def solve_image(vis: Visibility,
model: Image,
components=None,
predict=predict_2d,
invert=invert_2d,
**kwargs) -> (Visibility, Image, Image):
"""Solve for image using deconvolve_cube and specified predict, invert
This is the same as a majorcycle/minorcycle algorithm. The components are removed prior to deconvolution.
See also arguments for predict, invert, deconvolve_cube functions.2d
:param vis:
:param model: Model image
:param predict: Predict function e.g. predict_2d, predict_wstack
:param invert: Invert function e.g. invert_2d, invert_wstack
:return: Visibility, model
"""
nmajor = get_parameter(kwargs, 'nmajor', 5)
log.info("solve_image: Performing %d major cycles" % nmajor)
# The model is added to each major cycle and then the visibilities are
# calculated from the full model
vispred = copy_visibility(vis)
visres = copy_visibility(vis)
vispred = predict(vispred, model, **kwargs)
if components is not None:
vispred = predict_skycomponent_visibility(vispred, components)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert(visres, model, **kwargs)
psf, sumwt = invert(visres, model, dopsf=True, **kwargs)
thresh = get_parameter(kwargs, "threshold", 0.0)
for i in range(nmajor):
log.info("solve_image: Start of major cycle %d" % i)
cc, res = deconvolve_cube(dirty, psf, **kwargs)
res = None
model.data += cc.data
vispred = predict(vispred, model, **kwargs)
visres.data['vis'] = vis.data['vis'] - vispred.data['vis']
dirty, sumwt = invert(visres, model, **kwargs)
if numpy.abs(dirty.data).max() < 1.1 * thresh:
log.info("Reached stopping threshold %.6f Jy" % thresh)
break
log.info("solve_image: End of major cycle")
log.info("solve_image: End of major cycles")
return visres, model, dirty
def test_predict_sky_components_coalesce(self):
sc = create_low_test_skycomponents_from_gleam(
flux_limit=10.0,
polarisation_frame=PolarisationFrame("stokesI"),
frequency=self.frequency,
kind='cubic',
phasecentre=SkyCoord("17h20m31s", "-00d58m45s"),
radius=0.1)
self.config = create_named_configuration('LOWBD2-CORE')
self.phasecentre = SkyCoord("17h20m31s", "-00d58m45s")
sampling_time = 3.76
self.times = numpy.arange(0.0, +300 * sampling_time, sampling_time)
self.vis = create_blockvisibility(
self.config,
self.times,
self.frequency,
phasecentre=self.phasecentre,
weight=1.0,
polarisation_frame=PolarisationFrame('stokesI'),
channel_bandwidth=self.channel_bandwidth)
self.vis = predict_skycomponent_visibility(self.vis, sc)
cvt = coalesce_visibility(self.vis, time_coal=1.0)
assert cvt.cindex is not None
newwcs = image_graph.wcs.deepcopy() newwcs.wcs.cdelt[0] = -0.001 * 180 / np.pi newwcs.wcs.cdelt[1] = 0.001 * 180 / np.pi newshape = np.array(image_graph.data.shape) newshape[2] /= 2 newshape[3] /= 2 newphasecentre = SkyCoord(ra=+15.0 * u.deg, dec=-10.0 * u.deg, frame='icrs', equinox='J2000') image = copy.deepcopy(image_graph) new_vis = copy.deepcopy(vis) insert_skycomponent(image, comp) # TODO FACET为1时，并行和串行的结果相同，但FACET不为1时结果不同，为了便于后面的验证，因此不再进行这两步操作 # image = reproject_image(image, newwcs, newshape)[0] # image.data = fft(image.data) predict_facets(new_vis, image) new_vis = phaserotate_visibility(new_vis, newphasecentre, tangent=False) predict_skycomponent_visibility(new_vis, comp) # === Extract Lsm === extract_lsm = extract_lsm_handle() broadcast_lsm = sc.broadcast(extract_lsm.collect()) # === Local Sky Model === local_sky_model = local_sky_model_handle() # === Telescope Management === telescope_management = telescope_management_handle() # # === Visibility Buffer === visibility_buffer = visibility_buffer_handle(vis) visibility_buffer.cache() broads_input1 = sc.broadcast(visibility_buffer.collect())
def test_solve_gaintable(self):
'''
测试solve_gaintable
:return:
'''
#===串行===
newphasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-10.0 * u.deg,
frame='icrs',
equinox='J2000')
image = copy.deepcopy(image_graph)
image.data = np.zeros_like(image.data)
#将lsm插入到image中
insert_skycomponent(image, comp, insert_method="Sinc")
# 期间可能会有fft和reproject暂不考虑
#生成telescope_data
telescope_data = copy.deepcopy(vis)
