#randomise RA and Dec between 0,2pi and -pi/2, pi/2 respectively
##adjusted to smaller ranges to see if improves results
sourcealpha[i] = 2*np.pi*(r.uniform(0,1))
sourcedelta[i] = np.arccos(2*(r.uniform(0,1))-1)-np.pi/2
#performs barycentring functions for source i
emit = get_bary_in_loop(tGPS, detector,[sourcealpha[i], sourcedelta[i]], Eephem, Sephem)
#creates training vectors of time difference
[emitdt, emitte, emitdd, emitR, emitER, emitE, emitS] = emit
TS[i]= np.reshape(emitdt, wl)
## normalises training vectors
TS[i] /= np.sqrt(np.abs(greedy.dot_product(dt, TS[i], TS[i])))
#print[i]
##print('outloop')
# tolerance for stopping algorithm
tol = 1e-12
#forms normalised basis vectors, except happens far too quickly to be right
RB = greedy.greedy(TS, dt, tol)
##### Test this RB can span all random points
sourcealpha2 = np.zeros(10)
sourcedelta2 = np.zeros(10)
def greedy_bary(detector, tssize, wl, source, tGPS, Eephem, Sephem, dt):
"""
Generates emitdt data for sources and detector input. Creates Reduced Basis
for this using modified Gram-Schmidt process.
detector should be case insensitive string matching one of the below abbreviations
GREEN BANK = 'GB'
NARRABRI CS08 = 'NA'
ARECIBO XYZ (JPL) = 'AO'
Hobart, Tasmania = 'HO'
DSS 43 XYZ = 'TD'
PARKES XYZ (JER) = 'PK'
JODRELL BANK = 'JB'
GB 300FT = 'G3'
GB 140FT = 'G1RAD' (changed to distinguish it from GEO)
VLA XYZ = 'VL'
Nancay = 'NC'
Effelsberg = 'EF'
GW telescopes):
LIGO Hanford = 'LHO', 'H1', 'H2'
LIGO Livingston = 'LLO', 'L1'
GEO 600 = 'GEO', 'G1'
VIRGO = 'V1', 'VIRGO'
TAMA = 'T1', 'TAMA'
Earth geocentre = 'GEOCENTER'
tssize is number of training sets, integer
wl is length of training set, integer
source = [sourcealpha, sourcedelta],
sourcealpha 1d array of right ascension, length wl
sourcedelta 1d array of declination, length wl
tGPS is array of linearly spaced time values within 630720013 and 1261872018
of length wl
Eephem & Sephem contain ephemeris data for the Earth and Sun respectively
dt = tGPS[1]-tGPS[0]
"""
#initialises training sets
TS = np.zeros((tssize, wl))
for i in range(tssize):
#performs barycentring functions for source i
emit = get_bary_in_loop(tGPS, detector,[source[0][i], source[1][i]], Eephem, Sephem)
#creates training vectors of time difference
[emitdt, emitte, emitdd, emitR, emitER, emitE, emitS] = emit
TS[i]= np.reshape(emitdt, wl)
## normalises training vectors
TS[i] /= np.sqrt(np.abs(greedy.dot_product(dt, TS[i], TS[i])))
# tolerance for stopping algorithm
tol = 1e-12
#forms normalised basis vectors
RB = greedy.greedy(TS, dt, tol)
return RB
