5
MyGFF = UVM_GFF.init(vis = 'data', refant = 'LA', column = 'data',
6
model = 'p[0] + 6.28*(p[1]*(nu - nu0) + p[2]*t')
8
- Corre clearcal (si no existen las columnas)
9
- Inicia una instancia de UVM, con el modelo, etc.
11
GAINS = MyGFF.doSBD(scan = [4], column = 'data',
12
reinit_model = True, stokes = 'RR')
14
- Devuelve el array con las ganancias.
17
for sci in MyGFF.getScanNumbers('3C279'):
18
# MyGFF.doMBD(scan = [sci], column = 'data', stokes = 'RR',
19
# reinit_model = True, niter=0, pini = GAINS)
21
MyGFF.applyModel(scan = [sci], stokes = 'RR', gains = GAINS)
23
- Coge la columna elegida, la divide por las fases del modelo y REINICIA el modelo.
26
MyGFF.changeRefant('NL')
28
Estas dos deberian ser "equivalentes":
30
MBGAINS = MyGFF.doMBD(scan=[], column='corrected',
31
reinit_model = True, stokes = 'RR')
33
MBGAINS = MyGFF.doMBD(scan=[], column='data',
34
reinit_model = False, stokes = 'RR')
41
################################################
43
GAINSRR = MyGFF.doSBD(scan = [4], column = 'data',
44
reinit_model = True, stokes = 'RR')
46
GAINSLL = MyGFF.doSBD(scan = [4], column = 'data',
47
reinit_model = True, stokes = 'LL')
49
MyGFF.applyModel(scan = [4], stokes = 'RL', gains = [GAINSRR,GAINSLL])
52
GAINSXP = MyGFF.doSBD(scan = [4], column = 'corrected',
53
reinit_model = True, stokes = 'RL')
56
GAINSXP[:] = np.average(GAINSXP[MyGFF.ANTNAMES!=REFANT])
60
MyGFF.applyModel(scan=[], stokes = 'RR', gains = GAINSRR)
61
MyGFF.applyModel(scan=[], stokes = 'LL', gains = GAINSLL)
62
MyGFF.applyModel(scan=[], stokes = 'RL', gains = [GAINSRR, GAINSLL])
63
MyGFF.applyModel(scan=[], stokes = 'LR', gains = [GAINSLL, GAINSRR])
66
PARA HACER RL, PODEMOS AUMENTAR ANT2 = ANT2 + NANT
68
RL ===> MyGFF.uvm.ant2[i] += MyGFF.uvm.Nant
69
MyGFF.uvm.phase_gains.keys() += MyGFF.uvm.Nant
73
GAINS_I = MyGFF.doMBD(scan = [], column = 'corrected',
74
reinit_model = True, stokes = 'I')