#image做卷积并且degrid生成modelvis
model_vis = predict_facets(telescope_data, image)
model_vis = predict_skycomponent_visibility(model_vis, comp)
model_vis = decoalesce_visibility(model_vis)
#模拟望远镜实际接收到的visibility
blockvis_observed = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=phasecentre,
weight=1,
polarisation_frame=PolarisationFrame('linear'),
integration_time=1.0)
# 用自然数值填充vis，模拟实际接收到的block_visibility，并且各个vis的值各不相同易于区分
blockvis_observed.data['vis'] = range(blockvis_observed.nvis)
gt = solve_gaintable(blockvis_observed, model_vis)
apply_gaintable(blockvis_observed, gt)
#===并行===
# TODO暂未完成
# 生成telescope_data
telescope_data, visibility_share = visibility_to_visibility_para(
vis, 'npol')
ims, image_share = image_to_image_para(image_graph, FACETS)
ims2 = []
for i in ims:
insert_skycomponent_para(i, comp, insert_method='Sinc')
ims2.append(i)
model_vis1 = []
viss2 = []
for v in viss:
for im in ims2:
if v[0][0] == im.frequency:
temp = predict_facets_para(v[1], im)
# (chan, time, ant1, ant2)
model_vis1.append(
((v[0][0], v[0][1], v[0][2], v[0][3]), temp))
viss2.append(((v[0][0], v[0][1], v[0][2], v[0][3],
im.facet, im.polarisation),
phaserotate_visibility_para(temp,
newphasecentre,
tangent=False)))
predict_skycoponent_visibility_para_modified(viss2, comp, mode='test2')
# 将visibility的facet和polarisation合并起来
viss3 = defaultdict(list)
model_vis2 = defaultdict(list)
for v in range(0, len(viss2), 4 * 4):
temp = copy.deepcopy(viss2[v][1])
temp2 = copy.deepcopy(model_vis1[v][1])
for id in range(v + 1, v + 16):
temp.data['vis'] += viss2[id][1].data['vis']
temp2.data['vis'] += model_vis1[id][1].data['vis']
viss3[viss2[v][0][0:2]].append(((viss2[v][0][2:4]), temp))
model_vis2[model_vis1[v][0][0:2]].append(
((model_vis1[v][0][2:]), temp2))
# TODO 将并行程序从此开始填入
gts = []
for key in viss3:
xs = []
xwts = []
for v, mv in zip(viss3[key], model_vis2[key]):
x, xwt = solve_gaintable_para(v[1], mv[1])
xs.append(((0, 0, 0, 0, key[0], key[1], v[0][0], v[0][1]), x))
xwts.append(xwt)
g = create_gaintable_from_visibility_para(viss3[key][0][1], 3)
solve_from_X_para(xs, xwts, g, npol=viss3[key][0][1].npol)
gts.append((key, g))
gaintable_right(gt, gts)
new_gain = combine_gaintable(gt, gts)
for key in viss3:
print(key)
chan, time = key
temp = gaintable_for_para(new_gain.gain[time, :,
chan, :, :].reshape[1, 3,
2, 2])
apply_gaintable_para(viss3[key], temp)
for key in viss3:
chan, time = key
vis_serial = block_vis.vis[time, :, :chan, :]
for id, v in viss3[key]:
ant1 = id[2]
ant2 = id[3]
vis_serial = block_vis.vis[time, ant2, ant1, chan]
print(vis_serial)
print(v.vis)
print()
def serialize_program():
result = []
lowcore = create_named_configuration('LOWBD2-CORE')
times = metadata.create_time() # time = 5
frequency = metadata.create_frequency() # nchan = 5
channel_bandwidth = metadata.create_channel_bandwidth() # nchan = 5
#---predict_module---#
phasecentre = metadata.create_phasecentre()
comp = metadata.create_skycomponent()
image = create_image(
metadata.NY,
metadata.NX,
frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=metadata.create_polarisation_frame(),
)
insert_skycomponent(image, comp, insert_method="Sinc")
blockvis = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=phasecentre,
weight=1,
polarisation_frame=metadata.create_polarisation_frame(),
integration_time=1.0,
NAN=metadata.NAN)
visibility = coalesce_visibility(blockvis)
visibility = predict_facets(visibility, image, facets=metadata.FACETS)
# 不确定这一步是否需要还要再一步phaserotate，因为predict_facet已经在最后调用过一次phaserotate
newphasecentre = SkyCoord(ra=+10.0 * u.deg,
dec=-30.0 * u.deg,
frame='icrs',
equinox='J2000')
model_vis = phaserotate_visibility(visibility, newphasecentre)
predict_skycomponent_visibility(model_vis, comp)
result.append(model_vis)
model_vis = decoalesce_visibility(model_vis)
#---solve_module---#
# 模拟望远镜实际接收到的visibility
blockvis_observed = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=phasecentre,
weight=1,
polarisation_frame=metadata.create_polarisation_frame(),
integration_time=1.0,
NAN=metadata.NAN)
