# -*- 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.
"""
from __future__ import absolute_import, division, print_function
import numpy as np
import os
import warnings
import six
import h5py
from . import UVData
from . import utils as uvutils
# define HDF5 type for interpreting HERA correlator outputs (integers) as complex numbers
_hera_corr_dtype = np.dtype([('r', '<i4'), ('i', '<i4')])
def _check_uvh5_dtype(dtype):
"""
Check that a specified custom datatype conforms to uvh5 standards.
Arguments:
dtype: numpy datatype with an 'r' field and an 'i' field
Returns:
None
"""
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.
Arguments:
dset: reference to an HDF5 dataset on disk.
indices: tuple representing indices to extract. Should be either lists of indices or
numpy slice objects.
dtype_out: 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: array referenced in the dataset cast to complex values
"""
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 = [0, 0, 0, 0]
for i in range(len(dset_shape)):
if indices[i] == np.s_[:]:
dset_shape[i] = dset.shape[i]
else:
dset_shape[i] = len(indices[i])
output_array = np.empty(dset_shape, dtype=dtype_out)
dtype_in = dset.dtype
with dset.astype(dtype_in):
output_array.real = dset['r'][indices]
output_array.imag = dset['i'][indices]
return output_array
def _write_complex_astype(data, dset, indices):
"""
Write floating point complex data as a specified type.
Arguments:
data: 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: reference to an HDF5 dataset on disk.
indices: 4-tuple representing indices to write data to. Should be either lists of
indices or numpy slice objects.
Returns:
None
"""
# get datatype from dataset
dtype_out = dset.dtype
# make doubly sure dtype is valid; should be unless user is pathological
_check_uvh5_dtype(dtype_out)
with dset.astype(dtype_out):
dset[indices[0], indices[1], indices[2], indices[3], 'r'] = data.real
dset[indices[0], indices[1], indices[2], indices[3], 'i'] = data.imag
return
[docs]class UVH5(UVData):
"""
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):
"""
Internal function to read header information from a UVH5 file.
Args:
header: reference to an h5py data group that contains the header information.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
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 = uvutils._bytes_to_str(header['instrument'][()].tostring())
self.telescope_name = uvutils._bytes_to_str(header['telescope_name'][()].tostring())
# get source information
self.object_name = uvutils._bytes_to_str(header['object_name'][()].tostring())
# set history appropriately
self.history = uvutils._bytes_to_str(header['history'][()].tostring())
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 = uvutils._bytes_to_str(header['vis_units'][()].tostring())
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 = uvutils._bytes_to_str(header['rdate'][()].tostring())
if 'timesys' in header:
self.timesys = uvutils._bytes_to_str(header['timesys'][()].tostring())
if 'x_orientation' in header:
self.x_orientation = uvutils._bytes_to_str(header['x_orientation'][()].tostring())
if 'blt_order' in header:
blt_order_str = uvutils._bytes_to_str(header['blt_order'][()].tostring())
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 = uvutils._bytes_to_str(
header['eq_coeffs_convention'][()].tostring())
# check for phasing information
self.phase_type = uvutils._bytes_to_str(header['phase_type'][()].tostring())
if self.phase_type == 'phased':
self.set_phased()
self.phase_center_ra = float(header['phase_center_ra'][()])
self.phase_center_dec = float(header['phase_center_dec'][()])
self.phase_center_epoch = float(header['phase_center_epoch'][()])
if 'phase_center_frame' in header:
self.phase_center_frame = uvutils._bytes_to_str(header['phase_center_frame'][()].tostring())
elif self.phase_type == 'drift':
self.set_drift()
else:
self.set_unknown_phase_type()
# 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 = [uvutils._bytes_to_str(n.tostring()) 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[:]
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
latitude, longitude, altitude = self.telescope_location_lat_lon_alt_degrees
self.lst_array = uvutils.get_lst_for_time(self.time_array, latitude, longitude,
altitude)
# get frequency information
self.freq_array = header['freq_array'][:, :]
self.channel_width = float(header['channel_width'][()])
self.spw_array = header['spw_array'][:]
# get polarization information
self.polarization_array = header['polarization_array'][:]
# 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'][()])
# 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] = uvutils._bytes_to_str(header["extra_keywords"][key][()].tostring())
else:
self.extra_keywords[key] = header["extra_keywords"][key][()]
return
def _get_data(self, dgrp, antenna_nums, antenna_names, ant_str,
bls, frequencies, freq_chans, times, time_range, polarizations,
blt_inds, run_check, check_extra, run_check_acceptability,
data_array_dtype, keep_all_metadata):
"""
Internal function to read just the visibility, flag, and nsample data of the uvh5 file.
