https://github.com/RadioAstronomySoftwareGroup/pyuvdata
Raw File
Tip revision: 983ae27b57a28550b6ee1a58e13cc9161bb89327 authored by Bryna Hazelton on 15 January 2020, 19:15 UTC
update release date
Tip revision: 983ae27
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 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

if six.PY2:
    from collections import Iterable
else:
    from collections.abc import Iterable


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)

        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.')
        self._antenna_positions = uvp.UVParameter(
            'antenna_positions', description=desc, form=('Nants_telescope', 3),
            expected_type=np.float, tols=1e-3)  # 1 mm

        # -------- 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 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)

        desc = "Per-antenna and per-frequency equalization coefficients"
        self._eq_coeffs = uvp.UVParameter("eq_coeffs",
                                          required=False,
                                          description=desc,
                                          form=("Nants_telescope", "Nfreqs"),
                                          expected_type=np.float,
                                          spoof_val=1.0)

        desc = "Convention for how to remove eq_coeffs from data"
        self._eq_coeffs_convention = uvp.UVParameter("eq_coeffs_convention",
                                                     required=False,
                                                     description=desc,
                                                     form="str",
                                                     spoof_val="divide")

        super(UVData, self).__init__()

    @property
    def _data_params(self):
        """List of strings giving the data-like parameters"""
        return ['data_array', 'nsample_array', 'flag_array']

    @property
    def data_like_parameters(self):
        """An iterator of defined parameters which are data-like (not metadata-like)"""
        for key in self._data_params:
            if hasattr(self, key):
                yield getattr(self, key)

    @property
    def metadata_only(self):
        """
        Property that determines whether this is a metadata only object.

        An object is metadata only if data_array, nsample_array and flag_array
        are all None.
        """
        metadata_only = all(d is None for d in self.data_like_parameters)

        for param_name in self._data_params:
            getattr(self, "_" + param_name).required = not metadata_only

        return metadata_only

    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()

        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))

        # 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 copy(self, metadata_only=False):
        """Make and return a copy of the UVData object.

        Parameters
        ----------
        metadata_only : bool
            If True, only copy the metadata of the object.

        Returns
        -------
        uv : UVData
            Copy of self.
        """
        uv = UVData()
        for param in self:
            # parameter names have a leading underscore we want to ignore
            if metadata_only and param.lstrip("_") in self._data_params:
                continue
            setattr(uv, param, copy.deepcopy(getattr(self, param)))

        return uv

    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 or array_like of int
            baseline number

        Returns
        -------
        int or array_like of int
            first antenna number(s)
        int or array_like of int
            second antenna number(s)
        """
        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 or array_like of int
            first antenna number
        ant2 : int or array_like of 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 or array of 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
        if not self.metadata_only:
            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, allow_rephase=True, orig_phase_frame=None):
        """
        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.
        allow_rephase : bool
            If True, allow unphasing and rephasing if this object is already
            phased.
        orig_phase_frame : str
            The original phase frame of this object (to use in unphasing). Only
            used if the object is already phased, `allow_rephase` is True and
            the phase_center_ra/dec of the object does not match `ra` and `dec`.
            Defaults to using the 'phase_center_frame' attribute or 'icrs' if
            that attribute is None.

        Raises
        ------
        ValueError
            If the phase_type is not 'drift'

        """
        if self.phase_type == 'drift':
            pass
        elif self.phase_type == 'phased':
            if allow_rephase:
                if (not np.isclose(self.phase_center_ra, ra,
                                   rtol=self._phase_center_ra.tols[0],
                                   atol=self._phase_center_ra.tols[1])
                    or not np.isclose(self.phase_center_dec, dec,
                                      rtol=self._phase_center_dec.tols[0],
                                      atol=self._phase_center_dec.tols[1])):
                    self.unphase_to_drift(phase_frame=orig_phase_frame,
                                          use_ant_pos=use_ant_pos)
            else:
                raise ValueError('The data is already phased; set allow_rephase'
                                 ' to True to unphase and rephase.')
        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
        if not self.metadata_only:
            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,
                      allow_rephase=True, orig_phase_frame=None):
        """
        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 or float
            The time to phase to, an astropy Time object or a float Julian Date
        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.
        allow_rephase : bool
            If True, allow unphasing and rephasing if this object is already
            phased.
        orig_phase_frame : str
            The original phase frame of this object (to use in unphasing). Only
            used if the object is already phased, `allow_rephase` is True and
            the phase_center_ra/dec of the object does not match `ra` and `dec`.
            Defaults to using the 'phase_center_frame' attribute or 'icrs' if
            that attribute is None.

        Raises
        ------
        ValueError
            If the phase_type is not 'drift'
        TypeError
            If time is not an astropy.time.Time object or Julian Date as a float
        """
        if isinstance(time, (float, np.float32)):
            time = Time(time, format='jd')

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

        # 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, 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,
                   allow_rephase=allow_rephase, orig_phase_frame=orig_phase_frame)

    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:
                if not self.metadata_only:
                    warnings.warn('Data will be unphased and rephased '
                                  'to calculate UVWs, which might introduce small '
                                  'inaccuracies to the data.')
                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, _ = self.get_ENU_antpos(center=False)

        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, uvw_tol=0.0):
        """
        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.
        uvw_tol : float
            Defines a tolerance on uvw coordinates for setting the
            u>0, u<0, v>0, or v<0 conventions. Defaults to 0m.

        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] > uvw_tol)
                                         | (uvw_array_use[:, 1] > uvw_tol) & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol)
                                         | (uvw_array_use[:, 2] > uvw_tol)
                                         & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol)
                                         & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol)).nonzero()
            elif convention == 'u>0':
                index_array = np.asarray((uvw_array_use[:, 0] < -uvw_tol)
                                         | ((uvw_array_use[:, 1] < -uvw_tol) & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol))
                                         | ((uvw_array_use[:, 2] < -uvw_tol)
                                         & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol)
                                         & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol))).nonzero()
            elif convention == 'v<0':
                index_array = np.asarray((uvw_array_use[:, 1] > uvw_tol)
                                         | (uvw_array_use[:, 0] > uvw_tol) & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol)
                                         | (uvw_array_use[:, 2] > uvw_tol)
                                         & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol)
                                         & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol)).nonzero()
            elif convention == 'v>0':
                index_array = np.asarray((uvw_array_use[:, 1] < -uvw_tol)
                                         | (uvw_array_use[:, 0] < -uvw_tol) & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol)
                                         | (uvw_array_use[:, 2] < -uvw_tol)
                                         & np.isclose(uvw_array_use[:, 0], 0, atol=uvw_tol)
                                         & np.isclose(uvw_array_use[:, 1], 0, atol=uvw_tol)).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 reorder_blts(self, order='time', minor_order=None, conj_convention=None, uvw_tol=0.0,
                     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.
        uvw_tol : float
            If conjugating baselines, sets a tolerance for determining the signs
            of u,v, and w, and whether or not they are zero.
            See conjugate_bls for 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, uvw_tol=uvw_tol)

        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]
        if not self.metadata_only:
            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 sum_vis(self, other, run_check=True, check_extra=True,
                run_check_acceptability=True, inplace=False, difference=False):
        """
        Sums visibilities between two UVData objects.

        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.
        difference : bool
            If True, differences the visibilities of the two UVData objects
            rather than summing them.

        Returns
        ------
        UVData Object
            If inplace parameter is False.

        Raises
        ------
        ValueError
            If other is not a UVData object, 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)

        # Define the parameters that need to be the same for objects to be
        # summed or diffed.
        compatibility_params = list(this.__iter__())
        compatibility_params.remove('_data_array')
        compatibility_params.remove('_history')

        # Check each metadata element in compatibility_params
        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)

        # Do the summing / differencing
        if difference:
            this.data_array = this.data_array - other.data_array
            history_update_string = ' Visibilities differenced using pyuvdata.'
        else:
            this.data_array = this.data_array + other.data_array
            history_update_string = ' Visibilities summed using pyuvdata.'

