P13 , P23 = PIJs
print 'done'
psddict = { 'f':f , 'AE':P12 , 'AT':P13 , 'ET':P23 }
file = open( csddir + 'd001.pkl' , 'wb' ) ; cpkl.dump( psddict , file , -1 ) ; file.close()
( options , args ) = parser.parse_args()
if len( args ) < 1 :
parser.error( "You must specify PSDDIR! Type './x_simulate_expected_signal_PSD.py -h' for help " )
else :
psddir = args[ 0 ]
if options.whichtdi == 'ordinary' :
IIs = [ '11' , '22' , '33' ]
elif options.whichtdi == 'optimal' :
IIs = [ 'AA' , 'EE' , 'TT' ]
if psddir not in glob.glob( psddir ) :
os.system( 'mkdir %s' % psddir )
fdata = options.f0 + options.df * np.arange( options.Nf )
fscale = { 'Offset1':options.f0 , 'Cadence1':options.df }
f = AS.Coarsable( fdata , **fscale )
print "Calculating expected auto NSD..."
PIIdatas = [ mlisar.get_tdiNSD( options.tditype , options.tdigen , II[0] , II[1] , fdata ) for II in IIs ]
PIIs = [ AS.Coarsable( PIIdata , **fscale ) for PIIdata in PIIdatas ]
P11 , P22 , P33 = PIIs
print 'done'
psddict = { 'f':f , 'AA':P11 , 'EE':P22 , 'TT':P33 }
file = open( psddir + 'd001.pkl' , 'wb' ) ; cpkl.dump( psddict , file , -1 ) ; file.close()