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
uvh5.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 UVH5 files."""
import json
import os
import warnings
import h5py
import numpy as np
from .. import utils as uvutils
from .uvdata import UVData
__all__ = ["UVH5"]
# define HDF5 type for interpreting HERA correlator outputs (integers) as
# complex numbers
_hera_corr_dtype = np.dtype([("r", "<i4"), ("i", "<i4")])
hdf5plugin_present = True
try:
import hdf5plugin # noqa: F401
except ImportError as error:
hdf5plugin_present = False
hdf5plugin_error = error
def _check_uvh5_dtype(dtype):
"""
Check that a specified custom datatype conforms to UVH5 standards.
According to the UVH5 spec, the data type for the data array must be a
compound datatype with an "r" field and an "i" field. Additionally, both
datatypes must be the same (e.g., "<i4", "<r8", etc.).
Parameters
----------
dtype : numpy dtype
A numpy dtype object with an "r" field and an "i" field.
Returns
-------
None
Raises
------
ValueError
This is raised if dtype is not a numpy dtype, if the dtype does not have
an "r" field and an "i" field, or if the "r" field and "i" fields have
different types.
"""
if not isinstance(dtype, np.dtype):
raise ValueError("dtype in a uvh5 file must be a numpy dtype")
if "r" not in dtype.names or "i" not in dtype.names:
raise ValueError(
"dtype must be a compound datatype with an 'r' field and an 'i' field"
)
rkind = dtype["r"].kind
ikind = dtype["i"].kind
if rkind != ikind:
raise ValueError(
"dtype must have the same kind ('i4', 'r8', etc.) for both real "
"and imaginary fields"
)
return
def _read_complex_astype(dset, indices, dtype_out=np.complex64):
"""
Read the given data set of a specified type to floating point complex data.
Parameters
----------
dset : h5py dataset
A reference to an HDF5 dataset on disk.
indices : tuple
The indices to extract. Should be either lists of indices or numpy
slice objects.
dtype_out : str or numpy dtype
The datatype of the output array. One of (complex, np.complex64,
np.complex128). Default is np.complex64 (single-precision real and
imaginary floats).
Returns
-------
output_array : ndarray
The array referenced in the dataset cast to complex values.
Raises
------
ValueError
This is raised if dtype_out is not an acceptable value.
"""
if dtype_out not in (complex, np.complex64, np.complex128):
raise ValueError(
"output datatype must be one of (complex, np.complex64, np.complex128)"
)
dset_shape, indices = uvutils._get_dset_shape(dset, indices)
output_array = np.empty(dset_shape, dtype=dtype_out)
# dset is indexed in native dtype, but is upcast upon assignment
if dtype_out == np.complex64:
compound_dtype = [("r", "f4"), ("i", "f4")]
else:
compound_dtype = [("r", "f8"), ("i", "f8")]
output_array.view(compound_dtype)[:, :] = uvutils._index_dset(dset, indices)[:, :]
return output_array
def _write_complex_astype(data, dset, indices):
"""
Write floating point complex data as a specified type.
Parameters
----------
data : ndarray
The data array to write out. Should be a complex-valued array that
supports the .real and .imag attributes for accessing real and imaginary
components.
dset : h5py dataset
A reference to an HDF5 dataset on disk.
indices : tuple
A 3-tuple representing indices to write data to. Should be either lists
of indices or numpy slice objects. For data arrays with 4 dimensions, the second
dimension (the old spw axis) should not be included because it can only be
length one.
Returns
-------
None
"""
# get datatype from dataset
dtype_out = dset.dtype
if data.dtype == np.complex64:
compound_dtype = [("r", "f4"), ("i", "f4")]
else:
compound_dtype = [("r", "f8"), ("i", "f8")]
# make doubly sure dtype is valid; should be unless user is pathological
_check_uvh5_dtype(dtype_out)
if len(dset.shape) == 3:
# this is the future array shape
dset[indices[0], indices[1], indices[2]] = data.view(compound_dtype).astype(
dtype_out, copy=False
)
else:
dset[indices[0], np.s_[:], indices[1], indices[2]] = data.view(
compound_dtype
).astype(dtype_out, copy=False)
return
def _get_compression(compression):
"""
Get the HDF5 compression and compression options to use.
Parameters
----------
compression : str
HDF5 compression specification or "bitshuffle".
Returns
-------
compression_use : str
HDF5 compression specification
compression_opts : tuple
HDF5 compression options
"""
if compression == "bitshuffle":
if not hdf5plugin_present: # pragma: no cover
raise ImportError(
"The hdf5plugin package is not installed but is required to use "
"bitshuffle compression."
) from hdf5plugin_error
compression_use = 32008
compression_opts = (0, 2)
else:
compression_use = compression
compression_opts = None
return compression_use, compression_opts
class UVH5(UVData):
"""
A class for UVH5 file objects.
This class defines an HDF5-specific subclass of UVData for reading and
writing UVH5 files. This class should not be interacted with directly,
instead use the read_uvh5 and write_uvh5 methods on the UVData class.
"""
def _read_header(
self, header, filename, run_check_acceptability=True, background_lsts=True
):
"""
Read header information from a UVH5 file.
This is an internal function called by the user-space methods.
Properties of the UVData object are updated as the file is processed.
Parameters
----------
header : h5py datagroup
A reference to an h5py data group that contains the header
information. Should be "/Header" for UVH5 files conforming to spec.
run_check_acceptability : bool
Option to check acceptable range of the values of parameters after
reading in the file.
background_lsts : bool
When set to True, the lst_array is calculated in a background thread.
Returns
-------
None
"""
# get telescope information
latitude = header["latitude"][()]
longitude = header["longitude"][()]
altitude = header["altitude"][()]
self.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude)
self.instrument = bytes(header["instrument"][()]).decode("utf8")
self.telescope_name = bytes(header["telescope_name"][()]).decode("utf8")
# set history appropriately
self.history = bytes(header["history"][()]).decode("utf8")
if not uvutils._check_history_version(self.history, self.pyuvdata_version_str):
self.history += self.pyuvdata_version_str
# check for vis_units
if "vis_units" in header:
self.vis_units = bytes(header["vis_units"][()]).decode("utf8")
# Added here because older files allowed for both upper and lowercase
# formats, although since the attribute is case sensitive, we want to
# correct for this here.
if self.vis_units == "UNCALIB":
self.vis_units = "uncalib"
else:
# default to uncalibrated data
self.vis_units = "uncalib"
# check for optional values
if "dut1" in header:
self.dut1 = float(header["dut1"][()])
if "earth_omega" in header:
self.earth_omega = float(header["earth_omega"][()])
if "gst0" in header:
self.gst0 = float(header["gst0"][()])
if "rdate" in header:
self.rdate = bytes(header["rdate"][()]).decode("utf8")
if "timesys" in header:
self.timesys = bytes(header["timesys"][()]).decode("utf8")
if "x_orientation" in header:
self.x_orientation = bytes(header["x_orientation"][()]).decode("utf8")
if "blt_order" in header:
blt_order_str = bytes(header["blt_order"][()]).decode("utf8")
self.blt_order = tuple(blt_order_str.split(", "))
if self.blt_order == ("bda",):
self._blt_order.form = (1,)
if "antenna_diameters" in header:
self.antenna_diameters = header["antenna_diameters"][()]
if "uvplane_reference_time" in header:
self.uvplane_reference_time = int(header["uvplane_reference_time"][()])
if "eq_coeffs" in header:
self.eq_coeffs = header["eq_coeffs"][()]
if "eq_coeffs_convention" in header:
self.eq_coeffs_convention = bytes(
header["eq_coeffs_convention"][()]
).decode("utf8")
# get data shapes
self.Nfreqs = int(header["Nfreqs"][()])
self.Npols = int(header["Npols"][()])
self.Ntimes = int(header["Ntimes"][()])
self.Nblts = int(header["Nblts"][()])
self.Nspws = int(header["Nspws"][()])
