Revision 8b22682704c00bd278c44dae1686f726d261b718 authored by Steven Murray on 09 January 2023, 21:13:32 UTC, committed by Steven Murray on 09 January 2023, 21:13:32 UTC
1 parent e377413
calfits.py
# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License
"""Class for reading and writing calibration FITS files."""
import os
import warnings
import numpy as np
from astropy.io import fits
from .. import utils as uvutils
from .uvcal import UVCal
__all__ = ["CALFITS"]
class CALFITS(UVCal):
"""
Defines a calfits-specific class for reading and writing calfits files.
This class should not be interacted with directly, instead use the read_calfits
and write_calfits methods on the UVCal class.
"""
def write_calfits(
self,
filename,
run_check=True,
check_extra=True,
run_check_acceptability=True,
clobber=False,
):
"""
Write the data to a calfits file.
Parameters
----------
filename : str
The calfits file to write to.
run_check : bool
Option to check for the existence and proper shapes of
parameters before writing the file.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
parameters before writing the file.
clobber : bool
Option to overwrite the filename if the file already exists.
"""
if self.Nspws > 1:
raise ValueError(
"The calfits format does not support multiple spectral windows"
)
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
# calfits allows for frequency spacing to not equal channel widths as long as
# the frequencies are evenly spaced, so only raise spacing error
spacing_error, chanwidth_error = self._check_freq_spacing(raise_errors=False)
if spacing_error:
raise ValueError(
"Frequencies are not evenly spaced or have differing "
"values of channel widths. The calfits format does not support "
"unevenly spaced frequencies or varying channel widths."
)
# we've already run the check_freq_spacing, so spacings and channel widths are
# the same to our tolerances
if self.future_array_shapes:
if self.freq_array is not None:
ref_freq = self.freq_array[0]
else:
ref_freq = self.freq_range[0, 0]
else:
ref_freq = self.freq_array[0, 0]
if self.Nfreqs > 1:
if chanwidth_error:
# this means that the frequencies are evenly spaced but do not
# match our channel widths. Use some rounding to get a good delta.
if self.future_array_shapes:
freq_arr_use = self.freq_array
else:
freq_arr_use = self.freq_array[0, :]
rounded_spacing = np.around(
np.diff(freq_arr_use),
int(np.ceil(np.log10(self._freq_array.tols[1]) * -1)),
)
delta_freq_array = rounded_spacing[0]
else:
if self.future_array_shapes or self.flex_spw:
delta_freq_array = np.median(self.channel_width)
else:
delta_freq_array = self.channel_width
else:
if self.future_array_shapes or self.flex_spw:
if self.channel_width is not None:
delta_freq_array = self.channel_width[0]
else:
# default to 1 Hz for wide-band cals with Nfreqs=1 and no channel
# width info
delta_freq_array = 1.0
else:
delta_freq_array = self.channel_width
if self.Ntimes > 1:
if not uvutils._test_array_constant_spacing(self._time_array):
raise ValueError(
"The times are not evenly spaced (probably "
"because of a select operation). The calfits format "
"does not support unevenly spaced times."
)
time_spacing = np.diff(self.time_array)
if self.future_array_shapes:
if not uvutils._test_array_constant(self._integration_time):
raise ValueError(
"The integration times are variable. The calfits format "
"does not support variable integration times."
