def test_phasing_funcs():
# these tests are based on a notebook where I tested against the mwa_tools phasing code
ra_hrs = 12.1
dec_degs = -42.3
mjd = 55780.1
array_center_xyz = np.array([-2559454.08, 5095372.14, -2849057.18])
lat_lon_alt = uvutils.LatLonAlt_from_XYZ(array_center_xyz)
obs_time = Time(mjd,
format='mjd',
location=(lat_lon_alt[1], lat_lon_alt[0]))
icrs_coord = SkyCoord(ra=Angle(ra_hrs, unit='hr'),
dec=Angle(dec_degs, unit='deg'),
obstime=obs_time)
gcrs_coord = icrs_coord.transform_to('gcrs')
# in east/north/up frame (relative to array center) in meters: (Nants, 3)
ants_enu = np.array([-101.94, 0156.41, 0001.24])
ant_xyz_abs = uvutils.ECEF_from_ENU(ants_enu, lat_lon_alt[0],
lat_lon_alt[1], lat_lon_alt[2])
ant_xyz_rel_itrs = ant_xyz_abs - array_center_xyz
ant_xyz_rel_rot = uvutils.rotECEF_from_ECEF(ant_xyz_rel_itrs,
lat_lon_alt[1])
array_center_coord = SkyCoord(x=array_center_xyz[0] * units.m,
y=array_center_xyz[1] * units.m,
z=array_center_xyz[2] * units.m,
frame='itrs',
obstime=obs_time)
itrs_coord = SkyCoord(x=ant_xyz_abs[0] * units.m,
y=ant_xyz_abs[1] * units.m,
z=ant_xyz_abs[2] * units.m,
frame='itrs',
obstime=obs_time)
gcrs_array_center = array_center_coord.transform_to('gcrs')
gcrs_from_itrs_coord = itrs_coord.transform_to('gcrs')
gcrs_rel = (gcrs_from_itrs_coord.cartesian -
gcrs_array_center.cartesian).get_xyz().T
gcrs_uvw = uvutils.phase_uvw(gcrs_coord.ra.rad, gcrs_coord.dec.rad,
gcrs_rel.value)
mwa_tools_calcuvw_u = -97.122828
mwa_tools_calcuvw_v = 50.388281
mwa_tools_calcuvw_w = -151.27976
assert np.allclose(gcrs_uvw[0, 0], mwa_tools_calcuvw_u, atol=1e-3)
assert np.allclose(gcrs_uvw[0, 1], mwa_tools_calcuvw_v, atol=1e-3)
assert np.allclose(gcrs_uvw[0, 2], mwa_tools_calcuvw_w, atol=1e-3)
# also test unphasing
temp2 = uvutils.unphase_uvw(gcrs_coord.ra.rad, gcrs_coord.dec.rad,
np.squeeze(gcrs_uvw))
assert np.allclose(gcrs_rel.value, temp2)
def parse_telescope_params(tele_params):
"""
Parse the "telescope" section of a healvis obsparam.
