def test_build_aa_flat(self):
"""Test building a antenna array object with uniform sky response."""
lwa1 = lwa_common.lwa1
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 10e6)
aa = vis.build_sim_array(lwa1, antennas, freqs, force_flat=True)
aa[0].bm_response((0, 0, 1), pol='x')
aa[0].bm_response((0, 0, 1), pol='y')
bm1 = aa[0].get_beam_shape()
az = numpy.zeros((360, 90))
for i in range(360):
az[i, :] = i * numpy.pi / 180.0
alt = numpy.zeros((360, 90))
for i in range(90):
alt[:, i] = i * numpy.pi / 180.0
xyz = aipy.coord.azalt2top(numpy.concatenate([[az], [alt]]))
bm2 = aa[0].bm_response(xyz, pol='x')
numpy.testing.assert_allclose(bm1.transpose(2, 0, 1), bm2)
def test_clean_leastsq(self):
"""Test CLEANing using least squares in the image plane"""
# Setup
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 1e6)
aa = vis.build_sim_array(lwa1, antennas, freqs)
# Build the data dictionary
out = vis.build_sim_data(aa, vis.SOURCES, jd=2458962.16965)
with lsl.testing.SilentVerbose():
# Build an image
img = utils.build_gridded_image(out)
# CLEAN
deconv.lsq(aa,
out,
img,
max_iter=2,
verbose=False,
plot=run_plotting_tests)
def test_plotting_graticules(self):
"""Test adding a graticule to an image."""
# Setup
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 1e6)
aa = vis.build_sim_array(lwa1, antennas, freqs)
# Build the data dictionary
out = vis.build_sim_data(aa, vis.SOURCES, jd=2458962.16965)
# Build an image
img = utils.build_gridded_image(out)
# Plot
fig = plt.figure()
ax = fig.gca()
utils.plot_gridded_image(ax, img)
with self.subTest(type='RA/Dec.'):
overlay.graticule_radec(ax, aa)
with self.subTest(type='az/alt'):
overlay.graticule_azalt(ax, aa)
del fig
def test_build_aa(self):
"""Test building a antenna array object with realistic sky response."""
lwa1 = lwa_common.lwa1
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 1e6)
aa = vis.build_sim_array(lwa1, antennas, freqs)
aa.set_asp_filter('split')
aa[0].bm_response((0, 0, 1))
# Check the number of stands
self.assertEqual(len(aa.ants), len(antennas))
# Check the frequencies comming out
numpy.testing.assert_allclose(aa.get_afreqs(), freqs / 1e9)
# Check that other methods even run
aa.get_baseline_fast(0, 1)
aa.gen_uvw_fast(0, 1)
aa.gen_phs_fast('z', 0, 1)
aa.set_unixtime(1588026422.0)
vis.SOURCES['crab'].compute(aa)
aa.gen_phs_fast(vis.SOURCES['crab'], 0, 1)
def grid_visibilities(bl,
freqs,
vis,
tx_freq,
station,
valid_ants=None,
size=80,
res=0.5,
wres=0.10,
use_pol=0,
jd=None):
'''
Resamples the baseline-sampled visibilities on to a regular grid.
arguments:
bl = pairs of antenna objects representing baselines (list)
freqs = frequency channels for which we have correlations (list)
vis = visibility samples corresponding to the baselines (numpy array)
tx_freq = the frequency of the signal we want to locate
valid_ants = which antennas we actually want to use (list)
station = lsl station object - usually stations.lwasv
according to LSL docstring:
size = number of wavelengths which the UV matrix spans (this
determines the image resolution).
res = resolution of the UV matrix (determines image field of view).
wres: the gridding resolution of sqrt(w) when projecting to w=0.