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

        # 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 diff_vis(self, other, run_check=True, check_extra=True,
                 run_check_acceptability=True,
                 inplace=False):

        if inplace:
            self.sum_vis(other, difference=True, run_check=True,
                         check_extra=check_extra,
                         run_check_acceptability=run_check_acceptability,
                         inplace=inplace)
        else:
            return self.sum_vis(other, difference=True, run_check=True,
                                check_extra=check_extra,
                                run_check_acceptability=run_check_acceptability,
                                inplace=inplace)

    def __add__(self, other, phase_center_radec=None,
                unphase_to_drift=False, phase_frame='icrs',
                orig_phase_frame=None,
                use_ant_pos=False,
                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.
        phase_center_radec : array_like of float
            The phase center to phase the files to before adding the objects in
            radians (in the ICRS frame). Note that if this keyword is not set
            and the two UVData objects are phased to different phase centers
            or if one is phased and one is drift, this method will error
            because the objects are not compatible.
        unphase_to_drift : bool
            If True, unphase the objects to drift before combining them.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
            Only used if `phase_center_radec` is set.
        orig_phase_frame : str
            The original phase frame of the data (if it is already phased). Used
            for unphasing, only if `unphase_to_drift` or `phase_center_radec`
            are set. Defaults to using the 'phase_center_frame' attribute or
            'icrs' if that attribute is None.
        use_ant_pos : bool
            If True, calculate the phased or unphased uvws directly from the
            antenna positions rather than from the existing uvws.
            Only used if `unphase_to_drift` or `phase_center_radec` are set.
        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. One way they can not be
            compatible if if they have different phasing, in that case set
            `unphase_to_drift` or `phase_center_radec`to (un)phase them so they
            are compatible.
            If phase_center_radec is not None and is not length 2.

        """
        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)

        if phase_center_radec is not None and unphase_to_drift:
            raise ValueError('phase_center_radec cannot be set if '
                             'unphase_to_drift is True.')

        if unphase_to_drift:
            if (this.phase_type != 'drift'):
                warnings.warn("Unphasing this UVData object to drift")
                this.unphase_to_drift(phase_frame=orig_phase_frame,
                                      use_ant_pos=use_ant_pos)

            if (other.phase_type != 'drift'):
                warnings.warn("Unphasing other UVData object to drift")
                other.unphase_to_drift(phase_frame=orig_phase_frame,
                                       use_ant_pos=use_ant_pos)

        if phase_center_radec is not None:
            if np.array(phase_center_radec).size != 2:
                raise ValueError('phase_center_radec should have length 2.')

            # If this object is not phased or is not phased close to
            # phase_center_radec, (re)phase it.
            # Close is defined using the phase_center_ra/dec tolerances.
            if (this.phase_type == 'drift'
                or (not np.isclose(this.phase_center_ra, phase_center_radec[0],
                                   rtol=this._phase_center_ra.tols[0],
                                   atol=this._phase_center_ra.tols[1])
                    or not np.isclose(this.phase_center_dec, phase_center_radec[1],
                                      rtol=this._phase_center_dec.tols[0],
                                      atol=this._phase_center_dec.tols[1]))):
                warnings.warn("Phasing this UVData object to phase_center_radec")
                this.phase(phase_center_radec[0], phase_center_radec[1],
                           phase_frame=phase_frame,
                           orig_phase_frame=orig_phase_frame,
                           use_ant_pos=use_ant_pos, allow_rephase=True)

            # If other object is not phased or is not phased close to
            # phase_center_radec, (re)phase it.
            # Close is defined using the phase_center_ra/dec tolerances.
            if (other.phase_type == 'drift'
                or (not np.isclose(other.phase_center_ra, phase_center_radec[0],
                                   rtol=other._phase_center_ra.tols[0],
                                   atol=other._phase_center_ra.tols[1])
                    or not np.isclose(other.phase_center_dec, phase_center_radec[1],
                                      rtol=other._phase_center_dec.tols[0],
                                      atol=other._phase_center_dec.tols[1]))):
                warnings.warn("Phasing other UVData object to phase_center_radec")
                other.phase(phase_center_radec[0], phase_center_radec[1],
                            phase_frame=phase_frame,
                            orig_phase_frame=orig_phase_frame,
                            use_ant_pos=use_ant_pos, allow_rephase=True)

        # 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 not self.metadata_only and (
                len(both_pol) > 0 and len(both_freq) > 0 and 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
            if n_axes > 0:
                history_update_string += ', frequency'
            else:
                history_update_string += 'frequency'
            n_axes += 1
        else:
            fnew_inds = []

        # 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
            if n_axes > 0:
                history_update_string += ', polarization'
            else:
                history_update_string += 'polarization'
            n_axes += 1
        else:
            pnew_inds = []

        # 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)
            if not self.metadata_only:
                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:
            this.freq_array = np.concatenate([this.freq_array,
                                              other.freq_array[:, fnew_inds]], axis=1)
            f_order = np.argsort(this.freq_array[0, :])
            if not self.metadata_only:
                zero_pad = np.zeros((this.data_array.shape[0], this.Nspws, len(fnew_inds),
                                     this.Npols))
                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:
            this.polarization_array = np.concatenate([this.polarization_array,
                                                      other.polarization_array[pnew_inds]])
            p_order = np.argsort(np.abs(this.polarization_array))
            if not self.metadata_only:
                zero_pad = np.zeros((this.data_array.shape[0], this.Nspws,
                                     this.data_array.shape[2], len(pnew_inds)))
                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]
        if not self.metadata_only:
            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 not self.metadata_only:
            if len(bnew_inds) > 0:
                for name, param in zip(this._data_params, this.data_like_parameters):
                    setattr(this, name, param[blt_order, :, :, :])

            if len(fnew_inds) > 0:
                for name, param in zip(this._data_params, this.data_like_parameters):
                    setattr(this, name, param[:, :, f_order, :])

            if len(pnew_inds) > 0:
                for name, param in zip(this._data_params, this.data_like_parameters):
                    setattr(this, name, param[:, :, :, p_order])

        if len(fnew_inds) > 0:
            this.freq_array = this.freq_array[:, f_order]
        if len(pnew_inds) > 0:
            this.polarization_array = this.polarization_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 + this._channel_width.tols[1]:
                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, phase_center_radec=None, unphase_to_drift=False,
                 phase_frame='icrs', orig_phase_frame=None, use_ant_pos=False,
                 run_check=True, check_extra=True,
                 run_check_acceptability=True):
        """
        In place add.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        phase_center_radec : array_like of float
            The phase center to phase the files to before adding the objects in
            radians (in the ICRS frame). Note that if this keyword is not set
            and the two UVData objects are phased to different phase centers
            or if one is phased and one is drift, this method will error
            because the objects are not compatible.
        unphase_to_drift : bool
            If True, unphase the objects to drift before combining them.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
            Only used if `phase_center_radec` is set.
        orig_phase_frame : str
            The original phase frame of the data (if it is already phased). Used
            for unphasing, only if `unphase_to_drift` or `phase_center_radec`
            are set. Defaults to using the 'phase_center_frame' attribute or
            'icrs' if that attribute is None.
        use_ant_pos : bool
            If True, calculate the phased or unphased uvws directly from the
            antenna positions rather than from the existing uvws.
            Only used if `unphase_to_drift` or `phase_center_radec` are set.
        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.

        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, phase_center_radec=phase_center_radec,
                     unphase_to_drift=unphase_to_drift,
                     phase_frame=phase_frame, orig_phase_frame=orig_phase_frame,
                     use_ant_pos=use_ant_pos,
                     run_check=run_check, check_extra=check_extra,
                     run_check_acceptability=run_check_acceptability,
                     inplace=True)
        return self

    def fast_concat(self, other, axis, phase_center_radec=None,
                    unphase_to_drift=False,
                    phase_frame='icrs', orig_phase_frame=None, use_ant_pos=False,
                    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'.
        phase_center_radec : array_like of float
            The phase center to phase the files to before adding the objects in
            radians (in the ICRS frame). Note that if this keyword is not set
            and the two UVData objects are phased to different phase centers
            or if one is phased and one is drift, this method will error
            because the objects are not compatible.
        unphase_to_drift : bool
            If True, unphase the objects to drift before combining them.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
            Only used if `phase_center_radec` is set.
        orig_phase_frame : str
            The original phase frame of the data (if it is already phased). Used
            for unphasing, only if `unphase_to_drift` or `phase_center_radec`
            are set. Defaults to using the 'phase_center_frame' attribute or
            'icrs' if that attribute is None.
        use_ant_pos : bool
            If True, calculate the phased or unphased uvws directly from the
            antenna positions rather than from the existing uvws.
            Only used if `unphase_to_drift` or `phase_center_radec` are set.
        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)

        if phase_center_radec is not None and unphase_to_drift:
            raise ValueError('phase_center_radec cannot be set if '
                             'unphase_to_drift is True.')

        if unphase_to_drift:
            if (this.phase_type != 'drift'):
                warnings.warn("Unphasing this UVData object to drift")
                this.unphase_to_drift(phase_frame=orig_phase_frame,
                                      use_ant_pos=use_ant_pos)

            if (other.phase_type != 'drift'):
                warnings.warn("Unphasing other UVData object to drift")
                other.unphase_to_drift(phase_frame=orig_phase_frame,
                                       use_ant_pos=use_ant_pos)

        if phase_center_radec is not None:
            if np.array(phase_center_radec).size != 2:
                raise ValueError('phase_center_radec should have length 2.')

