def create_uvfits(antenna_xlocs, antenna_ylocs, save_filename):
Nants = len(antenna_xlocs)
antenna_locs_ENU = np.zeros((Nants, 3))
antenna_locs_ENU[:, 0] = antenna_xlocs
antenna_locs_ENU[:, 1] = antenna_ylocs
filename = '/home/ubuntu/1061316296.uvfits'
UV = UVData()
UV.read_uvfits(filename)
UV.Nants_data = Nants
UV.Nants_telescope = Nants
UV.Nbls = (Nants**2 + Nants) / 2
UV.Nblts = UV.Nbls * UV.Ntimes
UV.antenna_numbers = np.array(range(Nants), dtype=int)
UV.antenna_names = [str(ant) for ant in UV.antenna_numbers]
ant_1_array = np.zeros(UV.Nbls, dtype=int)
ant_2_array = np.zeros(UV.Nbls, dtype=int)
baseline_array = np.zeros(UV.Nbls, dtype=int)
index = 0
for ant1 in range(Nants):
for ant2 in range(ant1, Nants):
ant_1_array[index] = ant1
ant_2_array[index] = ant2
baseline_array[index] = 2048 * (ant1 + 1) + (ant2 + 1) + 2**16
index += 1
UV.ant_1_array = np.tile(ant_1_array, UV.Ntimes)
UV.ant_2_array = np.tile(ant_2_array, UV.Ntimes)
UV.baseline_array = np.tile(baseline_array, UV.Ntimes)
old_time_array = np.copy(UV.time_array)
UV.time_array = np.repeat(np.array(list(set(UV.time_array))), UV.Nbls)
UV.lst_array = np.array([
UV.lst_array[np.where(old_time_array == time)[0][0]]
for time in UV.time_array
])
# Add dummy data
UV.data_array = np.full((UV.Nblts, UV.Nspws, UV.Nfreqs, UV.Npols),
UV.data_array[0, 0, 0, 0],
dtype=complex)
UV.nsample_array = np.full((UV.Nblts, UV.Nspws, UV.Nfreqs, UV.Npols),
1.,
dtype=float)
# Unflag all
UV.flag_array = np.full((UV.Nblts, UV.Nspws, UV.Nfreqs, UV.Npols),
False,
dtype=bool)
# Calculate UVWs
UV.uvw_array = np.zeros((UV.Nblts, 3), dtype=float)
antenna_locs_ECEF = uvutils.ECEF_from_ENU(
antenna_locs_ENU.T, *UV.telescope_location_lat_lon_alt).T
UV.antenna_positions = antenna_locs_ECEF - UV.telescope_location
UV.set_uvws_from_antenna_positions(allow_phasing=True,
orig_phase_frame='gcrs',
output_phase_frame='icrs')
UV.phase_center_frame = 'icrs'
print 'Saving uvfits to {}'.format(save_filename)
UV.write_uvfits(save_filename, spoof_nonessential=True)
obs.set_fov(fov)
obs.set_pointings(time_arr)
obs.set_beam('gaussian', sigma=sigma)
print("Nprocs: ", Nprocs)
print("Shell = {:.4f}MB".format(shell0.nbytes/1e6))
visibs.append(obs.make_visibilities(shell0, Nprocs=Nprocs)[0])
# Visibilities are in Jy
# Get beam_sq_int
za, az = obs.calc_azza(Nside, obs.pointing_centers[0])
beam_sq_int = np.sum(obs.beam.beam_val(az, za)**2)
beam_sq_int = np.ones(Nfreqs) * beam_sq_int * om
uv = UVData()
uv.Nbls = 1
uv.Ntimes = Ntimes
uv.spw_array = [0]
uv.Nfreqs = Nfreqs
uv.freq_array = freqs[np.newaxis, :]
uv.Nblts = uv.Ntimes * uv.Nbls
uv.ant_1_array = np.zeros(uv.Nblts, dtype=int)
uv.ant_2_array = np.ones(uv.Nblts, dtype=int)
uv.baseline_array = uv.antnums_to_baseline(uv.ant_1_array, uv.ant_2_array)
uv.time_array = time_arr
uv.Npols = 1
uv.polarization_array=np.array([1])
uv.Nants_telescope = 2
uv.Nants_data = 2
uv.antenna_positions = uvutils.ECEF_from_ENU(np.stack([ant1_enu, ant2_enu]), latitude, longitude, altitude)
uv.Nspws = 1
def initialize_uvdata(uvtask_list,
source_list_name,
uvdata_file=None,
obs_param_file=None,
telescope_config_file=None,
antenna_location_file=None):
"""
Initialize an empty uvdata object to fill with simulation.
Args:
uvtask_list: List of uvtasks to simulate.
source_list_name: Name of source list file or mock catalog.
uvdata_file: Name of input UVData file or None if initializing from
config files.
obs_param_file: Name of observation parameter config file or None if
initializing from a UVData file.
telescope_config_file: Name of telescope config file or None if
initializing from a UVData file.
antenna_location_file: Name of antenna location file or None if
initializing from a UVData file.
"""
if not isinstance(source_list_name, str):
raise ValueError('source_list_name must be a string')
if uvdata_file is not None:
if not isinstance(uvdata_file, str):
raise ValueError('uvdata_file must be a string')
if (obs_param_file is not None or telescope_config_file is not None
or antenna_location_file is not None):
raise ValueError('If initializing from a uvdata_file, none of '
'obs_param_file, telescope_config_file or '
'antenna_location_file can be set.')
elif (obs_param_file is None or telescope_config_file is None
or antenna_location_file is None):
if not isinstance(obs_param_file, str):
raise ValueError('obs_param_file must be a string')
if not isinstance(telescope_config_file, str):
raise ValueError('telescope_config_file must be a string')
if not isinstance(antenna_location_file, str):
raise ValueError('antenna_location_file must be a string')
raise ValueError('If not initializing from a uvdata_file, all of '
'obs_param_file, telescope_config_file or '
'antenna_location_file must be set.')
