https://github.com/RadioAstronomySoftwareGroup/pyuvdata
Raw File
Tip revision: d1829efacb60da384f64a8f25a280441bfa9d68a authored by Bryna Hazelton on 24 May 2019, 01:18 UTC
increase version to 1.4.0
Tip revision: d1829ef
uvdata.py
# -*- mode: python; coding: utf-8 -*
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License

"""Primary container for radio interferometer datasets.

"""
from __future__ import absolute_import, division, print_function

import os
import copy
import collections
import re
import numpy as np
import six
import warnings
from astropy import constants as const
import astropy.units as units
from astropy.time import Time
from astropy.coordinates import SkyCoord, EarthLocation, FK5, Angle

from .uvbase import UVBase
from . import parameter as uvp
from . import telescopes as uvtel
from . import utils as uvutils


class UVData(UVBase):
    """
    A class for defining a radio interferometer dataset.

    Currently supported file types: uvfits, miriad, fhd.
    Provides phasing functions.

    Attributes
    ----------
    UVParameter objects :
        For full list see UVData Parameters
        (http://pyuvdata.readthedocs.io/en/latest/uvdata_parameters.html).
        Some are always required, some are required for certain phase_types
        and others are always optional.
    """

    def __init__(self):
        """Create a new UVData object."""
        # add the UVParameters to the class

        # standard angle tolerance: 10 mas in radians.
        # Should perhaps be decreased to 1 mas in the future
        radian_tol = 10 * 2 * np.pi * 1e-3 / (60.0 * 60.0 * 360.0)

        self._Ntimes = uvp.UVParameter('Ntimes', description='Number of times',
                                       expected_type=int)
        self._Nbls = uvp.UVParameter('Nbls', description='Number of baselines',
                                     expected_type=int)
        self._Nblts = uvp.UVParameter('Nblts', description='Number of baseline-times '
                                      '(i.e. number of spectra). Not necessarily '
                                      'equal to Nbls * Ntimes', expected_type=int)
        self._Nfreqs = uvp.UVParameter('Nfreqs', description='Number of frequency channels',
                                       expected_type=int)
        self._Npols = uvp.UVParameter('Npols', description='Number of polarizations',
                                      expected_type=int)

        desc = ('Array of the visibility data, shape: (Nblts, Nspws, Nfreqs, '
                'Npols), type = complex float, in units of self.vis_units')
        self._data_array = uvp.UVParameter('data_array', description=desc,
                                           form=('Nblts', 'Nspws',
                                                 'Nfreqs', 'Npols'),
                                           expected_type=np.complex)

        desc = 'Visibility units, options are: "uncalib", "Jy" or "K str"'
        self._vis_units = uvp.UVParameter('vis_units', description=desc,
                                          form='str', expected_type=str,
                                          acceptable_vals=["uncalib", "Jy", "K str"])

        desc = ('Number of data points averaged into each data element, '
                'NOT required to be an integer, type = float, same shape as data_array.'
                'The product of the integration_time and the nsample_array '
                'value for a visibility reflects the total amount of time '
                'that went into the visibility. Best practice is for the '
                'nsample_array to be used to track flagging within an integration_time '
                '(leading to a decrease of the nsample array value below 1) and '
                'LST averaging (leading to an increase in the nsample array '
                'value). So datasets that have not been LST averaged should '
                'have nsample array values less than or equal to 1.'
                'Note that many files do not follow this convention, but it is '
                'safe to assume that the product of the integration_time and '
                'the nsample_array is the total amount of time included in a visibility.')
        self._nsample_array = uvp.UVParameter('nsample_array', description=desc,
                                              form=('Nblts', 'Nspws',
                                                    'Nfreqs', 'Npols'),
                                              expected_type=(np.float))

        desc = 'Boolean flag, True is flagged, same shape as data_array.'
        self._flag_array = uvp.UVParameter('flag_array', description=desc,
                                           form=('Nblts', 'Nspws',
                                                 'Nfreqs', 'Npols'),
                                           expected_type=np.bool)

        self._Nspws = uvp.UVParameter('Nspws', description='Number of spectral windows '
                                      '(ie non-contiguous spectral chunks). '
                                      'More than one spectral window is not '
                                      'currently supported.', expected_type=int)

        self._spw_array = uvp.UVParameter('spw_array',
                                          description='Array of spectral window '
                                          'Numbers, shape (Nspws)', form=('Nspws',),
                                          expected_type=int)

        desc = ('Projected baseline vectors relative to phase center, '
                'shape (Nblts, 3), units meters. Convention is: uvw = xyz(ant2) - xyz(ant1).'
                'Note that this is the Miriad convention but it is different '
                'from the AIPS/FITS convention (where uvw = xyz(ant1) - xyz(ant2)).')
        self._uvw_array = uvp.UVParameter('uvw_array', description=desc,
                                          form=('Nblts', 3),
                                          expected_type=np.float,
                                          acceptable_range=(0, 1e8), tols=1e-3)

        desc = ('Array of times, center of integration, shape (Nblts), '
                'units Julian Date')
        self._time_array = uvp.UVParameter('time_array', description=desc,
                                           form=('Nblts',),
                                           expected_type=np.float,
                                           tols=1e-3 / (60.0 * 60.0 * 24.0))  # 1 ms in days

        desc = ('Array of lsts, center of integration, shape (Nblts), '
                'units radians')
        self._lst_array = uvp.UVParameter('lst_array', description=desc,
                                          form=('Nblts',),
                                          expected_type=np.float,
                                          tols=radian_tol)

        desc = ('Array of first antenna indices, shape (Nblts), '
                'type = int, 0 indexed')
        self._ant_1_array = uvp.UVParameter('ant_1_array', description=desc,
                                            expected_type=int, form=('Nblts',))
        desc = ('Array of second antenna indices, shape (Nblts), '
                'type = int, 0 indexed')
        self._ant_2_array = uvp.UVParameter('ant_2_array', description=desc,
                                            expected_type=int, form=('Nblts',))

        desc = ('Array of baseline indices, shape (Nblts), '
                'type = int; baseline = 2048 * (ant1+1) + (ant2+1) + 2^16')
        self._baseline_array = uvp.UVParameter('baseline_array',
                                               description=desc,
                                               expected_type=int, form=('Nblts',))

        # this dimensionality of freq_array does not allow for different spws
        # to have different dimensions
        desc = 'Array of frequencies, center of the channel, shape (Nspws, Nfreqs), units Hz'
        self._freq_array = uvp.UVParameter('freq_array', description=desc,
                                           form=('Nspws', 'Nfreqs'),
                                           expected_type=np.float,
                                           tols=1e-3)  # mHz

        desc = ('Array of polarization integers, shape (Npols). '
                'AIPS Memo 117 says: pseudo-stokes 1:4 (pI, pQ, pU, pV);  '
                'circular -1:-4 (RR, LL, RL, LR); linear -5:-8 (XX, YY, XY, YX). '
                'NOTE: AIPS Memo 117 actually calls the pseudo-Stokes polarizations '
                '"Stokes", but this is inaccurate as visibilities cannot be in '
                'true Stokes polarizations for physical antennas. We adopt the '
                'term pseudo-Stokes to refer to linear combinations of instrumental '
                'visibility polarizations (e.g. pI = xx + yy).')
        self._polarization_array = uvp.UVParameter('polarization_array',
                                                   description=desc,
                                                   expected_type=int,
                                                   acceptable_vals=list(
                                                       np.arange(-8, 0)) + list(np.arange(1, 5)),
                                                   form=('Npols',))

        desc = ('Length of the integration in seconds, shape (Nblts). '
                'The product of the integration_time and the nsample_array '
                'value for a visibility reflects the total amount of time '
                'that went into the visibility. Best practice is for the '
                'integration_time to reflect the length of time a visibility '
                'was integrated over (so it should vary in the case of '
                'baseline-dependent averaging and be a way to do selections '
                'for differently integrated baselines).'
                'Note that many files do not follow this convention, but it is '
                'safe to assume that the product of the integration_time and '
                'the nsample_array is the total amount of time included in a visibility.')
        self._integration_time = uvp.UVParameter('integration_time',
                                                 description=desc,
                                                 form=('Nblts',),
                                                 expected_type=np.float, tols=1e-3)  # 1 ms
        self._channel_width = uvp.UVParameter('channel_width',
                                              description='Width of frequency channels (Hz)',
                                              expected_type=np.float,
                                              tols=1e-3)  # 1 mHz

        # --- observation information ---
        self._object_name = uvp.UVParameter('object_name',
                                            description='Source or field '
                                            'observed (string)', form='str',
                                            expected_type=str)
        self._telescope_name = uvp.UVParameter('telescope_name',
                                               description='Name of telescope '
                                               '(string)', form='str',
                                               expected_type=str)
        self._instrument = uvp.UVParameter('instrument', description='Receiver or backend. '
                                           'Sometimes identical to telescope_name',
                                           form='str', expected_type=str)

        desc = ('Telescope location: xyz in ITRF (earth-centered frame). '
                'Can also be accessed using telescope_location_lat_lon_alt or '
                'telescope_location_lat_lon_alt_degrees properties')
        self._telescope_location = uvp.LocationParameter('telescope_location',
                                                         description=desc,
                                                         acceptable_range=(
                                                             6.35e6, 6.39e6),
                                                         tols=1e-3)

        self._history = uvp.UVParameter('history', description='String of history, units English',
                                        form='str', expected_type=str)

        # --- phasing information ---
        desc = ('String indicating phasing type. Allowed values are "drift", '
                '"phased" and "unknown"')
        self._phase_type = uvp.UVParameter('phase_type', form='str', expected_type=str,
                                           description=desc, value='unknown',
                                           acceptable_vals=['drift', 'phased', 'unknown'])

        desc = ('Required if phase_type = "phased". Epoch year of the phase '
                'applied to the data (eg 2000.)')
        self._phase_center_epoch = uvp.UVParameter('phase_center_epoch',
                                                   required=False,
                                                   description=desc,
                                                   expected_type=np.float)

        desc = ('Required if phase_type = "phased". Right ascension of phase '
                'center (see uvw_array), units radians. Can also be accessed using phase_center_ra_degrees.')
        self._phase_center_ra = uvp.AngleParameter('phase_center_ra',
                                                   required=False,
                                                   description=desc,
                                                   expected_type=np.float,
                                                   tols=radian_tol)

        desc = ('Required if phase_type = "phased". Declination of phase center '
                '(see uvw_array), units radians. Can also be accessed using phase_center_dec_degrees.')
        self._phase_center_dec = uvp.AngleParameter('phase_center_dec',
                                                    required=False,
                                                    description=desc,
                                                    expected_type=np.float,
                                                    tols=radian_tol)

        desc = ('Only relevant if phase_type = "phased". Specifies the frame the'
                ' data and uvw_array are phased to. Options are "gcrs" and "icrs",'
                ' default is "icrs"')
        self._phase_center_frame = uvp.UVParameter('phase_center_frame',
                                                   required=False,
                                                   description=desc,
                                                   expected_type=str,
                                                   acceptable_vals=['icrs', 'gcrs'])

        # --- antenna information ----
        desc = ('Number of antennas with data present (i.e. number of unique '
                'entries in ant_1_array and ant_2_array). May be smaller '
                'than the number of antennas in the array')
        self._Nants_data = uvp.UVParameter('Nants_data', description=desc,
                                           expected_type=int)

        desc = ('Number of antennas in the array. May be larger '
                'than the number of antennas with data')
        self._Nants_telescope = uvp.UVParameter('Nants_telescope',
                                                description=desc, expected_type=int)

        desc = ('List of antenna names, shape (Nants_telescope), '
                'with numbers given by antenna_numbers (which can be matched '
                'to ant_1_array and ant_2_array). There must be one entry '
                'here for each unique entry in ant_1_array and '
                'ant_2_array, but there may be extras as well.')
        self._antenna_names = uvp.UVParameter('antenna_names', description=desc,
                                              form=('Nants_telescope',),
                                              expected_type=str)

        desc = ('List of integer antenna numbers corresponding to antenna_names, '
                'shape (Nants_telescope). There must be one '
                'entry here for each unique entry in ant_1_array and '
                'ant_2_array, but there may be extras as well.')
        self._antenna_numbers = uvp.UVParameter('antenna_numbers', description=desc,
                                                form=('Nants_telescope',),
                                                expected_type=int)

        # -------- extra, non-required parameters ----------
        desc = ('Orientation of the physical dipole corresponding to what is '
                'labelled as the x polarization. Options are "east" '
                '(indicating east/west orientation) and "north" (indicating '
                'north/south orientation)')
        self._x_orientation = uvp.UVParameter('x_orientation', description=desc,
                                              required=False, expected_type=str,
                                              acceptable_vals=['east', 'north'])

        blt_order_options = ['time', 'baseline', 'ant1', 'ant2', 'bda']
        desc = ('Ordering of the data array along the blt axis. A tuple with '
                'the major and minor order (minor order is omitted if order is "bda"). '
                'The allowed values are: '
                + ' ,'.join([str(val) for val in blt_order_options]))
        self._blt_order = uvp.UVParameter('blt_order', description=desc, form=(2,),
                                          required=False, expected_type=str,
                                          acceptable_vals=blt_order_options)

        desc = ('Any user supplied extra keywords, type=dict. Keys should be '
                '8 character or less strings if writing to uvfits or miriad files. '
                'Use the special key "comment" for long multi-line string comments.')
        self._extra_keywords = uvp.UVParameter('extra_keywords', required=False,
                                               description=desc, value={},
                                               spoof_val={}, expected_type=dict)

        desc = ('Array giving coordinates of antennas relative to '
                'telescope_location (ITRF frame), shape (Nants_telescope, 3), '
                'units meters. See the tutorial page in the documentation '
                'for an example of how to convert this to topocentric frame.'
                'Will be a required parameter in a future version.')
        self._antenna_positions = uvp.AntPositionParameter('antenna_positions',
                                                           required=False,
                                                           description=desc,
                                                           form=(
                                                               'Nants_telescope', 3),
                                                           expected_type=np.float,
                                                           tols=1e-3)  # 1 mm

        desc = ('Array of antenna diameters in meters. Used by CASA to '
                'construct a default beam if no beam is supplied.')
        self._antenna_diameters = uvp.UVParameter('antenna_diameters',
                                                  required=False,
                                                  description=desc,
                                                  form=('Nants_telescope',),
                                                  expected_type=np.float,
                                                  tols=1e-3)  # 1 mm

        # --- other stuff ---
        # the below are copied from AIPS memo 117, but could be revised to
        # merge with other sources of data.
        self._gst0 = uvp.UVParameter('gst0', required=False,
                                     description='Greenwich sidereal time at '
                                                 'midnight on reference date',
                                     spoof_val=0.0, expected_type=np.float)
        self._rdate = uvp.UVParameter('rdate', required=False,
                                      description='Date for which the GST0 or '
                                                  'whatever... applies',
                                      spoof_val='', form='str')
        self._earth_omega = uvp.UVParameter('earth_omega', required=False,
                                            description='Earth\'s rotation rate '
                                                        'in degrees per day',
                                            spoof_val=360.985, expected_type=np.float)
        self._dut1 = uvp.UVParameter('dut1', required=False,
                                     description='DUT1 (google it) AIPS 117 '
                                                 'calls it UT1UTC',
                                     spoof_val=0.0, expected_type=np.float)
        self._timesys = uvp.UVParameter('timesys', required=False,
                                        description='We only support UTC',
                                        spoof_val='UTC', form='str')

        desc = ('FHD thing we do not understand, something about the time '
                'at which the phase center is normal to the chosen UV plane '
                'for phasing')
        self._uvplane_reference_time = uvp.UVParameter('uvplane_reference_time',
                                                       required=False,
                                                       description=desc,
                                                       spoof_val=0)

        super(UVData, self).__init__()

    def check(self, check_extra=True, run_check_acceptability=True):
        """
        Add some extra checks on top of checks on UVBase class.

        Check that required parameters exist. Check that parameters have
        appropriate shapes and optionally that the values are acceptable.

        Parameters
        ----------
        check_extra : bool
            If true, check all parameters, otherwise only check required parameters.
        run_check_acceptability : bool
            Option to check if values in parameters are acceptable.

        Returns
        -------
        bool
            True if check passes

        Raises
        ------
        ValueError
            if parameter shapes or types are wrong or do not have acceptable
            values (if run_check_acceptability is True)
        """
        # first run the basic check from UVBase
        # set the phase type based on object's value
        if self.phase_type == 'phased':
            self.set_phased()
        elif self.phase_type == 'drift':
            self.set_drift()
        else:
            self.set_unknown_phase_type()

        # check for deprecated x_orientation strings and convert to new values (if possible)
        if self.x_orientation is not None:
            if self.x_orientation not in self._x_orientation.acceptable_vals:
                warn_string = ('x_orientation {xval} is not one of [{vals}], '
                               .format(xval=self.x_orientation,
                                       vals=(', ').join(self._x_orientation.acceptable_vals)))
                if self.x_orientation.lower() == 'e':
                    self.x_orientation = 'east'
                    warn_string += 'converting to "east".'
                elif self.x_orientation.lower() == 'n':
                    self.x_orientation = 'north'
                    warn_string += 'converting to "north".'
                else:
                    warn_string += 'cannot be converted.'

                warnings.warn(warn_string + ' Only [{vals}] will be supported '
                              'starting in version 1.5'
                              .format(vals=(', ').join(self._x_orientation.acceptable_vals)),
                              DeprecationWarning)

        super(UVData, self).check(check_extra=check_extra,
                                  run_check_acceptability=run_check_acceptability)

        # Check internal consistency of numbers which don't explicitly correspond
        # to the shape of another array.
        nants_data_calc = int(len(np.unique(self.ant_1_array.tolist()
                                            + self.ant_2_array.tolist())))
        if self.Nants_data != nants_data_calc:
            raise ValueError('Nants_data must be equal to the number of unique '
                             'values in ant_1_array and ant_2_array')

        if self.Nbls != len(np.unique(self.baseline_array)):
            raise ValueError('Nbls must be equal to the number of unique '
                             'baselines in the data_array')

        if self.Ntimes != len(np.unique(self.time_array)):
            raise ValueError('Ntimes must be equal to the number of unique '
                             'times in the time_array')

        # require that all entries in ant_1_array and ant_2_array exist in antenna_numbers
        if not all(ant in self.antenna_numbers for ant in self.ant_1_array):
            raise ValueError('All antennas in ant_1_array must be in antenna_numbers.')
        if not all(ant in self.antenna_numbers for ant in self.ant_2_array):
            raise ValueError('All antennas in ant_2_array must be in antenna_numbers.')