            # If this object is not phased or is not phased close to
            # phase_center_radec, (re)phase it.
            # Close is defined using the phase_center_ra/dec tolerances.
            if (this.phase_type == 'drift'
                or (not np.isclose(this.phase_center_ra, phase_center_radec[0],
                                   rtol=this._phase_center_ra.tols[0],
                                   atol=this._phase_center_ra.tols[1])
                    or not np.isclose(this.phase_center_dec, phase_center_radec[1],
                                      rtol=this._phase_center_dec.tols[0],
                                      atol=this._phase_center_dec.tols[1]))):
                warnings.warn("Phasing this UVData object to phase_center_radec")
                this.phase(phase_center_radec[0], phase_center_radec[1],
                           phase_frame=phase_frame,
                           orig_phase_frame=orig_phase_frame,
                           use_ant_pos=use_ant_pos, allow_rephase=True)

            # If other object is not phased or is not phased close to
            # phase_center_radec, (re)phase it.
            # Close is defined using the phase_center_ra/dec tolerances.
            if (other.phase_type == 'drift'
                or (not np.isclose(other.phase_center_ra, phase_center_radec[0],
                                   rtol=other._phase_center_ra.tols[0],
                                   atol=other._phase_center_ra.tols[1])
                    or not np.isclose(other.phase_center_dec, phase_center_radec[1],
                                      rtol=other._phase_center_dec.tols[0],
                                      atol=other._phase_center_dec.tols[1]))):
                warnings.warn("Phasing other UVData object to phase_center_radec")
                other.phase(phase_center_radec[0], phase_center_radec[1],
                            phase_frame=phase_frame,
                            orig_phase_frame=orig_phase_frame,
                            use_ant_pos=use_ant_pos, allow_rephase=True)

        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 + this._channel_width.tols[1]:
                warnings.warn('Combined frequencies are not contiguous. This will make '
                              'it impossible to write this data out to some file types.')
            if not self.metadata_only:
                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.')

            if not self.metadata_only:
                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])
            if not self.metadata_only:
                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, time_range,
                           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. Cannot be used with `time_range`.
        time_range : array_like of float, optional
            The time range in Julian Date to keep in the object, must be
            length 2. Some of the times in the object should fall between the
            first and last elements. Cannot be used with `times`.
        polarizations : array_like of int, optional
            The polarizations numbers to 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 len(bls) == 0 or 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 intersection (and) to join antenna_names/nums & ant_pairs_nums
                ant_blt_inds = np.array(list(set(ant_blt_inds).intersection(bls_blt_inds)))
            else:
                ant_blt_inds = bls_blt_inds

        if ant_blt_inds is not None:
            if blt_inds is not None:
                # Use intersection (and) to join antenna_names/nums/ant_pairs_nums with blt_inds
                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:
            if time_range is not None:
                raise ValueError(
                    'Only one of "times" and "time_range" can be set')

            times = uvutils._get_iterable(times)
            if np.array(times).ndim > 1:
                times = np.array(times).flatten()

            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 time_range is not None:
            if np.size(time_range) != 2:
                raise ValueError('time_range must be length 2.')

            time_blt_inds = np.nonzero(
                (self.time_array <= time_range[1])
                & (self.time_array >= time_range[0]))[0]
            if time_blt_inds.size == 0:
                raise ValueError(
                    'No elements in time range between {t0} and t1'
                    .format(t0=time_range[0], t1=time_range[1]))

        if times is not None or time_range is not None:
            if n_selects > 0:
                history_update_string += ', times'
            else:
                history_update_string += 'times'
            n_selects += 1

            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, time_range=None, polarizations=None, blt_inds=None,
               run_check=True,
               check_extra=True, run_check_acceptability=True, inplace=True,
               metadata_only=None, 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. Cannot be used with `time_range`.
        time_range : array_like of float, optional
            The time range in Julian Date to keep in the object, must be
            length 2. Some of the times in the object should fall between the
            first and last elements. Cannot be used with `times`.
        polarizations : array_like of int, optional
            The polarizations numbers to 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. Note this option
            has been replaced by an automatic detection of whether the data like
            arrays are present. The keyword will be deprecated in version 1.6.
        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 not None:
            warnings.warn('The metadata_only option has been replaced by an '
                          'automatic detection of whether the data like arrays '
                          'are present. The keyword will be deprecated in version 1.6.',
                          DeprecationWarning)

            if metadata_only != self.metadata_only:
                raise ValueError('The metadata_only option can only be True if '
                                 'data_array, flag_array or nsample_array are '
                                 'all None and must be False otherwise.')

        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, time_range, 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 self.metadata_only:
            if not inplace:
                return uv_object
            else:
                return

        if blt_inds is not None:
            for param_name, param in zip(self._data_params, uv_object.data_like_parameters):
                setattr(uv_object, param_name, param[blt_inds, :, :, :])

        if freq_inds is not None:
            for param_name, param in zip(self._data_params, uv_object.data_like_parameters):
                setattr(uv_object, param_name, param[:, :, freq_inds, :])

        if pol_inds is not None:
            for param_name, param in zip(self._data_params, uv_object.data_like_parameters):
                setattr(uv_object, param_name, param[:, :, :, 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 == 'uvfits':
            from . import uvfits
            other_obj = uvfits.UVFITS()
        elif filetype == 'fhd':
            from . import fhd
            other_obj = fhd.FHD()
        elif filetype == 'miriad':
            from . import miriad
            other_obj = miriad.Miriad()
        elif filetype == '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,
                    time_range=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 a single uvfits file.

        Parameters
        ----------
        filename : str
            The uvfits file to read from. Support for a list of files will be
            deprecated in version 2.0 in favor of a call to the generic
            `read` method.
        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 in the file.
            Cannot be used with `time_range`.
        time_range : array_like of float, optional
            The time range in Julian Date to include when reading data into
            the object, must be length 2. Some of the times in the file should
            fall between the first and last elements.
            Cannot be used with `times`.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        keep_all_metadata : bool
            Option to keep all the metadata associated with antennas, even those
            that do not have data associated with them after the select option.
        read_data : bool
            Read in the visibility and flag data. If set to false, only the
            basic header info and metadata read in. Setting read_data to False
            results in a metdata only object.
        read_metadata : bool
            Deprecated, will be removed in version 2.0, after which metadata
            will always be read along with header data.
            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, which will result in an incompletely
            defined object -- check will not pass.
        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.

        Raises
        ------
        IOError
            If filename doesn't exist.
        ValueError
            If incompatible select keywords are set (e.g. `ant_str` with other
            antenna selectors, `times` and `time_range`) or select keywords
            exclude all data or if keywords are set to the wrong type.
            If the data are multi source or have multiple
            spectral windows.
            If the metadata are not internally consistent or missing.

        """
        from . import uvfits

        if isinstance(filename, (list, tuple, np.ndarray)):
            warnings.warn('Please use the generic `read` '
                          'method to read multiple uvfits files. Support for '
                          'reading multiple files with this method will be '
                          'removed in version 2.0.', DeprecationWarning)

            self.read(filename, file_type='uvfits', axis=axis,
                      antenna_nums=antenna_nums,
                      antenna_names=antenna_names, ant_str=ant_str,
                      bls=bls, frequencies=frequencies,
                      freq_chans=freq_chans, times=times, time_range=time_range,
                      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)
            return

        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,
                               time_range=time_range,
                               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)

    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).

        Raises
        ------
        ValueError
            The `phase_type` of the object is "drift" and the `force_phase` keyword is not set.
            The `phase_type` of the object is "unknown".
            If the frequencies are not evenly spaced or are separated by more
            than their channel width.
            The polarization values are not evenly spaced.
            Any of ['antenna_positions', 'gst0', 'rdate', 'earth_omega', 'dut1',
            'timesys'] are not set on the object and `spoof_nonessential` is False.
            If the `timesys` parameter is not set to "UTC".
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        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
            The measurement set root directory to read from.
            Support for a list/array of file directories will be
            deprecated in version 2.0 in favor of a call to the generic
            `read` method.
        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).

        Raises
        ------
        IOError
            If root file directory doesn't exist.
        ValueError
            If the `data_column` is not set to an allowed value.
            If the data are have multiple subarrays or are multi source or have
            multiple spectral windows.
            If the data have multiple data description ID values.