# Version string to add to history
history = get_version_string()
history += ' Sources from source list: ' + source_list_name + '.'
if uvdata_file is not None:
history += ' Based on UVData file: ' + uvdata_file + '.'
else:
history += (' Based on config files: ' + obs_param_file + ', ' +
telescope_config_file + ', ' + antenna_location_file)
history += ' Npus = ' + str(Npus) + '.'
task_freqs = []
task_bls = []
task_times = []
task_antnames = []
task_antnums = []
task_antpos = []
task_uvw = []
ant_1_array = []
ant_2_array = []
telescope_name = uvtask_list[0].telescope.telescope_name
telescope_location = uvtask_list[0].telescope.telescope_location.geocentric
source_0 = uvtask_list[0].source
freq_0 = uvtask_list[0].freq
for task in uvtask_list:
if not task.source == source_0:
continue
task_freqs.append(task.freq)
if task.freq == freq_0:
task_bls.append(task.baseline)
task_times.append(task.time)
task_antnames.append(task.baseline.antenna1.name)
task_antnames.append(task.baseline.antenna2.name)
ant_1_array.append(task.baseline.antenna1.number)
ant_2_array.append(task.baseline.antenna2.number)
task_antnums.append(task.baseline.antenna1.number)
task_antnums.append(task.baseline.antenna2.number)
task_antpos.append(task.baseline.antenna1.pos_enu)
task_antpos.append(task.baseline.antenna2.pos_enu)
task_uvw.append(task.baseline.uvw)
antnames, ant_indices = np.unique(task_antnames, return_index=True)
task_antnums = np.array(task_antnums)
task_antpos = np.array(task_antpos)
antnums = task_antnums[ant_indices]
antpos = task_antpos[ant_indices]
freqs = np.unique(task_freqs)
uv_obj = UVData()
# add pyuvdata version info
history += uv_obj.pyuvdata_version_str
uv_obj.telescope_name = telescope_name
uv_obj.telescope_location = np.array(
[tl.to('m').value for tl in telescope_location])
uv_obj.instrument = telescope_name
uv_obj.Nfreqs = freqs.size
uv_obj.Ntimes = np.unique(task_times).size
uv_obj.Nants_data = antnames.size
uv_obj.Nants_telescope = uv_obj.Nants_data
uv_obj.Nblts = len(ant_1_array)
uv_obj.antenna_names = antnames.tolist()
uv_obj.antenna_numbers = antnums
antpos_ecef = uvutils.ECEF_from_ENU(
antpos, *
uv_obj.telescope_location_lat_lon_alt) - uv_obj.telescope_location
uv_obj.antenna_positions = antpos_ecef
uv_obj.ant_1_array = np.array(ant_1_array)
uv_obj.ant_2_array = np.array(ant_2_array)
uv_obj.time_array = np.array(task_times)
uv_obj.uvw_array = np.array(task_uvw)
uv_obj.baseline_array = uv_obj.antnums_to_baseline(ant_1_array,
ant_2_array)
uv_obj.Nbls = np.unique(uv_obj.baseline_array).size
if uv_obj.Nfreqs == 1:
uv_obj.channel_width = 1. # Hz
else:
uv_obj.channel_width = np.diff(freqs)[0]
if uv_obj.Ntimes == 1:
uv_obj.integration_time = np.ones_like(uv_obj.time_array,
dtype=np.float64) # Second
else:
# Note: currently only support a constant spacing of times
uv_obj.integration_time = (
np.ones_like(uv_obj.time_array, dtype=np.float64) *
np.diff(np.unique(task_times))[0])
uv_obj.set_lsts_from_time_array()
uv_obj.zenith_ra = uv_obj.lst_array
uv_obj.zenith_dec = np.repeat(uv_obj.telescope_location_lat_lon_alt[0],
uv_obj.Nblts) # Latitude
uv_obj.object_name = 'zenith'
uv_obj.set_drift()
uv_obj.vis_units = 'Jy'
uv_obj.polarization_array = np.array([-5, -6, -7, -8])
uv_obj.spw_array = np.array([0])
uv_obj.freq_array = np.array([freqs])
uv_obj.Nspws = uv_obj.spw_array.size
uv_obj.Npols = uv_obj.polarization_array.size
uv_obj.data_array = np.zeros(
(uv_obj.Nblts, uv_obj.Nspws, uv_obj.Nfreqs, uv_obj.Npols),
dtype=np.complex)
uv_obj.flag_array = np.zeros(
(uv_obj.Nblts, uv_obj.Nspws, uv_obj.Nfreqs, uv_obj.Npols), dtype=bool)
uv_obj.nsample_array = np.ones_like(uv_obj.data_array, dtype=float)
uv_obj.history = history
uv_obj.check()
return uv_obj
def apply_bda(
uv, max_decorr, pre_fs_int_time, corr_fov_angle, max_time, corr_int_time=None
):
"""Apply baseline dependent averaging to a UVData object.
For each baseline in the UVData object, the expected decorrelation from
averaging in time is computed. Baselines are averaged together in powers-
of-two until the specified level of decorrelation is reached (rounded down).
Parameters
----------
uv : UVData object
The UVData object to apply BDA to. No changes are made to this object,
and instead a copy is returned.
max_decorr : float
The maximum decorrelation fraction desired in the output object. Must
be between 0 and 1.
pre_fs_int_time : astropy Quantity
The pre-finge-stopping integration time inside of the correlator. The
quantity should be compatible with units of time.
corr_fov_angle : astropy Angle
The opening angle at which the maximum decorrelation is to be
calculated. Because a priori it is not known in which direction the
decorrelation will be largest, the expected decorrelation is computed in
all 4 cardinal directions at `corr_fov_angle` degrees off of zenith,
and the largest one is used. This is a "worst case scenario"
decorrelation.
max_time : astropy Quantity
The maximum amount of time that spectra from different times should be
combined for. The ultimate integration time for a given baseline will be
for max_time or the integration time that is smaller than the specified
decorrelation level, whichever is smaller. The quantity should be
compatible with units of time.
corr_int_time : astropy Quantity, optional
The output time of the correlator. If not specified, the smallest
integration_time in the UVData object is used. If specified, the
quantity should be compatible with units of time.