)
median_int_time = np.median(self.integration_time)
if np.isclose(time_spacing[0], median_int_time / (24.0 * 60.0**2)):
time_spacing = median_int_time / (24.0 * 60.0**2)
else:
rounded_spacing = np.around(
time_spacing,
int(
np.ceil(
np.log10(self._time_array.tols[1] / self.Ntimes) * -1
)
+ 1
),
)
time_spacing = rounded_spacing[0]
else:
if np.isclose(
time_spacing[0], self.integration_time / (24.0 * 60.0**2)
):
time_spacing = self.integration_time / (24.0 * 60.0**2)
else:
rounded_spacing = np.around(
time_spacing,
int(
np.ceil(
np.log10(self._time_array.tols[1] / self.Ntimes) * -1
)
+ 1
),
)
time_spacing = rounded_spacing[0]
else:
if self.future_array_shapes:
time_spacing = self.integration_time[0] / (24.0 * 60.0**2)
else:
time_spacing = self.integration_time / (24.0 * 60.0**2)
if self.Njones > 1:
if not uvutils._test_array_constant_spacing(self._jones_array):
raise ValueError(
"The jones values are not evenly spaced."
"The calibration fits file format does not"
" support unevenly spaced polarizations."
)
jones_spacing = self.jones_array[1] - self.jones_array[0]
else:
jones_spacing = -1
prihdr = fits.Header()
if self.total_quality_array is not None:
totqualhdr = fits.Header()
totqualhdr["EXTNAME"] = "TOTQLTY"
if self.cal_type != "gain":
sechdr = fits.Header()
sechdr["EXTNAME"] = "FLAGS"
# Conforming to fits format
prihdr["SIMPLE"] = True
prihdr["TELESCOP"] = self.telescope_name
if self.telescope_location is not None:
prihdr["ARRAYX"] = self.telescope_location[0]
prihdr["ARRAYY"] = self.telescope_location[1]
prihdr["ARRAYZ"] = self.telescope_location[2]
prihdr["LAT"] = self.telescope_location_lat_lon_alt_degrees[0]
prihdr["LON"] = self.telescope_location_lat_lon_alt_degrees[1]
prihdr["ALT"] = self.telescope_location_lat_lon_alt[2]
prihdr["GNCONVEN"] = self.gain_convention
prihdr["CALTYPE"] = self.cal_type
prihdr["CALSTYLE"] = self.cal_style
if self.sky_field is not None:
prihdr["FIELD"] = self.sky_field
if self.sky_catalog is not None:
prihdr["CATALOG"] = self.sky_catalog
if self.ref_antenna_name is not None:
prihdr["REFANT"] = self.ref_antenna_name
if self.Nsources is not None:
prihdr["NSOURCES"] = self.Nsources
if self.baseline_range is not None:
prihdr["BL_RANGE"] = (
"[" + ", ".join([str(b) for b in self.baseline_range]) + "]"
)
if self.diffuse_model is not None:
prihdr["DIFFUSE"] = self.diffuse_model
if self.gain_scale is not None:
prihdr["GNSCALE"] = self.gain_scale
if self.future_array_shapes:
if self.Ntimes > 1:
prihdr["INTTIME"] = median_int_time
else:
prihdr["INTTIME"] = self.integration_time[0]
else:
prihdr["INTTIME"] = self.integration_time
if self.future_array_shapes or self.flex_spw:
if self.Nfreqs > 1:
prihdr["CHWIDTH"] = np.median(self.channel_width)
else:
prihdr["CHWIDTH"] = delta_freq_array
else:
prihdr["CHWIDTH"] = self.channel_width
prihdr["XORIENT"] = self.x_orientation
if self.future_array_shapes and self.freq_range is not None:
freq_range_use = self.freq_range[0, :]
else:
freq_range_use = self.freq_range
if self.cal_type == "delay":
prihdr["FRQRANGE"] = ",".join(map(str, freq_range_use))
elif self.freq_range is not None:
prihdr["FRQRANGE"] = ",".join(map(str, freq_range_use))
if self.time_range is not None:
prihdr["TMERANGE"] = ",".join(map(str, self.time_range))
if self.observer:
prihdr["OBSERVER"] = self.observer
if self.git_origin_cal:
prihdr["ORIGCAL"] = self.git_origin_cal
if self.git_hash_cal:
prihdr["HASHCAL"] = self.git_hash_cal
if self.cal_type == "unknown":
raise ValueError(
"unknown calibration type. Do not know how to " "store parameters"
)
# Define primary header values
# Arrays have (column-major) dimensions of
# [Nimages, Njones, Ntimes, Nfreqs, 1, Nantennas]
# For a "delay"-type calibration, Nfreqs is a shallow axis
# set the axis for number of arrays
prihdr["CTYPE1"] = ("Narrays", "Number of image arrays.")
