<reponame>e-koch/pyuvdata
# -*- 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."""
import os
import copy
from collections.abc import Iterable
import warnings
import threading

import numpy as np
from scipy import ndimage as nd
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 astropy import coordinates as coord

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

__all__ = ["UVData"]


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: 1 mas in radians.
        radian_tol = 1 * 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, 1, Nfreqs, "
            "Npols) or (Nblts, Nfreqs, Npols) if future_array_shapes=True, "
            "type = complex float, in units of self.vis_units"
        )
        # TODO: Spw axis to be collapsed in future release
        self._data_array = uvp.UVParameter(
            "data_array",
            description=desc,
            form=("Nblts", 1, "Nfreqs", "Npols"),
            expected_type=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", 1, "Nfreqs", "Npols"),
            expected_type=float,
        )

        desc = "Boolean flag, True is flagged, same shape as data_array."
        self._flag_array = uvp.UVParameter(
            "flag_array",
            description=desc,
            form=("Nblts", 1, "Nfreqs", "Npols"),
            expected_type=bool,
        )

        self._Nspws = uvp.UVParameter(
            "Nspws",
            description="Number of spectral windows "
            "(ie non-contiguous spectral chunks). ",
            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=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=float,
            tols=1e-3 / (60.0 * 60.0 * 24.0),
        )  # 1 ms in days

        desc = (
            "Array of local apparent sidereal times (LAST) at the center of "
            "integration, shape (Nblts), units radians."
        )
        self._lst_array = uvp.UVParameter(
            "lst_array",
            description=desc,
            form=("Nblts",),
            expected_type=float,
            tols=radian_tol,
        )

        desc = (
            "Array of numbers for the first antenna, which is matched to that in "
            "the antenna_numbers attribute. Shape (Nblts), type = int."
        )
        self._ant_1_array = uvp.UVParameter(
            "ant_1_array", description=desc, expected_type=int, form=("Nblts",)
        )

        desc = (
            "Array of numbers for the second antenna, which is matched to that in "
            "the antenna_numbers attribute. Shape (Nblts), type = int."
        )
        self._ant_2_array = uvp.UVParameter(
            "ant_2_array", description=desc, expected_type=int, form=("Nblts",)
        )

        desc = (
            "Array of baseline numbers, 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 (1, Nfreqs) or (Nfreqs,) if future_array_shapes=True, units Hz"
        )
        # TODO: Spw axis to be collapsed in future release
        self._freq_array = uvp.UVParameter(
            "freq_array",
            description=desc,
            form=(1, "Nfreqs"),
            expected_type=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=float,
            tols=1e-3,
        )  # 1 ms

        desc = (
            "Width of frequency channels (Hz). If flex_spw = False and "
            "future_array_shapes=False, then it is a "
            "single value of type = float, otherwise it is an array of shape "
            "(Nfreqs), type = float."
        )
        self._channel_width = uvp.UVParameter(
            "channel_width", description=desc, expected_type=float, tols=1e-3,
        )  # 1 mHz

        desc = (
            "Name(s) of source(s) or field(s) observed, type string. If "
            'multi_phase_center = True, set to "multi".'
        )
        self._object_name = uvp.UVParameter(
            "object_name", description=desc, form="str", expected_type=str,
        )

        # --- multi phase center handling ---
        desc = (
            'Only relevant if phase_type = "phased". Specifies the that the data set '
            "contains multiple sources within it."
        )
        self._multi_phase_center = uvp.UVParameter(
            "multi_phase_center", description=desc, expected_type=bool, value=False,
        )

        desc = (
            "Required if multi_phase_center = True. Specifies the number of sources "
            "contained within the data set."
        )

        self._Nphase = uvp.UVParameter(
            "Nphase", description=desc, expected_type=int, required=False,
        )

        desc = (
            "Only relevant if multi_phase_center = True. Dictionary that acts as a "
            "catalog, containing information on individual phase centers. Keys are the "
            "names of the different phase centers in the UVData object. At a minimum, "
            'each dictionary must contain the key "cat_type", which can be either '
            '"sidereal" (fixed position in RA/Dec), "ephem" (position in RA/Dec which'
            'moves with time), "driftscan" (fixed postion in Az/El, NOT the same as '
            '`phase_type`="drift") and "unphased" (baseline coordinates in ENU, but '
            'data are not phased, similar to `phase_type`="drift"). Other typical '
            'keyworks include "cat_lon" (longitude coord, e.g. RA), "cat_lat" '
            '(latitude coord, e.g. Dec.), "cat_frame" (coordinate frame, e.g. '
            'icrs), "cat_epoch" (epoch and equinox of the coordinate frame), '
            '"cat_times" (times for the coordinates, only used for "ephem" '
            'types), "cat_pm_ra" (proper motion in RA), "cat_pm_dec" (proper '
            'motion in Dec), "cat_dist" (physical distance), "cat_vrad" ('
            'rest frame velocity), "info_source" (describes where catalog info came '
            'from), and "cat_id" (matched to the parameter `phase_center_id_array`. '
            "See the documentation of the `phase` method for more details."
        )
        self._phase_center_catalog = uvp.UVParameter(
            "phase_center_catalog",
            description=desc,
            expected_type=dict,
            required=False,
        )

        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,
        )

        # --- flexible spectral window information ---

        desc = (
            'Option to construct a "flexible spectral window", which stores'
            "all spectral channels across the frequency axis of data_array. "
            "Allows for spectral windows of variable sizes, and channels of "
            "varying widths."
        )
        self._flex_spw = uvp.UVParameter(
            "flex_spw", description=desc, expected_type=bool, value=False,
        )

        desc = (
            "Required if flex_spw = True. Maps individual channels along the "
            "frequency axis to individual spectral windows, as listed in the "
            "spw_array. Shape (Nfreqs), type = int."
        )
        self._flex_spw_id_array = uvp.UVParameter(
            "flex_spw_id_array",
            description=desc,
            form=("Nfreqs",),
            expected_type=int,
            required=False,
        )

        desc = "Flag indicating that this object is using the future array shapes."
        self._future_array_shapes = uvp.UVParameter(
            "future_array_shapes", description=desc, expected_type=bool, value=False,
        )

        # --- phasing information ---
        desc = (
            'String indicating phasing type. Allowed values are "drift" and '
            '"phased" (n.b., "drift" is not the same as `cat_type="driftscan"`, '
            "the latter of which _is_ phased to a fixed az-el position)."
        )
        self._phase_type = uvp.UVParameter(
            "phase_type",
            form="str",
            expected_type=str,
            description=desc,
            value=None,
            acceptable_vals=["drift", "phased"],
        )

        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=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=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=float,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Apparent right ascension of phase '
            "center in the topocentric frame of the observatory, units radians."
            "Shape (Nblts,), type = float."
        )
        self._phase_center_app_ra = uvp.AngleParameter(
            "phase_center_app_ra",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Declination of phase center '
            "in the topocentric frame of the observatory, units radians. "
            "Shape (Nblts,), type = float."
        )
        self._phase_center_app_dec = uvp.AngleParameter(
            "phase_center_app_dec",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Position angle between the hour '
            "circle (which is a great circle that goes through the target postion and "
            "both poles) in the apparent/topocentric frame, and the frame given in "
            "the phase_center_frame attribute."
            "Shape (Nblts,), type = float."
        )
        # The tolerance here is set by the fact that is is calculated using an arctan,
        # the limiting precision of which happens around values of 1.
        self._phase_center_frame_pa = uvp.AngleParameter(
            "phase_center_frame_pa",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=2e-8,
        )

        desc = (
            'Only relevant if phase_type = "phased". Specifies the frame the'
            ' data and uvw_array are phased to. Options are "icrs", "gcrs", and "fk5";'
            ' default is "icrs"'
        )
        self._phase_center_frame = uvp.UVParameter(
            "phase_center_frame",
            required=False,
            description=desc,
            expected_type=str,
            acceptable_vals=["icrs", "gcrs", "fk5"],
        )

        desc = (
            "Required if multi_phase_center = True. Maps individual indices along the "
            "Nblt axis to an entry in `phase_center_catalog`, with the ID number of "
            "individual entries stored as `cat_id`, along with other metadata. "
            "Shape (Nblts), type = int."
        )
        self._phase_center_id_array = uvp.UVParameter(
            "phase_center_id_array",
            description=desc,
            form=("Nblts",),
            expected_type=int,
            required=False,
        )

        desc = (
            "Optional when reading a MS. Retains the  scan number when reading a MS."
            " Shape (Nblts), type = int."
        )
        self._scan_number_array = uvp.UVParameter(
            "scan_number_array",
            description=desc,
            form=("Nblts",),
            expected_type=int,
            required=False,
        )

        # --- 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."
            "Note that these are not indices -- they do not need to start "
            "at zero or be continuous."
        )
        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=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=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=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=float,
        )
        self._dut1 = uvp.UVParameter(
            "dut1",
            required=False,
            description="DUT1 (google it) AIPS 117 " "calls it UT1UTC",
            spoof_val=0.0,
            expected_type=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=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",
        )

        desc = (
            "List of strings containing the unique basenames (not the full path) of "
            "input files."
        )
        self._filename = uvp.UVParameter(
            "filename", required=False, description=desc, expected_type=str,
        )

        super(UVData, self).__init__()

    def _set_flex_spw(self):
        """
        Set flex_spw to True, and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by the file-reading methods to indicate that an object has multiple spectral
        windows concatenated together across the frequency axis.
        """
        # Mark once-optional arrays as now required
        self.flex_spw = True
        self._flex_spw_id_array.required = True
        # Now make sure that chan_width is set to be an array
        self._channel_width.form = ("Nfreqs",)

    def _set_scan_numbers(self, override=False):
        """
        Set scan numbers by grouping consecutive integrations on the same phase center.

        This approach mimics the definition of scan number in measurement sets and is
        especially helpful for distinguishing between repeated visits to multiple
        phase centers.

        Parameters
        ----------
        override : bool
            When True, will redefine existing scan numbers. Default is False.
        """
        if self.scan_number_array is None or override:

            # We are grouping based on integrations on a phase center.
            # If this isn't defined, we cannot define scan numbers in this way
            # and default to a single "scan".
            if self.phase_center_catalog is None:
                self.scan_number_array = np.ones((self.Nblts,), dtype=int)

            else:
                sou_list = list(self.phase_center_catalog.keys())
                sou_list.sort()

                slice_list = []
                # This loops over phase centers, finds contiguous integrations with
                # ndimage.label, and then finds the slices to return those contiguous
                # integrations with nd.find_objects.
                for idx in range(self.Nphase):
                    sou_id = self.phase_center_catalog[sou_list[idx]]["cat_id"]
                    slice_list.extend(
                        nd.find_objects(
                            nd.label(self.phase_center_id_array == sou_id)[0]
                        )
                    )

                # Sort by start integration number, which we can extract from
                # the start of each slice in the list.
                slice_list_ord = sorted(slice_list, key=lambda x: x[0].start)

                # Incrementally increase the scan number with each group in
                # slice_list_ord
                scan_array = np.zeros_like(self.phase_center_id_array)
                for ii, slice_scan in enumerate(slice_list_ord):
                    scan_array[slice_scan] = ii + 1

                self.scan_number_array = scan_array

    def _look_in_catalog(
        self,
        cat_name,
        phase_dict=None,
        cat_type=None,
        cat_lon=None,
        cat_lat=None,
        cat_frame=None,
        cat_epoch=None,
        cat_times=None,
        cat_pm_ra=None,
        cat_pm_dec=None,
        cat_dist=None,
        cat_vrad=None,
        ignore_name=False,
    ):
        """
        Check the catalog to see if an existing entry matches provided data.

        This is a helper function for verifying if an entry already exists within
        the catalog, contained within the attribute `phase_center_catalog`.

        Parameters
        ----------
        cat_name : str
            Name of the phase center, which should match a key in
            `phase_center_catalog`.
        phase_dict : dict
            Instead of providing individual parameters, one may provide a dict which
            matches that format used within `phase_center_catalog` for checking for
            existing entries. If used, all other parameters (save for `ignore_name` and
            `cat_name`) are disregarded.
        cat_type : str
            Type of phase center of the entry. Must be one of:
                "sidereal" (fixed RA/Dec),
                "ephem" (RA/Dec that moves with time),
                "driftscan" (fixed az/el position),
                "unphased" (no w-projection, equivalent to `phase_type` == "drift").
        cat_lon : float or ndarray
            Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_lat : float or ndarray
            Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_frame : str
            Coordinate frame that cat_lon and cat_lat are given in. Only used for
            sidereal and ephem phase centers. Can be any of the several supported frames
            in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).
        cat_epoch : str or float
            Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given
            in unites of fractional years, either as a float or as a string with the
            epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).
        cat_times : ndarray of floats
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        cat_pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        cat_pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        cat_dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        ignore_name : bool
            Nominally, `_look_in_catalog` will only look at entries where `cat_name`
            matches the name of an entry in the catalog. However, by setting this to
            True, the method will search all entries in the catalog and see if any
            match all of the provided data (excluding `cat_name`).

        Returns
        -------
        cat_id : int or None
            The unique ID number for the phase center added to the internal catalog.
            This value is used in the `phase_center_id_array` attribute to denote which
            source a given baseline-time corresponds to. If no catalog entry matches,
            then None is returned.
        cat_diffs : int
            The number of differences between the information provided and the catalog
            entry contained within `phase_center_catalog`. If everything matches, then
            `cat_diffs=0`.
        """
        # 1 marcsec tols
        radian_tols = (0, 1 * 2 * np.pi * 1e-3 / (60.0 * 60.0 * 360.0))
        default_tols = (1e-5, 1e-8)
        cat_id = None
        cat_diffs = 0

        # Emulate the defaults that are set if None is detected for
        # unphased and driftscan types.
        if (cat_type == "unphased") or (cat_type == "driftscan"):
            if cat_lon is None:
                cat_lon = 0.0
            if cat_lat is None:
                cat_lat = np.pi / 2
            if cat_frame is None:
                cat_frame = "altaz"

        if phase_dict is None:
            phase_dict = {
                "cat_type": cat_type,
                "cat_lon": cat_lon,
                "cat_lat": cat_lat,
                "cat_frame": cat_frame,
                "cat_epoch": cat_epoch,
                "cat_times": cat_times,
                "cat_pm_ra": cat_pm_ra,
                "cat_pm_dec": cat_pm_dec,
                "cat_dist": cat_dist,
                "cat_vrad": cat_vrad,
            }

        if self.multi_phase_center:
            check_dict = self.phase_center_catalog
        else:
            check_dict = {}
            is_phased = self.phase_type == "phased"
            check_dict[self.object_name] = {
                "cat_type": "sidereal" if is_phased else "unphased",
                "cat_lon": self.phase_center_ra if is_phased else 0.0,
                "cat_lat": self.phase_center_dec if is_phased else np.pi / 2.0,
                "cat_frame": self.phase_center_frame if is_phased else "altaz",
                "cat_epoch": self.phase_center_epoch if is_phased else None,
                "cat_times": None,
                "cat_pm_ra": None,
                "cat_pm_dec": None,
                "cat_dist": None,
                "cat_vrad": None,
                "cat_id": 0,
            }

        tol_dict = {
            "cat_type": None,
            "cat_lon": radian_tols,
            "cat_lat": radian_tols,
            "cat_frame": None,
            "cat_epoch": None,
            "cat_times": default_tols,
            "cat_pm_ra": default_tols,
            "cat_pm_dec": default_tols,
            "cat_dist": default_tols,
            "cat_vrad": default_tols,
        }

        if self.multi_phase_center:
            name_list = list(self.phase_center_catalog.keys())
        else:
            name_list = [self.object_name]

        for name in name_list:
            cat_diffs = 0
            if (cat_name != name) and (not ignore_name):
                continue
            for key in tol_dict.keys():
                if phase_dict.get(key) is not None:
                    if check_dict[name].get(key) is None:
                        cat_diffs += 1
                    elif tol_dict[key] is None:
                        # If no tolerance specified, expect attributes to be identical
                        cat_diffs += phase_dict.get(key) != check_dict[name].get(key)
                    else:
                        # Numpy will throw a Value error if you have two arrays
                        # of different shape, which we can catch to flag that
                        # the two arrays are actually not within tolerance.
                        if np.shape(phase_dict[key]) != np.shape(check_dict[name][key]):
                            cat_diffs += 1
                        else:
                            cat_diffs += not np.allclose(
                                phase_dict[key],
                                check_dict[name][key],
                                tol_dict[key][0],
                                tol_dict[key][1],
                            )
                else:
                    cat_diffs += check_dict[name][key] is not None
            if (cat_diffs == 0) or (cat_name == name):
                cat_id = check_dict[name]["cat_id"]
                break

        return cat_id, cat_diffs

    def _add_phase_center(
        self,
        cat_name,
        cat_type=None,
        cat_lon=None,
        cat_lat=None,
        cat_frame=None,
        cat_epoch=None,
        cat_times=None,
        cat_pm_ra=None,
        cat_pm_dec=None,
        cat_dist=None,
        cat_vrad=None,
        info_source="user",
        force_update=False,
        cat_id=None,
    ):
        """
        Add an entry to the internal object/source catalog.

        This is a helper function for adding a source to the internal
        catalog, contained within the attribute `phase_center_catalog`.

        Parameters
        ----------
        cat_name : str
            Name of the phase center to be added, must be unique (i.e., not contained
            as a key in the UVData attribute `phase_center_catalog`).
        cat_type : str
            Type of phase center to be added. Must be one of:
                "sidereal" (fixed RA/Dec),
                "ephem" (RA/Dec that moves with time),
                "driftscan" (fixed az/el position),
                "unphased" (no w-projection, equivalent to `phase_type` == "drift").
        cat_lon : float or ndarray
            Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_lat : float or ndarray
            Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_frame : str
            Coordinate frame that cat_lon and cat_lat are given in. Only used
            for sidereal and ephem targets. Can be any of the several supported frames
            in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).
        cat_epoch : str or float
            Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given
            in unites of fractional years, either as a float or as a string with the
            epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).
        cat_times : ndarray of floats
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        cat_pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        cat_pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        cat_dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        info_source : str
            Optional string describing the source of the information provided. Used
            primarily in UVData to denote when an ephemeris has been supplied by the
            JPL-Horizons system, user-supplied, or read in by one of the various file
            interpreters. Default is 'user'.
        force_update : bool
            Normally, `_add_phase_center` will throw an error if there already exists an
            identically named phase center with different properties. However, if one
            sets `force_update=True`, the method will overwrite the existing entry in
            `phase_center_catalog` with the paramters supplied, preserving only the
            parameters `cat_id` and `cat_name`. Note that doing this will _not_ update
            other atributes of the `UVData` object. Default is False.
        cat_id : int
            An integer signifying the ID number for the phase center, used in the
            `phase_center_id_array` attribute. The default is for the method to assign
            this value automatically.

        Returns
        -------
        cat_id : int
            The unique ID number for the phase center added to the internal catalog.
            This value is used in the `phase_center_id_array` attribute to denote which
            source a given baseline-time corresponds to.

        Raises
        ------
        ValueError
            If attempting to add a non-unique source name, attempting to use the method
            w/ a UVData object where multi_phase_center=False, or if adding a sidereal
            source without coordinates.
        """
        # Check whether we should actually be doing this in the first place
        if not self.multi_phase_center:
            raise ValueError("Cannot add a source if multi_phase_center != True.")

        if not isinstance(cat_name, str):
            raise ValueError("cat_name must be a string.")

        # The catalog name "unphased" is used internally whenever we have to make a
        # block of data as unphased in a data set. To avoid naming collisions, check
        # that someone hasn't tried to use it for any other purpose.
        if (cat_name == "unphased") and (cat_type != "unphased"):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "cat_name."
            )

        # We currently only have 4 supported types -- make sure the user supplied
        # one of those
        if cat_type not in ["sidereal", "ephem", "driftscan", "unphased"]:
            raise ValueError(
                "Only sidereal, ephem, driftscan or unphased may be used "
                "for cat_type."
            )

        # Both proper motion parameters need to be set together
        if (cat_pm_ra is None) != (cat_pm_dec is None):
            raise ValueError(
                "Must supply values for either both or neither of "
                "cat_pm_ra and cat_pm_dec."
            )

        # If left unset, unphased and driftscan defaulted to Az, El = (0, 90)
        if (cat_type == "unphased") or (cat_type == "driftscan"):
            if cat_lon is None:
                cat_lon = 0.0
            if cat_lat is None:
                cat_lat = np.pi / 2
            if cat_frame is None:
                cat_frame = "altaz"

        # Let's check some case-specific things and make sure all the entires are value
        if (cat_times is None) and (cat_type == "ephem"):
            raise ValueError("cat_times cannot be None for ephem object.")
        elif (cat_times is not None) and (cat_type != "ephem"):
            raise ValueError("cat_times cannot be used for non-ephem phase centers.")

        if (cat_lon is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_lon cannot be None for sidereal phase centers.")

        if (cat_lat is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_lat cannot be None for sidereal phase centers.")

        if (cat_frame is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_frame cannot be None for sidereal phase centers.")
        elif (cat_frame != "altaz") and (cat_type in ["driftscan", "unphased"]):
            raise ValueError(
                "cat_frame must be either None or 'altaz' when the cat type "
                "is either driftscan or unphased."
            )

        if (cat_type == "unphased") and (cat_lon != 0.0):
            raise ValueError(
                "Catalog entries that are unphased must have cat_lon set to either "
                "0 or None."
            )
        if (cat_type == "unphased") and (cat_lat != (np.pi / 2)):
            raise ValueError(
                "Catalog entries that are unphased must have cat_lat set to either "
                "pi/2 or None."
            )

        if (cat_type != "sidereal") and (
            (cat_pm_ra is not None) or (cat_pm_dec is not None)
        ):
            raise ValueError(
                "Non-zero proper motion values (cat_pm_ra, cat_pm_dec) "
                "for cat types other than sidereal are not supported."
            )

        if isinstance(cat_epoch, Time) or isinstance(cat_epoch, str):
            if cat_frame in ["fk4", "fk4noeterms"]:
                cat_epoch = Time(cat_epoch).byear
            else:
                cat_epoch = Time(cat_epoch).jyear
        elif cat_epoch is not None:
            cat_epoch = float(cat_epoch)

        if cat_type == "ephem":
            cat_times = np.array(cat_times, dtype=float).reshape(-1)
            cshape = cat_times.shape
            try:
                cat_lon = np.array(cat_lon, dtype=float).reshape(cshape)
                cat_lat = np.array(cat_lat, dtype=float).reshape(cshape)
                if cat_dist is not None:
                    cat_dist = np.array(cat_dist, dtype=float).reshape(cshape)
                if cat_vrad is not None:
                    cat_vrad = np.array(cat_vrad, dtype=float).reshape(cshape)
            except ValueError:
                raise ValueError(
                    "Object properties -- lon, lat, pm_ra, pm_dec, dist, vrad -- must "
                    "be of the same size as cat_times for ephem phase centers."
                )
        else:
            cat_lon = None if cat_lon is None else float(cat_lon)
            cat_lat = None if cat_lat is None else float(cat_lat)
            cat_pm_ra = None if cat_pm_ra is None else float(cat_pm_ra)
            cat_pm_dec = None if cat_pm_dec is None else float(cat_pm_dec)
            cat_dist = None if cat_dist is None else float(cat_dist)
            cat_vrad = None if cat_vrad is None else float(cat_vrad)

        # Names serve as dict keys, so we need to make sure that they're unique
        if not force_update:
            temp_id, cat_diffs = self._look_in_catalog(
                cat_name,
                cat_type=cat_type,
                cat_lon=cat_lon,
                cat_lat=cat_lat,
                cat_frame=cat_frame,
                cat_epoch=cat_epoch,
                cat_times=cat_times,
                cat_pm_ra=cat_pm_ra,
                cat_pm_dec=cat_pm_dec,
                cat_dist=cat_dist,
                cat_vrad=cat_vrad,
            )

            # If the source does have the same name, check to see if all the
            # atributes match. If so, no problem, go about your business
            if temp_id is not None:
                if cat_diffs == 0:
                    # Everything matches, return the catalog ID of the matching entry
                    return temp_id
                else:
                    raise ValueError(
                        "Cannot add different source with an non-unique name."
                    )

        # We want to create a unique ID for each source, for use in indexing arrays.
        # The logic below ensures that we pick the lowest positive integer that is
        # not currently being used by another source
        used_cat_ids = {
            self.phase_center_catalog[name]["cat_id"]: name
            for name in self.phase_center_catalog.keys()
        }

        if force_update and (cat_name in self.phase_center_catalog.keys()):
            cat_id = self.phase_center_catalog[cat_name]["cat_id"]
        elif cat_id is None:
            cat_id = int(
                np.arange(self.Nphase + 1)[
                    ~np.isin(np.arange(self.Nphase + 1), list(used_cat_ids.keys()))
                ][0]
            )
        elif cat_id in used_cat_ids.keys():
            raise ValueError(
                "Provided cat_id belongs to another source (%s)." % used_cat_ids[cat_id]
            )

        # If source is unique, begin creating a dictionary for it
        phase_dict = {
            "cat_id": cat_id,
            "cat_type": cat_type,
            "cat_lon": cat_lon,
            "cat_lat": cat_lat,
            "cat_frame": cat_frame,
            "cat_epoch": cat_epoch,
            "cat_times": cat_times,
            "cat_pm_ra": cat_pm_ra,
            "cat_pm_dec": cat_pm_dec,
            "cat_vrad": cat_vrad,
            "cat_dist": cat_dist,
            "info_source": info_source,
        }

        self.phase_center_catalog[cat_name] = phase_dict
        self.Nphase = len(self.phase_center_catalog.keys())
        return cat_id

    def _remove_phase_center(self, defunct_name):
        """
        Remove an entry from the internal object/source catalog.

        Removes an entry from the attribute `phase_center_catalog`. Only allowed when
        the UVData object in question is a multi phase center data set (i.e.,
        `multi_phase_center=True`).

        Parameters
        ----------
        defunct_name : str
            Name of the source to be removed

        Raises
        ------
        ValueError
            If multi_phase_center is not set to True
        IndexError
            If the name provided is not found as a key in `phase_center_catalog`
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot remove a phase center if multi_phase_center != True."
            )

        if defunct_name not in self.phase_center_catalog.keys():
            raise IndexError("No source by that name contained in the catalog.")

        del self.phase_center_catalog[defunct_name]
        self.Nphase = len(self.phase_center_catalog.keys())

    def _clear_unused_phase_centers(self):
        """
        Remove objects dictionaries and names that are no longer in use.

        Goes through the `phase_center_catalog` attribute in of a UVData object and
        clears out entries that are no longer being used, and appropriately updates
        `phase_center_id_array` accordingly. This function is not typically called
        by users, but instead is used by other methods.

        Raises
        ------
        ValueError
            If attempting to call the method when multi_phase_center=False.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot remove a phase center if multi_phase_center != True."
            )

        unique_cat_ids = np.unique(self.phase_center_id_array)
        defunct_list = []
        Nphase = 0
        for cat_name in self.phase_center_catalog.keys():
            cat_id = self.phase_center_catalog[cat_name]["cat_id"]
            if cat_id in unique_cat_ids:
                Nphase += 1
            else:
                defunct_list.append(cat_name)

        # Check the number of "good" sources we have -- if we haven't dropped any,
        # then we are free to bail, otherwise update the Nphase attribute
        if Nphase == self.Nphase:
            return

        # Time to kill the entries that are no longer in the source stack
        for defunct_name in defunct_list:
            self._remove_phase_center(defunct_name)

    def _check_for_unphased(self):
        """
        Check which Nblts are unphased in a multi phase center dataset.

        This convenience method returns back a boolean mask to identify which data
        along the Blt axis contains unphased objects (which is only applicable when
        multi_phase_center=True)

        Returns
        -------
        blt_mask : ndarray of bool
            A boolean mask for identifying which elements contain unphased objects
        """
        if self.multi_phase_center:
            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.
            nophase_dict = {
                self.phase_center_catalog[name]["cat_id"]: self.phase_center_catalog[
                    name
                ]["cat_type"]
                == "unphased"
                for name in self.phase_center_catalog.keys()
            }

            # Use dict to construct a bool array
            blt_mask = np.array(
                [nophase_dict[idx] for idx in self.phase_center_id_array], dtype=bool
            )
        else:
            # If not multi phase center, we just need to check the phase type
            blt_mask = np.repeat(self.phase_type == "drift", self.Nblts)

        return blt_mask

    def rename_phase_center(self, old_name, new_name):
        """
        Rename a phase center/catalog entry within a multi phase center data set.

        Parameters
        ----------
        old_name : str
            Phase center name for the data to be renamed.
        new_name : str
            New name for the phase center.

        Raises
        ------
        ValueError
            If attempting to run the method on a non multi phase center data set, if
            `old_name` is not found as a key in `phase_center_catalog`, if `new_name`
            already exists as a key in `phase_center_catalog`, or if attempting to
            name a source "unphased" (which is reserved).
        TypeError
            If `new_name` is not actually a string.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot rename a phase center if multi_phase_center != True."
            )
        if old_name not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % old_name)
        if not isinstance(new_name, str):
            raise TypeError("Value provided to new_name must be a string.")
        if new_name == old_name:
            # This is basically just a no-op, so return to user
            return
        if new_name in self.phase_center_catalog.keys():
            raise ValueError(
                "Must include a unique name for new_name, %s is already present "
                "in phase_center_catalog." % new_name
            )
        if (new_name == "unphased") and (
            self.phase_center_catalog[old_name]["cat_type"] != "unphased"
        ):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "new_name."
            )

        self.phase_center_catalog[new_name] = self.phase_center_catalog[old_name]
        self.Nphase = len(self.phase_center_catalog.keys())
        self._remove_phase_center(old_name)

    def split_phase_center(self, cat_name, new_name, select_mask, downselect=False):
        """
        Rename the phase center (but preserve other properties) of a subset of data.

