def visibility_scatter(vis: Visibility,
vis_iter,
vis_slices=1) -> List[Visibility]:
"""Scatter a visibility into a list of subvisibilities
If vis_iter is over time then the type of the outvisibilities will be the same as inout
If vis_iter is over w then the type of the output visibilities will always be Visibility
:param vis: Visibility
:param vis_iter: visibility iterator
:param vis_slices: Number of slices to be made
:return: list of subvisibilitys
"""
assert vis is not None
if vis_slices == 1:
return [vis]
visibility_list = list()
for i, rows in enumerate(vis_iter(vis, vis_slices=vis_slices)):
subvis = create_visibility_from_rows(vis, rows)
visibility_list.append(subvis)
return visibility_list
def test_create_visibility_from_rows_makecopy(self):
self.vis = create_visibility(self.lowcore, self.times, self.frequency, phasecentre=self.phasecentre,
weight=1.0, channel_bandwidth=self.channel_bandwidth)
rows = self.vis.time > 150.0
for makecopy in [True, False]:
selected_vis = create_visibility_from_rows(self.vis, rows, makecopy=makecopy)
assert selected_vis.nvis == numpy.sum(numpy.array(rows))
def test_coalesce_decoalesce_with_iter(self):
for rows in vis_timeslice_iter(self.blockvis):
visslice = create_visibility_from_rows(self.blockvis, rows)
cvisslice = convert_blockvisibility_to_visibility(visslice)
assert numpy.min(cvisslice.frequency) == numpy.min(self.frequency)
assert numpy.min(cvisslice.frequency) > 0.0
dvisslice = decoalesce_visibility(cvisslice)
assert dvisslice.nvis == visslice.nvis
def test_vis_wslice_iterator(self):
self.actualSetUp()
nchunks = vis_wslices(self.vis, wslice=10.0)
log.debug('Found %d chunks' % (nchunks))
assert nchunks > 1
total_rows = 0
for chunk, rows in enumerate(vis_wslice_iter(self.vis, nchunks)):
assert len(rows)
visslice = create_visibility_from_rows(self.vis, rows)
total_rows += visslice.nvis
assert numpy.sum(visslice.nvis) < self.vis.nvis
assert total_rows == self.vis.nvis, "Total rows iterated %d, Original rows %d" % (total_rows, self.vis.nvis)
def create_gaintable_from_screen(vis,
sc,
screen,
height=3e5,
vis_slices=None,
scale=1.0,
**kwargs):
""" Create gaintables from a screen calculated using ARatmospy
Screen axes are ['XX', 'YY', 'TIME', 'FREQ']
:param vis:
:param sc: Sky components for which pierce points are needed
:param screen:
:param height: Height (in m) of screen above telescope e.g. 3e5
:param scale: Multiply the screen by this factor
:return:
"""
assert isinstance(vis, BlockVisibility)
station_locations = vis.configuration.xyz
# Convert to TEC
# phase = image[pixel] * -8.44797245e9 / frequency
nant = station_locations.shape[0]
t2r = numpy.pi / 43200.0
gaintables = [
create_gaintable_from_blockvisibility(vis, **kwargs) for i in sc
]
# The time in the Visibility is hour angle in seconds!
for iha, rows in enumerate(vis_timeslice_iter(vis, vis_slices=vis_slices)):
v = create_visibility_from_rows(vis, rows)
ha = numpy.average(v.time)
tec2phase = -8.44797245e9 / numpy.array(vis.frequency)
number_bad = 0
number_good = 0
for icomp, comp in enumerate(sc):
pp = find_pierce_points(station_locations,
(comp.direction.ra.rad + t2r * ha) * u.rad,
comp.direction.dec,
height=height,
phasecentre=vis.phasecentre)
scr = numpy.zeros([nant])
for ant in range(nant):
pp0 = pp[ant][0:2]
worldloc = [pp0[0], pp0[1], ha, 1e8]
try:
pixloc = screen.wcs.wcs_world2pix([worldloc],
0)[0].astype('int')
scr[ant] = scale * screen.data[pixloc[3], pixloc[2],
pixloc[1], pixloc[0]]
number_good += 1
except:
number_bad += 1
scr[ant] = 0.0
scr = scr[:, numpy.newaxis] * tec2phase[numpy.newaxis, :]
# axes of gaintable.gain are time, ant, nchan, nrec
gaintables[icomp].gain[iha, :, :, :] = numpy.exp(
1j * scr[..., numpy.newaxis, numpy.newaxis])
gaintables[icomp].phasecentre = comp.direction
if number_bad > 0:
log.warning(
"create_gaintable_from_screen: %d pierce points are inside the screen image"
% (number_good))
log.warning(
"create_gaintable_from_screen: %d pierce points are outside the screen image"
% (number_bad))
return gaintables
def solve_gaintable(vis: BlockVisibility,
modelvis: BlockVisibility = None,
gt=None,
phase_only=True,
niter=30,
tol=1e-8,
crosspol=False,
normalise_gains=True,
**kwargs) -> GainTable:
"""Solve a gain table by fitting an observed visibility to a model visibility
If modelvis is None, a point source model is assumed.