prihdr["CUNIT1"] = "Integer"
prihdr["CDELT1"] = 1
prihdr["CRPIX1"] = 1
prihdr["CRVAL1"] = 1
# Jones axis
prihdr["CTYPE2"] = ("JONES", "Jones matrix array")
prihdr["CUNIT2"] = ("Integer", "representative integer for polarization.")
prihdr["CRPIX2"] = 1
prihdr["CRVAL2"] = self.jones_array[0] # always start with first jones.
prihdr["CDELT2"] = jones_spacing
# time axis
prihdr["CTYPE3"] = ("TIME", "Time axis.")
prihdr["CUNIT3"] = ("JD", "Time in julian date format")
prihdr["CRPIX3"] = 1
prihdr["CRVAL3"] = self.time_array[0]
prihdr["CDELT3"] = time_spacing
# freq axis
prihdr["CTYPE4"] = ("FREQS", "Frequency.")
prihdr["CUNIT4"] = "Hz"
prihdr["CRPIX4"] = 1
prihdr["CRVAL4"] = ref_freq
prihdr["CDELT4"] = delta_freq_array
# spw axis: number of spectral windows
prihdr["CTYPE5"] = ("IF", "Spectral window number.")
prihdr["CUNIT5"] = "Integer"
prihdr["CRPIX5"] = 1
prihdr["CRVAL5"] = 1
prihdr["CDELT5"] = 1
# antenna axis
prihdr["CTYPE6"] = ("ANTAXIS", "See ANTARR in ANTENNA extension for values.")
prihdr["CUNIT6"] = "Integer"
prihdr["CRPIX6"] = 1
prihdr["CRVAL6"] = 1
prihdr["CDELT6"] = -1
# end standard keywords; begin user-defined keywords
for key, value in self.extra_keywords.items():
# 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 calfits 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 calfits.".format(keyword=key, keytype=type(value))
)
if keyword == "COMMENT":
for line in value.splitlines():
prihdr.add_comment(line)
else:
prihdr[keyword] = value
for line in self.history.splitlines():
prihdr.add_history(line)
# define data section based on calibration type
if self.cal_type == "gain":
calfits_data_shape = (
self.Nants_data,
1,
self.Nfreqs,
self.Ntimes,
self.Njones,
1,
)
if self.input_flag_array is not None:
pridata = np.concatenate(
[
np.reshape(self.gain_array.real, calfits_data_shape),
np.reshape(self.gain_array.imag, calfits_data_shape),
np.reshape(self.flag_array, calfits_data_shape),
np.reshape(self.input_flag_array, calfits_data_shape),
np.reshape(self.quality_array, calfits_data_shape),
],
axis=-1,
)
else:
pridata = np.concatenate(
[
np.reshape(self.gain_array.real, calfits_data_shape),
np.reshape(self.gain_array.imag, calfits_data_shape),
np.reshape(self.flag_array, calfits_data_shape),
np.reshape(self.quality_array, calfits_data_shape),
],
axis=-1,
)
elif self.cal_type == "delay":
calfits_data_shape = (self.Nants_data, 1, 1, self.Ntimes, self.Njones, 1)
pridata = np.concatenate(
[
np.reshape(self.delay_array, calfits_data_shape),
np.reshape(self.quality_array, calfits_data_shape),
],
axis=-1,
)
# Set headers for the second hdu containing the flags. Only in
# cal_type=delay
# Can't put in primary header because frequency axis is shallow there,
# but not here
# Header values are the same as the primary header
sechdr["CTYPE1"] = ("Narrays", "Number of image arrays.")
