"""Class for reading and writing uvfits files."""
from astropy import constants as const
import astropy
from astropy.time import Time
from astropy.io import fits
import numpy as np
import warnings
from uvdata import UVData
import parameter as uvp
import utils as uvutils
class UVFITS(UVData):
"""
Defines a uvfits-specific subclass of UVData for reading and writing uvfits files.
This class should not be interacted with directly, instead use the read_uvfits
and write_uvfits methods on the UVData class.
Attributes:
uvfits_required_extra: Names of optional UVParameters that are required
for uvfits.
"""
uvfits_required_extra = ['antenna_positions', 'gst0', 'rdate',
'earth_omega', 'dut1', 'timesys']
def _get_parameter_data(self, vis_hdu):
"""
Internal function to read just the random parameters portion of the
uvfits file (referred to as metadata).
Separated from full read so that header, metadata and data can be read independently.
"""
# astropy.io fits reader scales date according to relevant PZER0 (?)
# uvfits standard is to have 2 DATE parameters, both floats:
# DATE (full day) and _DATE (fractional day)
# cotter uvfits files have one DATE that is a double
# using data.par('date') is general -- it will add them together if there are 2
self.time_array = vis_hdu.data.par('date')
if np.finfo(self.time_array[0]).precision < 5:
raise ValueError('JDs in this file are not precise to '
'better than a second.')
if (np.finfo(self.time_array[0]).precision > 5
and np.finfo(self.time_array[0]).precision < 8):
warnings.warn('The JDs in this file have sub-second '
'precision, but not sub-millisecond. '
'Use with caution.')
self.Ntimes = len(np.unique(self.time_array))
self.set_lsts_from_time_array()
# if antenna arrays are present, use them. otherwise use baseline array
if 'ANTENNA1' in vis_hdu.data.parnames and 'ANTENNA2' in vis_hdu.data.parnames:
# Note: uvfits antennas are 1 indexed,
# need to subtract one to get to 0-indexed
self.ant_1_array = np.int32(vis_hdu.data.par('ANTENNA1')) - 1
self.ant_2_array = np.int32(vis_hdu.data.par('ANTENNA2')) - 1
subarray = np.int32(vis_hdu.data.par('SUBARRAY')) - 1
# error on files with multiple subarrays
if len(set(subarray)) > 1:
raise ValueError('This file appears to have multiple subarray '
'values; only files with one subarray are '
'supported.')
else:
# cannot set this to be the baseline array because it uses the
# 256 convention, not our 2048 convention
bl_input_array = np.int64(vis_hdu.data.par('BASELINE'))
# get antenna arrays based on uvfits baseline array
self.ant_1_array, self.ant_2_array = \
self.baseline_to_antnums(bl_input_array)
# check for multi source files
if 'SOURCE' in vis_hdu.data.parnames:
source = vis_hdu.data.par('SOURCE')
if len(set(source)) > 1:
raise ValueError('This file has multiple sources. Only single '
'source observations are supported.')
# get self.baseline_array using our convention
self.baseline_array = \
self.antnums_to_baseline(self.ant_1_array,
self.ant_2_array)
self.Nbls = len(np.unique(self.baseline_array))
# initialize internal variables based on the antenna lists
self.Nants_data = int(
len(np.unique(self.ant_1_array.tolist() + self.ant_2_array.tolist())))
# read baseline vectors in units of seconds, return in meters
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
self.uvw_array = (-1) * (np.array(np.stack((vis_hdu.data.par('UU'),
vis_hdu.data.par('VV'),
vis_hdu.data.par('WW'))))
* const.c.to('m/s').value).T
if 'INTTIM' in vis_hdu.data.parnames:
self.integration_time = float(vis_hdu.data.par('INTTIM')[0])
else:
if self.Ntimes > 1:
self.integration_time = \
float(np.diff(np.sort(list(set(self.time_array))))
[0]) * 86400
else:
raise ValueError('integration time not specified and only '
'one time present')
def _get_data(self, vis_hdu, antenna_nums, antenna_names, ant_str,
ant_pairs_nums, frequencies, freq_chans, times, polarizations,
blt_inds, read_metadata, run_check, check_extra,
run_check_acceptability):
"""
Internal function to read just the visibility and flag data of the uvfits file.
Separated from full read so that header, metadata and data can be read independently.
