text: # -*- 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
  
  from . import UVData
  from . import utils as uvutils
  
  try:
      import h5py
  except ImportError:  # pragma: no cover
      uvutils._reraise_context('h5py is not installed but is required for '
                               'uvh5 functionality')
  
  # define HDF5 type for interpreting HERA correlator outputs (integers) as complex numbers
  _hera_corr_dtype = np.dtype([('r', '<i4'), ('i', '<i4')])
  
  
  def _read_uvh5_string(dataset, filename):
      """
      Handle backwards compatibility of string types for legacy uvh5 files.
  
      Args:
          dataset: HDF5 dataset containing string-like data
          filename: name of uvh5 file
  
      Returns:
          string: string of type <str> corresponding to data saved in dataset
  
      Notes:
          This function is only designed to work on scalar datasets. Arrays of strings should be
          handled differently. (See how antenna_names are handled below for an example.)
      """
      if dataset.dtype.type is np.object_:
          warnings.warn("Strings in metadata of {file} are not the correct type; rewrite with "
                        "write_uvh5 to ensure future compatibility".format(file=filename))
          try:
              return uvutils._bytes_to_str(dataset[()])
          except AttributeError:
              # dataset[()] is already <str> type, and doesn't need to be decoded
              return dataset[()]
      else:
          return uvutils._bytes_to_str(dataset[()].tostring())
  
  
  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 must be a numpy dtype")
      if 'r' not in dtype.names or 'i' not in dtype.names:
          raise ValueError("datatype must be 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("datatype 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
  
  
  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'][()]
          if np.abs(latitude) <= np.pi and np.abs(longitude) <= np.pi:
              warnings.warn("It seems that the latitude and longitude are in radians; "
                            "support for interpreting these quantities in radians will "
                            "not be supported in future versions. Rewrite with write_uvh5 "
                            "to ensure future compatibility.", DeprecationWarning)
              self.telescope_location_lat_lon_alt = (latitude, longitude, altitude)
          else:
              self.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude)
          self.instrument = _read_uvh5_string(header['instrument'], filename)
          self.telescope_name = _read_uvh5_string(header['telescope_name'], filename)
  
          # get source information
          self.object_name = _read_uvh5_string(header['object_name'], filename)
  
          # set history appropriately
          self.history = _read_uvh5_string(header['history'], filename)
          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 = _read_uvh5_string(header['vis_units'], filename)
          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 = _read_uvh5_string(header['rdate'], filename)
          if 'timesys' in header:
              self.timesys = _read_uvh5_string(header['timesys'], filename)
          if 'x_orientation' in header:
              self.x_orientation = _read_uvh5_string(header['x_orientation'], filename)
          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'][()])
  
          # check for phasing information
          self.phase_type = _read_uvh5_string(header['phase_type'], filename)
          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 = _read_uvh5_string(header['phase_center_frame'], filename)
          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']
          if integration_time.size == 1 and int(header['Nblts'][()]) > 1:
              warnings.warn('{file} appears to be an old uvh5 format '
                            'with a single valued integration_time which has been deprecated. '
                            'Rewrite this file with write_uvh5 to ensure '
                            'future compatibility.'.format(file=filename))
              self.integration_time = np.ones_like(self.time_array, dtype=np.float64) * integration_time[()]
          else:
              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] = _read_uvh5_string(header["extra_keywords"][key], filename)
                  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, 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, 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
  
      def read_uvh5(self, filename, antenna_nums=None, antenna_names=None,
                    ant_str=None, bls=None, frequencies=None, freq_chans=None,
                    times=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.
  