        # issue warning if extra_keywords keys are longer than 8 characters
        for key in self.extra_keywords.keys():
            if len(key) > 8:
                warnings.warn('key {key} in extra_keywords is longer than 8 '
                              'characters. It will be truncated to 8 if written '
                              'to uvfits or miriad file formats.'.format(key=key))

        # issue warning if extra_keywords values are lists, arrays or dicts
        for key, value in self.extra_keywords.items():
            if isinstance(value, (list, dict, np.ndarray)):
                warnings.warn('{key} in extra_keywords is a list, array or dict, '
                              'which will raise an error when writing uvfits or '
                              'miriad file types'.format(key=key))

        # issue deprecation warning if antenna positions are not set
        if self.antenna_positions is None:
            warnings.warn('antenna_positions are not defined. '
                          'antenna_positions will be a required parameter in '
                          'version 1.5', DeprecationWarning)

        # check auto and cross-corrs have sensible uvws
        autos = np.isclose(self.ant_1_array - self.ant_2_array, 0.0)
        if not np.all(np.isclose(self.uvw_array[autos], 0.0,
                                 rtol=self._uvw_array.tols[0],
                                 atol=self._uvw_array.tols[1])):
            raise ValueError("Some auto-correlations have non-zero "
                             "uvw_array coordinates.")
        if np.any(np.isclose([np.linalg.norm(uvw) for uvw in self.uvw_array[~autos]], 0.0,
                             rtol=self._uvw_array.tols[0],
                             atol=self._uvw_array.tols[1])):
            raise ValueError("Some cross-correlations have near-zero "
                             "uvw_array magnitudes.")

        return True

    def set_drift(self):
        """Set phase_type to 'drift' and adjust required parameters."""
        self.phase_type = 'drift'
        self._phase_center_epoch.required = False
        self._phase_center_ra.required = False
        self._phase_center_dec.required = False

    def set_phased(self):
        """Set phase_type to 'phased' and adjust required parameters."""
        self.phase_type = 'phased'
        self._phase_center_epoch.required = True
        self._phase_center_ra.required = True
        self._phase_center_dec.required = True

    def set_unknown_phase_type(self):
        """Set phase_type to 'unknown' and adjust required parameters."""
        self.phase_type = 'unknown'
        self._phase_center_epoch.required = False
        self._phase_center_ra.required = False
        self._phase_center_dec.required = False

    def known_telescopes(self):
        """
        Get a list of telescopes known to pyuvdata.

        This is just a shortcut to uvdata.telescopes.known_telescopes()

        Returns
        -------
        list of str
            List of names of known telescopes
        """
        return uvtel.known_telescopes()

    def set_telescope_params(self, overwrite=False):
        """
        Set telescope related parameters.

        If the telescope_name is in the known_telescopes, set any missing
        telescope-associated parameters (e.g. telescope location) to the value
        for the known telescope.

        Parameters
        ----------
        overwrite : bool
            Option to overwrite existing telescope-associated parameters with
            the values from the known telescope.

        Raises
        ------
        ValueError
            if the telescope_name is not in known telescopes
        """
        telescope_obj = uvtel.get_telescope(self.telescope_name)
        if telescope_obj is not False:
            params_set = []
            for p in telescope_obj:
                telescope_param = getattr(telescope_obj, p)
                self_param = getattr(self, p)
                if telescope_param.value is not None and (overwrite is True
                                                          or self_param.value is None):
                    telescope_shape = telescope_param.expected_shape(telescope_obj)
                    self_shape = self_param.expected_shape(self)
                    if telescope_shape == self_shape:
                        params_set.append(self_param.name)
                        prop_name = self_param.name
                        setattr(self, prop_name, getattr(telescope_obj, prop_name))
                    else:
                        # expected shapes aren't equal. This can happen e.g. with diameters,
                        # which is a single value on the telescope object but is
                        # an array of length Nants_telescope on the UVData object

                        # use an assert here because we want an error if this condition
                        # isn't true, but it's really an internal consistency check.
                        # This will error if there are changes to the Telescope
                        # object definition, but nothing that a normal user does will cause an error
                        assert(telescope_shape == () and self_shape != 'str')
                        array_val = np.zeros(self_shape,
                                             dtype=telescope_param.expected_type) + telescope_param.value
                        params_set.append(self_param.name)
                        prop_name = self_param.name
                        setattr(self, prop_name, array_val)

            if len(params_set) > 0:
                params_set_str = ', '.join(params_set)
                warnings.warn('{params} is not set. Using known values '
                              'for {telescope_name}.'.format(params=params_set_str,
                                                             telescope_name=telescope_obj.telescope_name))
        else:
            raise ValueError('Telescope {telescope_name} is not in '
                             'known_telescopes.'.format(telescope_name=self.telescope_name))

    def baseline_to_antnums(self, baseline):
        """
        Get the antenna numbers corresponding to a given baseline number.

        Parameters
        ----------
        baseline : int
            baseline number

        Returns
        -------
        int
            first antenna number
        int
            second antenna number
        """
        return uvutils.baseline_to_antnums(baseline, self.Nants_telescope)

    def antnums_to_baseline(self, ant1, ant2, attempt256=False):
        """
        Get the baseline number corresponding to two given antenna numbers.

        Parameters
        ----------
        ant1 : int
            first antenna number
        ant2 : int
            second antenna number
        attempt256 : bool
            Option to try to use the older 256 standard used in many uvfits files
            (will use 2048 standard if there are more than 256 antennas).

        Returns
        -------
        int
            baseline number corresponding to the two antenna numbers.
        """
        return uvutils.antnums_to_baseline(ant1, ant2, self.Nants_telescope, attempt256=attempt256)

    def set_lsts_from_time_array(self):
        """Set the lst_array based from the time_array."""
        latitude, longitude, altitude = self.telescope_location_lat_lon_alt_degrees
        unique_times, inverse_inds = np.unique(self.time_array, return_inverse=True)
        unique_lst_array = uvutils.get_lst_for_time(unique_times, latitude, longitude, altitude)
        self.lst_array = unique_lst_array[inverse_inds]

    def unphase_to_drift(self, phase_frame=None, use_ant_pos=False):
        """
        Convert from a phased dataset to a drift dataset.

        See the phasing memo under docs/references for more documentation.

        Parameters
        ----------
        phase_frame : str
            The astropy frame to phase from. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation, 'icrs' also includes abberation.
            Defaults to using the 'phase_center_frame' attribute or 'icrs'
            if that attribute is None.
        use_ant_pos : bool
            If True, calculate the uvws directly from the antenna positions
            rather than from the existing uvws.

        Raises
        ------
        ValueError
            If the phase_type is not 'phased'
        """
        if self.phase_type == 'phased':
            pass
        elif self.phase_type == 'drift':
            raise ValueError('The data is already drift scanning; can only '
                             'unphase phased data.')
        else:
            raise ValueError('The phasing type of the data is unknown. '
                             'Set the phase_type to drift or phased to '
                             'reflect the phasing status of the data')

        if phase_frame is None:
            if self.phase_center_frame is not None:
                phase_frame = self.phase_center_frame
            else:
                phase_frame = 'icrs'

        icrs_coord = SkyCoord(ra=self.phase_center_ra, dec=self.phase_center_dec,
                              unit='radian', frame='icrs')
        if phase_frame == 'icrs':
            frame_phase_center = icrs_coord
        else:
            # use center of observation for obstime for gcrs
            center_time = np.mean([np.max(self.time_array), np.min(self.time_array)])
            icrs_coord.obstime = Time(center_time, format='jd')
            frame_phase_center = icrs_coord.transform_to('gcrs')

        # This promotion is REQUIRED to get the right answer when we
        # add in the telescope location for ICRS
        # In some cases, the uvws are already float64, but sometimes they're not
        self.uvw_array = np.float64(self.uvw_array)

        # apply -w phasor
        w_lambda = (self.uvw_array[:, 2].reshape(self.Nblts, 1)
                    / const.c.to('m/s').value * self.freq_array.reshape(1, self.Nfreqs))
        phs = np.exp(-1j * 2 * np.pi * (-1) * w_lambda[:, None, :, None])
        self.data_array *= phs

        unique_times, unique_inds = np.unique(self.time_array, return_index=True)
        for ind, jd in enumerate(unique_times):
            inds = np.where(self.time_array == jd)[0]

            obs_time = Time(jd, format='jd')

            itrs_telescope_location = SkyCoord(x=self.telescope_location[0] * units.m,
                                               y=self.telescope_location[1] * units.m,
                                               z=self.telescope_location[2] * units.m,
                                               frame='itrs', obstime=obs_time)
            frame_telescope_location = itrs_telescope_location.transform_to(phase_frame)
            itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

            if use_ant_pos:
                ant_uvw = uvutils.phase_uvw(self.telescope_location_lat_lon_alt[1],
                                            self.telescope_location_lat_lon_alt[0],
                                            self.antenna_positions)

                for bl_ind in inds:
                    ant1_index = np.where(self.antenna_numbers == self.ant_1_array[bl_ind])[0][0]
                    ant2_index = np.where(self.antenna_numbers == self.ant_2_array[bl_ind])[0][0]
                    self.uvw_array[bl_ind, :] = ant_uvw[ant2_index, :] - ant_uvw[ant1_index, :]

            else:
                uvws_use = self.uvw_array[inds, :]

                uvw_rel_positions = uvutils.unphase_uvw(frame_phase_center.ra.rad,
                                                        frame_phase_center.dec.rad,
                                                        uvws_use)

                # astropy 2 vs 3 use a different keyword name
                if six.PY2:
                    rep_keyword = 'representation'
                else:
                    rep_keyword = 'representation_type'
                setattr(frame_telescope_location, rep_keyword, 'cartesian')

                rep_dict = {}
                rep_dict[rep_keyword] = 'cartesian'
                frame_uvw_coord = SkyCoord(x=uvw_rel_positions[:, 0] * units.m + frame_telescope_location.x,
                                           y=uvw_rel_positions[:, 1] * units.m + frame_telescope_location.y,
                                           z=uvw_rel_positions[:, 2] * units.m + frame_telescope_location.z,
                                           frame=phase_frame, obstime=obs_time,
                                           **rep_dict)

                itrs_uvw_coord = frame_uvw_coord.transform_to('itrs')

                # now convert them to ENU, which is the space uvws are in
                self.uvw_array[inds, :] = uvutils.ENU_from_ECEF(itrs_uvw_coord.cartesian.get_xyz().value.T,
                                                                *itrs_lat_lon_alt)

        # remove phase center
        self.phase_center_frame = None
        self.phase_center_ra = None
        self.phase_center_dec = None
        self.phase_center_epoch = None
        self.set_drift()

    def phase(self, ra, dec, epoch='J2000', phase_frame='icrs', use_ant_pos=False):
        """
        Phase a drift scan dataset to a single ra/dec at a particular epoch.

        See the phasing memo under docs/references for more documentation.

        Tested against MWA_Tools/CONV2UVFITS/convutils.
        Will not phase already phased data.

        Parameters
        ----------
        ra : float
            The ra to phase to in radians.
        dec : float
            The dec to phase to in radians.
        epoch : astropy.time.Time object or str
            The epoch to use for phasing. Either an astropy Time object or the
            string "J2000" (which is the default).
            Note that the epoch is only used to evaluate the ra & dec values,
            if the epoch is not J2000, the ra & dec values are interpreted
            as FK5 ra/dec values and translated to J2000, the data are then
            phased to the J2000 ra/dec values.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
        use_ant_pos : bool
            If True, calculate the uvws directly from the antenna positions
            rather than from the existing uvws.

        Raises
        ------
        ValueError
            If the phase_type is not 'drift'
        """
        if self.phase_type == 'drift':
            pass
        elif self.phase_type == 'phased':
            raise ValueError('The data is already phased; can only phase '
                             'drift scan data. Use unphase_to_drift to '
                             'convert to a drift scan.')
        else:
            raise ValueError('The phasing type of the data is unknown. '
                             'Set the phase_type to "drift" or "phased" to '
                             'reflect the phasing status of the data')

        if phase_frame not in ['icrs', 'gcrs']:
            raise ValueError('phase_frame can only be set to icrs or gcrs.')

        if epoch == "J2000" or epoch == 2000:
            icrs_coord = SkyCoord(ra=ra, dec=dec, unit='radian', frame='icrs')
        else:
            assert(isinstance(epoch, Time))
            phase_center_coord = SkyCoord(ra=ra, dec=dec, unit='radian',
                                          equinox=epoch, frame=FK5)
            # convert to icrs (i.e. J2000) to write to object
            icrs_coord = phase_center_coord.transform_to('icrs')

        self.phase_center_ra = icrs_coord.ra.radian
        self.phase_center_dec = icrs_coord.dec.radian
        self.phase_center_epoch = 2000.0

        if phase_frame == 'icrs':
            frame_phase_center = icrs_coord
        else:
            # use center of observation for obstime for gcrs
            center_time = np.mean([np.max(self.time_array), np.min(self.time_array)])
            icrs_coord.obstime = Time(center_time, format='jd')
            frame_phase_center = icrs_coord.transform_to('gcrs')

        # This promotion is REQUIRED to get the right answer when we
        # add in the telescope location for ICRS
        self.uvw_array = np.float64(self.uvw_array)

        unique_times, unique_inds = np.unique(self.time_array, return_index=True)
        for ind, jd in enumerate(unique_times):
            inds = np.where(self.time_array == jd)[0]

            obs_time = Time(jd, format='jd')

            itrs_telescope_location = SkyCoord(x=self.telescope_location[0] * units.m,
                                               y=self.telescope_location[1] * units.m,
                                               z=self.telescope_location[2] * units.m,
                                               frame='itrs', obstime=obs_time)
            itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

            frame_telescope_location = itrs_telescope_location.transform_to(phase_frame)

            # astropy 2 vs 3 use a different keyword name
            if six.PY2:
                rep_keyword = 'representation'
            else:
                rep_keyword = 'representation_type'
            setattr(frame_telescope_location, rep_keyword, 'cartesian')

            if use_ant_pos:
                # This promotion is REQUIRED to get the right answer when we
                # add in the telescope location for ICRS
                ecef_ant_pos = np.float64(self.antenna_positions) + self.telescope_location

                itrs_ant_coord = SkyCoord(x=ecef_ant_pos[:, 0] * units.m,
                                          y=ecef_ant_pos[:, 1] * units.m,
                                          z=ecef_ant_pos[:, 2] * units.m,
                                          frame='itrs', obstime=obs_time)

                frame_ant_coord = itrs_ant_coord.transform_to(phase_frame)

                frame_ant_rel = (frame_ant_coord.cartesian
                                 - frame_telescope_location.cartesian).get_xyz().T.value

                frame_ant_uvw = uvutils.phase_uvw(frame_phase_center.ra.rad,
                                                  frame_phase_center.dec.rad,
                                                  frame_ant_rel)

                for bl_ind in inds:
                    ant1_index = np.where(self.antenna_numbers == self.ant_1_array[bl_ind])[0][0]
                    ant2_index = np.where(self.antenna_numbers == self.ant_2_array[bl_ind])[0][0]
                    self.uvw_array[bl_ind, :] = frame_ant_uvw[ant2_index, :] - frame_ant_uvw[ant1_index, :]
            else:
                # Also, uvws should be thought of like ENU, not ECEF (or rotated ECEF)
                # convert them to ECEF to transform between frames
                uvws_use = self.uvw_array[inds, :]

                uvw_ecef = uvutils.ECEF_from_ENU(uvws_use, *itrs_lat_lon_alt)

                itrs_uvw_coord = SkyCoord(x=uvw_ecef[:, 0] * units.m,
                                          y=uvw_ecef[:, 1] * units.m,
                                          z=uvw_ecef[:, 2] * units.m,
                                          frame='itrs', obstime=obs_time)
                frame_uvw_coord = itrs_uvw_coord.transform_to(phase_frame)

                # this takes out the telescope location in the new frame,
                # so these are vectors again
                frame_rel_uvw = (frame_uvw_coord.cartesian.get_xyz().value.T
                                 - frame_telescope_location.cartesian.get_xyz().value)

                self.uvw_array[inds, :] = uvutils.phase_uvw(frame_phase_center.ra.rad,
                                                            frame_phase_center.dec.rad,
                                                            frame_rel_uvw)

        # calculate data and apply phasor
        w_lambda = (self.uvw_array[:, 2].reshape(self.Nblts, 1)
                    / const.c.to('m/s').value * self.freq_array.reshape(1, self.Nfreqs))
        phs = np.exp(-1j * 2 * np.pi * w_lambda[:, None, :, None])
        self.data_array *= phs

        self.phase_center_frame = phase_frame
        self.set_phased()

    def phase_to_time(self, time, phase_frame='icrs', use_ant_pos=False):
        """
        Phase a drift scan dataset to the ra/dec of zenith at a particular time.

        See the phasing memo under docs/references for more documentation.

        Parameters
        ----------
        time : astropy.time.Time object
            The time to phase to, an astropy Time object.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
        use_ant_pos : bool
            If True, calculate the uvws directly from the antenna positions
            rather than from the existing uvws.

        Raises
        ------
        ValueError
            If the phase_type is not 'drift'
        TypeError
            If time is not an astropy.time.Time object
        """
        if self.phase_type == 'drift':
            pass
        elif self.phase_type == 'phased':
            raise ValueError('The data is already phased; can only phase '
                             'drift scanning data.')
        else:
            raise ValueError('The phasing type of the data is unknown. '
                             'Set the phase_type to drift or phased to '
                             'reflect the phasing status of the data')

        if not isinstance(time, Time):
            raise(TypeError, "time must be an astropy.time.Time object")

        # Generate ra/dec of zenith at time in the phase_frame coordinate system
        # to use for phasing
        telescope_location = EarthLocation.from_geocentric(self.telescope_location[0],
                                                           self.telescope_location[1],
                                                           self.telescope_location[2],
                                                           unit='m')

        zenith_coord = SkyCoord(alt=Angle(90 * units.deg), az=Angle(0 * units.deg),
                                obstime=time, frame='altaz', location=telescope_location)

        obs_zenith_coord = zenith_coord.transform_to(phase_frame)
        zenith_ra = obs_zenith_coord.ra
        zenith_dec = obs_zenith_coord.dec

        self.phase(zenith_ra, zenith_dec, epoch='J2000', phase_frame=phase_frame,
                   use_ant_pos=use_ant_pos)

    def set_uvws_from_antenna_positions(self, allow_phasing=False,
                                        orig_phase_frame=None,
                                        output_phase_frame='icrs'):
        """
        Calculate UVWs based on antenna_positions

        Parameters
        ----------
        allow_phasing : bool
            Option for phased data. If data is phased and allow_phasing is set,
            data will be unphased, UVWs will be calculated, and then data will
            be rephased.
        orig_phase_frame : str
            The astropy frame to phase from. Either 'icrs' or 'gcrs'.
            Defaults to using the 'phase_center_frame' attribute or 'icrs' if
            that attribute is None. Only used if allow_phasing is True.
        output_phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'. Only used if
            allow_phasing is True.

        Raises
        ------
        ValueError
            If data is phased and allow_phasing is False.