"""
if self.time_array is None or read_metadata:
# first read in random group parameters
self._get_parameter_data(vis_hdu)
# figure out what data to read in
blt_inds, freq_inds, pol_inds, history_update_string = \
self._select_preprocess(antenna_nums, antenna_names, ant_str, ant_pairs_nums,
frequencies, freq_chans, times, polarizations, blt_inds)
if blt_inds is not None:
blt_frac = len(blt_inds) / float(self.Nblts)
else:
blt_frac = 1
if freq_inds is not None:
freq_frac = len(freq_inds) / float(self.Nfreqs)
else:
freq_frac = 1
if pol_inds is not None:
pol_frac = len(pol_inds) / float(self.Npols)
else:
pol_frac = 1
min_frac = np.min([blt_frac, freq_frac, pol_frac])
if min_frac == 1:
# no select, read in all the data
if vis_hdu.header['NAXIS'] == 7:
raw_data_array = vis_hdu.data.data[:, 0, 0, :, :, :, :]
assert(self.Nspws == raw_data_array.shape[1])
else:
# in many uvfits files the spw axis is left out,
# here we put it back in so the dimensionality stays the same
raw_data_array = vis_hdu.data.data[:, 0, 0, :, :, :]
raw_data_array = raw_data_array[:, np.newaxis, :, :]
else:
# do select operations on everything except data_array, flag_array and nsample_array
self._select_metadata(blt_inds, freq_inds, pol_inds, history_update_string)
# just read in the right portions of the data and flag arrays
if blt_frac == min_frac:
if vis_hdu.header['NAXIS'] == 7:
raw_data_array = vis_hdu.data.data[blt_inds, :, :, :, :, :, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :, :]
assert(self.Nspws == raw_data_array.shape[1])
else:
# in many uvfits files the spw axis is left out,
# here we put it back in so the dimensionality stays the same
raw_data_array = vis_hdu.data.data[blt_inds, :, :, :, :, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :]
raw_data_array = raw_data_array[:, np.newaxis, :, :, :]
if freq_frac < 1:
raw_data_array = raw_data_array[:, :, freq_inds, :, :]
if pol_frac < 1:
raw_data_array = raw_data_array[:, :, :, pol_inds, :]
elif freq_frac == min_frac:
if vis_hdu.header['NAXIS'] == 7:
raw_data_array = vis_hdu.data.data[:, :, :, :, freq_inds, :, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :, :]
assert(self.Nspws == raw_data_array.shape[1])
else:
# in many uvfits files the spw axis is left out,
# here we put it back in so the dimensionality stays the same
raw_data_array = vis_hdu.data.data[:, :, :, freq_inds, :, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :]
raw_data_array = raw_data_array[:, np.newaxis, :, :, :]
if blt_frac < 1:
raw_data_array = raw_data_array[blt_inds, :, :, :, :]
if pol_frac < 1:
raw_data_array = raw_data_array[:, :, :, pol_inds, :]
else:
if vis_hdu.header['NAXIS'] == 7:
raw_data_array = vis_hdu.data.data[:, :, :, :, :, pol_inds, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :, :]
assert(self.Nspws == raw_data_array.shape[1])
else:
# in many uvfits files the spw axis is left out,
# here we put it back in so the dimensionality stays the same
raw_data_array = vis_hdu.data.data[:, :, :, :, pol_inds, :]
raw_data_array = raw_data_array[:, 0, 0, :, :, :]
raw_data_array = raw_data_array[:, np.newaxis, :, :, :]
if blt_frac < 1:
raw_data_array = raw_data_array[blt_inds, :, :, :, :]
if freq_frac < 1:
raw_data_array = raw_data_array[:, :, freq_inds, :, :]
assert(len(raw_data_array.shape) == 5)
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
self.data_array = (raw_data_array[:, :, :, :, 0] - 1j * raw_data_array[:, :, :, :, 1])
self.flag_array = (raw_data_array[:, :, :, :, 2] <= 0)
self.nsample_array = np.abs(raw_data_array[:, :, :, :, 2])
# check if object has all required UVParameters set
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
def read_uvfits(self, filename, antenna_nums=None, antenna_names=None,
ant_str=None, ant_pairs_nums=None, frequencies=None,
freq_chans=None, times=None, polarizations=None, blt_inds=None,
read_data=True, read_metadata=True,
run_check=True, check_extra=True, run_check_acceptability=True):
"""
Read in header, metadata and data from a uvfits file. Supports reading
only selected portions of the data.