Args:
tele_params: Dictionary of telescope parameters
Returns:
dict of array properties:
| Nants_data: Number of antennas
| Nants_telescope: Number of antennas
| antenna_names: list of antenna names
| antenna_numbers: corresponding list of antenna numbers
| antenna_positions: Array of ECEF antenna positions
| telescope_location: ECEF array center location
| telescope_config_file: Path to configuration yaml file
| antenna_location_file: Path to csv layout file
| telescope_name: observatory name
"""
layout_csv = tele_params["array_layout"]
if not os.path.exists(layout_csv):
if not os.path.exists(layout_csv):
raise ValueError(
"layout_csv file from yaml does not exist: {}".format(
layout_csv))
ant_layout = _parse_layout_csv(layout_csv)
if isinstance(tele_params["telescope_location"], str):
tloc = tele_params["telescope_location"][1:-1] # drop parens
tloc = list(map(float, tloc.split(",")))
else:
tloc = list(tele_params["telescope_location"])
tloc[0] *= np.pi / 180.0
tloc[1] *= np.pi / 180.0 # Convert to radians
tloc_xyz = uvutils.XYZ_from_LatLonAlt(*tloc)
E, N, U = ant_layout["e"], ant_layout["n"], ant_layout["u"]
antnames = ant_layout["name"]
return_dict = {}
return_dict["Nants_data"] = antnames.size
return_dict["Nants_telescope"] = antnames.size
return_dict["antenna_names"] = np.array(antnames.tolist())
return_dict["antenna_numbers"] = np.array(ant_layout["number"])
antpos_enu = np.vstack((E, N, U)).T
return_dict["antenna_positions"] = (
uvutils.ECEF_from_ENU(antpos_enu, *tloc) - tloc_xyz)
return_dict["array_layout"] = layout_csv
return_dict["telescope_location"] = tloc_xyz
return_dict["telescope_name"] = tele_params["telescope_name"]
return return_dict
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 uv.antenna_numbers = np.array([0,1]) uv.antenna_names = ['ant0', 'ant1'] #uv.channel_width = np.ones(uv.Nblts) * np.diff(freqs)[0] uv.channel_width = np.diff(freqs)[0] uv.integration_time = np.ones(uv.Nblts) * np.diff(time_arr)[0] * 24 * 3600. # Seconds uv.uvw_array = np.tile(ant1_enu - ant2_enu, uv.Nblts).reshape(uv.Nblts, 3) uv.history = 'Eorsky simulated' uv.set_drift() uv.telescope_name = 'Eorsky gaussian' uv.instrument = 'simulator' uv.object_name = 'zenith' uv.vis_units = 'Jy' uv.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude) uv.set_lsts_from_time_array()
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
def phase(jd, ra, dec, telescope_location, uvws0):
"""
Compute UVWs phased to a given RA/DEC at a particular epoch.
This function was copied from the pyuvdata.UVData.phase method, and modified to be
simpler.
Parameters
----------
jd : float or array_like of float
The Julian date of the observation.
ra : float
The ra to phase to in radians.
dec : float
The dec to phase to in radians.
telescope_location : :class:`astropy.coordinates.EarthLocation`
The location of the reference point of the telescope, in geodetic frame (i.e.
it has lat, lon, height attributes).
uvws0 : array
The UVWs when phased to zenith.
Returns
-------
uvws : array
Array of the same shape as `uvws0`, with entries modified to the new phase
center.
"""
frame_phase_center = SkyCoord(ra=ra, dec=dec, unit="radian", frame="icrs")
obs_time = Time(np.atleast_1d(jd), format="jd")
itrs_telescope_location = telescope_location.get_itrs(obstime=obs_time)
frame_telescope_location = itrs_telescope_location.transform_to(ICRS)
frame_telescope_location.representation_type = "cartesian"
uvw_ecef = uvutils.ECEF_from_ENU(
uvws0,
telescope_location.lat.rad,
telescope_location.lon.rad,
telescope_location.height,
)
unique_times, r_inds = np.unique(obs_time, return_inverse=True)
uvws = np.zeros((uvw_ecef.shape[0], unique_times.size, 3),
dtype=np.float64)
for ind, jd in tqdm.tqdm(
enumerate(unique_times),
desc="computing UVWs",
total=len(unique_times),
unit="times",
disable=not config.PROGRESS or unique_times.size == 1,
):
itrs_uvw_coord = SkyCoord(
x=uvw_ecef[:, 0] * un.m,
y=uvw_ecef[:, 1] * un.m,
z=uvw_ecef[:, 2] * un.m,
frame="itrs",
obstime=jd,
)
frame_uvw_coord = itrs_uvw_coord.transform_to("icrs")
# this takes out the telescope location in the new frame,
# so these are vectors again
frame_rel_uvw = (
frame_uvw_coord.cartesian.get_xyz().value.T -
frame_telescope_location[ind].cartesian.get_xyz().value)
uvws[:, ind, :] = uvutils.phase_uvw(frame_phase_center.ra.rad,
frame_phase_center.dec.rad,
frame_rel_uvw)
return uvws[:, r_inds, :]
def test_ENU_tofrom_ECEF():
center_lat = -30.7215261207 * np.pi / 180.0
center_lon = 21.4283038269 * np.pi / 180.0
center_alt = 1051.7
lats = np.array([
-30.72218216, -30.72138101, -30.7212785, -30.7210011, -30.72159853,
-30.72206199, -30.72174614, -30.72188775, -30.72183915, -30.72100138
]) * np.pi / 180.0
lons = np.array([
21.42728211, 21.42811727, 21.42814544, 21.42795736, 21.42686739,
21.42918772, 21.42785662, 21.4286408, 21.42750933, 21.42896567
]) * np.pi / 180.0
alts = np.array([
1052.25, 1051.35, 1051.2, 1051., 1051.45, 1052.04, 1051.68, 1051.87,
1051.77, 1051.06
])