:param vis: BlockVisibility containing the observed data_models
:param modelvis: BlockVisibility containing the visibility predicted by a model
:param gt: Existing gaintable
:param phase_only: Solve only for the phases (default=True)
:param niter: Number of iterations (default 30)
:param tol: Iteration stops when the fractional change in the gain solution is below this tolerance
:param crosspol: Do solutions including cross polarisations i.e. XY, YX or RL, LR
:return: GainTable containing solution
"""
assert isinstance(vis, BlockVisibility), vis
if modelvis is not None:
assert isinstance(modelvis, BlockVisibility), modelvis
assert numpy.max(numpy.abs(
modelvis.vis)) > 0.0, "Model visibility is zero"
if phase_only:
log.debug('solve_gaintable: Solving for phase only')
else:
log.debug('solve_gaintable: Solving for complex gain')
if gt is None:
log.debug("solve_gaintable: creating new gaintable")
gt = create_gaintable_from_blockvisibility(vis, **kwargs)
else:
log.debug("solve_gaintable: starting from existing gaintable")
for row in range(gt.ntimes):
vis_rows = numpy.abs(vis.time - gt.time[row]) < gt.interval[row] / 2.0
if numpy.sum(vis_rows) > 0:
subvis = create_visibility_from_rows(vis, vis_rows)
if modelvis is not None:
model_subvis = create_visibility_from_rows(modelvis, vis_rows)
pointvis = divide_visibility(subvis, model_subvis)
x = numpy.sum(pointvis.vis * pointvis.weight, axis=0)
xwt = numpy.sum(pointvis.weight, axis=0)
else:
x = numpy.sum(subvis.vis * subvis.weight, axis=0)
xwt = numpy.sum(subvis.weight, axis=0)
mask = numpy.abs(xwt) > 0.0
x_shape = x.shape
x[mask] = x[mask] / xwt[mask]
x[~mask] = 0.0
x = x.reshape(x_shape)
gt = solve_from_X(gt,
x,
xwt,
row,
crosspol,
niter,
phase_only,
tol,
npol=vis.polarisation_frame.npol)
if normalise_gains and not phase_only:
gabs = numpy.average(numpy.abs(gt.data['gain'][row]))
gt.data['gain'][row] /= gabs
assert isinstance(gt, GainTable), "gt is not a GainTable: %r" % gt
assert_vis_gt_compatible(vis, gt)
return gt
def simulate_gaintable_from_voltage_patterns(vis, sc, vp_list, vp_coeffs, vis_slices=None, order=3, use_radec=False,
**kwargs):
""" Create gaintables for a set of zernikes
:param vis:
:param sc: Sky components for which pierce points are needed
:param vp: List of Voltage patterns in AZELGEO frame
:param vp_coeffs: Fractional contribution [nants, nvp]
:param order: order of spline (default is 3)
:return:
"""
ntimes, nant = vis.vis.shape[0:2]
vp_coeffs = numpy.array(vp_coeffs)
gaintables = [create_gaintable_from_blockvisibility(vis, **kwargs) for i in sc]
if not use_radec:
assert isinstance(vis, BlockVisibility)
assert vis.configuration.mount[0] == 'azel', "Mount %s not supported yet" % vis.configuration.mount[0]