Returns
-------
uv2 : UVData object
The UVData object with BDA applied.
Raises
------
ValueError
This is raised if the input parameters are not the appropriate type or
in the appropriate range. It is also raised if the input UVData object
is not in drift mode (the BDA code does rephasing within an averaged
set of baselines).
AssertionError
This is raised if the baselines of the UVData object are not time-
ordered.
"""
if not isinstance(uv, UVData):
raise ValueError(
"apply_bda must be passed a UVData object as its first argument"
)
if not isinstance(corr_fov_angle, Angle):
raise ValueError(
"corr_fov_angle must be an Angle object from astropy.coordinates"
)
if not isinstance(pre_fs_int_time, units.Quantity):
raise ValueError("pre_fs_int_time must be an astropy.units.Quantity")
try:
pre_fs_int_time.to(units.s)
except UnitConversionError:
raise ValueError("pre_fs_int_time must be a Quantity with units of time")
if (
corr_fov_angle.to(units.deg).value < 0
or corr_fov_angle.to(units.deg).value > 90
):
raise ValueError("corr_fov_angle must be between 0 and 90 degrees")
if max_decorr < 0 or max_decorr > 1:
raise ValueError("max_decorr must be between 0 and 1")
if not isinstance(max_time, units.Quantity):
raise ValueError("max_time must be an astropy.units.Quantity")
try:
max_time.to(units.s)
except UnitConversionError:
raise ValueError("max_time must be a Quantity with units of time")
if corr_int_time is None:
# assume the correlator integration time is the smallest int_time of the
# UVData object
corr_int_time = np.unique(uv.integration_time)[0] * units.s
else:
if not isinstance(corr_int_time, units.Quantity):
raise ValueError("corr_int_time must be an astropy.units.Quantity")
try:
corr_int_time.to(units.s)
except UnitConversionError:
raise ValueError("corr_int_time must be a Quantity with units of time")
if uv.phase_type != "drift":
raise ValueError("UVData object must be in drift mode to apply BDA")
# get relevant bits of metadata
freq = np.amax(uv.freq_array[0, :]) * units.Hz
chan_width = uv.channel_width * units.Hz
antpos_enu, ants = uv.get_ENU_antpos()
lat, lon, alt = uv.telescope_location_lat_lon_alt
antpos_ecef = uvutils.ECEF_from_ENU(antpos_enu, lat, lon, alt)
telescope_location = EarthLocation.from_geocentric(
uv.telescope_location[0],
uv.telescope_location[1],
uv.telescope_location[2],
unit="m",
)
# make a new UVData object to put BDA baselines in
uv2 = UVData()
# copy over metadata
uv2.Nbls = uv.Nbls
uv2.Nfreqs = uv.Nfreqs
uv2.Npols = uv.Npols
uv2.vis_units = uv.vis_units
uv2.Nspws = uv.Nspws
uv2.spw_array = uv.spw_array
uv2.freq_array = uv.freq_array
uv2.polarization_array = uv.polarization_array
uv2.channel_width = uv.channel_width
uv2.object_name = uv.object_name
uv2.telescope_name = uv.telescope_name
uv2.instrument = uv.instrument
uv2.telescope_location = uv.telescope_location
history = uv.history + " Baseline dependent averaging applied."
uv2.history = history
uv2.Nants_data = uv.Nants_data
uv2.Nants_telescope = uv.Nants_telescope
uv2.antenna_names = uv.antenna_names
uv2.antenna_numbers = uv.antenna_numbers
uv2.x_orientation = uv.x_orientation
uv2.extra_keywords = uv.extra_keywords
uv2.antenna_positions = uv.antenna_positions
uv2.antenna_diameters = uv.antenna_diameters
uv2.gst0 = uv.gst0
uv2.rdate = uv.rdate
uv2.earth_omega = uv.earth_omega
uv2.dut1 = uv.dut1
uv2.timesys = uv.timesys
uv2.uvplane_reference_time = uv.uvplane_reference_time
# initialize place-keeping variables and Nblt-sized metadata
start_index = 0
uv2.Nblts = 0
uv2.uvw_array = np.zeros_like(uv.uvw_array)
uv2.time_array = np.zeros_like(uv.time_array)
uv2.lst_array = np.zeros_like(uv.lst_array)
uv2.ant_1_array = np.zeros_like(uv.ant_1_array)
uv2.ant_2_array = np.zeros_like(uv.ant_2_array)
uv2.baseline_array = np.zeros_like(uv.baseline_array)
uv2.integration_time = np.zeros_like(uv.integration_time)
uv2.data_array = np.zeros_like(uv.data_array)
uv2.flag_array = np.zeros_like(uv.flag_array)
uv2.nsample_array = np.zeros_like(uv.nsample_array)
# iterate over baselines
for key in uv.get_antpairs():
print("averaging baseline ", key)
ind1, ind2, indp = uv._key2inds(key)
if len(ind2) != 0:
raise AssertionError(
"ind2 from _key2inds() is not 0--exiting. This should not happen, "
"please contact the package maintainers."