sechdr["CUNIT1"] = "Integer"
sechdr["CRPIX1"] = 1
sechdr["CRVAL1"] = 1
sechdr["CDELT1"] = 1
sechdr["CTYPE2"] = ("JONES", "Jones matrix array")
sechdr["CUNIT2"] = ("Integer", "representative integer for polarization.")
sechdr["CRPIX2"] = 1
sechdr["CRVAL2"] = self.jones_array[0] # always start with first jones.
sechdr["CDELT2"] = jones_spacing
sechdr["CTYPE3"] = ("TIME", "Time axis.")
sechdr["CUNIT3"] = ("JD", "Time in julian date format")
sechdr["CRPIX3"] = 1
sechdr["CRVAL3"] = self.time_array[0]
sechdr["CDELT3"] = time_spacing
sechdr["CTYPE4"] = ("FREQS", "Valid frequencies to apply delay.")
sechdr["CUNIT4"] = "Hz"
sechdr["CRPIX4"] = 1
sechdr["CRVAL4"] = ref_freq
sechdr["CDELT4"] = delta_freq_array
sechdr["CTYPE5"] = ("IF", "Spectral window number.")
sechdr["CUNIT5"] = "Integer"
sechdr["CRPIX5"] = 1
sechdr["CRVAL5"] = 1
sechdr["CDELT5"] = 1
sechdr["CTYPE6"] = (
"ANTAXIS",
"See ANTARR in ANTENNA extension for values.",
)
# convert from bool to int64; undone on read
calfits_data_shape = (
self.Nants_data,
1,
self.Nfreqs,
self.Ntimes,
self.Njones,
1,
)
if self.future_array_shapes:
# need to broadcast the flags back to the expected shape
flag_array_use = np.repeat(self.flag_array, self.Nfreqs, axis=2)
if self.input_flag_array is not None:
input_flag_array_use = np.repeat(
self.input_flag_array, self.Nfreqs, axis=2
)
else:
flag_array_use = self.flag_array
input_flag_array_use = self.input_flag_array
if self.input_flag_array is not None:
secdata = np.concatenate(
[
np.reshape(flag_array_use.astype(np.int64), calfits_data_shape),
np.reshape(
input_flag_array_use.astype(np.int64), calfits_data_shape
),
],
axis=-1,
)
else:
secdata = np.reshape(
flag_array_use.astype(np.int64), calfits_data_shape
)
if self.total_quality_array is not None:
# Set headers for the hdu containing the total_quality_array
# No antenna axis, so we have [Njones, Ntime, Nfreq, 1]
totqualhdr["CTYPE1"] = ("JONES", "Jones matrix array")
totqualhdr["CUNIT1"] = (
"Integer",
"representative integer for polarization.",
)
totqualhdr["CRPIX1"] = 1
totqualhdr["CRVAL1"] = self.jones_array[0] # always start with first jones.
totqualhdr["CDELT1"] = jones_spacing
totqualhdr["CTYPE2"] = ("TIME", "Time axis.")
totqualhdr["CUNIT2"] = ("JD", "Time in julian date format")
totqualhdr["CRPIX2"] = 1
totqualhdr["CRVAL2"] = self.time_array[0]
totqualhdr["CDELT2"] = time_spacing
totqualhdr["CTYPE3"] = ("FREQS", "Valid frequencies to apply delay.")
totqualhdr["CUNIT3"] = "Hz"
totqualhdr["CRPIX3"] = 1
totqualhdr["CRVAL3"] = ref_freq
totqualhdr["CDELT3"] = delta_freq_array
# spws axis: number of spectral windows
totqualhdr["CTYPE4"] = ("IF", "Spectral window number.")
