def make_calfits(fname,
data_array,
freq_array,
time_array,
jones_array,
ants,
flag_array=None,
channel_width=0.0,
gain_convention='multiply',
history='',
telescope_name='HERA',
x_orientation='east',
integration_time=10.0,
freq_range=None,
clobber=False):
"""
make a calfits file from data_array etc. objects
fname : str, filename
data_array : ndarray, shape=(Nants, Nfreqs, Ntimes, Npols)
freq_array : ndarray, shape=Nfreqs
time_array : ndarray, shape=Ntimes
jones_array : ndarray, shape=Npols
ants : ndarray, shape=Nants
"""
# specify ant params
Nants_data = len(ants)
Nants_telescope = len(ants)
ant_array = np.array(ants, np.int)
antenna_names = np.array(ants, np.str)
antenna_numbers = np.array(ants, np.int)
# frequency params
Nfreqs = len(freq_array)
if freq_range is None:
freq_range = np.array([freq_array[0], freq_array[-1]])
freq_array = freq_array.reshape(1, -1)
# pol params
Njones = len(jones_array)
# time params
Ntimes = len(time_array)
time_range = np.array([time_array[0], time_array[-1]])
# spw params
Nspws = 1
spw_array = np.array([0])
data_array = data_array[:, np.newaxis, :, :, :]
if flag_array is not None:
flag_array = flag_array[:, np.newaxis, :, :, :].astype(np.bool)
# data params
if data_array.shape[2] > 1:
gain_array = data_array
delay_array = None
cal_type = 'gain'
else:
gain_array = None
delay_array = data_array
cal_type = 'delay'
if flag_array is None:
flag_array = np.array(np.zeros_like(data_array), np.bool)
quality_array = np.zeros_like(data_array, np.float64)
# make blank uvc
uvc = UVCal()
params = [
'Nants_data', 'Nants_telescope', 'ant_array', 'antenna_names',
'antenna_numbers', 'Nfreqs', 'freq_array', 'Njones', 'Ntimes',
'time_range', 'Nspws', 'spw_array', 'data_array', 'gain_array',
'delay_array', 'jones_array', 'time_array', 'freq_array', 'cal_type',
'flag_array', 'quality_array', 'channel_width', 'gain_convention',
'history', 'telescope_name', 'x_orientation', 'integration_time',
'freq_range'
]
for p in params:
uvc.__setattr__(p, locals()[p])
uvc.write_calfits(fname, clobber=clobber)
def write_cal(fname,
gains,
freqs,
times,
flags=None,
quality=None,
total_qual=None,
write_file=True,
return_uvc=True,
outdir='./',
overwrite=False,
gain_convention='divide',
history=' ',
x_orientation="east",
telescope_name='HERA',
cal_style='redundant',
**kwargs):
'''Format gain solution dictionary into pyuvdata.UVCal and write to file
Arguments:
fname : type=str, output file basename
gains : type=dictionary, holds complex gain solutions. keys are antenna + pol
tuple pairs, e.g. (2, 'x'), and keys are 2D complex ndarrays with time
along [0] axis and freq along [1] axis.
freqs : type=ndarray, holds unique frequencies channels in Hz
times : type=ndarray, holds unique times of integration centers in Julian Date
flags : type=dictionary, holds boolean flags (True if flagged) for gains.
Must match shape of gains.
quality : type=dictionary, holds "quality" of calibration solution. Must match
shape of gains. See pyuvdata.UVCal doc for more details.
total_qual : type=dictionary, holds total_quality_array. Key(s) are polarization
string(s) and values are 2D (Ntimes, Nfreqs) ndarrays.
write_file : type=bool, if True, write UVCal to calfits file
return_uvc : type=bool, if True, return UVCal object
outdir : type=str, output file directory
overwrite : type=bool, if True overwrite output files
gain_convention : type=str, gain solutions formatted such that they 'multiply' into data
to get model, or 'divide' into data to get model
options=['multiply', 'divide']
history : type=str, history string for UVCal object.
x_orientation : type=str, orientation of X dipole, options=['east', 'north']
telescope_name : type=str, name of telescope
cal_style : type=str, style of calibration solutions, options=['redundant', 'sky']. If
cal_style == sky, additional params are required. See pyuvdata.UVCal doc.