        Allows you to rename a subset of the data phased to a particular phase center,
        marked by a different name than the original. Useful when you want to phase to
        one position, but want to differentiate different groups of data (e.g., marking
        every other integration to make jackknifing easier).

        Parameters
        ----------
        cat_name : str
            Name of the phase center to be split.
        new_name : str
            New name for the object.
        select_mask : array_like
            Selection mask for which data should be identified as belonging to the phase
            center labeled by `new_name`. Any array-like able to be used as an index
            is suitable -- the most typical is an array of bool with length `Nblts`,
            or an array of ints within the range (-Nblts, Nblts).
        downselect : bool
            If selecting data that is not marked as belonging to `cat_name`,
            normally an error is thrown. By setting this to True, `select_mask` will
            be modified to exclude data not marked as belonging to `cat_name`.

        Raises
        ------
        ValueError
            If attempting to run the method on a non multi phase center data set, if
            `old_name` is not found as a key in `phase_center_catalog`, if `new_name`
            already exists as a key in `phase_center_catalog`, or if attempting to
            name a source "unphased" (which is reserved). Also raised if `select_mask`
            contains data that doesn't belong to `cat_name`, unless setting
            `downselect` to True.
        IndexError
            If select_mask is not a valid indexing array.
        UserWarning
            If all data for `cat_name` was selected (in which case `rename_phase_center`
            is called instead), or if no valid data was selected.
        """
        # Check to make sure that everything lines up with
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use split_phase_center on a non-multi phase center data set."
            )
        if not isinstance(new_name, str):
            raise TypeError("Value provided to new_name must be a string.")
        if cat_name not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % cat_name)
        if new_name in self.phase_center_catalog.keys():
            raise ValueError(
                "The name %s is already found in the catalog, choose another name "
                "for new_name." % new_name
            )
        if (new_name == "unphased") and (
            self.phase_center_catalog[cat_name]["cat_type"] != "unphased"
        ):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "new_name."
            )

        try:
            inv_mask = np.ones(self.Nblts, dtype=bool)
            inv_mask[select_mask] = False
        except IndexError:
            raise IndexError(
                "select_mask must be an array-like, either of ints with shape (Nblts), "
                "or  of ints within the range (-Nblts, Nblts)."
            )
        # Now that we know nthat all the inputs are sensible, lets make sure that
        # the select_mask choice is sensible
        cat_id = self.phase_center_catalog[cat_name]["cat_id"]

        # If we have selected any entries that don't correspond to the cat_id
        # in question, either downselect or raise an error.
        if np.any(cat_id != self.phase_center_id_array[select_mask]):
            if downselect:
                select_mask = np.logical_and(
                    ~inv_mask, cat_id == self.phase_center_id_array
                )
                inv_mask = ~select_mask
            else:
                raise ValueError(
                    "Data selected with select_mask includes that which has not been "
                    "phased to %s. You can fix this by either revising select_mask or "
                    "setting downselect=True." % cat_name
                )

        # Now check for no(-ish) ops
        if np.all(inv_mask):
            # You didn't actually select anything we could change
            warnings.warn(
                "No relevant data selected - %s not added to the data set" % new_name
            )
        elif not np.any(cat_id == self.phase_center_id_array[inv_mask]):
            # No matching catalog IDs found outside the range, so this is really a
            # replace more than a split.
            warnings.warn(
                "All data for %s selected - using rename_phase_center instead of a "
                "split_phase_center." % cat_name
            )
            self.rename_phase_center(cat_name, new_name)
        else:
            temp_dict = self.phase_center_catalog[cat_name]
            cat_id = self._add_phase_center(
                new_name,
                temp_dict["cat_type"],
                cat_lon=temp_dict.get("cat_lon"),
                cat_lat=temp_dict.get("cat_lat"),
                cat_frame=temp_dict.get("cat_frame"),
                cat_epoch=temp_dict.get("cat_epoch"),
                cat_times=temp_dict.get("cat_times"),
                cat_pm_ra=temp_dict.get("cat_pm_ra"),
                cat_pm_dec=temp_dict.get("cat_pm_dec"),
                cat_dist=temp_dict.get("cat_dist"),
                cat_vrad=temp_dict.get("cat_vrad"),
            )
            self.phase_center_id_array[select_mask] = cat_id

    def merge_phase_centers(self, catname1, catname2, force_merge=False):
        """
        Merge two differently named objects into one within a mutli-phase-ctr data set.

        Recombines two different objects into a single catalog entry -- useful if
        having previously used `split_phase_center` or when multiple objects with
        different names share the same source parameters.

        Parameters
        ----------
        catname1 : str
            String containing the name of the first phase center. Note that this name
            will be preserved in the UVData object.
        catname2 : str
            String containing the name of the second phase center, which will be merged
            into the first phase center. Note that once the merge is complete, all
            information about this phase center is removed.
        force_merge : bool
            Normally, the method will throw an error if the phase center properties
            differ for `catname1` and `catname2`. This can be overriden by setting this
            to True. Default is False.

        Raises
        ------
        ValueError
            If catname1 or catname2 are not found in the UVData object, of if their
            properties differ (and `force_merge` is not set to True).
        UserWarning
            If forcing the merge of two objects with different properties.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use merge_phase_centers on a non-multi phase center data set."
            )
        if catname1 not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % catname1)
        if catname2 not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % catname2)
        temp_dict = self.phase_center_catalog[catname2]
        # First, let's check and see if the dict entries are identical
        cat_id, cat_diffs = self._look_in_catalog(
            catname1,
            cat_type=temp_dict["cat_type"],
            cat_lon=temp_dict.get("cat_lon"),
            cat_lat=temp_dict.get("cat_lat"),
            cat_frame=temp_dict.get("cat_frame"),
            cat_epoch=temp_dict.get("cat_epoch"),
            cat_times=None,
            cat_pm_ra=None,
            cat_pm_dec=None,
            cat_dist=None,
            cat_vrad=None,
        )
        if cat_diffs != 0:
            if force_merge:
                warnings.warn(
                    "Forcing %s and %s together, even though their attributes "
                    "differ" % (catname1, catname2)
                )
            else:
                raise ValueError(
                    "Attributes of %s and %s differ in phase_center_catalog, which "
                    "means that they are likely not referring to the same position in "
                    "the sky. You can ignore this error and force merge_phase_centers "
                    "to complete by setting force_merge=True, but this should be done "
                    "with substantial caution." % (catname1, catname2)
                )

        old_cat_id = self.phase_center_catalog[catname2]["cat_id"]
        self.phase_center_id_array[self.phase_center_id_array == old_cat_id] = cat_id
        self._remove_phase_center(catname2)

    def print_phase_center_info(
        self, cat_name=None, hms_format=None, return_str=False, print_table=True
    ):
        """
        Print out the details of objects in a mutli-phase-ctr data set.

        Prints out an ASCII table that contains the details of the
        `phase_center_catalog` attribute, which acts as the internal source catalog
        for UVData objects.

        Parameters
        ----------
        cat_name : str
            Optional parameter which, if provided, will cause the method to only return
            information on the phase center with the matching name. Default is to print
            out information on all catalog entries.
        hms_format : bool
            Optional parameter, which if selected, can be used to force coordinates to
            be printed out in Hours-Min-Sec (if set to True) or Deg-Min-Sec (if set to
            False) format. Default is to print out in HMS if all the objects have
            coordinate frames of icrs, gcrs, fk5, fk4, and top; otherwise, DMS format
            is used.
        return_str: bool
            If set to True, the method returns an ASCII string which contains all the
            table infrmation. Default is False.
        print_table : bool
            If set to True, prints the table to the terminal window. Default is True.

        Returns
        -------
        table_str : bool
            If return_str=True, an ASCII string containing the entire table text

        Raises
        ------
        ValueError
            If `cat_name` matches no keys in `phase_center_catalog`.
        """
        r2d = 180.0 / np.pi
        r2m = 60.0 * 180.0 / np.pi
        r2s = 3600.0 * 180.0 / np.pi
        ra_frames = ["icrs", "gcrs", "fk5", "fk4", "topo"]

        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use print_phase_center_info on a "
                "non-multi phase center data set."
            )
        if cat_name is None:
            name_list = list(self.phase_center_catalog.keys())
            dict_list = [self.phase_center_catalog[name] for name in name_list]
        elif cat_name in self.phase_center_catalog.keys():
            name_list = [cat_name]
            dict_list = [self.phase_center_catalog[cat_name]]
        else:
            raise ValueError("No entry by the name %s in the catalog." % cat_name)

        # We want to check and actually see which fields we need to
        # print
        any_lon = any_lat = any_frame = any_epoch = any_times = False
        any_pm_ra = any_pm_dec = any_dist = any_vrad = False

        cat_id_list = []
        for indv_dict in dict_list:
            cat_id_list.append(indv_dict["cat_id"])
            any_lon = any_lon or indv_dict.get("cat_lon") is not None
            any_lat = any_lat or indv_dict.get("cat_lat") is not None
            any_frame = any_frame or indv_dict.get("cat_frame") is not None
            any_epoch = any_epoch or indv_dict.get("cat_epoch") is not None
            any_times = any_times or indv_dict.get("cat_times") is not None
            any_pm_ra = any_pm_ra or indv_dict.get("cat_pm_ra") is not None
            any_pm_dec = any_pm_dec or indv_dict.get("cat_pm_dec") is not None
            any_dist = any_dist or indv_dict.get("cat_dist") is not None
            any_vrad = any_vrad or indv_dict.get("cat_vrad") is not None

            if any_lon and (hms_format is None):
                cat_frame = indv_dict.get("cat_frame")
                cat_type = indv_dict["cat_type"]
                if (cat_frame not in ra_frames) or (cat_type == "driftscan"):
                    hms_format = False

        if hms_format is None:
            hms_format = True

        col_list = []
        col_list.append(
            {"hdr": ("ID", "#"), "fmt": "% 4i", "field": " %4s ", "name": "cat_id"}
        )
        col_list.append(
            {
                "hdr": ("Cat Entry", "Name"),
                "fmt": "%12s",
                "field": " %12s ",
                "name": "cat_name",
            }
        )
        col_list.append(
            {"hdr": ("Type", ""), "fmt": "%9s", "field": " %9s ", "name": "cat_type"}
        )

        if any_lon:
            col_list.append(
                {
                    "hdr": ("Az/Lon/RA", "hours" if hms_format else "deg"),
                    "fmt": "% 3i:%02i:%05.2f",
                    "field": (" %12s " if hms_format else " %13s "),
                    "name": "cat_lon",
                }
            )
        if any_lat:
            col_list.append(
                {
                    "hdr": ("El/Lat/Dec", "deg"),
                    "fmt": "%1s%2i:%02i:%05.2f",
                    "field": " %12s ",
                    "name": "cat_lat",
                }
            )
        if any_frame:
            col_list.append(
                {
                    "hdr": ("Frame", ""),
                    "fmt": "%5s",
                    "field": " %5s ",
                    "name": "cat_frame",
                }
            )
        if any_epoch:
            col_list.append(
                {
                    "hdr": ("Epoch", ""),
                    "fmt": "%7s",
                    "field": " %7s ",
                    "name": "cat_epoch",
                }
            )
        if any_times:
            col_list.append(
                {
                    "hdr": ("   Ephem Range   ", "Start-MJD    End-MJD"),
                    "fmt": " %8.2f  % 8.2f",
                    "field": " %20s ",
                    "name": "cat_times",
                }
            )
        if any_pm_ra:
            col_list.append(
                {
                    "hdr": ("PM-Ra", "mas/yr"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_pm_ra",
                }
            )
        if any_pm_dec:
            col_list.append(
                {
                    "hdr": ("PM-Dec", "mas/yr"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_pm_dec",
                }
            )
        if any_dist:
            col_list.append(
                {
                    "hdr": ("Dist", "pc"),
                    "fmt": "%.1e",
                    "field": " %7s ",
                    "name": "cat_dist",
                }
            )
        if any_vrad:
            col_list.append(
                {
                    "hdr": ("V_rad", "km/s"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_vrad",
                }
            )

        top_str = ""
        bot_str = ""
        for col in col_list:
            top_str += col["field"] % col["hdr"][0]
            bot_str += col["field"] % col["hdr"][1]

        info_str = ""

        info_str += top_str + "\n"
        info_str += bot_str + "\n"
        info_str += ("-" * len(bot_str)) + "\n"
        # We want to print in the order of cat_id
        for idx in np.argsort(cat_id_list):
            tbl_str = ""
            for col in col_list:
                # If we have a "special" field that needs extra handling,
                # take care of that up front
                if col["name"] == "cat_name":
                    temp_val = name_list[idx]
                else:
                    temp_val = dict_list[idx][col["name"]]
                if temp_val is None:
                    temp_str = ""
                elif col["name"] == "cat_lon":
                    temp_val = np.median(temp_val)
                    temp_val /= 15.0 if hms_format else 1.0
                    coord_tuple = (
                        np.mod(temp_val * r2d, 360.0),
                        np.mod(temp_val * r2m, 60.0),
                        np.mod(temp_val * r2s, 60.0),
                    )
                    temp_str = col["fmt"] % coord_tuple
                elif col["name"] == "cat_lat":
                    temp_val = np.median(temp_val)
                    coord_tuple = (
                        "-" if temp_val < 0.0 else "+",
                        np.mod(np.abs(temp_val) * r2d, 360.0),
                        np.mod(np.abs(temp_val) * r2m, 60.0),
                        np.mod(np.abs(temp_val) * r2s, 60.0),
                    )
                    temp_str = col["fmt"] % coord_tuple
                elif col["name"] == "cat_epoch":
                    use_byrs = dict_list[idx]["cat_frame"] in ["fk4", "fk4noeterms"]
                    temp_val = ("B%6.1f" if use_byrs else "J%6.1f") % temp_val
                    temp_str = col["fmt"] % temp_val
                elif col["name"] == "cat_times":
                    time_tuple = (
                        np.min(temp_val) - 2400000.5,
                        np.max(temp_val) - 2400000.5,
                    )
                    temp_str = col["fmt"] % time_tuple
                elif (col["name"] == "cat_dist") or (col["name"] == "cat_vrad"):
                    temp_val = np.median(temp_val)
                    temp_str = col["fmt"] % temp_val
                else:
                    temp_str = col["fmt"] % temp_val
                tbl_str += col["field"] % temp_str
            info_str += tbl_str + "\n"

        if print_table:
            # We need this extra bit of code to handle trailing whitespace, since
            # otherwise some checks (e.g., doc check on tutorials) will balk
            print(
                "\n".join([line.rstrip() for line in info_str.split("\n")]), end=""
            )  # pragma: nocover
        if return_str:
            return info_str

    def _update_phase_center_id(self, cat_name, new_cat_id=None, reserved_ids=None):
        """
        Update a phase center with a new catalog ID number.

        Parameters
        ----------
        cat_name : str
            Name of the phase center, which corresponds to a key in the attribute
            `phase_center_catalog`.
        new_cat_id : int
            Optional argument. If supplied, then the method will attempt to use the
            provided value as the new catalog ID, provided that an existing catalog
            entry is not already using the same value. If not supplied, then the
            method will automatically assign a value.
        reserved_ids : array-like in int
            Optional argument. An array-like of ints that denotes which ID numbers
            are already reserved. Useful for when combining two separate catalogs.

        Raises
        ------
        ValueError
            If not using the method on a multi-phase-ctr data set, if there's no entry
            that matches `cat_name`, or of the value `new_cat_id` is already taken.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use _update_phase_center_id on a "
                "non-multi phase center data set."
            )

        if cat_name not in self.phase_center_catalog.keys():
            raise ValueError(
                "Cannot run _update_phase_center_id: no entry with name %s." % cat_name
            )

        old_cat_id = self.phase_center_catalog[cat_name]["cat_id"]

        used_cat_ids = [] if (reserved_ids is None) else reserved_ids.copy()
        for name in self.phase_center_catalog.keys():
            if name != cat_name:
                used_cat_ids.append(self.phase_center_catalog[name]["cat_id"])
        if new_cat_id is None:
            # If the old ID is in the reserved list, then we'll need to update it
            if old_cat_id not in used_cat_ids:
                # Don't need to actually update anything
                return
            else:
                new_cat_id = np.arange(len(used_cat_ids) + 1)[
                    ~np.isin(np.arange(len(used_cat_ids) + 1), used_cat_ids)
                ][0]
        else:
            if new_cat_id in used_cat_ids:
                raise ValueError("Catalog ID supplied already taken by another source.")

        self.phase_center_id_array[
            self.phase_center_id_array == old_cat_id
        ] = new_cat_id
        self.phase_center_catalog[cat_name]["cat_id"] = new_cat_id

    def _set_multi_phase_center(self, preserve_phase_center_info=False):
        """
        Set multi_phase_center to True, and adjust required paramteres.

        This method is typically not be called directly by users; instead it is called
        by the file-reading methods to indicate that an object has multiple phase
        centers with in the same data set.

        Parameters
        ----------
        preserve_phase_center_info : bool
            Preserve the source information located in `object_name`, and for phased
            data sets, also `phase_center_ra`, `phase_center_dec`, `phase_center_epoch`
            and `phase_center_frame`. Default is False. Note that setting this to
            False will mean that some required attributes will NOT be correctly set,
            e.g., `phase_center_id_array` -- these will need to be set after calling
            `preserve_phase_center_info` in order for the UVData object to be viable.

        Raises
        ------
        ValueError
            if the telescope_name is not in known telescopes
        """
        # If you have already set this, don't do anything
        if self.multi_phase_center:
            return

        # All multi phase center objects have phase_type="phased", even if they are
        # unphased.
        if self.phase_type == "phased":
            cat_type = "sidereal"
        else:
            self._set_phased()
            cat_type = "unphased"

        self.multi_phase_center = True

        # Mark once-option arrays as now required
        self._phase_center_id_array.required = True
        self._Nphase.required = True
        self._phase_center_catalog.required = True

        # This should technically be required for any phased data set, but for now,
        # we are only gonna make it mandatory for mutli-phase-ctr data sets.
        self._phase_center_app_ra.required = True
        self._phase_center_app_dec.required = True
        self._phase_center_frame_pa.required = True
        self.Nphase = 0
        self.phase_center_catalog = {}
        cat_name = self.object_name
        self.object_name = "multi"

        if preserve_phase_center_info:
            cat_id = self._add_phase_center(
                cat_name,
                cat_type=cat_type,
                cat_lon=self.phase_center_ra,
                cat_lat=self.phase_center_dec,
                cat_frame=self.phase_center_frame,
                cat_epoch=self.phase_center_epoch,
            )
            self.phase_center_id_array = np.zeros(self.Nblts, dtype=int) + cat_id

        self.phase_center_ra = 0.0
        self.phase_center_dec = 0.0
        if self.phase_center_frame is None:
            self.phase_center_frame = "icrs"
        if self.phase_center_epoch is None:
            self.phase_center_epoch = 2000.0

        if (cat_type == "unphased") and preserve_phase_center_info:
            # If moving from unphased, then we'll fill in app_ra and app_dec in
            # the way that we normally would if this were an "unphased" object.
            self._set_app_coords_helper()

    def _set_drift(self):
        """
        Set phase_type to 'drift' and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by phasing methods and file-reading methods to indicate the object has a
        `phase_type` of "drift" and define which metadata are required.
        """
        self.phase_type = "drift"
        self._phase_center_frame.required = False
        self._phase_center_ra.required = False
        self._phase_center_dec.required = False
        self._phase_center_app_ra.required = False
        self._phase_center_app_dec.required = False
        self._phase_center_frame_pa.required = False

    def _set_phased(self):
        """
        Set phase_type to 'phased' and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by phasing methods and file-reading methods to indicate the object has a
        `phase_type` of "phased" and define which metadata are required.
        """
        self.phase_type = "phased"
        self._phase_center_frame.required = True
        self._phase_center_ra.required = True
        self._phase_center_dec.required = True
        self._phase_center_app_ra.required = True
        self._phase_center_app_dec.required = True
        self._phase_center_frame_pa.required = True

    @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):
        """Iterate 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 _set_future_array_shapes(self):
        """
        Set future_array_shapes to True and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by file-reading methods and `use_future_array_shapes` to indicate the
        `future_array_shapes` is True and define expected parameter shapes.

        """
        self.future_array_shapes = True
        self._freq_array.form = ("Nfreqs",)
        self._channel_width.form = ("Nfreqs",)
        for param_name in self._data_params:
            getattr(self, "_" + param_name).form = ("Nblts", "Nfreqs", "Npols")

    def use_future_array_shapes(self):
        """
        Change the array shapes of this object to match the planned future shapes.

        This method sets allows users to convert to the planned array shapes changes
        before the changes go into effect. This method sets the `future_array_shapes`
        parameter on this object to True.

        """
        self._set_future_array_shapes()
        if not self.metadata_only:
            # remove the length-1 spw axis for all data-like parameters
            for param_name in self._data_params:
                setattr(self, param_name, (getattr(self, param_name))[:, 0, :, :])

        # remove the length-1 spw axis for the freq_array
        self.freq_array = self.freq_array[0, :]

        if not self.flex_spw:
            # make channel_width be an array of length Nfreqs rather than a single value
            # (not needed with flexible spws because this is already done in that case)
            self.channel_width = (
                np.zeros(self.Nfreqs, dtype=np.float64) + self.channel_width
            )

    def use_current_array_shapes(self):
        """
        Change the array shapes of this object to match the current future shapes.

        This method sets allows users to convert back to the current array shapes.
        This method sets the `future_array_shapes` parameter on this object to False.
        """
        if not self.flex_spw:
            unique_channel_widths = np.unique(self.channel_width)
            if unique_channel_widths.size > 1:
                raise ValueError(
                    "channel_width parameter contains multiple unique values, but "
                    "only one spectral window is present. Cannot collapse "
                    "channel_width to a single value."
                )
            self._channel_width.form = ()
            self.channel_width = unique_channel_widths[0]

        self.future_array_shapes = False
        for param_name in self._data_params:
            getattr(self, "_" + param_name).form = ("Nblts", 1, "Nfreqs", "Npols")
        if not self.metadata_only:
            for param_name in self._data_params:
                setattr(
                    self, param_name, (getattr(self, param_name))[:, np.newaxis, :, :]
                )

        self._freq_array.form = (
            1,
            "Nfreqs",
        )
        self.freq_array = self.freq_array[np.newaxis, :]

    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"
                        # this parameter is as of this comment most likely a float
                        # since only diameters and antenna positions will probably
                        # trigger this else statement
                        # assign float64 as the type of the array
                        array_val = (
                            np.zeros(self_shape, dtype=np.float64,)
                            + 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(
                f"Telescope {self.telescope_name} is not in known_telescopes."
            )

    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 _set_lsts_helper(self):
        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]
        return

    def _set_app_coords_helper(self, pa_only=False):
        """
        Set values for the apparent coordinate arrays.

        This is an internal helper function, which is not designed to be called by
        users, but rather individual read/write functions for the UVData object.
        Users should use the phase() method for updating/adjusting coordinate values.

        Parameters
        ----------
        pa_only : bool, False
            Skip the calculation of the apparent RA/Dec, and only calculate the
            position angle between `phase_center_frame` and the apparent coordinate
            system. Useful for reading in data formats that do not calculate a PA.
        """
        if self.phase_type != "phased":
            # Uhhh... what do you want me to do? If the dataset isn't phased, there
            # isn't an apparent position to calculate. Time to bail, I guess...
            return
        if pa_only:
            app_ra = self.phase_center_app_ra
            app_dec = self.phase_center_app_dec
        elif self.multi_phase_center:
            app_ra = np.zeros(self.Nblts, dtype=float)
            app_dec = np.zeros(self.Nblts, dtype=float)
            for name in self.phase_center_catalog.keys():
                temp_dict = self.phase_center_catalog[name]
                select_mask = self.phase_center_id_array == temp_dict["cat_id"]
                cat_type = temp_dict["cat_type"]
                lon_val = temp_dict.get("cat_lon")
                lat_val = temp_dict.get("cat_lat")
                epoch = temp_dict.get("cat_epoch")
                frame = temp_dict.get("cat_frame")
                pm_ra = temp_dict.get("cat_pm_ra")
                pm_dec = temp_dict.get("cat_pm_dec")
                vrad = temp_dict.get("vrad")
                dist = temp_dict.get("cat_dist")

                app_ra[select_mask], app_dec[select_mask] = uvutils.calc_app_coords(
                    lon_val,
                    lat_val,
                    frame,
                    coord_epoch=epoch,
                    pm_ra=pm_ra,
                    pm_dec=pm_dec,
                    vrad=vrad,
                    dist=dist,
                    time_array=self.time_array[select_mask],
                    lst_array=self.lst_array[select_mask],
                    telescope_loc=self.telescope_location_lat_lon_alt,
                    coord_type=cat_type,
                )
        else:
            # So this is actually the easier of the two cases -- just use the object
            # properties to fill in the relevant data
            app_ra, app_dec = uvutils.calc_app_coords(
                self.phase_center_ra,
                self.phase_center_dec,
                self.phase_center_frame,
                coord_epoch=self.phase_center_epoch,
                time_array=self.time_array,
                lst_array=self.lst_array,
                telescope_loc=self.telescope_location_lat_lon_alt,
                coord_type="sidereal",
            )

        # Now that we have the apparent coordinates sorted out, we can figure out what
        # it is we want to do with the position angle
        frame_pa = uvutils.calc_frame_pos_angle(
            self.time_array,
            app_ra,
            app_dec,
            self.telescope_location_lat_lon_alt,
            self.phase_center_frame,
            ref_epoch=self.phase_center_epoch,
        )
        self.phase_center_app_ra = app_ra
        self.phase_center_app_dec = app_dec
        self.phase_center_frame_pa = frame_pa

    def set_lsts_from_time_array(self, background=False):
        """Set the lst_array based from the time_array.

        Parameters
        ----------
        background : bool, False
            When set to True, start the calculation on a threading.Thread in the
            background and return the thread to the user.

        Returns
        -------
        proc : None or threading.Thread instance
            When background is set to True, a thread is returned which must be
            joined before the lst_array exists on the UVData object.

        """
        if not background:
            self._set_lsts_helper()
            return
        else:
            proc = threading.Thread(target=self._set_lsts_helper)
            proc.start()
            return proc

    def _check_flex_spw_contiguous(self):
        """
        Check if the spectral windows are contiguous for flex_spw datasets.

        This checks the flex_spw_id_array to make sure that all channels for each
        spectral window are together in one block, versus being interspersed (e.g.,
        channel #1 and #3 is in spw #1, channels #2 and #4 are in spw #2). In theory,
        UVH5 and UVData objects can handle this, but MIRIAD, MIR, UVFITS, and MS file
        formats cannot, so we just consider it forbidden.
        """
        if self.flex_spw:
            exp_spw_ids = np.unique(self.spw_array)
            # This is an internal consistency check to make sure that the indexes match
            # up as expected -- this shouldn't error unless someone is mucking with
            # settings they shouldn't be.
            assert np.all(np.unique(self.flex_spw_id_array) == exp_spw_ids)

            n_breaks = np.sum(self.flex_spw_id_array[1:] != self.flex_spw_id_array[:-1])
            if (n_breaks + 1) != self.Nspws:
                raise ValueError(
                    "Channels from different spectral windows are interspersed with "
                    "one another, rather than being grouped together along the "
                    "frequency axis. Most file formats do not support such "
                    "non-grouping of data."
                )
        else:
            # If this isn't a flex_spw data set, then there is only 1 spectral window,
            # which means that the check always passes
            pass
        return True

    def _check_freq_spacing(self, raise_errors=True):
        """
        Check if frequencies are evenly spaced and separated by their channel width.

        This is a requirement for writing uvfits & miriad files.

        Parameters
        ----------
        raise_errors : bool
            Option to raise errors if the various checks do not pass.

        Returns
        -------
        spacing_error : bool
            Flag that channel spacings or channel widths are not equal.
        chanwidth_error : bool
            Flag that channel spacing does not match channel width.