totqualhdr["CUNIT4"] = "Integer"
totqualhdr["CRPIX4"] = 1
totqualhdr["CRVAL4"] = 1
totqualhdr["CDELT4"] = 1
if self.cal_type == "gain":
calfits_tqa_shape = (1, self.Nfreqs, self.Ntimes, self.Njones)
else:
calfits_tqa_shape = (1, 1, self.Ntimes, self.Njones)
totqualdata = np.reshape(self.total_quality_array, calfits_tqa_shape)
# make HDUs
prihdu = fits.PrimaryHDU(data=pridata, header=prihdr)
# ant HDU
col1 = fits.Column(name="ANTNAME", format="8A", array=self.antenna_names)
col2 = fits.Column(name="ANTINDEX", format="D", array=self.antenna_numbers)
if self.Nants_data == self.Nants_telescope:
col3 = fits.Column(name="ANTARR", format="D", array=self.ant_array)
else:
# ant_array is shorter than the other columns.
# Pad the extra rows with -1s. Need to undo on read.
nants_add = self.Nants_telescope - self.Nants_data
ant_array_use = np.append(
self.ant_array, np.zeros(nants_add, dtype=np.int64) - 1
)
col3 = fits.Column(name="ANTARR", format="D", array=ant_array_use)
if self.antenna_positions is not None:
col4 = fits.Column(name="ANTXYZ", format="3D", array=self.antenna_positions)
cols = fits.ColDefs([col1, col2, col3, col4])
else:
cols = fits.ColDefs([col1, col2, col3])
ant_hdu = fits.BinTableHDU.from_columns(cols)
ant_hdu.header["EXTNAME"] = "ANTENNAS"
hdulist = fits.HDUList([prihdu, ant_hdu])
if self.cal_type != "gain":
sechdu = fits.ImageHDU(data=secdata, header=sechdr)
hdulist.append(sechdu)
if self.total_quality_array is not None:
totqualhdu = fits.ImageHDU(data=totqualdata, header=totqualhdr)
hdulist.append(totqualhdu)
hdulist.writeto(filename, overwrite=clobber)
hdulist.close()
def read_calfits(
self,
filename,
read_data=True,
background_lsts=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
):
"""
Read data from a calfits file.
Parameters
----------
filename : str
The calfits file to read from.
read_data : bool
Read in the gains or delays, quality arrays and flag arrays.
If set to False, only the metadata will be read in. Setting read_data to
False results in a metadata only object.
background_lsts : bool
When set to True, the lst_array is calculated in a background thread.
run_check : bool
Option to check for the existence and proper shapes of
parameters after reading in the file.
check_extra : bool
Option to check optional parameters as well as required ones.
run_check_acceptability : bool
Option to check acceptable range of the values of
parameters after reading in the file.
"""
# update filename attribute
basename = os.path.basename(filename)
self.filename = [basename]
self._filename.form = (1,)
with fits.open(filename) as fname:
hdr = fname[0].header.copy()
hdunames = uvutils._fits_indexhdus(fname)
anthdu = fname[hdunames["ANTENNAS"]]
self.Nants_telescope = anthdu.header["NAXIS2"]
antdata = anthdu.data
self.antenna_names = np.array(list(map(str, antdata["ANTNAME"])))
self.antenna_numbers = np.array(list(map(int, antdata["ANTINDEX"])))
self.ant_array = np.array(list(map(int, antdata["ANTARR"])))
if np.min(self.ant_array) < 0:
# ant_array was shorter than the other columns, so it was
# padded with -1s.
# Remove the padded entries.
self.ant_array = self.ant_array[np.where(self.ant_array >= 0)[0]]
if anthdu.header["TFIELDS"] > 3:
self.antenna_positions = antdata["ANTXYZ"]
self.channel_width = hdr.pop("CHWIDTH")
self.integration_time = hdr.pop("INTTIME")
self.telescope_name = hdr.pop("TELESCOP")
x_telescope = hdr.pop("ARRAYX", None)
y_telescope = hdr.pop("ARRAYY", None)
z_telescope = hdr.pop("ARRAYZ", None)
lat = hdr.pop("LAT", None)
lon = hdr.pop("LON", None)
alt = hdr.pop("ALT", None)
if (
x_telescope is not None
and y_telescope is not None
and z_telescope is not None
):
self.telescope_location = np.array(
[x_telescope, y_telescope, z_telescope]
)
elif lat is not None and lon is not None and alt is not None:
self.telescope_location_lat_lon_alt_degrees = (lat, lon, alt)
if self.telescope_location is None or self.antenna_positions is None:
try:
self.set_telescope_params()
except ValueError as ve:
warnings.warn(str(ve))
self.history = str(hdr.get("HISTORY", ""))
if not uvutils._check_history_version(
self.history, self.pyuvdata_version_str
):
if not self.history.endswith("\n"):
self.history += "\n"
self.history += self.pyuvdata_version_str
time_range = hdr.pop("TMERANGE", None)
if time_range is not None:
self.time_range = list(map(float, time_range.split(",")))
self.gain_convention = hdr.pop("GNCONVEN")
self.gain_scale = hdr.pop("GNSCALE", None)
self.x_orientation = hdr.pop("XORIENT")
self.cal_type = hdr.pop("CALTYPE")
if self.cal_type == "delay":
self.freq_range = list(map(float, hdr.pop("FRQRANGE").split(",")))
else:
if "FRQRANGE" in hdr:
self.freq_range = list(map(float, hdr.pop("FRQRANGE").split(",")))
self.cal_style = hdr.pop("CALSTYLE")
if self.cal_style == "sky":
self._set_sky()
elif self.cal_style == "redundant":
self._set_redundant()
self.sky_field = hdr.pop("FIELD", None)
self.sky_catalog = hdr.pop("CATALOG", None)
self.ref_antenna_name = hdr.pop("REFANT", None)
self.Nsources = hdr.pop("NSOURCES", None)
bl_range_string = hdr.pop("BL_RANGE", None)
if bl_range_string is not None:
self.baseline_range = [
float(b) for b in bl_range_string.strip("[").strip("]").split(",")
]
self.diffuse_model = hdr.pop("DIFFUSE", None)
self.observer = hdr.pop("OBSERVER", None)
self.git_origin_cal = hdr.pop("ORIGCAL", None)
self.git_hash_cal = hdr.pop("HASHCAL", None)
# generate polarization and time array for either cal_type.
self.Njones = hdr.pop("NAXIS2")
self.jones_array = uvutils._fits_gethduaxis(fname[0], 2)
self.Ntimes = hdr.pop("NAXIS3")
self.time_array = uvutils._fits_gethduaxis(fname[0], 3)
if self.telescope_location is not None:
proc = self.set_lsts_from_time_array(background=background_lsts)
else:
proc = None
self.Nspws = hdr.pop("NAXIS5")
assert self.Nspws == 1, (
"This file appears to have multiple spectral windows, which is not "
"supported by the calfits format."
)
# subtract 1 to be zero-indexed
self.spw_array = uvutils._fits_gethduaxis(fname[0], 5) - 1
self.Nants_data = hdr.pop("NAXIS6")
if self.cal_type == "gain":
self._set_gain()
self.Nfreqs = hdr.pop("NAXIS4")
self.freq_array = uvutils._fits_gethduaxis(fname[0], 4)
self.freq_array.shape = (1,) + self.freq_array.shape
if self.cal_type == "delay":
self._set_delay()
sechdu = fname[hdunames["FLAGS"]]
# generate frequency array from flag data unit
# (no freq axis in primary).
self.Nfreqs = sechdu.header["NAXIS4"]
assert self.Nspws == 1, (
"This file appears to have multiple spectral windows, which is not "
"supported by the calfits format."
