# Input Arrays
ary_dict('uvw', (3,'ntime','na'), 'ft',
default=0,
test=rand_uvw),
ary_dict('antenna1', ('ntime', 'nbl'), np.int32,
default=lambda slvr, ary: slvr.default_ant_pairs()[0],
test=lambda slvr, ary: slvr.default_ant_pairs()[0]),
ary_dict('antenna2', ('ntime', 'nbl'), np.int32,
default=lambda slvr, ary: slvr.default_ant_pairs()[1],
test=lambda slvr, ary: slvr.default_ant_pairs()[1]),
ary_dict('lm', (2,'nsrc'), 'ft',
default=0,
test=lambda slvr, ary: (rary(ary)-0.5)*1e-1),
ary_dict('brightness', (5,'ntime','nsrc'), 'ft',
default=np.array([1,0,0,1,0.8])[:,np.newaxis,np.newaxis],
test=lambda slvr, ary: (rary(ary))),
ary_dict('gauss_shape', (3, 'ngsrc'), 'ft',
default=np.array([0,0,1])[:,np.newaxis],
test=rand_gauss_shape),
ary_dict('sersic_shape', (3, 'nssrc'), 'ft',
default=np.array([0,0,0])[:,np.newaxis],
test=rand_sersic_shape),
ary_dict('wavelength', ('nchan',), 'ft',
default=lambda slvr, ary: montblanc.constants.C / \
Classifier.E_BEAM_INPUT,
Classifier.EKB_SQRT_INPUT,
Classifier.COHERENCIES_INPUT]),
default=lambda slvr, ary: np.linspace(1e9, 2e9, slvr.dim_local_size('nchan')),
test=lambda slvr, ary: np.linspace(1e9, 2e9, slvr.dim_local_size('nchan'))),
ary_dict('ref_frequency', ('nchan',), 'ft',
classifiers=frozenset([Classifier.B_SQRT_INPUT]),
default=1.4e9,
test=1.4e9),
# Reference antenna parallactic angle at each timestep
ary_dict('parallactic_angles', ('ntime', 'na'), 'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=0,
test=lambda s, a: rary(a)*np.pi),
# Holographic Beam
ary_dict('point_errors', ('ntime','na','nchan',2), 'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=0,
test=lambda slvr, ary: (rary(ary) - 0.5)*1e-2),
ary_dict('antenna_scaling', ('na','nchan',2), 'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=1,
test=lambda slvr, ary: rary(ary)),
ary_dict('E_beam', ('beam_lw', 'beam_mh', 'beam_nud', 4), 'ct',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=np.array([1,0,0,1])[np.newaxis,np.newaxis,np.newaxis,:],
default=lambda slvr, ary: np.linspace(
1e9, 2e9, slvr.dim_local_size('nchan')),
test=lambda slvr, ary: np.linspace(1e9, 2e9,
slvr.dim_local_size('nchan'))),
ary_dict('ref_frequency', ('nchan', ),
'ft',
classifiers=frozenset([Classifier.B_SQRT_INPUT]),
default=1.4e9,
test=1.4e9),
# Reference antenna parallactic angle at each timestep
ary_dict('parallactic_angles', ('ntime', 'na'),
'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=0,
test=lambda s, a: rary(a) * np.pi),
# Holographic Beam
ary_dict('point_errors', ('ntime', 'na', 'nchan', 2),
'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=0,
test=lambda slvr, ary: (rary(ary) - 0.5) * 1e-2),
ary_dict('antenna_scaling', ('na', 'nchan', 2),
'ft',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
default=1,
test=lambda slvr, ary: rary(ary)),
ary_dict('E_beam', ('beam_lw', 'beam_mh', 'beam_nud', 4),
'ct',
classifiers=frozenset([Classifier.E_BEAM_INPUT]),