use_pol = which polarization to use (only 0 is supported right now)
returns:
gridded_image
'''
# In order to do the gridding, we need to build a VisibilityDataSet using
# lsl.imaging.data.VisibilityDataSet. We have to build a bunch of stuff to
# pass to its constructor.
if valid_ants is None:
valid_ants, n_baselines = select_antennas(station.antennas, use_pol)
# we only want the bin nearest to our frequency
target_bin = np.argmin([abs(tx_freq - f) for f in freqs])
# Build antenna array
freqs = np.array(freqs)
antenna_array = simVis.build_sim_array(station,
valid_ants,
freqs / 1e9,
jd=jd,
force_flat=True)
uvw = np.empty((len(bl), 3, len(freqs)))
for i, f in enumerate(freqs):
# wavelength = 3e8/f # TODO this should be fixed. What is currently happening is not true. Well it is, but only if you're looking for a specific transmitter frequency. Which I guess we are. I just mean it's not generalized.
wavelength = 3e8 / tx_freq
uvw[:, :, i] = uvw_from_antenna_pairs(bl, wavelength=wavelength)
dataSet = VisibilityDataSet(jd=jd,
freq=freqs,
baselines=bl,
uvw=uvw,
antennarray=antenna_array)
if use_pol == 0:
pol_string = 'XX'
else:
raise RuntimeError("Only pol. XX supported right now.")
polDataSet = PolarizationDataSet(pol_string, data=vis)
dataSet.append(polDataSet)
# Use lsl.imaging.utils.build_gridded_image (takes a VisibilityDataSet)
gridded_image = build_gridded_image(dataSet,
pol=pol_string,
chan=target_bin,
size=size,
res=res,
wres=wres)
return gridded_image
def main(args):
h5fi = h5py.File(args.input_file, 'r')
h5fo = h5py.File(args.output_file,'w')
# Copy over important attributes
for key in h5fi.attrs.keys():
h5fo.attrs[key]=h5fi.attrs[key]
# Copy over important datasets
for key in h5fi.keys():
temp_arr = h5fi[key]
h5fo.create_dataset('vis_{}'.format(key),data=temp_arr)
h5fo.attrs['grid_size']=args.size
h5fo.attrs['grid_res']=args.res
h5fo.attrs['grid_wres']=args.wres
h5fo.create_dataset('l_est', (len(h5fo['vis_l_est']),))
h5fo.create_dataset('m_est', (len(h5fo['vis_l_est']),))
h5fo.create_dataset('extent', (len(h5fo['vis_l_est']),4))
h5fo.create_dataset('elevation', (len(h5fo['vis_l_est']),))
h5fo.create_dataset('azimuth', (len(h5fo['vis_l_est']),))
h5fo.create_dataset('height', (len(h5fo['vis_l_est']),))
h5fi.close() # done with input data now
## Begin doing stuff
antennas = station.antennas
valid_ants, n_baselines = select_antennas(antennas, h5fo.attrs['use_pol'], exclude=[256]) # to exclude outrigger
tx_coords = h5fo.attrs['tx_coordinates']
rx_coords = [station.lat * 180/np.pi, station.lon * 180/np.pi]
## Build freqs (same for every 'integration')
freqs = np.empty((h5fo.attrs['fft_len'],),dtype=np.float64)
#! Need to think of intelligent way of doing this.
#! target_bin will probably not matter since all vis is the same
freqs5 = [5284999.9897182, 5291249.9897182, 5297499.9897182, 5303749.9897182,
5309999.9897182, 5316249.9897182, 5322499.9897182, 5328749.9897182,
5334999.9897182, 5341249.9897182, 5347499.9897182, 5353749.9897182,
5359999.9897182, 5366249.9897182, 5372499.9897182, 5378749.9897182]
for i in range(len(freqs)):
freqs[i]=freqs5[i]
## Build bl (same for every 'integration')
pol_string = 'xx' if h5fo.attrs['use_pol'] == 0 else 'yy'
pol1, pol2 = pol_to_pols(pol_string)
antennas1 = [a for a in valid_ants if a.pol == pol1]
antennas2 = [a for a in valid_ants if a.pol == pol2]
nStands = len(antennas1)
baselines = uvutils.get_baselines(antennas1, antennas2=antennas2, include_auto=False, indicies=True)
antennaBaselines = []
for bl in range(len(baselines)):
antennaBaselines.append( (antennas1[baselines[bl][0]], antennas2[baselines[bl][1]]) )
bl = antennaBaselines
uvw_m = np.array([np.array([b[0].stand.x - b[1].stand.x, b[0].stand.y - b[1].stand.y, b[0].stand.z - b[1].stand.z]) for b in bl])
uvw = np.empty((len(bl), 3, len(freqs)))
for i, f in enumerate(freqs):
# wavelength = 3e8/f # TODO this should be fixed. What is currently happening is not true. Well it is, but only if you're looking for a specific transmitter frequency. Which I guess we are. I just mean it's not generalized.