# The time in the Visibility is hour angle in seconds!
number_bad = 0
number_good = 0
# Cache the splines, one per voltage pattern
real_splines = list()
imag_splines = list()
for ivp, vp in enumerate(vp_list):
assert vp.wcs.wcs.ctype[0] == 'AZELGEO long', vp.wcs.wcs.ctype[0]
assert vp.wcs.wcs.ctype[1] == 'AZELGEO lati', vp.wcs.wcs.ctype[1]
nchan, npol, ny, nx = vp.data.shape
real_splines.append(RectBivariateSpline(range(ny), range(nx), vp.data[0, 0, ...].real, kx=order,
ky=order))
imag_splines.append(RectBivariateSpline(range(ny), range(nx), vp.data[0, 0, ...].imag, kx=order,
ky=order))
latitude = vis.configuration.location.lat.rad
r2d = 180.0 / numpy.pi
s2r = numpy.pi / 43200.0
# For each hourangle, we need to calculate the location of a component
# in AZELGEO. With that we can then look up the relevant gain from the
# voltage pattern
for iha, rows in enumerate(vis_timeslice_iter(vis, vis_slices=vis_slices)):
v = create_visibility_from_rows(vis, rows)
ha = numpy.average(v.time)
har = s2r * ha
# Calculate the az el for this hourangle and the phasecentre declination
azimuth_centre, elevation_centre = hadec_to_azel(har, vis.phasecentre.dec.rad, latitude)
for icomp, comp in enumerate(sc):
antgain = numpy.zeros([nant], dtype='complex')
# Calculate the location of the component in AZELGEO, then add the pointing offset
# for each antenna
hacomp = comp.direction.ra.rad - vis.phasecentre.ra.rad + har
deccomp = comp.direction.dec.rad
azimuth_comp, elevation_comp = hadec_to_azel(hacomp, deccomp, latitude)
for ant in range(nant):
for ivp, vp in enumerate(vp_list):
nchan, npol, ny, nx = vp.data.shape
wcs_azel = vp.wcs.deepcopy()
az_comp = azimuth_centre * r2d
el_comp = elevation_centre * r2d
# We use WCS sensible coordinate handling by labelling the axes misleadingly
wcs_azel.wcs.crval[0] = az_comp
wcs_azel.wcs.crval[1] = el_comp
wcs_azel.wcs.ctype[0] = 'RA---SIN'
wcs_azel.wcs.ctype[1] = 'DEC--SIN'
worldloc = [azimuth_comp*r2d, elevation_comp*r2d,
vp.wcs.wcs.crval[2], vp.wcs.wcs.crval[3]]
try:
pixloc = wcs_azel.sub(2).wcs_world2pix([worldloc[:2]], 1)[0]
assert pixloc[0] > 2
assert pixloc[0] < nx - 3
assert pixloc[1] > 2
assert pixloc[1] < ny - 3
gain = real_splines[ivp].ev(pixloc[1], pixloc[0]) \
+ 1j * imag_splines[ivp](pixloc[1], pixloc[0])
antgain[ant] += vp_coeffs[ant, ivp] * gain
number_good += 1
except:
number_bad += 1
antgain[ant] = 0.0
antgain[ant] = 1.0/antgain[ant]
gaintables[icomp].gain[iha, :, :, :] = antgain[:, numpy.newaxis, numpy.newaxis, numpy.newaxis]
gaintables[icomp].phasecentre = comp.direction
else:
assert isinstance(vis, BlockVisibility)
number_bad = 0
number_good = 0
# Cache the splines, one per voltage pattern
real_splines = list()
imag_splines = list()
for ivp, vp in enumerate(vp_list):
nchan, npol, ny, nx = vp.data.shape
real_splines.append(RectBivariateSpline(range(ny), range(nx), vp.data[0, 0, ...].real, kx=order,
ky=order))
imag_splines.append(RectBivariateSpline(range(ny), range(nx), vp.data[0, 0, ...].imag, kx=order,
ky=order))
for iha, rows in enumerate(vis_timeslice_iter(vis, vis_slices=vis_slices)):
# The time in the Visibility is hour angle in seconds!
r2d = 180.0 / numpy.pi
# For each hourangle, we need to calculate the location of a component
# in AZELGEO. With that we can then look up the relevant gain from the
# voltage pattern
v = create_visibility_from_rows(vis, rows)
ha = numpy.average(v.time)
for icomp, comp in enumerate(sc):
antgain = numpy.zeros([nant], dtype='complex')
antwt = numpy.zeros([nant])
ra_comp = comp.direction.ra.rad
dec_comp = comp.direction.dec.rad
for ant in range(nant):
for ivp, vp in enumerate(vp_list):
assert vp.wcs.wcs.ctype[0] == 'RA---SIN', vp.wcs.wcs.ctype[0]
assert vp.wcs.wcs.ctype[1] == 'DEC--SIN', vp.wcs.wcs.ctype[1]
worldloc = [ra_comp * r2d, dec_comp * r2d,
vp.wcs.wcs.crval[2], vp.wcs.wcs.crval[3]]
nchan, npol, ny, nx = vp.data.shape
try:
pixloc = vp.wcs.sub(2).wcs_world2pix([worldloc[:2]], 1)[0]
assert pixloc[0] > 2
assert pixloc[0] < nx - 3
assert pixloc[1] > 2
assert pixloc[1] < ny - 3
gain = real_splines[ivp].ev(pixloc[1], pixloc[0]) \
+ 1j * imag_splines[ivp](pixloc[1], pixloc[0])
antgain[ant] += vp_coeffs[ant, ivp] * gain
antwt[ant] = 1.0
number_good += 1
except:
number_bad += 1
antgain[ant] = 1e15
antwt[ant] = 0.0
antgain[ant] = 1.0/antgain[ant]
gaintables[icomp].gain[iha, :, :, :] = antgain[:, numpy.newaxis, numpy.newaxis, numpy.newaxis]
gaintables[icomp].weight[iha, :, :, :] = antwt[:, numpy.newaxis, numpy.newaxis, numpy.newaxis]
gaintables[icomp].phasecentre = comp.direction
if number_bad > 0:
log.warning(
"simulate_gaintable_from_voltage_patterns: %d points are inside the voltage pattern image" % (number_good))
log.warning(
"simulate_gaintable_from_voltage_patterns: %d points are outside the voltage pattern image" % (number_bad))
return gaintables