        Warns
        -----
        UserWarning
            If the phase_type is 'phased'
        """
        phase_type = self.phase_type
        if phase_type == 'phased':
            if allow_phasing:
                warnings.warn('Warning: Data will be unphased and rephased '
                              'to calculate UVWs.'
                              )
                if orig_phase_frame not in [None, 'icrs', 'gcrs']:
                    raise ValueError('Invalid parameter orig_phase_frame. '
                                     'Options are "icrs", "gcrs", or None.')
                if output_phase_frame not in ['icrs', 'gcrs']:
                    raise ValueError('Invalid parameter output_phase_frame. '
                                     'Options are "icrs" or "gcrs".')
                phase_center_ra = self.phase_center_ra
                phase_center_dec = self.phase_center_dec
                phase_center_epoch = self.phase_center_epoch
                self.unphase_to_drift(phase_frame=orig_phase_frame)
            else:
                raise ValueError('UVW calculation requires unphased data. '
                                 'Use unphase_to_drift or set '
                                 'allow_phasing=True.'
                                 )
        antenna_locs_ENU = uvutils.ENU_from_ECEF(
            (self.antenna_positions + self.telescope_location),
            *self.telescope_location_lat_lon_alt)
        uvw_array = np.zeros((self.baseline_array.size, 3))
        for baseline in list(set(self.baseline_array)):
            baseline_inds = np.where(self.baseline_array == baseline)[0]
            ant1_index = np.where(self.antenna_numbers
                                  == self.ant_1_array[baseline_inds[0]])[0][0]
            ant2_index = np.where(self.antenna_numbers
                                  == self.ant_2_array[baseline_inds[0]])[0][0]
            uvw_array[baseline_inds, :] = (antenna_locs_ENU[ant2_index, :]
                                           - antenna_locs_ENU[ant1_index, :])
        self.uvw_array = uvw_array
        if phase_type == 'phased':
            self.phase(phase_center_ra, phase_center_dec, phase_center_epoch,
                       phase_frame=output_phase_frame)

    def conjugate_bls(self, convention='ant1<ant2', use_enu=True):
        """
        Conjugate baselines according to one of the supported conventions.

        This will fail if only one of the cross pols is present (because
        conjugation requires changing the polarization number for cross pols).

        Parameters
        ----------
        convention : str or array_like of int
            A convention for the directions of the baselines, options are:
            'ant1<ant2', 'ant2<ant1', 'u<0', 'u>0', 'v<0', 'v>0' or an
            index array of blt indices to conjugate.
        use_enu : bool
            Use true antenna positions to determine uv location (as opposed to
            uvw array). Only applies if `convention` is 'u<0', 'u>0', 'v<0', 'v>0'.
            Set to False to use uvw array values.

        Raises
        ------
        ValueError
            If convention is not an allowed value or if not all conjugate pols exist.
        """
        if isinstance(convention, (np.ndarray, list, tuple)):
            convention = np.array(convention)
            if (np.max(convention) >= self.Nblts or np.min(convention) < 0
                    or convention.dtype not in [int, np.int, np.int32, np.int64]):
                raise ValueError('If convention is an index array, it must '
                                 'contain integers and have values greater '
                                 'than zero and less than NBlts')
        else:
            if convention not in ['ant1<ant2', 'ant2<ant1', 'u<0', 'u>0', 'v<0', 'v>0']:
                raise ValueError("convention must be one of 'ant1<ant2', "
                                 "'ant2<ant1', 'u<0', 'u>0', 'v<0', 'v>0' or "
                                 "an index array with values less than NBlts")

        if isinstance(convention, str):
            if convention in ['u<0', 'u>0', 'v<0', 'v>0']:
                if use_enu is True:
                    enu, anum = self.get_ENU_antpos()
                    anum = anum.tolist()
                    uvw_array_use = np.zeros_like(self.uvw_array)
                    for i, bl in enumerate(self.baseline_array):
                        a1, a2 = self.ant_1_array[i], self.ant_2_array[i]
                        i1, i2 = anum.index(a1), anum.index(a2)
                        uvw_array_use[i, :] = enu[i2] - enu[i1]
                else:
                    uvw_array_use = copy.copy(self.uvw_array)

            if convention == 'ant1<ant2':
                index_array = np.asarray(self.ant_1_array > self.ant_2_array).nonzero()
            elif convention == 'ant2<ant1':
                index_array = np.asarray(self.ant_2_array > self.ant_1_array).nonzero()
            elif convention == 'u<0':
                index_array = np.asarray((uvw_array_use[:, 0] > 0)
                                         | (uvw_array_use[:, 1] < 0) & (uvw_array_use[:, 0] == 0)
                                         | ((uvw_array_use[:, 2] < 0)
                                            & (uvw_array_use[:, 0] == 0)
                                            & (uvw_array_use[:, 1] == 0))).nonzero()
            elif convention == 'u>0':
                index_array = np.asarray((uvw_array_use[:, 0] < 0)
                                         | (uvw_array_use[:, 1] < 0) & (uvw_array_use[:, 0] == 0)
                                         | ((uvw_array_use[:, 2] < 0)
                                            & (uvw_array_use[:, 0] == 0)
                                            & (uvw_array_use[:, 1] == 0))).nonzero()
            elif convention == 'v<0':
                index_array = np.asarray((uvw_array_use[:, 1] > 0)
                                         | (uvw_array_use[:, 0] < 0) & (uvw_array_use[:, 1] == 0)
                                         | ((uvw_array_use[:, 2] < 0)
                                            & (uvw_array_use[:, 0] == 0)
                                            & (uvw_array_use[:, 1] == 0))).nonzero()
            elif convention == 'v>0':
                index_array = np.asarray((uvw_array_use[:, 1] < 0)
                                         | (uvw_array_use[:, 0] < 0) & (uvw_array_use[:, 1] == 0)
                                         | ((uvw_array_use[:, 2] < 0)
                                            & (uvw_array_use[:, 0] == 0)
                                            & (uvw_array_use[:, 1] == 0))).nonzero()
        else:
            index_array = convention

        if index_array[0].size > 0:
            new_pol_inds = uvutils.reorder_conj_pols(self.polarization_array)

            self.uvw_array[index_array] *= (-1)
            orig_data_array = copy.copy(self.data_array)

            for pol_ind in np.arange(self.Npols):
                self.data_array[index_array, :, :, new_pol_inds[pol_ind]] = \
                    np.conj(orig_data_array[index_array, :, :, pol_ind])

            ant_1_vals = self.ant_1_array[index_array]
            ant_2_vals = self.ant_2_array[index_array]
            self.ant_1_array[index_array] = ant_2_vals
            self.ant_2_array[index_array] = ant_1_vals
            self.baseline_array[index_array] = self.antnums_to_baseline(
                self.ant_1_array[index_array], self.ant_2_array[index_array])
            self.Nbls = np.unique(self.baseline_array).size

    def reorder_pols(self, order='AIPS', run_check=True, check_extra=True,
                     run_check_acceptability=True):
        """
        Rearrange polarizations in the event they are not uvfits compatible.

        Parameters
        ----------
        order : str
            Either a string specifying a cannonical ordering ('AIPS' or 'CASA')
            or an index array of length Npols that specifies how to shuffle the
            data (this is not the desired final pol order).
            CASA ordering has cross-pols in between (e.g. XX,XY,YX,YY)
            AIPS ordering has auto-pols followed by cross-pols (e.g. XX,YY,XY,YX)
            Default ('AIPS') will sort by absolute value of pol values.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after reordering.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reordering.

        Raises
        ------
        ValueError
            If the order is not one of the allowed values.
        """
        if isinstance(order, (np.ndarray, list, tuple)):
            order = np.array(order)
            if (order.size != self.Npols
                    or order.dtype not in [int, np.int, np.int32, np.int64]
                    or np.min(order) < 0 or np.max(order) >= self.Npols):
                raise ValueError('If order is an index array, it must '
                                 'contain integers and be length Npols.')
            index_array = order
        elif order == 'AIPS':
            index_array = np.argsort(np.abs(self.polarization_array))
        elif order == 'CASA':
            casa_order = np.array([1, 2, 3, 4, -1, -3, -4, -2, -5, -7, -8, -6])
            pol_inds = []
            for pol in self.polarization_array:
                pol_inds.append(np.where(casa_order == pol)[0][0])
            index_array = np.argsort(pol_inds)
        else:
            raise ValueError("order must be one of: 'AIPS', 'CASA', or an "
                             "index array of length Npols")

        self.polarization_array = self.polarization_array[index_array]
        self.data_array = self.data_array[:, :, :, index_array]
        self.nsample_array = self.nsample_array[:, :, :, index_array]
        self.flag_array = self.flag_array[:, :, :, index_array]

        # check if object is self-consistent
        if run_check:
            self.check(check_extra=check_extra,
                       run_check_acceptability=run_check_acceptability)

    def order_pols(self, order='AIPS'):
        """
        Will be deprecated in version 1.5, now just calls reorder_pols.

        Parameters
        ----------
        order : str
            either 'CASA' or 'AIPS'.

        Raises
        ------
        ValueError
            If the order is not one of the allowed values.

        Warns
        -----
        DeprecationWarning
            Always, because this method will be deprecated in version 1.5
        """
        warnings.warn('order_pols method will be deprecated in favor of '
                      'reorder_pols in version 1.5', DeprecationWarning)
        self.reorder_pols(order=order)

    def reorder_blts(self, order='time', minor_order=None, conj_convention=None,
                     conj_convention_use_enu=True, run_check=True, check_extra=True,
                     run_check_acceptability=True):
        """
        Arrange blt axis according to desired order. Optionally conjugate some baselines.

        Parameters
        ----------
        order : str or array_like of int
            A string describing the desired order along the blt axis.
            Options are: `time`, `baseline`, `ant1`, `ant2`, `bda` or an
            index array of length Nblts that specifies the new order.
        minor_order : str
            Optionally specify a secondary ordering. Default depends on how
            order is set: if order is 'time', this defaults to `baseline`,
            if order is `ant1`, or `ant2` this defaults to the other antenna,
            if order is `baseline` the only allowed value is `time`. Ignored if
            order is `bda` If this is the same as order, it is reset to the default.
        conj_convention : str or array_like of int
            Optionally conjugate baselines to make the baselines have the
            desired orientation. See conjugate_bls for allowed values and details.
        conj_convention_use_enu: bool
            If `conj_convention` is set, this is passed to conjugate_bls, see that
            method for details.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after reordering.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reordering.

        Raises
        ------
        ValueError
            If parameter values are inappropriate
        """
        if isinstance(order, (np.ndarray, list, tuple)):
            order = np.array(order)
            if (order.size != self.Nblts
                    or order.dtype not in [int, np.int, np.int32, np.int64]):
                raise ValueError('If order is an index array, it must '
                                 'contain integers and be length Nblts.')
            if minor_order is not None:
                raise ValueError('Minor order cannot be set if order is an index array.')
        else:
            if order not in ['time', 'baseline', 'ant1', 'ant2', 'bda']:
                raise ValueError("order must be one of 'time', 'baseline', "
                                 "'ant1', 'ant2', 'bda' or an index array of "
                                 "length Nblts")

            if minor_order == order:
                minor_order = None

            if minor_order is not None:
                if minor_order not in ['time', 'baseline', 'ant1', 'ant2']:
                    raise ValueError("minor_order can only be one of 'time', "
                                     "'baseline', 'ant1', 'ant2'")
                if isinstance(order, np.ndarray) or order == 'bda':
                    raise ValueError("minor_order cannot be specified if order is "
                                     "'bda' or an index array.")
                if order == 'baseline':
                    if minor_order in ['ant1', 'ant2']:
                        raise ValueError('minor_order conflicts with order')
            else:
                if order == 'time':
                    minor_order = 'baseline'
                elif order == 'ant1':
                    minor_order = 'ant2'
                elif order == 'ant2':
                    minor_order = 'ant1'
                elif order == 'baseline':
                    minor_order = 'time'

        if conj_convention is not None:
            self.conjugate_bls(convention=conj_convention,
                               use_enu=conj_convention_use_enu)

        if isinstance(order, str):
            if minor_order is None:
                self.blt_order = (order,)
                self._blt_order.form = (1,)
            else:
                self.blt_order = (order, minor_order)
                # set it back to the right shape in case it was set differently before
                self._blt_order.form = (2,)
        else:
            self.blt_order = None

        if not isinstance(order, np.ndarray):
            # Use lexsort to sort along different arrays in defined order.
            if order == 'time':
                arr1 = self.time_array
                if minor_order == 'ant1':
                    arr2 = self.ant_1_array
                    arr3 = self.ant_2_array
                elif minor_order == 'ant2':
                    arr2 = self.ant_2_array
                    arr3 = self.ant_1_array
                else:
                    # minor_order is baseline
                    arr2 = self.baseline_array
                    arr3 = self.baseline_array
            elif order == 'ant1':
                arr1 = self.ant_1_array
                if minor_order == 'time':
                    arr2 = self.time_array
                    arr3 = self.ant_2_array
                elif minor_order == 'ant2':
                    arr2 = self.ant_2_array
                    arr3 = self.time_array
                else:  # minor_order is baseline
                    arr2 = self.baseline_array
                    arr3 = self.time_array
            elif order == 'ant2':
                arr1 = self.ant_2_array
                if minor_order == 'time':
                    arr2 = self.time_array
                    arr3 = self.ant_1_array
                elif minor_order == 'ant1':
                    arr2 = self.ant_1_array
                    arr3 = self.time_array
                else:
                    # minor_order is baseline
                    arr2 = self.baseline_array
                    arr3 = self.time_array
            elif order == 'baseline':
                arr1 = self.baseline_array
                # only allowed minor order is time
                arr2 = self.time_array
                arr3 = self.time_array
            elif order == 'bda':
                arr1 = self.integration_time
                # only allowed minor order is time
                arr2 = self.baseline_array
                arr3 = self.time_array

            # lexsort uses the listed arrays from last to first (so the primary sort is on the last one)
            index_array = np.lexsort((arr3, arr2, arr1))
        else:
            index_array = order

        # actually do the reordering
        self.ant_1_array = self.ant_1_array[index_array]
        self.ant_2_array = self.ant_2_array[index_array]
        self.baseline_array = self.baseline_array[index_array]
        self.uvw_array = self.uvw_array[index_array, :]
        self.time_array = self.time_array[index_array]
        self.lst_array = self.lst_array[index_array]
        self.integration_time = self.integration_time[index_array]
        self.data_array = self.data_array[index_array, :, :, :]
        self.flag_array = self.flag_array[index_array, :, :, :]
        self.nsample_array = self.nsample_array[index_array, :, :, :]

        # check if object is self-consistent
        if run_check:
            self.check(check_extra=check_extra,
                       run_check_acceptability=run_check_acceptability)

    def __add__(self, other, run_check=True, check_extra=True,
                run_check_acceptability=True, inplace=False):
        """
        Combine two UVData objects along frequency, polarization and/or baseline-time.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after combining objects.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            combining objects.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.

        Raises
        ------
        ValueError
            If other is not a UVData object, self and other are not compatible
            or if data in self and other overlap.
        """
        if inplace:
            this = self
        else:
            this = copy.deepcopy(self)
        # Check that both objects are UVData and valid
        this.check(check_extra=check_extra, run_check_acceptability=run_check_acceptability)
        if not issubclass(other.__class__, this.__class__):
            if not issubclass(this.__class__, other.__class__):
                raise ValueError('Only UVData (or subclass) objects can be '
                                 'added to a UVData (or subclass) object')
        other.check(check_extra=check_extra, run_check_acceptability=run_check_acceptability)

        # Define parameters that must be the same to add objects
        # But phase_center should be the same, even if in drift (empty parameters)
        compatibility_params = ['_vis_units', '_channel_width', '_object_name',
                                '_telescope_name', '_instrument',
                                '_telescope_location', '_phase_type',
                                '_Nants_telescope', '_antenna_names',
                                '_antenna_numbers', '_antenna_positions',
                                '_phase_center_ra', '_phase_center_dec',
                                '_phase_center_epoch']

        # Build up history string
        history_update_string = ' Combined data along '
        n_axes = 0

        # Create blt arrays for convenience
        prec_t = - 2 * \
            np.floor(np.log10(this._time_array.tols[-1])).astype(int)
        prec_b = 8
        this_blts = np.array(["_".join(["{1:.{0}f}".format(prec_t, blt[0]),
                                        str(blt[1]).zfill(prec_b)]) for blt in
                              zip(this.time_array, this.baseline_array)])
        other_blts = np.array(["_".join(["{1:.{0}f}".format(prec_t, blt[0]),
                                         str(blt[1]).zfill(prec_b)]) for blt in
                               zip(other.time_array, other.baseline_array)])
        # Check we don't have overlapping data
        both_pol, this_pol_ind, other_pol_ind = np.intersect1d(
            this.polarization_array, other.polarization_array, return_indices=True)
        both_freq, this_freq_ind, other_freq_ind = np.intersect1d(
            this.freq_array[0, :], other.freq_array[0, :], return_indices=True)
        both_blts, this_blts_ind, other_blts_ind = np.intersect1d(
            this_blts, other_blts, return_indices=True)
        if len(both_pol) > 0:
            if len(both_freq) > 0:
                if len(both_blts) > 0:
                    # check that overlapping data is not valid
                    this_all_zero = np.all(this.data_array[this_blts_ind][
                        :, :, this_freq_ind][:, :, :, this_pol_ind] == 0)
                    this_all_flag = np.all(this.flag_array[this_blts_ind][
                        :, :, this_freq_ind][:, :, :, this_pol_ind])
                    other_all_zero = np.all(other.data_array[other_blts_ind][
                        :, :, other_freq_ind][:, :, :, other_pol_ind] == 0)
                    other_all_flag = np.all(other.flag_array[other_blts_ind][
                        :, :, other_freq_ind][:, :, :, other_pol_ind])
                    if (this_all_zero and this_all_flag):
                        # we're fine to overwrite; update history accordingly
                        history_update_string = ' Overwrote invalid data using pyuvdata.'
                        this.history += history_update_string
                    elif (other_all_zero and other_all_flag):
                        raise ValueError('To combine these data, please run the add operation again, '
                                         'but with the object whose data is to be overwritten as the '
                                         'first object in the add operation.')
                    else:
                        raise ValueError('These objects have overlapping data and'
                                         ' cannot be combined.')