)
data = uv._smart_slicing(
uv.data_array, ind1, ind2, indp, squeeze="none", force_copy=True
)
flags = uv._smart_slicing(
uv.flag_array, ind1, ind2, indp, squeeze="none", force_copy=True
)
nsamples = uv._smart_slicing(
uv.nsample_array, ind1, ind2, indp, squeeze="none", force_copy=True
)
# get lx and ly for baseline
ant1 = np.where(ants == key[0])[0][0]
ant2 = np.where(ants == key[1])[0][0]
x1, y1, z1 = antpos_ecef[ant1, :]
x2, y2, z2 = antpos_ecef[ant2, :]
lx = np.abs(x2 - x1) * units.m
ly = np.abs(y2 - y1) * units.m
# figure out how many time samples we can combine together
if key[0] == key[1]:
# autocorrelation--don't average
n_two_foldings = 0
else:
n_two_foldings = dc.bda_compression_factor(
max_decorr,
freq,
lx,
ly,
corr_fov_angle,
chan_width,
pre_fs_int_time,
corr_int_time,
)
# convert from max_time to max_samples
max_samples = (max_time / corr_int_time).to(units.dimensionless_unscaled)
max_two_foldings = int(np.floor(np.log2(max_samples)))
n_two_foldings = min(n_two_foldings, max_two_foldings)
n_int = 2 ** (n_two_foldings)
print("averaging {:d} time samples...".format(n_int))
# figure out how many output samples we're going to have
n_in = len(ind1)
n_out = n_in // n_int + min(1, n_in % n_int)
# get relevant metdata
uvw_array = uv.uvw_array[ind1, :]
times = uv.time_array[ind1]
if not np.all(times == np.sort(times)):
raise AssertionError(
"times of uvdata object are not monotonically increasing; "
"throwing our hands up"
)
lsts = uv.lst_array[ind1]
int_time = uv.integration_time[ind1]
# do the averaging
input_shape = data.shape
assert input_shape == (n_in, 1, uv.Nfreqs, uv.Npols)
output_shape = (n_out, 1, uv.Nfreqs, uv.Npols)
data_out = np.empty(output_shape, dtype=np.complex128)
flags_out = np.empty(output_shape, dtype=np.bool_)
nsamples_out = np.empty(output_shape, dtype=np.float32)
uvws_out = np.empty((n_out, 3), dtype=np.float64)
times_out = np.empty((n_out,), dtype=np.float64)
lst_out = np.empty((n_out,), dtype=np.float64)
int_time_out = np.empty((n_out,), dtype=np.float64)
if n_out == n_in:
# we don't need to average
current_index = start_index + n_out
uv2.data_array[start_index:current_index, :, :, :] = data
uv2.flag_array[start_index:current_index, :, :, :] = flags
uv2.nsample_array[start_index:current_index, :, :, :] = nsamples
uv2.uvw_array[start_index:current_index, :] = uvw_array
uv2.time_array[start_index:current_index] = times
uv2.lst_array[start_index:current_index] = lsts
uv2.integration_time[start_index:current_index] = int_time
uv2.ant_1_array[start_index:current_index] = key[0]
uv2.ant_2_array[start_index:current_index] = key[1]
uv2.baseline_array[start_index:current_index] = uvutils.antnums_to_baseline(
ant1, ant2, None
)
start_index = current_index
else:
# rats, we actually have to do work...
# phase up the data along each chunk of times
for i in range(n_out):
# compute zenith of the desired output time
i1 = i * n_int
i2 = min((i + 1) * n_int, n_in)
assert i2 - i1 > 0
t0 = Time((times[i1] + times[i2 - 1]) / 2, scale="utc", format="jd")
zenith_coord = SkyCoord(
alt=Angle(90 * units.deg),
az=Angle(0 * units.deg),
obstime=t0,
frame="altaz",
location=telescope_location,
)
obs_zenith_coord = zenith_coord.transform_to("icrs")
zenith_ra = obs_zenith_coord.ra
zenith_dec = obs_zenith_coord.dec
# get data, flags, and nsamples of slices
data_chunk = data[i1:i2, :, :, :]
flags_chunk = flags[i1:i2, :, :, :]
nsamples_chunk = nsamples[i1:i2, :, :, :]
# actually phase now
# compute new uvw coordinates
icrs_coord = SkyCoord(
ra=zenith_ra, dec=zenith_dec, unit="radian", frame="icrs"
)
uvws = np.float64(uvw_array[i1:i2, :])
itrs_telescope_location = SkyCoord(
x=uv.telescope_location[0] * units.m,
y=uv.telescope_location[1] * units.m,
z=uv.telescope_location[2] * units.m,
representation_type="cartesian",
frame="itrs",
obstime=t0,
)
itrs_lat_lon_alt = uv.telescope_location_lat_lon_alt
frame_telescope_location = itrs_telescope_location.transform_to("icrs")
frame_telescope_location.representation_type = "cartesian"
uvw_ecef = uvutils.ECEF_from_ENU(uvws, *itrs_lat_lon_alt)
itrs_uvw_coord = SkyCoord(
x=uvw_ecef[:, 0] * units.m,
y=uvw_ecef[:, 1] * units.m,
z=uvw_ecef[:, 2] * units.m,
representation_type="cartesian",
frame="itrs",
obstime=t0,
)
frame_uvw_coord = itrs_uvw_coord.transform_to("icrs")
frame_rel_uvw = (
frame_uvw_coord.cartesian.get_xyz().value.T
- frame_telescope_location.cartesian.get_xyz().value
)
new_uvws = uvutils.phase_uvw(
icrs_coord.ra.rad, icrs_coord.dec.rad, frame_rel_uvw
)
# average these uvws together to get the "average" position in
# the uv-plane
avg_uvws = np.average(new_uvws, axis=0)
# calculate and apply phasor
w_lambda = (
new_uvws[:, 2].reshape((i2 - i1), 1)
/ const.c.to("m/s").value
* uv.freq_array.reshape(1, uv.Nfreqs)
)
phs = np.exp(-1j * 2 * np.pi * w_lambda[:, None, :, None])
data_chunk *= phs
# sum data, propagate flag array, and adjusting nsample accordingly
data_slice = np.sum(data_chunk, axis=0)
flag_slice = np.sum(flags_chunk, axis=0)