wavelength = 3e8/h5fo.attrs['tx_freq']
uvw[:,:,i] = uvw_m/wavelength
# Build antenna array (gets used in the VisibilityDataSet)
# jd can't matter, right?
jd = 2458847.2362531545
antenna_array = simVis.build_sim_array(station, valid_ants, freqs/1e9, jd=jd, force_flat=True)
# we only want the bin nearest to our frequency
target_bin = np.argmin([abs(h5fo.attrs['tx_freq'] - f) for f in freqs])
# Needed for PolarizationDataSet
if h5fo.attrs['use_pol'] == 0:
pol_string = 'XX'
p=0 # this is related to the enumerate in lsl.imaging.utils.CorrelatedIDI().get_data_set() (for when there are multiple pols in a single dataset)
else:
raise RuntimeError("Only pol. XX supported right now.")
if args.all_sky:
fig, ax = plt.subplots()
for k in np.arange(len(h5fo['vis_l_est'])):
l_in = h5fo['vis_l_est'][k]
m_in = h5fo['vis_m_est'][k]
## Build vis
vismodel = point_source_visibility_model_uv(uvw[:,0,0],uvw[:,1,0],l_in,m_in)
vis = np.empty((len(vismodel), len(freqs)), dtype=np.complex64)
for i in np.arange(vis.shape[1]):
vis[:,i] = vismodel
if args.export_npy:
print(args.export_npy)
print("Exporting modelled u, v, w, and visibility")
np.save('model-uvw{}.npy'.format(k), uvw)
np.save('model-vis{}.npy'.format(k), vis)
## Start to build up the data structure for VisibilityDataSet
dataSet = VisibilityDataSet(jd=jd, freq=freqs, baselines=bl, uvw=uvw, antennarray=antenna_array)
polDataSet = PolarizationDataSet(pol_string, data=vis)
dataSet.append(polDataSet)
print('| Gridding and imaging with size={}, res={}, wres={}'.format(args.size, args.res, args.wres))
gridded_image = build_gridded_image(dataSet, pol=pol_string,
chan=target_bin, size=args.size,
res=args.res, wres=args.wres)
if args.export_npy:
print("Exporting gridded u, v, and visibility")
u,v = gridded_image.get_uv()
np.save('gridded-u{}.npy'.format(k), u)
np.save('gridded-v{}.npy'.format(k), v)
np.save('gridded-vis{}.npy'.format(k), gridded_image.uv)
l,m,img,extent=get_gimg_max(gridded_image, return_img=True)
# Compute other values of interest
elev, az = lm_to_ea(l, m)
height = flatmirror_height(tx_coords, rx_coords, elev)
h5fo['l_est'][k] = l
h5fo['m_est'][k] = m
h5fo['extent'][k] = extent
h5fo['elevation'][k] = elev
h5fo['azimuth'][k] = az
h5fo['height'][k] = height
if args.all_sky:
ax.imshow(img, extent=extent, origin='lower', interpolation='nearest')
ax.set_title('size={}, res={}, wres={}, iteration={}'.format(args.size,args.res,args.wres,k))
ax.set_xlabel('l')
ax.set_ylabel('m')
ax.plot(l,m,marker='o', color='k', label='Image Max.')