        """
        from . import ms

        if isinstance(filepath, (list, tuple, np.ndarray)):

            warnings.warn('Please use the generic `read` '
                          'method to read multiple ms files. Support for '
                          'reading multiple files with this method will be '
                          'removed in version 2.0.', DeprecationWarning)

            self.read(filepath, file_type='ms', axis=axis, run_check=run_check,
                      check_extra=check_extra,
                      run_check_acceptability=run_check_acceptability,
                      data_column=data_column, pol_order=pol_order)
            return

        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 : array_like of str
            The list/array of FHD save files to read from. Must include at
            least one polarization file, a params file and a flag file.
            Support for a list of lists of files for multiple data sets will be
            deprecated in version 2.0 in favor of a call to the generic
            `read` method.
        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).

        Raises
        ------
        ValueError
            If required files are missing or multiple files for any polarization
            are included in filelist.
            If there is no recognized key for visibility weights in the flags_file.

        """
        from . import fhd
        if isinstance(filelist[0], (list, tuple, np.ndarray)):
            warnings.warn('Please use the generic `read` '
                          'method to read multiple fhd files. Support for '
                          'reading multiple files with this method will be '
                          'removed in version 2.0.', DeprecationWarning)

            self.read(filelist, file_type='fhd', axis=axis, use_model=use_model,
                      run_check=run_check,
                      check_extra=check_extra,
                      run_check_acceptability=run_check_acceptability)
            return

        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
            The miriad root directory to read from.
            Support for a list/array of file directories will be
            deprecated in version 2.0 in favor of a call to the generic
            `read` method.
        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.

        Raises
        ------
        IOError
            If root file directory doesn't exist.
        ValueError
            If incompatible select keywords are set (e.g. `ant_str` with other
            antenna selectors, `times` and `time_range`) or select keywords
            exclude all data or if keywords are set to the wrong type.
            If the data are multi source or have multiple
            spectral windows.
            If the metadata are not internally consistent.
        """
        from . import miriad
        if isinstance(filepath, (list, tuple, np.ndarray)):
            warnings.warn('Please use the generic `read` '
                          'method to read multiple miriad files. Support for '
                          'reading multiple files with this method will be '
                          'removed in version 2.0.', DeprecationWarning)

            self.read(filepath, file_type='miriad', axis=axis,
                      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)
            return

        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,
                               read_data=read_data,
                               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)

    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 root 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.

        Raises
        ------
        ValueError
            If the frequencies are not evenly spaced or are separated by more
            than their channel width.
            The `phase_type` of the object is "unknown".
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        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_mwa_corr_fits(self, filelist, axis=None, use_cotter_flags=False,
                           correct_cable_len=False, phase_to_pointing_center=False,
                           phase_data=None, phase_center=None, run_check=True,
                           check_extra=True, run_check_acceptability=True):
        """
        Read in MWA correlator gpu box files.

        Parameters
        ----------
        filelist : list of str
            The list of MWA correlator files to read from. Must include at
            least one fits file and only one metafits file per data set.
            Support for a list of lists of files for multiple data sets will be
            deprecated in version 2.0 in favor of a call to the generic
            `read` method.
        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.
        use_cotter_flags : bool
            Option to use cotter output mwaf flag files. Otherwise flagging
            will only be applied to missing data and bad antennas.
        correct_cable_len : bool
            Option to apply a cable delay correction.
        phase_to_pointing_center : bool
            Option to phase to the observation pointing center.
        phase_data : bool
            Deprecated, use phase_to_pointing_center. Option to phase data,
            default is no phasing.
        phase_center : tuple, optional
            Deprecated, use the `read` method to phase to arbitrary locations.
            A tuple containing the ra and dec coordinates in radians of a
            specific location to phase data to.
        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).

        Raises
        ------
        ValueError
            If required files are missing or multiple files metafits files are included in filelist.
            If files from different observations are included in filelist.
            If files in fileslist have different fine channel widths
            If file types other than fits, metafits, and mwaf files are included in filelist.

        """
        from . import mwa_corr_fits

        call_read = False
        if phase_center is not None:
            warnings.warn('The `phase_center` keyword is deprecated. '
                          'Please use the generic `read` '
                          'method to phase to an arbitrary phase center. '
                          'Support for the `phase_center` keyword will be '
                          'removed in version 2.0.', DeprecationWarning)
            call_read = True

        if phase_data is not None:
            warnings.warn('The `phase_data` keyword is deprecated. '
                          'Please use the `phase_to_pointing_center` '
                          'keyword to phase to the pointing center. '
                          'Support for the `phase_data` keyword will be '
                          'removed in version 2.0.', DeprecationWarning)
            if phase_center is None:
                phase_to_pointing_center = True
            else:
                phase_to_pointing_center = False

        if isinstance(filelist[0], (list, tuple, np.ndarray)):
            warnings.warn('Please use the generic `read` '
                          'method to read multiple mwa_corr_fits file sets. '
                          'Support for '
                          'reading multiple file sets with this method will be '
                          'removed in version 2.0.', DeprecationWarning)
            call_read = True

        if call_read:

            self.read(filelist, file_type='mwa_corr_fits', axis=axis,
                      use_cotter_flags=use_cotter_flags,
                      correct_cable_len=correct_cable_len,
                      phase_to_pointing_center=phase_to_pointing_center,
                      phase_center_radec=phase_center, run_check=run_check,
                      check_extra=check_extra,
                      run_check_acceptability=run_check_acceptability)
            return

        corr_obj = mwa_corr_fits.MWACorrFITS()
        corr_obj.read_mwa_corr_fits(filelist, use_cotter_flags=use_cotter_flags,
                                    correct_cable_len=correct_cable_len,
                                    phase_to_pointing_center=phase_to_pointing_center,
                                    run_check=run_check, check_extra=check_extra,
                                    run_check_acceptability=run_check_acceptability)
        self._convert_from_filetype(corr_obj)
        del(corr_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, time_range=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
             The UVH5 file to read from.
             Support for a list/array of files will be
             deprecated in version 2.0 in favor of a call to the generic
             `read` method.
        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 in the file.
            Cannot be used with `time_range`.
        time_range : array_like of float, optional
            The time range in Julian Date to include when reading data into
            the object, must be length 2. Some of the times in the file should
            fall between the first and last elements.
            Cannot be used with `times`.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        keep_all_metadata : bool
            Option to keep all the metadata associated with antennas, even those
            that do not have data associated with them after the select option.
        read_data : bool
            Read in the visibility and flag data. If set to false, only the
            basic header info and metadata will be read in. Setting read_data to
            False results in an incompletely defined object (check will not pass).
        data_array_dtype : numpy dtype
            Datatype to store the output data_array as. Must be either
            np.complex64 (single-precision real and imaginary) or np.complex128 (double-
            precision real and imaginary). Only used if the datatype of the visibility
            data on-disk is not 'c8' or 'c16'.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.

        Raises
        ------
        IOError
            If filename doesn't exist.
        ValueError
            If the data_array_dtype is not a complex dtype.
            If incompatible select keywords are set (e.g. `ant_str` with other
            antenna selectors, `times` and `time_range`) or select keywords
            exclude all data or if keywords are set to the wrong type.

        """
        from . import uvh5
        if isinstance(filename, (list, tuple, np.ndarray)):
            warnings.warn('Please use the generic `read` '
                          'method to read multiple uvh5 files. Support for '
                          'reading multiple files with this method will be '
                          'removed in version 2.0.', DeprecationWarning)

            self.read(filename, file_type='uvh5', axis=axis,
                      antenna_nums=antenna_nums,
                      antenna_names=antenna_names, ant_str=ant_str, bls=bls,
                      frequencies=frequencies, freq_chans=freq_chans,
                      times=times, time_range=time_range,
                      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)
            return

        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, time_range=time_range,
                           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, allow_rephase=True,
             phase_center_radec=None, unphase_to_drift=False, phase_frame='icrs',
             orig_phase_frame=None, phase_use_ant_pos=False,
             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,
             use_cotter_flags=False, correct_cable_len=False,
             phase_to_pointing_center=False,
             run_check=True, check_extra=True, run_check_acceptability=True):
        """
        Read a generic file into a UVData object.