Args:
filename: The uvfits file to read from.
antenna_nums: The antennas numbers to include when reading data into
the object (antenna positions and names for the excluded antennas
will be retained). This cannot be provided if antenna_names is
also provided. Ignored if read_data is False.
antenna_names: The antennas names to include when reading data into
the object (antenna positions and names for the excluded antennas
will be retained). This cannot be provided if antenna_nums is
also provided. Ignored if read_data is False.
ant_pairs_nums: A list of antenna number tuples (e.g. [(0,1), (3,2)])
specifying baselines to include when reading data into the object.
Ordering of the numbers within the tuple does not matter.
Ignored if read_data is False.
ant_str: A string containing information about what antenna numbers
and polarizations to include when reading data into the object.
Can be 'auto', 'cross', 'all', or combinations of antenna numbers
and polarizations (e.g. '1', '1_2', '1x_2y').
See tutorial for more examples of valid strings and
the behavior of different forms for ant_str.
If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
be kept for both baselines (1,2) and (2,3) to return a valid
pyuvdata object.
An ant_str cannot be passed in addition to any of the above antenna
args or the polarizations arg.
Ignored if read_data is False.
frequencies: The frequencies to include when reading data into the
object. Ignored if read_data is False.
freq_chans: The frequency channel numbers to include when reading
data into the object. Ignored if read_data is False.
times: The times to include when reading data into the object.
Ignored if read_data is False.
polarizations: The polarizations to include when reading data into
the object. Ignored if read_data is False.
blt_inds: The baseline-time indices to include when reading data into
the object. This is not commonly used. Ignored if read_data is False.
read_data: Read in the visibility and flag data. If set to false,
only the basic header info and metadata (if read_metadata is True)
will be read in. Results in an incompletely defined object
(check will not pass). Default True.
read_metadata: Read in metadata (times, baselines, uvws) as well as
basic header info. Only used if read_data is False
(metadata will be read if data is read). If both read_data and
read_metadata are false, only basic header info is read in. Default True.
run_check: Option to check for the existence and proper shapes of
parameters after reading in the file. Default is True.
Ignored if read_data is False.
check_extra: Option to check optional parameters as well as required
ones. Default is True. Ignored if read_data is False.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
Ignored if read_data is False.
"""
if not read_data:
run_check = False
hdu_list = fits.open(filename, memmap=True)
vis_hdu = hdu_list[0] # assumes the visibilities are in the primary hdu
vis_hdr = vis_hdu.header.copy()
hdunames = uvutils.fits_indexhdus(hdu_list) # find the rest of the tables
# First get everything we can out of the header.
self.set_phased()
# check if we have an spw dimension
if vis_hdr['NAXIS'] == 7:
if vis_hdr['NAXIS5'] > 1:
raise ValueError('Sorry. Files with more than one spectral'
'window (spw) are not yet supported. A '
'great project for the interested student!')
self.Nspws = vis_hdr.pop('NAXIS5')
self.spw_array = np.int32(uvutils.fits_gethduaxis(vis_hdu, 5)) - 1
# the axis number for phase center depends on if the spw exists
self.phase_center_ra_degrees = np.float(vis_hdr.pop('CRVAL6'))
self.phase_center_dec_degrees = np.float(vis_hdr.pop('CRVAL7'))
else:
self.Nspws = 1
self.spw_array = np.array([0])
# the axis number for phase center depends on if the spw exists
self.phase_center_ra_degrees = np.float(vis_hdr.pop('CRVAL5'))
self.phase_center_dec_degrees = np.float(vis_hdr.pop('CRVAL6'))
# get shapes
self.Nfreqs = vis_hdr.pop('NAXIS4')
self.Npols = vis_hdr.pop('NAXIS3')
self.Nblts = vis_hdr.pop('GCOUNT')
self.freq_array = uvutils.fits_gethduaxis(vis_hdu, 4)