Separated from full read so that header/metadata and data can be read independently.
"""
# 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,
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])
# 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
if custom_dtype:
inds = (np.s_[:], np.s_[:], np.s_[:], np.s_[:])
self.data_array = _read_complex_astype(dgrp['visdata'], inds, data_array_dtype)
else:
self.data_array = dgrp['visdata'][:, :, :, :]
self.flag_array = dgrp['flags'][:, :, :, :]
self.nsample_array = dgrp['nsamples'][:, :, :, :]
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,
keep_all_metadata)
# open references to datasets
visdata_dset = dgrp['visdata']
flags_dset = dgrp['flags']
nsamples_dset = dgrp['nsamples']
# just read in the right portions of the data and flag arrays
if blt_frac == min_frac:
if custom_dtype:
inds = (blt_inds, np.s_[:], np.s_[:], np.s_[:])
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = visdata_dset[blt_inds, :, :, :]
flags = flags_dset[blt_inds, :, :, :]
nsamples = nsamples_dset[blt_inds, :, :, :]
assert(self.Nspws == visdata.shape[1])
if freq_frac < 1:
visdata = visdata[:, :, freq_inds, :]
flags = flags[:, :, freq_inds, :]
nsamples = nsamples[:, :, freq_inds, :]
if pol_frac < 1:
visdata = visdata[:, :, :, pol_inds]
flags = flags[:, :, :, pol_inds]
nsamples = nsamples[:, :, :, pol_inds]
elif freq_frac == min_frac:
if custom_dtype:
inds = (np.s_[:], np.s_[:], freq_inds, np.s_[:])
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = visdata_dset[:, :, freq_inds, :]
flags = flags_dset[:, :, freq_inds, :]
nsamples = nsamples_dset[:, :, freq_inds, :]
if blt_frac < 1:
visdata = visdata[blt_inds, :, :, :]
flags = flags[blt_inds, :, :, :]
nsamples = nsamples[blt_inds, :, :, :]
if pol_frac < 1:
visdata = visdata[:, :, :, pol_inds]
flags = flags[:, :, :, pol_inds]
nsamples = nsamples[:, :, :, pol_inds]
else:
if custom_dtype:
inds = (np.s_[:], np.s_[:], np.s_[:], pol_inds)
visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype)
else:
visdata = visdata_dset[:, :, :, pol_inds]
flags = flags_dset[:, :, :, pol_inds]
nsamples = nsamples_dset[:, :, :, pol_inds]
if blt_frac < 1:
visdata = visdata[blt_inds, :, :, :]
flags = flags[blt_inds, :, :, :]
nsamples = nsamples[blt_inds, :, :, :]
if freq_frac < 1:
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
# check if object has all required UVParameters set
if run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
return
[docs] 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, polarizations=None,
blt_inds=None, read_data=True,
run_check=True, check_extra=True, run_check_acceptability=True,
data_array_dtype=np.complex128, keep_all_metadata=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`.
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.
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 and flag data. If set to false, only the
basic header info and metadata will be read in. Setting read_data to
False results in an incompletely defined object (check will not pass).
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'.
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.
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')
# 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)
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
dgrp = f['/Data']
self._get_data(dgrp, antenna_nums, antenna_names, ant_str,
bls, frequencies, freq_chans, times, time_range,
polarizations, blt_inds, run_check,
check_extra, run_check_acceptability,
data_array_dtype, keep_all_metadata)
return
def _write_header(self, header):
"""Internal function to write uvh5 header information.