        # find the blt indices in "other" but not in "this"
        temp = np.nonzero(~np.in1d(other_blts, this_blts))[0]
        if len(temp) > 0:
            bnew_inds = temp
            new_blts = other_blts[temp]
            history_update_string += 'baseline-time'
            n_axes += 1
        else:
            bnew_inds, new_blts = ([], [])
            # add metadata to be checked to compatibility params
            extra_params = ['_integration_time', '_uvw_array', '_lst_array']
            compatibility_params.extend(extra_params)

        # find the freq indices in "other" but not in "this"
        temp = np.nonzero(
            ~np.in1d(other.freq_array[0, :], this.freq_array[0, :]))[0]
        if len(temp) > 0:
            fnew_inds = temp
            new_freqs = other.freq_array[0, temp]
            if n_axes > 0:
                history_update_string += ', frequency'
            else:
                history_update_string += 'frequency'
            n_axes += 1
        else:
            fnew_inds, new_freqs = ([], [])

        # find the pol indices in "other" but not in "this"
        temp = np.nonzero(~np.in1d(other.polarization_array,
                                   this.polarization_array))[0]
        if len(temp) > 0:
            pnew_inds = temp
            new_pols = other.polarization_array[temp]
            if n_axes > 0:
                history_update_string += ', polarization'
            else:
                history_update_string += 'polarization'
            n_axes += 1
        else:
            pnew_inds, new_pols = ([], [])

        # Actually check compatibility parameters
        for a in compatibility_params:
            if a == "_integration_time":
                # only check that overlapping blt indices match
                params_match = np.allclose(this.integration_time[this_blts_ind],
                                           other.integration_time[other_blts_ind],
                                           rtol=this._integration_time.tols[0],
                                           atol=this._integration_time.tols[1])
            elif a == "_uvw_array":
                # only check that overlapping blt indices match
                params_match = np.allclose(this.uvw_array[this_blts_ind, :],
                                           other.uvw_array[other_blts_ind, :],
                                           rtol=this._uvw_array.tols[0],
                                           atol=this._uvw_array.tols[1])
            elif a == "_lst_array":
                # only check that overlapping blt indices match
                params_match = np.allclose(this.lst_array[this_blts_ind],
                                           other.lst_array[other_blts_ind],
                                           rtol=this._lst_array.tols[0],
                                           atol=this._lst_array.tols[1])
            else:
                params_match = (getattr(this, a) == getattr(other, a))
            if not params_match:
                msg = 'UVParameter ' + \
                    a[1:] + ' does not match. Cannot combine objects.'
                raise ValueError(msg)

        # Pad out self to accommodate new data
        if len(bnew_inds) > 0:
            this_blts = np.concatenate((this_blts, new_blts))
            blt_order = np.argsort(this_blts)
            zero_pad = np.zeros(
                (len(bnew_inds), this.Nspws, this.Nfreqs, this.Npols))
            this.data_array = np.concatenate([this.data_array, zero_pad], axis=0)
            this.nsample_array = np.concatenate([this.nsample_array, zero_pad], axis=0)
            this.flag_array = np.concatenate([this.flag_array,
                                              1 - zero_pad], axis=0).astype(np.bool)
            this.uvw_array = np.concatenate([this.uvw_array,
                                             other.uvw_array[bnew_inds, :]], axis=0)[blt_order, :]
            this.time_array = np.concatenate([this.time_array,
                                              other.time_array[bnew_inds]])[blt_order]
            this.integration_time = np.concatenate([this.integration_time,
                                                    other.integration_time[bnew_inds]])[blt_order]
            this.lst_array = np.concatenate(
                [this.lst_array, other.lst_array[bnew_inds]])[blt_order]
            this.ant_1_array = np.concatenate([this.ant_1_array,
                                               other.ant_1_array[bnew_inds]])[blt_order]
            this.ant_2_array = np.concatenate([this.ant_2_array,
                                               other.ant_2_array[bnew_inds]])[blt_order]
            this.baseline_array = np.concatenate([this.baseline_array,
                                                  other.baseline_array[bnew_inds]])[blt_order]

        if len(fnew_inds) > 0:
            zero_pad = np.zeros((this.data_array.shape[0], this.Nspws, len(fnew_inds),
                                 this.Npols))
            this.freq_array = np.concatenate([this.freq_array,
                                              other.freq_array[:, fnew_inds]], axis=1)
            f_order = np.argsort(this.freq_array[0, :])
            this.data_array = np.concatenate([this.data_array, zero_pad], axis=2)
            this.nsample_array = np.concatenate([this.nsample_array, zero_pad], axis=2)
            this.flag_array = np.concatenate([this.flag_array, 1 - zero_pad],
                                             axis=2).astype(np.bool)
        if len(pnew_inds) > 0:
            zero_pad = np.zeros((this.data_array.shape[0], this.Nspws,
                                 this.data_array.shape[2], len(pnew_inds)))
            this.polarization_array = np.concatenate([this.polarization_array,
                                                      other.polarization_array[pnew_inds]])
            p_order = np.argsort(np.abs(this.polarization_array))
            this.data_array = np.concatenate([this.data_array, zero_pad], axis=3)
            this.nsample_array = np.concatenate([this.nsample_array, zero_pad], axis=3)
            this.flag_array = np.concatenate([this.flag_array, 1 - zero_pad],
                                             axis=3).astype(np.bool)

        # Now populate the data
        pol_t2o = np.nonzero(
            np.in1d(this.polarization_array, other.polarization_array))[0]
        freq_t2o = np.nonzero(
            np.in1d(this.freq_array[0, :], other.freq_array[0, :]))[0]
        blt_t2o = np.nonzero(np.in1d(this_blts, other_blts))[0]
        this.data_array[np.ix_(blt_t2o, [0], freq_t2o,
                               pol_t2o)] = other.data_array
        this.nsample_array[np.ix_(
            blt_t2o, [0], freq_t2o, pol_t2o)] = other.nsample_array
        this.flag_array[np.ix_(blt_t2o, [0], freq_t2o,
                               pol_t2o)] = other.flag_array
        if len(bnew_inds) > 0:
            this.data_array = this.data_array[blt_order, :, :, :]
            this.nsample_array = this.nsample_array[blt_order, :, :, :]
            this.flag_array = this.flag_array[blt_order, :, :, :]
        if len(fnew_inds) > 0:
            this.freq_array = this.freq_array[:, f_order]
            this.data_array = this.data_array[:, :, f_order, :]
            this.nsample_array = this.nsample_array[:, :, f_order, :]
            this.flag_array = this.flag_array[:, :, f_order, :]
        if len(pnew_inds) > 0:
            this.polarization_array = this.polarization_array[p_order]
            this.data_array = this.data_array[:, :, :, p_order]
            this.nsample_array = this.nsample_array[:, :, :, p_order]
            this.flag_array = this.flag_array[:, :, :, p_order]

        # Update N parameters (e.g. Npols)
        this.Ntimes = len(np.unique(this.time_array))
        this.Nbls = len(np.unique(this.baseline_array))
        this.Nblts = this.uvw_array.shape[0]
        this.Nfreqs = this.freq_array.shape[1]
        this.Npols = this.polarization_array.shape[0]
        this.Nants_data = len(
            np.unique(this.ant_1_array.tolist() + this.ant_2_array.tolist()))

        # Check specific requirements
        if this.Nfreqs > 1:
            freq_separation = np.diff(this.freq_array[0, :])
            if not np.isclose(np.min(freq_separation), np.max(freq_separation),
                              rtol=this._freq_array.tols[0], atol=this._freq_array.tols[1]):
                warnings.warn('Combined frequencies are not evenly spaced. This will '
                              'make it impossible to write this data out to some file types.')
            elif np.max(freq_separation) > this.channel_width:
                warnings.warn('Combined frequencies are not contiguous. This will make '
                              'it impossible to write this data out to some file types.')

        if this.Npols > 2:
            pol_separation = np.diff(this.polarization_array)
            if np.min(pol_separation) < np.max(pol_separation):
                warnings.warn('Combined polarizations are not evenly spaced. This will '
                              'make it impossible to write this data out to some file types.')

        if n_axes > 0:
            history_update_string += ' axis using pyuvdata.'
            this.history += history_update_string

        this.history = uvutils._combine_histories(this.history, other.history)

        # Check final object is self-consistent
        if run_check:
            this.check(check_extra=check_extra,
                       run_check_acceptability=run_check_acceptability)

        if not inplace:
            return this

    def __iadd__(self, other):
        """
        In place add.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.

        Raises
        ------
        ValueError
            If other is not a UVData object, self and other are not compatible
            or if data in self and other overlap.
        """
        self.__add__(other, inplace=True)
        return self

    def fast_concat(self, other, axis, run_check=True, check_extra=True,
                    run_check_acceptability=True, inplace=False):
        """
        Concatenate two UVData objects along specified axis with almost no checking of metadata.

        Warning! This method assumes all the metadata along other axes is sorted
        the same way. The __add__ method is much safer, it checks all the metadata,
        but it is slower. Some quick checks are run, but this method doesn't
        make any guarantees that the resulting object is correct.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. Allowed values are: 'blt', 'freq', 'polarization'.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after combining objects.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            combining objects.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.

        Raises
        ------
        ValueError
            If other is not a UVData object, axis is not an allowed value or if
            self and other are not compatible.
        """
        if inplace:
            this = self
        else:
            this = copy.deepcopy(self)
        # Check that both objects are UVData and valid
        this.check(check_extra=check_extra, run_check_acceptability=run_check_acceptability)
        if not issubclass(other.__class__, this.__class__):
            if not issubclass(this.__class__, other.__class__):
                raise ValueError('Only UVData (or subclass) objects can be '
                                 'added to a UVData (or subclass) object')
        other.check(check_extra=check_extra, run_check_acceptability=run_check_acceptability)

        allowed_axes = ['blt', 'freq', 'polarization']
        if axis not in allowed_axes:
            raise ValueError('If axis is specifed it must be one of: '
                             + ', '.join(allowed_axes))

        compatibility_params = ['_vis_units', '_channel_width', '_object_name',
                                '_telescope_name', '_instrument',
                                '_telescope_location', '_phase_type',
                                '_Nants_telescope', '_antenna_names',
                                '_antenna_numbers', '_antenna_positions',
                                '_phase_center_ra', '_phase_center_dec',
                                '_phase_center_epoch']

        history_update_string = ' Combined data along '

        if axis == 'freq':
            history_update_string += 'frequency'
            compatibility_params += ['_polarization_array', '_ant_1_array',
                                     '_ant_2_array', '_integration_time',
                                     '_uvw_array', '_lst_array']
        elif axis == 'polarization':
            history_update_string += 'polarization'
            compatibility_params += ['_freq_array', '_ant_1_array',
                                     '_ant_2_array', '_integration_time',
                                     '_uvw_array', '_lst_array']
        elif axis == 'blt':
            history_update_string += 'baseline-time'
            compatibility_params += ['_freq_array', '_polarization_array']

        history_update_string += ' axis using pyuvdata.'
        this.history += history_update_string

        this.history = uvutils._combine_histories(this.history, other.history)

        # Actually check compatibility parameters
        for a in compatibility_params:
            params_match = (getattr(this, a) == getattr(other, a))
            if not params_match:
                msg = 'UVParameter ' + \
                    a[1:] + ' does not match. Cannot combine objects.'
                raise ValueError(msg)

        if axis == 'freq':
            this.freq_array = np.concatenate([this.freq_array, other.freq_array], axis=1)
            this.Nfreqs = this.Nfreqs + other.Nfreqs

            freq_separation = np.diff(this.freq_array[0, :])
            if not np.isclose(np.min(freq_separation), np.max(freq_separation),
                              rtol=this._freq_array.tols[0], atol=this._freq_array.tols[1]):
                warnings.warn('Combined frequencies are not evenly spaced. This will '
                              'make it impossible to write this data out to some file types.')
            elif np.max(freq_separation) > this.channel_width:
                warnings.warn('Combined frequencies are not contiguous. This will make '
                              'it impossible to write this data out to some file types.')

            this.data_array = np.concatenate([this.data_array, other.data_array], axis=2)
            this.nsample_array = np.concatenate([this.nsample_array, other.nsample_array], axis=2)
            this.flag_array = np.concatenate([this.flag_array, other.flag_array], axis=2)
        elif axis == 'polarization':
            this.polarization_array = np.concatenate([this.polarization_array,
                                                     other.polarization_array])
            this.Npols = this.Npols + other.Npols

            pol_separation = np.diff(this.polarization_array)
            if np.min(pol_separation) < np.max(pol_separation):
                warnings.warn('Combined polarizations are not evenly spaced. This will '
                              'make it impossible to write this data out to some file types.')

            this.data_array = np.concatenate([this.data_array, other.data_array], axis=3)
            this.nsample_array = np.concatenate([this.nsample_array, other.nsample_array], axis=3)
            this.flag_array = np.concatenate([this.flag_array, other.flag_array], axis=3)
        elif axis == 'blt':
            this.Nblts = this.Nblts + other.Nblts
            this.ant_1_array = np.concatenate([this.ant_1_array,
                                              other.ant_1_array])
            this.ant_2_array = np.concatenate([this.ant_2_array,
                                              other.ant_2_array])
            this.Nants_data = int(len(np.unique(self.ant_1_array.tolist()
                                                + self.ant_2_array.tolist())))
            this.uvw_array = np.concatenate([this.uvw_array,
                                            other.uvw_array], axis=0)
            this.time_array = np.concatenate([this.time_array,
                                             other.time_array])
            this.Ntimes = len(np.unique(this.time_array))
            this.lst_array = np.concatenate([this.lst_array,
                                            other.lst_array])
            this.baseline_array = np.concatenate([this.baseline_array,
                                                 other.baseline_array])
            this.Nbls = len(np.unique(this.baseline_array))
            this.integration_time = np.concatenate([this.integration_time,
                                                   other.integration_time])
            this.data_array = np.concatenate([this.data_array, other.data_array], axis=0)
            this.nsample_array = np.concatenate([this.nsample_array, other.nsample_array], axis=0)
            this.flag_array = np.concatenate([this.flag_array, other.flag_array], axis=0)

        # Check final object is self-consistent
        if run_check:
            this.check(check_extra=check_extra,
                       run_check_acceptability=run_check_acceptability)

        if not inplace:
            return this

    def _select_preprocess(self, antenna_nums, antenna_names, ant_str, bls,
                           frequencies, freq_chans, times, polarizations, blt_inds):
        """
        Internal function to build up blt_inds, freq_inds, pol_inds
        and history_update_string for select.

        Parameters
        ----------
        antenna_nums : array_like of int, optional
            The antennas numbers to keep in 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.
        antenna_names : array_like of str, optional
            The antennas names to keep in 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.
        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 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, `polarizations` must be None.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to keep in 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.
        frequencies : array_like of float, optional
            The frequencies to keep in the object, each value passed here should
            exist in the freq_array.
        freq_chans : array_like of int, optional
            The frequency channel numbers to keep in the object.
        times : array_like of float, optional
            The times to keep in the object, each value passed here should
            exist in the time_array.
        polarizations : array_like of int, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array.
        blt_inds : array_like of int, optional
            The baseline-time indices to keep in the object. This is
            not commonly used.

        Returns
        -------
        blt_inds : list of int
            list of baseline-time indices to keep. Can be None (to keep everything).
        freq_inds : list of int
            list of frequency indices to keep. Can be None (to keep everything).
        pol_inds : list of int
            list of polarization indices to keep. Can be None (to keep everything).
        history_update_string : str
            string to append to the end of the history.
        """
        # build up history string as we go
        history_update_string = '  Downselected to specific '
        n_selects = 0

        if ant_str is not None:
            if not (antenna_nums is None and antenna_names is None
                    and bls is None and polarizations is None):
                raise ValueError(
                    'Cannot provide ant_str with antenna_nums, antenna_names, '
                    'bls, or polarizations.')
            else:
                bls, polarizations = self.parse_ants(ant_str)

        # Antennas, times and blt_inds all need to be combined into a set of
        # blts indices to keep.

        # test for blt_inds presence before adding inds from antennas & times
        if blt_inds is not None:
            blt_inds = uvutils._get_iterable(blt_inds)
            if np.array(blt_inds).ndim > 1:
                blt_inds = np.array(blt_inds).flatten()
            history_update_string += 'baseline-times'
            n_selects += 1

        if antenna_names is not None:
            if antenna_nums is not None:
                raise ValueError(
                    'Only one of antenna_nums and antenna_names can be provided.')

            if not isinstance(antenna_names, (list, tuple, np.ndarray)):
                antenna_names = (antenna_names,)
            if np.array(antenna_names).ndim > 1:
                antenna_names = np.array(antenna_names).flatten()
            antenna_nums = []
            for s in antenna_names:
                if s not in self.antenna_names:
                    raise ValueError(
                        'Antenna name {a} is not present in the antenna_names array'.format(a=s))
                antenna_nums.append(self.antenna_numbers[np.where(
                    np.array(self.antenna_names) == s)][0])

        if antenna_nums is not None:
            antenna_nums = uvutils._get_iterable(antenna_nums)
            if np.array(antenna_nums).ndim > 1:
                antenna_nums = np.array(antenna_nums).flatten()
            if n_selects > 0:
                history_update_string += ', antennas'
            else:
                history_update_string += 'antennas'
            n_selects += 1
            inds1 = np.zeros(0, dtype=np.int)
            inds2 = np.zeros(0, dtype=np.int)
            for ant in antenna_nums:
                if ant in self.ant_1_array or ant in self.ant_2_array:
                    wh1 = np.where(self.ant_1_array == ant)[0]
                    wh2 = np.where(self.ant_2_array == ant)[0]
                    if len(wh1) > 0:
                        inds1 = np.append(inds1, list(wh1))
                    if len(wh2) > 0:
                        inds2 = np.append(inds2, list(wh2))
                else:
                    raise ValueError('Antenna number {a} is not present in the '
                                     'ant_1_array or ant_2_array'.format(a=ant))

            ant_blt_inds = np.array(
                list(set(inds1).intersection(inds2)), dtype=np.int)
        else:
            ant_blt_inds = None

        if bls is not None:
            if isinstance(bls, tuple) and (len(bls) == 2 or len(bls) == 3):
                bls = [bls]
            if not all(isinstance(item, tuple) for item in bls):
                raise ValueError(
                    'bls must be a list of tuples of antenna numbers (optionally with polarization).')
            if not all([isinstance(item[0], six.integer_types + (np.integer,)) for item in bls]
                       + [isinstance(item[1], six.integer_types + (np.integer,)) for item in bls]):
                raise ValueError(
                    'bls must be a list of tuples of antenna numbers (optionally with polarization).')
            if all([len(item) == 3 for item in bls]):
                if polarizations is not None:
                    raise ValueError('Cannot provide length-3 tuples and also specify polarizations.')
                if not all([isinstance(item[2], str) for item in bls]):
                    raise ValueError('The third element in each bl must be a polarization string')

            if ant_str is None:
                if n_selects > 0:
                    history_update_string += ', baselines'
                else:
                    history_update_string += 'baselines'
            else:
                history_update_string += 'antenna pairs'
            n_selects += 1
            bls_blt_inds = np.zeros(0, dtype=np.int)
            bl_pols = set()
            for bl in bls:
                if not (bl[0] in self.ant_1_array or bl[0] in self.ant_2_array):
                    raise ValueError('Antenna number {a} is not present in the '
                                     'ant_1_array or ant_2_array'.format(a=bl[0]))
                if not (bl[1] in self.ant_1_array or bl[1] in self.ant_2_array):
                    raise ValueError('Antenna number {a} is not present in the '
                                     'ant_1_array or ant_2_array'.format(a=bl[1]))
                wh1 = np.where(np.logical_and(
                    self.ant_1_array == bl[0], self.ant_2_array == bl[1]))[0]
                wh2 = np.where(np.logical_and(
                    self.ant_1_array == bl[1], self.ant_2_array == bl[0]))[0]
                if len(wh1) > 0:
                    bls_blt_inds = np.append(bls_blt_inds, list(wh1))
                    if len(bl) == 3:
                        bl_pols.add(bl[2])
                elif len(wh2) > 0:
                    bls_blt_inds = np.append(bls_blt_inds, list(wh2))
                    if len(bl) == 3:
                        bl_pols.add(bl[2][::-1])  # reverse polarization string
                else:
                    raise ValueError('Antenna pair {p} does not have any data '
                                     'associated with it.'.format(p=bl))
            if len(bl_pols) > 0:
                polarizations = list(bl_pols)

            if ant_blt_inds is not None:
                # Use union (or) to join antenna_names/nums & ant_pairs_nums
                ant_blt_inds = np.array(list(set(ant_blt_inds).union(bls_blt_inds)))
            else:
                ant_blt_inds = bls_blt_inds

        if ant_blt_inds is not None:
            if blt_inds is not None:
                # Use intesection (and) to join antenna_names/nums/ant_pairs_nums with blt_inds
                # handled differently because of the time aspect (which is anded with antennas below)
                blt_inds = np.array(
                    list(set(blt_inds).intersection(ant_blt_inds)), dtype=np.int)
            else:
                blt_inds = ant_blt_inds