self.freq_array.shape = (self.Nspws,) + self.freq_array.shape
self.channel_width = vis_hdr.pop('CDELT4')
self.polarization_array = np.int32(uvutils.fits_gethduaxis(vis_hdu, 3))
# other info -- not required but frequently used
self.object_name = vis_hdr.pop('OBJECT', None)
self.telescope_name = vis_hdr.pop('TELESCOP', None)
self.instrument = vis_hdr.pop('INSTRUME', None)
latitude_degrees = vis_hdr.pop('LAT', None)
longitude_degrees = vis_hdr.pop('LON', None)
altitude = vis_hdr.pop('ALT', None)
self.x_orientation = vis_hdr.pop('XORIENT', None)
self.history = str(vis_hdr.get('HISTORY', ''))
if not uvutils.check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
while 'HISTORY' in vis_hdr.keys():
vis_hdr.remove('HISTORY')
self.vis_units = vis_hdr.pop('BUNIT', 'UNCALIB')
self.phase_center_epoch = vis_hdr.pop('EPOCH', None)
# remove standard FITS header items that are still around
std_fits_substrings = ['SIMPLE', 'BITPIX', 'EXTEND', 'BLOCKED',
'GROUPS', 'PCOUNT', 'BSCALE', 'BZERO', 'NAXIS',
'PTYPE', 'PSCAL', 'PZERO', 'CTYPE', 'CRVAL',
'CRPIX', 'CDELT', 'CROTA', 'CUNIT', 'DATE-OBS']
for key in vis_hdr.keys():
for sub in std_fits_substrings:
if key.find(sub) > -1:
vis_hdr.remove(key)
# find all the remaining header items and keep them as extra_keywords
for key in vis_hdr:
if key == 'COMMENT':
self.extra_keywords[key] = str(vis_hdr.get(key))
elif key != '':
self.extra_keywords[key] = vis_hdr.get(key)
# Next read the antenna table
ant_hdu = hdu_list[hdunames['AIPS AN']]
# stuff in the header
if self.telescope_name is None:
self.telescope_name = ant_hdu.header['ARRNAM']
self.gst0 = ant_hdu.header['GSTIA0']
self.rdate = ant_hdu.header['RDATE']
self.earth_omega = ant_hdu.header['DEGPDY']
self.dut1 = ant_hdu.header['UT1UTC']
if 'TIMESYS' in ant_hdu.header.keys():
self.timesys = ant_hdu.header['TIMESYS']
else:
# CASA misspells this one
self.timesys = ant_hdu.header['TIMSYS']
if 'FRAME' in ant_hdu.header.keys():
xyz_telescope_frame = ant_hdu.header['FRAME']
else:
warnings.warn('Required Antenna frame keyword not set, '
'setting to ????')
xyz_telescope_frame = '????'
# get telescope location and antenna positions.
# VLA incorrectly sets ARRAYX/ARRAYY/ARRAYZ to 0, and puts array center
# in the antenna positions themselves
if (np.isclose(ant_hdu.header['ARRAYX'], 0)
and np.isclose(ant_hdu.header['ARRAYY'], 0)
and np.isclose(ant_hdu.header['ARRAYZ'], 0)):
x_telescope = np.mean(ant_hdu.data['STABXYZ'][:, 0])
y_telescope = np.mean(ant_hdu.data['STABXYZ'][:, 1])
z_telescope = np.mean(ant_hdu.data['STABXYZ'][:, 2])
self.antenna_positions = (ant_hdu.data.field('STABXYZ')
- np.array([x_telescope,
y_telescope,
z_telescope]))
else:
x_telescope = ant_hdu.header['ARRAYX']
y_telescope = ant_hdu.header['ARRAYY']
z_telescope = ant_hdu.header['ARRAYZ']
# AIPS memo #117 says that antenna_positions should be relative to
# the array center, but in a rotated ECEF frame so that the x-axis
# goes through the local meridian.
rot_ecef_positions = ant_hdu.data.field('STABXYZ')
latitude, longitude, altitude = \
uvutils.LatLonAlt_from_XYZ(np.array([x_telescope, y_telescope, z_telescope]))
self.antenna_positions = uvutils.ECEF_from_rotECEF(rot_ecef_positions,
longitude)
if xyz_telescope_frame == 'ITRF':
self.telescope_location = np.array(
[x_telescope, y_telescope, z_telescope])
else:
if latitude_degrees is not None and longitude_degrees is not None and altitude is not None:
self.telescope_location_lat_lon_alt_degrees = (
latitude_degrees, longitude_degrees, altitude)
# stuff in columns
ant_names = ant_hdu.data.field('ANNAME').tolist()
self.antenna_names = []
for name in ant_names:
self.antenna_names.append(name.replace('\x00!', ''))
# subtract one to get to 0-indexed values rather than 1-indexed values
self.antenna_numbers = ant_hdu.data.field('NOSTA') - 1
self.Nants_telescope = len(self.antenna_numbers)
if 'DIAMETER' in ant_hdu.columns.names:
self.antenna_diameters = ant_hdu.data.field('DIAMETER')
try:
self.set_telescope_params()
except ValueError, ve:
warnings.warn(str(ve))
if not read_data and not read_metadata:
# don't read in the data or metadata. This means the object is incomplete,
# but that may not matter for many purposes.
return
# Now read in the random parameter info
self._get_parameter_data(vis_hdu)
if not read_data:
# don't read in the data. This means the object is incomplete,
# but that may not matter for many purposes.
return
# Now read in the data
self._get_data(vis_hdu, antenna_nums, antenna_names, ant_str,
ant_pairs_nums, frequencies, freq_chans, times, polarizations,
blt_inds, False, run_check, check_extra, run_check_acceptability)
def read_uvfits_metadata(self, filename):
"""
Read in metadata (random parameter info) but not data from a uvfits file
(useful for an object that already has the associated header info and
full visibility data isn't needed).
Args:
filename: The uvfits file to read from.
"""
hdu_list = fits.open(filename, memmap=True)
vis_hdu = hdu_list[0] # assumes the visibilities are in the primary hdu
if self.data_array is not None:
raise ValueError('data_array is already defined, cannot read metadata')
self._get_parameter_data(vis_hdu)
del(vis_hdu)
def read_uvfits_data(self, filename, antenna_nums=None, antenna_names=None,
ant_str=None, ant_pairs_nums=None, frequencies=None,
freq_chans=None, times=None, polarizations=None,
blt_inds=None, read_metadata=True, run_check=True,
check_extra=True, run_check_acceptability=True):
"""
Read in data but not header info from a uvfits file
(useful for an object that already has the associated header info).
Args:
filename: The uvfits file to read from.
antenna_nums: The antennas numbers to include when reading data into
the object (antenna positions and names for the excluded antennas
will be retained). This cannot be provided if antenna_names is
also provided.
antenna_names: The antennas names to include when reading data into
the object (antenna positions and names for the excluded antennas
will be retained). This cannot be provided if antenna_nums is
also provided.
ant_pairs_nums: A list of antenna number tuples (e.g. [(0,1), (3,2)])
specifying baselines to include when reading data into the object.
Ordering of the numbers within the tuple does not matter.
ant_str: A string containing information about what antenna numbers
and polarizations to include when reading data into the object.
Can be 'auto', 'cross', 'all', or combinations of antenna numbers
and polarizations (e.g. '1', '1_2', '1x_2y').
See tutorial for more examples of valid strings and
the behavior of different forms for ant_str.
If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
be kept for both baselines (1,2) and (2,3) to return a valid
pyuvdata object.
An ant_str cannot be passed in addition to any of the above antenna
args or the polarizations arg.
frequencies: The frequencies to include when reading data into the
object.
freq_chans: The frequency channel numbers to include when reading
data into the object.
times: The times to include when reading data into the object.
polarizations: The polarizations to include when reading data into
the object.
blt_inds: The baseline-time indices to include when reading data into
the object. This is not commonly used.
read_metadata: Option to read metadata even if it already exists
(to ensure data and metadata match). Default is True.
run_check: Option to check for the existence and proper shapes of
parameters after reading in the file. Default is True.
check_extra: Option to check optional parameters as well as required
ones. Default is True.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
"""
hdu_list = fits.open(filename, memmap=True)
vis_hdu = hdu_list[0] # assumes the visibilities are in the primary hdu
self._get_data(vis_hdu, antenna_nums, antenna_names, ant_str,
ant_pairs_nums, frequencies, freq_chans, times, polarizations,
blt_inds, read_metadata, run_check, check_extra,
run_check_acceptability)
del(vis_hdu)
def write_uvfits(self, filename, spoof_nonessential=False,
force_phase=False, run_check=True, check_extra=True,
run_check_acceptability=True):
"""
Write the data to a uvfits file.
Args:
filename: The uvfits file to write to.
spoof_nonessential: Option to spoof the values of optional
UVParameters that are not set but are required for uvfits files.
Default is False.
force_phase: Option to automatically phase drift scan data to
zenith of the first timestamp. Default is False.
run_check: Option to check for the existence and proper shapes of
parameters before writing the file. Default is True.
check_extra: Option to check optional parameters as well as required
ones. Default is True.
run_check_acceptability: Option to check acceptable range of the values of
parameters before writing the file. Default is True.