"""
# 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)
header['object_name'] = np.string_(self.object_name)
# 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
# handle antenna_names
if six.PY2:
n_names = len(self.antenna_names)
max_len_names = np.amax([len(n) for n in self.antenna_names])
dtype = "S{:d}".format(max_len_names)
header.create_dataset('antenna_names', (n_names,), dtype=dtype, data=self.antenna_names)
else:
header['antenna_names'] = np.string_(self.antenna_names, keepdims=True)
# write out phasing information
header['phase_type'] = np.string_(self.phase_type)
if self.phase_center_ra is not None:
header['phase_center_ra'] = self.phase_center_ra
if self.phase_center_dec is not None:
header['phase_center_dec'] = self.phase_center_dec
if self.phase_center_epoch is not None:
header['phase_center_epoch'] = self.phase_center_epoch
if self.phase_center_frame is not None:
header['phase_center_frame'] = np.string_(self.phase_center_frame)
# 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)
# 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])
else:
extra_keywords[k] = self.extra_keywords[k]
# write out history
header['history'] = np.string_(self.history)
return
[docs] def write_uvh5(self, filename, run_check=True, check_extra=True,
run_check_acceptability=True, clobber=False,
data_compression=None, flags_compression="lzf", nsample_compression="lzf",
data_write_dtype=None, add_to_history=None):
"""
Write an in-memory UVData object to a UVH5 file.
Args:
filename: The UVH5 file to write to.
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.
clobber: Option to overwrite the file if it already exists.
Default is False. If False and file exists, raises an IOError.
data_compression: HDF5 filter to apply when writing the data_array. Default is
None (no filter/compression).
flags_compression: HDF5 filter to apply when writing the flags_array. Default is
the LZF filter.
nsample_compression: HDF5 filter to apply when writing the nsample_array. Default is
the LZF filter.
data_write_dtype: datatype of output visibility data. If 'None', then the same datatype
as data_array will be used. 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. Default is None.
Returns:
None
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-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 run_check:
self.check(check_extra=check_extra,
run_check_acceptability=run_check_acceptability)
if os.path.exists(filename):
if clobber:
print("File exists; clobbering")
else:
raise IOError("File exists; skipping")
# 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=True,
compression=data_compression, dtype=data_write_dtype)
indices = (np.s_[:], np.s_[:], np.s_[:], np.s_[:])
_write_complex_astype(self.data_array, visdata, indices)
else:
visdata = dgrp.create_dataset("visdata", chunks=True,
data=self.data_array,
compression=data_compression,
dtype=data_write_dtype)
dgrp.create_dataset("flags", chunks=True,
data=self.flag_array,
compression=flags_compression)
dgrp.create_dataset("nsamples", chunks=True,
data=self.nsample_array.astype(np.float32),
compression=nsample_compression)
return
[docs] def initialize_uvh5_file(self, filename, clobber=False, 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.
Args:
filename: The UVH5 file to write to.
clobber: Option to overwrite the file if it already exists.
Default is False. If False and file exists, raises an IOError.
data_compression: HDF5 filter to apply when writing the data_array. Default is
None (no filter/compression).
flags_compression: HDF5 filter to apply when writing the flags_array. Default is
the LZF filter.
nsample_compression: HDF5 filter to apply when writing the nsample_array. Default is
the LZF filter.
data_write_dtype: 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. Default is None.
Returns:
None
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")
# 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.Nspws, 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=True,
dtype=data_write_dtype, compression=data_compression)
dgrp.create_dataset("flags", data_size, chunks=True,
dtype='b1', compression=flags_compression)
dgrp.create_dataset("nsamples", data_size, chunks=True,
dtype='f4', compression=nsample_compression)
return
def _check_header(self, filename, run_check_acceptability=True):
"""
Check that the metadata present in a file header matches the object's metadata.
Args:
header: reference to an h5py data group that contains the header information.
run_check_acceptability: Option to check acceptable range of the values of
parameters after reading in the file. Default is True.
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)
# 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
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
[docs] 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,
polarizations=None, blt_inds=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`.
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.
add_to_history : str
String to append to history before write out. Default is no appending.
Returns
-------
None
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))
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,
polarizations, blt_inds)
# 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, 1, Nfreqs, Npols)
if data_array.shape != proper_shape:
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, np.s_[:], 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, np.s_[:], 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, np.s_[:], 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, np.s_[:], 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, np.s_[:], 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)
return