ax.plot(l_in,m_in,marker='x', color='r', label='Model (input)')
plt.legend(loc='lower right')
plt.savefig('allsky{}.png'.format(k))
plt.cla()
save_pkl_gridded = args.pkl_gridded and k in args.pkl_gridded
if save_pkl_gridded:
quickDict={'image':img, 'extent':extent}
with open('gridded{}.pkl'.format(k), 'wb') as f:
pickle.dump(quickDict, f, protocol=pickle.HIGHEST_PROTOCOL)
h5fo.close()
def main(args):
# Loop over the input files
for filename in args.filename:
## Is this file valid?
try:
db = PasiImageDB(filename, 'r')
except Exception as e:
print("ERROR: %s" % str(e))
continue
## Setup the array
if db.header.station == 'LWA1':
aa = simVis.build_sim_array(lwa1, lwa1.antennas[0::2],
numpy.array([
38e6,
]))
else:
aa = None
## Go!
for i, (hdr, img, spec) in enumerate(db):
if args.dataset != 0 and args.dataset != (i + 1):
continue
mjd = int(hdr.startTime)
mpm = int((hdr.startTime - mjd) * 86400 * 1000.0)
tStart = mjdmpm_to_datetime(mjd, mpm)
if aa is not None:
aa.set_jultime(hdr.centroidTime + astro.MJD_OFFSET)
stokes = hdr.stokesParams.split(',')
### Save the image size for later
imSize = img.shape[-1]
### Zero outside of the horizon so avoid problems
pCntr = imSize / 2 + 1 + 0.5 * ((imSize + 1) % 2)
pScale = hdr['xPixelSize']
sRad = 360.0 / pScale / numpy.pi / 2
x = numpy.arange(1, img.shape[-2] + 1, dtype=numpy.float32) - pCntr
y = numpy.arange(1, img.shape[-1] + 1, dtype=numpy.float32) - pCntr
x /= -sRad
y /= sRad
x, y = numpy.meshgrid(x, y)
invalid = numpy.where((x**2 + y**2) > 1)
img[:, invalid[0], invalid[1]] = 0.0
### Try and set the image scale correctly for the display
x2 = x - 1 / sRad
y2 = y - 1 / sRad
extent = (x2.max(), x2.min(), y.min(), y.max())
### Loop over Stokes parameters
fig = plt.figure()
for j, label in enumerate(stokes):
ax = fig.add_subplot(2, 2, j + 1)
ax.imshow(img[j, :, :].T,
origin='lower',
interpolation='nearest',
extent=extent)
ax.set_title(label)
ax.set_xlim((1, -1))
ax.set_ylim((-1, 1))
## Turn off tick marks
ax.xaxis.set_major_formatter(NullFormatter())
ax.yaxis.set_major_formatter(NullFormatter())
## Is we know where we are, overlay some stuff
if aa is not None:
# Horizon
overlay.horizon(ax, aa)
# RA/Dec graticle
if not args.no_grid:
overlay.graticule_radec(ax, aa)
# Source positions
overlay.sources(ax,
aa,
simVis.SOURCES,
label=(not args.no_labels))
fig.suptitle(
'%.3f MHz @ %s' %
(hdr.freq / 1e6, tStart.strftime("%Y/%m/%d %H:%M:%S")))
plt.show()
## Done
db.close()
def test_scale_data(self):
"""Test that we can scale a data dictionary without error"""
# Setup
lwa1 = lwa_common.lwa1
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 1e6)
aa = vis.build_sim_array(lwa1, antennas, freqs)
# Build the data dictionary
out = vis.build_sim_data(aa, vis.SOURCES)
# Scale
amp = vis.scale_data(out,
numpy.ones(len(antennas)) * 2,
numpy.zeros(len(antennas)))
# Delay
phs = vis.scale_data(out, numpy.ones(len(antennas)),
numpy.ones(len(antennas)))
#
# Single-channel test
#
# Setup
lwa1 = lwa_common.lwa1
antennas = lwa1.antennas[0:20]
freqs = numpy.array([
30e6,
])
aa = vis.build_sim_array(lwa1, antennas, freqs)
# Build the data dictionary
out = vis.build_sim_data(aa, vis.SOURCES)
# Scale
amp = vis.scale_data(out,
numpy.ones(len(antennas)) * 2,
numpy.zeros(len(antennas)))
# Delay
phs = vis.scale_data(out, numpy.ones(len(antennas)),
numpy.ones(len(antennas)))
#
# VisibilityData test
#
# Setup
lwa1 = lwa_common.lwa1
antennas = lwa1.antennas[0:20]
freqs = numpy.arange(30e6, 50e6, 1e6)
aa = vis.build_sim_array(lwa1, antennas, freqs)
# Build the data dictionary
out = vis.build_sim_data(aa, vis.SOURCES)
out2 = VisibilityData(out)
# Scale
amp2 = vis.scale_data(out2,
numpy.ones(len(antennas)) * 2,
numpy.zeros(len(antennas)))
# Delay
phs2 = vis.scale_data(out2, numpy.ones(len(antennas)),
numpy.ones(len(antennas)))
def main(args):
## Check we should bother doing anything
if not args.export_npy and not args.export_h5 and not args.all_sky and not args.pkl_gridded:
raise RuntimeError(
"You have not selected a data output of any type. Read the docstring and pick something for me to do."