"""
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
if self.phase_type == 'phased':
pass
elif self.phase_type == 'drift':
if force_phase:
print('The data are in drift mode and do not have a '
'defined phase center. Phasing to zenith of the first '
'timestamp.')
self.phase_to_time(self.time_array[0])
else:
raise ValueError('The data are in drift mode. '
'Set force_phase to true to phase the data '
'to zenith of the first timestamp before '
'writing a uvfits file.')
else:
raise ValueError('The phasing type of the data is unknown. '
'Set the phase_type to drift or phased to '
'reflect the phasing status of the data')
if self.Nfreqs > 1:
freq_spacing = self.freq_array[0, 1:] - self.freq_array[0, :-1]
if not np.isclose(np.min(freq_spacing), np.max(freq_spacing),
rtol=self._freq_array.tols[0], atol=self._freq_array.tols[1]):
raise ValueError('The frequencies are not evenly spaced (probably '
'because of a select operation). The uvfits format '
'does not support unevenly spaced frequencies.')
if not np.isclose(freq_spacing[0], self.channel_width,
rtol=self._freq_array.tols[0], atol=self._freq_array.tols[1]):
raise ValueError('The frequencies are separated by more than their '
'channel width (probably because of a select operation). '
'The uvfits format does not support frequencies '
'that are spaced by more than their channel width.')
freq_spacing = freq_spacing[0]
else:
freq_spacing = 1.0
if self.Npols > 1:
pol_spacing = np.diff(self.polarization_array)
if np.min(pol_spacing) < np.max(pol_spacing):
raise ValueError('The polarization values are not evenly spaced (probably '
'because of a select operation). The uvfits format '
'does not support unevenly spaced polarizations.')
pol_spacing = pol_spacing[0]
else:
pol_spacing = 1
for p in self.extra():
param = getattr(self, p)
if param.name in self.uvfits_required_extra:
if param.value is None:
if spoof_nonessential:
# spoof extra keywords required for uvfits
if isinstance(param, uvp.AntPositionParameter):
param.apply_spoof(self, 'Nants_telescope')
else:
param.apply_spoof()
setattr(self, p, param)
else:
raise ValueError('Required attribute {attribute} '
'for uvfits not defined. Define or '
'set spoof_nonessential to True to '
'spoof this attribute.'
.format(attribute=p))
# check for unflagged data with nsample = 0. Warn if any found
wh_nsample0 = np.where(self.nsample_array == 0)
if np.any(~self.flag_array[wh_nsample0]):
warnings.warn('Some unflagged data has nsample = 0. Flags and '
'nsamples are combined in uvfits files such that '
'these data will appear to be flagged.')
weights_array = self.nsample_array * \
np.where(self.flag_array, -1, 1)
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
data_array = np.conj(self.data_array[:, np.newaxis,
np.newaxis, :, :, :, np.newaxis])
weights_array = weights_array[:, np.newaxis, np.newaxis, :, :, :,
np.newaxis]
# uvfits_array_data shape will be (Nblts,1,1,[Nspws],Nfreqs,Npols,3)
uvfits_array_data = np.concatenate([data_array.real,
data_array.imag,
weights_array], axis=6)
# FITS uvw direction convention is opposite ours and Miriad's.
# So conjugate the visibilities and flip the uvws:
uvw_array_sec = -1 * self.uvw_array / const.c.to('m/s').value
# jd_midnight = np.floor(self.time_array[0] - 0.5) + 0.5
tzero = np.float32(self.time_array[0])
# uvfits convention is that time_array + relevant PZERO = actual JD
# We are setting PZERO4 = float32(first time of observation)
time_array = np.float32(self.time_array - np.float64(tzero))
int_time_array = (np.zeros_like((time_array), dtype=np.float)
+ self.integration_time)
baselines_use = self.antnums_to_baseline(self.ant_1_array,
self.ant_2_array,
attempt256=True)
# Set up dictionaries for populating hdu
# Note that uvfits antenna arrays are 1-indexed so we add 1
# to our 0-indexed arrays
group_parameter_dict = {'UU ': uvw_array_sec[:, 0],
'VV ': uvw_array_sec[:, 1],
'WW ': uvw_array_sec[:, 2],
'DATE ': time_array,
'BASELINE': baselines_use,
'ANTENNA1': self.ant_1_array + 1,
'ANTENNA2': self.ant_2_array + 1,
'SUBARRAY': np.ones_like(self.ant_1_array),
'INTTIM': int_time_array}
pscal_dict = {'UU ': 1.0, 'VV ': 1.0, 'WW ': 1.0,
'DATE ': 1.0, 'BASELINE': 1.0, 'ANTENNA1': 1.0,
'ANTENNA2': 1.0, 'SUBARRAY': 1.0, 'INTTIM': 1.0}
pzero_dict = {'UU ': 0.0, 'VV ': 0.0, 'WW ': 0.0,
'DATE ': tzero, 'BASELINE': 0.0, 'ANTENNA1': 0.0,
'ANTENNA2': 0.0, 'SUBARRAY': 0.0, 'INTTIM': 0.0}
# list contains arrays of [u,v,w,date,baseline];
# each array has shape (Nblts)
if (np.max(self.ant_1_array) < 255
and np.max(self.ant_2_array) < 255):