        """
        spacing_error = False
        chanwidth_error = False
        if self.future_array_shapes:
            freq_spacing = np.diff(self.freq_array)
            freq_array_use = self.freq_array
        else:
            freq_spacing = np.diff(self.freq_array[0])
            freq_array_use = self.freq_array[0]
        if self.Nfreqs == 1:
            # Skip all of this if there is only 1 channel
            pass
        elif self.flex_spw:
            # Check to make sure that the flexible spectral window has indicies set up
            # correctly (grouped together) for this check
            self._check_flex_spw_contiguous()
            diff_chanwidth = np.diff(self.channel_width)
            freq_dir = []
            # We want to grab unique spw IDs, in the order that they appear in the data
            select_mask = np.append((np.diff(self.flex_spw_id_array) != 0), True)
            for idx in self.flex_spw_id_array[select_mask]:
                chan_mask = self.flex_spw_id_array == idx
                freq_dir += [
                    np.sign(np.mean(np.diff(freq_array_use[chan_mask])))
                ] * np.sum(chan_mask)

            # Pop off the first entry, since the above arrays are diff'd
            # (and thus one element shorter)
            freq_dir = np.array(freq_dir[1:])
            # Ignore cases where looking at the boundaries of spectral windows
            bypass_check = self.flex_spw_id_array[1:] != self.flex_spw_id_array[:-1]
            if not np.all(
                np.logical_or(
                    bypass_check,
                    np.isclose(
                        diff_chanwidth,
                        0.0,
                        rtol=self._freq_array.tols[0],
                        atol=self._freq_array.tols[1],
                    ),
                )
            ):
                spacing_error = True
            if not np.all(
                np.logical_or(
                    bypass_check,
                    np.isclose(
                        freq_spacing,
                        self.channel_width[1:] * freq_dir,
                        rtol=self._freq_array.tols[0],
                        atol=self._freq_array.tols[1],
                    ),
                )
            ):
                chanwidth_error = True
        else:
            freq_dir = np.sign(np.mean(freq_spacing))
            if not np.isclose(
                np.min(freq_spacing),
                np.max(freq_spacing),
                rtol=self._freq_array.tols[0],
                atol=self._freq_array.tols[1],
            ):
                spacing_error = True
            if self.future_array_shapes:
                if not np.isclose(
                    np.min(self.channel_width),
                    np.max(self.channel_width),
                    rtol=self._freq_array.tols[0],
                    atol=self._freq_array.tols[1],
                ):
                    spacing_error = True
                else:
                    if not np.isclose(
                        np.mean(freq_spacing),
                        np.mean(self.channel_width) * freq_dir,
                        rtol=self._channel_width.tols[0],
                        atol=self._channel_width.tols[1],
                    ):
                        chanwidth_error = True
            else:
                if not np.isclose(
                    np.mean(freq_spacing),
                    self.channel_width * freq_dir,
                    rtol=self._channel_width.tols[0],
                    atol=self._channel_width.tols[1],
                ):
                    chanwidth_error = True
        if raise_errors and spacing_error:
            raise ValueError(
                "The frequencies are not evenly spaced (probably "
                "because of a select operation) or has differing "
                "values of channel widths. Some file formats "
                "(e.g. uvfits, miriad) and methods (frequency_average) "
                "do not support unevenly spaced frequencies."
            )
        if raise_errors and chanwidth_error:
            raise ValueError(
                "The frequencies are separated by more than their "
                "channel width (probably because of a select operation). "
                "Some file formats (e.g. uvfits, miriad) and "
                "methods (frequency_average) do not support "
                "frequencies that are spaced by more than their "
                "channel width."
            )

        return spacing_error, chanwidth_error

    def _calc_nants_data(self):
        """Calculate the number of antennas from ant_1_array and ant_2_array arrays."""
        return int(np.union1d(self.ant_1_array, self.ant_2_array).size)

    def check(
        self,
        check_extra=True,
        run_check_acceptability=True,
        check_freq_spacing=False,
        strict_uvw_antpos_check=False,
        allow_flip_conj=False,
    ):
        """
        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.
        check_freq_spacing :  bool
            Option to check if frequencies are evenly spaced and the spacing is
            equal to their channel_width. This is not required for UVData
            objects in general but is required to write to uvfits and miriad files.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        allow_flip_conj : bool
            If set to True, and the UVW coordinates do not match antenna positions,
            check and see if flipping the conjugation of the baselines (i.e, multiplying
            the UVWs by -1) resolves  the apparent discrepancy -- and if it does, fix
            the apparent conjugation error in `uvw_array` and `data_array`. Default is
            False.

        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:
            raise ValueError('Phase type must be either "phased" or "drift"')

        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.
        if self.Nants_data != self._calc_nants_data():
            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 set(np.unique(self.ant_1_array)).issubset(self.antenna_numbers):
            raise ValueError("All antennas in ant_1_array must be in antenna_numbers.")
        if not set(np.unique(self.ant_2_array)).issubset(self.antenna_numbers):
            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)
                )

        if run_check_acceptability:
            # check that the uvws make sense given the antenna positions
            # make a metadata only copy of this object to properly calculate uvws
            temp_obj = self.copy(metadata_only=True)

            if temp_obj.phase_center_frame is not None:
                output_phase_frame = temp_obj.phase_center_frame
            else:
                output_phase_frame = "icrs"

            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                temp_obj.set_uvws_from_antenna_positions(
                    allow_phasing=True, output_phase_frame=output_phase_frame,
                )

            if not np.allclose(temp_obj.uvw_array, self.uvw_array, atol=1):
                max_diff = np.max(np.abs(temp_obj.uvw_array - self.uvw_array))
                if allow_flip_conj and np.allclose(
                    -temp_obj.uvw_array, self.uvw_array, atol=1
                ):
                    warnings.warn(
                        "UVW orientation appears to be flipped, attempting to "
                        "fix by changing conjugation of baselines."
                    )
                    self.uvw_array *= -1
                    self.data_array = np.conj(self.data_array)
                elif not strict_uvw_antpos_check:
                    warnings.warn(
                        "The uvw_array does not match the expected values given "
                        "the antenna positions. The largest discrepancy is "
                        f"{max_diff} meters. This is a fairly common situation "
                        "but might indicate an error in the antenna positions, "
                        "the uvws or the phasing."
                    )
                else:
                    raise ValueError(
                        "The uvw_array does not match the expected values given "
                        "the antenna positions. The largest discrepancy is "
                        f"{max_diff} meters."
                    )

            # 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(
                    # this line used to use np.linalg.norm but it turns out
                    # squaring and sqrt is slightly more efficient unless the array
                    # is "very large".
                    np.sqrt(
                        self.uvw_array[~autos, 0] ** 2
                        + self.uvw_array[~autos, 1] ** 2
                        + self.uvw_array[~autos, 2] ** 2
                    ),
                    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."
                )

        if check_freq_spacing:
            self._check_freq_spacing()

        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
        -------
        UVData
            Copy of self.
        """
        if not metadata_only:
            return super(UVData, self).copy()
        else:
            uv = UVData()
            # include all attributes, not just UVParameter ones.
            for attr in self.__iter__(uvparams_only=False):
                # skip properties
                if isinstance(getattr(type(self), attr, None), property):
                    continue

                # skip data like parameters
                # parameter names have a leading underscore we want to ignore
                if attr.lstrip("_") in self._data_params:
                    continue
                setattr(uv, attr, copy.deepcopy(getattr(self, attr)))

            if uv.future_array_shapes:
                for param_name in uv._data_params:
                    getattr(uv, "_" + param_name).form = ("Nblts", "Nfreqs", "Npols")

            return uv

    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 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
            its 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 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(
                                f"Baseline {key} not found for polarization "
                                "array in data."
                            )
                        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(
                            f"Baseline {key} not found for polarization array in data."
                        )
                    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
    ):
        """
        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]:
                    if self.future_array_shapes:
                        out = data[
                            blt_start:blt_stop:dblt, :, p_start[0] : p_stop[0] : dp[0]
                        ]
                    else:
                        out = data[
                            blt_start:blt_stop:dblt,
                            :,
                            :,
                            p_start[0] : p_stop[0] : dp[0],
                        ]
                else:
                    if self.future_array_shapes:
                        out = data[blt_start:blt_stop:dblt, :, indp[0]]
                    else:
                        out = data[blt_start:blt_stop:dblt, :, :, indp[0]]
            else:
                out = data[ind1]
                if p_reg_spaced[0]:
                    if self.future_array_shapes:
                        out = out[:, :, p_start[0] : p_stop[0] : dp[0]]
                    else:
                        out = out[:, :, :, p_start[0] : p_stop[0] : dp[0]]
                else:
                    if self.future_array_shapes:
                        out = out[:, :, indp[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]:
                    if self.future_array_shapes:
                        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,
                                :,
                                :,
                                p_start[1] : p_stop[1] : dp[1],
                            ]
                        )
                else:
                    if self.future_array_shapes:
                        out = np.conj(data[blt_start:blt_stop:dblt, :, indp[1]])
                    else:
                        out = np.conj(data[blt_start:blt_stop:dblt, :, :, indp[1]])
            else:
                out = data[ind2]
                if p_reg_spaced[1]:
                    if self.future_array_shapes:
                        out = np.conj(out[:, :, p_start[1] : p_stop[1] : dp[1]])
                    else:
                        out = np.conj(out[:, :, :, p_start[1] : p_stop[1] : dp[1]])
                else:
                    if self.future_array_shapes:
                        out = np.conj(out[:, :, indp[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]:
                if self.future_array_shapes:
                    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][:, :, :, p_start[0] : p_stop[0] : dp[0]],
                        out[1][:, :, :, p_start[1] : p_stop[1] : dp[1]],
                        axis=0,
                    )
            else:
                if self.future_array_shapes:
                    out = np.append(
                        out[0][:, :, indp[0]], out[1][:, :, indp[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 self.future_array_shapes:
                if out.shape[2] == 1:
                    # one polarization dimension
                    out = np.squeeze(out, axis=2)
            else:
                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_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_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 list(zip(*self.baseline_to_antnums(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 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)
        # When we select conjugated baselines, there is a call to np.conj()
        # inside of _smart_slicing to correct the data array. This has the
        # unintended consequence of promoting the dtype of an array of np.bool_
        # to np.int8. Rather than having a bunch of special handling for this
        # ~corner case, we instead explicitly cast back to np.bool_ before we
        # hand back to the user.
        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 get_lsts(self, key1, key2=None, key3=None):
        """
        Get the LSTs 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
            LSTs from the lst_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.lst_array[np.append(inds1, inds2)]

    def get_ENU_antpos(self, center=False, pick_data_ants=False):
        """
        Get 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 = np.in1d(telescope_ants, data_ants)
            antpos = antpos[select, :]
            ants = telescope_ants[select]

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

        return antpos, ants

    def _set_method_helper(self, dshape, key1, key2=None, key3=None):
        """
        Extract the indices for setting data, flags, or nsample arrays.

        This is a helper method designed to work with set_data, set_flags, and
        set_nsamples. Given the shape of the data-like array and the keys
        corresponding to where the data should end up, it finds the indices
        that are needed for the `_index_dset` method.

        Parameters
        ----------
        dshape : tuple of int
            The shape of the data-like array. This is used to ensure the array
            is compatible with the indices selected.
        key1, key2, key3 : int or tuple of ints
            Identifier of which flags to set, 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, set all flags for
                    that pol.
                else:
                    interpreted as a baseline number, set all flags for that baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                set all flags for that baseline, pol. pol may be a string or int.

        Returns
        -------
        inds : tuple of int
            The indices in the data-like array to slice into.

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        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)
        if len(ind2) != 0:
            raise ValueError(
                "the requested key is present on the object, but conjugated. Please "
                "conjugate data and keys appropriately and try again"
            )

        if self.future_array_shapes:
            expected_shape = (len(ind1), self.Nfreqs, len(indp[0]))
        else:
            expected_shape = (len(ind1), 1, self.Nfreqs, len(indp[0]))
        if dshape != expected_shape:
            raise ValueError(
                "the input array is not compatible with the shape of the destination. "
                f"Input array shape is {dshape}, expected shape is {expected_shape}."
            )

        blt_slices, blt_sliceable = uvutils._convert_to_slices(
            ind1, max_nslice_frac=0.1
        )
        pol_slices, pol_sliceable = uvutils._convert_to_slices(
            indp[0], max_nslice_frac=0.5
        )

        if self.future_array_shapes:
            inds = [ind1, np.s_[:], indp[0]]
        else:
            inds = [ind1, np.s_[:], np.s_[:], indp[0]]
        if blt_sliceable:
            inds[0] = blt_slices
        if pol_sliceable:
            inds[-1] = pol_slices

        return tuple(inds)

    def set_data(self, data, key1, key2=None, key3=None):
        """
        Set the data array to some values provided by the user.

        Parameters
        ----------
        data : ndarray of complex
            The data to overwrite into the data_array. Must be the same shape as
            the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to set, 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.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = data.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.data_array, inds, data)

        return

    def set_flags(self, flags, key1, key2=None, key3=None):
        """
        Set the flag array to some values provided by the user.

        Parameters
        ----------
        flag : ndarray of boolean
            The flags to overwrite into the fkag_array. Must be the same shape
            as the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which flags to set, 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, set all flags for
                    that pol.
                else:
                    interpreted as a baseline number, set all flags for that baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                set all flags for that baseline, pol. pol may be a string or int.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = flags.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.flag_array, inds, flags)

        return

    def set_nsamples(self, nsamples, key1, key2=None, key3=None):
        """
        Set the nsamples array to some values provided by the user.

        Parameters
        ----------
        nsamples : ndarray of float
            The nsamples to overwrite into the nsample_array. Must be the same
            shape as the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which nsamples to set, 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, set all data for
                    that pol.
                else:
                    interpreted as a baseline number, set all nsamples for that
                    baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2,
                pol), set all nsamples for that baseline, pol. pol may be a
                string or int.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = nsamples.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.nsample_array, inds, nsamples)

        return

    def antpairpol_iter(self, squeeze="default"):
        """
        Iterate 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 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

            if not self.metadata_only:
                orig_data_array = copy.copy(self.data_array)
                for pol_ind in np.arange(self.Npols):
                    if self.future_array_shapes:
                        self.data_array[
                            index_array, :, new_pol_inds[pol_ind]
                        ] = np.conj(orig_data_array[index_array, :, pol_ind])
                    else:
                        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,
        strict_uvw_antpos_check=False,
    ):
        """
        Rearrange polarizations in the event they are not uvfits compatible.

        Parameters
        ----------
        order : str
            Either a string specifying a canonical 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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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]
        if not self.metadata_only:
            # data array is special and large, take is faster here
            if self.future_array_shapes:
                self.data_array = np.take(self.data_array, index_array, axis=2)
                self.nsample_array = self.nsample_array[:, :, index_array]
                self.flag_array = self.flag_array[:, :, index_array]
            else:
                self.data_array = np.take(self.data_array, index_array, axis=3)
                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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    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,
        strict_uvw_antpos_check=False,
    ):
        """
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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 self.phase_center_app_ra is not None:
            self.phase_center_app_ra = self.phase_center_app_ra[index_array]
        if self.phase_center_app_dec is not None:
            self.phase_center_app_dec = self.phase_center_app_dec[index_array]
        if self.phase_center_frame_pa is not None:
            self.phase_center_frame_pa = self.phase_center_frame_pa[index_array]
        if self.multi_phase_center:
            self.phase_center_id_array = self.phase_center_id_array[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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    def reorder_freqs(
        self,
        spw_order=None,
        channel_order=None,
        select_spw=None,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Arrange frequency axis according to desired order.

        Can be applied across the entire frequency axis, or just a subset.

        Parameters
        ----------
        spw_order : str or array_like of int
            A string describing the desired order of spectral windows along the
            frequecy axis. Allowed strings include `number` (sort on spectral window
            number) and `freq` (sort on median frequency). A '-' can be appended
            to signify descending order instead of the default ascending order,
            e.g., if you have SPW #1 and 2, and wanted them ordered as [2, 1],
            you would specify `-number`. Alternatively, one can supply an array
            of length Nspws that specifies the new order, with values matched to
            the specral window number given in `spw_array`. Default is to apply no
            sorting of spectral windows.
        channel_order : str or array_like of int
            A string describing the desired order of frequency channels within a
            spectral window. Allowed strings include `freq`, which will sort channels
            within a spectral window by frequency. A '-' can be optionally appended
            to signify descending order instead of the default ascending order.
            Alternatively, one can supply an index array of length Nfreqs that
            specifies the new order. Default is to apply no sorting of channels
            within a single spectral window. Note that proving an array_like of ints
            will cause the values given to `spw_order` and `select_spw` to be ignored.
        select_spw : int or array_like of int
            An int or array_like of ints which specifies which spectral windows to
            apply sorting. Note that setting this argument will cause the value
            given to `spw_order` to be ignored.
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Returns
        -------
        None

        Raises
        ------
        UserWarning
            Raised if providing arguments to select_spw and freq_screen (the latter
            overrides the former).
        ValueError
            Raised if select_spw contains values not in spw_array, or if freq_screen
            is not the same length as freq_array.

        """
        if (spw_order is None) and (channel_order is None):
            warnings.warn(
                "Not specifying either spw_order or channel_order causes "
                "no sorting actions to be applied. Returning object unchanged."
            )
            return

        # Check to see if there are arguments we should be ignoring
        if isinstance(channel_order, (np.ndarray, list, tuple)):
            if select_spw is not None:
                warnings.warn(
                    "The select_spw argument is ignored when providing an "
                    "array_like of int for channel_order"
                )
            if spw_order is not None:
                warnings.warn(
                    "The spw_order argument is ignored when providing an "
                    "array_like of int for channel_order"
                )
            if not np.all(np.sort(channel_order) == np.arange(self.Nfreqs)):
                raise ValueError(
                    "Index array for channel_order must contain all indicies for "
                    "the frequency axis, without duplicates."
                )
            index_array = channel_order
        else:
            index_array = np.arange(self.Nfreqs)
            # Multipy by 1.0 here to make a cheap copy of the array to manipulate
            temp_freqs = 1.0 * (
                self.freq_array if self.future_array_shapes else self.freq_array[0, :]
            )
            # Same trick for ints -- add 0 to make a cheap copy
            temp_spws = 0 + (
                self.flex_spw_id_array
                if self.flex_spw
                else (np.zeros(self.Nfreqs) + self.spw_array)
            )

            # Check whether or not we need to sort the channels in individual windows
            sort_spw = {idx: channel_order is not None for idx in self.spw_array}
            if select_spw is not None:
                if spw_order is not None:
                    warnings.warn(
                        "The spw_order argument is ignored when providing an "
                        "argument for select_spw"
                    )
                if channel_order is None:
                    warnings.warn(
                        "Specifying select_spw without providing channel_order causes "
                        "no sorting actions to be applied. Returning object unchanged."
                    )
                    return
                if isinstance(select_spw, (np.ndarray, list, tuple)):
                    sort_spw = {idx: idx in select_spw for idx in self.spw_array}
                else:
                    sort_spw = {idx: idx == select_spw for idx in self.spw_array}
            elif spw_order is not None:
                if isinstance(spw_order, (np.ndarray, list, tuple)):
                    if not np.all(np.sort(spw_order) == np.sort(self.spw_array)):
                        raise ValueError(
                            "Index array for spw_order must contain all indicies for "
                            "the frequency axis, without duplicates."
                        )
                elif spw_order not in ["number", "freq", "-number", "-freq", None]:
                    raise ValueError(
                        "spw_order can only be one of 'number', '-number', "
                        "'freq', '-freq', or None"
                    )
                elif self.Nspws > 1:
                    # Only need to do this step if we actually have multiple spws.

                    # If the string starts with a '-', then we will flip the order at
                    # the end of the operation
                    flip_spws = spw_order[0] == "-"

                    if "number" in spw_order:
                        spw_order = np.sort(self.spw_array)
                    elif "freq" in spw_order:
                        spw_order = self.spw_array[
                            np.argsort(
                                [
                                    np.median(temp_freqs[temp_spws == idx])
                                    for idx in self.spw_array
                                ]
                            )
                        ]
                    if flip_spws:
                        spw_order = np.flip(spw_order)
                # Now that we know the spw order, we can apply the first sort
                index_array = np.concatenate(
                    [index_array[temp_spws == idx] for idx in spw_order]
                )
                temp_freqs = temp_freqs[index_array]
                temp_spws = temp_spws[index_array]
            # Spectral windows are assumed sorted at this point
            if channel_order is not None:
                if channel_order not in ["freq", "-freq"]:
                    raise ValueError(
                        "channel_order can only be one of 'freq' or '-freq'"
                    )
                for idx in self.spw_array:
                    if sort_spw[idx]:
                        select_mask = temp_spws == idx
                        subsort_order = index_array[select_mask]
                        subsort_order = subsort_order[
                            np.argsort(temp_freqs[select_mask])
                        ]
                        index_array[select_mask] = (
                            np.flip(subsort_order)
                            if channel_order[0] == "-"
                            else subsort_order
                        )

        if np.all(index_array[1:] > index_array[:-1]):
            # Nothing to do - the data are already sorted!
            return

        # Now update all of the arrays.
        if self.future_array_shapes:
            self.freq_array = self.freq_array[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, :]
        else:
            self.freq_array = self.freq_array[:, 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, :]
        if self.flex_spw:
            self.flex_spw_id_array = self.flex_spw_id_array[index_array]
            self.channel_width = self.channel_width[index_array]
            # Reorder the spw-axis items based on their first appearance in the data
            unique_index = np.sort(
                np.unique(self.flex_spw_id_array, return_index=True)[1]
            )
            self.spw_array = self.flex_spw_id_array[unique_index]

        if self.eq_coeffs is not None:
            self.eq_coeffs = self.eq_coeffs[:, index_array]
        # check if object is self-consistent
        if run_check:
            self.check(
                check_extra=check_extra,
                run_check_acceptability=run_check_acceptability,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    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] *= eq_coeff1 * eq_coeff2
            else:
                self.data_array[blt_inds] /= eq_coeff1 * eq_coeff2

        return

    def _apply_w_proj(self, new_w_vals, old_w_vals, select_mask=None):
        """
        Apply corrections based on changes to w-coord.

        Adjusts the data to account for a change along the w-axis of a baseline.

        Parameters
        ----------
        new_w_vals: float or ndarray of float
            New w-coordinates for the baselines, in units of meters. Can either be a
            solitary float (helpful for unphasing data, where new_w_vals can be set to
            0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).
        old_w_vals: float or ndarray of float
            Old w-coordinates for the baselines, in units of meters. Can either be a
            solitary float (helpful for unphasing data, where new_w_vals can be set to
            0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).
        select_mask: ndarray of bool
            Array is of shape (Nblts,), where the sum of all enties marked True is
            equal to Nselect (mentioned above).

        Raises
        ------
        IndexError
            If the length of new_w_vals or old_w_vals isn't compatible with
            select_mask, or if select mask isn't the right length.
        """
        # If we only have metadata, then we have no work to do. W00t!
        if self.metadata_only or (self.data_array is None):
            return

        if select_mask is None:
            select_len = self.Nblts
        else:
            try:
                inv_mask = np.ones(self.Nblts, dtype=bool)
                inv_mask[select_mask] = False
                select_mask = ~inv_mask
                select_len = np.sum(select_mask)
            except IndexError:
                raise IndexError(
                    "select_mask must be an array-like, either of ints with shape "
                    "(Nblts), or of ints within the range (-Nblts, Nblts)."
                )

        # Promote everything to float64 ndarrays if they aren't already
        old_w_vals = np.array(old_w_vals, dtype=np.float64)
        old_w_vals.shape += (1,) if (old_w_vals.ndim == 0) else ()
        new_w_vals = np.array(new_w_vals, dtype=np.float64)
        new_w_vals.shape += (1,) if (new_w_vals.ndim == 0) else ()

        # Make sure the lengths of everything make sense
        new_val_len = len(new_w_vals)
        old_val_len = len(old_w_vals)

        if new_val_len not in [1, select_len]:
            raise IndexError(
                "The length of new_w_vals is wrong (expected 1 or %i, got %i)!"
                % (select_len, new_val_len)
            )
        if old_val_len not in [1, select_len]:
            raise IndexError(
                "The length of old_w_vals is wrong (expected 1 or %i, got %i)!"
                % (select_len, old_val_len)
            )

        # Calculate the difference in w terms as a function of freq. Note that the
        # 1/c is there to speed of processing (faster to multiply than divide)
        delta_w_lambda = (
            (new_w_vals - old_w_vals).reshape(-1, 1)
            * (1.0 / const.c.to("m/s").value)
            * self.freq_array.reshape(1, self.Nfreqs)
        )
        if select_mask is None or np.all(select_mask):
            # If all the w values are changing, it turns out to be twice as fast
            # to ditch any sort of selection mask and just do the full multiply.
            if self.future_array_shapes:
                self.data_array *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, :, None]
                )
            else:
                self.data_array *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, None, :, None]
                )
        elif np.any(select_mask):
            # In the case we are _not_ doing all baselines, use a selection mask to
            # only update the values we need. In the worse case, it slows down the
            # processing by ~2x, but it can save a lot on time and memory if only
            # needing to update a select number of baselines.
            if self.future_array_shapes:
                self.data_array[select_mask] *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, :, None]
                )
            else:
                self.data_array[select_mask] *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, None, :, None]
                )

    def unphase_to_drift(
        self, phase_frame=None, use_ant_pos=True, use_old_proj=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. Default is True.
        use_old_proj : bool
            If True, uses the 'old' way of calculating baseline projections.
            Default is False.

        Raises
        ------
        ValueError
            If the phase_type is not 'phased'
        """
        if self.phase_type == "phased":
            pass
        else:
            raise ValueError(
                "The data is already drift scanning; can only unphase phased data."
            )

        if not use_old_proj:
            # Check to make sure that these attributes are actually filled. Otherwise,
            # you probably want to use the old phase method.
            if (
                (not use_ant_pos)
                and (self.phase_center_app_ra is None)
                or (self.phase_center_app_dec is None)
            ):
                raise AttributeError(
                    "Object missing phase_center_ra_app or phase_center_dec_app, "
                    "which implies that the data were phased using the 'old' "
                    "method for phasing (which is not compatible with the new "
                    "version of the code). Please run unphase_to_drift with "
                    "use_old_proj=True to continue."
                )

            telescope_location = self.telescope_location_lat_lon_alt

            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.
            select_mask = ~self._check_for_unphased()

            new_uvw = uvutils.calc_uvw(
                lst_array=self.lst_array,
                use_ant_pos=use_ant_pos,
                uvw_array=self.uvw_array,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=self.ant_1_array,
                ant_2_array=self.ant_2_array,
                old_app_ra=self.phase_center_app_ra,
                old_app_dec=self.phase_center_app_dec,
                old_frame_pa=self.phase_center_frame_pa,
                telescope_lat=telescope_location[0],
                telescope_lon=telescope_location[1],
                to_enu=True,
            )

            self._apply_w_proj(0.0, self.uvw_array[select_mask, 2], select_mask)
            self.uvw_array = new_uvw

            # remove/update phase center
            if self.multi_phase_center:
                self.phase_center_id_array[:] = self._add_phase_center(
                    "unphased", "unphased"
                )
                self.phase_center_app_ra = self.lst_array.copy()
                self.phase_center_app_dec[:] = (
                    np.zeros(self.Nblts) + self.telescope_location[0]
                )
                self.phase_center_frame_pa = np.zeros(self.Nblts)
            else:
                self.phase_center_frame = None
                self.phase_center_ra = None
                self.phase_center_dec = None
                self.phase_center_epoch = None
                self.phase_center_app_ra = None
                self.phase_center_app_dec = None
                self.phase_center_frame_pa = None
                self._set_drift()
            return

        # If you are a multi phase center data set, there's no valid reason to be going
        # back to the old phase method. Time to bail!
        if self.multi_phase_center:
            raise ValueError(
                "Multi phase center data sets are not compatible with the old phasing "
                "method, please set use_old_proj=False."
            )

        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)
            )
            if self.future_array_shapes:
                phs = np.exp(-1j * 2 * np.pi * (-1) * w_lambda[:, :, None])
            else:
                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)

        telescope_location = EarthLocation.from_geocentric(
            *self.telescope_location, unit=units.m
        )
        obs_times = Time(unique_times, format="jd")
        itrs_telescope_locations = telescope_location.get_itrs(obstime=obs_times)
        itrs_telescope_locations = SkyCoord(itrs_telescope_locations)
        # just calling transform_to(coord.GCRS) will delete the obstime information
        # need to re-add obstimes for a GCRS transformation
        if phase_frame == "gcrs":
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())(obstime=obs_times)
            )
        else:
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())
            )

        frame_telescope_locations.representation_type = "cartesian"

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

            obs_time = obs_times[ind]

            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,
                )
                # instead of looping through every ind, find the spot in antenna number
                # array where ant_num <= ant1 < ant_number and similarly for ant2
                # for all baselines in inds
                # then find the uvw coordinate for all at the same time

                # antenna_numbers does not necessarily need to be in order on the object
                # but needs to be in order for the searchsorted to work.
                # ant1_index and ant2_index arrays will preserve the order of blts
                ant_sort = np.argsort(self.antenna_numbers)
                ant1_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_1_array[inds]
                )
                ant2_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_2_array[inds]
                )
                self.uvw_array[inds] = (
                    ant_uvw[ant_sort][ant2_index, :] - ant_uvw[ant_sort][ant1_index, :]
                )

            else:
                frame_telescope_location = frame_telescope_locations[ind]
                itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

                uvws_use = self.uvw_array[inds, :]

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

                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,
                    representation_type="cartesian",
                )

                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_dict_helper(
        self,
        ra,
        dec,
        epoch,
        phase_frame,
        ephem_times,
        cat_type,
        pm_ra,
        pm_dec,
        dist,
        vrad,
        cat_name,
        lookup_name,
        select_mask,
        time_array,
    ):
        """
        Supplies a dictionary with parametrs for the phase method to use.