)
self.freq_array = uvutils._fits_gethduaxis(sechdu, 4)
self.freq_array.shape = (1,) + self.freq_array.shape
spw_array = uvutils._fits_gethduaxis(sechdu, 5) - 1
if not np.allclose(spw_array, self.spw_array):
raise ValueError(
"Spectral window values are different in FLAGS HDU than"
" in primary HDU"
)
time_array = uvutils._fits_gethduaxis(sechdu, 3)
if not np.allclose(
time_array,
self.time_array,
rtol=self._time_array.tols[0],
atol=self._time_array.tols[0],
):
raise ValueError(
"Time values are different in FLAGS HDU than in primary HDU"
)
jones_array = uvutils._fits_gethduaxis(sechdu, 2)
if not np.allclose(
jones_array,
self.jones_array,
rtol=self._jones_array.tols[0],
atol=self._jones_array.tols[0],
):
raise ValueError(
"Jones values are different in FLAGS HDU than in primary HDU"
)
# get data.
if read_data:
data = fname[0].data
if self.cal_type == "gain":
self.gain_array = (
data[:, :, :, :, :, 0] + 1j * data[:, :, :, :, :, 1]
)
self.flag_array = data[:, :, :, :, :, 2].astype("bool")
if hdr.pop("NAXIS1") == 5:
self.input_flag_array = data[:, :, :, :, :, 3].astype("bool")
self.quality_array = data[:, :, :, :, :, 4]
else:
self.quality_array = data[:, :, :, :, :, 3]
if self.cal_type == "delay":
self.delay_array = data[:, :, :, :, :, 0]
self.quality_array = data[:, :, :, :, :, 1]
flag_data = sechdu.data
if sechdu.header["NAXIS1"] == 2:
self.flag_array = flag_data[:, :, :, :, :, 0].astype("bool")
self.input_flag_array = flag_data[:, :, :, :, :, 1].astype(
"bool"
)
else:
self.flag_array = flag_data[:, :, :, :, :, 0].astype("bool")
# get total quality array if present
if "TOTQLTY" in hdunames:
totqualhdu = fname[hdunames["TOTQLTY"]]
self.total_quality_array = totqualhdu.data
spw_array = uvutils._fits_gethduaxis(totqualhdu, 4) - 1
if not np.allclose(spw_array, self.spw_array):
raise ValueError(
"Spectral window values are different in "
"TOTQLTY HDU than in primary HDU. primary HDU "
"has {pspw}, TOTQLTY has {tspw}".format(
pspw=self.spw_array, tspw=spw_array
)
)
if self.cal_type != "delay":
# delay-type files won't have a freq_array
freq_array = uvutils._fits_gethduaxis(totqualhdu, 3)
freq_array.shape = (1,) + freq_array.shape
if not np.allclose(
freq_array,
self.freq_array,
rtol=self._freq_array.tols[0],
atol=self._freq_array.tols[0],
):
raise ValueError(
"Frequency values are different in TOTQLTY HDU than"
" in primary HDU"
)
time_array = uvutils._fits_gethduaxis(totqualhdu, 2)
if not np.allclose(
time_array,
self.time_array,
rtol=self._time_array.tols[0],
atol=self._time_array.tols[0],
):
raise ValueError(
"Time values are different in TOTQLTY HDU than in "
"primary HDU"
)
jones_array = uvutils._fits_gethduaxis(totqualhdu, 1)
if not np.allclose(
jones_array,
self.jones_array,
rtol=self._jones_array.tols[0],
atol=self._jones_array.tols[0],
):
raise ValueError(
"Jones values are different in TOTQLTY HDU than in "
"primary HDU"
)
else:
self.total_quality_array = None
self.extra_keywords = uvutils._get_fits_extra_keywords(hdr)
# wait for LSTs if set in background
if proc is not None:
proc.join()
if run_check:
self.check(
check_extra=check_extra, run_check_acceptability=run_check_acceptability
)
Computing file changes ...