# used pymap3d, which implements matlab code, as a reference.
x = [
5109327.46674067, 5109339.76407785, 5109344.06370947, 5109365.11297147,
5109372.115673, 5109266.94314734, 5109329.89620962, 5109295.13656657,
5109337.21810468, 5109329.85680612
]
y = [
2005130.57953031, 2005221.35184577, 2005225.93775268, 2005214.8436201,
2005105.42364036, 2005302.93158317, 2005190.65566222, 2005257.71335575,
2005157.78980089, 2005304.7729239
]
z = [
-3239991.24516348, -3239914.4185286, -3239904.57048431,
-3239878.02656316, -3239935.20415493, -3239979.68381865,
-3239949.39266985, -3239962.98805772, -3239958.30386264,
-3239878.08403833
]
east = [
-97.87631659, -17.87126443, -15.17316938, -33.19049252, -137.60520964,
84.67346748, -42.84049408, 32.28083937, -76.1094745, 63.40285935
]
north = [
-72.7437482, 16.09066646, 27.45724573, 58.21544651, -8.02964511,
-59.41961437, -24.39698388, -40.09891961, -34.70965816, 58.18410876
]
up = [
0.54883333, -0.35004539, -0.50007736, -0.70035299, -0.25148791,
0.33916067, -0.02019057, 0.16979185, 0.06945155, -0.64058124
]
xyz = uvutils.XYZ_from_LatLonAlt(lats, lons, alts)
assert np.allclose(np.stack((x, y, z), axis=1), xyz, atol=1e-3)
enu = uvutils.ENU_from_ECEF(xyz, center_lat, center_lon, center_alt)
assert np.allclose(np.stack((east, north, up), axis=1), enu, atol=1e-3)
# check warning if array transposed
uvtest.checkWarnings(uvutils.ENU_from_ECEF,
[xyz.T, center_lat, center_lon, center_alt],
message='The expected shape of ECEF xyz array',
category=DeprecationWarning)
# check warning if 3 x 3 array
uvtest.checkWarnings(uvutils.ENU_from_ECEF,
[xyz[0:3], center_lat, center_lon, center_alt],
message='The xyz array in ENU_from_ECEF is',
category=DeprecationWarning)
# check error if only 2 coordinates
pytest.raises(ValueError, uvutils.ENU_from_ECEF, xyz[:, 0:2], center_lat,
center_lon, center_alt)