        This method should not be called directly by users; it is instead a function
        called by the `phase` method, which packages up phase center information
        into a single dictionary to allow for consistent behavior between different
        instantiations of `UVData` objects.
        """
        cat_id = None
        info_source = "user"
        if self.multi_phase_center:
            name_list = list(self.phase_center_catalog.keys())
        else:
            name_list = [self.object_name]

        # We only want to use the JPL-Horizons service if using a non-mutli-phase-ctr
        # instance of a UVData object.
        if lookup_name and (cat_name not in name_list) and self.multi_phase_center:
            if (cat_type is None) or (cat_type == "ephem"):
                [
                    cat_times,
                    cat_lon,
                    cat_lat,
                    cat_dist,
                    cat_vrad,
                ] = uvutils.lookup_jplhorizons(
                    cat_name,
                    time_array,
                    telescope_loc=self.telescope_location_lat_lon_alt,
                )
                cat_type = "ephem"
                cat_pm_ra = cat_pm_dec = None
                cat_epoch = 2000.0
                cat_frame = "icrs"
                info_source = "jplh"
            else:
                raise ValueError(
                    "Unable to find %s in among the existing sources "
                    "recorded in the catalog. Please supply source "
                    "information (e.g., RA and Dec coordinates) and "
                    "set lookup_name=False." % cat_name
                )
        elif (cat_name in name_list) and self.multi_phase_center:
            # If the name of the source matches, then verify that all of its
            # properties are the same as what is stored in phase_center_catalog.
            if lookup_name:
                cat_id = self.phase_center_catalog[cat_name]["cat_id"]
                cat_diffs = 0
            else:
                cat_id, cat_diffs = self._look_in_catalog(
                    cat_name,
                    cat_type=cat_type,
                    cat_lon=ra,
                    cat_lat=dec,
                    cat_frame=phase_frame,
                    cat_epoch=epoch,
                    cat_times=ephem_times,
                    cat_pm_ra=pm_ra,
                    cat_pm_dec=pm_dec,
                    cat_dist=dist,
                    cat_vrad=vrad,
                )
            # If cat_diffs > 0, it means that the catalog entries dont match
            if cat_diffs != 0:
                # Last chance here -- if we have selected all of the data phased
                # to this phase center, then we are still okay.
                if select_mask is None:
                    # We have selected all data, so we're good
                    pass
                elif np.all(
                    np.not_equal(
                        self.phase_center_id_array[~select_mask],
                        self.phase_center_catalog[cat_name]["cat_id"],
                    )
                ):
                    # We have selected a subset of the data that contains
                    # everything that was phased to the object
                    pass
                else:
                    raise ValueError(
                        "The entry name %s is not unique, but arguments to phase "
                        "do not match that stored in phase_center_catalog. Try using a "
                        "different name, using select_mask to select all data "
                        "phased to this phase center, or using the existing phase "
                        "center information by setting lookup_name=True." % cat_name
                    )
                cat_type = "sidereal" if cat_type is None else cat_type
                cat_lon = ra
                cat_lat = dec
                cat_frame = phase_frame
                cat_epoch = epoch
                cat_times = ephem_times
                cat_pm_ra = pm_ra
                cat_pm_dec = pm_dec
                cat_dist = dist
                cat_vrad = vrad
            else:
                temp_dict = self.phase_center_catalog[cat_name]
                cat_id = temp_dict["cat_id"]
                cat_type = temp_dict["cat_type"]
                info_source = temp_dict["info_source"]
                # Get here will return None if no key found, which we want
                cat_lon = temp_dict.get("cat_lon")
                cat_lat = temp_dict.get("cat_lat")
                cat_frame = temp_dict.get("cat_frame")
                cat_epoch = temp_dict.get("cat_epoch")
                cat_times = temp_dict.get("cat_times")
                cat_pm_ra = temp_dict.get("cat_pm_ra")
                cat_pm_dec = temp_dict.get("cat_pm_dec")
                cat_dist = temp_dict.get("cat_dist")
                cat_vrad = temp_dict.get("cat_vrad")
        else:
            # Either this is not a multi phase center data set, or the name of the
            # source is unique!
            cat_type = "sidereal" if cat_type is None else cat_type
            cat_lon = ra
            cat_lat = dec
            cat_frame = phase_frame
            cat_epoch = epoch
            cat_times = ephem_times
            cat_pm_ra = pm_ra
            cat_pm_dec = pm_dec
            cat_dist = dist
            cat_vrad = vrad

        if cat_epoch is None:
            cat_epoch = 1950.0 if (cat_frame in ["fk4", "fk4noeterms"]) else 2000.0
        if isinstance(cat_epoch, str) or isinstance(cat_epoch, Time):
            cat_epoch = Time(cat_epoch).to_value(
                "byear" if cat_frame in ["fk4", "fk4noeterms"] else "jyear"
            )

        # One last check - if we have an ephem phase center, lets make sure that the
        # time range of the ephemeris encapsulates the entire range of time_array
        check_ephem = False
        if cat_type == "ephem":
            # Take advantage of this to make sure that lat, lon, and times are all
            # ndarray types
            cat_lon = np.array(cat_lon, dtype=float)
            cat_lat = np.array(cat_lat, dtype=float)
            cat_times = np.array(cat_times, dtype=float)
            cat_lon.shape += (1,) if (cat_lon.ndim == 0) else ()
            cat_lat.shape += (1,) if (cat_lat.ndim == 0) else ()
            cat_times.shape += (1,) if (cat_times.ndim == 0) else ()
            check_ephem = np.min(time_array) < np.min(cat_times)
            check_ephem = check_ephem or (np.max(time_array) > np.max(cat_times))
            # If the ephem was supplied by JPL-Horizons, then we can easily expand
            # it to cover the requested range.
            if check_ephem and (info_source == "jplh"):
                # Concat the two time ranges to make sure that we cover both the
                # requested time range _and_ the original time range.
                [
                    cat_times,
                    cat_lon,
                    cat_lat,
                    cat_dist,
                    cat_vrad,
                ] = uvutils.lookup_jplhorizons(
                    cat_name,
                    np.concatenate((np.reshape(time_array, -1), cat_times)),
                    telescope_loc=self.telescope_location_lat_lon_alt,
                )
            elif check_ephem:
                # The ephem was user-supplied during the call to the phase method,
                # raise an error to ask for more ephem data.
                raise ValueError(
                    "Ephemeris data does not cover the entirety of the time range "
                    "attempted to be phased. Please supply additional ephem data "
                    "(and if used, set lookup_name=False)."
                )
        # Time to repackage everything into a dict
        phase_dict = {
            "cat_name": cat_name,
            "cat_type": cat_type,
            "cat_lon": cat_lon,
            "cat_lat": cat_lat,
            "cat_frame": cat_frame,
            "cat_epoch": cat_epoch,
            "cat_times": cat_times,
            "cat_pm_ra": cat_pm_ra,
            "cat_pm_dec": cat_pm_dec,
            "cat_dist": cat_dist,
            "cat_vrad": cat_vrad,
            "info_source": info_source,
            "cat_id": cat_id,
        }

        # Finally, make sure everything is a float or an ndarray of floats
        for key in phase_dict.keys():
            if isinstance(phase_dict[key], np.ndarray):
                phase_dict[key] = phase_dict[key].astype(float)
            elif (key == "cat_id") and (phase_dict[key] is not None):
                # If this is the cat_id, make it an int
                phase_dict[key] == int(phase_dict[key])
            elif not ((phase_dict[key] is None) or isinstance(phase_dict[key], str)):
                phase_dict[key] = float(phase_dict[key])

        return phase_dict

    def phase(
        self,
        ra,
        dec,
        epoch="J2000",
        phase_frame="icrs",
        cat_type=None,
        ephem_times=None,
        pm_ra=None,
        pm_dec=None,
        dist=None,
        vrad=None,
        cat_name=None,
        lookup_name=False,
        use_ant_pos=True,
        allow_rephase=True,
        orig_phase_frame=None,
        select_mask=None,
        cleanup_old_sources=True,
        use_old_proj=False,
        fix_old_proj=True,
    ):
        """
        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.

        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 transformed 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.
        cat_type : str
            Type of phase center to be added. Must be one of:
            "sidereal" (fixed RA/Dec), "ephem" (RA/Dec that moves with time),
            "driftscan" (fixed az/el position). Default is "sidereal", other selections
            are only permissible if `multi_phase_center=True`.
        ephem_times : ndarray of float
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_name :str
            Name of the phase center being phased to. Required if
            `multi_phase_center=True`, otherwise `object_name` set to this value.
        lookup_name : bool
            Only used if `multi_phase_center=True`, allows the user to lookup phase
            center infomation in `phase_center_catalog` (for the entry matching
            `cat_name`). Setting this to `True` will ignore the values supplied to the
            `ra`, `dec`, `epoch`, `phase_frame`, `pm_ra`, `pm_dec`, `dist`, `vrad`.
        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.
        select_mask : ndarray of bool
            Optional mask for selecting which data to operate on along the blt-axis,
            only used if with multi phase center data sets (i.e.,
            `multi_phase_center=True`). Shape is (Nblts,).
        use_old_proj : bool
            If True, use the "old" method for calculating baseline uvw-coordinates,
            which involved using astropy to move antenna positions (in ITRF) into
            the requested reference frame (either GCRS or ICRS). Default is False.
        fix_old_proj : bool
            If True, the method will convert a data set with coordinates calculated
            using the "old" method, correct them, and then produce new coordinates
            using the "new" method.

        Raises
        ------
        ValueError
            If the phase_type is 'phased' and allow_rephase is False
        """
        # Non-multi phase center datasets don't (yet) have a way of recording the
        # 'extra' source properties, or selection mask, so make sure that these aren't
        # using any of those if looking at a single object.
        if not self.multi_phase_center:
            if select_mask is not None:
                raise ValueError(
                    "Cannot apply a selection mask if multi_phase_center=False. "
                    "Remove the select_mask argument to continue."
                )

            check_params = [pm_ra, pm_dec, dist, vrad]
            check_names = ["pm_ra", "pm_dec", "dist", "vrad"]
            for name, value in zip(check_names, check_params):
                if value not in [0, None]:
                    raise ValueError(
                        "Non-zero values of %s not supported when "
                        "multi_phase_center=False." % name
                    )

            if (cat_type != "sidereal") and (cat_type is not None):
                raise ValueError(
                    "Only sidereal sources are supported when multi_phase_center=False"
                )
            if lookup_name:
                raise ValueError(
                    "Object name lookup is not supported when multi_phase_center=False"
                )
        else:
            if cat_name is None:
                raise ValueError(
                    "Must supply a unique name for cat_name when phasing a "
                    "multi phase center data set."
                )

        # If you are a multi phase center data set, there's no valid reason to be going
        # back to the old phase method. Time to bail!
        if self.multi_phase_center and use_old_proj:
            raise NotImplementedError(
                "Multi phase center data sets are not compatible with the old phasing "
                "method, please set use_old_proj=False."
            )

        if not allow_rephase and (self.phase_type == "phased"):
            raise ValueError(
                "The data is already phased; set allow_rephase"
                " to True to unphase and rephase."
            )

        # Right up front, we're gonna split off the piece of the code that
        # does the phasing using the "new" method, since its a lot more flexible
        # and because I think at some point, everything outside of this loop
        # can be deprecated
        if not use_old_proj:
            needs_fix = (
                (not use_ant_pos)
                and (self.phase_type == "phased")
                and (
                    self.phase_center_app_ra is None
                    or self.phase_center_app_dec is None
                )
            )
            if needs_fix:
                if fix_old_proj:
                    # So to fix the 'old' projection, we use the unphase_to_drift
                    # method with the 'old' projection to bring the data set back
                    # to ENU, and then we can move from there. Of course, none of
                    # this is actually neccessary if calculating the coordinates
                    # from antenna positions, so you do you, puvudataset.
                    self.unphase_to_drift(
                        phase_frame=orig_phase_frame,
                        use_old_proj=True,
                        use_ant_pos=use_ant_pos,
                    )
                else:
                    raise AttributeError(
                        "Data missing phase_center_ra_app or phase_center_dec_app, "
                        "which implies that the data were phased using the 'old' "
                        "method for phasing (which is not compatible with the new "
                        "version of the code). You can fix this by calling the "
                        "phase method with fix_old_proj=True, or can otherwise "
                        "proceed by using the 'old' projection method by setting "
                        "use_old_proj=True."
                    )

            # Grab all the meta-data we need for the rotations
            time_array = self.time_array
            lst_array = self.lst_array
            uvw_array = self.uvw_array
            ant_1_array = self.ant_1_array
            ant_2_array = self.ant_2_array
            old_w_vals = self.uvw_array[:, 2].copy()
            old_w_vals[self._check_for_unphased()] = 0.0
            old_app_ra = self.phase_center_app_ra
            old_app_dec = self.phase_center_app_dec
            old_frame_pa = self.phase_center_frame_pa
            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.

            if select_mask is not None:
                if len(select_mask) != self.Nblts:
                    raise IndexError("Selection mask must be of length Nblts.")
                time_array = time_array[select_mask]
                lst_array = lst_array[select_mask]
                uvw_array = uvw_array[select_mask, :]
                ant_1_array = ant_1_array[select_mask]
                ant_2_array = ant_2_array[select_mask]
                if isinstance(old_w_vals, np.ndarray):
                    old_w_vals = old_w_vals[select_mask]

            # Before moving forward with the heavy calculations, we need to do some
            # basic housekeeping to make sure that we've got the coordinate data that
            # we need in order to proceed.
            phase_dict = self._phase_dict_helper(
                ra,
                dec,
                epoch,
                phase_frame,
                ephem_times,
                cat_type,
                pm_ra,
                pm_dec,
                dist,
                vrad,
                cat_name,
                lookup_name,
                select_mask,
                time_array,
            )

            # We got the meta-data, now handle calculating the apparent coordinates.
            # First, check if we need to look up the phase center in question
            new_app_ra, new_app_dec = uvutils.calc_app_coords(
                phase_dict["cat_lon"],
                phase_dict["cat_lat"],
                coord_frame=phase_dict["cat_frame"],
                coord_epoch=phase_dict["cat_epoch"],
                coord_times=phase_dict["cat_times"],
                coord_type=phase_dict["cat_type"],
                time_array=time_array,
                lst_array=lst_array,
                pm_ra=phase_dict["cat_pm_ra"],
                pm_dec=phase_dict["cat_pm_dec"],
                vrad=phase_dict["cat_vrad"],
                dist=phase_dict["cat_dist"],
                telescope_loc=self.telescope_location_lat_lon_alt,
            )

            # Now calculate position angles. If this is a single phase center data set,
            # the ref frame is always equal to the source coordinate frame. In a multi
            # phase center data set, those two components are allowed to be decoupled.
            new_frame_pa = uvutils.calc_frame_pos_angle(
                time_array,
                new_app_ra,
                new_app_dec,
                self.telescope_location_lat_lon_alt,
                self.phase_center_frame if self.multi_phase_center else phase_frame,
                ref_epoch=self.phase_center_epoch if self.multi_phase_center else epoch,
            )

            # Now its time to do some rotations and calculate the new coordinates
            new_uvw = uvutils.calc_uvw(
                app_ra=new_app_ra,
                app_dec=new_app_dec,
                frame_pa=new_frame_pa,
                lst_array=lst_array,
                use_ant_pos=use_ant_pos,
                uvw_array=uvw_array,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=ant_1_array,
                ant_2_array=ant_2_array,
                old_app_ra=old_app_ra,
                old_app_dec=old_app_dec,
                old_frame_pa=old_frame_pa,
                telescope_lat=self.telescope_location_lat_lon_alt[0],
                telescope_lon=self.telescope_location_lat_lon_alt[1],
                from_enu=(self.phase_type == "drift"),
            )

            # With all operations complete, we now start manipulating the UVData object
            if self.multi_phase_center:
                cat_id = self._add_phase_center(
                    phase_dict["cat_name"],
                    phase_dict["cat_type"],
                    cat_lon=phase_dict["cat_lon"],
                    cat_lat=phase_dict["cat_lat"],
                    cat_frame=phase_dict["cat_frame"],
                    cat_epoch=phase_dict["cat_epoch"],
                    cat_times=phase_dict["cat_times"],
                    cat_pm_ra=phase_dict["cat_pm_ra"],
                    cat_pm_dec=phase_dict["cat_pm_dec"],
                    cat_dist=phase_dict["cat_dist"],
                    cat_vrad=phase_dict["cat_vrad"],
                    info_source=phase_dict["info_source"],
                    cat_id=phase_dict["cat_id"],
                    force_update=True,
                )

            # Now its time to update the raw data. This will return empty if
            # metadata_only is set to True. Note that cat_type is only allowed
            # to be unphased if this is a multi_phase_center data set.
            new_w_vals = 0.0 if (cat_type == "unphased") else new_uvw[:, 2]
            self._apply_w_proj(new_w_vals, old_w_vals, select_mask=select_mask)

            # Finally, we now take it upon ourselves to update some metadata. What we
            # do here will depend a little bit on whether or not we have a selection
            # mask active, since most everything is affected by that.
            if select_mask is not None:
                self.uvw_array[select_mask] = new_uvw
                self.phase_center_app_ra[select_mask] = new_app_ra
                self.phase_center_app_dec[select_mask] = new_app_dec
                self.phase_center_frame_pa[select_mask] = new_frame_pa
                if self.multi_phase_center:
                    self.phase_center_id_array[select_mask] = cat_id
            else:
                self.uvw_array = new_uvw
                self.phase_center_app_ra = new_app_ra
                self.phase_center_app_dec = new_app_dec
                self.phase_center_frame_pa = new_frame_pa
                if self.multi_phase_center:
                    self.phase_center_id_array[:] = cat_id

            # If not multi phase center, make sure to update the ra/dec values, since
            # otherwise we'll have no record of source properties.
            if not self.multi_phase_center:
                # Make sure this is actually marked as a phased dataset now
                self._set_phased()

                # Update the phase center properties
                self.phase_center_ra = phase_dict["cat_lon"]
                self.phase_center_dec = phase_dict["cat_lat"]
                self.phase_center_epoch = phase_dict["cat_epoch"]
                self.phase_center_frame = phase_dict["cat_frame"]
                if cat_name is not None:
                    self.object_name = cat_name
            else:
                self.phase_center_ra = 0.0
                self.phase_center_dec = 0.0
                self.phase_center_epoch = 2000.0
                if cleanup_old_sources:
                    self._clear_unused_phase_centers()
            # All done w/ the new phase method
            return
        warnings.warn(
            "The original `phase` method is deprecated, and will be removed in "
            "pyuvdata v3.0 (although `fix_phase` will remain for longer). "
            "Note that the old and new phase methods are NOT compatible with one "
            "another, so if you have phased using the old method, you should call "
            "the phase method with fix_old_proj=True, or otherwise can use the "
            "unphase_to_drift method with use_old_proj=True to undo the old "
            "corrections before using the new version of the phase method.",
            DeprecationWarning,
        )
        if self.phase_type == "drift":
            pass
        elif self.phase_type == "phased":
            # To get to this point, allow_rephase has to be true
            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,
                    use_old_proj=True,
                )
        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
        self.phase_center_app_ra = None
        self.phase_center_app_dec = None
        self.phase_center_frame_pa = None

        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)

        telescope_location = EarthLocation.from_geocentric(
            *self.telescope_location, unit=units.m
        )
        obs_times = Time(unique_times, format="jd")

        itrs_telescope_locations = telescope_location.get_itrs(obstime=obs_times)
        itrs_telescope_locations = SkyCoord(itrs_telescope_locations)
        # just calling transform_to(coord.GCRS) will delete the obstime information
        # need to re-add obstimes for a GCRS transformation
        if phase_frame == "gcrs":
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())(obstime=obs_times)
            )
        else:
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())
            )
        # set the representation_type to cartensian to get xyz later
        frame_telescope_locations.representation_type = "cartesian"

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

            obs_time = obs_times[ind]

            itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

            frame_telescope_location = frame_telescope_locations[ind]

            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
                )
                # instead of looping through every ind, find the spot in antenna number
                # array where ant_num <= ant1 < ant_number and similarly for ant2
                # for all baselines in inds
                # then find the uvw coordinate for all at the same time

                # antenna_numbers does not necessarily need to be in order on the object
                # but needs to be in order for the searchsorted to work.
                # ant1_index and ant2_index arrays will preserve the order of blts
                ant_sort = np.argsort(self.antenna_numbers)
                ant1_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_1_array[inds]
                )
                ant2_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_2_array[inds]
                )
                self.uvw_array[inds] = (
                    frame_ant_uvw[ant_sort][ant2_index, :]
                    - frame_ant_uvw[ant_sort][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)
            )
            if self.future_array_shapes:
                phs = np.exp(-1j * 2 * np.pi * w_lambda[:, :, None])
            else:
                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=True,
        use_old_proj=False,
        allow_rephase=True,
        orig_phase_frame=None,
        select_mask=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.
        select_mask : array_like
            Selection mask for which data should be rephased, only applicable if
            `multi_phase_center=True`. Any array-like able to be used as an index
            is suitable -- the most typical is an array of bool with length `Nblts`,
            or an array of ints within the range (-Nblts, Nblts).

        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.floating)):
            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.rad
        zenith_dec = obs_zenith_coord.dec.rad

        self.phase(
            zenith_ra,
            zenith_dec,
            epoch="J2000",
            phase_frame=phase_frame,
            use_ant_pos=use_ant_pos,
            use_old_proj=use_old_proj,
            allow_rephase=allow_rephase,
            orig_phase_frame=orig_phase_frame,
            select_mask=select_mask,
            cat_name=("zenith_at_jd%f" % self.time_array[0])
            if self.multi_phase_center
            else None,
        )

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

        Parameters
        ----------
        allow_phasing : bool
            Option for phased data. If data is phased and allow_phasing=True,
            UVWs will be calculated and the visibilities will be rephased. Default
            is False.
        require_phasing : bool
            Option for phased data. If data is phased and require_phasing=True, then
            the method will throw an error unless allow_phasing=True, otherwise if
            `require_phasing=False` and `allow_phasing=False`, the UVWs will be
            recalculated but the data will NOT be rephased. This feature should only be
            used in limited circumstances (e.g., when certain metadata like exact time
            are not trusted), as misuse can significantly corrupt data.
        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 and use_old_proj
            is True.
        output_phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'. Only used if
            allow_phasing is True, and use_old_proj is True.
        use_old_proj : bool
            If set to True, uses the 'old' method of calculating baseline vectors.
            Default is False, which will instead use the 'new' method.

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

        Warns
        -----
        UserWarning
            If the phase_type is 'phased'

        """
        if not use_old_proj and not (
            self.phase_center_app_ra is None or self.phase_center_app_dec is None
        ):
            if (self.phase_type == "phased") and (
                not (allow_phasing) and require_phasing
            ):
                raise ValueError(
                    "UVW recalculation requires either unphased data or the ability "
                    "to rephase data. Use unphase_to_drift or set allow_phasing=True."
                )

            telescope_location = self.telescope_location_lat_lon_alt
            new_uvw = uvutils.calc_uvw(
                app_ra=self.phase_center_app_ra,
                app_dec=self.phase_center_app_dec,
                frame_pa=self.phase_center_frame_pa,
                lst_array=self.lst_array,
                use_ant_pos=True,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=self.ant_1_array,
                ant_2_array=self.ant_2_array,
                telescope_lat=telescope_location[0],
                telescope_lon=telescope_location[1],
                from_enu=(self.phase_type != "phased"),
                to_enu=(self.phase_type != "phased"),
            )
            if self.phase_type == "phased":
                if allow_phasing:
                    old_w_vals = self.uvw_array[:, 2].copy()
                    old_w_vals[self._check_for_unphased()] = 0.0
                    self._apply_w_proj(new_uvw[:, 2], old_w_vals)
                else:
                    warnings.warn(
                        "Recalculating uvw_array without adjusting visibility phases "
                        "-- this can introduce significant errors if used incorrectly."
                    )
            # If the data are phased, we've already adjusted the phases. Now we just
            # need to update the uvw's and we are home free.
            self.uvw_array = new_uvw
            return

        # mutli-phase-ctr datasets should never use the 'old' uvw calculation method
        if self.multi_phase_center:
            raise NotImplementedError(
                "Multi phase center data sets are not compatible with the old uvw "
                "calculation  method, please set use_old_proj=False."
            )

        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, use_old_proj=True,
                )
            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)
        # this code used to loop through every bl in the unique,
        # find the index into self.antenna_array of ant1 and ant2
        # and fill out the self.uvw_array for all matching bls.

        # instead, find the indices and reverse inds from the unique,
        # create the unique ant1 and ant2 arrays
        # use searchsorted to find the index of the antenna numbers into ant1 and ant2
        # create the unique uvw array then broadcast to self.uvw_array
        bls, unique_inds, reverse_inds = np.unique(
            self.baseline_array, return_index=True, return_inverse=True
        )

        # antenna_numbers does not necessarily need to be in order on the object
        # but needs to be in order for the searchsorted to work.
        # ant1_index and ant2_index arrays will preserve the order of blts
        ant_sort = np.argsort(self.antenna_numbers)
        ant1_index = np.searchsorted(
            self.antenna_numbers[ant_sort], self.ant_1_array[unique_inds],
        )
        ant2_index = np.searchsorted(
            self.antenna_numbers[ant_sort], self.ant_2_array[unique_inds],
        )
        _uvw_array = np.zeros((bls.size, 3))
        _uvw_array = (
            antenna_locs_ENU[ant_sort][ant2_index, :]
            - antenna_locs_ENU[ant_sort][ant1_index, :]
        )
        self.uvw_array = _uvw_array[reverse_inds]
        if phase_type == "phased":
            self.phase(
                phase_center_ra,
                phase_center_dec,
                phase_center_epoch,
                phase_frame=output_phase_frame,
                use_old_proj=use_old_proj,
            )

    def fix_phase(
        self, use_ant_pos=True,
    ):
        """
        Fix the data to be consistent with the new phasing method.

        This is a simple utility function for updating UVW coordinates calculated using
        the 'old' phasing algorithm with those calculated by the 'new' algorithm. Note
        that this step is required for using the new methods with data phased using the
        `phase` methiod prior to pyuvdata v2.2.

        Parameters
        ----------
        use_ant_pos : bool
            Use the antenna positions for determining UVW coordinates. Default is True.
        """
        # If we are missing apparent coordinates, we should calculate those now
        if (self.phase_center_app_ra is None) or (self.phase_center_app_dec is None):
            self._set_app_coords_helper()

        # If we are just using the antenna positions, we don't actually need to do
        # anything, since the new baseline vectors will be unaffected by the prior
        # phasing method, and the delta_w values already get correctly corrected for.
        if use_ant_pos:
            self.set_uvws_from_antenna_positions(
                allow_phasing=True, use_old_proj=False,
            )
        elif self.multi_phase_center:
            raise ValueError(
                "Cannot run fix_phase on a mutli-phase-ctr dataset without using the "
                "antenna positions. Please set use_ant_pos=True."
            )
        else:
            # Record the old values
            phase_center_ra = self.phase_center_ra
            phase_center_dec = self.phase_center_dec
            phase_center_frame = self.phase_center_frame
            phase_center_epoch = self.phase_center_epoch
            cat_name = self.object_name

            # Bring the UVWs back to ENU/unphased
            self.unphase_to_drift(
                phase_frame=self.phase_center_frame,
                use_ant_pos=False,
                use_old_proj=True,
            )

            # Check for any autos, since their uvws get potentially corrupted
            # by the above operation
            auto_mask = self.ant_1_array == self.ant_2_array
            if any(auto_mask):
                self.uvw_array[auto_mask, :] = 0.0

            # And rephase the data using the new algorithm
            self.phase(
                phase_center_ra,
                phase_center_dec,
                phase_frame=phase_center_frame,
                epoch=phase_center_epoch,
                cat_name=cat_name,
                use_ant_pos=False,
            )

    def __add__(
        self,
        other,
        inplace=False,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        orig_phase_frame=None,
        use_ant_pos=True,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        make_multi_phase=False,
        ignore_name=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.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            two objects are different, the combined object will keep all the history of
            both input objects (if many objects are combined in succession this can
            lead to very long histories). If False and if the histories for the two
            objects are different, the combined object will have the history of the
            first object and only the parts of the second object history that are unique
            (this is done word by word and can result in hard to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. Setting this to true will allow for two
            UVData objects to be combined, even if the phase center properties do not
            agree (so long as the names are unique for each phase center). Default is
            False.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.


