def test_random_like(self):
"""
Test that the random_like function produces sensible data
"""
# Try for floats and complex data
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
# Test random array creation with same
# shape and type as existing array
shape = (np.random.randint(1, 50), np.random.randint(1, 50))
ary = np.empty(shape=shape, dtype=dtype)
random_ary = mbu.random_like(ary)
# Test that that the shape and type is correct
self.assertTrue(random_ary.shape == ary.shape)
self.assertTrue(random_ary.dtype == dtype)
# Test that we're getting complex data out
if np.issubdtype(dtype, np.complexfloating):
proportion_cplx = np.sum(np.iscomplex(random_ary)) / random_ary.size
self.assertTrue(proportion_cplx > 0.9)
# Test random array creation with supplied shape and type
shape = (np.random.randint(1, 50), np.random.randint(1, 50))
random_ary = mbu.random_like(shape=shape, dtype=dtype)
# Test that that the shape and type is correct
self.assertTrue(random_ary.shape == shape)
self.assertTrue(random_ary.dtype == dtype)
# Test that we're getting complex data out
if np.issubdtype(dtype, np.complexfloating):
proportion_cplx = np.sum(np.iscomplex(random_ary)) / random_ary.size
self.assertTrue(proportion_cplx > 0.9)
def test_random_like(self):
"""
Test that the random_like function produces sensible data
"""
# Try for floats and complex data
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
# Test random array creation with same
# shape and type as existing array
shape = (np.random.randint(1, 50), np.random.randint(1, 50))
ary = np.empty(shape=shape, dtype=dtype)
random_ary = mbu.random_like(ary)
# Test that that the shape and type is correct
self.assertTrue(random_ary.shape == ary.shape)
self.assertTrue(random_ary.dtype == dtype)
# Test that we're getting complex data out
if np.issubdtype(dtype, np.complexfloating):
proportion_cplx = np.sum(np.iscomplex(random_ary)) / random_ary.size
self.assertTrue(proportion_cplx > 0.9)
# Test random array creation with supplied shape and type
shape = (np.random.randint(1, 50), np.random.randint(1, 50))
random_ary = mbu.random_like(shape=shape, dtype=dtype)
# Test that that the shape and type is correct
self.assertTrue(random_ary.shape == shape)
self.assertTrue(random_ary.dtype == dtype)
# Test that we're getting complex data out
if np.issubdtype(dtype, np.complexfloating):
proportion_cplx = np.sum(np.iscomplex(random_ary)) / random_ary.size
self.assertTrue(proportion_cplx > 0.9)
montblanc.log.setLevel(logging.INFO)
slvr_cfg = montblanc.rime_solver_cfg(
msfile=args.msfile,
sources=montblanc.sources(point=args.npsrc,
gaussian=args.ngsrc,
sersic=args.nssrc),
init_weights='weight',
weight_vector=False,
dtype='float',
auto_correlations=args.auto_correlations,
version=args.version)
with montblanc.rime_solver(slvr_cfg) as slvr:
# Random point source coordinates in the l,m,n (brightness image) domain
slvr.lm[:] = mbu.random_like(slvr.lm) * 0.1
# Need a positive semi-definite brightness matrix
I, Q, U, V = slvr.stokes[:, :,
0], slvr.stokes[:, :,
1], slvr.stokes[:, :,
2], slvr.stokes[:, :,
3]
Q[:] = np.random.random(size=Q.shape) - 0.5
U[:] = np.random.random(size=U.shape) - 0.5
V[:] = np.random.random(size=V.shape) - 0.5
noise = np.random.random(size=(Q.shape)) * 0.1
# Determinant of a brightness matrix
# is I^2 - Q^2 - U^2 - V^2, noise ensures
# positive semi-definite matrix
I[:] = np.sqrt(Q**2 + U**2 + V**2 + noise)
with montblanc.rime_solver(slvr_cfg) as slvr:
# Get the lm coordinates
lm = sky_parse.shape_arrays(['l','m'], slvr.lm.shape, slvr.lm.dtype)
# Get the stokes and alpha parameters
stokes, alpha = repeat_brightness_over_time(slvr, sky_parse)