# check that a round trip gives the original value.
xyz_from_enu = uvutils.ECEF_from_ENU(enu, center_lat, center_lon,
center_alt)
assert np.allclose(xyz, xyz_from_enu, atol=1e-3)
# check warning if array transposed
uvtest.checkWarnings(uvutils.ECEF_from_ENU,
[enu.T, center_lat, center_lon, center_alt],
message='The expected shape the ENU array',
category=DeprecationWarning)
# check warning if 3 x 3 array
uvtest.checkWarnings(uvutils.ECEF_from_ENU,
[enu[0:3], center_lat, center_lon, center_alt],
message='The enu array in ECEF_from_ENU is',
category=DeprecationWarning)
# check error if only 2 coordinates
pytest.raises(ValueError, uvutils.ENU_from_ECEF, enu[:, 0:2], center_lat,
center_lon, center_alt)
# check passing a single value
enu_single = uvutils.ENU_from_ECEF(xyz[0, :], center_lat, center_lon,
center_alt)
assert np.allclose(np.array((east[0], north[0], up[0])),
enu[0, :],
atol=1e-3)
xyz_from_enu = uvutils.ECEF_from_ENU(enu_single, center_lat, center_lon,
center_alt)
assert np.allclose(xyz[0, :], xyz_from_enu, atol=1e-3)
# error checking
pytest.raises(ValueError, uvutils.ENU_from_ECEF, xyz[:, 0:1], center_lat,
center_lon, center_alt)
pytest.raises(ValueError, uvutils.ECEF_from_ENU, enu[:, 0:1], center_lat,
center_lon, center_alt)
pytest.raises(ValueError, uvutils.ENU_from_ECEF, xyz / 2., center_lat,
center_lon, center_alt)
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 write_vis(fname,
data,
lst_array,
freq_array,
antpos,
time_array=None,
flags=None,
nsamples=None,
filetype='miriad',
write_file=True,
outdir="./",
overwrite=False,
verbose=True,
history=" ",
return_uvd=False,
longitude=21.42830,
start_jd=None,
instrument="HERA",
telescope_name="HERA",
object_name='EOR',
vis_units='uncalib',
dec=-30.72152,
telescope_location=np.array(
[5109325.85521063, 2005235.09142983, -3239928.42475395]),
integration_time=None,
**kwargs):
"""
Take DataContainer dictionary, export to UVData object and write to file. See pyuvdata.UVdata
documentation for more info on these attributes.
Parameters:
-----------
fname : type=str, output filename of visibliity data
data : type=DataContainer, holds complex visibility data.
lst_array : type=float ndarray, contains unique LST time bins [radians] of data (center of integration).
freq_array : type=ndarray, contains frequency bins of data [Hz].
antpos : type=dictionary, antenna position dictionary. keys are antenna integers and values
are position vectors in meters in ENU (TOPO) frame.
time_array : type=ndarray, contains unique Julian Date time bins of data (center of integration).
flags : type=DataContainer, holds data flags, matching data in shape.
nsamples : type=DataContainer, holds number of points averaged into each bin in data (if applicable).
filetype : type=str, filetype to write-out, options=['miriad'].
write_file : type=boolean, write UVData to file if True.
outdir : type=str, output directory for output file.
overwrite : type=boolean, if True, overwrite output files.
verbose : type=boolean, if True, report feedback to stdout.
history : type=str, history string for UVData object
return_uvd : type=boolean, if True return UVData instance.
longitude : type=float, longitude of observer in degrees East
start_jd : type=float, starting integer Julian Date of time_array if time_array is None.
instrument : type=str, instrument name.
telescope_name : type=str, telescope name.
object_name : type=str, observing object name.
vis_unit : type=str, visibility units.
dec : type=float, declination of observer in degrees North.
telescope_location : type=ndarray, telescope location in xyz in ITRF (earth-centered frame).
integration_time : type=float, integration duration in seconds for data_array. This does not necessarily have
to be equal to the diff(time_array): for the case of LST-binning, this is not the duration of the LST-bin
but the integration time of the pre-binned data.
kwargs : type=dictionary, additional parameters to set in UVData object.
Output:
-------
if return_uvd: return UVData instance
"""
## configure UVData parameters
# get pols
pols = np.unique(map(lambda k: k[-1], data.keys()))
Npols = len(pols)
polarization_array = np.array(map(lambda p: polstr2num[p], pols))
# get times
if time_array is None:
if start_jd is None:
raise AttributeError(
"if time_array is not fed, start_jd must be fed")
time_array = hc.utils.LST2JD(lst_array, start_jd, longitude=longitude)
Ntimes = len(time_array)
if integration_time is None:
integration_time = np.median(np.diff(time_array)) * 24 * 3600.