          Args:
              filename: The file name to read.
              antenna_nums: The antennas numbers to include when reading data into
                  the object (antenna positions and names for the excluded antennas
                  will be retained). This cannot be provided if antenna_names is
                  also provided. Ignored if read_data is False.
              antenna_names: The antennas names to include when reading data into
                  the object (antenna positions and names for the excluded antennas
                  will be retained). This cannot be provided if antenna_nums is
                  also provided. Ignored if read_data is False.
              bls: 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 keep in 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,
                  the polarizations argument below must be None. Ignored if read_data is False.
              ant_str: A string containing information about what antenna numbers
                  and polarizations to include when reading data into the object.
                  Can be 'auto', 'cross', 'all', or combinations of antenna numbers
                  and polarizations (e.g. '1', '1_2', '1x_2y').
                  See tutorial for more examples of valid strings and
                  the behavior of different forms for ant_str.
                  If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
                  be kept for both baselines (1,2) and (2,3) to return a valid
                  pyuvdata object.
                  An ant_str cannot be passed in addition to any of the above antenna
                  args or the polarizations arg.
                  Ignored if read_data is False.
              frequencies: The frequencies to include when reading data into the
                  object. Ignored if read_data is False.
              freq_chans: The frequency channel numbers to include when reading
                  data into the object. Ignored if read_data is False.
              times: The times to include when reading data into the object.
                  Ignored if read_data is False.
              polarizations: The polarizations to include when reading data into
                  the object. Ignored if read_data is False.
              blt_inds: The baseline-time indices to include when reading data into
                  the object. This is not commonly used. Ignored if read_data is False.
              read_data: Read in the visibility and flag data. If set to false,
                  only the header info and metadata will be read in. Results in an
                  incompletely defined object (check will not pass). Default True.
              run_check: Option to check for the existence and proper shapes of
                  parameters after reading in the file. Default is True.
              check_extra: Option to check optional parameters as well as required
                  ones. Default is True.
              run_check_acceptability: Option to check acceptable range of the values of
                  parameters after reading in the file. Default is True.
              data_array_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'. Default is np.complex128.
              keep_all_metadata: Option to keep all the metadata associated with antennas,
                  even those that do not remain after the select option. Default is True.
  
          Returns:
              None
          """
          if not os.path.exists(filename):
              raise IOError(filename + ' not found')
  
          if not read_data:
              run_check = False
  
          # 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, 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)
  
          # 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.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
  
          # 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
  
      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)
              flags = dgrp.create_dataset("flags", chunks=True,
                                          data=self.flag_array,
                                          compression=flags_compression)
              nsample_array = dgrp.create_dataset("nsamples", chunks=True,
                                                  data=self.nsample_array.astype(np.float32),
                                                  compression=nsample_compression)
  
          return
  
      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)
              visdata = dgrp.create_dataset("visdata", data_size, chunks=True,
                                            dtype=data_write_dtype, compression=data_compression)
              flags = dgrp.create_dataset("flags", data_size, chunks=True,
                                          dtype='b1', compression=flags_compression)
              nsample_array = 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
  
      def write_uvh5_part(self, filename, data_array, flags_array, nsample_array, check_header=True,
                          antenna_nums=None, antenna_names=None, ant_str=None, bls=None,
                          frequencies=None, freq_chans=None, times=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.
  
          Args:
              filename: 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: 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.
              flags_array: 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: 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: option to check that the metadata present in the header
                  on disk matches that in the object. Default is True.
              run_check_acceptability: If check_header, additional option to check
                  acceptable range of the values of parameters after reading in the file.
                  Default is True.
              antenna_nums: 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: 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: 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: 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: The frequencies to include when writing data to the file.
              freq_chans: The frequency channel numbers to include when writing data to the file.
              times: The times to include when writing data to the file.
              polarizations: The polarizations to include when writing data to the file.
              blt_inds: The baseline-time indices to include when writing data to the file.
                  This is not commonly used.
              add_to_history: 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,
              polarizations, blt_inds)
  
          # make sure that the dimensions of the data to write are correct
          if data_array.shape != flags_array.shape:
              raise AssertionError("data_array and flags_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] = flags_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] = flags_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] = flags_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] = flags_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] = flags_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
  