)
# Normalize all inputs to the same length
sizes = [int(item) for item in args.size.split(',')]
reses = [float(item) for item in args.res.split(',')]
wreses = [float(item) for item in args.wres.split(',')]
maxinputlen = max(len(sizes), len(reses), len(wreses))
if len(sizes) not in [1, maxinputlen] or len(reses) not in [
1, maxinputlen
] or len(wreses) not in [1, maxinputlen]:
raise RuntimeError(" \
For size, res and wres you must pass either the same number of values as the max or a single value.\n \
For example:\n \
ALLOWED -> sizes=175,180,190, res=0.5, wres=0.5\n \
-> sizes=175,180,190, res=0.5, wres=0.5,0.6,0.7\n \
NOT ALLOWED -> sizes=175,180,190, res=0.5, wres=0.5,0.6 \
")
if len(
sizes
) != maxinputlen: # You'd think there must be a good way to do this with list comprehension.
sizes = sizes * maxinputlen
if len(reses) != maxinputlen:
reses = reses * maxinputlen
if len(wreses) != maxinputlen:
wreses = wreses * maxinputlen
all_grid_params = []
while len(sizes) > 0:
all_grid_params.append({
'size': sizes.pop(),
'res': reses.pop(),
'wres': wreses.pop()
})
## Begin doing stuff
tx_coords = known_transmitters.parse_args(args)
if not transmitter_coords:
print("Please specify a transmitter location")
return
rx_coords = [station.lat * 180 / np.pi, station.lon * 180 / np.pi]
antennas = station.antennas
valid_ants, n_baselines = select_antennas(antennas, args.use_pol)
if args.export_h5:
h5fname = "simulation-results.h5"
print("Output will be written to {}".format(h5fname))
h5f = h5py.File(h5fname, 'w')
ats = h5f.attrs
ats['transmitter'] = args.transmitter
ats['tx_freq'] = args.tx_freq
ats['valid_ants'] = [a.id for a in valid_ants]
ats['n_baselines'] = n_baselines
ats['fft_len'] = args.fft_len
ats['use_pol'] = args.use_pol
ats['int_length'] = args.integration_length
ats['l_model'] = args.l_model
ats['m_model'] = args.m_model
h5f.create_dataset('l_est', (len(all_grid_params), ))
h5f.create_dataset('m_est', (len(all_grid_params), ))
h5f.create_dataset('wres', (len(all_grid_params), ))
h5f.create_dataset('res', (len(all_grid_params), ))
h5f.create_dataset('size', (len(all_grid_params), ))
h5f.create_dataset('extent', (len(all_grid_params), 4))
h5f.create_dataset('elevation', (len(all_grid_params), ))
h5f.create_dataset('azimuth', (len(all_grid_params), ))
h5f.create_dataset('height', (len(all_grid_params), ))
## Build freqs
freqs = np.empty((args.fft_len, ), dtype=np.float64)
#! Need to think of intelligent way of doing this.