        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 is 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 = self.copy()

        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )

        # Check to make sure that both objects are consistent w/ use of flex_spw
        if this.flex_spw != other.flex_spw:
            raise ValueError(
                "To combine these data, flex_spw must be set to the same "
                "value (True or False) for both objects."
            )

        # check that both objects have the same array shapes
        if this.future_array_shapes != other.future_array_shapes:
            raise ValueError(
                "Both objects must have the same `future_array_shapes` parameter. "
                "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                "methods to convert them."
            )

        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",
            "_telescope_name",
            "_instrument",
            "_telescope_location",
            "_phase_type",
            "_Nants_telescope",
            "_antenna_names",
            "_antenna_numbers",
            "_antenna_positions",
            "_phase_center_frame",
            "_phase_center_epoch",
        ]
        if not this.future_array_shapes and not this.flex_spw:
            compatibility_params.append("_channel_width")

        multi_obj_check = False
        if this.multi_phase_center == other.multi_phase_center:
            # If the names are different and we are making a mutli-phase-ctr data set,
            # then we can skip the step of checking the ra and dec, otherwise we need to
            # check it
            multi_obj_check = make_multi_phase or this.multi_phase_center
            if not ((this.object_name != other.object_name) and multi_obj_check):
                compatibility_params.append("_phase_center_ra")
                compatibility_params.append("_phase_center_dec")

            # Also, if we are not supposed to ignore the name, then make sure that its
            # one of the parameters we check for compatibility.
            if not (ignore_name or multi_obj_check):
                compatibility_params.append("_object_name")
        elif not (this.multi_phase_center or make_multi_phase):
            raise ValueError(
                "To combine these data, please run the add operation with the UVData "
                "object with multi_phase_center set to True as the first object in the "
                "add operation."
            )

        # 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
        )

        # If we have a flexible spectral window, the handling here becomes a bit funky,
        # because we are allowed to have channels with the same frequency *if* they
        # belong to different spectral windows (one real-life example: you might want
        # to preserve guard bands in the correlator, which can have overlaping RF
        # frequency channels)
        if this.flex_spw:
            this_freq_ind = np.array([], dtype=np.int64)
            other_freq_ind = np.array([], dtype=np.int64)
            both_freq = np.array([], dtype=float)
            both_spw = np.intersect1d(this.spw_array, other.spw_array)
            for idx in both_spw:
                this_mask = np.where(this.flex_spw_id_array == idx)[0]
                other_mask = np.where(other.flex_spw_id_array == idx)[0]
                if this.future_array_shapes:
                    both_spw_freq, this_spw_ind, other_spw_ind = np.intersect1d(
                        this.freq_array[this_mask],
                        other.freq_array[other_mask],
                        return_indices=True,
                    )
                else:
                    both_spw_freq, this_spw_ind, other_spw_ind = np.intersect1d(
                        this.freq_array[0, this_mask],
                        other.freq_array[0, other_mask],
                        return_indices=True,
                    )
                this_freq_ind = np.append(this_freq_ind, this_mask[this_spw_ind])
                other_freq_ind = np.append(other_freq_ind, other_mask[other_spw_ind])
                both_freq = np.append(both_freq, both_spw_freq)
        else:
            if this.future_array_shapes:
                both_freq, this_freq_ind, other_freq_ind = np.intersect1d(
                    this.freq_array, other.freq_array, return_indices=True
                )
            else:
                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
            if this.future_array_shapes:
                this_inds = np.ravel_multi_index(
                    (
                        this_blts_ind[:, np.newaxis, np.newaxis],
                        this_freq_ind[np.newaxis, :, np.newaxis],
                        this_pol_ind[np.newaxis, np.newaxis, :],
                    ),
                    this.data_array.shape,
                ).flatten()
                other_inds = np.ravel_multi_index(
                    (
                        other_blts_ind[:, np.newaxis, np.newaxis],
                        other_freq_ind[np.newaxis, :, np.newaxis],
                        other_pol_ind[np.newaxis, np.newaxis, :],
                    ),
                    other.data_array.shape,
                ).flatten()
            else:
                this_inds = np.ravel_multi_index(
                    (
                        this_blts_ind[:, np.newaxis, np.newaxis, np.newaxis],
                        np.zeros((1, 1, 1, 1), dtype=np.int64),
                        this_freq_ind[np.newaxis, np.newaxis, :, np.newaxis],
                        this_pol_ind[np.newaxis, np.newaxis, np.newaxis, :],
                    ),
                    this.data_array.shape,
                ).flatten()
                other_inds = np.ravel_multi_index(
                    (
                        other_blts_ind[:, np.newaxis, np.newaxis, np.newaxis],
                        np.zeros((1, 1, 1, 1), dtype=np.int64),
                        other_freq_ind[np.newaxis, np.newaxis, :, np.newaxis],
                        other_pol_ind[np.newaxis, np.newaxis, np.newaxis, :],
                    ),
                    other.data_array.shape,
                ).flatten()
            this_all_zero = np.all(this.data_array.flatten()[this_inds] == 0)
            this_all_flag = np.all(this.flag_array.flatten()[this_inds])
            other_all_zero = np.all(other.data_array.flatten()[other_inds] == 0)
            other_all_flag = np.all(other.flag_array.flatten()[other_inds])

            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 = ([], [])

        # if there's any overlap in blts, check extra params
        temp = np.nonzero(np.in1d(other_blts, this_blts))[0]
        if len(temp) > 0:
            # add metadata to be checked to compatibility params
            extra_params = [
                "_integration_time",
                "_uvw_array",
                "_lst_array",
                "_phase_center_app_ra",
                "_phase_center_app_dec",
                "_phase_center_frame_pa",
                "_phase_center_id_array",
                "_phase_center_catalog",
                "_Nphase",
            ]
            compatibility_params.extend(extra_params)
            if not ignore_name and ("_object_name" not in compatibility_params):
                compatibility_params.append("_object_name")

        # TODO: Add handling for what happens when you have two different source
        # catalogs that you want to combine

        # find the freq indices in "other" but not in "this"
        if self.flex_spw:
            other_mask = np.ones_like(other.flex_spw_id_array, dtype=bool)
            for idx in np.intersect1d(this.spw_array, other.spw_array):
                if this.future_array_shapes:
                    other_mask[other.flex_spw_id_array == idx] = np.isin(
                        other.freq_array[other.flex_spw_id_array == idx],
                        this.freq_array[this.flex_spw_id_array == idx],
                        invert=True,
                    )
                else:
                    other_mask[other.flex_spw_id_array == idx] = np.isin(
                        other.freq_array[0, other.flex_spw_id_array == idx],
                        this.freq_array[0, this.flex_spw_id_array == idx],
                        invert=True,
                    )
            temp = np.where(other_mask)[0]
        else:
            if this.future_array_shapes:
                temp = np.nonzero(~np.in1d(other.freq_array, this.freq_array))[0]
            else:
                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 = []

        # if channel width is an array and there's any overlap in freqs,
        # check extra params
        if this.future_array_shapes or this.flex_spw:
            if this.future_array_shapes:
                temp = np.nonzero(np.in1d(other.freq_array, this.freq_array))[0]
            else:
                temp = np.nonzero(
                    np.in1d(other.freq_array[0, :], this.freq_array[0, :])
                )[0]
            if len(temp) > 0:
                # add metadata to be checked to compatibility params
                extra_params = ["_channel_width"]
                compatibility_params.extend(extra_params)

        # 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 cp in compatibility_params:
            if cp == "_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 cp == "_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 cp == "_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],
                )
            elif cp == "_channel_width" and this.future_array_shapes or this.flex_spw:
                # only check that overlapping freq indices match
                params_match = np.allclose(
                    this.channel_width[this_freq_ind],
                    other.channel_width[other_freq_ind],
                    rtol=this._channel_width.tols[0],
                    atol=this._channel_width.tols[1],
                )
            elif (cp == "_phase_center_app_ra") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_app_ra[this_blts_ind],
                    other.phase_center_app_ra[other_blts_ind],
                    rtol=this._phase_center_app_ra.tols[0],
                    atol=this._phase_center_app_ra.tols[1],
                )
            elif (cp == "_phase_center_app_dec") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_app_dec[this_blts_ind],
                    other.phase_center_app_dec[other_blts_ind],
                    rtol=this._phase_center_app_dec.tols[0],
                    atol=this._phase_center_app_dec.tols[1],
                )
            elif (cp == "_phase_center_frame_pa") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_frame_pa[this_blts_ind],
                    other.phase_center_frame_pa[other_blts_ind],
                    rtol=this._phase_center_frame_pa.tols[0],
                    atol=this._phase_center_frame_pa.tols[1],
                )
            else:
                params_match = getattr(this, cp) == getattr(other, cp)
            if not params_match:
                msg = (
                    "UVParameter " + cp[1:] + " does not match. Cannot combine objects."
                )
                if cp[1:] == "object_name":
                    msg += (
                        " This can potentially be remedied by setting "
                        "ignore_name=True, or by allowing the creation of a "
                        "mutli-phase-ctr dataset (by setting make_multi_phase=True)."
                    )
                raise ValueError(msg)

        # At this point, we are assuming that the two data sets _mostly_ compatible.
        # Last thing we need to check is if these are mutli-phase-ctr data sets, whether
        # or not they are compatible.
        if this.multi_phase_center or make_multi_phase:
            if other.multi_phase_center:
                other_names = list(other.phase_center_catalog.keys())
                other_cat = other.phase_center_catalog
            else:
                other_names = [other.object_name]
                other_cat = {
                    other_names[0]: {
                        "cat_type": "sidereal",
                        "cat_lon": other.phase_center_ra,
                        "cat_lat": other.phase_center_dec,
                        "cat_frame": other.phase_center_frame,
                        "cat_epoch": other.phase_center_epoch,
                    },
                }

            for name in other_names:
                cat_id, cat_diffs = this._look_in_catalog(
                    name, phase_dict=other_cat[name]
                )
                if (cat_id is not None) and (cat_diffs != 0):
                    # We have a name conflict, raise an error now
                    raise ValueError(
                        "There exists a target named %s in both objects in the "
                        "sum, but their properties are different. Use the rename_"
                        "phase_center method in order to rename it in one object."
                        % name
                    )

        # Begin manipulating the objects.
        if make_multi_phase and (not this.multi_phase_center):
            this._set_multi_phase_center(preserve_phase_center_info=True)
        if other.multi_phase_center:
            # This to get adding stuff to the catalog
            reserved_ids = [
                other.phase_center_catalog[name]["cat_id"]
                for name in other.phase_center_catalog.keys()
            ]
            # First loop, we want to look at the sources that are in this, but not
            # other, since we need to choose catalog IDs that won't collide with the
            # catalog that exists.
            for name in this.phase_center_catalog.keys():
                if name not in other.phase_center_catalog.keys():
                    this._update_phase_center_id(name, reserved_ids=reserved_ids)
            # Next loop, we want to update the IDs of sources that are in both
            for name in this.phase_center_catalog.keys():
                if name in other.phase_center_catalog.keys():
                    this._update_phase_center_id(
                        name, new_cat_id=other.phase_center_catalog[name]["cat_id"],
                    )
            # Finally, add those other objects not found in this
            for name in other.phase_center_catalog.keys():
                if name not in this.phase_center_catalog.keys():
                    this._add_phase_center(
                        name,
                        cat_type=other.phase_center_catalog[name]["cat_type"],
                        cat_lon=other.phase_center_catalog[name]["cat_lon"],
                        cat_lat=other.phase_center_catalog[name]["cat_lat"],
                        cat_frame=other.phase_center_catalog[name]["cat_frame"],
                        cat_epoch=other.phase_center_catalog[name]["cat_epoch"],
                        cat_times=other.phase_center_catalog[name]["cat_times"],
                        cat_pm_ra=other.phase_center_catalog[name]["cat_pm_ra"],
                        cat_pm_dec=other.phase_center_catalog[name]["cat_pm_dec"],
                        cat_dist=other.phase_center_catalog[name]["cat_dist"],
                        cat_vrad=other.phase_center_catalog[name]["cat_vrad"],
                        info_source=other.phase_center_catalog[name]["info_source"],
                        cat_id=other.phase_center_catalog[name]["cat_id"],
                    )
        elif this.multi_phase_center:
            # If other is not multi phase center, then we'll go ahead and add the object
            # information here.
            other_cat_id = this._add_phase_center(
                other.object_name,
                cat_type="sidereal",
                cat_lon=other.phase_center_ra,
                cat_lat=other.phase_center_dec,
                cat_frame=other.phase_center_frame,
                cat_epoch=other.phase_center_epoch,
            )

        # 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:
                if this.future_array_shapes:
                    zero_pad = np.zeros((len(bnew_inds), this.Nfreqs, this.Npols))
                else:
                    zero_pad = np.zeros((len(bnew_inds), 1, 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 this.phase_type == "phased":
                this.phase_center_app_ra = np.concatenate(
                    [this.phase_center_app_ra, other.phase_center_app_ra[bnew_inds]]
                )[blt_order]
                this.phase_center_app_dec = np.concatenate(
                    [this.phase_center_app_dec, other.phase_center_app_dec[bnew_inds]]
                )[blt_order]
                this.phase_center_frame_pa = np.concatenate(
                    [this.phase_center_frame_pa, other.phase_center_frame_pa[bnew_inds]]
                )[blt_order]
            if this.multi_phase_center:
                if other.multi_phase_center:
                    this.phase_center_id_array = np.concatenate(
                        [
                            this.phase_center_id_array,
                            other.phase_center_id_array[bnew_inds],
                        ]
                    )[blt_order]
                else:
                    this.phase_center_id_array = np.concatenate(
                        [this.phase_center_id_array, [other_cat_id] * len(bnew_inds)]
                    )[blt_order]

        if len(fnew_inds) > 0:
            if this.future_array_shapes:
                this.freq_array = np.concatenate(
                    [this.freq_array, other.freq_array[fnew_inds]]
                )
            else:
                this.freq_array = np.concatenate(
                    [this.freq_array, other.freq_array[:, fnew_inds]], axis=1
                )

            if this.flex_spw or this.future_array_shapes:
                this.channel_width = np.concatenate(
                    [this.channel_width, other.channel_width[fnew_inds]]
                )

            if this.flex_spw:
                this.flex_spw_id_array = np.concatenate(
                    [this.flex_spw_id_array, other.flex_spw_id_array[fnew_inds]]
                )
                this.spw_array = np.concatenate([this.spw_array, other.spw_array])
                # We want to preserve per-spw information based on first appearance
                # in the concatenated array.
                unique_index = np.sort(
                    np.unique(this.flex_spw_id_array, return_index=True)[1]
                )
                this.spw_array = this.flex_spw_id_array[unique_index]
                this.Nspws = len(this.spw_array)

            # If we have a flex/multi-spw data set, need to sort out the order of the
            # individual windows first.
            if this.flex_spw:
                f_order = np.concatenate(
                    [
                        np.where(this.flex_spw_id_array == idx)[0]
                        for idx in sorted(this.spw_array)
                    ]
                )

                # With spectral windows sorted, check and see if channels within
                # windows need sorting. If they are ordered in ascending or descending
                # fashion, leave them be. If not, sort in ascending order
                for idx in this.spw_array:
                    select_mask = this.flex_spw_id_array[f_order] == idx
                    check_freqs = (
                        this.freq_array[f_order[select_mask]]
                        if this.future_array_shapes
                        else this.freq_array[0, f_order[select_mask]]
                    )
                    if (not np.all(check_freqs[1:] > check_freqs[:-1])) and (
                        not np.all(check_freqs[1:] < check_freqs[:-1])
                    ):
                        subsort_order = f_order[select_mask]
                        f_order[select_mask] = subsort_order[np.argsort(check_freqs)]
            else:
                if this.future_array_shapes:
                    f_order = np.argsort(this.freq_array)
                else:
                    f_order = np.argsort(this.freq_array[0, :])

            if not self.metadata_only:
                if this.future_array_shapes:
                    zero_pad = np.zeros(
                        (this.data_array.shape[0], len(fnew_inds), this.Npols)
                    )
                    this.data_array = np.concatenate(
                        [this.data_array, zero_pad], axis=1
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array, zero_pad], axis=1
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array, 1 - zero_pad], axis=1
                    ).astype(np.bool_)
                else:
                    zero_pad = np.zeros(
                        (this.data_array.shape[0], 1, 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:
                if this.future_array_shapes:
                    zero_pad = np.zeros(
                        (
                            this.data_array.shape[0],
                            this.data_array.shape[1],
                            len(pnew_inds),
                        )
                    )
                    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_)
                else:
                    zero_pad = np.zeros(
                        (
                            this.data_array.shape[0],
                            1,
                            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]
        if this.future_array_shapes:
            freq_t2o = np.nonzero(np.in1d(this.freq_array, other.freq_array))[0]
        else:
            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:
            if this.future_array_shapes:
                this.data_array[np.ix_(blt_t2o, freq_t2o, pol_t2o)] = other.data_array
                this.nsample_array[
                    np.ix_(blt_t2o, freq_t2o, pol_t2o)
                ] = other.nsample_array
                this.flag_array[np.ix_(blt_t2o, freq_t2o, pol_t2o)] = other.flag_array
            else:
                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 this.future_array_shapes:
                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])
            else:
                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:
            if this.future_array_shapes:
                this.freq_array = this.freq_array[f_order]
            else:
                this.freq_array = this.freq_array[:, f_order]
            if this.flex_spw or this.future_array_shapes:
                this.channel_width = this.channel_width[f_order]
            if this.flex_spw:
                this.flex_spw_id_array = this.flex_spw_id_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.size
        this.Npols = this.polarization_array.shape[0]
        this.Nants_data = this._calc_nants_data()

        # Update filename parameter
        this.filename = uvutils._combine_filenames(this.filename, other.filename)
        if this.filename is not None:
            this._filename.form = (len(this.filename),)

        # Check specific requirements
        if this.Nfreqs > 1:
            spacing_error, chanwidth_error = this._check_freq_spacing(
                raise_errors=False
            )

            if spacing_error:
                warnings.warn(
                    "Combined frequencies are not evenly spaced or have differing "
                    "values of channel widths. This will make it impossible to write "
                    "this data out to some file types."
                )
            elif chanwidth_error:
                warnings.warn(
                    "Combined frequencies are separated by more than their "
                    "channel width. This will make it impossible to write this data "
                    "out to some file types."
                )

        if n_axes > 0:
            history_update_string += " axis using pyuvdata."

            histories_match = uvutils._check_histories(this.history, other.history)

            this.history += history_update_string
            if not histories_match:
                if verbose_history:
                    this.history += " Next object history follows. " + other.history
                else:
                    extra_history = uvutils._combine_history_addition(
                        this.history, other.history
                    )
                    if extra_history is not None:
                        this.history += (
                            " Unique part of next object history follows. "
                            + extra_history
                        )

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

        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=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. Setting this to true will allow for two
            UVData objects to be combined, even if the phase center properties do not
            agree (so long as the names are unique for each phase center). Default is
            False.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.


        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 is 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.

        """
        self.__add__(
            other,
            inplace=True,
            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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            make_multi_phase=make_multi_phase,
            ignore_name=ignore_name,
        )
        return self

    def fast_concat(
        self,
        other,
        axis,
        inplace=False,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        orig_phase_frame=None,
        use_ant_pos=True,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        ignore_name=False,
    ):
        """
        Concatenate two UVData objects along specified axis with almost no checking.

        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 or list of UVData objects
            UVData object or list of UVData objects 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'.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            objects are different, the combined object will keep all the history of
            all input objects (if many objects are combined this can lead to very long
            histories). If False and if the histories for the objects are different,
            the combined object will have the history of the first object and only the
            parts of the other object histories that are unique (this is done word by
            word and can result in hard to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.

        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 = self.copy()
        if not isinstance(other, (list, tuple, np.ndarray)):
            # if this is a UVData object already, stick it in a list
            other = [other]
        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        for obj in other:
            if not issubclass(obj.__class__, this.__class__):
                if not issubclass(this.__class__, obj.__class__):
                    raise ValueError(
                        "Only UVData (or subclass) objects can be "
                        "added to a UVData (or subclass) object"
                    )
            obj.check(
                check_extra=check_extra,
                run_check_acceptability=run_check_acceptability,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

        # check that all objects have the same array shapes
        for obj in other:
            if this.future_array_shapes != obj.future_array_shapes:
                raise ValueError(
                    "All objects must have the same `future_array_shapes` parameter. "
                    "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                    "methods to convert them."
                )

        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
                )

            for obj in other:
                if obj.phase_type != "drift":
                    warnings.warn("Unphasing other UVData object to drift")
                    obj.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.
            for obj in other:
                if obj.phase_type == "drift" or (
                    not np.isclose(
                        obj.phase_center_ra,
                        phase_center_radec[0],
                        rtol=obj._phase_center_ra.tols[0],
                        atol=obj._phase_center_ra.tols[1],
                    )
                    or not np.isclose(
                        obj.phase_center_dec,
                        phase_center_radec[1],
                        rtol=obj._phase_center_dec.tols[0],
                        atol=obj._phase_center_dec.tols[1],
                    )
                ):
                    warnings.warn("Phasing other UVData object to phase_center_radec")
                    obj.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",
            "_telescope_name",
            "_instrument",
            "_telescope_location",
            "_phase_type",
            "_Nants_telescope",
            "_antenna_names",
            "_antenna_numbers",
            "_antenna_positions",
            "_phase_center_ra",
            "_phase_center_dec",
            "_phase_center_epoch",
            "_multi_phase_center",
            "_phase_center_catalog",
            "_Nphase",
        ]
        if not this.future_array_shapes and not this.flex_spw:
            compatibility_params.append("_channel_width")

        if not (this.multi_phase_center or ignore_name):
            compatibility_params += ["_object_name"]

        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",
                "_phase_center_id_array",
            ]
        elif axis == "polarization":
            history_update_string += "polarization"
            compatibility_params += [
                "_freq_array",
                "_ant_1_array",
                "_ant_2_array",
                "_integration_time",
                "_uvw_array",
                "_lst_array",
                "_phase_center_id_array",
            ]
        elif axis == "blt":
            history_update_string += "baseline-time"
            compatibility_params += ["_freq_array", "_polarization_array"]

        history_update_string += " axis using pyuvdata."

        histories_match = []
        for obj in other:
            histories_match.append(uvutils._check_histories(this.history, obj.history))

        this.history += history_update_string
        for obj_num, obj in enumerate(other):
            if not histories_match[obj_num]:
                if verbose_history:
                    this.history += " Next object history follows. " + obj.history
                else:
                    extra_history = uvutils._combine_history_addition(
                        this.history, obj.history
                    )
                    if extra_history is not None:
                        this.history += (
                            " Unique part of next object history follows. "
                            + extra_history
                        )

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

        if axis == "freq":
            this.Nfreqs = sum([this.Nfreqs] + [obj.Nfreqs for obj in other])
            if this.future_array_shapes:
                this.freq_array = np.concatenate(
                    [this.freq_array] + [obj.freq_array for obj in other]
                )
            else:
                this.freq_array = np.concatenate(
                    [this.freq_array] + [obj.freq_array for obj in other], axis=1
                )
            if this.flex_spw or this.future_array_shapes:
                this.channel_width = np.concatenate(
                    [this.channel_width] + [obj.channel_width for obj in other]
                )
            if this.flex_spw:
                this.flex_spw_id_array = np.concatenate(
                    [this.flex_spw_id_array] + [obj.flex_spw_id_array for obj in other]
                )
                this.spw_array = np.concatenate(
                    [this.spw_array] + [obj.spw_array for obj in other]
                )
                # We want to preserve per-spw information based on first appearance
                # in the concatenated array.
                unique_index = np.sort(
                    np.unique(this.flex_spw_id_array, return_index=True)[1]
                )
                this.spw_array = this.flex_spw_id_array[unique_index]

                this.Nspws = len(this.spw_array)

            spacing_error, chanwidth_error = this._check_freq_spacing(
                raise_errors=False
            )
            if spacing_error:
                warnings.warn(
                    "Combined frequencies are not evenly spaced or have differing "
                    "values of channel widths. This will make it impossible to write "
                    "this data out to some file types."
                )
            elif chanwidth_error:
                warnings.warn(
                    "Combined frequencies are separated by more than their "
                    "channel width. This will make it impossible to write this data "
                    "out to some file types."
                )

            if not self.metadata_only:
                if this.future_array_shapes:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=1,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=1,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=1,
                    )
                else:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=2,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=2,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=2,
                    )
        elif axis == "polarization":
            this.polarization_array = np.concatenate(
                [this.polarization_array] + [obj.polarization_array for obj in other]
            )
            this.Npols = sum([this.Npols] + [obj.Npols for obj in other])

            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:
                if this.future_array_shapes:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=2,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=2,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=2,
                    )
                else:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=3,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=3,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=3,
                    )
        elif axis == "blt":
            this.Nblts = sum([this.Nblts] + [obj.Nblts for obj in other])
            this.ant_1_array = np.concatenate(
                [this.ant_1_array] + [obj.ant_1_array for obj in other]
            )
            this.ant_2_array = np.concatenate(
                [this.ant_2_array] + [obj.ant_2_array for obj in other]
            )
            this.Nants_data = this._calc_nants_data()
            this.uvw_array = np.concatenate(
                [this.uvw_array] + [obj.uvw_array for obj in other], axis=0
            )
            this.time_array = np.concatenate(
                [this.time_array] + [obj.time_array for obj in other]
            )
            this.Ntimes = len(np.unique(this.time_array))
            this.lst_array = np.concatenate(
                [this.lst_array] + [obj.lst_array for obj in other]
            )
            this.baseline_array = np.concatenate(
                [this.baseline_array] + [obj.baseline_array for obj in other]
            )
            this.Nbls = len(np.unique(this.baseline_array))
            this.integration_time = np.concatenate(
                [this.integration_time] + [obj.integration_time for obj in other]
            )
            if not self.metadata_only:
                this.data_array = np.concatenate(
                    [this.data_array] + [obj.data_array for obj in other], axis=0,
                )
                this.nsample_array = np.concatenate(
                    [this.nsample_array] + [obj.nsample_array for obj in other], axis=0,
                )
                this.flag_array = np.concatenate(
                    [this.flag_array] + [obj.flag_array for obj in other], axis=0,
                )
            if this.phase_type == "phased":
                this.phase_center_app_ra = np.concatenate(
                    [this.phase_center_app_ra]
                    + [obj.phase_center_app_ra for obj in other]
                )
                this.phase_center_app_dec = np.concatenate(
                    [this.phase_center_app_dec]
                    + [obj.phase_center_app_dec for obj in other]
                )
                this.phase_center_frame_pa = np.concatenate(
                    [this.phase_center_frame_pa]
                    + [obj.phase_center_frame_pa for obj in other]
                )
            if this.multi_phase_center:
                this.phase_center_id_array = np.concatenate(
                    [this.phase_center_id_array]
                    + [obj.phase_center_id_array for obj in other]
                )

        # update filename attribute
        for obj in other:
            this.filename = uvutils._combine_filenames(this.filename, obj.filename)
        if this.filename is not None:
            this._filename.form = len(this.filename)

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

        if not inplace:
            return this

    def sum_vis(
        self,
        other,
        inplace=False,
        difference=False,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        override_params=None,
    ):
        """
        Sum visibilities between two UVData objects.

        By default requires that all UVParameters are the same on the two objects
        except for `history`, `data_array`, `object_name`, and `extra_keywords`.
        The `object_name` values are concatenated if they are different. If keys
        in `extra_keywords` have different values the values from the first
        object are taken.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        difference : bool
            If True, differences the visibilities of the two UVData objects
            rather than summing them.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            two objects are different, the combined object will keep all the history of
            both input objects (this can lead to long histories). If False and if the
            histories for the two objects are different, the combined object will have
            the history of the first object and only the parts of the second object
            history that are unique (this is done word by word and can result in hard
            to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        override_params : array_like of strings
            List of object UVParameters to omit from compatibility check. Overridden
            parameters will not be compared between the objects, and the values
            for these parameters will be taken from the first object.

        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 = self.copy()

        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )

        # check that both objects have the same array shapes
        if this.future_array_shapes != other.future_array_shapes:
            raise ValueError(
                "Both objects must have the same `future_array_shapes` parameter. "
                "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                "methods to convert them."
            )

        compatibility_params = list(this.__iter__())
        remove_params = ["_history", "_data_array", "_object_name", "_extra_keywords"]

        # Add underscores to override_params to match list from __iter__()
        # Add to parameters to be removed
        if override_params and all(isinstance(param, str) for param in override_params):
            for param in override_params:
                if param[0] != "_":
                    param = "_" + param
                if param not in compatibility_params:
                    msg = (
                        "Provided parameter " + param[1:] + " is not a recognizable "
                        "UVParameter."
                    )
                    raise ValueError(msg)
                remove_params.append(param)

        # compatibility_params should define the parameters that need to
        # be the same for objects to be summed or diffed
        compatibility_params = list(set(compatibility_params) - set(remove_params))

        # Check each UVParameter in compatibility_params
        for param in compatibility_params:
            params_match = getattr(this, param) == getattr(other, param)
            if not params_match:
                msg = (
                    "UVParameter " + param[1:] + " does not match. Cannot "
                    "combine objects."
                )
                raise ValueError(msg)

        # Merge extra keywords and object_name
        for intersection in set(this.extra_keywords.keys()) & set(
            other.extra_keywords.keys()
        ):
            if this.extra_keywords[intersection] != other.extra_keywords[intersection]:
                warnings.warn(
                    "Keyword " + intersection + " in _extra_keywords is different "
                    "in the two objects. Taking the first object's entry."
                )

        # Merge extra_keywords lists, taking values from the first object
        this.extra_keywords = dict(
            list(other.extra_keywords.items()) + list(this.extra_keywords.items())
        )

        # Merge object_name if different.
        if this.object_name != other.object_name:
            this.object_name = this.object_name + "-" + other.object_name

        # 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."

        histories_match = uvutils._check_histories(this.history, other.history)

        this.history += history_update_string
        if not histories_match:
            if verbose_history:
                this.history += " Second object history follows. " + other.history
            else:
                extra_history = uvutils._combine_history_addition(
                    this.history, other.history
                )
                if extra_history is not None:
                    this.history += (
                        " Unique part of second object history follows. "
                        + extra_history
                    )

        # merge file names
        this.filename = uvutils._combine_filenames(this.filename, other.filename)

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

        if not inplace:
            return this

    def diff_vis(
        self,
        other,
        inplace=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        override_params=None,
    ):
        """
        Difference visibilities between two UVData objects.

        By default requires that all UVParameters are the same on the two objects
        except for `history`, `data_array`, `object_name`, and `extra_keywords`.
        The `object_name` values are concatenated if they are different. If keys
        in `extra_keywords` have different values the values from the first
        object are taken.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        override_params : array_like of strings
            List of object UVParameters to omit from compatibility check. Overridden
            parameters will not be compared between the objects, and the values
            for these parameters will be taken from the first object.

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

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

        Used to support 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.