        if times is not None:
            times = uvutils._get_iterable(times)
            if np.array(times).ndim > 1:
                times = np.array(times).flatten()
            if n_selects > 0:
                history_update_string += ', times'
            else:
                history_update_string += 'times'
            n_selects += 1

            time_blt_inds = np.zeros(0, dtype=np.int)
            for jd in times:
                if jd in self.time_array:
                    time_blt_inds = np.append(
                        time_blt_inds, np.where(self.time_array == jd)[0])
                else:
                    raise ValueError(
                        'Time {t} is not present in the time_array'.format(t=jd))

            if blt_inds is not None:
                # Use intesection (and) to join antenna_names/nums/ant_pairs_nums/blt_inds with times
                blt_inds = np.array(
                    list(set(blt_inds).intersection(time_blt_inds)), dtype=np.int)
            else:
                blt_inds = time_blt_inds

        if blt_inds is not None:
            if len(blt_inds) == 0:
                raise ValueError(
                    'No baseline-times were found that match criteria')
            if max(blt_inds) >= self.Nblts:
                raise ValueError(
                    'blt_inds contains indices that are too large')
            if min(blt_inds) < 0:
                raise ValueError('blt_inds contains indices that are negative')

            blt_inds = list(sorted(set(list(blt_inds))))

        if freq_chans is not None:
            freq_chans = uvutils._get_iterable(freq_chans)
            if np.array(freq_chans).ndim > 1:
                freq_chans = np.array(freq_chans).flatten()
            if frequencies is None:
                frequencies = self.freq_array[0, freq_chans]
            else:
                frequencies = uvutils._get_iterable(frequencies)
                frequencies = np.sort(list(set(frequencies)
                                           | set(self.freq_array[0, freq_chans])))

        if frequencies is not None:
            frequencies = uvutils._get_iterable(frequencies)
            if np.array(frequencies).ndim > 1:
                frequencies = np.array(frequencies).flatten()
            if n_selects > 0:
                history_update_string += ', frequencies'
            else:
                history_update_string += 'frequencies'
            n_selects += 1

            freq_inds = np.zeros(0, dtype=np.int)
            # this works because we only allow one SPW. This will have to be reworked when we support more.
            freq_arr_use = self.freq_array[0, :]
            for f in frequencies:
                if f in freq_arr_use:
                    freq_inds = np.append(
                        freq_inds, np.where(freq_arr_use == f)[0])
                else:
                    raise ValueError(
                        'Frequency {f} is not present in the freq_array'.format(f=f))

            if len(frequencies) > 1:
                freq_ind_separation = freq_inds[1:] - freq_inds[:-1]
                if np.min(freq_ind_separation) < np.max(freq_ind_separation):
                    warnings.warn('Selected frequencies are not evenly spaced. This '
                                  'will make it impossible to write this data out to '
                                  'some file types')
                elif np.max(freq_ind_separation) > 1:
                    warnings.warn('Selected frequencies are not contiguous. This '
                                  'will make it impossible to write this data out to '
                                  'some file types.')

            freq_inds = list(sorted(set(list(freq_inds))))
        else:
            freq_inds = None

        if polarizations is not None:
            polarizations = uvutils._get_iterable(polarizations)
            if np.array(polarizations).ndim > 1:
                polarizations = np.array(polarizations).flatten()
            if n_selects > 0:
                history_update_string += ', polarizations'
            else:
                history_update_string += 'polarizations'
            n_selects += 1

            pol_inds = np.zeros(0, dtype=np.int)
            for p in polarizations:
                if isinstance(p, str):
                    p_num = uvutils.polstr2num(p, x_orientation=self.x_orientation)
                else:
                    p_num = p
                if p_num in self.polarization_array:
                    pol_inds = np.append(pol_inds, np.where(
                        self.polarization_array == p_num)[0])
                else:
                    raise ValueError(
                        'Polarization {p} is not present in the polarization_array'.format(p=p))

            if len(pol_inds) > 2:
                pol_ind_separation = pol_inds[1:] - pol_inds[:-1]
                if np.min(pol_ind_separation) < np.max(pol_ind_separation):
                    warnings.warn('Selected polarization values are not evenly spaced. This '
                                  'will make it impossible to write this data out to '
                                  'some file types')

            pol_inds = list(sorted(set(list(pol_inds))))
        else:
            pol_inds = None

        history_update_string += ' using pyuvdata.'

        return blt_inds, freq_inds, pol_inds, history_update_string

    def _select_metadata(self, blt_inds, freq_inds, pol_inds, history_update_string,
                         keep_all_metadata=True):
        """
        Internal function to perform select on everything except the data-sized arrays.

        Parameters
        ----------
        blt_inds : list of int
            list of baseline-time indices to keep. Can be None (to keep everything).
        freq_inds : list of int
            list of frequency indices to keep. Can be None (to keep everything).
        pol_inds : list of int
            list of polarization indices to keep. Can be None (to keep everything).
        history_update_string : str
            string to append to the end of the history.
        keep_all_metadata : bool
            Option to keep metadata for antennas that are no longer in the dataset.
        """
        if blt_inds is not None:
            self.Nblts = len(blt_inds)
            self.baseline_array = self.baseline_array[blt_inds]
            self.Nbls = len(np.unique(self.baseline_array))
            self.time_array = self.time_array[blt_inds]
            self.integration_time = self.integration_time[blt_inds]
            self.lst_array = self.lst_array[blt_inds]
            self.uvw_array = self.uvw_array[blt_inds, :]

            self.ant_1_array = self.ant_1_array[blt_inds]
            self.ant_2_array = self.ant_2_array[blt_inds]
            self.Nants_data = int(
                len(set(self.ant_1_array.tolist() + self.ant_2_array.tolist())))

            self.Ntimes = len(np.unique(self.time_array))
            if not keep_all_metadata:
                ants_to_keep = set(self.ant_1_array.tolist() + self.ant_2_array.tolist())
                inds_to_keep = [self.antenna_numbers.tolist().index(ant) for ant in ants_to_keep]
                self.antenna_names = [self.antenna_names[ind] for ind in inds_to_keep]
                self.antenna_numbers = self.antenna_numbers[inds_to_keep]
                self.antenna_positions = self.antenna_positions[inds_to_keep, :]
                if self.antenna_diameters is not None:
                    self.antenna_diameters = self.antenna_diameters[inds_to_keep]
                self.Nants_telescope = int(len(ants_to_keep))

        if freq_inds is not None:
            self.Nfreqs = len(freq_inds)
            self.freq_array = self.freq_array[:, freq_inds]

        if pol_inds is not None:
            self.Npols = len(pol_inds)
            self.polarization_array = self.polarization_array[pol_inds]

        self.history = self.history + history_update_string

    def select(self, 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=True,
               check_extra=True, run_check_acceptability=True, inplace=True,
               metadata_only=False, keep_all_metadata=True):
        """
        Downselect data to keep on the object along various axes.

        Axes that can be selected along include antenna names or numbers,
        antenna pairs, frequencies, times and polarizations. Specific
        baseline-time indices can also be selected, but this is not commonly used.
        The history attribute on the object will be updated to identify the
        operations performed.

        Parameters
        ----------
        antenna_nums : array_like of int, optional
            The antennas numbers to keep in 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.
        antenna_names : array_like of str, optional
            The antennas names to keep in 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.
        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 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, `polarizations` must be None.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to keep in 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.
        frequencies : array_like of float, optional
            The frequencies to keep in the object, each value passed here should
            exist in the freq_array.
        freq_chans : array_like of int, optional
            The frequency channel numbers to keep in the object.
        times : array_like of float, optional
            The times to keep in the object, each value passed here should
            exist in the time_array.
        polarizations : array_like of int, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array.
        blt_inds : array_like of int, optional
            The baseline-time indices to keep in the object. This is
            not commonly used.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after downselecting data on this object (the default is True,
            meaning the check will be run).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            downselecting data on this object (the default is True, meaning the
            acceptable range check will be done).
        inplace : bool
            Option to perform the select directly on self or return a new UVData
            object with just the selected data (the default is True, meaning the
            select will be done on self).
        metadata_only : bool
            Option to only do the select on the metadata. Not allowed if the
            data_array, flag_array or nsample_array is not None.
        keep_all_metadata : bool
            Option to keep all the metadata associated with antennas, even those
            that do do not have data associated with them after the select option.

        Returns
        -------
        UVData object or None
            None is returned if inplace is True, otherwise a new UVData object
            with just the selected data is returned

        Raises
        ------
        ValueError
            If any of the parameters are set to inappropriate values.
        """
        if metadata_only is True and (self.data_array is not None
                                      or self.flag_array is not None
                                      or self.nsample_array is not None):
            raise ValueError('metadata_only option cannot be used if data_array, '
                             'flag_array or nsample_array is not None')

        if inplace:
            uv_object = self
        else:
            uv_object = copy.deepcopy(self)

        blt_inds, freq_inds, pol_inds, history_update_string = \
            uv_object._select_preprocess(antenna_nums, antenna_names, ant_str, bls,
                                         frequencies, freq_chans, times, polarizations, blt_inds)

        # do select operations on everything except data_array, flag_array and nsample_array
        uv_object._select_metadata(blt_inds, freq_inds, pol_inds, history_update_string,
                                   keep_all_metadata)

        if metadata_only is True:
            if not inplace:
                return uv_object
            else:
                return

        if blt_inds is not None:
            uv_object.data_array = uv_object.data_array[blt_inds, :, :, :]
            uv_object.flag_array = uv_object.flag_array[blt_inds, :, :, :]
            uv_object.nsample_array = uv_object.nsample_array[blt_inds, :, :, :]

        if freq_inds is not None:
            uv_object.data_array = uv_object.data_array[:, :, freq_inds, :]
            uv_object.flag_array = uv_object.flag_array[:, :, freq_inds, :]
            uv_object.nsample_array = uv_object.nsample_array[:, :, freq_inds, :]

        if pol_inds is not None:
            uv_object.data_array = uv_object.data_array[:, :, :, pol_inds]
            uv_object.flag_array = uv_object.flag_array[:, :, :, pol_inds]
            uv_object.nsample_array = uv_object.nsample_array[:, :, :, pol_inds]

        # check if object is uv_object-consistent
        if run_check:
            uv_object.check(check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability)

        if not inplace:
            return uv_object

    def _convert_from_filetype(self, other):
        """
        Internal function to convert from a file-type specific object to a UVData object.

        Used in reads.

        Parameters
        ----------
        other : object that inherits from UVData
            File type specific object to convert to UVData
        """
        for p in other:
            param = getattr(other, p)
            setattr(self, p, param)

    def _convert_to_filetype(self, filetype):
        """
        Internal function to convert from a UVData object to a file-type specific object.

        Used in writes.

        Parameters
        ----------
        filetype : str
            Specifies what file type object to convert to. Options are: 'uvfits',
            'fhd', 'miriad', 'uvh5'

        Raises
        ------
        ValueError
            if filetype is not a known type
        """
        if filetype is 'uvfits':
            from . import uvfits
            other_obj = uvfits.UVFITS()
        elif filetype is 'fhd':
            from . import fhd
            other_obj = fhd.FHD()
        elif filetype is 'miriad':
            from . import miriad
            other_obj = miriad.Miriad()
        elif filetype is 'uvh5':
            from . import uvh5
            other_obj = uvh5.UVH5()
        else:
            raise ValueError('filetype must be uvfits, miriad, fhd, or uvh5')
        for p in self:
            param = getattr(self, p)
            setattr(other_obj, p, param)
        return other_obj

    def read_uvfits(self, filename, axis=None, antenna_nums=None, antenna_names=None,
                    ant_str=None, bls=None, frequencies=None,
                    freq_chans=None, times=None, polarizations=None, blt_inds=None,
                    keep_all_metadata=True, read_data=True, read_metadata=True,
                    run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read in header, metadata and data from uvfits file(s).

        Parameters
        ----------
        filename : str or list of str
            The uvfits file or list of files to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        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. Ignored if read_data is False.
        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 (if read_metadata is True) will be
            read in. Setting read_data to False results in an incompletely
            defined object (check will not pass).
        read_metadata: : bool
            Read in metadata (times, baselines, uvws) as well as basic header
            info. Only used if read_data is False (metadata will be read if data
            is read). If both read_data and read_metadata are false, only basic
            header info is read in.
        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.
        """
        from . import uvfits
        # work out what function should be called depending on what's
        # already defined on the object
        if self.freq_array is not None:
            hdr_loaded = True
        else:
            hdr_loaded = False
        if self.data_array is not None:
            data_loaded = True
        else:
            data_loaded = False

        if not read_data and not read_metadata:
            # not reading data or metadata, use read_uvfits to get header
            func = 'read_uvfits'
        elif not read_data:
            # reading metadata but not data
            if hdr_loaded:
                # header already read, use read_uvfits_metadata
                # (which will error if the data have already been read)
                func = 'read_uvfits_metadata'
            else:
                # header not read, use read_uvfits
                func = 'read_uvfits'
        else:
            # reading data
            if hdr_loaded and not data_loaded:
                # header already read, data not read, use read_uvfits_data
                # (which will read metadata if it doesn't exist)
                func = 'read_uvfits_data'
            else:
                # header not read or object already fully defined,
                # use read_uvfits to get a new object
                func = 'read_uvfits'

        if isinstance(filename, (list, tuple)):
            if not read_data:
                raise ValueError('read_data cannot be False for a list of uvfits files')
            if func == 'read_uvfits_data':
                raise ValueError('A list of files cannot be used when just reading data')

            self.read_uvfits(filename[0], antenna_nums=antenna_nums,
                             antenna_names=antenna_names, ant_str=ant_str,
                             bls=bls, frequencies=frequencies,
                             freq_chans=freq_chans, times=times,
                             polarizations=polarizations, blt_inds=blt_inds,
                             run_check=run_check, check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability,
                             keep_all_metadata=keep_all_metadata)
            if len(filename) > 1:
                for f in filename[1:]:
                    uv2 = UVData()
                    uv2.read_uvfits(f, antenna_nums=antenna_nums,
                                    antenna_names=antenna_names, ant_str=ant_str,
                                    bls=bls, frequencies=frequencies,
                                    freq_chans=freq_chans, times=times,
                                    polarizations=polarizations, blt_inds=blt_inds,
                                    run_check=run_check, check_extra=check_extra,
                                    run_check_acceptability=run_check_acceptability,
                                    keep_all_metadata=keep_all_metadata)
                    if axis is not None:
                        self.fast_concat(uv2, axis, run_check=run_check,
                                         check_extra=check_extra,
                                         run_check_acceptability=run_check_acceptability,
                                         inplace=True)
                    else:
                        self += uv2
                del(uv2)
        else:
            if func == 'read_uvfits':
                uvfits_obj = uvfits.UVFITS()
                uvfits_obj.read_uvfits(filename, antenna_nums=antenna_nums,
                                       antenna_names=antenna_names, ant_str=ant_str,
                                       bls=bls, frequencies=frequencies,
                                       freq_chans=freq_chans, times=times,
                                       polarizations=polarizations, blt_inds=blt_inds,
                                       read_data=read_data, read_metadata=read_metadata,
                                       run_check=run_check, check_extra=check_extra,
                                       run_check_acceptability=run_check_acceptability,
                                       keep_all_metadata=keep_all_metadata)
                self._convert_from_filetype(uvfits_obj)
                del(uvfits_obj)
            elif func == 'read_uvfits_metadata':
                # can only be one file, it would have errored earlier because read_data=False
                uvfits_obj = self._convert_to_filetype('uvfits')
                uvfits_obj.read_uvfits_metadata(
                    filename, run_check_acceptability=run_check_acceptability)
                self._convert_from_filetype(uvfits_obj)
                del(uvfits_obj)
            elif func == 'read_uvfits_data':
                uvfits_obj = self._convert_to_filetype('uvfits')
                uvfits_obj.read_uvfits_data(filename, antenna_nums=antenna_nums,
                                            antenna_names=antenna_names, ant_str=ant_str,
                                            bls=bls, frequencies=frequencies,
                                            freq_chans=freq_chans, times=times,
                                            polarizations=polarizations, blt_inds=blt_inds,
                                            run_check=run_check, check_extra=check_extra,
                                            run_check_acceptability=run_check_acceptability,
                                            keep_all_metadata=keep_all_metadata)
                self._convert_from_filetype(uvfits_obj)
                del(uvfits_obj)

    def write_uvfits(self, filename, spoof_nonessential=False, write_lst=True,
                     force_phase=False, run_check=True, check_extra=True,
                     run_check_acceptability=True):
        """
        Write the data to a uvfits file.

        Parameters
        ----------
        filename : str
            The uvfits file to write to.
        spoof_nonessential : bool
            Option to spoof the values of optional UVParameters that are not set
            but are required for uvfits files.
        write_lst : bool
            Option to write the LSTs to the metadata (random group parameters).
        force_phase:  : bool
            Option to automatically phase drift scan data to zenith of the first
            timestamp.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after before writing the file (the default is True,
            meaning the check will be run).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file (the default is True, meaning the acceptable
            range check will be done).
        """
        uvfits_obj = self._convert_to_filetype('uvfits')
        uvfits_obj.write_uvfits(filename, spoof_nonessential=spoof_nonessential,
                                write_lst=write_lst, force_phase=force_phase,
                                run_check=run_check, check_extra=check_extra,
                                run_check_acceptability=run_check_acceptability)
        del(uvfits_obj)

    def read_ms(self, filepath, axis=None, data_column='DATA', pol_order='AIPS',
                run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read in data from a measurement set

        Parameters
        ----------
        filepath : str or list of str
            The measurement set file directory or list of directories to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        data_column : str
            name of CASA data column to read into data_array. Options are:
            'DATA', 'MODEL', or 'CORRECTED_DATA'
        pol_order : str
            Option to specify polarizations order convention, options are 'CASA' or 'AIPS'.
        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).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        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).
        """
        from . import ms

        if isinstance(filepath, (list, tuple)):
            self.read_ms(filepath[0], run_check=run_check, check_extra=check_extra,
                         run_check_acceptability=run_check_acceptability,
                         data_column=data_column, pol_order=pol_order)
            if len(filepath) > 1:
                for f in filepath[1:]:
                    uv2 = UVData()
                    uv2.read_ms(f, run_check=run_check, check_extra=check_extra,
                                run_check_acceptability=run_check_acceptability,
                                data_column=data_column, pol_order=pol_order)
                    if axis is not None:
                        self.fast_concat(uv2, axis, run_check=run_check,
                                         check_extra=check_extra,
                                         run_check_acceptability=run_check_acceptability,
                                         inplace=True)
                    else:
                        self += uv2
                del(uv2)
        else:
            ms_obj = ms.MS()
            ms_obj.read_ms(filepath, run_check=run_check, check_extra=check_extra,
                           run_check_acceptability=run_check_acceptability,
                           data_column=data_column, pol_order=pol_order)
            self._convert_from_filetype(ms_obj)
            del(ms_obj)

    def read_fhd(self, filelist, use_model=False, axis=None,
                 run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read in data from a list of FHD files.