# We've added a few new keywords that did not exist before, so check to see if
# any of them are in the header, and if not, mark the data set as being
# "regular" (e.g., not a flexible spectral window setup, single source only).
if "flex_spw" in header:
if bool(header["flex_spw"][()]):
self._set_flex_spw()
if "flex_spw_id_array" in header:
self.flex_spw_id_array = header["flex_spw_id_array"][:]
if "flex_spw_polarization_array" in header:
self.flex_spw_polarization_array = header["flex_spw_polarization_array"][:]
# Here is where we start handing phase center information. If we have a
# multi phase center dataset, we need to get different header items
if "phase_center_catalog" in header:
self.Nphase = int(header["Nphase"][()])
self.phase_center_id_array = header["phase_center_id_array"][:]
# Here is where we collect the other source/phasing info
self.phase_center_catalog = {}
key_list = list(header["phase_center_catalog"].keys())
if isinstance(header["phase_center_catalog"][key_list[0]], h5py.Group):
# This is the new, correct way
for pc, pc_dict in header["phase_center_catalog"].items():
pc_id = int(pc)
self.phase_center_catalog[pc_id] = {}
for key, dset in pc_dict.items():
if issubclass(dset.dtype.type, np.bytes_):
self.phase_center_catalog[pc_id][key] = bytes(
dset[()]
).decode("utf8")
elif dset.shape is None:
self.phase_center_catalog[pc_id][key] = None
else:
self.phase_center_catalog[pc_id][key] = dset[()]
else:
# This is the old way this was written
for key in header["phase_center_catalog"].keys():
pc_dict = json.loads(
bytes(header["phase_center_catalog"][key][()]).decode("utf8")
)
pc_dict["cat_name"] = key
pc_id = pc_dict.pop("cat_id")
self.phase_center_catalog[pc_id] = pc_dict
else:
# check for older phasing information
phase_type = bytes(header["phase_type"][()]).decode("utf8")
if "object_name" in header:
cat_name = bytes(header["object_name"][()]).decode("utf8")
else:
cat_name = None
if "phase_center_ra" in header:
phase_center_ra = float(header["phase_center_ra"][()])
if "phase_center_dec" in header:
phase_center_dec = float(header["phase_center_dec"][()])
if "phase_center_frame" in header:
phase_center_frame = bytes(header["phase_center_frame"][()]).decode(
"utf8"
)
if "phase_center_epoch" in header:
phase_center_epoch = float(header["phase_center_epoch"][()])
if phase_type not in ["phased", "drift"]:
warnings.warn(
"Unknown phase types are no longer supported, marking this "
"object as unprojected (unphased) by default."
)
phase_type = "drift"
if phase_type == "drift":
if cat_name is None:
cat_name = "unprojected"
cat_id = self._add_phase_center(cat_name, cat_type="unprojected")
else:
cat_id = self._add_phase_center(
cat_name,
cat_type="sidereal",
cat_lon=phase_center_ra,
cat_lat=phase_center_dec,
cat_frame=phase_center_frame,
cat_epoch=phase_center_epoch,
)
self.phase_center_id_array = np.zeros(self.Nblts, dtype=int) + cat_id
if "phase_center_app_ra" in header and "phase_center_app_dec" in header:
self.phase_center_app_ra = header["phase_center_app_ra"][:]
self.phase_center_app_dec = header["phase_center_app_dec"][:]
if "phase_center_frame_pa" in header:
self.phase_center_frame_pa = header["phase_center_frame_pa"][:]
# get antenna arrays
# cast to native python int type
self.Nants_data = int(header["Nants_data"][()])
self.Nants_telescope = int(header["Nants_telescope"][()])
self.ant_1_array = header["ant_1_array"][:]
self.ant_2_array = header["ant_2_array"][:]
self.antenna_names = [
bytes(n).decode("utf8") for n in header["antenna_names"][:]
]
self.antenna_numbers = header["antenna_numbers"][:]
self.antenna_positions = header["antenna_positions"][:]
# set telescope params
try:
self.set_telescope_params()
except ValueError as ve:
warnings.warn(str(ve))
# get baseline array
self.baseline_array = self.antnums_to_baseline(
self.ant_1_array, self.ant_2_array
)
self.Nbls = len(np.unique(self.baseline_array))
# get uvw array
self.uvw_array = header["uvw_array"][:, :]
# get time information
self.time_array = header["time_array"][:]
integration_time = header["integration_time"]
self.integration_time = integration_time[:]
proc = None
if "lst_array" in header:
self.lst_array = header["lst_array"][:]
# check that lst_array in file is self-consistent
if run_check_acceptability:
(
latitude,
longitude,
altitude,
) = self.telescope_location_lat_lon_alt_degrees
lst_array = uvutils.get_lst_for_time(
self.time_array, latitude, longitude, altitude
)
if not np.all(
np.isclose(
self.lst_array,
lst_array,
rtol=self._lst_array.tols[0],
atol=self._lst_array.tols[1],
)
):
warnings.warn(
"LST values stored in {file} are not self-consistent "
"with time_array and telescope location. Consider "
"recomputing with utils.get_lst_for_time.".format(file=filename)
)
else:
# compute lst_array from time_array and telescope location
proc = self.set_lsts_from_time_array(background=background_lsts)
# get frequency information
self.freq_array = header["freq_array"][:]
self.spw_array = header["spw_array"][:]
if self.freq_array.ndim == 1:
arr_shape_msg = (
"The size of arrays in this file are not internally consistent, "
"which should not happen. Please file an issue in our GitHub issue "
"log so that we can fix it."
)
assert (
np.asarray(header["channel_width"]).size == self.freq_array.size
), arr_shape_msg
self._set_future_array_shapes()
# Pull in the channel_width parameter as either an array or as a single float,
# depending on whether or not the data is stored with a flexible spw.
if self.flex_spw or np.asarray(header["channel_width"]).ndim == 1:
self.channel_width = header["channel_width"][:]
else:
self.channel_width = float(header["channel_width"][()])
# get polarization information
self.polarization_array = header["polarization_array"][:]
# get extra_keywords
if "extra_keywords" in header:
self.extra_keywords = {}
for key in header["extra_keywords"].keys():
if header["extra_keywords"][key].dtype.type in (np.string_, np.object_):
self.extra_keywords[key] = bytes(
header["extra_keywords"][key][()]
).decode("utf8")
else:
# special handling for empty datasets == python `None` type
if header["extra_keywords"][key].shape is None:
self.extra_keywords[key] = None
else:
self.extra_keywords[key] = header["extra_keywords"][key][()]
if proc is not None:
# if lsts are in the background wait for them to return
proc.join()
return
def _get_data(
self,
dgrp,
antenna_nums,
antenna_names,
ant_str,
bls,
frequencies,
freq_chans,
times,
time_range,
lsts,
lst_range,
polarizations,
blt_inds,
phase_center_ids,
data_array_dtype,
keep_all_metadata,
multidim_index,
run_check,
check_extra,
run_check_acceptability,
strict_uvw_antpos_check,
fix_old_proj,
fix_use_ant_pos,
check_autos,
fix_autos,
):
"""
Read the data-size arrays (data, flags, nsamples) from a file.