#! target_bin will probably not matter since all vis is the same
freqs5 = [
5284999.9897182, 5291249.9897182, 5297499.9897182, 5303749.9897182,
5309999.9897182, 5316249.9897182, 5322499.9897182, 5328749.9897182,
5334999.9897182, 5341249.9897182, 5347499.9897182, 5353749.9897182,
5359999.9897182, 5366249.9897182, 5372499.9897182, 5378749.9897182
]
for i in range(len(freqs)):
freqs[i] = freqs5[i]
## Build bl
pol_string = 'xx' if args.use_pol == 0 else 'yy'
pol1, pol2 = pol_to_pols(pol_string)
antennas1 = [a for a in valid_ants if a.pol == pol1]
antennas2 = [a for a in valid_ants if a.pol == pol2]
nStands = len(antennas1)
baselines = uvutils.get_baselines(antennas1,
antennas2=antennas2,
include_auto=False,
indicies=True)
antennaBaselines = []
for bl in range(len(baselines)):
antennaBaselines.append(
(antennas1[baselines[bl][0]], antennas2[baselines[bl][1]]))
bl = antennaBaselines
uvw_m = np.array([
np.array([
b[0].stand.x - b[1].stand.x, b[0].stand.y - b[1].stand.y,
b[0].stand.z - b[1].stand.z
]) for b in bl
])
uvw = np.empty((len(bl), 3, len(freqs)))
for i, f in enumerate(freqs):
# wavelength = 3e8/f # TODO this should be fixed. What is currently happening is not true. Well it is, but only if you're looking for a specific transmitter frequency. Which I guess we are. I just mean it's not generalized.
wavelength = 3e8 / args.tx_freq
uvw[:, :, i] = uvw_m / wavelength
## Build vis
vismodel = point_source_visibility_model_uv(uvw[:, 0, 0], uvw[:, 1, 0],
args.l_model, args.m_model)
vis = np.empty((len(vismodel), len(freqs)), dtype=np.complex64)
for i in np.arange(vis.shape[1]):
vis[:, i] = vismodel
if args.export_npy:
print(args.export_npy)
print("Exporting modelled u, v, w, and visibility")
np.save('model-uvw.npy', uvw)
np.save('model-vis.npy', vis)
## Start to build up the data structure for VisibilityDataSet
# we only want the bin nearest to our frequency
target_bin = np.argmin([abs(args.tx_freq - f) for f in freqs])
# This can't matter, right?
# jd = tbnf.get_info('start_time').jd
jd = 2458847.2362531545
# Build antenna array
antenna_array = simVis.build_sim_array(station,
antennas,
freqs / 1e9,
jd=jd,
force_flat=True)
dataSet = VisibilityDataSet(jd=jd,
freq=freqs,
baselines=bl,
uvw=uvw,
antennarray=antenna_array)
if args.use_pol == 0:
pol_string = 'XX'
p = 0 # this is related to the enumerate in lsl.imaging.utils.CorrelatedIDI().get_data_set() (for when there are multiple pols in a single dataset)
else:
raise RuntimeError("Only pol. XX supported right now.")