        Parameters
        ----------
        filelist : list of str
            The list of FHD save files to read from. Must include at least one
            polarization file, a params file and a flag file. Can also be a list
            of lists to read multiple data sets.
        use_model : bool
            Option to read in the model visibilities rather than the dirty
            visibilities (the default is False, meaning the dirty visibilities
            will be read).
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple data sets are passed.
        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).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        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).
        """
        from . import fhd
        if isinstance(filelist[0], (list, tuple)):
            self.read_fhd(filelist[0], use_model=use_model, run_check=run_check,
                          check_extra=check_extra,
                          run_check_acceptability=run_check_acceptability)
            if len(filelist) > 1:
                for f in filelist[1:]:
                    uv2 = UVData()
                    uv2.read_fhd(f, use_model=use_model, run_check=run_check,
                                 check_extra=check_extra,
                                 run_check_acceptability=run_check_acceptability)
                    if axis is not None:
                        self.fast_concat(uv2, axis, run_check=run_check,
                                         check_extra=check_extra,
                                         run_check_acceptability=run_check_acceptability,
                                         inplace=True)
                    else:
                        self += uv2
                del(uv2)
        else:
            fhd_obj = fhd.FHD()
            fhd_obj.read_fhd(filelist, use_model=use_model, run_check=run_check,
                             check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability)
            self._convert_from_filetype(fhd_obj)
            del(fhd_obj)

    def read_miriad(self, filepath, axis=None, antenna_nums=None, ant_str=None,
                    bls=None, polarizations=None, time_range=None, read_data=True,
                    phase_type=None, correct_lat_lon=True, run_check=True,
                    check_extra=True, run_check_acceptability=True):
        """
        Read in data from a miriad file.

        Parameters
        ----------
        filepath : str or list of str
            The miriad file directory or list of directories to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to read into the object.
        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.
        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`,
            `bls` or `polarizations` parameters, if it is a ValueError will be raised.
        polarizations : array_like of int or str, optional
            List of polarization integers or strings to read-in. e.g. ['xx', 'yy', ...]
        time_range : list of float, optional
            len-2 list containing min and max range of times in Julian Date to
            include when reading data into the object. e.g. [2458115.20, 2458115.40]
        read_data : bool
            Read in the visibility and flag data. If set to false,
            only the metadata will be read in. Setting read_data to False
            results in an incompletely defined object (check will not pass).
        phase_type : str, optional
            Option to specify the phasing status of the data. Options are 'drift',
            'phased' or None. 'drift' means the data are zenith drift data,
            'phased' means the data are phased to a single RA/Dec. Default is None
            meaning it will be guessed at based on the file contents.
        correct_lat_lon : bool
            Option to update the latitude and longitude from the known_telescopes
            list if the altitude is missing.
        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.
        """
        from . import miriad
        if isinstance(filepath, (list, tuple)):
            if not read_data:
                raise ValueError('read_data cannot be False for a list of miriad files')

            self.read_miriad(filepath[0], correct_lat_lon=correct_lat_lon,
                             run_check=run_check, check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability,
                             phase_type=phase_type, antenna_nums=antenna_nums,
                             ant_str=ant_str, bls=bls,
                             polarizations=polarizations, time_range=time_range)
            if len(filepath) > 1:
                for f in filepath[1:]:
                    uv2 = UVData()
                    uv2.read_miriad(f, correct_lat_lon=correct_lat_lon,
                                    run_check=run_check, check_extra=check_extra,
                                    run_check_acceptability=run_check_acceptability,
                                    phase_type=phase_type, antenna_nums=antenna_nums,
                                    ant_str=ant_str, bls=bls,
                                    polarizations=polarizations, time_range=time_range)
                    if axis is not None:
                        self.fast_concat(uv2, axis, run_check=run_check,
                                         check_extra=check_extra,
                                         run_check_acceptability=run_check_acceptability,
                                         inplace=True)
                    else:
                        self += uv2
                del(uv2)
        else:
            # work out what function should be called
            if read_data:
                # reading data, use read_miriad
                miriad_obj = miriad.Miriad()
                miriad_obj.read_miriad(filepath, correct_lat_lon=correct_lat_lon,
                                       run_check=run_check, check_extra=check_extra,
                                       run_check_acceptability=run_check_acceptability,
                                       phase_type=phase_type, antenna_nums=antenna_nums,
                                       ant_str=ant_str, bls=bls,
                                       polarizations=polarizations, time_range=time_range)
                self._convert_from_filetype(miriad_obj)
                del(miriad_obj)
            else:
                # not reading data. Will error if data_array is already defined.
                miriad_obj = self._convert_to_filetype('miriad')
                miriad_obj.read_miriad_metadata(filepath, correct_lat_lon=correct_lat_lon)
                self._convert_from_filetype(miriad_obj)
                del(miriad_obj)

    def write_miriad(self, filepath, run_check=True, check_extra=True,
                     run_check_acceptability=True, clobber=False, no_antnums=False):
        """
        Write the data to a miriad file.

        Parameters
        ----------
        filename : str
            The miriad file directory to write to.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after before writing the file (the default is True,
            meaning the check will be run).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file (the default is True, meaning the acceptable
            range check will be done).
        clobber : bool
            Option to overwrite the filename if the file already exists.
        no_antnums : bool
            Option to not write the antnums variable to the file.
            Should only be used for testing purposes.
        """
        miriad_obj = self._convert_to_filetype('miriad')
        miriad_obj.write_miriad(filepath, run_check=run_check, check_extra=check_extra,
                                run_check_acceptability=run_check_acceptability,
                                clobber=clobber, no_antnums=no_antnums)
        del(miriad_obj)

    def read_uvh5(self, filename, axis=None, antenna_nums=None, antenna_names=None,
                  ant_str=None, bls=None, frequencies=None, freq_chans=None,
                  times=None, polarizations=None, blt_inds=None,
                  keep_all_metadata=True, read_data=True, data_array_dtype=np.complex128,
                  run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read a UVH5 file.

        Parameters
        ----------
        filename : str or list of str
             The UVH5 file or list of files to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        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. Ignored if read_data is False.
        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 (if read_metadata is True) 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.
        """
        from . import uvh5
        if isinstance(filename, (list, tuple)):
            if not read_data and len(filename) > 1:
                raise ValueError('read_data cannot be False for a list of uvh5 files')

            self.read_uvh5(filename[0], antenna_nums=antenna_nums,
                           antenna_names=antenna_names, ant_str=ant_str, bls=bls,
                           frequencies=frequencies, freq_chans=freq_chans, times=times,
                           polarizations=polarizations, blt_inds=blt_inds,
                           read_data=read_data, run_check=run_check,
                           check_extra=check_extra,
                           run_check_acceptability=run_check_acceptability,
                           data_array_dtype=data_array_dtype,
                           keep_all_metadata=keep_all_metadata)
            if len(filename) > 1:
                for f in filename[1:]:
                    uv2 = UVData()
                    uv2.read_uvh5(f, axis=axis, antenna_nums=antenna_nums,
                                  antenna_names=antenna_names, ant_str=ant_str, bls=bls,
                                  frequencies=frequencies, freq_chans=freq_chans,
                                  times=times, polarizations=polarizations,
                                  blt_inds=blt_inds, read_data=read_data,
                                  run_check=run_check, check_extra=check_extra,
                                  run_check_acceptability=run_check_acceptability,
                                  data_array_dtype=data_array_dtype,
                                  keep_all_metadata=keep_all_metadata)
                    if axis is not None:
                        self.fast_concat(uv2, axis, run_check=run_check,
                                         check_extra=check_extra,
                                         run_check_acceptability=run_check_acceptability,
                                         inplace=True)
                    else:
                        self += uv2
                del(uv2)
        else:
            uvh5_obj = uvh5.UVH5()
            uvh5_obj.read_uvh5(filename, antenna_nums=antenna_nums,
                               antenna_names=antenna_names, ant_str=ant_str, bls=bls,
                               frequencies=frequencies, freq_chans=freq_chans, times=times,
                               polarizations=polarizations, blt_inds=blt_inds,
                               read_data=read_data, run_check=run_check, check_extra=check_extra,
                               run_check_acceptability=run_check_acceptability,
                               data_array_dtype=data_array_dtype,
                               keep_all_metadata=keep_all_metadata)
            self._convert_from_filetype(uvh5_obj)
            del(uvh5_obj)

    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):
        """
        Write a completely in-memory UVData object to a UVH5 file.

        Parameters
        ----------
        filename : str
             The UVH5 file to write to.
        clobber : bool
            Option to overwrite the file if it already exists.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter.
        data_write_dtype : numpy 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.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after before writing the file (the default is True,
            meaning the check will be run).
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file (the default is True, meaning the acceptable
            range check will be done).
        """
        uvh5_obj = self._convert_to_filetype('uvh5')
        uvh5_obj.write_uvh5(filename, run_check=run_check,
                            check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            clobber=clobber, data_compression=data_compression,
                            flags_compression=flags_compression,
                            nsample_compression=nsample_compression,
                            data_write_dtype=data_write_dtype)
        del(uvh5_obj)

    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 with the header metadata and empty data arrays.

        Parameters
        ----------
        filename : str
             The UVH5 file to write to.
        clobber : bool
            Option to overwrite the file if it already exists.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter.
        data_write_dtype : numpy 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.

        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.
        """
        uvh5_obj = self._convert_to_filetype('uvh5')
        uvh5_obj.initialize_uvh5_file(filename, clobber=clobber,
                                      data_compression=data_compression,
                                      flags_compression=flags_compression,
                                      nsample_compression=nsample_compression,
                                      data_write_dtype=data_write_dtype)
        del(uvh5_obj)

    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 data to a UVH5 file that has already been initialized.

        Parameters
        ----------
        filename : str
            The UVH5 file to write to. It must already exist, and is assumed to
            have been initialized with initialize_uvh5_file.
        data_array : ndarray
            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 : ndarray
            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 : ndarray
            The nsample 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.
        check_header : bool
            Option to check that the metadata present in the header on disk
            matches that in the object.
        antenna_nums : array_like of int, optional
            The antennas numbers to include when writing data into the file
            (antenna positions and names for the removed 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 file
            (antenna positions and names for the removed antennas will be retained).
            This cannot be provided if `antenna_nums` is also provided.
        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 writing data into 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, `polarizations` must be
            None.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include writing data into the file.
            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.
        frequencies : array_like of float, optional
            The frequencies to include when writing data into the file, each
            value passed here should exist in the freq_array.
        freq_chans : array_like of int, optional
            The frequency channel numbers to include writing data into the file.
        times : array_like of float, optional
            The times to include when writing data into the file, each value
            passed here should exist in the time_array.
        polarizations : array_like of int, optional
            The polarizations numbers to include when writing data into the file,
            each value passed here should exist in the polarization_array.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when writing data into the file.
            This is not commonly used.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file (the default is True, meaning the acceptable
            range check will be done).
        add_to_history : str
            String to append to history before write out. Default is no appending.
        """
        uvh5_obj = self._convert_to_filetype('uvh5')
        uvh5_obj.write_uvh5_part(filename, data_array, flags_array, nsample_array,
                                 check_header=check_header, antenna_nums=antenna_nums,
                                 antenna_names=antenna_names, bls=bls, ant_str=ant_str,
                                 frequencies=frequencies, freq_chans=freq_chans,
                                 times=times, polarizations=polarizations,
                                 blt_inds=blt_inds,
                                 run_check_acceptability=run_check_acceptability,
                                 add_to_history=add_to_history)
        del(uvh5_obj)

    def read(self, filename, axis=None, file_type=None,
             antenna_nums=None, antenna_names=None, ant_str=None, bls=None,
             frequencies=None, freq_chans=None, times=None, polarizations=None,
             blt_inds=None, time_range=None, keep_all_metadata=True,
             read_metadata=True, read_data=True,
             phase_type=None, correct_lat_lon=True, use_model=False,
             data_column='DATA', pol_order='AIPS', data_array_dtype=np.complex128,
             run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read a generic file into a UVData object.

        Parameters
        ----------
        filename : str or list of str
            The file(s) or list(s) of files to read from.
        file_type : str
            One of ['uvfits', 'miriad', 'fhd', 'ms', 'uvh5'] or None.
            If None, the code attempts to guess what the file type is.
            For miriad and ms types, this is based on the standard directory
            structure. For FHD, uvfits and uvh5 files it's based on file
            extensions (FHD: .sav, .txt; uvfits: .uvfits; uvh5: .uvh5).
            Note that if a list of datasets is passed, the file type is
            determined from the first dataset.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        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. Ignored if read_data is False.
        time_range : list of float, optional
            len-2 list containing min and max range of times in Julian Date to
            include when reading data into the object. e.g. [2458115.20, 2458115.40]
            Cannot be set 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_metadata : bool
            Option to read in metadata (times, baselines, uvws) as well as
            basic header info. Only used if file_type is 'uvfits' and read_data
            is False (metadata will be read if data is read). If file_type is
            'uvfits' and both read_data and read_metadata are false, only basic
            header info is read in.
        read_data : bool
            Read in the data. Only used if file_type is 'uvfits',
            'miriad' or 'uvh5'. If set to False, only the metadata will be
            read in (for uvfits, this can be further restricted to just the
            header if read_metadata is False). Setting read_data to False
            results in an incompletely defined object (check will not pass).
        phase_type : str, optional
            Option to specify the phasing status of the data. Only used if
            file_type is 'miriad'. Options are 'drift', 'phased' or None.
            'drift' means the data are zenith drift data, 'phased' means the
            data are phased to a single RA/Dec. Default is None
            meaning it will be guessed at based on the file contents.
        correct_lat_lon : bool
            Option to update the latitude and longitude from the known_telescopes
            list if the altitude is missing. Only used if file_type is 'miriad'.
        use_model : bool
            Option to read in the model visibilities rather than the dirty
            visibilities (the default is False, meaning the dirty visibilities
            will be read). Only used if file_type is 'fhd'.
        data_column : str
            name of CASA data column to read into data_array. Options are:
            'DATA', 'MODEL', or 'CORRECTED_DATA'. Only used if file_type is 'ms'.
        pol_order : str
            Option to specify polarizations order convention, options are
            'CASA' or 'AIPS'. Only used if file_type is 'ms'.
        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'. Only used if file_type is 'uvh5'.
        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.
        """
        if isinstance(filename, (list, tuple)):
            # this is either a list of separate files to read or a list of FHD files
            if isinstance(filename[0], (list, tuple)):
                # this must be a list of lists for FHD
                file_type = 'fhd'
                multi = True
            else:
                basename, extension = os.path.splitext(filename[0])
                if extension == '.sav' or extension == '.txt':
                    file_type = 'fhd'
                    multi = False
                else:
                    multi = True
        else:
            multi = False

        if file_type is None:
            if multi:
                file_test = filename[0]
            else:
                file_test = filename

            if os.path.isdir(file_test):
                # it's a directory, so it's either miriad or ms file type
                if os.path.exists(os.path.join(file_test, 'vartable')):
                    # It's miriad.
                    file_type = 'miriad'
                elif os.path.exists(os.path.join(file_test, 'OBSERVATION')):
                    # It's a measurement set.
                    file_type = 'ms'
            else:
                basename, extension = os.path.splitext(file_test)
                if extension == '.uvfits':
                    file_type = 'uvfits'
                elif extension == '.uvh5':
                    file_type = 'uvh5'

        if file_type is None:
            raise ValueError('File type could not be determined.')

        if (time_range is not None):
            if times is not None:
                raise ValueError(
                    'Only one of times and time_range can be provided.')

        if antenna_names is not None and antenna_nums is not None:
            raise ValueError('Only one of antenna_nums and antenna_names can be provided.')

        if file_type == 'uvfits':
            if (time_range is not None):
                select = True
                warnings.warn('Warning: "time_range" keyword is set which is not '
                              'supported by read_uvfits. This select will be '
                              'done after reading the file.')
            else:
                select = False

            self.read_uvfits(filename, antenna_nums=antenna_nums,
                             antenna_names=antenna_names, ant_str=ant_str,
                             bls=bls, frequencies=frequencies,
                             freq_chans=freq_chans, times=times,
                             polarizations=polarizations, blt_inds=blt_inds,
                             read_data=read_data, read_metadata=read_metadata,
                             run_check=run_check, check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability,
                             keep_all_metadata=keep_all_metadata, axis=axis)

            if select:
                unique_times = np.unique(self.time_array)
                times_to_keep = unique_times[np.where((unique_times >= np.min(time_range))
                                                      & (unique_times <= np.max(time_range)))]
                self.select(times=times_to_keep, run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            keep_all_metadata=keep_all_metadata)

        elif file_type == 'miriad':
            if (antenna_names is not None or frequencies is not None or freq_chans is not None
                    or times is not None or blt_inds is not None):

                if blt_inds is not None:
                    if (antenna_nums is not None or ant_str is not None
                            or bls is not None or time_range is not None):
                        warnings.warn('Warning: blt_inds is set along with select '
                                      'on read keywords that are supported by '
                                      'read_miriad and may downselect blts. '
                                      'This may result in incorrect results '
                                      'because the select on read will happen '
                                      'before the blt_inds selection so the '
                                      'indices may not match the expected locations.')
                else:
                    warnings.warn('Warning: a select on read keyword is set that is not '
                                  'supported by read_miriad. This select will be '
                                  'done after reading the file.')
                select = True
            else:
                select = False

            self.read_miriad(filename, antenna_nums=antenna_nums, ant_str=ant_str,
                             bls=bls, polarizations=polarizations,
                             time_range=time_range, read_data=read_data,
                             phase_type=phase_type, correct_lat_lon=correct_lat_lon,
                             run_check=run_check, check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability,
                             axis=axis)

            if select:
                self.select(antenna_names=antenna_names, frequencies=frequencies,
                            freq_chans=freq_chans, times=times,
                            blt_inds=blt_inds, run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            keep_all_metadata=keep_all_metadata)

        elif file_type == 'fhd':
            if (antenna_nums is not None or antenna_names is not None
                    or ant_str is not None or bls is not None
                    or frequencies is not None or freq_chans is not None
                    or times is not None or polarizations is not None
                    or blt_inds is not None):
                select = True
                warnings.warn('Warning: select on read keyword set, but '
                              'file_type is "fhd" which does not support select '
                              'on read. Entire file will be read and then select '
                              'will be performed')
            else:
                select = False

            self.read_fhd(filename, use_model=use_model, run_check=run_check,
                          check_extra=check_extra,
                          run_check_acceptability=run_check_acceptability,
                          axis=axis)

            if select:
                self.select(antenna_nums=antenna_nums, antenna_names=antenna_names,
                            ant_str=ant_str, bls=bls, frequencies=frequencies,
                            freq_chans=freq_chans, times=times,
                            polarizations=polarizations, blt_inds=blt_inds,
                            run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            keep_all_metadata=keep_all_metadata)
        elif file_type == 'ms':
            if (antenna_nums is not None or antenna_names is not None
                    or ant_str is not None or bls is not None
                    or frequencies is not None or freq_chans is not None
                    or times is not None or polarizations is not None
                    or blt_inds is not None):
                select = True
                warnings.warn('Warning: select on read keyword set, but '
                              'file_type is "fhd" which does not support select '
                              'on read. Entire file will be read and then select '
                              'will be performed')
            else:
                select = False

            self.read_ms(filename, run_check=run_check, check_extra=check_extra,
                         run_check_acceptability=run_check_acceptability,
                         data_column=data_column, pol_order=pol_order, axis=axis)

            if select:
                self.select(antenna_nums=antenna_nums, antenna_names=antenna_names,
                            ant_str=ant_str, bls=bls, frequencies=frequencies,
                            freq_chans=freq_chans, times=times,
                            polarizations=polarizations, blt_inds=blt_inds,
                            run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            keep_all_metadata=keep_all_metadata)
        elif file_type == 'uvh5':
            if (time_range is not None):
                select = True
                warnings.warn('Warning: "time_range" keyword is set which is not '
                              'supported by read_uvh5. This select will be '
                              'done after reading the file.')
            else:
                select = False

            self.read_uvh5(filename, antenna_nums=antenna_nums,
                           antenna_names=antenna_names, ant_str=ant_str, bls=bls,
                           frequencies=frequencies, freq_chans=freq_chans, times=times,
                           polarizations=polarizations, blt_inds=blt_inds,
                           read_data=read_data, run_check=run_check, check_extra=check_extra,
                           run_check_acceptability=run_check_acceptability,
                           data_array_dtype=data_array_dtype,
                           keep_all_metadata=keep_all_metadata, axis=axis)

            if select:
                unique_times = np.unique(self.time_array)
                times_to_keep = unique_times[np.where((unique_times >= np.min(time_range))
                                                      & (unique_times <= np.max(time_range)))]
                self.select(times=times_to_keep, run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            keep_all_metadata=keep_all_metadata)

    def get_ants(self):
        """
        Get the unique antennas that have data associated with them.