        Parameters
        ----------
        filename : str or array_like of str
            The file(s) or list(s) (or array(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.
        allow_rephase :  bool
            Allow rephasing of phased file data so that data from files with
            different phasing can be combined.
        phase_center_radec : array_like of float
            The phase center to phase the files to before adding the objects in
            radians (in the ICRS frame). If set to None and multiple files are
            read with different phase centers, the phase center of the first
            file will be used.
        unphase_to_drift : bool
            Unphase the data from the files before combining them.
        phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'.
            'gcrs' accounts for precession & nutation,
            'icrs' accounts for precession, nutation & abberation.
            Only used if `phase_center_radec` is set.
        orig_phase_frame : str
            The original phase frame of the data (if it is already phased). Used
            for unphasing, only if `unphase_to_drift` or `phase_center_radec`
            are set. Defaults to using the 'phase_center_frame' attribute or
            'icrs' if that attribute is None.
        phase_use_ant_pos : bool
            If True, calculate the phased or unphased uvws directly from the
            antenna positions rather than from the existing uvws.
            Only used if `unphase_to_drift` or `phase_center_radec` are set.
        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.
        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.
        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`,
            `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 reading data into the object, each
            value passed here should exist in the freq_array.
        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 in the file.
            Cannot be used with `time_range`.
        time_range : array_like of float, optional
            The time range in Julian Date to include when reading data into
            the object, must be length 2. Some of the times in the file should
            fall between the first and last elements.
            Cannot be used with `times`.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used.
        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
            Deprecated, will be removed in version 2.0, after which metadata
            will always be read along with header data.
            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. Setting read_data to False results in a metdata only
            object.
        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'.
        use_cotter_flags : bool
            Flag to apply cotter flags. Only used if file_type is 'mwa_corr_fits'.
        correct_cable_len : bool
            Flag to apply cable length correction. Only used if file_type is
            'mwa_corr_fits'.
        phase_to_pointing_center : bool
            Flag to phase to the pointing center. Only used if file_type is
            'mwa_corr_fits'. Cannot be set if phase_center_radec is not None.
        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.

        Raises
        ------
        ValueError
            If the file_type is not set and cannot be determined from the file name.
            If incompatible select keywords are set (e.g. `ant_str` with other
            antenna selectors, `times` and `time_range`) or select keywords
            exclude all data or if keywords are set to the wrong type.
            If the data are multi source or have multiple
            spectral windows.
            If phase_center_radec is not None and is not length 2.

        """
        if isinstance(filename, (list, tuple, np.ndarray)):
            # this is either a list of separate files to read or a list of
            # FHD files or MWA correlator FITS files
            if isinstance(filename[0], (list, tuple, np.ndarray)):
                if file_type is None:
                    # this must be a list of lists of FHD or MWA correlator FITS
                    basename, extension = os.path.splitext(filename[0][0])
                    if extension == '.sav' or extension == '.txt':
                        file_type = 'fhd'
                    elif (extension == '.fits' or extension == '.metafits'
                          or extension == '.mwaf'):
                        file_type = 'mwa_corr_fits'
                multi = True
            else:
                if file_type is None:
                    basename, extension = os.path.splitext(filename[0])
                    if extension == '.sav' or extension == '.txt':
                        file_type = 'fhd'
                    elif (extension == '.fits' or extension == '.metafits'
                          or extension == '.mwaf'):
                        file_type = 'mwa_corr_fits'

                if file_type == 'fhd' or file_type == 'mwa_corr_fits':
                    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, use the '
                             'file_type keyword to specify the type.')

        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 multi:
            if file_type == 'uvfits':
                if not read_data and not read_metadata:
                    raise ValueError('A list of files cannot be used when just '
                                     'reading the header (read_data and '
                                     'read_metadata are both False)')

            self.read(filename[0], file_type=file_type,
                      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, time_range=time_range,
                      keep_all_metadata=keep_all_metadata,
                      read_metadata=read_metadata, read_data=read_data,
                      phase_type=phase_type, correct_lat_lon=correct_lat_lon,
                      use_model=use_model,
                      data_column=data_column, pol_order=pol_order,
                      data_array_dtype=data_array_dtype,
                      run_check=run_check, check_extra=check_extra,
                      run_check_acceptability=run_check_acceptability)

            if (allow_rephase and phase_center_radec is None
                    and not unphase_to_drift and self.phase_type == 'phased'):
                # set the phase center to be the phase center of the first file
                phase_center_radec = [self.phase_center_ra,
                                      self.phase_center_dec]

            if len(filename) > 1:
                for f in filename[1:]:
                    uv2 = UVData()
                    uv2.read(f, file_type=file_type,
                             phase_center_radec=phase_center_radec,
                             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, time_range=time_range,
                             keep_all_metadata=keep_all_metadata,
                             read_metadata=read_metadata, read_data=read_data,
                             phase_type=phase_type,
                             correct_lat_lon=correct_lat_lon,
                             use_model=use_model,
                             data_column=data_column, pol_order=pol_order,
                             data_array_dtype=data_array_dtype,
                             run_check=run_check, check_extra=check_extra,
                             run_check_acceptability=run_check_acceptability)
                    if axis is not None:
                        self.fast_concat(
                            uv2, axis, phase_center_radec=phase_center_radec,
                            unphase_to_drift=unphase_to_drift,
                            phase_frame=phase_frame,
                            orig_phase_frame=orig_phase_frame,
                            use_ant_pos=phase_use_ant_pos,
                            run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            inplace=True)
                    else:
                        self.__iadd__(
                            uv2, phase_center_radec=phase_center_radec,
                            unphase_to_drift=unphase_to_drift,
                            phase_frame=phase_frame,
                            orig_phase_frame=orig_phase_frame,
                            use_ant_pos=phase_use_ant_pos,
                            run_check=run_check, check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability)

                del(uv2)
        else:
            if file_type in ['fhd', 'ms', 'mwa_corr_fits']:
                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 time_range 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 "{ftype}" which does not support select '
                        'on read. Entire file will be read and then select '
                        'will be performed'.format(ftype=file_type))
                    # these file types do not have select on read, so set all
                    # select parameters
                    select_antenna_nums = antenna_nums
                    select_antenna_names = antenna_names
                    select_ant_str = ant_str
                    select_bls = bls
                    select_frequencies = frequencies
                    select_freq_chans = freq_chans
                    select_times = times
                    select_time_range = time_range
                    select_polarizations = polarizations
                    select_blt_inds = blt_inds
                else:
                    select = False
            elif file_type in ['uvfits', 'uvh5']:
                select = False
            elif file_type in ['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
                    # these are all done by partial read, so set to None
                    select_antenna_nums = None
                    select_ant_str = None
                    select_bls = None
                    select_time_range = None
                    select_polarizations = None

                    # these aren't supported by partial read, so do it in select
                    select_antenna_names = antenna_names
                    select_frequencies = frequencies
                    select_freq_chans = freq_chans
                    select_times = times
                    select_blt_inds = blt_inds
                else:
                    select = False

            # reading a single "file". Call the appropriate file-type read
            if file_type == 'uvfits':
                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, time_range=time_range,
                    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)

            elif file_type == 'miriad':
                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)

            elif file_type == 'mwa_corr_fits':
                self.read_mwa_corr_fits(
                    filename, run_check=run_check,
                    use_cotter_flags=use_cotter_flags,
                    correct_cable_len=correct_cable_len,
                    phase_to_pointing_center=phase_to_pointing_center,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability)

            elif file_type == 'fhd':
                self.read_fhd(filename, use_model=use_model,
                              run_check=run_check, check_extra=check_extra,
                              run_check_acceptability=run_check_acceptability)

            elif file_type == 'ms':
                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)

            elif file_type == 'uvh5':
                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, time_range=time_range,
                    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)
                select = False

            if select:
                self.select(
                    antenna_nums=select_antenna_nums,
                    antenna_names=select_antenna_names,
                    ant_str=select_ant_str,
                    bls=select_bls,
                    frequencies=select_frequencies,
                    freq_chans=select_freq_chans,
                    times=select_times,
                    time_range=select_time_range,
                    polarizations=select_polarizations,
                    blt_inds=select_blt_inds,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    keep_all_metadata=keep_all_metadata)

            if unphase_to_drift:
                if (self.phase_type != 'drift'):
                    warnings.warn("Unphasing this UVData object to drift")
                    self.unphase_to_drift(phase_frame=orig_phase_frame,
                                          use_ant_pos=phase_use_ant_pos)

            if phase_center_radec is not None:
                if np.array(phase_center_radec).size != 2:
                    raise ValueError('phase_center_radec should have length 2.')