# if the number of antennas is less than 256 then include both the
# baseline array and the antenna arrays in the group parameters.
# Otherwise just use the antenna arrays
parnames_use = ['UU ', 'VV ', 'WW ',
'DATE ', 'BASELINE', 'ANTENNA1',
'ANTENNA2', 'SUBARRAY', 'INTTIM']
else:
parnames_use = ['UU ', 'VV ', 'WW ', 'DATE ',
'ANTENNA1', 'ANTENNA2', 'SUBARRAY', 'INTTIM']
group_parameter_list = [group_parameter_dict[parname] for
parname in parnames_use]
hdu = fits.GroupData(uvfits_array_data, parnames=parnames_use,
pardata=group_parameter_list, bitpix=-32)
hdu = fits.GroupsHDU(hdu)
for i, key in enumerate(parnames_use):
hdu.header['PSCAL' + str(i + 1) + ' '] = pscal_dict[key]
hdu.header['PZERO' + str(i + 1) + ' '] = pzero_dict[key]
# ISO string of first time in self.time_array
hdu.header['DATE-OBS'] = Time(self.time_array[0], scale='utc',
format='jd').isot
hdu.header['CTYPE2 '] = 'COMPLEX '
hdu.header['CRVAL2 '] = 1.0
hdu.header['CRPIX2 '] = 1.0
hdu.header['CDELT2 '] = 1.0
# Note: This axis is called STOKES to comply with the AIPS memo 117
# However, this confusing because it is NOT a true Stokes axis,
# it is really the polarization axis.
hdu.header['CTYPE3 '] = 'STOKES '
hdu.header['CRVAL3 '] = self.polarization_array[0]
hdu.header['CRPIX3 '] = 1.0
hdu.header['CDELT3 '] = pol_spacing
hdu.header['CTYPE4 '] = 'FREQ '
hdu.header['CRVAL4 '] = self.freq_array[0, 0]
hdu.header['CRPIX4 '] = 1.0
hdu.header['CDELT4 '] = freq_spacing
hdu.header['CTYPE5 '] = 'IF '
hdu.header['CRVAL5 '] = 1.0
hdu.header['CRPIX5 '] = 1.0
hdu.header['CDELT5 '] = 1.0
hdu.header['CTYPE6 '] = 'RA'
hdu.header['CRVAL6 '] = self.phase_center_ra_degrees
hdu.header['CTYPE7 '] = 'DEC'
hdu.header['CRVAL7 '] = self.phase_center_dec_degrees
hdu.header['BUNIT '] = self.vis_units
hdu.header['BSCALE '] = 1.0
hdu.header['BZERO '] = 0.0
hdu.header['OBJECT '] = self.object_name
hdu.header['TELESCOP'] = self.telescope_name
hdu.header['LAT '] = self.telescope_location_lat_lon_alt_degrees[0]
hdu.header['LON '] = self.telescope_location_lat_lon_alt_degrees[1]
hdu.header['ALT '] = self.telescope_location_lat_lon_alt[2]
hdu.header['INSTRUME'] = self.instrument
hdu.header['EPOCH '] = float(self.phase_center_epoch)
if self.x_orientation is not None:
hdu.header['XORIENT'] = self.x_orientation
for line in self.history.splitlines():
hdu.header.add_history(line)
# end standard keywords; begin user-defined keywords
for key, value in self.extra_keywords.iteritems():
# header keywords have to be 8 characters or less
if len(str(key)) > 8:
warnings.warn('key {key} in extra_keywords is longer than 8 '
'characters. It will be truncated to 8 as required '
'by the uvfits file format.'.format(key=key))
keyword = key[:8].upper()
if isinstance(value, (dict, list, np.ndarray)):
raise TypeError('Extra keyword {keyword} is of {keytype}. '
'Only strings and numbers are '
'supported in uvfits.'.format(keyword=key,
keytype=type(value)))
if keyword == 'COMMENT':
for line in value.splitlines():
hdu.header.add_comment(line)
else:
hdu.header[keyword] = value
# ADD the ANTENNA table
staxof = np.zeros(self.Nants_telescope)
# 0 specifies alt-az, 6 would specify a phased array
mntsta = np.zeros(self.Nants_telescope)
# beware, X can mean just about anything
poltya = np.full((self.Nants_telescope), 'X', dtype=np.object_)
polaa = [90.0] + np.zeros(self.Nants_telescope)
poltyb = np.full((self.Nants_telescope), 'Y', dtype=np.object_)
polab = [0.0] + np.zeros(self.Nants_telescope)
col1 = fits.Column(name='ANNAME', format='8A',
array=self.antenna_names)