        Returns
        -------
        ndarray of int
            Array of unique antennas with data associated with them.
        """
        return np.unique(np.append(self.ant_1_array, self.ant_2_array))

    def get_ENU_antpos(self, center=None, pick_data_ants=False):
        """
        Returns antenna positions in ENU (topocentric) coordinates in units of meters.

        Parameters
        ----------
        center : bool
            If True, subtract median of array position from antpos
        pick_data_ants : bool
            If True, return only antennas found in data

        Returns
        -------
        antpos : ndarray
            Antenna positions in ENU (topocentric) coordinates in units of meters, shape=(Nants, 3)
        ants : ndarray
            Antenna numbers matching ordering of antpos, shape=(Nants,)
        """
        if center is None:
            center = False
            warnings.warn('The default for the `center` keyword has changed. '
                          'Previously it defaulted to True, using the median '
                          'antennna location; now it defaults to False, '
                          'using the telescope_location. This warning will be '
                          'removed in version 1.5', DeprecationWarning)

        antpos = uvutils.ENU_from_ECEF((self.antenna_positions + self.telescope_location),
                                       *self.telescope_location_lat_lon_alt)
        ants = self.antenna_numbers

        if pick_data_ants:
            data_ants = np.unique(np.concatenate([self.ant_1_array, self.ant_2_array]))
            telescope_ants = self.antenna_numbers
            select = [x in data_ants for x in telescope_ants]
            antpos = antpos[select, :]
            ants = telescope_ants[select]

        if center is True:
            antpos -= np.median(antpos, axis=0)

        return antpos, ants

    def get_baseline_nums(self):
        """
        Get the unique baselines that have data associated with them.

        Returns
        -------
        ndarray of int
            Array of unique baselines with data associated with them.
        """
        return np.unique(self.baseline_array)

    def get_antpairs(self):
        """
        Get the unique antpair tuples that have data associated with them.

        Returns
        -------
        list of tuples of int
            list of unique antpair tuples (ant1, ant2) with data associated with them.
        """
        return [self.baseline_to_antnums(bl) for bl in self.get_baseline_nums()]

    def get_pols(self):
        """
        Get the polarizations in the data.

        Returns
        -------
        list of str
            list of polarizations (as strings) in the data.
        """
        return uvutils.polnum2str(self.polarization_array, x_orientation=self.x_orientation)

    def get_antpairpols(self):
        """
        Get the unique antpair + pol tuples that have data associated with them.

        Returns
        -------
        list of tuples of int
            list of unique antpair + pol tuples (ant1, ant2, pol) with data associated with them.
        """
        bli = 0
        pols = self.get_pols()
        bls = self.get_antpairs()
        return [(bl) + (pol,) for bl in bls for pol in pols]

    def get_feedpols(self):
        """
        Get the unique antenna feed polarizations in the data.

        Returns
        -------
        list of str
            list of antenna feed polarizations (e.g. ['X', 'Y']) in the data.

        Raises
        ------
        ValueError
            If any pseudo-Stokes visibilities are present
        """
        if np.any(self.polarization_array > 0):
            raise ValueError('Pseudo-Stokes visibilities cannot be interpreted as feed polarizations')
        else:
            return list(set(''.join(self.get_pols())))

    def antpair2ind(self, ant1, ant2=None, ordered=True):
        """
        Get indices along the baseline-time axis for a given antenna pair.

        This will search for either the key as specified, or the key and its
        conjugate.

        Parameters
        ----------
        ant1, ant2 : int
            Either an antenna-pair key, or key expanded as arguments,
            e.g. antpair2ind( (10, 20) ) or antpair2ind(10, 20)
        ordered : bool
            If True, search for antpair as provided, else search for it and it's conjugate.

        Returns
        -------
        inds : ndarray of int-64
            indices of the antpair along the baseline-time axis.
        """
        # check for expanded antpair or key
        if ant2 is None:
            if not isinstance(ant1, tuple):
                raise ValueError("antpair2ind must be fed an antpair tuple "
                                 "or expand it as arguments")
            ant2 = ant1[1]
            ant1 = ant1[0]
        else:
            if not isinstance(ant1, (int, np.integer)):
                raise ValueError("antpair2ind must be fed an antpair tuple or "
                                 "expand it as arguments")
        if not isinstance(ordered, (bool, np.bool)):
            raise ValueError("ordered must be a boolean")

        # if getting auto-corr, ordered must be True
        if ant1 == ant2:
            ordered = True

        # get indices
        inds = np.where((self.ant_1_array == ant1) & (self.ant_2_array == ant2))[0]
        if ordered:
            return inds
        else:
            ind2 = np.where((self.ant_1_array == ant2) & (self.ant_2_array == ant1))[0]
            inds = np.asarray(np.append(inds, ind2), dtype=np.int64)
            return inds

    def _key2inds(self, key):
        """
        Interpret user specified key as a combination of antenna pair and/or polarization.

        Parameters
        ----------
        key : tuple of int
            Identifier of data. Key can be length 1, 2, or 3:

            if len(key) == 1:
                if (key < 5) or (type(key) is str):  interpreted as a
                             polarization number/name, return all blts for that pol.
                else: interpreted as a baseline number. Return all times and
                      polarizations for that baseline.

            if len(key) == 2: interpreted as an antenna pair. Return all
                times and pols for that baseline.

            if len(key) == 3: interpreted as antenna pair and pol (ant1, ant2, pol).
                Return all times for that baseline, pol. pol may be a string.

        Returns
        ----------
        blt_ind1 : ndarray of int
            blt indices for antenna pair.
        blt_ind2 : ndarray of int
            blt indices for conjugate antenna pair.
            Note if a cross-pol baseline is requested, the polarization will
            also be reversed so the appropriate correlations are returned.
            e.g. asking for (1, 2, 'xy') may return conj(2, 1, 'yx'), which
            is equivalent to the requesting baseline. See utils.conj_pol() for
            complete conjugation mapping.
        pol_ind : tuple of ndarray of int
            polarization indices for blt_ind1 and blt_ind2
        """
        key = uvutils._get_iterable(key)
        if type(key) is str:
            # Single string given, assume it is polarization
            pol_ind1 = np.where(self.polarization_array
                                == uvutils.polstr2num(key, x_orientation=self.x_orientation))[0]
            if len(pol_ind1) > 0:
                blt_ind1 = np.arange(self.Nblts, dtype=np.int64)
                blt_ind2 = np.array([], dtype=np.int64)
                pol_ind2 = np.array([], dtype=np.int64)
                pol_ind = (pol_ind1, pol_ind2)
            else:
                raise KeyError('Polarization {pol} not found in data.'.format(pol=key))
        elif len(key) == 1:
            key = key[0]  # For simplicity
            if isinstance(key, collections.Iterable):
                # Nested tuple. Call function again.
                blt_ind1, blt_ind2, pol_ind = self._key2inds(key)
            elif key < 5:
                # Small number, assume it is a polarization number a la AIPS memo
                pol_ind1 = np.where(self.polarization_array == key)[0]
                if len(pol_ind1) > 0:
                    blt_ind1 = np.arange(self.Nblts)
                    blt_ind2 = np.array([], dtype=np.int64)
                    pol_ind2 = np.array([], dtype=np.int64)
                    pol_ind = (pol_ind1, pol_ind2)
                else:
                    raise KeyError('Polarization {pol} not found in data.'.format(pol=key))
            else:
                # Larger number, assume it is a baseline number
                inv_bl = self.antnums_to_baseline(self.baseline_to_antnums(key)[1],
                                                  self.baseline_to_antnums(key)[0])
                blt_ind1 = np.where(self.baseline_array == key)[0]
                blt_ind2 = np.where(self.baseline_array == inv_bl)[0]
                if len(blt_ind1) + len(blt_ind2) == 0:
                    raise KeyError('Baseline {bl} not found in data.'.format(bl=key))
                if len(blt_ind1) > 0:
                    pol_ind1 = np.arange(self.Npols)
                else:
                    pol_ind1 = np.array([], dtype=np.int64)
                if len(blt_ind2) > 0:
                    try:
                        pol_ind2 = uvutils.reorder_conj_pols(self.polarization_array)
                    except ValueError:
                        if len(blt_ind1) == 0:
                            raise KeyError('Baseline {bl} not found for polarization'
                                           + ' array in data.'.format(bl=key))
                        else:
                            pol_ind2 = np.array([], dtype=np.int64)
                            blt_ind2 = np.array([], dtype=np.int64)
                else:
                    pol_ind2 = np.array([], dtype=np.int64)
                pol_ind = (pol_ind1, pol_ind2)
        elif len(key) == 2:
            # Key is an antenna pair
            blt_ind1 = self.antpair2ind(key[0], key[1])
            blt_ind2 = self.antpair2ind(key[1], key[0])
            if len(blt_ind1) + len(blt_ind2) == 0:
                raise KeyError('Antenna pair {pair} not found in data'.format(pair=key))
            if len(blt_ind1) > 0:
                pol_ind1 = np.arange(self.Npols)
            else:
                pol_ind1 = np.array([], dtype=np.int64)
            if len(blt_ind2) > 0:
                try:
                    pol_ind2 = uvutils.reorder_conj_pols(self.polarization_array)
                except ValueError:
                    if len(blt_ind1) == 0:
                        raise KeyError('Baseline {bl} not found for polarization'
                                       + ' array in data.'.format(bl=key))
                    else:
                        pol_ind2 = np.array([], dtype=np.int64)
                        blt_ind2 = np.array([], dtype=np.int64)
            else:
                pol_ind2 = np.array([], dtype=np.int64)
            pol_ind = (pol_ind1, pol_ind2)
        elif len(key) == 3:
            # Key is an antenna pair + pol
            blt_ind1 = self.antpair2ind(key[0], key[1])
            blt_ind2 = self.antpair2ind(key[1], key[0])
            if len(blt_ind1) + len(blt_ind2) == 0:
                raise KeyError('Antenna pair {pair} not found in '
                               'data'.format(pair=(key[0], key[1])))
            if type(key[2]) is str:
                # pol is str
                if len(blt_ind1) > 0:
                    pol_ind1 = np.where(
                        self.polarization_array
                        == uvutils.polstr2num(key[2],
                                              x_orientation=self.x_orientation))[0]
                else:
                    pol_ind1 = np.array([], dtype=np.int64)
                if len(blt_ind2) > 0:
                    pol_ind2 = np.where(
                        self.polarization_array
                        == uvutils.polstr2num(uvutils.conj_pol(key[2]),
                                              x_orientation=self.x_orientation))[0]
                else:
                    pol_ind2 = np.array([], dtype=np.int64)
            else:
                # polarization number a la AIPS memo
                if len(blt_ind1) > 0:
                    pol_ind1 = np.where(self.polarization_array == key[2])[0]
                else:
                    pol_ind1 = np.array([], dtype=np.int64)
                if len(blt_ind2) > 0:
                    pol_ind2 = np.where(self.polarization_array == uvutils.conj_pol(key[2]))[0]
                else:
                    pol_ind2 = np.array([], dtype=np.int64)
            pol_ind = (pol_ind1, pol_ind2)
            if len(blt_ind1) * len(pol_ind[0]) + len(blt_ind2) * len(pol_ind[1]) == 0:
                raise KeyError('Polarization {pol} not found in data.'.format(pol=key[2]))
        # Catch autos
        if np.array_equal(blt_ind1, blt_ind2):
            blt_ind2 = np.array([], dtype=np.int64)
        return (blt_ind1, blt_ind2, pol_ind)

    def _smart_slicing(self, data, ind1, ind2, indp, squeeze='default',
                       force_copy=False):
        """
        Method to quickly get the relevant section of a data-like array.

        Used in get_data, get_flags and get_nsamples.

        Parameters
        ----------
        data : ndarray
            4-dimensional array shaped like self.data_array
        ind1 : array_like of int
            blt indices for antenna pair (e.g. from self._key2inds)
        ind2 : array_like of int
            blt indices for conjugate antenna pair. (e.g. from self._key2inds)
        indp : tuple array_like of int
            polarization indices for ind1 and ind2 (e.g. from self._key2inds)
        squeeze : str
            string specifying how to squeeze the returned array. Options are:
            'default': squeeze pol and spw dimensions if possible;
            'none': no squeezing of resulting numpy array;
            'full': squeeze all length 1 dimensions.
        force_copy : bool
            Option to explicitly make a copy of the data.

        Returns
        -------
        ndarray
            copy (or if possible, a read-only view) of relevant section of data
        """
        p_reg_spaced = [False, False]
        p_start = [0, 0]
        p_stop = [0, 0]
        dp = [1, 1]
        for i, pi in enumerate(indp):
            if len(pi) == 0:
                continue
            if len(set(np.ediff1d(pi))) <= 1:
                p_reg_spaced[i] = True
                p_start[i] = pi[0]
                p_stop[i] = pi[-1] + 1
                if len(pi) != 1:
                    dp[i] = pi[1] - pi[0]

        if len(ind2) == 0:
            # only unconjugated baselines
            if len(set(np.ediff1d(ind1))) <= 1:
                blt_start = ind1[0]
                blt_stop = ind1[-1] + 1
                if len(ind1) == 1:
                    dblt = 1
                else:
                    dblt = ind1[1] - ind1[0]
                if p_reg_spaced[0]:
                    out = data[blt_start:blt_stop:dblt, :, :, p_start[0]:p_stop[0]:dp[0]]
                else:
                    out = data[blt_start:blt_stop:dblt, :, :, indp[0]]
            else:
                out = data[ind1, :, :, :]
                if p_reg_spaced[0]:
                    out = out[:, :, :, p_start[0]:p_stop[0]:dp[0]]
                else:
                    out = out[:, :, :, indp[0]]
        elif len(ind1) == 0:
            # only conjugated baselines
            if len(set(np.ediff1d(ind2))) <= 1:
                blt_start = ind2[0]
                blt_stop = ind2[-1] + 1
                if len(ind2) == 1:
                    dblt = 1
                else:
                    dblt = ind2[1] - ind2[0]
                if p_reg_spaced[1]:
                    out = np.conj(data[blt_start:blt_stop:dblt, :, :, p_start[1]:p_stop[1]:dp[1]])
                else:
                    out = np.conj(data[blt_start:blt_stop:dblt, :, :, indp[1]])
            else:
                out = data[ind2, :, :, :]
                if p_reg_spaced[1]:
                    out = np.conj(out[:, :, :, p_start[1]:p_stop[1]:dp[1]])
                else:
                    out = np.conj(out[:, :, :, indp[1]])
        else:
            # both conjugated and unconjugated baselines
            out = (data[ind1, :, :, :], np.conj(data[ind2, :, :, :]))
            if p_reg_spaced[0] and p_reg_spaced[1]:
                out = np.append(out[0][:, :, :, p_start[0]:p_stop[0]:dp[0]],
                                out[1][:, :, :, p_start[1]:p_stop[1]:dp[1]], axis=0)
            else:
                out = np.append(out[0][:, :, :, indp[0]],
                                out[1][:, :, :, indp[1]], axis=0)

        if squeeze == 'full':
            out = np.squeeze(out)
        elif squeeze == 'default':
            if out.shape[3] is 1:
                # one polarization dimension
                out = np.squeeze(out, axis=3)
            if out.shape[1] is 1:
                # one spw dimension
                out = np.squeeze(out, axis=1)
        elif squeeze != 'none':
            raise ValueError('"' + str(squeeze) + '" is not a valid option for squeeze.'
                             'Only "default", "none", or "full" are allowed.')

        if force_copy:
            out = np.array(out)
        elif out.base is not None:
            # if out is a view rather than a copy, make it read-only
            out.flags.writeable = False

        return out

    def get_data(self, key1, key2=None, key3=None, squeeze='default',
                 force_copy=False):
        """
        Get the data corresonding to a baseline and/or polarization.

        Parameters
        ----------
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to get, can be passed as 1, 2, or 3 arguments
            or as a single tuple of length 1, 2, or 3. These are collectively
            called the key.

            If key is length 1:
                if (key < 5) or (type(key) is str):
                    interpreted as a polarization number/name, get all data for
                    that pol.
                else:
                    interpreted as a baseline number, get all data for that baseline.

            if key is length 2: interpreted as an antenna pair, get all data
                for that baseline.

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                get all data for that baseline, pol. pol may be a string or int.
        squeeze : str
            string specifying how to squeeze the returned array. Options are:
            'default': squeeze pol and spw dimensions if possible;
            'none': no squeezing of resulting numpy array;
            'full': squeeze all length 1 dimensions.
        force_copy : bool
            Option to explicitly make a copy of the data.

        Returns
        -------
        ndarray
            copy (or if possible, a read-only view) of relevant section of data.
            If data exists conjugate to requested antenna pair, it will be conjugated
            before returning.
        """
        key = []
        for val in [key1, key2, key3]:
            if isinstance(val, str):
                key.append(val)
            elif val is not None:
                key += list(uvutils._get_iterable(val))
        if len(key) > 3:
            raise ValueError('no more than 3 key values can be passed')
        ind1, ind2, indp = self._key2inds(key)
        out = self._smart_slicing(self.data_array, ind1, ind2, indp,
                                  squeeze=squeeze, force_copy=force_copy)
        return out

    def get_flags(self, key1, key2=None, key3=None, squeeze='default',
                  force_copy=False):
        """
        Get the flags corresonding to a baseline and/or polarization.

        Parameters
        ----------
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to get, can be passed as 1, 2, or 3 arguments
            or as a single tuple of length 1, 2, or 3. These are collectively
            called the key.

            If key is length 1:
                if (key < 5) or (type(key) is str):
                    interpreted as a polarization number/name, get all flags for
                    that pol.
                else:
                    interpreted as a baseline number, get all flags for that baseline.

            if key is length 2: interpreted as an antenna pair, get all flags
                for that baseline.