                # If this object is not phased or is not phased close to
                # phase_center_radec, (re)phase it.
                # Close is defined using the phase_center_ra/dec tolerances.
                if (self.phase_type == 'drift'
                    or (not np.isclose(self.phase_center_ra, phase_center_radec[0],
                                       rtol=self._phase_center_ra.tols[0],
                                       atol=self._phase_center_ra.tols[1])
                        or not np.isclose(self.phase_center_dec, phase_center_radec[1],
                                          rtol=self._phase_center_dec.tols[0],
                                          atol=self._phase_center_dec.tols[1]))):
                    warnings.warn("Phasing this UVData object to phase_center_radec")
                    self.phase(phase_center_radec[0], phase_center_radec[1],
                               phase_frame=phase_frame,
                               orig_phase_frame=orig_phase_frame,
                               use_ant_pos=phase_use_ant_pos,
                               allow_rephase=True)

    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=False, 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,)
        """
        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.
        """
        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, 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] == 1:
                # one polarization dimension
                out = np.squeeze(out, axis=3)
            if out.shape[1] == 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_redundancies(self, tol=1.0, use_antpos=False,
                         include_conjugates=False, include_autos=True,
                         conjugate_bls=False):
        """
        Get redundant baselines to a given tolerance. This can be used to identify
        redundant baselines present in the data, or find all possible redundant
        baselines given the antenna positions.

        Parameters
        ----------
        tol : float
            Redundancy tolerance in meters (default 1m).
        use_antpos : bool
            Use antenna positions to find all possible redundant groups for this
            telescope (default False).
            The returned baselines are in the 'u>0' convention.
        include_conjugates : bool
            Option to include baselines that are redundant under conjugation.
            Only used if use_antpos is False.
        include_autos : bool
            Option to include autocorrelations in the full redundancy list.
            Only used if use_antpos is True.
        conjugate_bls : bool
            If using antenna positions, this will conjugate baselines on this
            object to correspond with those in the returned groups.

        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
        conjugates : list of int, or None, optional
            List of indices for baselines that must be conjugated to fit into their
            redundant groups.
            Will return None if use_antpos is True and include_conjugates is True
            Only returned if include_conjugates is True

        Notes
        -----
        If use_antpos is set, then this function will find all redundant baseline groups
        for this telescope, under the u>0 antenna ordering convention. If use_antpos is not set,
        this function will look for redundant groups in the data.

        """
        if use_antpos:
            antpos, numbers = self.get_ENU_antpos(center=False)
            result = uvutils.get_antenna_redundancies(numbers, antpos, tol=tol,
                                                      include_autos=include_autos)
            if conjugate_bls:
                self.conjugate_bls(convention='u>0', uvw_tol=tol)

            if include_conjugates:
                result = result + (None,)
            return result

        _, 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=include_conjugates)

    def get_antenna_redundancies(self, *args, **kwargs):
        """
        Deprecated -- Please use `get_redundancies` instead.
        """

        warnings.warn("UVData.get_antenna_redundancies has been replaced with get_redundancies,"
                      "and will be removed in version 1.6.",
                      DeprecationWarning)
        kwargs['use_antpos'] = True
        red_gps, blvecs, lens = self.get_redundancies(*args, **kwargs)
        return red_gps, blvecs, lens

    def get_baseline_redundancies(self, *args, **kwargs):
        """
        Deprecated -- Please use `get_redundancies` instead.
        """
        warnings.warn("UVData.get_baseline_redundancies has been replaced with get_redundancies,"
                      "and will be removed in version 1.6.",
                      DeprecationWarning)
        kwargs['include_conjugates'] = True
        red_gps, blvecs, lens, conjs = self.get_redundancies(*args, **kwargs)
        return red_gps, blvecs, lens, conjs

    def compress_by_redundancy(self, tol=1.0, inplace=True, metadata_only=None,
                               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. Note this option
            has been replaced by an automatic detection of whether the data like
            arrays are present. The keyword will be deprecated in version 1.6.
        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_redundancies(tol, include_conjugates=True)

        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`)
        """

        self.conjugate_bls(convention='u>0')
        red_gps, centers, lengths = self.get_redundancies(tol=tol, use_antpos=True, 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()

    def _harmonize_resample_arrays(
            self,
            inds_to_keep,
            temp_baseline,
            temp_time,
            temp_int_time,
            temp_data,
            temp_flag,
            temp_nsample,
    ):
        """
        Make a self-consistent object after up/downsampling.

        This function is called by both upsample_in_time and downsample_in_time.
        See those functions for more information about arguments.
        """
        self.baseline_array = self.baseline_array[inds_to_keep]
        self.time_array = self.time_array[inds_to_keep]
        self.integration_time = self.integration_time[inds_to_keep]

        self.baseline_array = np.concatenate((self.baseline_array, temp_baseline))
        self.time_array = np.concatenate((self.time_array, temp_time))
        self.integration_time = np.concatenate((self.integration_time, temp_int_time))
        if not self.metadata_only:
            self.data_array = self.data_array[inds_to_keep]
            self.flag_array = self.flag_array[inds_to_keep]
            self.nsample_array = self.nsample_array[inds_to_keep]

            # concatenate temp array with existing arrays
            self.data_array = np.concatenate((self.data_array, temp_data), axis=0)
            self.flag_array = np.concatenate((self.flag_array, temp_flag), axis=0)
            self.nsample_array = np.concatenate((self.nsample_array, temp_nsample), axis=0)

        # set antenna arrays from baseline_array
        self.ant_1_array, self.ant_2_array = self.baseline_to_antnums(self.baseline_array)

        # update metadata
        self.Nblts = self.baseline_array.shape[0]
        self.Ntimes = np.unique(self.time_array).size
        self.uvw_array = np.zeros((self.Nblts, 3))

        # set lst array
        self.set_lsts_from_time_array()

        # temporarily store the metadata only to calculate UVWs correctly
        uv_temp = self.copy(metadata_only=True)

        # properly calculate the UVWs self-consistently
        uv_temp.set_uvws_from_antenna_positions(allow_phasing=True)
        self.uvw_array = uv_temp.uvw_array

        return

    def upsample_in_time(self, max_int_time, blt_order="time", minor_order="baseline",
                         summing_correlator_mode=False, allow_drift=False):
        """
        Resample to a shorter integration time.

        This method will resample a UVData object such that all data samples have
        an integration time less than or equal to the `max_int_time`. The new
        samples are copied from the original samples (not interpolated).

        Parameters
        ----------
        max_int_time : float
            Maximum integration time to upsample to in seconds.
        blt_order : str
            Major baseline ordering for output object. Default is "time". See
            the documentation on the `reorder_blts` method for more info.
        minor_order : str
            Minor baseline ordering for output object. Default is "baseline".
        summing_correlator_mode : bool
            Option to split the flux from the original samples into the new
            samples rather than duplicating the original samples in all the new
            samples (undoing an integration rather than an average) to emulate
            undoing the behavior in some correlators (e.g. HERA).
        allow_drift : bool
            Option to allow resampling of drift mode data. If this is False,
            drift mode data will be phased before resampling and then unphased
            after resampling. Phasing and unphasing can introduce small errors,
            but resampling in drift mode may result in unexpected behavior.

        Returns
        -------
        None

        """
        # check that max_int_time is sensible given integration_time
        min_integration_time = np.amin(self.integration_time)
        sensible_min = 1e-2 * min_integration_time
        if max_int_time < sensible_min:
            raise ValueError("Decreasing the integration time by more than a "
                             "factor of 100 is not supported. Also note that "
                             "max_int_time should be in seconds.")

        # figure out where integration_time is longer than max_int_time
        inds_to_upsample = np.nonzero((self.integration_time > max_int_time)
                                      & (~np.isclose(self.integration_time, max_int_time,
                                                     rtol=self._integration_time.tols[0],
                                                     atol=self._integration_time.tols[1])))
        if len(inds_to_upsample[0]) == 0:
            warnings.warn("All values in the integration_time array are already "
                          "longer than the value specified; doing nothing.")
            return

        input_phase_type = self.phase_type
        if input_phase_type == "drift":
            if allow_drift:
                print('Data are in drift mode and allow_drift is True, so '
                      'resampling will be done without phasing.')
            else:
                # phase to RA/dec of zenith
                print('Data are in drift mode, phasing before resampling.')
                phase_time = Time(self.time_array[0], format='jd')
                self.phase_to_time(phase_time)

        # we want the ceil of this, but we don't want to get the wrong answer
        # when the number is very close to an integer but just barely above it.
        temp_new_samples = self.integration_time[inds_to_upsample] / max_int_time
        mask_close_floor = np.isclose(temp_new_samples, np.floor(temp_new_samples))
        temp_new_samples[mask_close_floor] = np.floor(temp_new_samples[mask_close_floor])

        n_new_samples = np.asarray(list(map(int, np.ceil(temp_new_samples))))

        temp_Nblts = np.sum(n_new_samples)

        temp_baseline = np.zeros((temp_Nblts,), dtype=np.int)
        temp_time = np.zeros((temp_Nblts,))
        temp_int_time = np.zeros((temp_Nblts,))
        if self.metadata_only:
            temp_data = None
            temp_flag = None
            temp_nsample = None
        else:
            temp_data = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                 dtype=self.data_array.dtype)
            temp_flag = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                 dtype=self.flag_array.dtype)
            temp_nsample = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                    dtype=self.nsample_array.dtype)

        i0 = 0
        for i, ind in enumerate(inds_to_upsample[0]):
            i1 = i0 + n_new_samples[i]
            temp_baseline[i0:i1] = self.baseline_array[ind]
            if not self.metadata_only:
                if summing_correlator_mode:
                    temp_data[i0:i1] = self.data_array[ind] / n_new_samples[i]
                else:
                    temp_data[i0:i1] = self.data_array[ind]
                temp_flag[i0:i1] = self.flag_array[ind]
                temp_nsample[i0:i1] = self.nsample_array[ind]