# If there are gaussian sources, create their
# shape matrix and transfer it.
if slvr.dim_global_size('ngsrc') > 0:
gauss.shape = sky_parse.shape_arrays(['el','em','eR'],
slvr.gauss.shape.shape, slvr.gauss.shape.dtype)
# Create observed visibilities and upload them to the GPU
observed_vis = mbu.random_like(slvr.observed_vis)
slvr.transfer_observed_vis(observed_vis)
# Generate random antenna pointing errors
point_errors = mbu.random_like(slvr.point_errors)
# Generate and transfer a noise vector.
weight_vector = mbu.random_like(slvr.weight_vector)
slvr.transfer_weight_vector(weight_vector)
# Execute the pipeline
for i in range(args.count):
# Set data on the solver object. Uploads to GPU
slvr.transfer_lm(lm)
slvr.transfer_stokes(stokes)
slvr.transfer_alpha(alpha)
args = parser.parse_args(sys.argv[1:])
# Set the logging level
montblanc.log.setLevel(logging.INFO)
slvr_cfg = montblanc.rime_solver_cfg(msfile=args.msfile,
sources=montblanc.sources(point=args.npsrc,
gaussian=args.ngsrc, sersic=args.nssrc),
init_weights='weight', weight_vector=False,
dtype='float', auto_correlations=args.auto_correlations,
version=args.version)
with montblanc.rime_solver(slvr_cfg) as slvr:
# Random point source coordinates in the l,m,n (brightness image) domain
slvr.lm[:] = mbu.random_like(slvr.lm)*0.1
# Need a positive semi-definite brightness matrix
I, Q, U, V = slvr.stokes[:,:,0], slvr.stokes[:,:,1], slvr.stokes[:,:,2], slvr.stokes[:,:,3]
Q[:] = np.random.random(size=Q.shape)-0.5
U[:] = np.random.random(size=U.shape)-0.5
V[:] = np.random.random(size=V.shape)-0.5
noise = np.random.random(size=(Q.shape))*0.1
# Determinant of a brightness matrix
# is I^2 - Q^2 - U^2 - V^2, noise ensures
# positive semi-definite matrix
I[:] = np.sqrt(Q**2 + U**2 + V**2 + noise)
slvr.alpha[:] = mbu.random_like(slvr.alpha)
# E beam
slvr.E_beam[:] = mbu.random_like(slvr.E_beam)
# Set the logging level
montblanc.log.setLevel(logging.INFO)
slvr_cfg = montblanc.rime_solver_cfg(msfile=args.msfile,
sources=montblanc.sources(
point=args.npsrc,
gaussian=args.ngsrc,
sersic=args.nssrc),
init_weights='weight', weight_vector=False,
auto_correlations=args.auto_correlations,
dtype='double', version=args.version)
with montblanc.rime_solver(slvr_cfg) as slvr:
# Random point source coordinates in the l,m,n (brightness image) domain
lm = mbu.random_like(slvr.lm)*0.1
if args.version in [Options.VERSION_TWO]:
# Random brightness matrix for the point sources
brightness = mbu.random_like(slvr.brightness)
elif args.version in [Options.VERSION_FOUR]:
# Need a positive semi-definite brightness
# matrix for v4 and v5
stokes = np.empty(shape=slvr.stokes.shape, dtype=slvr.stokes.dtype)
I, Q, U, V = stokes[:,:,0], stokes[:,:,1], stokes[:,:,2], stokes[:,:,3]
Q[:] = np.random.random(size=Q.shape)-0.5
U[:] = np.random.random(size=U.shape)-0.5
V[:] = np.random.random(size=V.shape)-0.5
noise = np.random.random(size=(Q.shape))*0.1
# Determinant of a brightness matrix
# is I^2 - Q^2 - U^2 - V^2, noise ensures