polDataSet = PolarizationDataSet(pol_string, data=vis)
dataSet.append(polDataSet)
if args.all_sky:
fig, ax = plt.subplots()
# Iterate over size/res/wres and generate multiple grids/images
k = 0
for grid_params in all_grid_params:
print('| Gridding and imaging with size={}, res={}, wres={}'.format(
grid_params['size'], grid_params['res'], grid_params['wres']))
gridded_image = build_gridded_image(dataSet,
pol=pol_string,
chan=target_bin,
size=grid_params['size'],
res=grid_params['res'],
wres=grid_params['wres'])
if args.export_npy:
print("Exporting gridded u, v, and visibility")
u, v = gridded_image.get_uv()
np.save(
'gridded-u-size-{}-res-{}-wres-{}.npy'.format(
grid_params['size'], grid_params['res'],
grid_params['wres']), u)
np.save(
'gridded-v-size-{}-res-{}-wres-{}.npy'.format(
grid_params['size'], grid_params['res'],
grid_params['wres']), v)
np.save(
'gridded-vis-size-{}-res-{}-wres-{}.npy'.format(
grid_params['size'], grid_params['res'],
grid_params['wres']), gridded_image.uv)
l, m, img, extent = get_gimg_max(gridded_image, return_img=True)
# Compute other values of interest
elev, az = lm_to_ea(l, m)
height = flatmirror_height(tx_coords, rx_coords, elev)
if args.export_h5:
h5f['l_est'][k] = l
h5f['m_est'][k] = m
h5f['wres'][k] = grid_params['wres']
h5f['res'][k] = grid_params['res']
h5f['size'][k] = grid_params['size']
h5f['extent'][k] = extent
h5f['elevation'][k] = elev
h5f['azimuth'][k] = az
h5f['height'][k] = height
if args.all_sky:
ax.imshow(img,
extent=extent,
origin='lower',
interpolation='nearest')
ax.set_title('size={}, res={}, wres={}'.format(
grid_params['size'], grid_params['res'], grid_params['wres']))
ax.set_xlabel('l')
ax.set_ylabel('m')
ax.plot(l, m, marker='o', color='k', label='Image Max.')
ax.plot(args.l_model,
args.m_model,
marker='x',
color='r',
label='Model (input)')
plt.legend(loc='lower right')
plt.savefig('allsky_size_{}_res_{}_wres_{}.png'.format(
grid_params['size'], grid_params['res'], grid_params['wres']))
plt.cla()
save_pkl_gridded = args.pkl_gridded and k in args.pkl_gridded
if save_pkl_gridded:
quickDict = {'image': img, 'extent': extent}
with open(
'gridded_size_{}_res_{}_wres_{}.pkl'.format(
grid_params['size'], grid_params['res'],
grid_params['wres']), 'wb') as f:
pickle.dump(quickDict, f, protocol=pickle.HIGHEST_PROTOCOL)
k += 1
if args.export_h5:
h5f.close()
c = stations.Cable(f"Cable{ant.id:03d}-Pol{pol}", 0.0)
a = stations.Antenna(ant.id*2+pol, stand=s, cable=c, pol=pol)
ovro2.antennas.append(a)
ovro = ovro2
# Simulation setup
nant = len(ovro.antennas) // 2
nbl = nant*(nant+1)//2
chan0 = 1234
nchan = 192
CHAN_BW = 196e6 / 8192
jd = AstroTime.now().jd
# Simulation array
freqs = (chan0 + numpy.arange(nchan)) * CHAN_BW + CHAN_BW/2
aa = simVis.build_sim_array(ovro, ovro.antennas[0::2], freqs/1e9, jd=jd)
# Simulate with bright sources only
dataSet = simVis.build_sim_data(aa, simVis.SOURCES, pols=['xx','yy'], jd=jd)
# Make the final data set that can be used with dr_visibilities.py
# NOTE: XY and XY are 1% of XX and have sign flip between XY and YX
vis = numpy.zeros((nbl,nchan,4), dtype=numpy.complex64)
k = 0
l = 0
for i in range(nant):
for j in range(i, nant):
vis[l,:,0] = dataSet.XX.data[k,:]
vis[l,:,1] = dataSet.XX.data[k,:]* 0.01
vis[l,:,2] = dataSet.XX.data[k,:]*-0.01
vis[l,:,3] = dataSet.YY.data[k,:]