nsamples_slice = np.sum(nsamples_chunk, axis=0) / (i2 - i1)
data_out[i, :, :, :] = data_slice
flags_out[i, :, :, :] = flag_slice
nsamples_out[i, :, :, :] = nsamples_slice
# update metadata
uvws_out[i, :] = avg_uvws
times_out[i] = (times[i1] + times[i2 - 1]) / 2
lst_out[i] = (lsts[i1] + lsts[i2 - 1]) / 2
int_time_out[i] = np.average(int_time[i1:i2]) * (i2 - i1)
# update data and metadata when we're done with this baseline
current_index = start_index + n_out
uv2.data_array[start_index:current_index, :, :, :] = data_out
uv2.flag_array[start_index:current_index, :, :, :] = flags_out
uv2.nsample_array[start_index:current_index, :, :, :] = nsamples_out
uv2.uvw_array[start_index:current_index, :] = uvws_out
uv2.time_array[start_index:current_index] = times_out
uv2.lst_array[start_index:current_index] = lst_out
uv2.integration_time[start_index:current_index] = int_time_out
uv2.ant_1_array[start_index:current_index] = key[0]
uv2.ant_2_array[start_index:current_index] = key[1]
uv2.baseline_array[start_index:current_index] = uvutils.antnums_to_baseline(
ant1, ant2, None
)
start_index = current_index
# clean up -- shorten all arrays to actually be size nblts
nblts = start_index
uv2.Nblts = nblts
uv2.data_array = uv2.data_array[:nblts, :, :, :]
uv2.flag_array = uv2.flag_array[:nblts, :, :, :]
uv2.nsample_array = uv2.nsample_array[:nblts, :, :, :]
uv2.uvw_array = uv2.uvw_array[:nblts, :]
uv2.time_array = uv2.time_array[:nblts]
uv2.lst_array = uv2.lst_array[:nblts]
uv2.integration_time = uv2.integration_time[:nblts]
uv2.ant_1_array = uv2.ant_1_array[:nblts]
uv2.ant_2_array = uv2.ant_2_array[:nblts]
uv2.baseline_array = uv2.baseline_array[:nblts]
uv2.Ntimes = len(np.unique(uv2.time_array))
# set phasing info
uv2.phase_type = "phased"
uv2.phase_center_ra = zenith_ra.rad
uv2.phase_center_dec = zenith_dec.rad
uv2.phase_center_frame = 2000.0
# fix up to correct old phasing method
uv2.phase(zenith_ra.rad, zenith_dec.rad, epoch="J2000", fix_old_proj=True)
# run a check
uv2.check()
return uv2
avg_vis = False
if avg_vis is True:
print_message("averaging visibilities", type=1)
uvfile = or_xx_files[0]
uvd = UVData()
uvd.read_miriad(uvfile)
# create uvdata object
uvd = UVData()
uvd.Nants_telescope = self.Nants
uvd.Nants_data = len(np.unique(bl_array))
uvd.Nbls = Nbls
uvd.Ntimes = Ntimes
uvd.Nblts = Nbls * Ntimes
uvd.Nfreqs = self.Nfreqs
uvd.Npols = self.Nxpols
uvd.Nspws = 1
uvd.antenna_numbers = self.ant_nums
uvd.antenna_names = np.array(self.ant_nums, dtype=str)
uvd.ant_1_array = np.concatenate(
[map(lambda x: x[0], bl_array) for i in range(Ntimes)])
uvd.ant_2_array = np.concatenate(
[map(lambda x: x[1], bl_array) for i in range(Ntimes)])
uvd.baseline_array = np.concatenate(
[np.arange(Nbls) for i in range(Ntimes)])
uvd.freq_array = (self.freqs * 1e6).reshape(1, -1)
uvd.time_array = np.repeat(np.array(JD_array)[:, np.newaxis], Nbls,
if isinstance(antnums, int):
antnums = [antnums]
bls = [(a1, a2) for (a1, a2) in bls if a1 in antnums or a2 in antnums]
uv_obj.antenna_nums = antnums
if 'redundancy' in sel:
red_tol = sel['redundancy']
reds, vec_bin_centers, lengths = uvutils.get_antenna_redundancies(anums, enu, tol=red_tol, include_autos=False)
bls = [r[0] for r in reds]
bls = [uvutils.baseline_to_antnums(bl_ind, Nants) for bl_ind in bls]
uv_obj.Nants_data = np.unique(bls).size
for (a1, a2) in bls:
i1, i2 = np.where(anums == a1), np.where(anums == a2)
array.append(visibility.baseline(enu[i1], enu[i2]))
bl_array.append(uvutils.antnums_to_baseline(a1, a2, Nants))
Nbls = len(bl_array)
uv_obj.Nbls = Nbls
uv_obj.Nblts = Nbls * Ntimes
bl_array = np.array(bl_array)
freqs = freq_dict['freq_array'][0] #Hz
obs = visibility.observatory(np.degrees(lat), np.degrees(lon), array=array, freqs=freqs)
obs.set_fov(fov)
print("Observatory built.")
print("Nbls: ", Nbls)
print("Ntimes: ", Ntimes)
sys.stdout.flush()
# ---------------------------
# Pointings
# ---------------------------
uvd.freq_array = np.array([aa.get_afreqs() * 1e9])
#Baseline array:
ant_exclude = []
if hasattr(uvd, 'bad_antenna'):
ant_exclude = uvd.bad_antenna #List antennas to be excluded here, for whatever reason.
bls = np.array([
uvd.antnums_to_baseline(j, i, attempt256=True) for i in range(0, len(aa))
for j in range(i, len(aa)) if not j in ant_exclude and not i in ant_exclude
])
uvd.baseline_array = np.tile(bls, uvd.Ntimes)
#number of baselines
nbl = len(bls)
uvd.Nbls = nbl
#Antennas
uvd.antenna_indices = np.arange(1, Nants + 1, 1) #1 indexed, not 0
uvd.antenna_names = ["ANT" + str(i) for i in uvd.antenna_indices]
uvd.antenna_positions = np.array([ant.pos for ant in aa])
uvd.ant_1_array, uvd.ant_2_array = \
uvd.baseline_to_antnums(uvd.baseline_array)
#Delays
#if uvd.instrument == 'MWA':
uvd.extra_keywords['delays'] = '0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0'
if hasattr(uvd, 'file_gap'):
t0 = tzero + en * (uvd.Ntimes * dt + uvd.file_gap * dayspersec)
else:
def fake_data_generator():
"""Generate a fake dataset as a module-level fixture."""