        """
        return uvutils.parse_ants(
            uv=self,
            ant_str=ant_str,
            print_toggle=print_toggle,
            x_orientation=self.x_orientation,
        )

    def _select_preprocess(
        self,
        antenna_nums,
        antenna_names,
        ant_str,
        bls,
        frequencies,
        freq_chans,
        times,
        time_range,
        lsts,
        lst_range,
        polarizations,
        blt_inds,
    ):
        """
        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 or list of int, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]), a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]), or a list of
            baseline numbers (e.g. [67599, 71699, 73743]) 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`, `lsts`, or
            `lst_array`.
        time_range : array_like of float, optional
            The time range in Julian Date to keep in the object, must be length
            2. Some of the times in the object should fall between the first and
            last elements. Cannot be used with `times`, `lsts`, or `lst_array`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int or str, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array. If passing strings, the
            canonical polarization strings (e.g. "xx", "rr") are supported and if the
            `x_orientation` attribute is set, the physical dipole strings
            (e.g. "nn", "ee") are also supported.
        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)
                if bls is not None and len(bls) == 0:
                    raise ValueError(
                        f"There is no data matching ant_str={ant_str} in this object."
                    )

        # 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
            # Check to make sure that we actually have these antenna nums in the data
            ant_check = np.logical_or(
                np.isin(antenna_nums, self.ant_1_array),
                np.isin(antenna_nums, self.ant_2_array),
            )
            if not np.all(ant_check):
                raise ValueError(
                    "Antenna number % i is not present in the ant_1_array or "
                    "ant_2_array" % antenna_nums[~ant_check][0]
                )
            ant_blt_inds = np.where(
                np.logical_and(
                    np.isin(self.ant_1_array, antenna_nums),
                    np.isin(self.ant_2_array, antenna_nums),
                )
            )[0]
        else:
            ant_blt_inds = None

        if bls is not None:
            if isinstance(bls, list) and all(
                isinstance(bl_ind, (int, np.integer,),) for bl_ind in bls
            ):
                for bl_ind in bls:
                    if not (bl_ind in self.baseline_array):
                        raise ValueError(
                            "Baseline number {i} is not present in the "
                            "baseline_array".format(i=bl_ind)
                        )
                bls = list(zip(*self.baseline_to_antnums(bls)))
            elif 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) or a list of baseline numbers."
                )
            if not all(
                [isinstance(item[0], (int, np.integer,),) for item in bls]
                + [isinstance(item[1], (int, np.integer,),) for item in bls]
            ):
                raise ValueError(
                    "bls must be a list of tuples of antenna numbers "
                    "(optionally with polarization) or a list of baseline numbers."
                )
            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.int64)
            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:
                        # find conjugate polarization
                        bl_pols.add(uvutils.conj_pol(bl[2]))
                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.int64
                )
            else:
                blt_inds = ant_blt_inds

        have_times = times is not None
        have_time_range = time_range is not None
        have_lsts = lsts is not None
        have_lst_range = lst_range is not None
        if (
            np.count_nonzero([have_times, have_time_range, have_lsts, have_lst_range])
            > 1
        ):
            raise ValueError(
                "Only one of [times, time_range, lsts, lst_range] may be "
                "specified per selection operation."
            )

        if times is not None:
            times = uvutils._get_iterable(times)
            if np.array(times).ndim > 1:
                times = np.array(times).flatten()

            time_blt_inds = np.zeros(0, dtype=np.int64)
            for jd in times:
                if np.any(
                    np.isclose(
                        self.time_array,
                        jd,
                        rtol=self._time_array.tols[0],
                        atol=self._time_array.tols[1],
                    )
                ):
                    time_blt_inds = np.append(
                        time_blt_inds,
                        np.where(
                            np.isclose(
                                self.time_array,
                                jd,
                                rtol=self._time_array.tols[0],
                                atol=self._time_array.tols[1],
                            )
                        )[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(
                    f"No elements in time range between {time_range[0]} and "
                    f"{time_range[1]}."
                )

        if lsts is not None:
            if np.any(np.asarray(lsts) > 2 * np.pi):
                warnings.warn(
                    "The lsts parameter contained a value greater than 2*pi. "
                    "LST values are assumed to be in radians, not hours."
                )
            lsts = uvutils._get_iterable(lsts)
            if np.array(lsts).ndim > 1:
                lsts = np.array(lsts).flatten()

            time_blt_inds = np.zeros(0, dtype=np.int64)
            for lst in lsts:
                if np.any(
                    np.isclose(
                        self.lst_array,
                        lst,
                        rtol=self._lst_array.tols[0],
                        atol=self._lst_array.tols[1],
                    )
                ):
                    time_blt_inds = np.append(
                        time_blt_inds,
                        np.where(
                            np.isclose(
                                self.lst_array,
                                lst,
                                rtol=self._lst_array.tols[0],
                                atol=self._lst_array.tols[1],
                            )
                        )[0],
                    )
                else:
                    raise ValueError(f"LST {lst} is not present in the lst_array")

        if lst_range is not None:
            if np.size(lst_range) != 2:
                raise ValueError("lst_range must be length 2.")
            if np.any(np.asarray(lst_range) > 2 * np.pi):
                warnings.warn(
                    "The lst_range contained a value greater than 2*pi. "
                    "LST values are assumed to be in radians, not hours."
                )
            if lst_range[1] < lst_range[0]:
                # we're wrapping around LST = 2*pi = 0
                lst_range_1 = [lst_range[0], 2 * np.pi]
                lst_range_2 = [0, lst_range[1]]
                time_blt_inds1 = np.nonzero(
                    (self.lst_array <= lst_range_1[1])
                    & (self.lst_array >= lst_range_1[0])
                )[0]
                time_blt_inds2 = np.nonzero(
                    (self.lst_array <= lst_range_2[1])
                    & (self.lst_array >= lst_range_2[0])
                )[0]
                time_blt_inds = np.union1d(time_blt_inds1, time_blt_inds2)
            else:
                time_blt_inds = np.nonzero(
                    (self.lst_array <= lst_range[1]) & (self.lst_array >= lst_range[0])
                )[0]
            if time_blt_inds.size == 0:
                raise ValueError(
                    f"No elements in LST range between {lst_range[0]} and "
                    f"{lst_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.int64
                )
            else:
                blt_inds = time_blt_inds

        if lsts is not None or lst_range is not None:
            if n_selects > 0:
                history_update_string += ", lsts"
            else:
                history_update_string += "lsts"
            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.int64
                )
            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 = sorted(set(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:
                if self.future_array_shapes:
                    frequencies = self.freq_array[freq_chans]
                else:
                    frequencies = self.freq_array[0, freq_chans]
            else:
                frequencies = uvutils._get_iterable(frequencies)
                if self.future_array_shapes:
                    frequencies = np.sort(
                        list(set(frequencies) | set(self.freq_array[freq_chans]))
                    )
                else:
                    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

            if self.future_array_shapes:
                freq_arr_use = self.freq_array
            else:
                freq_arr_use = self.freq_array[0, :]
            # Check and see that all requested freqs are available
            freq_check = np.isin(frequencies, freq_arr_use)
            if not np.all(freq_check):
                raise ValueError(
                    "Frequency %g is not present in the freq_array"
                    % frequencies[np.where(~freq_check)[0][0]]
                )
            freq_inds = np.where(np.isin(freq_arr_use, frequencies))[0]

            if len(frequencies) > 1:
                freq_ind_separation = freq_inds[1:] - freq_inds[:-1]
                if self.flex_spw:
                    freq_ind_separation = freq_ind_separation[
                        np.diff(self.flex_spw_id_array[freq_inds]) == 0
                    ]
                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 = sorted(set(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.int64)
            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 = sorted(set(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,
    ):
        """
        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 = self._calc_nants_data()

            if self.phase_center_app_ra is not None:
                self.phase_center_app_ra = self.phase_center_app_ra[blt_inds]
            if self.phase_center_app_dec is not None:
                self.phase_center_app_dec = self.phase_center_app_dec[blt_inds]
            if self.phase_center_frame_pa is not None:
                self.phase_center_frame_pa = self.phase_center_frame_pa[blt_inds]
            if self.multi_phase_center:
                self.phase_center_id_array = self.phase_center_id_array[blt_inds]

            self.Ntimes = len(np.unique(self.time_array))
            if not keep_all_metadata:
                ants_to_keep = set(np.unique(self.ant_1_array)).union(
                    np.unique(self.ant_2_array)
                )

                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)
            if self.future_array_shapes:
                self.freq_array = self.freq_array[freq_inds]
            else:
                self.freq_array = self.freq_array[:, freq_inds]
            if self.flex_spw or self.future_array_shapes:
                self.channel_width = self.channel_width[freq_inds]
            if self.flex_spw:
                self.flex_spw_id_array = self.flex_spw_id_array[freq_inds]
                # Use the spw ID array to check and see which SPWs are left
                self.spw_array = self.spw_array[
                    np.isin(self.spw_array, self.flex_spw_id_array)
                ]
                self.Nspws = len(self.spw_array)

        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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        inplace=True,
        keep_all_metadata=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int or str, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array. If passing strings, the
            canonical polarization strings (e.g. "xx", "rr") are supported and if the
            `x_orientation` attribute is set, the physical dipole strings
            (e.g. "nn", "ee") are also supported.
        blt_inds : array_like of int, optional
            The baseline-time indices to keep in the object. This is
            not commonly used.
        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).
        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.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

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

        (
            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,
            lsts,
            lst_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:
            if self.future_array_shapes:
                for param_name, param in zip(
                    self._data_params, uv_object.data_like_parameters
                ):
                    setattr(uv_object, param_name, param[:, freq_inds, :])
            else:
                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:
            if self.future_array_shapes:
                for param_name, param in zip(
                    self._data_params, uv_object.data_like_parameters
                ):
                    setattr(uv_object, param_name, param[:, :, pol_inds])
            else:
                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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

        if not inplace:
            return uv_object

    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))

        # update app source coords to new times
        self._set_app_coords_helper()

        # 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.int64)
        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:
            if self.future_array_shapes:
                temp_data = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols),
                    dtype=self.nsample_array.dtype,
                )
            else:
                temp_data = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, 1, 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=None,
        n_times_to_avg=None,
        blt_order="time",
        minor_order="baseline",
        keep_ragged=True,
        summing_correlator_mode=False,
        allow_drift=False,
    ):
        """
        Average to a longer integration time.

        This method will average a UVData object either by an integer factor
        (by setting `n_times_to_avg`) or by a factor that can differ by
        baseline-time sample such that after averaging, the samples have an
        integration time greater than or equal to the `min_int_time` (up to the
        tolerance on the integration_time).

        Note that if the integrations for a baseline do not divide evenly by the
        `n_times_to_avg` or into the specified `min_int_time`, the final
        integrations for that baseline may have integration times less than
        `min_int_time` or be composed of fewer input integrations than `n_times_to_avg`.
        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 average the UVData integration_time to
            in seconds.
        n_times_to_avg : int
            Number of time integrations to average together.
        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,
            or that have a number of integrations that do not evenly divide by
            n_times_to_avg, keep_ragged controls whether to keep the (averaged)
            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 averaging 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 averaging in drift mode may result in more decoherence.

        Returns
        -------
        None

        """
        if min_int_time is None and n_times_to_avg is None:
            raise ValueError("Either min_int_time or n_times_to_avg must be set.")

        if min_int_time is not None and n_times_to_avg is not None:
            raise ValueError("Only one of min_int_time or n_times_to_avg can be set.")

        if self.Ntimes == 1:
            raise ValueError("Only one time in this object, cannot downsample.")

        if min_int_time is not 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 "
                    "longer than the value specified; doing nothing."
                )
                return
        else:
            if not isinstance(n_times_to_avg, (int, np.integer)):
                raise ValueError("n_times_to_avg must be an integer.")
        # 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()

        if min_int_time is not None:
            # 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],
                )
            )
            bls_to_downsample = np.unique(self.baseline_array[inds_to_downsample])
        else:
            bls_to_downsample = np.unique(self.baseline_array)

        # figure out how many baseline times we'll end up with at the end
        n_new_samples = 0
        for bl in bls_to_downsample:
            bl_inds = np.nonzero(self.baseline_array == bl)[0]
            int_times = self.integration_time[bl_inds]

            if min_int_time is not None:
                running_int_time = 0.0
                for itime, int_time in enumerate(int_times):
                    running_int_time += int_time
                    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
                        n_new_samples += 1
                        running_int_time = 0.0
            else:
                n_bl_times = self.time_array[bl_inds].size
                nsample_temp = np.sum(n_bl_times / n_times_to_avg)
                if keep_ragged and not np.isclose(nsample_temp, np.floor(nsample_temp)):
                    n_new_samples += np.ceil(nsample_temp).astype(int)
                else:
                    n_new_samples += np.floor(nsample_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 = int_times
            if len(np.unique(int_times)) == 1:
                # this baseline has all the same integration times
                if len(np.unique(dtime)) > 1 and not np.isclose(
                    np.max(dtime),
                    np.min(dtime),
                    rtol=self._integration_time.tols[0],
                    atol=self._integration_time.tols[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],
                    rtol=self._integration_time.tols[0],
                    atol=self._integration_time.tols[1],
                ):
                    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.int64)
        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:
            if self.future_array_shapes:
                temp_data = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols),
                    dtype=self.nsample_array.dtype,
                )
            else:
                temp_data = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, 1, 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
                if min_int_time is not None:
                    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],
                    )
                else:
                    over_min_int_time = n_sum >= n_times_to_avg
                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():
                            if self.future_array_shapes:
                                ax1_inds, ax2_inds = np.nonzero(temp_flag[temp_idx])
                                mask[:, ax1_inds, ax2_inds] = False
                            else:
                                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
                        )

                        # nsample array is the fraction of data that we actually kept,
                        # relative to the amount that went into the sum or average
                        nsample_dtype = self.nsample_array.dtype.type
                        # promote nsample dtype if half-precision
                        if nsample_dtype is np.float16:
                            masked_nsample_dtype = np.float32
                        else:
                            masked_nsample_dtype = nsample_dtype
                        masked_nsample = np.ma.masked_array(
                            self.nsample_array[averaging_idx],
                            mask=mask,
                            dtype=masked_nsample_dtype,
                        )

                        if self.future_array_shapes:
                            int_time_arr = self.integration_time[
                                averaging_idx, np.newaxis, np.newaxis
                            ]
                        else:
                            int_time_arr = self.integration_time[
                                averaging_idx, np.newaxis, np.newaxis, np.newaxis
                            ]
                        masked_int_time = np.ma.masked_array(
                            np.ones_like(
                                self.data_array[averaging_idx],
                                dtype=self.integration_time.dtype,
                            )
                            * int_time_arr,
                            mask=mask,
                        )
                        if summing_correlator_mode:
                            temp_data[temp_idx] = np.sum(masked_data, axis=0)
                        else:
                            # take potential non-uniformity of integration_time
                            # and nsamples into account
                            weights = masked_nsample * masked_int_time
                            weighted_data = masked_data * weights
                            temp_data[temp_idx] = np.sum(
                                weighted_data, axis=0
                            ) / np.sum(weights, axis=0)

                        # output of masked array calculation should be coerced
                        # to the datatype of temp_nsample (which has the same
                        # precision as the original nsample_array)
                        temp_nsample[temp_idx] = np.sum(
                            masked_nsample * masked_int_time, axis=0
                        ) / np.sum(self.integration_time[averaging_idx])
                    # 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
        if min_int_time is not None:
            bls_not_downsampled = set(self.baseline_array) - set(bls_to_downsample)
            inds_to_keep = []
            for bl in bls_not_downsampled:
                inds_to_keep += np.nonzero(self.baseline_array == bl)[0].tolist()
            inds_to_keep = np.array(inds_to_keep, dtype=np.int64)
        else:
            inds_to_keep = np.array([], dtype=bool)
        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
        if min_int_time is not None:
            history_update_string = (
                " Downsampled data to {:f} second integration "
                "time using pyuvdata.".format(min_int_time)
            )
        else:
            history_update_string = (
                " Downsampled data by a factor of {} in "
                "time using pyuvdata.".format(n_times_to_avg)
            )
        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 not downsample and not upsample:
            warnings.warn(
                "No resampling will be done because target time is not "
                "a factor of 2 or more off from integration_time. To "
                "force resampling set only_upsample or only_downsample "
                "keywords or call upsample_in_time or downsample_in_time."
            )
            return

        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 frequency_average(
        self, n_chan_to_avg, summing_correlator_mode=False, propagate_flags=False
    ):
        """
        Average in frequency.

        Does a simple average over an integer number of input channels, leaving
        flagged samples out of the average.

        In the future, this method will support non-equally spaced channels
        and varying channel widths. It will also support setting the frequency
        to the true mean of the averaged non-flagged frequencies rather than
        the simple mean of the input channel frequencies. For now it does not.

        Parameters
        ----------
        n_chan_to_avg : int
            Number of channels to average together. If Nfreqs does not divide
            evenly by this number, the frequencies at the end of the freq_array
            will be dropped to make it evenly divisable. To control which
            frequencies are removed, use select before calling this 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).
        propagate_flags: bool
            Option to flag an averaged entry even if some of its contributors
            are not flagged. The averaged result will still leave the flagged
            samples out of the average, except when all contributors are
            flagged.
        """
        if self.flex_spw:
            raise NotImplementedError(
                "Frequency averaging not (yet) available for flexible spectral windows"
            )
        self._check_freq_spacing()

        n_final_chan = int(np.floor(self.Nfreqs / n_chan_to_avg))
        nfreq_mod_navg = self.Nfreqs % n_chan_to_avg
        if nfreq_mod_navg != 0:
            # not an even number of final channels
            warnings.warn(
                "Nfreqs does not divide by `n_chan_to_avg` evenly. "
                "The final {} frequencies will be excluded, to "
                "control which frequencies to exclude, use a "
                "select to control.".format(nfreq_mod_navg)
            )
            chan_to_keep = np.arange(n_final_chan * n_chan_to_avg)
            self.select(freq_chans=chan_to_keep)

        if self.future_array_shapes:
            self.freq_array = self.freq_array.reshape(
                (n_final_chan, n_chan_to_avg)
            ).mean(axis=1)
            self.channel_width = self.channel_width.reshape(
                (n_final_chan, n_chan_to_avg)
            ).sum(axis=1)
        else:
            self.freq_array = self.freq_array.reshape(
                (1, n_final_chan, n_chan_to_avg)
            ).mean(axis=2)
            self.channel_width = self.channel_width * n_chan_to_avg
        self.Nfreqs = n_final_chan

        if self.eq_coeffs is not None:
            eq_coeff_diff = np.diff(self.eq_coeffs, axis=1)
            if np.abs(np.max(eq_coeff_diff)) > 0:
                warnings.warn(
                    "eq_coeffs vary by frequency. They should be "
                    "applied to the data using `remove_eq_coeffs` "
                    "before frequency averaging."
                )
            self.eq_coeffs = self.eq_coeffs.reshape(
                (self.Nants_telescope, n_final_chan, n_chan_to_avg)
            ).mean(axis=2)

        if not self.metadata_only:
            if self.future_array_shapes:
                shape_tuple = (
                    self.Nblts,
                    n_final_chan,
                    n_chan_to_avg,
                    self.Npols,
                )
            else:
                shape_tuple = (
                    self.Nblts,
                    1,
                    n_final_chan,
                    n_chan_to_avg,
                    self.Npols,
                )

            mask = self.flag_array.reshape(shape_tuple)

            if propagate_flags:
                # if any contributors are flagged, the result should be flagged
                if self.future_array_shapes:
                    self.flag_array = np.any(
                        self.flag_array.reshape(shape_tuple), axis=2
                    )
                else:
                    self.flag_array = np.any(
                        self.flag_array.reshape(shape_tuple), axis=3
                    )
            else:
                # 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
                if self.future_array_shapes:
                    self.flag_array = (
                        np.sum(~self.flag_array.reshape(shape_tuple), axis=2) == 0
                    )
                else:
                    self.flag_array = (
                        np.sum(~self.flag_array.reshape(shape_tuple), axis=3) == 0
                    )

            # need to update mask if a downsampled visibility will be flagged
            # so that we don't set it to zero
            for n_chan in np.arange(n_final_chan):
                if self.future_array_shapes:
                    if (self.flag_array[:, n_chan]).any():
                        ax0_inds, ax2_inds = np.nonzero(self.flag_array[:, n_chan, :])
                        # Only if all entries are masked
                        # May not happen due to propagate_flags keyword
                        # mask should be left alone otherwise
                        if np.all(mask[ax0_inds, n_chan, :, ax2_inds]):
                            mask[ax0_inds, n_chan, :, ax2_inds] = False
                else:
                    if (self.flag_array[:, :, n_chan]).any():
                        ax0_inds, ax1_inds, ax3_inds = np.nonzero(
                            self.flag_array[:, :, n_chan, :]
                        )
                        # Only if all entries are masked
                        # May not happen due to propagate_flags keyword
                        # mask should be left alone otherwise
                        if np.all(mask[ax0_inds, ax1_inds, n_chan, :, ax3_inds]):
                            mask[ax0_inds, ax1_inds, n_chan, :, ax3_inds] = False

            masked_data = np.ma.masked_array(
                self.data_array.reshape(shape_tuple), mask=mask
            )

            self.nsample_array = self.nsample_array.reshape(shape_tuple)
            # promote nsample dtype if half-precision
            nsample_dtype = self.nsample_array.dtype.type
            if nsample_dtype is np.float16:
                masked_nsample_dtype = np.float32
            else:
                masked_nsample_dtype = nsample_dtype
            masked_nsample = np.ma.masked_array(
                self.nsample_array, mask=mask, dtype=masked_nsample_dtype
            )

            if summing_correlator_mode:
                if self.future_array_shapes:
                    self.data_array = np.sum(masked_data, axis=2).data
                else:
                    self.data_array = np.sum(masked_data, axis=3).data
            else:
                # need to weight by the nsample_array
                if self.future_array_shapes:
                    self.data_array = (
                        np.sum(masked_data * masked_nsample, axis=2)
                        / np.sum(masked_nsample, axis=2)
                    ).data
                else:
                    self.data_array = (
                        np.sum(masked_data * masked_nsample, axis=3)
                        / np.sum(masked_nsample, axis=3)
                    ).data

            # nsample array is the fraction of data that we actually kept,
            # relative to the amount that went into the sum or average.
            # Need to take care to return precision back to original value.
            if self.future_array_shapes:
                self.nsample_array = (
                    np.sum(masked_nsample, axis=2) / float(n_chan_to_avg)
                ).data.astype(nsample_dtype)
            else:
                self.nsample_array = (
                    np.sum(masked_nsample, axis=3) / float(n_chan_to_avg)
                ).data.astype(nsample_dtype)

    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 compress_by_redundancy(
        self, method="select", tol=1.0, inplace=True, keep_all_metadata=True
    ):
        """
        Downselect or average to only have one baseline per redundant group.

        Either select the first baseline in the redundant group or average over
        the baselines in the redundant group.

        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.
        method : str
            Options are "select", which just keeps the first baseline in each
            redundant group or "average" which averages over the baselines in each
            redundant group and assigns the average to the first baseline in the group.
        inplace : bool
            Option to do selection on current object.
        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

        """
        allowed_methods = ["select", "average"]
        if method not in allowed_methods:
            raise ValueError(f"method must be one of {allowed_methods}")

        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]

        if method == "average":
            # do a metadata only select to get all the metadata right
            new_obj = self.copy(metadata_only=True)
            new_obj.select(bls=bl_ants, keep_all_metadata=keep_all_metadata)

            if not self.metadata_only:
                # initalize the data like arrays
                if new_obj.future_array_shapes:
                    temp_data_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.data_array.dtype,
                    )
                    temp_nsample_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.nsample_array.dtype,
                    )
                    temp_flag_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.flag_array.dtype,
                    )
                else:
                    temp_data_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.data_array.dtype,
                    )
                    temp_nsample_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.nsample_array.dtype,
                    )
                    temp_flag_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.flag_array.dtype,
                    )
            for grp_ind, group in enumerate(red_gps):
                if len(conjugates) > 0:
                    conj_group = set(group).intersection(conjugates)
                    reg_group = list(set(group) - conj_group)
                    conj_group = list(conj_group)
                else:
                    reg_group = group
                    conj_group = []
                group_times = []
                group_inds = []
                conj_group_inds = []
                conj_group_times = []
                for bl in reg_group:
                    bl_inds = np.where(self.baseline_array == bl)[0]
                    group_inds.extend(bl_inds)
                    group_times.extend(self.time_array[bl_inds])
                for bl in conj_group:
                    bl_inds = np.where(self.baseline_array == bl)[0]
                    conj_group_inds.extend(bl_inds)
                    conj_group_times.extend(self.time_array[bl_inds])

                group_inds = np.array(group_inds, dtype=np.int64)
                conj_group_inds = np.array(conj_group_inds, dtype=np.int64)
                # now we have to figure out which times are the same to a tolerance
                # so we can average over them.
                time_inds = np.arange(len(group_times + conj_group_times))
                time_gps = uvutils.find_clusters(
                    time_inds,
                    np.array(group_times + conj_group_times),
                    self._time_array.tols[1],
                )

                # average over the same times
                obj_bl = bl_ants[grp_ind]
                obj_inds = new_obj._key2inds(obj_bl)[0]
                obj_times = new_obj.time_array[obj_inds]

                for gp in time_gps:
                    # Note that this average time is just used for identifying the
                    # index to use for the blt axis on the averaged data set.
                    # We do not update the actual time on that data set because it can
                    # result in confusing behavior -- small numerical rounding errors
                    # can result in many more unique times in the final data set than
                    # in the initial data set.
                    avg_time = np.average(np.array(group_times + conj_group_times)[gp])

                    obj_time_ind = np.where(
                        np.abs(obj_times - avg_time) < self._time_array.tols[1]
                    )[0]

                    if obj_time_ind.size == 1:
                        this_obj_ind = obj_inds[obj_time_ind[0]]
                    else:
                        warnings.warn(
                            "Index baseline in the redundant group does not "
                            "have all the times, compressed object will be "
                            "missing those times."
                        )
                        continue

                    # time_ind contains indices for both regular and conjugated bls
                    # because we needed to group them together in time.
                    # The regular ones are first and extend the length of group_times,
                    # so we use that to split them back up.
                    regular_orientation = np.array(
                        [time_ind for time_ind in gp if time_ind < len(group_times)],
                        dtype=np.int64,
                    )
                    regular_inds = group_inds[np.array(regular_orientation)]
                    conj_orientation = np.array(
                        [
                            time_ind - len(group_times)
                            for time_ind in gp
                            if time_ind >= len(group_times)
                        ],
                        dtype=np.int64,
                    )
                    conj_inds = conj_group_inds[np.array(conj_orientation)]
                    # check that the integration times are all the same
                    int_times = np.concatenate(
                        (
                            self.integration_time[regular_inds],
                            self.integration_time[conj_inds],
                        )
                    )
                    if not np.all(
                        np.abs(int_times - new_obj.integration_time[obj_time_ind])
                        < new_obj._integration_time.tols[1]
                    ):
                        warnings.warn(
                            "Integrations times are not identical in a redundant "
                            "group. Averaging anyway but this may cause unexpected "
                            "behavior."
                        )

                    if not self.metadata_only:
                        vis_to_avg = np.concatenate(
                            (
                                self.data_array[regular_inds],
                                np.conj(self.data_array[conj_inds]),
                            )
                        )
                        nsample_to_avg = np.concatenate(
                            (
                                self.nsample_array[regular_inds],
                                self.nsample_array[conj_inds],
                            )
                        )
                        flags_to_avg = np.concatenate(
                            (self.flag_array[regular_inds], self.flag_array[conj_inds],)
                        )
                        # if all data is flagged, average it all as if it were not
                        if np.all(flags_to_avg):
                            mask = np.zeros_like(flags_to_avg)
                        else:
                            mask = flags_to_avg

                        vis_to_avg = np.ma.masked_array(vis_to_avg, mask=mask)

                        nsample_to_avg = np.ma.masked_array(nsample_to_avg, mask=mask)

                        avg_vis = np.ma.average(
                            vis_to_avg, weights=nsample_to_avg, axis=0
                        )
                        avg_nsample = np.sum(nsample_to_avg, axis=0)
                        avg_flag = np.all(flags_to_avg, axis=0)

                        temp_data_array[this_obj_ind] = avg_vis
                        temp_nsample_array[this_obj_ind] = avg_nsample
                        temp_flag_array[this_obj_ind] = avg_flag

            if inplace:
                self.select(bls=bl_ants, keep_all_metadata=keep_all_metadata)
                if not self.metadata_only:
                    self.data_array = temp_data_array
                    self.nsample_array = temp_nsample_array
                    self.flag_array = temp_flag_array
                self.check()
                return
            else:
                if not self.metadata_only:
                    new_obj.data_array = temp_data_array
                    new_obj.nsample_array = temp_nsample_array
                    new_obj.flag_array = temp_flag_array
                new_obj.check()
                return new_obj
        else:
            return self.select(
                bls=bl_ants, inplace=inplace, 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 = self._calc_nants_data()
        self.Nbls = np.unique(self.baseline_array).size
        self.Nblts = Nblts_full

        if self.phase_center_app_ra is not None:
            self.phase_center_app_ra = self.phase_center_app_ra[blt_map]
        if self.phase_center_app_dec is not None:
            self.phase_center_app_dec = self.phase_center_app_dec[blt_map]
        if self.phase_center_frame_pa is not None:
            self.phase_center_frame_pa = self.phase_center_frame_pa[blt_map]
        if self.multi_phase_center:
            self.phase_center_id_array = self.phase_center_id_array[blt_map]

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

        self.check()

    def _convert_from_filetype(self, other):
        """
        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):
        """
        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', 'mir', 'ms'

        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()
        elif filetype == "mir":
            from . import mir

            other_obj = mir.Mir()
        elif filetype == "ms":
            from . import ms

            other_obj = ms.MS()
        else:
            raise ValueError("filetype must be uvfits, mir, miriad, ms, fhd, or uvh5")
        for p in self:
            param = getattr(self, p)
            setattr(other_obj, p, param)
        return other_obj

    def read_fhd(
        self,
        filelist,
        use_model=False,
        axis=None,
        read_data=True,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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, a layout file and a flag file.
            An obs file is also required if `read_data` is False.
        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.
        read_data : bool
            Read in the visibility, nsample and flag data. If set to False, only
            the metadata will be read in. Setting read_data to False results in
            a metadata only object. If read_data is False, an obs file must be
            included in the filelist.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        fhd_obj = fhd.FHD()
        fhd_obj.read_fhd(
            filelist,
            use_model=use_model,
            background_lsts=background_lsts,
            read_data=read_data,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        self._convert_from_filetype(fhd_obj)
        del fhd_obj

    def read_mir(
        self,
        filepath,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
    ):
        """
        Read in data from an SMA MIR file.

        Note that with the exception of filepath, the reset of the parameters are
        used to sub-select a range of data that matches the limitations of the current
        instantiation of pyuvdata  -- namely 1 spectral window, 1 source. These could
        be dropped in the future, as pyuvdata capabilities grow.

        Parameters
        ----------
        filepath : str
             The file path to the MIR folder to read from.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values. Default is false.
        """
        from . import mir

        mir_obj = mir.Mir()
        mir_obj.read_mir(
            filepath,
            isource=isource,
            irec=irec,
            isb=isb,
            corrchunk=corrchunk,
            pseudo_cont=pseudo_cont,
        )
        self._convert_from_filetype(mir_obj)
        del mir_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,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        calc_lst=True,
        fix_old_proj=False,
        fix_use_ant_pos=True,
    ):
        """
        Read in data from a miriad file.