This is an internal function to read just the visibility, flag, and
nsample data of the UVH5 file. This is separated from full read so that
header/metadata and data can be read independently. See the
documentation of `read_uvh5` for a full description of most of the
descriptions of parameters. Below we only include a description of args
unique to this function.
Parameters
----------
dgrp : h5py datagroup
The HDF5 datagroup containing the datasets. Should be "/Data" for
UVH5 files conforming to spec.
Returns
-------
None
Raises
------
ValueError
This is raised if the data array read from the file is not a complex
datatype (np.complex64 or np.complex128).
"""
# check for bitshuffle data; bitshuffle filter number is 32008
# TODO should we check for any other filters?
if "32008" in dgrp["visdata"]._filters:
if not hdf5plugin_present: # pragma: no cover
raise ImportError(
"hdf5plugin is not installed but is required to read this dataset"
) from hdf5plugin_error
# 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,
bls,
frequencies,
freq_chans,
times,
time_range,
lsts,
lst_range,
polarizations,
blt_inds,
phase_center_ids,
)
# figure out which axis is the most selective
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])
arr_shape_msg = (
"The size of arrays in this file are not internally consistent, "
"which should not happen. Please file an issue in our GitHub issue "
"log so that we can fix it."
)
if dgrp["visdata"].ndim == 3:
assert self.freq_array.ndim == 1, arr_shape_msg
assert self.channel_width.size == self.freq_array.size, arr_shape_msg
self._set_future_array_shapes()
# get the fundamental datatype of the visdata; if integers, we need to
# cast to floats
visdata_dtype = dgrp["visdata"].dtype
if visdata_dtype not in ("complex64", "complex128"):
_check_uvh5_dtype(visdata_dtype)
if data_array_dtype not in (np.complex64, np.complex128):
raise ValueError(
"data_array_dtype must be np.complex64 or np.complex128"
)
custom_dtype = True
else:
custom_dtype = False
if min_frac == 1:
# no select, read in all the data
inds = (np.s_[:], np.s_[:], np.s_[:])
if custom_dtype:
self.data_array = _read_complex_astype(
dgrp["visdata"], inds, data_array_dtype
)
else:
self.data_array = uvutils._index_dset(dgrp["visdata"], inds)
self.flag_array = uvutils._index_dset(dgrp["flags"], inds)
self.nsample_array = uvutils._index_dset(dgrp["nsamples"], inds)
else:
# do select operations on everything except data_array, flag_array
# and nsample_array
self._select_by_index(
blt_inds, freq_inds, pol_inds, history_update_string, keep_all_metadata
)
# determine which axes can be sliced, rather than fancy indexed
# max_nslice_frac of 0.1 yields slice speedup over fancy index for HERA data
# See pyuvdata PR #805
if blt_inds is not None:
blt_slices, blt_sliceable = uvutils._convert_to_slices(
blt_inds, max_nslice_frac=0.1
)
else:
blt_inds, blt_slices = np.s_[:], np.s_[:]
blt_sliceable = True
if freq_inds is not None:
freq_slices, freq_sliceable = uvutils._convert_to_slices(
freq_inds, max_nslice_frac=0.1
)
else:
freq_inds, freq_slices = np.s_[:], np.s_[:]
freq_sliceable = True
if pol_inds is not None:
pol_slices, pol_sliceable = uvutils._convert_to_slices(
pol_inds, max_nslice_frac=0.5
)
else:
pol_inds, pol_slices = np.s_[:], np.s_[:]
pol_sliceable = True
# open references to datasets
visdata_dset = dgrp["visdata"]
flags_dset = dgrp["flags"]
nsamples_dset = dgrp["nsamples"]
# check that multidim_index is appropriate
if multidim_index:
# if more than one dim is not sliceable, then not appropriate
if sum([blt_sliceable, freq_sliceable, pol_sliceable]) < 2:
multidim_index = False
# just read in the right portions of the data and flag arrays
if blt_frac == min_frac:
# construct inds list given simultaneous sliceability
inds = [blt_inds, np.s_[:], np.s_[:]]
if blt_sliceable:
inds[0] = blt_slices
if multidim_index:
if freq_sliceable:
inds[1] = freq_slices
else:
inds[1] = freq_inds
if multidim_index:
if pol_sliceable:
inds[2] = pol_slices
else:
inds[2] = pol_inds
inds = tuple(inds)
# index datasets
if custom_dtype:
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = uvutils._index_dset(visdata_dset, inds)
flags = uvutils._index_dset(flags_dset, inds)
nsamples = uvutils._index_dset(nsamples_dset, inds)
# down select on other dimensions if necessary
# use indices not slices here: generally not the bottleneck
if not multidim_index and freq_frac < 1:
if self.future_array_shapes:
visdata = visdata[:, freq_inds, :]
flags = flags[:, freq_inds, :]
nsamples = nsamples[:, freq_inds, :]
else:
visdata = visdata[:, :, freq_inds, :]
flags = flags[:, :, freq_inds, :]
nsamples = nsamples[:, :, freq_inds, :]
if not multidim_index and pol_frac < 1:
if self.future_array_shapes:
visdata = visdata[:, :, pol_inds]
flags = flags[:, :, pol_inds]
nsamples = nsamples[:, :, pol_inds]
else:
visdata = visdata[:, :, :, pol_inds]
flags = flags[:, :, :, pol_inds]
nsamples = nsamples[:, :, :, pol_inds]
elif freq_frac == min_frac:
# construct inds list given simultaneous sliceability
inds = [np.s_[:], freq_inds, np.s_[:]]
if freq_sliceable:
inds[1] = freq_slices
if multidim_index:
if blt_sliceable:
inds[0] = blt_slices
else:
inds[0] = blt_inds
if multidim_index:
if pol_sliceable:
inds[2] = pol_slices
else:
inds[2] = pol_inds
inds = tuple(inds)
# index datasets
if custom_dtype:
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = uvutils._index_dset(visdata_dset, inds)
flags = uvutils._index_dset(flags_dset, inds)
nsamples = uvutils._index_dset(nsamples_dset, inds)
# down select on other dimensions if necessary
# use indices not slices here: generally not the bottleneck
if not multidim_index and blt_frac < 1:
visdata = visdata[blt_inds]
flags = flags[blt_inds]
nsamples = nsamples[blt_inds]
if not multidim_index and pol_frac < 1:
if self.future_array_shapes:
visdata = visdata[:, :, pol_inds]
flags = flags[:, :, pol_inds]
nsamples = nsamples[:, :, pol_inds]
else:
visdata = visdata[:, :, :, pol_inds]
flags = flags[:, :, :, pol_inds]
nsamples = nsamples[:, :, :, pol_inds]
else:
# construct inds list given simultaneous sliceability
inds = [np.s_[:], np.s_[:], pol_inds]
if pol_sliceable:
inds[2] = pol_slices
if multidim_index:
if blt_sliceable:
inds[0] = blt_slices
else:
inds[0] = blt_inds
if multidim_index:
if freq_sliceable:
inds[1] = freq_slices
else:
inds[1] = freq_inds
inds = tuple(inds)
# index datasets
if custom_dtype:
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = uvutils._index_dset(visdata_dset, inds)
flags = uvutils._index_dset(flags_dset, inds)
nsamples = uvutils._index_dset(nsamples_dset, inds)
# down select on other dimensions if necessary
# use indices not slices here: generally not the bottleneck
if not multidim_index and blt_frac < 1:
visdata = visdata[blt_inds]
flags = flags[blt_inds]
nsamples = nsamples[blt_inds]
if not multidim_index and freq_frac < 1:
if self.future_array_shapes:
visdata = visdata[:, freq_inds, :]
flags = flags[:, freq_inds, :]
nsamples = nsamples[:, freq_inds, :]
else:
visdata = visdata[:, :, freq_inds, :]
flags = flags[:, :, freq_inds, :]