# generate a fake data file for testing BDA application
uvd = UVData()
t0 = 2459000.0
dt = (2 * units.s).to(units.day)
t1 = t0 + dt.value
t2 = t0 + 2 * dt.value
# define baseline-time spacing
uvd.ant_1_array = np.asarray([0, 0, 0, 0, 0, 0, 0, 0, 0], dtype=np.int_)
uvd.ant_2_array = np.asarray([0, 1, 2, 0, 1, 2, 0, 1, 2], dtype=np.int_)
uvd.time_array = np.asarray([t0, t0, t0, t1, t1, t1, t2, t2, t2],
dtype=np.float64)
uvd.Ntimes = 3
uvd.Nbls = 3
uvd.Nblts = uvd.Ntimes * uvd.Nbls
# define frequency array
nfreqs = 1024
freq_array = np.linspace(50e6, 250e6, num=nfreqs)
uvd.freq_array = freq_array[np.newaxis, :]
uvd.spw_array = [0]
uvd.Nfreqs = nfreqs
uvd.Nspws = 1
# define polarization array
uvd.polarization_array = [-5]
uvd.Npols = 1
# make random data for data array
data_shape = (uvd.Nblts, 1, uvd.Nfreqs, uvd.Npols)
uvd.data_array = np.zeros(data_shape, dtype=np.complex128)
rng = default_rng()
uvd.data_array += rng.standard_normal(data_shape)
uvd.data_array += 1j * rng.standard_normal(data_shape)
uvd.flag_array = np.zeros_like(uvd.data_array, dtype=np.bool_)
uvd.nsample_array = np.ones_like(uvd.data_array, dtype=np.float32)
# set telescope and antenna positions
hera_telescope = pyuvdata.telescopes.get_telescope("HERA")
uvd.telescope_location_lat_lon_alt = hera_telescope.telescope_location_lat_lon_alt
antpos0 = np.asarray([0, 0, 0], dtype=np.float64)
antpos1 = np.asarray([14, 0, 0], dtype=np.float64)
antpos2 = np.asarray([28, 0, 0], dtype=np.float64)
antpos_enu = np.vstack((antpos0, antpos1, antpos2))
antpos_xyz = uvutils.ECEF_from_ENU(antpos_enu,
*uvd.telescope_location_lat_lon_alt)
uvd.antenna_positions = antpos_xyz - uvd.telescope_location
uvw_array = np.zeros((uvd.Nblts, 3), dtype=np.float64)
uvw_array[::3, :] = antpos0 - antpos0
uvw_array[1::3, :] = antpos1 - antpos0
uvw_array[2::3, :] = antpos2 - antpos0
uvd.uvw_array = uvw_array
uvd.antenna_numbers = np.asarray([0, 1, 2], dtype=np.int_)
uvd.antenna_names = np.asarray(["H0", "H1", "H2"], dtype=np.str_)
uvd.Nants_data = 3
uvd.Nants_telescope = 3
# set other metadata
uvd.vis_units = "uncalib"
uvd.channel_width = 5e4 # 50 kHz
uvd.phase_type = "drift"
uvd.baseline_array = uvd.antnums_to_baseline(uvd.ant_1_array,
uvd.ant_2_array)
uvd.history = "BDA test file"
uvd.instrument = "HERA"
uvd.telescope_name = "HERA"
uvd.object_name = "zenith"
uvd.integration_time = 2 * np.ones_like(uvd.baseline_array,
dtype=np.float64)
uvd.set_lsts_from_time_array()
# run a check
uvd.check()
yield uvd
# clean up when done
del uvd
return
def uvdata_from_sim_data(
array_lat,
array_lon,
array_height,
jd_axis,
nu_axis,
r_axis,
ant_pairs,
V_sim,
integration_time="derived",
channel_width="derived",
antenna_numbers="derived",
antenna_names="derived",
instrument="left blank by user",
telescope_name="left blank by user",
history="left blank by user",
object_name="left blank by user",
):
HERA_LAT = array_lat
HERA_LON = array_lon
HERA_HEIGHT = array_height
HERA_LAT_LON_ALT = (HERA_LAT, HERA_LON, HERA_HEIGHT)
HERA_LOC = coord.EarthLocation(
lat=HERA_LAT * units.rad,
lon=HERA_LON * units.rad,
height=HERA_HEIGHT * units.meter,
)
jd_obj = Time(jd_axis, format="jd", location=HERA_LOC)
lst_axis = jd_obj.sidereal_time("apparent").radian
if integration_time == "derived":
del_jd = jd_obj[1] - jd_obj[0]
integration_time = del_jd.sec
uvd = UVData()
uvd.telescope_location = HERA_LOC.value
uvd.telescope_location_lat_lon_alt = HERA_LAT_LON_ALT
if antenna_numbers == "derived":
uvd.antenna_numbers = np.arange(r_axis.shape[0], dtype=np.int64)
ant_num_pairs = ant_pairs
else:
uvd.antenna_numbers = antenna_numbers
ant_num_pairs = np.array([(antenna_numbers[ap[0]],
antenna_numbers[ap[1]])
for ap in ant_pairs])
if antenna_names == "derived":
uvd.antenna_names = [str(ant_ind) for ant_ind in uvd.antenna_numbers]
else:
uvd.antenna_names = antenna_names
ant_pos_ECEF = pyuvdata.utils.ECEF_from_ENU(r_axis, HERA_LOC.lat.rad,
HERA_LOC.lon.rad,
HERA_LOC.height.value)
uvd.antenna_positions = ant_pos_ECEF - uvd.telescope_location
bls = [
uvd.antnums_to_baseline(ant_num_pair[0], ant_num_pair[1])
for ant_num_pair in ant_num_pairs
]
uvd.freq_array = nu_axis.reshape((1, nu_axis.size))
pols = ["xx", "yy", "xy", "yx"]
uvd.polarization_array = np.array(
[polstr2num(pol_str) for pol_str in pols])
uvd.x_orientation = "east"
if channel_width == "derived":
uvd.channel_width = nu_axis[1] - nu_axis[0]
else:
uvd.channel_width = channel_width
uvd.Nfreqs = nu_axis.size
uvd.Nspws = 1
uvd.Npols = len(pols)
bl_arr, lst_arr = np.meshgrid(np.array(bls), lst_axis)
uvd.baseline_array = bl_arr.flatten()
uvd.lst_array = lst_arr.flatten()
_, time_arr = np.meshgrid(np.array(bls), jd_axis)
uvd.time_array = time_arr.flatten()
ant1_arr, _ = np.meshgrid(ant_num_pairs[:, 0], lst_axis)
ant2_arr, _ = np.meshgrid(ant_num_pairs[:, 1], lst_axis)
uvd.ant_1_array = ant1_arr.flatten()
uvd.ant_2_array = ant2_arr.flatten()
# Nants_data might be less than r_axis.shape[0] for redundant arrays
uvd.Nants_data = len(
np.unique(np.r_[ant1_arr.flatten(),
ant2_arr.flatten()]))
uvd.Nants_telescope = r_axis.shape[0]
uvd.set_uvws_from_antenna_positions()
uvd.Nbls = len(bls)
uvd.Ntimes = lst_axis.size
uvd.Nblts = bl_arr.size
uvd.data_array = np.zeros((uvd.Nblts, uvd.Nspws, uvd.Nfreqs, uvd.Npols),
dtype=np.complex128)
uvd.flag_array = np.zeros_like(uvd.data_array, dtype=np.bool)
uvd.nsample_array = np.ones((uvd.Nblts, uvd.Nspws, uvd.Nfreqs, uvd.Npols),
dtype=np.float64)
uvd.spw_array = np.ones(1, dtype=np.int64)
uvd.integration_time = integration_time * np.ones(uvd.Nblts)
uvd.phase_type = "drift"