# AIPS memo #117 says that antenna_positions should be relative to
# the array center, but in a rotated ECEF frame so that the x-axis
# goes through the local meridian.
longitude = self.telescope_location_lat_lon_alt[1]
rot_ecef_positions = uvutils.rotECEF_from_ECEF(self.antenna_positions,
longitude)
col2 = fits.Column(name='STABXYZ', format='3D',
array=rot_ecef_positions)
# convert to 1-indexed from 0-indexed indicies
col3 = fits.Column(name='NOSTA', format='1J',
array=self.antenna_numbers + 1)
col4 = fits.Column(name='MNTSTA', format='1J', array=mntsta)
col5 = fits.Column(name='STAXOF', format='1E', array=staxof)
col6 = fits.Column(name='POLTYA', format='1A', array=poltya)
col7 = fits.Column(name='POLAA', format='1E', array=polaa)
# col8 = fits.Column(name='POLCALA', format='3E', array=polcala)
col9 = fits.Column(name='POLTYB', format='1A', array=poltyb)
col10 = fits.Column(name='POLAB', format='1E', array=polab)
# col11 = fits.Column(name='POLCALB', format='3E', array=polcalb)
# note ORBPARM is technically required, but we didn't put it in
col_list = [col1, col2, col3, col4, col5, col6, col7, col9, col10]
if self.antenna_diameters is not None:
col12 = fits.Column(name='DIAMETER', format='1E', array=self.antenna_diameters)
col_list.append(col12)
cols = fits.ColDefs(col_list)
ant_hdu = fits.BinTableHDU.from_columns(cols)
ant_hdu.header['EXTNAME'] = 'AIPS AN'
ant_hdu.header['EXTVER'] = 1
# write XYZ coordinates if not already defined
ant_hdu.header['ARRAYX'] = self.telescope_location[0]
ant_hdu.header['ARRAYY'] = self.telescope_location[1]
ant_hdu.header['ARRAYZ'] = self.telescope_location[2]
ant_hdu.header['FRAME'] = 'ITRF'
ant_hdu.header['GSTIA0'] = self.gst0
ant_hdu.header['FREQ'] = self.freq_array[0, 0]
ant_hdu.header['RDATE'] = self.rdate
ant_hdu.header['UT1UTC'] = self.dut1
ant_hdu.header['TIMSYS'] = self.timesys
if self.timesys != 'UTC':
raise ValueError('This file has a time system {tsys}. '
'Only "UTC" time system files are supported'.format(tsys=self.timesys))
ant_hdu.header['ARRNAM'] = self.telescope_name
ant_hdu.header['NO_IF'] = self.Nspws
ant_hdu.header['DEGPDY'] = self.earth_omega
# ant_hdu.header['IATUTC'] = 35.
# set mandatory parameters which are not supported by this object
# (or that we just don't understand)
ant_hdu.header['NUMORB'] = 0
# note: Bart had this set to 3. We've set it 0 after aips 117. -jph
ant_hdu.header['NOPCAL'] = 0
ant_hdu.header['POLTYPE'] = 'X-Y LIN'
# note: we do not support the concept of "frequency setups"
# -- lists of spws given in a SU table.
ant_hdu.header['FREQID'] = -1
# if there are offsets in images, this could be the culprit
ant_hdu.header['POLARX'] = 0.0
ant_hdu.header['POLARY'] = 0.0
ant_hdu.header['DATUTC'] = 0 # ONLY UTC SUPPORTED
# we always output right handed coordinates
ant_hdu.header['XYZHAND'] = 'RIGHT'
# ADD the FQ table
# skipping for now and limiting to a single spw
# write the file
hdulist = fits.HDUList(hdus=[hdu, ant_hdu])
if float(astropy.__version__[0:3]) < 1.3:
hdulist.writeto(filename, clobber=True)
else:
hdulist.writeto(filename, overwrite=True)