# get freqs
Nfreqs = len(freq_array)
channel_width = np.median(np.diff(freq_array))
freq_array = freq_array.reshape(1, -1)
spw_array = np.array([0])
Nspws = 1
# get baselines keys
bls = sorted(data.bls())
Nbls = len(bls)
Nblts = Nbls * Ntimes
# reconfigure time_array and lst_array
time_array = np.repeat(time_array[np.newaxis], Nbls, axis=0).ravel()
lst_array = np.repeat(lst_array[np.newaxis], Nbls, axis=0).ravel()
# get data array
data_array = np.moveaxis(
map(lambda p: map(lambda bl: data[str(p)][bl], bls), pols), 0, -1)
# resort time and baseline axes
data_array = data_array.reshape(Nblts, 1, Nfreqs, Npols)
if nsamples is None:
nsample_array = np.ones_like(data_array, np.float)
else:
nsample_array = np.moveaxis(
map(lambda p: map(lambda bl: nsamples[str(p)][bl], bls), pols), 0,
-1)
nsample_array = nsample_array.reshape(Nblts, 1, Nfreqs, Npols)
# flags
if flags is None:
flag_array = np.zeros_like(data_array, np.float).astype(np.bool)
else:
flag_array = np.moveaxis(
map(
lambda p: map(lambda bl: flags[str(p)][bl].astype(np.bool), bls
), pols), 0, -1)
flag_array = flag_array.reshape(Nblts, 1, Nfreqs, Npols)
# configure baselines
bls = np.repeat(np.array(bls), Ntimes, axis=0)
# get ant_1_array, ant_2_array
ant_1_array = bls[:, 0]
ant_2_array = bls[:, 1]
# get baseline array
baseline_array = 2048 * (ant_1_array + 1) + (ant_2_array + 1) + 2**16
# get antennas in data
data_ants = np.unique(np.concatenate([ant_1_array, ant_2_array]))
Nants_data = len(data_ants)
# get telescope ants
antenna_numbers = np.unique(antpos.keys())
Nants_telescope = len(antenna_numbers)
antenna_names = map(lambda a: "HH{}".format(a), antenna_numbers)
# set uvw assuming drift phase i.e. phase center is zenith
uvw_array = np.array(
[antpos[k[1]] - antpos[k[0]] for k in zip(ant_1_array, ant_2_array)])
# get antenna positions in ITRF frame
tel_lat_lon_alt = uvutils.LatLonAlt_from_XYZ(telescope_location)
antenna_positions = np.array(map(lambda k: antpos[k], antenna_numbers))
antenna_positions = uvutils.ECEF_from_ENU(
antenna_positions.T, *tel_lat_lon_alt).T - telescope_location
# get zenith location: can only write drift phase
phase_type = 'drift'
zenith_dec_degrees = np.ones_like(baseline_array) * dec
zenith_ra_degrees = hc.utils.JD2RA(time_array, longitude)
zenith_dec = zenith_dec_degrees * np.pi / 180
zenith_ra = zenith_ra_degrees * np.pi / 180
# instantiate object
uvd = UVData()
# assign parameters
params = [
'Nants_data', 'Nants_telescope', 'Nbls', 'Nblts', 'Nfreqs', 'Npols',
'Nspws', 'Ntimes', 'ant_1_array', 'ant_2_array', 'antenna_names',
'antenna_numbers', 'baseline_array', 'channel_width', 'data_array',
'flag_array', 'freq_array', 'history', 'instrument',
'integration_time', 'lst_array', 'nsample_array', 'object_name',
'phase_type', 'polarization_array', 'spw_array', 'telescope_location',
'telescope_name', 'time_array', 'uvw_array', 'vis_units',
'antenna_positions', 'zenith_dec', 'zenith_ra'
]
local_params = locals()
# overwrite paramters by kwargs
local_params.update(kwargs)
# set parameters in uvd
for p in params:
uvd.__setattr__(p, local_params[p])
# write to file
if write_file:
if filetype == 'miriad':
# check output
fname = os.path.join(outdir, fname)
if os.path.exists(fname) and overwrite is False:
if verbose:
print("{} exists, not overwriting".format(fname))
else:
if verbose:
print("saving {}".format(fname))
uvd.write_miriad(fname, clobber=True)
else:
raise AttributeError(
"didn't recognize filetype: {}".format(filetype))
if return_uvd:
return uvd