nsamples = nsamples[:, :, freq_inds, :]
# save arrays in object
self.data_array = visdata
self.flag_array = flags
self.nsample_array = nsamples
if self.data_array.ndim == 3:
assert self.freq_array.ndim == 1, arr_shape_msg
assert self.channel_width.size == self.freq_array.size, arr_shape_msg
self._set_future_array_shapes()
return
def read_uvh5(
self,
filename,
antenna_nums=None,
antenna_names=None,
ant_str=None,
bls=None,
frequencies=None,
freq_chans=None,
times=None,
time_range=None,
lsts=None,
lst_range=None,
polarizations=None,
blt_inds=None,
phase_center_ids=None,
keep_all_metadata=True,
read_data=True,
data_array_dtype=np.complex128,
multidim_index=False,
remove_flex_pol=True,
background_lsts=True,
run_check=True,
check_extra=True,
run_check_acceptability=True,
strict_uvw_antpos_check=False,
fix_old_proj=None,
fix_use_ant_pos=True,
check_autos=True,
fix_autos=True,
):
"""
Read in data from a UVH5 file.
Parameters
----------
filename : str
The UVH5 file to read from.
antenna_nums : array_like of int, optional
The antennas numbers to include when reading data into the object
(antenna positions and names for the removed antennas will be retained
unless `keep_all_metadata` is False). This cannot be provided if
`antenna_names` is also provided. Ignored if read_data is False.
antenna_names : array_like of str, optional
The antennas names to include when reading data into the object
(antenna positions and names for the removed antennas will be retained
unless `keep_all_metadata` is False). This cannot be provided if
`antenna_nums` is also provided. Ignored if read_data is False.
bls : list of tuple, optional
A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
to include when reading data into the object. For length-2 tuples,
the ordering of the numbers within the tuple does not matter. For
length-3 tuples, the polarization string is in the order of the two
antennas. If length-3 tuples are provided, `polarizations` must be
None. Ignored if read_data is False.
ant_str : str, optional
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 `antenna_nums`,
`antenna_names`, `bls` args or the `polarizations` parameters,
if it is a ValueError will be raised. Ignored if read_data is False.
frequencies : array_like of float, optional
The frequencies to include when reading data into the object, each
value passed here should exist in the freq_array. Ignored if
read_data is False.
freq_chans : array_like of int, optional
The frequency channel numbers to include when reading data into the
object. Ignored if read_data is False.
times : array_like of float, optional
The times to include when reading data into the object, each value
passed here should exist in the time_array. Cannot be used with
`time_range`.
time_range : array_like of float, optional
The time range in Julian Date to keep in the object, must be
length 2. Some of the times in the object should fall between the
first and last elements. Cannot be used with `times`.
lsts : array_like of float, optional
The local sidereal times (LSTs) to keep in the object, each value
passed here should exist in the lst_array. Cannot be used with
`times`, `time_range`, or `lst_range`.
lst_range : array_like of float, optional
The local sidereal time (LST) range in radians to keep in the
object, must be of length 2. Some of the LSTs in the object should
fall between the first and last elements. If the second value is
smaller than the first, the LSTs are treated as having phase-wrapped
around LST = 2*pi = 0, and the LSTs kept on the object will run from
the larger value, through 0, and end at the smaller value.
polarizations : array_like of int, optional
The polarizations numbers to include when reading data into the
object, each value passed here should exist in the polarization_array.
Ignored if read_data is False.
blt_inds : array_like of int, optional
The baseline-time indices to include when reading data into the
object. This is not commonly used. Ignored if read_data is False.
phase_center_ids : array_like of int, optional
Phase center IDs to include when reading data into the object (effectively
a selection on baseline-times).
keep_all_metadata : bool
Option to keep all the metadata associated with antennas, even those
that do not have data associated with them after the select option.
read_data : bool
Read in the visibility, nsample and flag data. If set to False, only
the metadata will be read in. Setting read_data to False results in
a metadata only object.
data_array_dtype : numpy dtype
Datatype to store the output data_array as. Must be either
np.complex64 (single-precision real and imaginary) or np.complex128 (double-
precision real and imaginary). Only used if the datatype of the visibility
data on-disk is not 'c8' or 'c16'.
multidim_index : bool
If True, attempt to index the HDF5 dataset
simultaneously along all data axes. Otherwise index one axis at-a-time.
This only works if data selection is sliceable along all but one axis.
If indices are not well-matched to data chunks, this can be slow.
remove_flex_pol : bool
If True and if the file is a flex_pol file, convert back to a standard
UVData 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 after reading in the file (the default is True,
meaning the check will be run). Ignored if read_data is False.
check_extra : bool
Option to check optional parameters as well as required ones (the
default is True, meaning the optional parameters will be checked).
Ignored if read_data is False.
run_check_acceptability : bool
Option to check acceptable range of the values of parameters after
reading in the file (the default is True, meaning the acceptable
range check will be done). Ignored if read_data is False.
strict_uvw_antpos_check : bool
Option to raise an error rather than a warning if the check that
uvws match antenna positions does not pass.
fix_old_proj : bool
Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
method was used prior to writing the data, which had errors of the order of
one part in 1e4 - 1e5. See the phasing memo for more details. Default is
to apply the correction if the attributes `phase_center_app_ra` and
`phase_center_app_dec` are missing (as they were introduced alongside the
new phasing method).
fix_use_ant_pos : bool
If setting `fix_old_proj` to True, use the antenna positions to derive the
correct uvw-coordinates rather than using the baseline vectors. Default is
True.
check_autos : bool
Check whether any auto-correlations have non-zero imaginary values in
data_array (which should not mathematically exist). Default is True.
fix_autos : bool
If auto-correlations with imaginary values are found, fix those values so
that they are real-only in data_array. Default is True.
Returns
-------
None
Raises
------
IOError
If filename doesn't exist.
ValueError
If the data_array_dtype is not a complex dtype.
If incompatible select keywords are set (e.g. `ant_str` with other
antenna selectors, `times` and `time_range`) or select keywords
exclude all data or if keywords are set to the wrong type.