# (0,0) <-> 'xx' <-> East-East, etc.
# matches order of uvd.polarization_array
pol_map = {(0, 0): 0, (1, 1): 1, (0, 1): 2, (1, 0): 3}
for i_a in range(2):
for i_b in range(2):
i_p = pol_map[(i_a, i_b)]
for k in range(ant_num_pairs.shape[0]):
a_i, a_j = ant_num_pairs[k]
bl_num = uvd.antnums_to_baseline(a_i, a_j)
bl_ind = np.where(uvd.baseline_array == bl_num)[0]
uvd.data_array[bl_ind, 0, :, i_p] = np.copy(V_sim[:, :, k, i_a,
i_b])
uvd.vis_units = "Jy"
uvd.instrument = instrument
uvd.telescope_name = telescope_name
uvd.history = history
uvd.object_name = object_name
uvd.check()
return uvd
if a1 == a2:
bram.imag = 0
bram.real = bram.real / float(
feng.corr.acc_len * feng.corr.spec_per_acc)
else:
bram = bram / float(
feng.corr.acc_len * feng.corr.spec_per_acc)
c.append(bram)
poco.append(np.transpose(c))
# Write output to pyuvdata file
data = UVData()
data.Nfreqs = len(fqs)
data.Nspws = 1
data.Npols = 1
data.Nbls = len(bls)
data.Ntimes = len(times)
data.Nblts = data.Ntimes
# Init flag array with no flags
data.flag_array = np.empty(
(data.Nblts, data.Nspws, data.Nfreqs, data.Npols), dtype=bool)
data.flag_array[:] = False
# Init nsamples array with unity weights
data.nsample_array = np.ones(
(data.Nblts, data.Nspws, data.Nfreqs, data.Npols), dtype=float)
# Create frequency array
data.freq_array = np.zeros((data.Nspws, data.Nfreqs))
data.freq_array[0, :] = fqs
input_uv.antenna_numbers = ants_new
input_uv.antenna_names = ants_new.astype(str)
Nants = ants_new.size
# For now, all use the same beam model
beam_dict = dict(zip(input_uv.antenna_names, np.zeros(Nants, dtype=int)))
input_uv.antenna_positions = input_uv.antenna_positions[:Nants, :]
input_uv.Nants_data = Nants
input_uv.Nants_telescope = Nants
# Time selection:
inds = np.array([
np.arange(args.Nbls) + i * input_uv.Nbls for i in range(args.Ntimes)
]).flatten()
input_uv.time_array = input_uv.time_array[inds]
input_uv.lst_array = input_uv.lst_array[inds]
input_uv.Nbls = args.Nbls
input_uv.Nblts = args.Nbls * args.Ntimes
# Beam selection:
# Default is uniform
if args.beam == 'hera':
beam = UVBeam()
beamfile = '/users/alanman/data/alanman/NickFagnoniBeams/HERA_NicCST_fullfreq.uvbeam'
beam_list = [beamfile]
mock_keywords = {
'mock_arrangement': 'random',
'Nsrcs': args.Nsrcs,
'min_alt': min_alt,
'time': input_uv.time_array[0]
}
def sim_obs(self,
bl_array,
JD_array,
pool=None,
write_miriad=False,
fname=None,
clobber=False,
one_beam_model=True,
interp=True,
Tnoise=None,
fast_noise=False):
"""
Simulate a visibility observation of the sky
Input:
------
bl_array : list, shape=(Nbls, 2), entry_format=tuple(int, int)
list of baselines (antenna pairs)
in (ant1, ant2) with type(ant1) == int
JD_array : list, shape=(Ntimes,), dtype=float
list of Julian Dates for observations
"""
# get array info
Nbls = len(bl_array)
str_bls = [(str(x[0]), str(x[1])) for x in bl_array]
Ntimes = len(JD_array)
rel_ants = np.unique(np.concatenate(bl_array)) # relevant antennas
rel_ant2ind = OrderedDict(
zip(np.array(rel_ants, str), np.arange(len(rel_ants))))
# get antenna indices
ant_inds = map(lambda x: self.ant2ind[str(x)], rel_ants)
# get antenna positions for each baseline
ant1_pos = np.array(map(lambda x: self.ant_pos[x[0]], str_bls))
ant2_pos = np.array(map(lambda x: self.ant_pos[x[1]], str_bls))
# get vector in u-v plane
wavelengths = 2.9979e8 / (self.freqs * 1e6)
self.uv_vecs = (ant2_pos - ant1_pos).reshape(Nbls, 3, -1) / wavelengths
# Get direction unit vector, s-hat
x, y, z = hp.pix2vec(self.beam_nside, np.arange(self.beam_npix))
self.s_hat = np.array([y, x, z])
# get phase map in topocentric frame (sqrt of full phase term)
self.phase = np.exp(-1j * np.pi *
np.einsum('ijk,jl->ikl', self.uv_vecs, self.s_hat))
# build beams for each antenna in topocentric coordinates
self.build_beams(ant_inds=ant_inds, one_beam_model=one_beam_model)
# create phase and beam product maps (Npol, Nbl, Nfreqs, Npix)
self.beam_phs = self.ant_beam_models.reshape(
self.Npols, -1, self.Nfreqs, self.beam_npix) * self.phase
# loop over JDs
self.vis_data = []
for jd in JD_array:
# map through each antenna and project polarized, multi-frequency beam model onto sky
if one_beam_model == True:
self.proj_beam_phs = self.project_beams(
jd,
self.sky_theta,
self.sky_phi,
beam_models=self.beam_phs,
output=True,
interp=interp)
else:
#proj_ant_beam_models = self.project_beams(JD, self.sky_theta, self.sky_phi,
#beam_models=np.array(map(lambda x: self.ant_beam_models[str(x)], self.ant_nums)), output=True)
self.proj_beam_phs = self.project_beams(
jd,
self.sky_theta,
self.sky_phi,
beam_models=self.beam_phs,
output=True,
interp=interp)
# calculate visibility
if pool is None:
M = map
else:
M = pool.map
# add noise
if Tnoise is None or fast_noise is True:
sky_models = self.sky_models
else:
sky_models = self.sky_models + self.generate_noise_map(
Tnoise, self.sky_models.shape)
if one_beam_model is True:
if self.onepol is True:
vis = np.einsum("ijkl, ikl -> ijk", self.proj_beam_phs**2,
sky_models)
else:
vis = np.einsum("ijkl, imkl, mjkl -> imjk",
self.proj_beam_phs, sky_models,
self.proj_beam_phs)
else:
vis = np.array(
map(
lambda x: np.einsum(
"ijk, jk, ljk -> ilj", self.proj_beam_phs[
rel_ant2ind[x[1][0]]], sky_models, self.