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                get all flags for that baseline, pol. pol may be a string or int.
        squeeze : str
            string specifying how to squeeze the returned array. Options are:
            'default': squeeze pol and spw dimensions if possible;
            'none': no squeezing of resulting numpy array;
            'full': squeeze all length 1 dimensions.
        force_copy : bool
            Option to explicitly make a copy of the data.

        Returns
        -------
        ndarray
            copy (or if possible, a read-only view) of relevant section of flags.
        """
        key = []
        for val in [key1, key2, key3]:
            if isinstance(val, str):
                key.append(val)
            elif val is not None:
                key += list(uvutils._get_iterable(val))
        if len(key) > 3:
            raise ValueError('no more than 3 key values can be passed')
        ind1, ind2, indp = self._key2inds(key)
        out = self._smart_slicing(self.flag_array, ind1, ind2, indp,
                                  squeeze=squeeze, force_copy=force_copy).astype(np.bool)
        return out

    def get_nsamples(self, key1, key2=None, key3=None, squeeze='default',
                     force_copy=False):
        """
        Get the nsamples corresonding to a baseline and/or polarization.

        Parameters
        ----------
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to get, can be passed as 1, 2, or 3 arguments
            or as a single tuple of length 1, 2, or 3. These are collectively
            called the key.

            If key is length 1:
                if (key < 5) or (type(key) is str):
                    interpreted as a polarization number/name, get all nsamples for
                    that pol.
                else:
                    interpreted as a baseline number, get all nsamples for that baseline.

            if key is length 2: interpreted as an antenna pair, get all nsamples
                for that baseline.

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                get all nsamples for that baseline, pol. pol may be a string or int.
        squeeze : str
            string specifying how to squeeze the returned array. Options are:
            'default': squeeze pol and spw dimensions if possible;
            'none': no squeezing of resulting numpy array;
            'full': squeeze all length 1 dimensions.
        force_copy : bool
            Option to explicitly make a copy of the data.

        Returns
        -------
        ndarray
            copy (or if possible, a read-only view) of relevant section of nsample_array.
        """
        key = []
        for val in [key1, key2, key3]:
            if isinstance(val, str):
                key.append(val)
            elif val is not None:
                key += list(uvutils._get_iterable(val))
        if len(key) > 3:
            raise ValueError('no more than 3 key values can be passed')
        ind1, ind2, indp = self._key2inds(key)
        out = self._smart_slicing(self.nsample_array, ind1, ind2, indp,
                                  squeeze=squeeze, force_copy=force_copy)
        return out

    def get_times(self, key1, key2=None, key3=None):
        """
        Get the times for a given antpair or baseline number.

        Meant to be used in conjunction with get_data function.

        Parameters
        ----------
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to get, can be passed as 1, 2, or 3 arguments
            or as a single tuple of length 1, 2, or 3. These are collectively
            called the key.

            If key is length 1:
                if (key < 5) or (type(key) is str):
                    interpreted as a polarization number/name, get all times.
                else:
                    interpreted as a baseline number, get all times for that baseline.

            if key is length 2: interpreted as an antenna pair, get all times
                for that baseline.

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                get all times for that baseline.

        Returns
        -------
        ndarray
            times from the time_array for the given antpair or baseline.
        """
        key = []
        for val in [key1, key2, key3]:
            if isinstance(val, str):
                key.append(val)
            elif val is not None:
                key += list(uvutils._get_iterable(val))
        if len(key) > 3:
            raise ValueError('no more than 3 key values can be passed')
        inds1, inds2, indp = self._key2inds(key)
        return self.time_array[np.append(inds1, inds2)]

    def antpairpol_iter(self, squeeze='default'):
        """
        Iterator to get the data for each antpair, polarization combination.

        Parameters
        ----------
        squeeze : str
            string specifying how to squeeze the returned array. Options are:
            'default': squeeze pol and spw dimensions if possible;
            'none': no squeezing of resulting numpy array;
            'full': squeeze all length 1 dimensions.

        Yields
        ------
        key : tuple
            antenna1, antenna2, and polarization string
        data : ndarray of complex
            data for the ant pair and polarization specified in key
        """
        antpairpols = self.get_antpairpols()
        for key in antpairpols:
            yield (key, self.get_data(key, squeeze=squeeze))

    def parse_ants(self, ant_str, print_toggle=False):
        """
        Get antpair and polarization from parsing an aipy-style ant string.

        Used to support the the select function.
        Generates two lists of antenna pair tuples and polarization indices based
        on parsing of the string ant_str.  If no valid polarizations (pseudo-Stokes
        params, or combinations of [lr] or [xy]) or antenna numbers are found in
        ant_str, ant_pairs_nums and polarizations are returned as None.

        Parameters
        ----------
        ant_str : str
            String containing antenna information to parse. Can be 'all',
            'auto', 'cross', or combinations of antenna numbers and polarization
            indicators 'l' and 'r' or 'x' and 'y'.  Minus signs can also be used
            in front of an antenna number or baseline to exclude it from being
            output in ant_pairs_nums. If ant_str has a minus sign as the first
            character, 'all,' will be appended to the beginning of the string.
            See the tutorial for examples of valid strings and their behavior.
        print_toggle : bool
            Boolean for printing parsed baselines for a visual user check.

        Returns
        -------
        ant_pairs_nums : list of tuples of int or None
            List of tuples containing the parsed pairs of antenna numbers, or
            None if ant_str is 'all' or a pseudo-Stokes polarizations.
        polarizations : list of int or None
            List of desired polarizations or None if ant_str does not contain a
            polarization specification.
        """

        ant_re = r'(\(((-?\d+[lrxy]?,?)+)\)|-?\d+[lrxy]?)'
        bl_re = '(^(%s_%s|%s),?)' % (ant_re, ant_re, ant_re)
        str_pos = 0
        ant_pairs_nums = []
        polarizations = []
        ants_data = self.get_ants()
        ant_pairs_data = self.get_antpairs()
        pols_data = self.get_pols()
        warned_ants = []
        warned_pols = []

        if ant_str.startswith('-'):
            ant_str = 'all,' + ant_str

        while str_pos < len(ant_str):
            m = re.search(bl_re, ant_str[str_pos:])
            if m is None:
                if ant_str[str_pos:].upper().startswith('ALL'):
                    if len(ant_str[str_pos:].split(',')) > 1:
                        ant_pairs_nums = self.get_antpairs()
                elif ant_str[str_pos:].upper().startswith('AUTO'):
                    for pair in ant_pairs_data:
                        if (pair[0] == pair[1]
                                and pair not in ant_pairs_nums):
                            ant_pairs_nums.append(pair)
                elif ant_str[str_pos:].upper().startswith('CROSS'):
                    for pair in ant_pairs_data:
                        if not (pair[0] == pair[1]
                                or pair in ant_pairs_nums):
                            ant_pairs_nums.append(pair)
                elif ant_str[str_pos:].upper().startswith('PI'):
                    polarizations.append(uvutils.polstr2num('pI'))
                elif ant_str[str_pos:].upper().startswith('PQ'):
                    polarizations.append(uvutils.polstr2num('pQ'))
                elif ant_str[str_pos:].upper().startswith('PU'):
                    polarizations.append(uvutils.polstr2num('pU'))
                elif ant_str[str_pos:].upper().startswith('PV'):
                    polarizations.append(uvutils.polstr2num('pV'))
                else:
                    raise ValueError('Unparsible argument {s}'.format(s=ant_str))

                comma_cnt = ant_str[str_pos:].find(',')
                if comma_cnt >= 0:
                    str_pos += comma_cnt + 1
                else:
                    str_pos = len(ant_str)
            else:
                m = m.groups()
                str_pos += len(m[0])
                if m[2] is None:
                    ant_i_list = [m[8]]
                    ant_j_list = list(self.get_ants())
                else:
                    if m[3] is None:
                        ant_i_list = [m[2]]
                    else:
                        ant_i_list = m[3].split(',')

                    if m[6] is None:
                        ant_j_list = [m[5]]
                    else:
                        ant_j_list = m[6].split(',')

                for ant_i in ant_i_list:
                    include_i = True
                    if type(ant_i) == str and ant_i.startswith('-'):
                        ant_i = ant_i[1:]  # nibble the - off the string
                        include_i = False

                    for ant_j in ant_j_list:
                        include_j = True
                        if type(ant_j) == str and ant_j.startswith('-'):
                            ant_j = ant_j[1:]
                            include_j = False

                        pols = None
                        ant_i, ant_j = str(ant_i), str(ant_j)
                        if not ant_i.isdigit():
                            ai = re.search(r'(\d+)([x,y,l,r])', ant_i).groups()

                        if not ant_j.isdigit():
                            aj = re.search(r'(\d+)([x,y,l,r])', ant_j).groups()

                        if ant_i.isdigit() and ant_j.isdigit():
                            ai = [ant_i, '']
                            aj = [ant_j, '']
                        elif ant_i.isdigit() and not ant_j.isdigit():
                            if ('x' in ant_j or 'y' in ant_j):
                                pols = ['x' + aj[1], 'y' + aj[1]]
                            else:
                                pols = ['l' + aj[1], 'r' + aj[1]]
                            ai = [ant_i, '']
                        elif not ant_i.isdigit() and ant_j.isdigit():
                            if ('x' in ant_i or 'y' in ant_i):
                                pols = [ai[1] + 'x', ai[1] + 'y']
                            else:
                                pols = [ai[1] + 'l', ai[1] + 'r']
                            aj = [ant_j, '']
                        elif not ant_i.isdigit() and not ant_j.isdigit():
                            pols = [ai[1] + aj[1]]

                        ant_tuple = tuple((abs(int(ai[0])), abs(int(aj[0]))))

                        # Order tuple according to order in object
                        if ant_tuple in ant_pairs_data:
                            pass
                        elif ant_tuple[::-1] in ant_pairs_data:
                            ant_tuple = ant_tuple[::-1]
                        else:
                            if not (ant_tuple[0] in ants_data
                                    or ant_tuple[0] in warned_ants):
                                warned_ants.append(ant_tuple[0])
                            if not (ant_tuple[1] in ants_data
                                    or ant_tuple[1] in warned_ants):
                                warned_ants.append(ant_tuple[1])
                            if pols is not None:
                                for pol in pols:
                                    if not (pol.lower() in pols_data
                                            or pol in warned_pols):
                                        warned_pols.append(pol)
                            continue

                        if include_i and include_j:
                            if ant_tuple not in ant_pairs_nums:
                                ant_pairs_nums.append(ant_tuple)
                            if pols is not None:
                                for pol in pols:
                                    if (pol.lower() in pols_data
                                            and uvutils.polstr2num(pol, x_orientation=self.x_orientation)
                                            not in polarizations):
                                        polarizations.append(
                                            uvutils.polstr2num(pol,
                                                               x_orientation=self.x_orientation))
                                    elif not (pol.lower() in pols_data
                                              or pol in warned_pols):
                                        warned_pols.append(pol)
                        else:
                            if pols is not None:
                                for pol in pols:
                                    if pol.lower() in pols_data:
                                        if (self.Npols == 1
                                                and [pol.lower()] == pols_data):
                                            ant_pairs_nums.remove(ant_tuple)
                                        if uvutils.polstr2num(
                                                pol, x_orientation=self.x_orientation) in polarizations:
                                            polarizations.remove(
                                                uvutils.polstr2num(
                                                    pol, x_orientation=self.x_orientation))
                                    elif not (pol.lower() in pols_data
                                              or pol in warned_pols):
                                        warned_pols.append(pol)
                            elif ant_tuple in ant_pairs_nums:
                                ant_pairs_nums.remove(ant_tuple)

        if ant_str.upper() == 'ALL':
            ant_pairs_nums = None
        elif len(ant_pairs_nums) == 0:
            if (not ant_str.upper() in ['AUTO', 'CROSS']):
                ant_pairs_nums = None

        if len(polarizations) == 0:
            polarizations = None
        else:
            polarizations.sort(reverse=True)

        if print_toggle:
            print('\nParsed antenna pairs:')
            if ant_pairs_nums is not None:
                for pair in ant_pairs_nums:
                    print(pair)

            print('\nParsed polarizations:')
            if polarizations is not None:
                for pol in polarizations:
                    print(uvutils.polnum2str(pol, x_orientation=self.x_orientation))

        if len(warned_ants) > 0:
            warnings.warn('Warning: Antenna number {a} passed, but not present '
                          'in the ant_1_array or ant_2_array'
                          .format(a=(',').join(map(str, warned_ants))))

        if len(warned_pols) > 0:
            warnings.warn('Warning: Polarization {p} is not present in '
                          'the polarization_array'
                          .format(p=(',').join(warned_pols).upper()))

        return ant_pairs_nums, polarizations

    def _calc_single_integration_time(self):
        """
        Calculate a single integration time in seconds when not otherwise specified.

        This function computes the shortest time difference present in the
        time_array, and returns it to be used as the integration time for all
        samples.

        Returns
        ----------
        int_time : int
            integration time in seconds to be assigned to all samples in the data.
        """
        # The time_array is in units of days, and integration_time has units of
        # seconds, so we need to convert.
        return np.diff(np.sort(list(set(self.time_array))))[0] * 86400

    def get_antenna_redundancies(self, tol=1.0, include_autos=True,
                                 conjugate_bls=False):
        """
        Get redundant baselines to a given tolerance from antenna positions.

        Finds all possible redundant baselines (antenna pairs) not just those with data.

        Parameters
        ----------
        tol : float
            Redundancy tolerance in meters (default 1m).
        include_autos : bool
            Option to include autocorrelations in the full redundancy list.
        conjugate_bls : bool
            Option to conjugate baselines on this object to the 'u>0' convention.
            Set this to True to ensure that the returned baseline numbers will
            match the baseline numbers in the data (if they exist in the data).

        Returns
        -------
        baseline_groups : list of lists of int
            List of lists of redundant baseline numbers
        vec_bin_centers : list of ndarray of float
            List of vectors describing redundant group uvw centers
        lengths : list of float
            List of redundant group baseline lengths in meters

        Notes
        -----
        Note that this method finds all possible redundant baselines in the 'u>0'
        part of the uv plane. In order for the returned baseline numbers to match
        baselines in this object, this method will conjugate baselines on this
        object to the 'u>0' convention unless `no_conjugate` is set to True.
        """
        if conjugate_bls:
            self.conjugate_bls(convention='u>0')
        antpos, numbers = self.get_ENU_antpos(center=False)
        return uvutils.get_antenna_redundancies(numbers, antpos, tol=tol,
                                                include_autos=include_autos)

    def get_baseline_redundancies(self, tol=1.0):
        """
        Get baseline redundancies to a given tolerance from uvw_array.

        Parameters
        ----------
        tol : float
            Redundancy tolerance in meters, default is 1.0 corresponding to 1 meter.

        Returns
        -------
        baseline_groups : list of lists of int
            List of lists of redundant baseline numbers
        vec_bin_centers : list of ndarray of float
            List of vectors describing redundant group uvw centers
        lengths : list of float
            List of redundant group baseline lengths in meters
        baseline_ind_conj : list of int
            List of baselines that are redundant when reversed.
        """
        _, unique_inds = np.unique(self.baseline_array, return_index=True)
        unique_inds.sort()
        baseline_vecs = np.take(self.uvw_array, unique_inds, axis=0)
        baselines = np.take(self.baseline_array, unique_inds)

        return uvutils.get_baseline_redundancies(baselines, baseline_vecs,
                                                 tol=tol, with_conjugates=True)

    def compress_by_redundancy(self, tol=1.0, inplace=True, metadata_only=False,
                               keep_all_metadata=True):
        """
        Downselect to only have one baseline per redundant group on the object.

        Uses utility functions to find redundant baselines to the given tolerance,
        then select on those.

        Parameters
        ----------
        tol : float
            Redundancy tolerance in meters, default is 1.0 corresponding to 1 meter.
        inplace : bool
            Option to do selection on current object.
        metadata_only : bool
            Option to only do the select on the metadata. Not allowed
            if the data_array, flag_array or nsample_array is not None.
        keep_all_metadata : bool
            Option to keep all the metadata associated with antennas,
            even those that do not remain after the select option.

        Returns
        -------
        UVData object or None
            if inplace is False, return the compressed UVData object
        """

        red_gps, centers, lengths, conjugates = self.get_baseline_redundancies(tol)

        bl_ants = [self.baseline_to_antnums(gp[0]) for gp in red_gps]
        return self.select(bls=bl_ants, inplace=inplace, metadata_only=metadata_only,
                           keep_all_metadata=keep_all_metadata)

    def inflate_by_redundancy(self, tol=1.0, blt_order='time', blt_minor_order=None):
        """
        Expand data to full size, copying data among redundant baselines.

        Note that this method conjugates baselines to the 'u>0' convention in order
        to inflate the redundancies.

        Parameters
        ----------
        tol : float
            Redundancy tolerance in meters, default is 1.0 corresponding to 1 meter.
        blt_order : str
            string specifying primary order along the blt axis (see `reorder_blts`)
        blt_minor_order : str
            string specifying minor order along the blt axis (see `reorder_blts`)
        """

        red_gps, centers, lengths = self.get_antenna_redundancies(tol=tol,
                                                                  conjugate_bls=True)

        # Stack redundant groups into one array.
        group_index, bl_array_full = zip(*[(i, bl) for i, gp in enumerate(red_gps) for bl in gp])

        # TODO should be an assert that each baseline only ends up in one group

        # Map group index to blt indices in the compressed array.
        bl_array_comp = self.baseline_array
        uniq_bl = np.unique(bl_array_comp)

        group_blti = {}
        Nblts_full = 0
        for i, gp in enumerate(red_gps):
            for bl in gp:
                # First baseline in the group that is also in the compressed baseline array.
                if bl in uniq_bl:
                    group_blti[i] = np.where(bl == bl_array_comp)[0]
                    # add number of blts for this group
                    Nblts_full += group_blti[i].size * len(gp)
                    break

        blt_map = np.zeros(Nblts_full, dtype=int)
        full_baselines = np.zeros(Nblts_full, dtype=int)
        missing = []
        counter = 0
        for bl, gi in zip(bl_array_full, group_index):
            try:
                # this makes the time the fastest axis
                blt_map[counter:counter + group_blti[gi].size] = group_blti[gi]
                full_baselines[counter:counter + group_blti[gi].size] = bl
                counter += group_blti[gi].size
            except KeyError:
                missing.append(bl)
                pass

        if np.any(missing):
            warnings.warn("Missing some redundant groups. Filling in available data.")

        # blt_map is an index array mapping compressed blti indices to uncompressed
        self.data_array = self.data_array[blt_map, ...]
        self.nsample_array = self.nsample_array[blt_map, ...]
        self.flag_array = self.flag_array[blt_map, ...]
        self.time_array = self.time_array[blt_map]
        self.lst_array = self.lst_array[blt_map]
        self.integration_time = self.integration_time[blt_map]
        self.uvw_array = self.uvw_array[blt_map, ...]

        self.baseline_array = full_baselines
        self.ant_1_array, self.ant_2_array = self.baseline_to_antnums(self.baseline_array)
        self.Nants_data = np.unique(self.ant_1_array.tolist() + self.ant_2_array.tolist()).size
        self.Nbls = np.unique(self.baseline_array).size
        self.Nblts = Nblts_full

        self.reorder_blts(order=blt_order, minor_order=blt_minor_order)

        self.check()
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