"""
if not os.path.exists(filename):
raise IOError(filename + " not found")
# update filename attribute
basename = os.path.basename(filename)
self.filename = [basename]
self._filename.form = (1,)
# open hdf5 file for reading
with h5py.File(filename, "r") as f:
# extract header information
header = f["/Header"]
self._read_header(
header,
filename,
run_check_acceptability=run_check_acceptability,
background_lsts=background_lsts,
)
if read_data:
# Now read in the data
dgrp = f["/Data"]
self._get_data(
dgrp,
antenna_nums,
antenna_names,
ant_str,
bls,
frequencies,
freq_chans,
times,
time_range,
lsts,
lst_range,
polarizations,
blt_inds,
phase_center_ids,
data_array_dtype,
keep_all_metadata,
multidim_index,
run_check,
check_extra,
run_check_acceptability,
strict_uvw_antpos_check,
fix_old_proj,
fix_use_ant_pos,
check_autos,
fix_autos,
)
# Finally, backfill the apparent coords if they aren't in the original datafile
add_app_coords = (
self.phase_center_app_ra is None
or (self.phase_center_app_dec is None)
or (self.phase_center_frame_pa is None)
)
if add_app_coords:
self._set_app_coords_helper()
# Default behavior for UVH5 is to fix phasing if the problem is
# detected, since the absence of the app coord attributes is the most
# clear indicator of the old phasing algorithm being used. Double-check
# the multi-phase-ctr attribute just to be extra safe.
old_phase_compatible, _ = self._old_phase_attributes_compatible()
if np.any(~self._check_for_cat_type("unprojected")) and old_phase_compatible:
if (fix_old_proj) or (fix_old_proj is None and add_app_coords):
self.fix_phase(use_ant_pos=fix_use_ant_pos)
elif add_app_coords:
warnings.warn(
"This data appears to have been phased-up using the old "
"`phase` method, which is incompatible with the current set of "
"methods. Please run the `fix_phase` method (or set "
"`fix_old_proj=True` when loading the dataset) to address this "
"issue."
)
# check if object has all required UVParameters set
if run_check:
self.check(
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
strict_uvw_antpos_check=strict_uvw_antpos_check,
allow_flip_conj=True,
check_autos=check_autos,
fix_autos=fix_autos,
)
if remove_flex_pol:
self.remove_flex_pol()
# For now, always use current shapes when data is read in, even if the file
# has the future shapes.
if self.future_array_shapes:
self.use_current_array_shapes()
return
def _write_header(self, header):
"""
Write data to the header datagroup of a UVH5 file.
Parameters
----------
header : h5py datagroup
The datagroup to write the header information to. For a UVH5 file
conforming to the spec, it should be "/Header"
Returns
-------
None
"""
# write out UVH5 version information
assert_err_msg = (
"This is a bug, please make an issue in our issue log at "
"https://github.com/RadioAstronomySoftwareGroup/pyuvdata/issues"
)
assert self.future_array_shapes, assert_err_msg
header["version"] = np.string_("1.1")
# write out telescope and source information
header["latitude"] = self.telescope_location_lat_lon_alt_degrees[0]
header["longitude"] = self.telescope_location_lat_lon_alt_degrees[1]
header["altitude"] = self.telescope_location_lat_lon_alt_degrees[2]
header["telescope_name"] = np.string_(self.telescope_name)
header["instrument"] = np.string_(self.instrument)
# write out required UVParameters
header["Nants_data"] = self.Nants_data
header["Nants_telescope"] = self.Nants_telescope
header["Nbls"] = self.Nbls
header["Nblts"] = self.Nblts
header["Nfreqs"] = self.Nfreqs
header["Npols"] = self.Npols
header["Nspws"] = self.Nspws
header["Ntimes"] = self.Ntimes
header["antenna_numbers"] = self.antenna_numbers
header["uvw_array"] = self.uvw_array
header["vis_units"] = np.string_(self.vis_units)
header["channel_width"] = self.channel_width
header["time_array"] = self.time_array
header["freq_array"] = self.freq_array
header["integration_time"] = self.integration_time
header["lst_array"] = self.lst_array
header["polarization_array"] = self.polarization_array
header["spw_array"] = self.spw_array
header["ant_1_array"] = self.ant_1_array
header["ant_2_array"] = self.ant_2_array
header["antenna_positions"] = self.antenna_positions
header["flex_spw"] = self.flex_spw
# handle antenna_names; works for lists or arrays
header["antenna_names"] = np.asarray(self.antenna_names, dtype="bytes")
# write out phasing information
# Write out the catalog, if available
header["phase_center_id_array"] = self.phase_center_id_array
header["Nphase"] = self.Nphase
# this is a dict of dicts. Top level key is the phase_center_id,
# next level keys give details for each phase center.
pc_group = header.create_group("phase_center_catalog")
for pc, pc_dict in self.phase_center_catalog.items():
this_group = pc_group.create_group(str(pc))
for key, value in pc_dict.items():
if isinstance(value, str):
this_group[key] = np.bytes_(value)
elif value is None:
this_group[key] = h5py.Empty("f")
else:
this_group[key] = value
header["phase_center_app_ra"] = self.phase_center_app_ra
header["phase_center_app_dec"] = self.phase_center_app_dec
header["phase_center_frame_pa"] = self.phase_center_frame_pa
# write out optional parameters
if self.dut1 is not None:
header["dut1"] = self.dut1
if self.earth_omega is not None:
header["earth_omega"] = self.earth_omega
if self.gst0 is not None:
header["gst0"] = self.gst0
if self.rdate is not None:
header["rdate"] = np.string_(self.rdate)
if self.timesys is not None:
header["timesys"] = np.string_(self.timesys)
if self.x_orientation is not None:
header["x_orientation"] = np.string_(self.x_orientation)
if self.blt_order is not None:
header["blt_order"] = np.string_(", ".join(self.blt_order))
if self.antenna_diameters is not None:
header["antenna_diameters"] = self.antenna_diameters
if self.uvplane_reference_time is not None:
header["uvplane_reference_time"] = self.uvplane_reference_time
if self.eq_coeffs is not None:
header["eq_coeffs"] = self.eq_coeffs
if self.eq_coeffs_convention is not None:
header["eq_coeffs_convention"] = np.string_(self.eq_coeffs_convention)
if self.flex_spw_id_array is not None:
header["flex_spw_id_array"] = self.flex_spw_id_array
if self.flex_spw_polarization_array is not None:
header["flex_spw_polarization_array"] = self.flex_spw_polarization_array
# write out extra keywords if it exists and has elements
if self.extra_keywords:
extra_keywords = header.create_group("extra_keywords")
for k in self.extra_keywords.keys():
if isinstance(self.extra_keywords[k], str):
extra_keywords[k] = np.string_(self.extra_keywords[k])
elif self.extra_keywords[k] is None:
# save as empty/null dataset
extra_keywords[k] = h5py.Empty("f")
else:
extra_keywords[k] = self.extra_keywords[k]
# write out history
header["history"] = np.string_(self.history)
return
def write_uvh5(
self,
filename,
clobber=False,
chunks=True,
data_compression=None,
flags_compression="lzf",
nsample_compression="lzf",
data_write_dtype=None,
add_to_history=None,
run_check=True,
check_extra=True,
run_check_acceptability=True,
strict_uvw_antpos_check=False,
check_autos=True,
fix_autos=False,
):
"""
Write an in-memory UVData object to a UVH5 file.