def main(args):
filenames = args.filenames
filenames.sort()
times = []
for filename in filenames:
dataDict = numpy.load(filename)
tStart = datetime.utcfromtimestamp(dataDict['tStart'])
tInt = dataDict['tInt']
try:
srate = dataDict['srate']
except KeyError:
srate = 19.6e6
freq1 = dataDict['freq1']
freq2 = dataDict['freq2']
stand1, stand2 = dataDict['stands']
times.append( tStart)
print("Got %i files from %s to %s (%s)" % (len(filenames), times[0].strftime("%Y/%m/%d %H:%M:%S"), times[-1].strftime("%Y/%m/%d %H:%M:%S"), (times[-1]-times[0])))
iTimes = []
for i in xrange(1, len(times)):
dt = times[i] - times[i-1]
iTimes.append(dt.days*24*3600 + dt.seconds + dt.microseconds/1e6)
iTimes = numpy.array(iTimes)
print(" -> Interval: %.3f +/- %.3f seconds (%.3f to %.3f seconds)" % (iTimes.mean(), iTimes.std(), iTimes.min(), iTimes.max()))
print("Number of frequency channels: %i (~%.1f Hz/channel)" % (len(freq1)+1, freq1[1]-freq1[0]))
# Build up the station
if args.lwasv:
site = stations.lwasv
else:
site = stations.lwa1
rawAntennas = site.antennas
antennas = []
for ant in rawAntennas:
if ant.stand.id == stand1 and ant.pol == 0:
antennas.append(ant)
for ant in rawAntennas:
if ant.stand.id == stand2 and ant.pol == 0:
antennas.append(ant)
if len(antennas) != 2:
raise RuntimeError("Can only find stand %i, %i and %i found in the NPZ files" % (antennas[0].stand.id, stand1, stand2))
# Create the simulated array
refJD = unix_to_utcjd(timegm(times[0].timetuple()))
aa1 = simVis.build_sim_array(site, antennas, freq1/1e9, jd=refJD)
aa2 = simVis.build_sim_array(site, antennas, freq2/1e9, jd=refJD)
# Build the model times and range.
jdList = []
dTimes = []
for i in xrange(len(times)):
tNow = timegm(times[i].timetuple())
jdNow = unix_to_utcjd(tNow)
jdList.append(jdNow)
dTimes.append( (times[i]-times[0]).seconds )
# Actually run the simulations
simDict1 = simVis.build_sim_data(aa1, simVis.SOURCES, jd=jdList, pols=['xx',], verbose=False)
simDict2 = simVis.build_sim_data(aa2, simVis.SOURCES, jd=jdList, pols=['xx',], verbose=False)
# Plot
fig = plt.figure()
ax1 = fig.add_subplot(2, 1, 1)
ax2 = fig.add_subplot(2, 1, 2)
vis1 = []
for vis in simDict1['vis']['xx']:
vis1.append( vis )
vis2 = []
for vis in simDict2['vis']['xx']:
vis2.append( vis )
vis1 = numpy.array(vis1)
vis1 = numpy.ma.array(vis1, mask=~numpy.isfinite(vis1))
vis2 = numpy.array(vis2)
vis2 = numpy.ma.array(vis2, mask=~numpy.isfinite(vis2))
data = numpy.abs(vis1)
data = data.ravel()
data.sort()
vmin1 = data[int(round(0.15*len(data)))]
vmax1 = data[int(round(0.85*len(data)))]
print('Plot range for tuning 1:', vmin1, vmax1)
data = numpy.abs(vis2)
data = data.ravel()
data.sort()
vmin2 = data[int(round(0.15*len(data)))]
vmax2 = data[int(round(0.85*len(data)))]
print('Plot range for tuning 2:', vmin2, vmax2)
ax1.imshow(numpy.abs(vis1), extent=(freq1[0], freq1[-1], dTimes[0], dTimes[-1]), origin='lower',
vmin=vmin1, vmax=vmax1)
ax2.imshow(numpy.abs(vis2), extent=(freq1[0], freq1[-1], dTimes[0], dTimes[-1]), origin='lower',
vmin=vmin2, vmax=vmax2)
ax1.axis('auto')
ax2.axis('auto')
fig.suptitle("%s to %s UTC" % (times[0].strftime("%Y/%m/%d %H:%M"), times[-1].strftime("%Y/%m/%d %H:%M")))
ax1.set_xlabel('Frequency [MHz]')
ax2.set_xlabel('Frequency [MHz]')
ax1.set_ylabel('Elapsed Time [s]')
ax2.set_ylabel('Elapsed Time [s]')
plt.show()