kwargs : additional atrributes to set in pyuvdata.UVCal
Returns:
if return_uvc: returns UVCal object
else: returns None
'''
# helpful dictionaries for antenna polarization of gains
str2pol = {'x': -5, 'y': -6}
pol2str = {-5: 'x', -6: 'y'}
# get antenna info
ant_array = np.array(sorted(map(lambda k: k[0], gains.keys())), np.int)
antenna_numbers = copy.copy(ant_array)
antenna_names = np.array(map(lambda a: "ant{}".format(a), antenna_numbers))
Nants_data = len(ant_array)
Nants_telescope = len(antenna_numbers)
# get polarization info
pol_array = np.array(sorted(set(map(lambda k: k[1].lower(),
gains.keys()))))
jones_array = np.array(map(lambda p: str2pol[p], pol_array), np.int)
Njones = len(jones_array)
# get time info
time_array = np.array(times, np.float)
Ntimes = len(time_array)
time_range = np.array([time_array.min(), time_array.max()], np.float)
if len(time_array) > 1:
integration_time = np.median(np.diff(time_array)) * 24. * 3600.
else:
integration_time = 0.0
# get frequency info
freq_array = np.array(freqs, np.float)
Nfreqs = len(freq_array)
Nspws = 1
freq_array = freq_array[None, :]
spw_array = np.arange(Nspws)
channel_width = np.median(np.diff(freq_array))
# form gain, flags and qualities
gain_array = np.empty((Nants_data, Nspws, Nfreqs, Ntimes, Njones),
np.complex)
flag_array = np.empty((Nants_data, Nspws, Nfreqs, Ntimes, Njones), np.bool)
quality_array = np.empty((Nants_data, Nspws, Nfreqs, Ntimes, Njones),
np.float)
total_quality_array = np.empty((Nspws, Nfreqs, Ntimes, Njones), np.float)
for i, p in enumerate(pol_array):
if total_qual is not None:
total_quality_array[0, :, :, i] = total_qual[p].T[None, :, :]
for j, a in enumerate(ant_array):
# ensure (a, p) is in gains
if (a, p) in gains:
gain_array[j, :, :, :, i] = gains[(a, p)].T[None, :, :]
if flags is not None:
flag_array[j, :, :, :, i] = flags[(a, p)].T[None, :, :]
else:
flag_array[j, :, :, :, i] = np.zeros(
(Nspws, Nfreqs, Ntimes), np.bool)
if quality is not None:
quality_array[j, :, :, :,
i] = quality[(a, p)].T[None, :, :]
else:
quality_array[j, :, :, :, i] = np.ones(
(Nspws, Nfreqs, Ntimes), np.float)
else:
gain_array[j, :, :, :, i] = np.ones((Nspws, Nfreqs, Ntimes),
np.complex)
flag_array[j, :, :, :, i] = np.ones((Nspws, Nfreqs, Ntimes),
np.bool)
quality_array[j, :, :, :, i] = np.ones((Nspws, Nfreqs, Ntimes),
np.float)
if total_qual is None:
total_quality_array = None
# Check gain_array for values close to zero, if so, set to 1
zero_check = np.isclose(gain_array, 0, rtol=1e-10, atol=1e-10)
gain_array[zero_check] = 1.0 + 0j
flag_array[zero_check] += True
if zero_check.max() is True:
print(
"Some of values in self.gain_array were zero and are flagged and set to 1."
)
# instantiate UVCal
uvc = UVCal()
# enforce 'gain' cal_type
uvc.cal_type = "gain"
# create parameter list
params = [
"Nants_data", "Nants_telescope", "Nfreqs", "Ntimes", "Nspws", "Njones",
"ant_array", "antenna_numbers", "antenna_names", "cal_style",
"history", "channel_width", "flag_array", "gain_array",
"quality_array", "jones_array", "time_array", "spw_array",
"freq_array", "history", "integration_time", "time_range",
"x_orientation", "telescope_name", "gain_convention",
"total_quality_array"
]
# create local parameter dict
local_params = locals()
# overwrite with kwarg parameters
local_params.update(kwargs)
# set parameters
for p in params:
uvc.__setattr__(p, local_params[p])
# run check
uvc.check()
# write to file
if write_file:
# check output
fname = os.path.join(outdir, fname)
if os.path.exists(fname) and overwrite is False:
print("{} exists, not overwriting...".format(fname))
else:
print "saving {}".format(fname)
uvc.write_calfits(fname, clobber=True)
# return object
if return_uvc:
return uvc