Parameters
----------
filename : str
The UVH5 file to write to.
clobber : bool
Option to overwrite the file if it already exists.
chunks : tuple or bool
h5py.create_dataset chunks keyword. Tuple for chunk shape,
True for auto-chunking, None for no chunking. Default is True.
data_compression : str
HDF5 filter to apply when writing the data_array. Default is None
(no filter/compression). In addition to the normal HDF5 filter values, the
user may specify "bitshuffle" which will set the compression to `32008` for
bitshuffle and will set the `compression_opts` to `(0, 2)` to allow
bitshuffle to automatically determine the block size and to use the LZF
filter after bitshuffle. Using `bitshuffle` requires having the
`hdf5plugin` package installed. Dataset must be chunked to use compression.
flags_compression : str
HDF5 filter to apply when writing the flags_array. Default is the
LZF filter. Dataset must be chunked.
nsample_compression : str
HDF5 filter to apply when writing the nsample_array. Default is the
LZF filter. Dataset must be chunked.
data_write_dtype : numpy dtype
The datatype of output visibility data. If 'None', then the same
datatype as data_array will be used. The user may specify 'c8' for
single-precision floats or 'c16' for double-presicion. Otherwise, a
numpy dtype object must be specified with an 'r' field and an 'i'
field for real and imaginary parts, respectively. See uvh5.py for
an example of defining such a datatype.
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.
strict_uvw_antpos_check : bool
Option to raise an error rather than a warning if the check that
uvws match antenna positions does not pass.
check_autos : bool
Check whether any auto-correlations have non-zero imaginary values in
data_array (which should not mathematically exist). Default is True.
fix_autos : bool
If auto-correlations with imaginary values are found, fix those values so
that they are real-only in data_array. Default is False.
Returns
-------
None
Raises
------
IOError
If the file located at `filename` already exists and clobber=False,
an IOError is raised.
Notes
-----
The HDF5 library allows for the application of "filters" when writing
data, which can provide moderate to significant levels of compression
for the datasets in question. Testing has shown that for some typical
cases of UVData objects (empty/sparse flag_array objects, and/or uniform
nsample_arrays), the built-in LZF filter provides significant
compression for minimal computational overhead.
Note that for typical HERA data files written after mid-2020, the
bitshuffle filter was applied to the data_array. Because of the lack of
portability, it is not included as an option here; in the future, it may
be added. Note that as long as bitshuffle is installed on the system in
a way that h5py can find it, no action needs to be taken to _read_ a
data_array encoded with bitshuffle (or an error will be raised).
"""
if run_check:
self.check(
check_extra=check_extra,
run_check_acceptability=run_check_acceptability,
strict_uvw_antpos_check=strict_uvw_antpos_check,
check_autos=check_autos,
fix_autos=fix_autos,
)
if os.path.exists(filename):
if clobber:
print("File exists; clobbering")
else:
raise IOError("File exists; skipping")
revert_fas = False
if not self.future_array_shapes:
# We force using future array shapes here to always write version 1.* files.
# We capture the current state so that it can be reverted later if needed.
revert_fas = True
self.use_future_array_shapes()
data_compression, data_compression_opts = _get_compression(data_compression)
# open file for writing
with h5py.File(filename, "w") as f:
# write header
header = f.create_group("Header")
self._write_header(header)
# write out data, flags, and nsample arrays
dgrp = f.create_group("Data")
if data_write_dtype is None:
if self.data_array.dtype == "complex64":
data_write_dtype = "c8"
else:
data_write_dtype = "c16"
if data_write_dtype not in ("c8", "c16"):
_check_uvh5_dtype(data_write_dtype)
visdata = dgrp.create_dataset(
"visdata",
self.data_array.shape,
chunks=chunks,
compression=data_compression,
compression_opts=data_compression_opts,
dtype=data_write_dtype,
)
indices = (np.s_[:], np.s_[:], np.s_[:])
_write_complex_astype(self.data_array, visdata, indices)
else:
visdata = dgrp.create_dataset(
"visdata",
chunks=chunks,
data=self.data_array,
compression=data_compression,
compression_opts=data_compression_opts,
dtype=data_write_dtype,
)
dgrp.create_dataset(
"flags",
chunks=chunks,
data=self.flag_array,
compression=flags_compression,
)
dgrp.create_dataset(
"nsamples",
chunks=chunks,
data=self.nsample_array.astype(np.float32),
compression=nsample_compression,
)
if revert_fas:
self.use_current_array_shapes()
return
def initialize_uvh5_file(
self,
filename,
clobber=False,
chunks=True,
data_compression=None,
flags_compression="lzf",
nsample_compression="lzf",
data_write_dtype=None,
):
"""
Initialize a UVH5 file on disk to be written to in parts.
Parameters
----------
filename : str
The UVH5 file to write to.
clobber : bool
Option to overwrite the file if it already exists.
chunks : tuple or bool
h5py.create_dataset chunks keyword. Tuple for chunk shape,
True for auto-chunking, None for no chunking. Default is True.
data_compression : str
HDF5 filter to apply when writing the data_array. Default is None
(no filter/compression). In addition to the normal HDF5 filter values, the
user may specify "bitshuffle" which will set the compression to `32008` for
bitshuffle and will set the `compression_opts` to `(0, 2)` to allow
bitshuffle to automatically determine the block size and to use the LZF
filter after bitshuffle. Using `bitshuffle` requires having the
`hdf5plugin` package installed. Dataset must be chunked to use compression.
flags_compression : str
HDF5 filter to apply when writing the flags_array. Default is the
LZF filter. Dataset must be chunked.
nsample_compression : str
HDF5 filter to apply when writing the nsample_array. Default is the
LZF filter. Dataset must be chunked.
data_write_dtype : str or numpy dtype
The datatype of output visibility data. If 'None', then double-
precision floats will be used. The user may specify 'c8' for
single-precision floats or 'c16' for double-presicion. Otherwise, a
numpy dtype object must be specified with an 'r' field and an 'i'
field for real and imaginary parts, respectively. See uvh5.py for
an example of defining such a datatype.
Returns
-------
None
Raises
------
IOError
If the file located at `filename` already exists and clobber=False,
an IOError is raised.
Notes
-----
When partially writing out data, this function should be called first to
initialize the file on disk. The data is then actually written by
calling the write_uvh5_part method, with the same filename as the one
specified in this function. See the tutorial for a worked example.
The HDF5 library allows for the application of "filters" when writing
data, which can provide moderate to significant levels of compression
for the datasets in question. Testing has shown that for some typical
cases of UVData objects (empty/sparse flag_array objects, and/or uniform
nsample_arrays), the built-in LZF filter provides significant
compression for minimal computational overhead.
Note that for typical HERA data files written after mid-2018, the
bitshuffle filter was applied to the data_array. Because of the lack of
portability, it is not included as an option here; in the future, it may
be added. Note that as long as bitshuffle is installed on the system in
a way that h5py can find it, no action needs to be taken to _read_ a
data_array encoded with bitshuffle (or an error will be raised).