            # compute the new times of the upsampled array
            t0 = self.time_array[ind]
            dt = self.integration_time[ind] / n_new_samples[i]

            # `offset` will be 0.5 or 1, depending on whether n_new_samples for
            # this baseline is even or odd.
            offset = 0.5 + 0.5 * (n_new_samples[i] % 2)
            n2 = n_new_samples[i] // 2

            # Figure out the new center for sample ii taking offset into
            # account. Because `t0` is the central time for the original time
            # sample, `nt` will range from negative to positive so that
            # `temp_time` will result in the central time for the new samples.
            # `idx2` tells us how to far to shift and in what direction for each
            # new sample.
            for ii, idx in enumerate(range(i0, i1)):
                idx2 = ii + offset + n2 - n_new_samples[i]
                nt = ((t0 * units.day) + (dt * idx2 * units.s)).to(units.day).value
                temp_time[idx] = nt

            temp_int_time[i0:i1] = dt

            i0 = i1

        # harmonize temporary arrays with existing ones
        inds_to_keep = np.nonzero(self.integration_time <= max_int_time)
        self._harmonize_resample_arrays(
            inds_to_keep,
            temp_baseline,
            temp_time,
            temp_int_time,
            temp_data,
            temp_flag,
            temp_nsample,
        )

        if input_phase_type == "drift" and not allow_drift:
            print('Unphasing back to drift mode.')
            self.unphase_to_drift()

        # reorganize along blt axis
        self.reorder_blts(order=blt_order, minor_order=minor_order)

        # check the resulting object
        self.check()

        # add to the history
        history_update_string = (" Upsampled data to {:f} second integration time "
                                 "using pyuvdata.".format(max_int_time))
        self.history = self.history + history_update_string

        return

    def downsample_in_time(self, min_int_time, blt_order="time", minor_order="baseline",
                           keep_ragged=True, summing_correlator_mode=False,
                           allow_drift=False):
        """
        Resample to a longer integration time.

        This method will resample a UVData object such that nearly all data
        samples have an integration time greater than or equal to the `min_int_time`.
        Note that if the integrations for a baseline do not divide evenly into
        the specified `min_int_time`, the final integrations for that baseline
        in the output may have integration times less than `min_int_time`.
        This behavior can be controlled with the `keep_ragged` argument. The new
        samples are averages of the original samples (not interpolations).

        Parameters
        ----------
        min_int_time : float
            Minimum integration time to downsample the UVData integration_time to
            in seconds.
        blt_order : str
            Major baseline ordering for output object. Default is "time". See the
            documentation on the `reorder_blts` method for more details.
        minor_order : str
            Minor baseline ordering for output object. Default is "baseline".
        keep_ragged : bool
            When averaging baselines that do not evenly divide into min_int_time,
            keep_ragged controls whether to keep the (summed) integrations
            corresponding to the remaining samples (keep_ragged=True), or
            discard them (keep_ragged=False).
        summing_correlator_mode : bool
            Option to integrate the flux from the original samples rather than
            average the flux to emulate the behavior in some correlators (e.g. HERA).
        allow_drift : bool
            Option to allow resampling of drift mode data. If this is False,
            drift mode data will be phased before resampling and then unphased
            after resampling. Phasing and unphasing can introduce small errors,
            but resampling in drift mode may result in unexpected behavior.

        Returns
        -------
        None

        """
        # check that min_int_time is sensible given integration_time
        max_integration_time = np.amax(self.integration_time)
        sensible_max = 1e2 * max_integration_time
        if min_int_time > sensible_max:
            raise ValueError("Increasing the integration time by more than a "
                             "factor of 100 is not supported. Also note that "
                             "min_int_time should be in seconds.")

        # first figure out where integration_time is shorter than min_int_time
        inds_to_downsample = np.nonzero((self.integration_time < min_int_time)
                                        & (~np.isclose(self.integration_time, min_int_time,
                                                       rtol=self._integration_time.tols[0],
                                                       atol=self._integration_time.tols[1])))

        if len(inds_to_downsample[0]) == 0:
            warnings.warn("All values in the integration_time array are already "
                          "shorter than the value specified; doing nothing.")
            return
        # If we're going to do actual work, reorder the baselines to ensure time is
        # monotonically increasing.
        # Default of reorder_blts is baseline major, time minor, which is what we want.
        self.reorder_blts()

        # now re-compute inds_to_downsample, in case things have changed
        inds_to_downsample = np.nonzero((self.integration_time < min_int_time)
                                        & (~np.isclose(self.integration_time, min_int_time,
                                                       rtol=self._integration_time.tols[0],
                                                       atol=self._integration_time.tols[1])))

        # figure out how many baselines we'll end up with at the end
        bls_to_downsample = np.unique(self.baseline_array[inds_to_downsample])
        n_new_samples = 0
        for bl in bls_to_downsample:
            bl_inds = np.nonzero(self.baseline_array == bl)[0]
            n_sample_temp = np.sum(self.integration_time[bl_inds] / min_int_time)
            if keep_ragged and not np.isclose(n_sample_temp, np.floor(n_sample_temp)):
                n_new_samples += np.ceil(n_sample_temp).astype(int)
            else:
                n_new_samples += np.floor(n_sample_temp).astype(int)
            # figure out if there are any time gaps in the data
            # meaning that the time differences are larger than the integration times
            # time_array is in JD, need to convert to seconds for the diff
            dtime = np.ediff1d(self.time_array[bl_inds]) * 24 * 3600
            int_times = self.integration_time[bl_inds]
            if len(np.unique(int_times)) == 1:
                # this baseline has all the same integration times
                if len(np.unique(dtime)) > 1:
                    warnings.warn("There is a gap in the times of baseline {bl}. "
                                  "The output may include averages across long "
                                  "time gaps.".format(bl=self.baseline_to_antnums(bl)))
                elif not np.isclose(dtime[0], int_times[0]):
                    warnings.warn("The time difference between integrations is "
                                  "not the same as the integration time for "
                                  "baseline {bl}. The output may average across "
                                  "longer time intervals than "
                                  "expected".format(bl=self.baseline_to_antnums(bl)))

            else:
                # varying integration times for this baseline, need to be more careful
                expected_dtimes = (int_times[:-1] + int_times[1:]) / 2
                wh_diff = np.nonzero(~np.isclose(dtime, expected_dtimes))
                if wh_diff[0].size > 1:
                    warnings.warn("The time difference between integrations is "
                                  "different than the expected given the "
                                  "integration times for baseline {bl}. The "
                                  "output may include averages across long time "
                                  "gaps.".format(bl=self.baseline_to_antnums(bl)))

        temp_Nblts = n_new_samples

        input_phase_type = self.phase_type
        if input_phase_type == "drift":
            if allow_drift:
                print('Data are in drift mode and allow_drift is True, so '
                      'resampling will be done without phasing.')
            else:
                # phase to RA/dec of zenith
                print('Data are in drift mode, phasing before resampling.')
                phase_time = Time(self.time_array[0], format='jd')
                self.phase_to_time(phase_time)