        Parameters
        ----------
        filepath : str
            The miriad root directory to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to read into the object.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `bls` or `polarizations` parameters, if it is a ValueError will be raised.
        polarizations : array_like of int or str, optional
            List of polarization integers or strings to read-in. e.g. ['xx', 'yy', ...]
        time_range : list of float, optional
            len-2 list containing min and max range of times in Julian Date to
            include when reading data into the object. e.g. [2458115.20, 2458115.40]
        read_data : bool
            Read in the visibility and flag data. If set to false,
            only the metadata will be read in. Setting read_data to False
            results in an incompletely defined object (check will not pass).
        phase_type : str, optional
            Option to specify the phasing status of the data. Options are 'drift',
            'phased' or None. 'drift' means the data are zenith drift data,
            'phased' means the data are phased to a single RA/Dec. Default is None
            meaning it will be guessed at based on the file contents.
        correct_lat_lon : bool
            Option to update the latitude and longitude from the known_telescopes
            list if the altitude is missing.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        miriad_obj = miriad.Miriad()
        miriad_obj.read_miriad(
            filepath,
            correct_lat_lon=correct_lat_lon,
            read_data=read_data,
            phase_type=phase_type,
            antenna_nums=antenna_nums,
            ant_str=ant_str,
            bls=bls,
            polarizations=polarizations,
            time_range=time_range,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            calc_lst=calc_lst,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(miriad_obj)
        del miriad_obj

    def read_ms(
        self,
        filepath,
        axis=None,
        data_column="DATA",
        pol_order="AIPS",
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        ignore_single_chan=True,
        raise_error=True,
        read_weights=True,
    ):
        """
        Read in data from a measurement set.

        Parameters
        ----------
        filepath : str
            The measurement set root directory to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        data_column : str
            name of CASA data column to read into data_array. Options are:
            'DATA', 'MODEL', or 'CORRECTED_DATA'
        pol_order : str
            Option to specify polarizations order convention, options are
            'CASA' or 'AIPS'.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        ignore_single_chan : bool
            Some measurement sets (e.g., those from ALMA) use single channel spectral
            windows for recording pseudo-continuum channels or storing other metadata
            in the track when the telescopes are not on source. Because of the way
            the object is strutured (where all spectral windows are assumed to be
            simultaneously recorded), this can significantly inflate the size and memory
            footprint of UVData objects. By default, single channel windows are ignored
            to avoid this issue, although they can be included if setting this parameter
            equal to True.
        raise_error : bool
            The measurement set format allows for different spectral windows and
            polarizations to have different metdata for the same time-baseline
            combination, but UVData objects do not. If detected, by default the reader
            will throw an error. However, if set to False, the reader will simply give
            a warning, and will use the first value read in the file as the "correct"
            metadata in the UVData object.
        read_weights : bool
            Read in the weights from the MS file, default is True. If false, the method
            will set the `nsamples_array` to the same uniform value (namely 1.0).

        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.

        """
        if isinstance(filepath, (list, tuple, np.ndarray)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        from . import ms

        ms_obj = ms.MS()
        ms_obj.read_ms(
            filepath,
            data_column=data_column,
            pol_order=pol_order,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            ignore_single_chan=ignore_single_chan,
            raise_error=raise_error,
            read_weights=read_weights,
        )
        self._convert_from_filetype(ms_obj)
        del ms_obj

    def read_mwa_corr_fits(
        self,
        filelist,
        axis=None,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        read_data=True,
        data_array_dtype=np.complex64,
        nsample_array_dtype=np.float32,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Read in MWA correlator gpu box files.

        The default settings remove some of the instrumental effects in the bandpass
        by dividing out the digital gains and the coarse band shape.
        If the desired output is raw correlator data, set remove_dig_gains=False,
        remove_coarse_band=False, correct_cable_len=False, and
        phase_to_pointing_center=False.

        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.
        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_aoflagger_flags : bool
            Option to use aoflagger mwaf flag files. Defaults to true if aoflagger
            flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Option to divide out digital gains.
        remove_coarse_band : bool
            Option to divide out coarse band shape.
        correct_cable_len : bool
            Option to apply a cable delay correction.
        correct_van_vleck : bool
            Option to apply a van vleck correction.
        cheby_approx : bool
            Only used if correct_van_vleck is True. Option to implement the van
            vleck correction with a chebyshev polynomial approximation.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna.
        flag_small_sig_ants : bool
            Being replaced with flag_small_auto_ants and will be removed in v2.4.
        propagate_coarse_flags : bool
            Option to propagate flags for missing coarse channel integrations
            across frequency.
        flag_init: bool
            Set to True in order to do routine flagging of coarse channel edges,
            start or end integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if flag_init is True. Set to the width to flag on the edge
            of each coarse channel, in hz. Errors if not equal to integer
            multiple of channel_width. Set to 0 for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if flag_init is True. Set to the number of seconds to flag
            at the end of the observation. Set to 0 for no flagging. Errors if
            not an integer multiple of the integration time.
        flag_dc_offset: bool
            Only used if flag_init is True. Set to True to flag the center fine
            channel of each coarse channel. Only used if file_type is
            'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
        phase_to_pointing_center : bool
            Option to phase to the observation pointing center.
        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.
        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).
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3).
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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

        if isinstance(filelist[0], (list, tuple, np.ndarray)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )
        if use_cotter_flags is not None:
            use_aoflagger_flags = use_cotter_flags
            warnings.warn(
                "Use `use_aoflagger_flags` instead of `use_cotter_flags`."
                "`use_cotter_flags` is deprecated, and will be removed in "
                "pyuvdata v2.4.",
                DeprecationWarning,
            )
        if flag_small_sig_ants is not None:
            flag_small_auto_ants = flag_small_sig_ants
            warnings.warn(
                "Use `flag_small_auto_ants` instead of `flag_small_sig_ants`."
                "`flag_small_sig_ants` is deprecated, and will be removed in "
                "pyuvdata v2.4.",
                DeprecationWarning,
            )

        corr_obj = mwa_corr_fits.MWACorrFITS()
        corr_obj.read_mwa_corr_fits(
            filelist,
            use_aoflagger_flags=use_aoflagger_flags,
            remove_dig_gains=remove_dig_gains,
            remove_coarse_band=remove_coarse_band,
            correct_cable_len=correct_cable_len,
            correct_van_vleck=correct_van_vleck,
            cheby_approx=cheby_approx,
            flag_small_auto_ants=flag_small_auto_ants,
            propagate_coarse_flags=propagate_coarse_flags,
            flag_init=flag_init,
            edge_width=edge_width,
            start_flag=start_flag,
            end_flag=end_flag,
            flag_dc_offset=flag_dc_offset,
            remove_flagged_ants=remove_flagged_ants,
            phase_to_pointing_center=phase_to_pointing_center,
            read_data=read_data,
            data_array_dtype=data_array_dtype,
            nsample_array_dtype=nsample_array_dtype,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        self._convert_from_filetype(corr_obj)
        del corr_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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        keep_all_metadata=True,
        read_data=True,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        fix_old_proj=None,
        fix_use_ant_pos=True,
    ):
        """
        Read in header, metadata and data from a single uvfits file.

        Parameters
        ----------
        filename : str
            The uvfits file to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_names` is also provided. Ignored if read_data is False.
        antenna_names : array_like of str, optional
            The antennas names to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_nums` is also provided. Ignored if read_data is False.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None. Ignored if read_data is False.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `antenna_names`, `bls` args or the `polarizations` parameters,
            if it is a ValueError will be raised. Ignored if read_data is False.
        frequencies : array_like of float, optional
            The frequencies to include when reading data into the object, each
            value passed here should exist in the freq_array. Ignored if
            read_data is False.
        freq_chans : array_like of int, optional
            The frequency channel numbers to include when reading data into the
            object. Ignored if read_data is False.
        times : array_like of float, optional
            The times to include when reading data into the object, each value
            passed here should exist in the time_array 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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        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.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        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,
            lsts=lsts,
            lst_range=lst_range,
            polarizations=polarizations,
            blt_inds=blt_inds,
            keep_all_metadata=keep_all_metadata,
            read_data=read_data,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(uvfits_obj)
        del uvfits_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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        keep_all_metadata=True,
        read_data=True,
        data_array_dtype=np.complex128,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        fix_old_proj=None,
        fix_use_ant_pos=True,
    ):
        """
        Read a UVH5 file.

        Parameters
        ----------
        filename : str
             The UVH5 file to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_names` is also provided. Ignored if read_data is False.
        antenna_names : array_like of str, optional
            The antennas names to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_nums` is also provided. Ignored if read_data is False.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None. Ignored if read_data is False.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `antenna_names`, `bls` args or the `polarizations` parameters,
            if it is a ValueError will be raised. Ignored if read_data is False.
        frequencies : array_like of float, optional
            The frequencies to include when reading data into the object, each
            value passed here should exist in the freq_array. Ignored if
            read_data is False.
        freq_chans : array_like of int, optional
            The frequency channel numbers to include when reading data into the
            object. Ignored if read_data is False.
        times : array_like of float, optional
            The times to include when reading data into the object, each value
            passed here should exist in the time_array 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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        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'.
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            to apply the correction if the attributes `phase_center_app_ra` and
            `phase_center_app_dec` are missing (as they were introduced alongside the
            new phasing method).
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        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,
            lsts=lsts,
            lst_range=lst_range,
            polarizations=polarizations,
            blt_inds=blt_inds,
            data_array_dtype=data_array_dtype,
            keep_all_metadata=keep_all_metadata,
            read_data=read_data,
            multidim_index=multidim_index,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(uvh5_obj)
        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",
        phase_epoch=None,
        orig_phase_frame=None,
        phase_use_ant_pos=True,
        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_data=True,
        phase_type=None,
        correct_lat_lon=True,
        use_model=False,
        data_column="DATA",
        pol_order="AIPS",
        data_array_dtype=np.complex128,
        nsample_array_dtype=np.float32,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        skip_bad_files=False,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
        lsts=None,
        lst_range=None,
        calc_lst=True,
        fix_old_proj=None,
        fix_use_ant_pos=True,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        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_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' or
            'mwa_corr_fits'.
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3). Only used if
            file_type is 'mwa_corr_fits'.
        use_aoflagger_flags : bool
            Option to use aoflagger mwaf flag files. Defaults to true if aoflagger
            flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out digital
            gains.
        remove_coarse_band : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse
            band shape.
        correct_cable_len : bool
            Flag to apply cable length correction. Only used if file_type is
            'mwa_corr_fits'.
        correct_van_vleck : bool
            Flag to apply a van vleck correction. Only used if file_type is
            'mwa_corr_fits'.
        cheby_approx : bool
            Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.
            Option to implement the van vleck correction with a chebyshev polynomial
            approximation. Set to False to run the integral version of the correction.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna. Only used if file_type is
            'mwa_corr_fits'.
        flag_small_sig_ants : bool
            Being replaced by flag_small_auto_ants and will be removed in v2.4.
        propogate_coarse_flags : bool
            Option to propogate flags for missing coarse channel integrations
            across frequency. Only used if file_type is 'mwa_corr_fits'.
        flag_init: bool
            Only used if file_type is 'mwa_corr_fits'. Set to True in order to
            do routine flagging of coarse channel edges, start or end
            integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the width to flag on the edge of each coarse channel, in hz.
            Errors if not equal to integer multiple of channel_width. Set to 0
            for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the number of seconds to flag at the end of the observation. Set
            to 0 for no flagging. Errors if not an integer multiple of the
            integration time.
        flag_dc_offset: bool
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to True to flag the center fine channel of each coarse channel. Only
            used if file_type is 'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
            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.
        skip_bad_files : bool
            Option when reading multiple files to catch read errors such that
            the read continues even if one or more files are corrupted. Files
            that produce errors will be printed. Default is False (files will
            not be skipped).
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values in MIR dataset. Default is false.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True. Only applies to MIRIAD files.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False, unless reading a UVH5 file that is missing the `phase_center_app_ra`
            and `phase_center_app_dec` attributes (as these were introduced at the same
            time as the new `phase` method), in which case the default is True.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. By default, this is only done if reading
            in a file with multiple sources.
        ignore_name : bool
            Only relevant when reading in multiple files, which are concatenated into a
            single UVData object. Option to ignore the name of the phase center when
            combining multiple files, which would otherwise result in an error being
            raised because of attributes not matching. Doing so effectively adopts the
            name found in the first file read in. Default 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, mir, 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"
                elif os.path.exists(os.path.join(file_test, "sch_read")):
                    # It's Submillimeter Array mir format.
                    file_type = "mir"

            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:

            file_num = 0
            file_warnings = ""
            unread = True
            f = filename[file_num]
            while unread and file_num < len(filename):
                try:
                    self.read(
                        filename[file_num],
                        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_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,
                        nsample_array_dtype=nsample_array_dtype,
                        skip_bad_files=skip_bad_files,
                        background_lsts=background_lsts,
                        run_check=run_check,
                        check_extra=check_extra,
                        run_check_acceptability=run_check_acceptability,
                        strict_uvw_antpos_check=strict_uvw_antpos_check,
                        isource=None,
                        irec=irec,
                        isb=isb,
                        corrchunk=corrchunk,
                        pseudo_cont=pseudo_cont,
                        calc_lst=calc_lst,
                        fix_old_proj=fix_old_proj,
                        fix_use_ant_pos=fix_use_ant_pos,
                        make_multi_phase=make_multi_phase,
                    )
                    unread = False
                except KeyError as err:
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to KeyError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
                except ValueError as err:
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to ValueError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
                except OSError as err:  # pragma: nocover
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to OSError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
            if (
                allow_rephase
                and phase_center_radec is None
                and not unphase_to_drift
                and self.phase_type == "phased"
                and not self.multi_phase_center
                and not make_multi_phase
            ):
                # set the phase center to be the phase center of the first file
                phase_center_radec = [self.phase_center_ra, self.phase_center_dec]
                phase_frame = self.phase_center_frame
                phase_epoch = self.phase_center_epoch

            uv_list = []
            if len(filename) > file_num + 1:
                for f in filename[file_num + 1 :]:
                    uv2 = UVData()
                    try:
                        uv2.read(
                            f,
                            file_type=file_type,
                            phase_center_radec=phase_center_radec,
                            phase_frame=phase_frame,
                            phase_epoch=phase_epoch,
                            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_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,
                            nsample_array_dtype=nsample_array_dtype,
                            skip_bad_files=skip_bad_files,
                            background_lsts=background_lsts,
                            run_check=run_check,
                            check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            strict_uvw_antpos_check=strict_uvw_antpos_check,
                            isource=None,
                            irec=irec,
                            isb=isb,
                            corrchunk=corrchunk,
                            pseudo_cont=pseudo_cont,
                            calc_lst=calc_lst,
                            fix_old_proj=fix_old_proj,
                            fix_use_ant_pos=fix_use_ant_pos,
                            make_multi_phase=make_multi_phase,
                        )
                        uv_list.append(uv2)
                    except KeyError as err:
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to KeyError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
                    except ValueError as err:
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to ValueError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
                    except OSError as err:  # pragma: nocover
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to OSError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
            if unread is True:
                warnings.warn(
                    "########################################################\n"
                    "ALL FILES FAILED ON READ - NO READABLE FILES IN FILENAME\n"
                    "########################################################"
                )
            elif len(file_warnings) > 0:
                warnings.warn(file_warnings)

            # Concatenate once at end
            if axis is not None:
                # Rewrote fast_concat to operate on lists
                self.fast_concat(
                    uv_list,
                    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,
                    ignore_name=ignore_name,
                )
            else:
                # Too much work to rewrite __add__ to operate on lists
                # of files, so instead doing a binary tree merge
                uv_list = [self] + uv_list
                while len(uv_list) > 1:
                    for uv1, uv2 in zip(uv_list[0::2], uv_list[1::2]):
                        uv1.__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,
                            ignore_name=ignore_name,
                        )
                    uv_list = uv_list[0::2]
                # Because self was at the beginning of the list,
                # everything is merged into it at the end of this loop

        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,
                    lsts=lsts,
                    lst_range=lst_range,
                    polarizations=polarizations,
                    blt_inds=blt_inds,
                    read_data=read_data,
                    keep_all_metadata=keep_all_metadata,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )

            elif file_type == "mir":
                self.read_mir(
                    filename,
                    isource=isource,
                    irec=irec,
                    isb=isb,
                    corrchunk=corrchunk,
                    pseudo_cont=pseudo_cont,
                )
                select = False

            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,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    calc_lst=calc_lst,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )

            elif file_type == "mwa_corr_fits":
                self.read_mwa_corr_fits(
                    filename,
                    use_aoflagger_flags=use_aoflagger_flags,
                    use_cotter_flags=use_cotter_flags,
                    remove_dig_gains=remove_dig_gains,
                    remove_coarse_band=remove_coarse_band,
                    correct_cable_len=correct_cable_len,
                    correct_van_vleck=correct_van_vleck,
                    cheby_approx=cheby_approx,
                    flag_small_auto_ants=flag_small_auto_ants,
                    flag_small_sig_ants=flag_small_sig_ants,
                    propagate_coarse_flags=propagate_coarse_flags,
                    flag_init=flag_init,
                    edge_width=edge_width,
                    start_flag=start_flag,
                    end_flag=end_flag,
                    flag_dc_offset=True,
                    remove_flagged_ants=remove_flagged_ants,
                    phase_to_pointing_center=phase_to_pointing_center,
                    read_data=read_data,
                    data_array_dtype=data_array_dtype,
                    nsample_array_dtype=nsample_array_dtype,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            elif file_type == "fhd":
                self.read_fhd(
                    filename,
                    use_model=use_model,
                    background_lsts=background_lsts,
                    read_data=read_data,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            elif file_type == "ms":
                self.read_ms(
                    filename,
                    data_column=data_column,
                    pol_order=pol_order,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            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,
                    lsts=lsts,
                    lst_range=lst_range,
                    polarizations=polarizations,
                    blt_inds=blt_inds,
                    read_data=read_data,
                    data_array_dtype=data_array_dtype,
                    keep_all_metadata=keep_all_metadata,
                    multidim_index=multidim_index,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )
                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,
                    keep_all_metadata=keep_all_metadata,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            if make_multi_phase:
                self._set_multi_phase_center(preserve_phase_center_info=True)

            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],
                    )
                    or (self.phase_center_frame != phase_frame)
                    or (self.phase_center_epoch != phase_epoch)
                ):
                    warnings.warn("Phasing this UVData object to phase_center_radec")
                    self.phase(
                        phase_center_radec[0],
                        phase_center_radec[1],
                        epoch=phase_epoch,
                        phase_frame=phase_frame,
                        orig_phase_frame=orig_phase_frame,
                        use_ant_pos=phase_use_ant_pos,
                        allow_rephase=True,
                    )

    @classmethod
    def from_file(
        cls,
        filename,
        axis=None,
        file_type=None,
        allow_rephase=True,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        phase_epoch=None,
        orig_phase_frame=None,
        phase_use_ant_pos=True,
        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_data=True,
        phase_type=None,
        correct_lat_lon=True,
        use_model=False,
        data_column="DATA",
        pol_order="AIPS",
        data_array_dtype=np.complex128,
        nsample_array_dtype=np.float32,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        skip_bad_files=False,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
        lsts=None,
        lst_range=None,
        calc_lst=True,
        fix_old_proj=None,
        fix_use_ant_pos=True,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        Initialize a new UVData object by reading the input file.

        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_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' or
            'mwa_corr_fits'.
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3). Only used if
            file_type is 'mwa_corr_fits'.
        use_aoflagger_flags : bool
            Only used if file_type is 'mwa_corr_fits'. Option to use aoflagger mwaf
            flag files. Defaults to true if aoflagger flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out digital
            gains.
        remove_coarse_band : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse
            band shape.
        correct_cable_len : bool
            Flag to apply cable length correction. Only used if file_type is
            'mwa_corr_fits'.
        correct_van_vleck : bool
            Flag to apply a van vleck correction. Only used if file_type is
            'mwa_corr_fits'.
        cheby_approx : bool
            Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.
            Option to implement the van vleck correction with a chebyshev polynomial
            approximation. Set to False to run the integral version of the correction.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna. Only used if file_type is
            'mwa_corr_fits'.
        flag_small_sig_ants : bool
            Being replaced by flag_small_auto_ants and will be removed in v2.4.
        propogate_coarse_flags : bool
            Option to propogate flags for missing coarse channel integrations
            across frequency. Only used if file_type is 'mwa_corr_fits'.
        flag_init: bool
            Only used if file_type is 'mwa_corr_fits'. Set to True in order to
            do routine flagging of coarse channel edges, start or end
            integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the width to flag on the edge of each coarse channel, in hz.
            Errors if not equal to integer multiple of channel_width. Set to 0
            for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the number of seconds to flag at the end of the observation. Set
            to 0 for no flagging. Errors if not an integer multiple of the
            integration time.
        flag_dc_offset: bool
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to True to flag the center fine channel of each coarse channel. Only
            used if file_type is 'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
            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.
        skip_bad_files : bool
            Option when reading multiple files to catch read errors such that
            the read continues even if one or more files are corrupted. Files
            that produce errors will be printed. Default is False (files will
            not be skipped).
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values in MIR dataset. Default is false.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True. Only applies to MIRIAD files.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False, unless reading a UVH5 file that is missing the `phase_center_app_ra`
            and `phase_center_app_dec` attributes (as these were introduced at the same
            time as the new `phase` method), in which case the default is True.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. By default, this is only done if reading
            in a file with multiple sources.
        ignore_name : bool
            Only relevant when reading in multiple files, which are concatenated into a
            single UVData object. Option to ignore the name of the phase center when
            combining multiple files, which would otherwise result in an error being
            raised because of attributes not matching. Doing so effectively adopts the
            name found in the first file read in. Default 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.

        """
        uvd = cls()
        uvd.read(
            filename,
            axis=axis,
            file_type=file_type,
            allow_rephase=allow_rephase,
            phase_center_radec=phase_center_radec,
            unphase_to_drift=unphase_to_drift,
            phase_frame=phase_frame,
            phase_epoch=phase_epoch,
            orig_phase_frame=orig_phase_frame,
            phase_use_ant_pos=phase_use_ant_pos,
            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_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,
            nsample_array_dtype=nsample_array_dtype,
            use_aoflagger_flags=use_aoflagger_flags,
            use_cotter_flags=use_cotter_flags,
            remove_dig_gains=remove_dig_gains,
            remove_coarse_band=remove_coarse_band,
            correct_cable_len=correct_cable_len,
            correct_van_vleck=correct_van_vleck,
            cheby_approx=cheby_approx,
            flag_small_auto_ants=flag_small_auto_ants,
            flag_small_sig_ants=flag_small_sig_ants,
            propagate_coarse_flags=propagate_coarse_flags,
            flag_init=flag_init,
            edge_width=edge_width,
            start_flag=start_flag,
            end_flag=end_flag,
            flag_dc_offset=flag_dc_offset,
            remove_flagged_ants=remove_flagged_ants,
            phase_to_pointing_center=phase_to_pointing_center,
            skip_bad_files=skip_bad_files,
            multidim_index=multidim_index,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            isource=isource,
            irec=irec,
            isb=isb,
            corrchunk=corrchunk,
            pseudo_cont=pseudo_cont,
            lsts=lsts,
            lst_range=lst_range,
            calc_lst=calc_lst,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
            make_multi_phase=make_multi_phase,
            ignore_name=ignore_name,
        )
        return uvd

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

        Parameters
        ----------
        filename : str
            The miriad root directory to write to.
        clobber : bool
            Option to overwrite the filename if the file already exists.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        no_antnums : bool
            Option to not write the antnums variable to the file.
            Should only be used for testing purposes.
        calc_lst : bool
            Recalculate the LST values upon writing the file. This is done to perform
            higher-precision accounting for the difference in MIRAD timestamps vs
            pyuvdata (the former marks the beginning of an integration, the latter
            marks the midpoint). Default is False, which instead uses a simple formula
            for correcting the LSTs, expected to be accurate to approximately 0.1 µsec
            precision.

        Raises
        ------
        ValueError
            If the frequencies are not evenly spaced or are separated by more
            than their channel width or if the UVData object is a metadata only object.
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a miriad file.")

        miriad_obj = self._convert_to_filetype("miriad")
        miriad_obj.write_miriad(
            filepath,
            clobber=clobber,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            no_antnums=no_antnums,
            calc_lst=calc_lst,
        )
        del miriad_obj

    def write_mir(
        self, filepath,
    ):
        """
        Write the data to a mir file.

        Parameters
        ----------
        filename : str
            The mir root directory to write to.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.
        NotImplementedError
            Method is not fully implemented yet, and thus will raise an error

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a mir file.")

        mir_obj = self._convert_to_filetype("mir")
        mir_obj.write_mir(filepath,)
        del mir_obj

    def write_ms(
        self,
        filename,
        force_phase=False,
        clobber=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Write a CASA measurement set (MS).

        Parameters
        ----------
        filename : str
            The measurement set file path to write to (a measurement set is really
            a folder with many files).
        force_phase : bool
            Option to automatically phase drift scan data to zenith of the first
            timestamp.
        clobber : bool
            Option to overwrite the file if it already exists.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            before writing the file.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.

        """
        if self.metadata_only:
            raise ValueError(
                "Cannot write out metadata only objects to a measurement set file."
            )

        ms_obj = self._convert_to_filetype("ms")
        ms_obj.write_ms(
            filename,
            force_phase=force_phase,
            clobber=clobber,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del ms_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,
        strict_uvw_antpos_check=False,
    ):
        """
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            The `phase_type` of the object is "drift" and the `force_phase`
            keyword is not set.
            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".
            If the UVData object is a metadata only object.
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a uvfits file.")

        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del uvfits_obj

    def write_uvh5(
        self,
        filename,
        clobber=False,
        chunks=True,
        data_compression=None,
        flags_compression="lzf",
        nsample_compression="lzf",
        data_write_dtype=None,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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.
        chunks : tuple or bool
            h5py.create_dataset chunks keyword. Tuple for chunk shape,
            True for auto-chunking, None for no chunking. Default is True.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression. Dataset must be chunked.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.