"""
if os.path.exists(filename):
if clobber:
print("File exists; clobbering")
else:
raise IOError("File exists; skipping")
data_compression, data_compression_opts = _get_compression(data_compression)
revert_fas = False
if not self.future_array_shapes:
# We force using future array shapes here to always write version 1.* files.
# We capture the current state so that it can be reverted later if needed.
revert_fas = True
self.use_future_array_shapes()
# write header and empty arrays to file
with h5py.File(filename, "w") as f:
# write header
header = f.create_group("Header")
self._write_header(header)
# initialize the data groups on disk
data_size = (self.Nblts, self.Nfreqs, self.Npols)
dgrp = f.create_group("Data")
if data_write_dtype is None:
# we don't know what kind of data we'll get--default to double-precision
data_write_dtype = "c16"
if data_write_dtype not in ("c8", "c16"):
# make sure the data type is correct
_check_uvh5_dtype(data_write_dtype)
dgrp.create_dataset(
"visdata",
data_size,
chunks=chunks,
dtype=data_write_dtype,
compression=data_compression,
compression_opts=data_compression_opts,
)
dgrp.create_dataset(
"flags",
data_size,
chunks=chunks,
dtype="b1",
compression=flags_compression,
)
dgrp.create_dataset(
"nsamples",
data_size,
chunks=chunks,
dtype="f4",
compression=nsample_compression,
)
if revert_fas:
self.use_current_array_shapes()
return
def _check_header(
self, filename, run_check_acceptability=True, background_lsts=True
):
"""
Check that the metadata in a file header matches the object's metadata.
Parameters
----------
header : h5py datagroup
A reference to an h5py data group that contains the header
information. For UVH5 files conforming to the spec, this should be
"/Header".
run_check_acceptability : bool
Option to check acceptable range of the values of parameters after
reading in the file.
background_lsts : bool
When set to True, the lst_array is calculated in a background thread.
Returns
-------
None
Notes
-----
This function creates a new UVData object an reads in the header
information saved on disk to compare with the object in memory. Note
that this adds some small memory overhead, but this amount is typically
much smaller than the size of the data.
"""
uvd_file = UVH5()
with h5py.File(filename, "r") as f:
header = f["/Header"]
uvd_file._read_header(
header,
filename,
run_check_acceptability=run_check_acceptability,
background_lsts=background_lsts,
)
# temporarily remove data, flag, and nsample arrays, so we only check metadata
if self.data_array is not None:
data_array = self.data_array
self.data_array = None
replace_data = True
else:
replace_data = False
if self.flag_array is not None:
flag_array = self.flag_array
self.flag_array = None
replace_flags = True
else:
replace_flags = False
if self.nsample_array is not None:
nsample_array = self.nsample_array
self.nsample_array = None
replace_nsamples = True
else:
replace_nsamples = False
# also ignore filename attribute
uvd_file.filename = self.filename
uvd_file._filename.form = self._filename.form
if self != uvd_file:
raise AssertionError(
"The object metadata in memory and metadata on disk are different"
)
else:
# clean up after ourselves
if replace_data:
self.data_array = data_array
if replace_flags:
self.flag_array = flag_array
if replace_nsamples:
self.nsample_array = nsample_array
del uvd_file
return
def write_uvh5_part(
self,
filename,
data_array,
flag_array,
nsample_array,
check_header=True,
antenna_nums=None,
antenna_names=None,
ant_str=None,
bls=None,
frequencies=None,
freq_chans=None,
times=None,
time_range=None,
lsts=None,
lst_range=None,
polarizations=None,
blt_inds=None,
phase_center_ids=None,
run_check_acceptability=True,
add_to_history=None,
):
"""
Write out a part of a UVH5 file that has been previously initialized.
Parameters
----------
filename : str
The file on disk to write data to. It must already exist,
and is assumed to have been initialized with initialize_uvh5_file.
data_array : array of float
The data to write to disk. A check is done to ensure that
the dimensions of the data passed in conform to the ones specified by
the "selection" arguments.
flag_array : array of bool
The flags array to write to disk. A check is done to ensure
that the dimensions of the data passed in conform to the ones specified
by the "selection" arguments.
nsample_array : array of float
The nsample array to write to disk. A check is done to ensure
that the dimensions fo the data passed in conform to the ones specified
by the "selection" arguments.
check_header : bool
Option to check that the metadata present in the header
on disk matches that in the object.
run_check_acceptability : bool
If check_header, additional option to check
acceptable range of the values of parameters after reading in the file.
antenna_nums : array_like of int, optional
The antennas numbers to include when writing 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 : array_like of str, optional
The antennas names to include when writing 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.
bls : list of tuples, optional
A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
to write to the file. For length-2 tuples, the ordering of the numbers
within the tuple does not matter. For length-3 tuples, the polarization
string is in the order of the two antennas. If length-3 tuples are provided,
the polarizations argument below must be None.
ant_str : str, optional
A string containing information about what antenna numbers
and polarizations to include when writing 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 written for both baselines (1, 2) and (2, 3) to reflect a valid
pyuvdata object.
An ant_str cannot be passed in addition to any of the above antenna
args or the polarizations arg.
frequencies : array_like of float, optional
The frequencies to include when writing data to the file.
freq_chans : array_like of int, optional
The frequency channel numbers to include when writing data to the file.
times : array_like of float, optional
The times in Julian Day to include when writing data to the file.
time_range : array_like of float, optional
The time range in Julian Date to include when writing data to the
file, must be length 2. Some of the times in the object should fall
between the first and last elements. Cannot be used with `times`.
lsts : array_like of float, optional
The local sidereal times (LSTs) to keep in the object, each value
passed here should exist in the lst_array. Cannot be used with
`times`, `time_range`, or `lst_range`.
lst_range : array_like of float, optional
The local sidereal time (LST) range in radians to keep in the
object, must be of length 2. Some of the LSTs in the object should
fall between the first and last elements. If the second value is
smaller than the first, the LSTs are treated as having phase-wrapped
around LST = 2*pi = 0, and the LSTs kept on the object will run from
the larger value, through 0, and end at the smaller value.
polarizations : array_like of int, optional
The polarizations to include when writing data to the file.
blt_inds : array_like of int, optional
The baseline-time indices to include when writing data to the file.
This is not commonly used.
phase_center_ids : array_like of int, optional
Phase center IDs to include when writing data into the file (effectively
a selection on baseline-times).
add_to_history : str
String to append to history before write out. Default is no appending.
Returns
-------
None
Raises
------
AssertionError
An AsserionError is raised if: (1) the location specified by
`filename` does not exist; (2) the data_array, flag_array, and
nsample_array do not all have the same shape; (3) the shape of the
data arrays do not correspond to the sizes specified by the
properties to write out.
Notes
-----
When partially writing out data, this function should be called after
calling initialize_uvh5_file. The same filename is passed in, with an
optional check to ensure that the object's metadata in-memory matches
the header on-disk. See the tutorial for a worked example.