# 用整数填充vis， 模拟实际接收到的block_visibility
vis_observed = coalesce_visibility(blockvis_observed)
vis_observed.data['vis'].flat = range(vis_observed.nvis * 4)
blockvis_observed = decoalesce_visibility(vis_observed)
gaintable = solve_gaintable(blockvis_observed, model_vis)
apply_gaintable(blockvis_observed, gaintable)
blockvis_observed.data[
'vis'] = blockvis_observed.data['vis'] - model_vis.data['vis']
visibility = coalesce_visibility(blockvis_observed,
time_coal=metadata.time_coal,
frequency_coal=metadata.frequency_coal)
result.append(visibility)
#---backwards_module---#
image = create_image(metadata.NY,
metadata.NX,
frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=metadata.create_polarisation_frame(
)) # 空的image，接受visibility的invert
image, wt = invert_facets(visibility, image, facets=metadata.FACETS)
psf_image = create_image(
metadata.NY,
metadata.NX,
frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=metadata.create_polarisation_frame())
psf_image, psf_wt = invert_facets(visibility,
psf_image,
dopsf=True,
facets=metadata.FACETS)
dirty_taylor, psf_taylor = deconvolve_cube_sumfacet(
image, psf_image, moments=metadata.MOMENTS)
result.append((dirty_taylor, psf_taylor))
#---deconvolution_module---#
comp_image, residual_image = deconvolve_cube_identify(image,
dirty_taylor,
psf_taylor,
niter=metadata.niter)
result.append((comp_image, residual_image))
return result
def serialize_program():
result = []
lowcore = create_named_configuration('LOWBD2-CORE')
times = numpy.linspace(-3, +3, 5) * (numpy.pi / 12.0) # time = 5
frequency = numpy.array([1e8, 1.1e8, 1.2e8, 1.3e8, 1.4e8]) # nchan = 5
channel_bandwidth = numpy.array([1e7, 1e7, 1e7, 1e7, 1e7]) # nchan = 5
f = numpy.array([100.0, 110.0, 120.0, 130.0]) # npol = 4
flux = numpy.array([f, f + 100.0, f + 200.0, f + 300.0,
f + 400.0]) # nchan,npol = 2, 4
phasecentre = SkyCoord(ra=+15.0 * u.deg,
dec=-35.0 * u.deg,
frame='icrs',
equinox='J2000')
compabsdirection = SkyCoord(ra=17.0 * u.deg,
dec=-36.5 * u.deg,
frame='icrs',
equinox='J2000')
comp = create_skycomponent(flux=flux,
frequency=frequency,
direction=compabsdirection,
polarisation_frame=PolarisationFrame('linear'))
image = create_image(
NY,
NX,
frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=PolarisationFrame('linear'),
)
insert_skycomponent(image, comp, insert_method="Sinc")
blockvis = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=phasecentre,
weight=1,
polarisation_frame=PolarisationFrame('linear'),
integration_time=1.0)
visibility = coalesce_visibility(blockvis)
visibility = predict_facets(visibility, image, facets=FACETS)
# 不确定这一步是否需要还要再一步phaserotate，因为predict_facet已经在最后调用过一次phaserotate
newphasecentre = SkyCoord(ra=+10.0 * u.deg,
dec=-30.0 * u.deg,
frame='icrs',
equinox='J2000')
model_vis = phaserotate_visibility(visibility, newphasecentre)
predict_skycomponent_visibility(model_vis, comp)
result.append(model_vis)
model_vis = decoalesce_visibility(model_vis)
# 模拟望远镜实际接收到的visibility
blockvis_observed = create_blockvisibility(
lowcore,
times=times,
frequency=frequency,
channel_bandwidth=channel_bandwidth,
phasecentre=phasecentre,
weight=1,
polarisation_frame=PolarisationFrame('linear'),
integration_time=1.0)
# 用整数填充vis， 模拟实际接收到的block_visibility
vis_observed = coalesce_visibility(blockvis_observed)
vis_observed.data['vis'].flat = range(vis_observed.nvis * 4)
blockvis_observed = decoalesce_visibility(vis_observed)
gaintable = solve_gaintable(blockvis_observed, model_vis)
apply_gaintable(blockvis_observed, gaintable)
blockvis_observed.data[
'vis'] = blockvis_observed.data['vis'] - model_vis.data['vis']
visibility = coalesce_visibility(
blockvis_observed) # 空的image，接受visibility的invert
result.append(visibility)
image = create_image(NY,
NX,
frequency=frequency,
phasecentre=phasecentre,
cellsize=0.001,
polarisation_frame=PolarisationFrame('linear'))
image, wt = invert_facets(visibility, image)
new_image = Image()
new_image.wcs = image.wcs
new_image.polarisation_frame = image.polarisation_frame
new_image.data = np.sum(image.data, axis=0).reshape((1, NPOL, NY, NX)) * 4
result.append(new_image)
return result