        # make temporary arrays
        temp_baseline = np.zeros((temp_Nblts,), dtype=np.int)
        temp_time = np.zeros((temp_Nblts,))
        temp_int_time = np.zeros((temp_Nblts,))
        if self.metadata_only:
            temp_data = None
            temp_flag = None
            temp_nsample = None
        else:
            temp_data = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                 dtype=self.data_array.dtype)
            temp_flag = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                 dtype=self.flag_array.dtype)
            temp_nsample = np.zeros((temp_Nblts, self.Nspws, self.Nfreqs, self.Npols),
                                    dtype=self.nsample_array.dtype)

        temp_idx = 0
        for bl in bls_to_downsample:
            bl_inds = np.nonzero(self.baseline_array == bl)[0]
            running_int_time = 0.0
            summing_idx = 0
            n_sum = 0
            for itime, int_time in enumerate(self.integration_time[bl_inds]):
                running_int_time += int_time
                n_sum += 1
                over_min_int_time = (running_int_time > min_int_time
                                     or np.isclose(running_int_time, min_int_time,
                                                   rtol=self._integration_time.tols[0],
                                                   atol=self._integration_time.tols[1]))
                last_sample = (itime == len(bl_inds) - 1)
                # We sum up all the samples found so far if we're over the target minimum
                # time, or we've hit the end of the time samples for this baseline.
                if over_min_int_time or last_sample:
                    if last_sample and not (over_min_int_time or keep_ragged):
                        # don't do anything -- implicitly drop these integrations
                        continue
                    # sum together that number of samples
                    temp_baseline[temp_idx] = bl
                    # this might be wrong if some of the constituent times are *totally* flagged
                    averaging_idx = bl_inds[summing_idx:summing_idx + n_sum]
                    # take potential non-uniformity of integration_time into account
                    temp_time[temp_idx] = (
                        np.sum(self.time_array[averaging_idx]
                               * self.integration_time[averaging_idx])
                        / np.sum(self.integration_time[averaging_idx])
                    )
                    temp_int_time[temp_idx] = running_int_time
                    if not self.metadata_only:
                        # if all inputs are flagged, the flag array should be True,
                        # otherwise it should be False.
                        # The sum below will be zero if it's all flagged and
                        # greater than zero otherwise
                        # Then we use a test against 0 to turn it into a Boolean
                        temp_flag[temp_idx] = np.sum(~self.flag_array[averaging_idx], axis=0) == 0

                        mask = self.flag_array[averaging_idx]
                        # need to update mask if a downsampled visibility will be flagged
                        # so that we don't set it to zero
                        if (temp_flag[temp_idx]).any():
                            ax1_inds, ax2_inds, ax3_inds = np.nonzero(temp_flag[temp_idx])
                            mask[:, ax1_inds, ax2_inds, ax3_inds] = False

                        masked_data = np.ma.masked_array(self.data_array[averaging_idx],
                                                         mask=mask)
                        if summing_correlator_mode:
                            temp_data[temp_idx] = np.sum(masked_data, axis=0)
                        else:
                            # take potential non-uniformity of integration_time into
                            # account
                            masked_int_time = np.ma.masked_array(
                                np.ones_like(
                                    self.data_array[averaging_idx], dtype=self.integration_time.dtype
                                )
                                * self.integration_time[
                                    averaging_idx,
                                    np.newaxis,
                                    np.newaxis,
                                    np.newaxis
                                ],
                                mask=mask,
                            )
                            weighted_data = masked_data * masked_int_time
                            temp_data[temp_idx] = (
                                np.sum(weighted_data, axis=0) / np.sum(masked_int_time, axis=0)
                            )
                        # nsample array is the fraction of data that we actually kept,
                        # relative to the amount that went into the sum or average
                        masked_nsample = np.ma.masked_array(self.nsample_array[averaging_idx],
                                                            mask=mask)
                        temp_nsample[temp_idx] = (np.sum(masked_nsample, axis=0)
                                                  / float(self.flag_array[averaging_idx].shape[0]))
                    # increment counters and reset values
                    temp_idx += 1
                    summing_idx += n_sum
                    running_int_time = 0.0
                    n_sum = 0

        # make sure we've populated the right number of baseline-times
        assert temp_idx == temp_Nblts, ("Wrong number of baselines. Got {:d}, "
                                        "expected {:d}. This is a bug, please "
                                        "make an issue at https://github.com/"
                                        "RadioAstronomySoftwareGroup/pyuvdata/"
                                        "issues".format(temp_idx, temp_Nblts))

        # harmonize temporary arrays with existing ones
        inds_to_keep = np.nonzero(self.integration_time >= min_int_time)
        self._harmonize_resample_arrays(
            inds_to_keep,
            temp_baseline,
            temp_time,
            temp_int_time,
            temp_data,
            temp_flag,
            temp_nsample,
        )

        if input_phase_type == "drift" and not allow_drift:
            print('Unphasing back to drift mode.')
            self.unphase_to_drift()

        # reorganize along blt axis
        self.reorder_blts(order=blt_order, minor_order=minor_order)

        # check the resulting object
        self.check()

        # add to the history
        history_update_string = (" Downsampled data to {:f} second integration "
                                 "time using pyuvdata.".format(min_int_time))
        self.history = self.history + history_update_string

        return

    def resample_in_time(self, target_time, only_downsample=False, only_upsample=False,
                         blt_order="time", minor_order="baseline", keep_ragged=True,
                         summing_correlator_mode=False, allow_drift=False):
        """Intelligently upsample or downsample a UVData object to the target time.

        Parameters
        ----------
        target_time : float
            The target integration time to resample to, in seconds.
        only_downsample : bool
            Option to only call bda_downsample.
        only_upsample : bool
            Option to only call bda_upsample.
        blt_order : str
            Major baseline ordering for output object. Default is "time". See the
            documentation on the `reorder_blts` method for more details.
        minor_order : str
            Minor baseline ordering for output object. Default is "baseline".
        keep_ragged : bool
            When averaging baselines that do not evenly divide into min_int_time,
            keep_ragged controls whether to keep the (summed) integrations
            corresponding to the remaining samples (keep_ragged=True), or
            discard them (keep_ragged=False). Note this option only applies to the
            `bda_downsample` method.
        summing_correlator_mode : bool
            Option to integrate or split the flux from the original samples
            rather than average or duplicate the flux from the original samples
            to emulate the behavior in some correlators (e.g. HERA).
        allow_drift : bool
            Option to allow resampling of drift mode data. If this is False,
            drift mode data will be phased before resampling and then unphased
            after resampling. Phasing and unphasing can introduce small errors,
            but resampling in drift mode may result in unexpected behavior.

        Returns
        -------
        None
        """
        # figure out integration times relative to target time
        min_int_time = np.amin(self.integration_time)
        max_int_time = np.amax(self.integration_time)

        if int(np.floor(target_time / min_int_time)) >= 2 and not only_upsample:
            downsample = True
        else:
            downsample = False

        if int(np.floor(max_int_time / target_time)) >= 2 and not only_downsample:
            upsample = True
        else:
            upsample = False

        if downsample:
            self.downsample_in_time(
                target_time,
                blt_order=blt_order,
                minor_order=minor_order,
                keep_ragged=keep_ragged,
                summing_correlator_mode=summing_correlator_mode,
                allow_drift=allow_drift,
            )
        if upsample:
            self.upsample_in_time(
                target_time,
                blt_order=blt_order,
                minor_order=minor_order,
                summing_correlator_mode=summing_correlator_mode,
                allow_drift=allow_drift,
            )

        return

    def remove_eq_coeffs(self):
        """Remove equalization coefficients from the data.

        Some telescopes, e.g. HERA, apply per-antenna, per-frequency gain
        coefficients as part of the signal chain. These are stored in the
        `eq_coeffs` attribute of the object. This method will remove them, so
        that the data are in "unnormalized" raw units.

        Parameters
        ----------
        None

        Returns
        -------
        None

        Raises
        ------
        ValueError
            Raised if eq_coeffs or eq_coeffs_convention are not defined on the
            object, or if eq_coeffs_convention is not one of "multiply" or "divide".
        """
        if self.eq_coeffs is None:
            raise ValueError(
                "The eq_coeffs attribute must be defined on the object to apply them."
            )
        if self.eq_coeffs_convention is None:
            raise ValueError(
                "The eq_coeffs_convention attribute must be defined on the object "
                "to apply them."
            )
        if self.eq_coeffs_convention not in ("multiply", "divide"):
            raise ValueError(
                "Got unknown convention {}. Must be one of: "
                '"multiply", "divide"'.format(self.eq_coeffs_convention)
            )

        # apply coefficients for each baseline
        for key in self.get_antpairs():
            # get indices for this key
            blt_inds = self.antpair2ind(key)

            ant1_index = np.asarray(self.antenna_numbers == key[0]).nonzero()[0][0]
            ant2_index = np.asarray(self.antenna_numbers == key[1]).nonzero()[0][0]

            eq_coeff1 = self.eq_coeffs[ant1_index, :]
            eq_coeff2 = self.eq_coeffs[ant2_index, :]

            # make sure coefficients are the right size to broadcast
            eq_coeff1 = np.repeat(eq_coeff1[:, np.newaxis], self.Npols, axis=1)
            eq_coeff2 = np.repeat(eq_coeff2[:, np.newaxis], self.Npols, axis=1)

            if self.eq_coeffs_convention == "multiply":
                self.data_array[blt_inds, 0, :, :] *= eq_coeff1 * eq_coeff2
            else:
                self.data_array[blt_inds, 0, :, :] /= eq_coeff1 * eq_coeff2

        return
back to top