"""
# check that the file already exists
if not os.path.exists(filename):
raise AssertionError(
"{0} does not exists; please first initialize it with "
"initialize_uvh5_file".format(filename)
)
revert_fas = False
if not self.future_array_shapes:
# We force using future array shapes here to always write version 1.* files.
# We capture the current state so that it can be reverted later if needed.
revert_fas = True
self.use_future_array_shapes()
if check_header:
self._check_header(
filename, run_check_acceptability=run_check_acceptability
)
# figure out which "full file" indices to write data to
blt_inds, freq_inds, pol_inds, _ = self._select_preprocess(
antenna_nums,
antenna_names,
ant_str,
bls,
frequencies,
freq_chans,
times,
time_range,
lsts,
lst_range,
polarizations,
blt_inds,
phase_center_ids,
)
# make sure that the dimensions of the data to write are correct
if data_array.shape != flag_array.shape:
raise AssertionError("data_array and flag_array must have the same shape")
if data_array.shape != nsample_array.shape:
raise AssertionError(
"data_array and nsample_array must have the same shape"
)
# check what part of each dimension to grab
# we can use numpy slice objects to index the h5py indices
if blt_inds is not None:
Nblts = len(blt_inds)
# test if blts are regularly spaced
if len(set(np.ediff1d(blt_inds))) <= 1:
blt_reg_spaced = True
blt_start = blt_inds[0]
blt_end = blt_inds[-1] + 1
if len(blt_inds) == 1:
d_blt = 1
else:
d_blt = blt_inds[1] - blt_inds[0]
blt_inds = np.s_[blt_start:blt_end:d_blt]
else:
blt_reg_spaced = False
else:
Nblts = self.Nblts
blt_reg_spaced = True
blt_inds = np.s_[:]
if freq_inds is not None:
Nfreqs = len(freq_inds)
# test if frequencies are regularly spaced
if len(set(np.ediff1d(freq_inds))) <= 1:
freq_reg_spaced = True
freq_start = freq_inds[0]
freq_end = freq_inds[-1] + 1
if len(freq_inds) == 1:
d_freq = 1
else:
d_freq = freq_inds[1] - freq_inds[0]
freq_inds = np.s_[freq_start:freq_end:d_freq]
else:
freq_reg_spaced = False
else:
Nfreqs = self.Nfreqs
freq_reg_spaced = True
freq_inds = np.s_[:]
if pol_inds is not None:
Npols = len(pol_inds)
# test if pols are regularly spaced
if len(set(np.ediff1d(pol_inds))) <= 1:
pol_reg_spaced = True
pol_start = pol_inds[0]
pol_end = pol_inds[-1] + 1
if len(pol_inds) == 1:
d_pol = 1
else:
d_pol = pol_inds[1] - pol_inds[0]
pol_inds = np.s_[pol_start:pol_end:d_pol]
else:
pol_reg_spaced = False
else:
Npols = self.Npols
pol_reg_spaced = True
pol_inds = np.s_[:]
# check for proper size of input arrays
proper_shape = (Nblts, Nfreqs, Npols)
if data_array.shape != proper_shape:
if revert_fas and data_array.shape == (Nblts, 1, Nfreqs, Npols):
data_array = data_array[:, 0, :, :]
flag_array = flag_array[:, 0, :, :]
nsample_array = nsample_array[:, 0, :, :]
else:
raise AssertionError(
"data_array has shape {0}; was expecting {1}".format(
data_array.shape, proper_shape
)
)
# actually write the data
with h5py.File(filename, "r+") as f:
dgrp = f["/Data"]
visdata_dset = dgrp["visdata"]
flags_dset = dgrp["flags"]
nsamples_dset = dgrp["nsamples"]
visdata_dtype = visdata_dset.dtype
if visdata_dtype not in ("complex64", "complex128"):
custom_dtype = True
else:
custom_dtype = False
# check if we can do fancy indexing
# as long as at least 2 out of 3 axes can be written as slices,
# we can be fancy
n_reg_spaced = np.count_nonzero(
[blt_reg_spaced, freq_reg_spaced, pol_reg_spaced]
)
if n_reg_spaced >= 2:
if custom_dtype:
indices = (blt_inds, freq_inds, pol_inds)
_write_complex_astype(data_array, visdata_dset, indices)
else:
visdata_dset[blt_inds, freq_inds, pol_inds] = data_array
flags_dset[blt_inds, freq_inds, pol_inds] = flag_array
nsamples_dset[blt_inds, freq_inds, pol_inds] = nsample_array
elif n_reg_spaced == 1:
# figure out which axis is regularly spaced
if blt_reg_spaced:
for ifreq, freq_idx in enumerate(freq_inds):
for ipol, pol_idx in enumerate(pol_inds):
if custom_dtype:
indices = (blt_inds, freq_idx, pol_idx)
_write_complex_astype(
data_array[:, ifreq, ipol], visdata_dset, indices
)
else:
visdata_dset[blt_inds, freq_idx, pol_idx] = data_array[
:, ifreq, ipol
]
flags_dset[blt_inds, freq_idx, pol_idx] = flag_array[
:, ifreq, ipol
]
nsamples_dset[blt_inds, freq_idx, pol_idx] = nsample_array[
:, ifreq, ipol
]
elif freq_reg_spaced:
for iblt, blt_idx in enumerate(blt_inds):
for ipol, pol_idx in enumerate(pol_inds):
if custom_dtype:
indices = (blt_idx, freq_inds, pol_idx)
_write_complex_astype(
data_array[iblt, :, ipol], visdata_dset, indices
)
else:
visdata_dset[blt_idx, freq_inds, pol_idx] = data_array[
iblt, :, ipol
]
flags_dset[blt_idx, freq_inds, pol_idx] = flag_array[
iblt, :, ipol
]
nsamples_dset[blt_idx, freq_inds, pol_idx] = nsample_array[
iblt, :, ipol
]
else: # pol_reg_spaced
for iblt, blt_idx in enumerate(blt_inds):
for ifreq, freq_idx in enumerate(freq_inds):
if custom_dtype:
indices = (blt_idx, freq_idx, pol_inds)
_write_complex_astype(
data_array[iblt, ifreq, :], visdata_dset, indices
)
else:
visdata_dset[blt_idx, freq_idx, pol_inds] = data_array[
iblt, ifreq, :
]
flags_dset[blt_idx, freq_idx, pol_inds] = flag_array[
iblt, ifreq, :
]
nsamples_dset[blt_idx, freq_idx, pol_inds] = nsample_array[
iblt, ifreq, :
]
else:
# all axes irregularly spaced
# perform a triple loop -- probably very slow!
for iblt, blt_idx in enumerate(blt_inds):
for ifreq, freq_idx in enumerate(freq_inds):
for ipol, pol_idx in enumerate(pol_inds):
if custom_dtype:
indices = (blt_idx, freq_idx, pol_idx)
_write_complex_astype(
data_array[iblt, ifreq, ipol], visdata_dset, indices
)
else:
visdata_dset[blt_idx, freq_idx, pol_idx] = data_array[
iblt, ifreq, ipol
]
flags_dset[blt_idx, freq_idx, pol_idx] = flag_array[
iblt, ifreq, ipol
]
nsamples_dset[blt_idx, freq_idx, pol_idx] = nsample_array[
iblt, ifreq, ipol
]
# append to history if desired
if add_to_history is not None:
history = np.string_(self.history) + np.string_(add_to_history)
if "history" in f["Header"]:
# erase dataset first b/c it has fixed-length string datatype
del f["Header"]["history"]
f["Header"]["history"] = np.string_(history)
if revert_fas:
self.use_current_array_shapes()
return
Computing file changes ...