proj_beam_phs[rel_ant2ind[x[1][1]]]),
enumerate(str_bls)))
self.vis_data.append(vis)
if self.onepol is True:
self.vis_data = np.moveaxis(np.array(self.vis_data), 0, 2)
else:
self.vis_data = np.moveaxis(np.array(self.vis_data), 0, 3)
# normalize by the effective-beam solid angle
if one_beam_model == True:
self.ant_beam_sa = np.repeat(
np.einsum('ijk,ijk->ij', self.ant_beam_models,
self.ant_beam_models)[:, np.newaxis, :], Nbls, 1)
else:
self.ant_beam_sa = np.array(
map(
lambda x: np.einsum(
'ijk,ljk->ilj', self.proj_ant_beam_models[rel_ant2ind[
x[1][0]]], self.proj_ant_beam_models[rel_ant2ind[x[
1][1]]]), enumerate(str_bls)))
if self.onepol is True:
self.vis_data /= self.ant_beam_sa[:, :, np.newaxis, :]
self.vis_data_shape = "(1, Nbls, Ntimes, Nfreqs)"
else:
self.vis_data /= self.ant_beam_sa[:, :, :, np.newaxis, :]
self.vis_data_shape = "(2, 2, Nbls, Ntimes, Nfreqs)"
if fast_noise is True and Tnoise is not None:
self.vis_data += self.generate_vis_noise(
Tnoise,
self.freqs * 1e6,
self.ant_beam_sa.squeeze(),
self.vis_data.shape,
bandwidth=self.bandwidth * 1e6,
int_time=10.7)
# write to file
if write_miriad == True:
# create uvdata object
uvd = UVData()
uvd.Nants_telescope = self.Nants
uvd.Nants_data = len(np.unique(bl_array))
uvd.Nbls = Nbls
uvd.Ntimes = Ntimes
uvd.Nblts = Nbls * Ntimes
uvd.Nfreqs = self.Nfreqs
uvd.Npols = self.Nxpols
uvd.Nspws = 1
uvd.antenna_numbers = self.ant_nums
uvd.antenna_names = np.array(self.ant_nums, dtype=str)
uvd.ant_1_array = np.concatenate(
[map(lambda x: x[0], bl_array) for i in range(Ntimes)])
uvd.ant_2_array = np.concatenate(
[map(lambda x: x[1], bl_array) for i in range(Ntimes)])
uvd.baseline_array = np.concatenate(
[np.arange(Nbls) for i in range(Ntimes)])
uvd.freq_array = (self.freqs * 1e6).reshape(1, -1)
uvd.time_array = np.repeat(np.array(JD_array)[:, np.newaxis],
Nbls,
axis=1).ravel()
uvd.channel_width = (self.freqs * 1e6)[1] - (self.freqs * 1e6)[0]
uvd.data_array = self.vis_data.reshape(uvd.Nblts, 1, self.Nfreqs,
self.Nxpols)
uvd.flag_array = np.ones_like(uvd.data_array, dtype=np.bool)
uvd.history = " "
uvd.instrument = " "
uvd.integration_time = 10.7
uvd.lst_array = np.repeat(np.array(
map(lambda x: self.JD2LST(self.loc, x), JD_array))[:,
np.newaxis],
Nbls,
axis=1).ravel()
uvd.nsample_array = np.ones_like(uvd.data_array, dtype=np.float)
uvd.object_name = ""
uvd.phase_type = "drift"
uvd.polarization_array = np.array(
map(lambda x: {
"XX": -5,
"YY": -6,
"XY": -7,
"YX": -8
}[x], self.xpols))
uvd.spw_array = np.array([0])
uvd.telescope_location = np.array([
6378137. * np.cos(self.loc.lon) * np.cos(self.loc.lat),
6378137. * np.sin(self.loc.lon) * np.cos(self.loc.lat),
6378137. * np.sin(self.loc.lat)
])
uvd.telescope_name = " "
uvd.uvw_array = np.ones((uvd.Nblts, 3))
uvd.vis_units = "Jy"
zen_dec, zen_ra = self.loc.radec_of(0, np.pi / 2)
uvd.zenith_dec = np.ones(uvd.Nblts) * zen_dec
uvd.zenith_ra = np.ones(uvd.Nblts) * zen_ra
uvd.write_miriad(fname, clobber=clobber)