        """
        if self.metadata_only:
            raise ValueError(
                "Cannot write out metadata only objects to a uvh5 file. To initialize "
                "a uvh5 file for partial writing, use the `initialize_uvh5_file` "
                "method."
            )

        uvh5_obj = self._convert_to_filetype("uvh5")
        uvh5_obj.write_uvh5(
            filename,
            clobber=clobber,
            chunks=chunks,
            data_compression=data_compression,
            flags_compression=flags_compression,
            nsample_compression=nsample_compression,
            data_write_dtype=data_write_dtype,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del uvh5_obj

    def initialize_uvh5_file(
        self,
        filename,
        clobber=False,
        chunks=True,
        data_compression=None,
        flags_compression="lzf",
        nsample_compression="lzf",
        data_write_dtype=None,
    ):
        """
        Initialize a UVH5 file on disk 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.
        chunks : tuple or bool
            h5py.create_dataset chunks keyword. Tuple for chunk shape,
            True for auto-chunking, None for no chunking. Default is True.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression. Dataset must be chunked.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        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,
            chunks=chunks,
            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,
        add_to_history=None,
        run_check_acceptability=True,
    ):
        """
        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.
        add_to_history : str
            String to append to history before write out. Default is no appending.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        """
        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,
            add_to_history=add_to_history,
            run_check_acceptability=run_check_acceptability,
        )
        del uvh5_obj

# -*- 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."""
import os
import copy
from collections.abc import Iterable
import warnings
import threading

import numpy as np
from scipy import ndimage as nd
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 astropy import coordinates as coord

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

__all__ = ["UVData"]


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: 1 mas in radians.
        radian_tol = 1 * 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, 1, Nfreqs, "
            "Npols) or (Nblts, Nfreqs, Npols) if future_array_shapes=True, "
            "type = complex float, in units of self.vis_units"
        )
        # TODO: Spw axis to be collapsed in future release
        self._data_array = uvp.UVParameter(
            "data_array",
            description=desc,
            form=("Nblts", 1, "Nfreqs", "Npols"),
            expected_type=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", 1, "Nfreqs", "Npols"),
            expected_type=float,
        )

        desc = "Boolean flag, True is flagged, same shape as data_array."
        self._flag_array = uvp.UVParameter(
            "flag_array",
            description=desc,
            form=("Nblts", 1, "Nfreqs", "Npols"),
            expected_type=bool,
        )

        self._Nspws = uvp.UVParameter(
            "Nspws",
            description="Number of spectral windows "
            "(ie non-contiguous spectral chunks). ",
            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=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=float,
            tols=1e-3 / (60.0 * 60.0 * 24.0),
        )  # 1 ms in days

        desc = (
            "Array of local apparent sidereal times (LAST) at the center of "
            "integration, shape (Nblts), units radians."
        )
        self._lst_array = uvp.UVParameter(
            "lst_array",
            description=desc,
            form=("Nblts",),
            expected_type=float,
            tols=radian_tol,
        )

        desc = (
            "Array of numbers for the first antenna, which is matched to that in "
            "the antenna_numbers attribute. Shape (Nblts), type = int."
        )
        self._ant_1_array = uvp.UVParameter(
            "ant_1_array", description=desc, expected_type=int, form=("Nblts",)
        )

        desc = (
            "Array of numbers for the second antenna, which is matched to that in "
            "the antenna_numbers attribute. Shape (Nblts), type = int."
        )
        self._ant_2_array = uvp.UVParameter(
            "ant_2_array", description=desc, expected_type=int, form=("Nblts",)
        )

        desc = (
            "Array of baseline numbers, 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 (1, Nfreqs) or (Nfreqs,) if future_array_shapes=True, units Hz"
        )
        # TODO: Spw axis to be collapsed in future release
        self._freq_array = uvp.UVParameter(
            "freq_array",
            description=desc,
            form=(1, "Nfreqs"),
            expected_type=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=float,
            tols=1e-3,
        )  # 1 ms

        desc = (
            "Width of frequency channels (Hz). If flex_spw = False and "
            "future_array_shapes=False, then it is a "
            "single value of type = float, otherwise it is an array of shape "
            "(Nfreqs), type = float."
        )
        self._channel_width = uvp.UVParameter(
            "channel_width", description=desc, expected_type=float, tols=1e-3,
        )  # 1 mHz

        desc = (
            "Name(s) of source(s) or field(s) observed, type string. If "
            'multi_phase_center = True, set to "multi".'
        )
        self._object_name = uvp.UVParameter(
            "object_name", description=desc, form="str", expected_type=str,
        )

        # --- multi phase center handling ---
        desc = (
            'Only relevant if phase_type = "phased". Specifies the that the data set '
            "contains multiple sources within it."
        )
        self._multi_phase_center = uvp.UVParameter(
            "multi_phase_center", description=desc, expected_type=bool, value=False,
        )

        desc = (
            "Required if multi_phase_center = True. Specifies the number of sources "
            "contained within the data set."
        )

        self._Nphase = uvp.UVParameter(
            "Nphase", description=desc, expected_type=int, required=False,
        )

        desc = (
            "Only relevant if multi_phase_center = True. Dictionary that acts as a "
            "catalog, containing information on individual phase centers. Keys are the "
            "names of the different phase centers in the UVData object. At a minimum, "
            'each dictionary must contain the key "cat_type", which can be either '
            '"sidereal" (fixed position in RA/Dec), "ephem" (position in RA/Dec which'
            'moves with time), "driftscan" (fixed postion in Az/El, NOT the same as '
            '`phase_type`="drift") and "unphased" (baseline coordinates in ENU, but '
            'data are not phased, similar to `phase_type`="drift"). Other typical '
            'keyworks include "cat_lon" (longitude coord, e.g. RA), "cat_lat" '
            '(latitude coord, e.g. Dec.), "cat_frame" (coordinate frame, e.g. '
            'icrs), "cat_epoch" (epoch and equinox of the coordinate frame), '
            '"cat_times" (times for the coordinates, only used for "ephem" '
            'types), "cat_pm_ra" (proper motion in RA), "cat_pm_dec" (proper '
            'motion in Dec), "cat_dist" (physical distance), "cat_vrad" ('
            'rest frame velocity), "info_source" (describes where catalog info came '
            'from), and "cat_id" (matched to the parameter `phase_center_id_array`. '
            "See the documentation of the `phase` method for more details."
        )
        self._phase_center_catalog = uvp.UVParameter(
            "phase_center_catalog",
            description=desc,
            expected_type=dict,
            required=False,
        )

        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,
        )

        # --- flexible spectral window information ---

        desc = (
            'Option to construct a "flexible spectral window", which stores'
            "all spectral channels across the frequency axis of data_array. "
            "Allows for spectral windows of variable sizes, and channels of "
            "varying widths."
        )
        self._flex_spw = uvp.UVParameter(
            "flex_spw", description=desc, expected_type=bool, value=False,
        )

        desc = (
            "Required if flex_spw = True. Maps individual channels along the "
            "frequency axis to individual spectral windows, as listed in the "
            "spw_array. Shape (Nfreqs), type = int."
        )
        self._flex_spw_id_array = uvp.UVParameter(
            "flex_spw_id_array",
            description=desc,
            form=("Nfreqs",),
            expected_type=int,
            required=False,
        )

        desc = "Flag indicating that this object is using the future array shapes."
        self._future_array_shapes = uvp.UVParameter(
            "future_array_shapes", description=desc, expected_type=bool, value=False,
        )

        # --- phasing information ---
        desc = (
            'String indicating phasing type. Allowed values are "drift" and '
            '"phased" (n.b., "drift" is not the same as `cat_type="driftscan"`, '
            "the latter of which _is_ phased to a fixed az-el position)."
        )
        self._phase_type = uvp.UVParameter(
            "phase_type",
            form="str",
            expected_type=str,
            description=desc,
            value=None,
            acceptable_vals=["drift", "phased"],
        )

        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=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=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=float,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Apparent right ascension of phase '
            "center in the topocentric frame of the observatory, units radians."
            "Shape (Nblts,), type = float."
        )
        self._phase_center_app_ra = uvp.AngleParameter(
            "phase_center_app_ra",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Declination of phase center '
            "in the topocentric frame of the observatory, units radians. "
            "Shape (Nblts,), type = float."
        )
        self._phase_center_app_dec = uvp.AngleParameter(
            "phase_center_app_dec",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=radian_tol,
        )

        desc = (
            'Required if phase_type = "phased". Position angle between the hour '
            "circle (which is a great circle that goes through the target postion and "
            "both poles) in the apparent/topocentric frame, and the frame given in "
            "the phase_center_frame attribute."
            "Shape (Nblts,), type = float."
        )
        # The tolerance here is set by the fact that is is calculated using an arctan,
        # the limiting precision of which happens around values of 1.
        self._phase_center_frame_pa = uvp.AngleParameter(
            "phase_center_frame_pa",
            required=False,
            form=("Nblts",),
            expected_type=float,
            description=desc,
            tols=2e-8,
        )

        desc = (
            'Only relevant if phase_type = "phased". Specifies the frame the'
            ' data and uvw_array are phased to. Options are "icrs", "gcrs", and "fk5";'
            ' default is "icrs"'
        )
        self._phase_center_frame = uvp.UVParameter(
            "phase_center_frame",
            required=False,
            description=desc,
            expected_type=str,
            acceptable_vals=["icrs", "gcrs", "fk5"],
        )

        desc = (
            "Required if multi_phase_center = True. Maps individual indices along the "
            "Nblt axis to an entry in `phase_center_catalog`, with the ID number of "
            "individual entries stored as `cat_id`, along with other metadata. "
            "Shape (Nblts), type = int."
        )
        self._phase_center_id_array = uvp.UVParameter(
            "phase_center_id_array",
            description=desc,
            form=("Nblts",),
            expected_type=int,
            required=False,
        )

        desc = (
            "Optional when reading a MS. Retains the  scan number when reading a MS."
            " Shape (Nblts), type = int."
        )
        self._scan_number_array = uvp.UVParameter(
            "scan_number_array",
            description=desc,
            form=("Nblts",),
            expected_type=int,
            required=False,
        )

        # --- 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."
            "Note that these are not indices -- they do not need to start "
            "at zero or be continuous."
        )
        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=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=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=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=float,
        )
        self._dut1 = uvp.UVParameter(
            "dut1",
            required=False,
            description="DUT1 (google it) AIPS 117 " "calls it UT1UTC",
            spoof_val=0.0,
            expected_type=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=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",
        )

        desc = (
            "List of strings containing the unique basenames (not the full path) of "
            "input files."
        )
        self._filename = uvp.UVParameter(
            "filename", required=False, description=desc, expected_type=str,
        )

        super(UVData, self).__init__()

    def _set_flex_spw(self):
        """
        Set flex_spw to True, and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by the file-reading methods to indicate that an object has multiple spectral
        windows concatenated together across the frequency axis.
        """
        # Mark once-optional arrays as now required
        self.flex_spw = True
        self._flex_spw_id_array.required = True
        # Now make sure that chan_width is set to be an array
        self._channel_width.form = ("Nfreqs",)

    def _set_scan_numbers(self, override=False):
        """
        Set scan numbers by grouping consecutive integrations on the same phase center.

        This approach mimics the definition of scan number in measurement sets and is
        especially helpful for distinguishing between repeated visits to multiple
        phase centers.

        Parameters
        ----------
        override : bool
            When True, will redefine existing scan numbers. Default is False.
        """
        if self.scan_number_array is None or override:

            # We are grouping based on integrations on a phase center.
            # If this isn't defined, we cannot define scan numbers in this way
            # and default to a single "scan".
            if self.phase_center_catalog is None:
                self.scan_number_array = np.ones((self.Nblts,), dtype=int)

            else:
                sou_list = list(self.phase_center_catalog.keys())
                sou_list.sort()

                slice_list = []
                # This loops over phase centers, finds contiguous integrations with
                # ndimage.label, and then finds the slices to return those contiguous
                # integrations with nd.find_objects.
                for idx in range(self.Nphase):
                    sou_id = self.phase_center_catalog[sou_list[idx]]["cat_id"]
                    slice_list.extend(
                        nd.find_objects(
                            nd.label(self.phase_center_id_array == sou_id)[0]
                        )
                    )

                # Sort by start integration number, which we can extract from
                # the start of each slice in the list.
                slice_list_ord = sorted(slice_list, key=lambda x: x[0].start)

                # Incrementally increase the scan number with each group in
                # slice_list_ord
                scan_array = np.zeros_like(self.phase_center_id_array)
                for ii, slice_scan in enumerate(slice_list_ord):
                    scan_array[slice_scan] = ii + 1

                self.scan_number_array = scan_array

    def _look_in_catalog(
        self,
        cat_name,
        phase_dict=None,
        cat_type=None,
        cat_lon=None,
        cat_lat=None,
        cat_frame=None,
        cat_epoch=None,
        cat_times=None,
        cat_pm_ra=None,
        cat_pm_dec=None,
        cat_dist=None,
        cat_vrad=None,
        ignore_name=False,
    ):
        """
        Check the catalog to see if an existing entry matches provided data.

        This is a helper function for verifying if an entry already exists within
        the catalog, contained within the attribute `phase_center_catalog`.

        Parameters
        ----------
        cat_name : str
            Name of the phase center, which should match a key in
            `phase_center_catalog`.
        phase_dict : dict
            Instead of providing individual parameters, one may provide a dict which
            matches that format used within `phase_center_catalog` for checking for
            existing entries. If used, all other parameters (save for `ignore_name` and
            `cat_name`) are disregarded.
        cat_type : str
            Type of phase center of the entry. Must be one of:
                "sidereal" (fixed RA/Dec),
                "ephem" (RA/Dec that moves with time),
                "driftscan" (fixed az/el position),
                "unphased" (no w-projection, equivalent to `phase_type` == "drift").
        cat_lon : float or ndarray
            Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_lat : float or ndarray
            Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_frame : str
            Coordinate frame that cat_lon and cat_lat are given in. Only used for
            sidereal and ephem phase centers. Can be any of the several supported frames
            in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).
        cat_epoch : str or float
            Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given
            in unites of fractional years, either as a float or as a string with the
            epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).
        cat_times : ndarray of floats
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        cat_pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        cat_pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        cat_dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        ignore_name : bool
            Nominally, `_look_in_catalog` will only look at entries where `cat_name`
            matches the name of an entry in the catalog. However, by setting this to
            True, the method will search all entries in the catalog and see if any
            match all of the provided data (excluding `cat_name`).

        Returns
        -------
        cat_id : int or None
            The unique ID number for the phase center added to the internal catalog.
            This value is used in the `phase_center_id_array` attribute to denote which
            source a given baseline-time corresponds to. If no catalog entry matches,
            then None is returned.
        cat_diffs : int
            The number of differences between the information provided and the catalog
            entry contained within `phase_center_catalog`. If everything matches, then
            `cat_diffs=0`.
        """
        # 1 marcsec tols
        radian_tols = (0, 1 * 2 * np.pi * 1e-3 / (60.0 * 60.0 * 360.0))
        default_tols = (1e-5, 1e-8)
        cat_id = None
        cat_diffs = 0

        # Emulate the defaults that are set if None is detected for
        # unphased and driftscan types.
        if (cat_type == "unphased") or (cat_type == "driftscan"):
            if cat_lon is None:
                cat_lon = 0.0
            if cat_lat is None:
                cat_lat = np.pi / 2
            if cat_frame is None:
                cat_frame = "altaz"

        if phase_dict is None:
            phase_dict = {
                "cat_type": cat_type,
                "cat_lon": cat_lon,
                "cat_lat": cat_lat,
                "cat_frame": cat_frame,
                "cat_epoch": cat_epoch,
                "cat_times": cat_times,
                "cat_pm_ra": cat_pm_ra,
                "cat_pm_dec": cat_pm_dec,
                "cat_dist": cat_dist,
                "cat_vrad": cat_vrad,
            }

        if self.multi_phase_center:
            check_dict = self.phase_center_catalog
        else:
            check_dict = {}
            is_phased = self.phase_type == "phased"
            check_dict[self.object_name] = {
                "cat_type": "sidereal" if is_phased else "unphased",
                "cat_lon": self.phase_center_ra if is_phased else 0.0,
                "cat_lat": self.phase_center_dec if is_phased else np.pi / 2.0,
                "cat_frame": self.phase_center_frame if is_phased else "altaz",
                "cat_epoch": self.phase_center_epoch if is_phased else None,
                "cat_times": None,
                "cat_pm_ra": None,
                "cat_pm_dec": None,
                "cat_dist": None,
                "cat_vrad": None,
                "cat_id": 0,
            }

        tol_dict = {
            "cat_type": None,
            "cat_lon": radian_tols,
            "cat_lat": radian_tols,
            "cat_frame": None,
            "cat_epoch": None,
            "cat_times": default_tols,
            "cat_pm_ra": default_tols,
            "cat_pm_dec": default_tols,
            "cat_dist": default_tols,
            "cat_vrad": default_tols,
        }

        if self.multi_phase_center:
            name_list = list(self.phase_center_catalog.keys())
        else:
            name_list = [self.object_name]

        for name in name_list:
            cat_diffs = 0
            if (cat_name != name) and (not ignore_name):
                continue
            for key in tol_dict.keys():
                if phase_dict.get(key) is not None:
                    if check_dict[name].get(key) is None:
                        cat_diffs += 1
                    elif tol_dict[key] is None:
                        # If no tolerance specified, expect attributes to be identical
                        cat_diffs += phase_dict.get(key) != check_dict[name].get(key)
                    else:
                        # Numpy will throw a Value error if you have two arrays
                        # of different shape, which we can catch to flag that
                        # the two arrays are actually not within tolerance.
                        if np.shape(phase_dict[key]) != np.shape(check_dict[name][key]):
                            cat_diffs += 1
                        else:
                            cat_diffs += not np.allclose(
                                phase_dict[key],
                                check_dict[name][key],
                                tol_dict[key][0],
                                tol_dict[key][1],
                            )
                else:
                    cat_diffs += check_dict[name][key] is not None
            if (cat_diffs == 0) or (cat_name == name):
                cat_id = check_dict[name]["cat_id"]
                break

        return cat_id, cat_diffs

    def _add_phase_center(
        self,
        cat_name,
        cat_type=None,
        cat_lon=None,
        cat_lat=None,
        cat_frame=None,
        cat_epoch=None,
        cat_times=None,
        cat_pm_ra=None,
        cat_pm_dec=None,
        cat_dist=None,
        cat_vrad=None,
        info_source="user",
        force_update=False,
        cat_id=None,
    ):
        """
        Add an entry to the internal object/source catalog.

        This is a helper function for adding a source to the internal
        catalog, contained within the attribute `phase_center_catalog`.

        Parameters
        ----------
        cat_name : str
            Name of the phase center to be added, must be unique (i.e., not contained
            as a key in the UVData attribute `phase_center_catalog`).
        cat_type : str
            Type of phase center to be added. Must be one of:
                "sidereal" (fixed RA/Dec),
                "ephem" (RA/Dec that moves with time),
                "driftscan" (fixed az/el position),
                "unphased" (no w-projection, equivalent to `phase_type` == "drift").
        cat_lon : float or ndarray
            Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_lat : float or ndarray
            Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.
            No default, not used when `cat_type="unphased"`. Expected to be a float for
            sidereal and driftscan phase centers, and an ndarray of floats of shape
            (Npts,) for ephem phase centers.
        cat_frame : str
            Coordinate frame that cat_lon and cat_lat are given in. Only used
            for sidereal and ephem targets. Can be any of the several supported frames
            in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).
        cat_epoch : str or float
            Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given
            in unites of fractional years, either as a float or as a string with the
            epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).
        cat_times : ndarray of floats
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        cat_pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        cat_pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        cat_dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        info_source : str
            Optional string describing the source of the information provided. Used
            primarily in UVData to denote when an ephemeris has been supplied by the
            JPL-Horizons system, user-supplied, or read in by one of the various file
            interpreters. Default is 'user'.
        force_update : bool
            Normally, `_add_phase_center` will throw an error if there already exists an
            identically named phase center with different properties. However, if one
            sets `force_update=True`, the method will overwrite the existing entry in
            `phase_center_catalog` with the paramters supplied, preserving only the
            parameters `cat_id` and `cat_name`. Note that doing this will _not_ update
            other atributes of the `UVData` object. Default is False.
        cat_id : int
            An integer signifying the ID number for the phase center, used in the
            `phase_center_id_array` attribute. The default is for the method to assign
            this value automatically.

        Returns
        -------
        cat_id : int
            The unique ID number for the phase center added to the internal catalog.
            This value is used in the `phase_center_id_array` attribute to denote which
            source a given baseline-time corresponds to.

        Raises
        ------
        ValueError
            If attempting to add a non-unique source name, attempting to use the method
            w/ a UVData object where multi_phase_center=False, or if adding a sidereal
            source without coordinates.
        """
        # Check whether we should actually be doing this in the first place
        if not self.multi_phase_center:
            raise ValueError("Cannot add a source if multi_phase_center != True.")

        if not isinstance(cat_name, str):
            raise ValueError("cat_name must be a string.")

        # The catalog name "unphased" is used internally whenever we have to make a
        # block of data as unphased in a data set. To avoid naming collisions, check
        # that someone hasn't tried to use it for any other purpose.
        if (cat_name == "unphased") and (cat_type != "unphased"):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "cat_name."
            )

        # We currently only have 4 supported types -- make sure the user supplied
        # one of those
        if cat_type not in ["sidereal", "ephem", "driftscan", "unphased"]:
            raise ValueError(
                "Only sidereal, ephem, driftscan or unphased may be used "
                "for cat_type."
            )

        # Both proper motion parameters need to be set together
        if (cat_pm_ra is None) != (cat_pm_dec is None):
            raise ValueError(
                "Must supply values for either both or neither of "
                "cat_pm_ra and cat_pm_dec."
            )

        # If left unset, unphased and driftscan defaulted to Az, El = (0, 90)
        if (cat_type == "unphased") or (cat_type == "driftscan"):
            if cat_lon is None:
                cat_lon = 0.0
            if cat_lat is None:
                cat_lat = np.pi / 2
            if cat_frame is None:
                cat_frame = "altaz"

        # Let's check some case-specific things and make sure all the entires are value
        if (cat_times is None) and (cat_type == "ephem"):
            raise ValueError("cat_times cannot be None for ephem object.")
        elif (cat_times is not None) and (cat_type != "ephem"):
            raise ValueError("cat_times cannot be used for non-ephem phase centers.")

        if (cat_lon is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_lon cannot be None for sidereal phase centers.")

        if (cat_lat is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_lat cannot be None for sidereal phase centers.")

        if (cat_frame is None) and (cat_type in ["sidereal", "ephem"]):
            raise ValueError("cat_frame cannot be None for sidereal phase centers.")
        elif (cat_frame != "altaz") and (cat_type in ["driftscan", "unphased"]):
            raise ValueError(
                "cat_frame must be either None or 'altaz' when the cat type "
                "is either driftscan or unphased."
            )

        if (cat_type == "unphased") and (cat_lon != 0.0):
            raise ValueError(
                "Catalog entries that are unphased must have cat_lon set to either "
                "0 or None."
            )
        if (cat_type == "unphased") and (cat_lat != (np.pi / 2)):
            raise ValueError(
                "Catalog entries that are unphased must have cat_lat set to either "
                "pi/2 or None."
            )

        if (cat_type != "sidereal") and (
            (cat_pm_ra is not None) or (cat_pm_dec is not None)
        ):
            raise ValueError(
                "Non-zero proper motion values (cat_pm_ra, cat_pm_dec) "
                "for cat types other than sidereal are not supported."
            )

        if isinstance(cat_epoch, Time) or isinstance(cat_epoch, str):
            if cat_frame in ["fk4", "fk4noeterms"]:
                cat_epoch = Time(cat_epoch).byear
            else:
                cat_epoch = Time(cat_epoch).jyear
        elif cat_epoch is not None:
            cat_epoch = float(cat_epoch)

        if cat_type == "ephem":
            cat_times = np.array(cat_times, dtype=float).reshape(-1)
            cshape = cat_times.shape
            try:
                cat_lon = np.array(cat_lon, dtype=float).reshape(cshape)
                cat_lat = np.array(cat_lat, dtype=float).reshape(cshape)
                if cat_dist is not None:
                    cat_dist = np.array(cat_dist, dtype=float).reshape(cshape)
                if cat_vrad is not None:
                    cat_vrad = np.array(cat_vrad, dtype=float).reshape(cshape)
            except ValueError:
                raise ValueError(
                    "Object properties -- lon, lat, pm_ra, pm_dec, dist, vrad -- must "
                    "be of the same size as cat_times for ephem phase centers."
                )
        else:
            cat_lon = None if cat_lon is None else float(cat_lon)
            cat_lat = None if cat_lat is None else float(cat_lat)
            cat_pm_ra = None if cat_pm_ra is None else float(cat_pm_ra)
            cat_pm_dec = None if cat_pm_dec is None else float(cat_pm_dec)
            cat_dist = None if cat_dist is None else float(cat_dist)
            cat_vrad = None if cat_vrad is None else float(cat_vrad)

        # Names serve as dict keys, so we need to make sure that they're unique
        if not force_update:
            temp_id, cat_diffs = self._look_in_catalog(
                cat_name,
                cat_type=cat_type,
                cat_lon=cat_lon,
                cat_lat=cat_lat,
                cat_frame=cat_frame,
                cat_epoch=cat_epoch,
                cat_times=cat_times,
                cat_pm_ra=cat_pm_ra,
                cat_pm_dec=cat_pm_dec,
                cat_dist=cat_dist,
                cat_vrad=cat_vrad,
            )

            # If the source does have the same name, check to see if all the
            # atributes match. If so, no problem, go about your business
            if temp_id is not None:
                if cat_diffs == 0:
                    # Everything matches, return the catalog ID of the matching entry
                    return temp_id
                else:
                    raise ValueError(
                        "Cannot add different source with an non-unique name."
                    )

        # We want to create a unique ID for each source, for use in indexing arrays.
        # The logic below ensures that we pick the lowest positive integer that is
        # not currently being used by another source
        used_cat_ids = {
            self.phase_center_catalog[name]["cat_id"]: name
            for name in self.phase_center_catalog.keys()
        }

        if force_update and (cat_name in self.phase_center_catalog.keys()):
            cat_id = self.phase_center_catalog[cat_name]["cat_id"]
        elif cat_id is None:
            cat_id = int(
                np.arange(self.Nphase + 1)[
                    ~np.isin(np.arange(self.Nphase + 1), list(used_cat_ids.keys()))
                ][0]
            )
        elif cat_id in used_cat_ids.keys():
            raise ValueError(
                "Provided cat_id belongs to another source (%s)." % used_cat_ids[cat_id]
            )

        # If source is unique, begin creating a dictionary for it
        phase_dict = {
            "cat_id": cat_id,
            "cat_type": cat_type,
            "cat_lon": cat_lon,
            "cat_lat": cat_lat,
            "cat_frame": cat_frame,
            "cat_epoch": cat_epoch,
            "cat_times": cat_times,
            "cat_pm_ra": cat_pm_ra,
            "cat_pm_dec": cat_pm_dec,
            "cat_vrad": cat_vrad,
            "cat_dist": cat_dist,
            "info_source": info_source,
        }

        self.phase_center_catalog[cat_name] = phase_dict
        self.Nphase = len(self.phase_center_catalog.keys())
        return cat_id

    def _remove_phase_center(self, defunct_name):
        """
        Remove an entry from the internal object/source catalog.

        Removes an entry from the attribute `phase_center_catalog`. Only allowed when
        the UVData object in question is a multi phase center data set (i.e.,
        `multi_phase_center=True`).

        Parameters
        ----------
        defunct_name : str
            Name of the source to be removed

        Raises
        ------
        ValueError
            If multi_phase_center is not set to True
        IndexError
            If the name provided is not found as a key in `phase_center_catalog`
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot remove a phase center if multi_phase_center != True."
            )

        if defunct_name not in self.phase_center_catalog.keys():
            raise IndexError("No source by that name contained in the catalog.")

        del self.phase_center_catalog[defunct_name]
        self.Nphase = len(self.phase_center_catalog.keys())

    def _clear_unused_phase_centers(self):
        """
        Remove objects dictionaries and names that are no longer in use.

        Goes through the `phase_center_catalog` attribute in of a UVData object and
        clears out entries that are no longer being used, and appropriately updates
        `phase_center_id_array` accordingly. This function is not typically called
        by users, but instead is used by other methods.

        Raises
        ------
        ValueError
            If attempting to call the method when multi_phase_center=False.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot remove a phase center if multi_phase_center != True."
            )

        unique_cat_ids = np.unique(self.phase_center_id_array)
        defunct_list = []
        Nphase = 0
        for cat_name in self.phase_center_catalog.keys():
            cat_id = self.phase_center_catalog[cat_name]["cat_id"]
            if cat_id in unique_cat_ids:
                Nphase += 1
            else:
                defunct_list.append(cat_name)

        # Check the number of "good" sources we have -- if we haven't dropped any,
        # then we are free to bail, otherwise update the Nphase attribute
        if Nphase == self.Nphase:
            return

        # Time to kill the entries that are no longer in the source stack
        for defunct_name in defunct_list:
            self._remove_phase_center(defunct_name)

    def _check_for_unphased(self):
        """
        Check which Nblts are unphased in a multi phase center dataset.

        This convenience method returns back a boolean mask to identify which data
        along the Blt axis contains unphased objects (which is only applicable when
        multi_phase_center=True)

        Returns
        -------
        blt_mask : ndarray of bool
            A boolean mask for identifying which elements contain unphased objects
        """
        if self.multi_phase_center:
            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.
            nophase_dict = {
                self.phase_center_catalog[name]["cat_id"]: self.phase_center_catalog[
                    name
                ]["cat_type"]
                == "unphased"
                for name in self.phase_center_catalog.keys()
            }

            # Use dict to construct a bool array
            blt_mask = np.array(
                [nophase_dict[idx] for idx in self.phase_center_id_array], dtype=bool
            )
        else:
            # If not multi phase center, we just need to check the phase type
            blt_mask = np.repeat(self.phase_type == "drift", self.Nblts)

        return blt_mask

    def rename_phase_center(self, old_name, new_name):
        """
        Rename a phase center/catalog entry within a multi phase center data set.

        Parameters
        ----------
        old_name : str
            Phase center name for the data to be renamed.
        new_name : str
            New name for the phase center.

        Raises
        ------
        ValueError
            If attempting to run the method on a non multi phase center data set, if
            `old_name` is not found as a key in `phase_center_catalog`, if `new_name`
            already exists as a key in `phase_center_catalog`, or if attempting to
            name a source "unphased" (which is reserved).
        TypeError
            If `new_name` is not actually a string.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot rename a phase center if multi_phase_center != True."
            )
        if old_name not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % old_name)
        if not isinstance(new_name, str):
            raise TypeError("Value provided to new_name must be a string.")
        if new_name == old_name:
            # This is basically just a no-op, so return to user
            return
        if new_name in self.phase_center_catalog.keys():
            raise ValueError(
                "Must include a unique name for new_name, %s is already present "
                "in phase_center_catalog." % new_name
            )
        if (new_name == "unphased") and (
            self.phase_center_catalog[old_name]["cat_type"] != "unphased"
        ):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "new_name."
            )

        self.phase_center_catalog[new_name] = self.phase_center_catalog[old_name]
        self.Nphase = len(self.phase_center_catalog.keys())
        self._remove_phase_center(old_name)

    def split_phase_center(self, cat_name, new_name, select_mask, downselect=False):
        """
        Rename the phase center (but preserve other properties) of a subset of data.

        Allows you to rename a subset of the data phased to a particular phase center,
        marked by a different name than the original. Useful when you want to phase to
        one position, but want to differentiate different groups of data (e.g., marking
        every other integration to make jackknifing easier).

        Parameters
        ----------
        cat_name : str
            Name of the phase center to be split.
        new_name : str
            New name for the object.
        select_mask : array_like
            Selection mask for which data should be identified as belonging to the phase
            center labeled by `new_name`. Any array-like able to be used as an index
            is suitable -- the most typical is an array of bool with length `Nblts`,
            or an array of ints within the range (-Nblts, Nblts).
        downselect : bool
            If selecting data that is not marked as belonging to `cat_name`,
            normally an error is thrown. By setting this to True, `select_mask` will
            be modified to exclude data not marked as belonging to `cat_name`.

        Raises
        ------
        ValueError
            If attempting to run the method on a non multi phase center data set, if
            `old_name` is not found as a key in `phase_center_catalog`, if `new_name`
            already exists as a key in `phase_center_catalog`, or if attempting to
            name a source "unphased" (which is reserved). Also raised if `select_mask`
            contains data that doesn't belong to `cat_name`, unless setting
            `downselect` to True.
        IndexError
            If select_mask is not a valid indexing array.
        UserWarning
            If all data for `cat_name` was selected (in which case `rename_phase_center`
            is called instead), or if no valid data was selected.
        """
        # Check to make sure that everything lines up with
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use split_phase_center on a non-multi phase center data set."
            )
        if not isinstance(new_name, str):
            raise TypeError("Value provided to new_name must be a string.")
        if cat_name not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % cat_name)
        if new_name in self.phase_center_catalog.keys():
            raise ValueError(
                "The name %s is already found in the catalog, choose another name "
                "for new_name." % new_name
            )
        if (new_name == "unphased") and (
            self.phase_center_catalog[cat_name]["cat_type"] != "unphased"
        ):
            raise ValueError(
                "The name unphased is reserved. Please choose another value for "
                "new_name."
            )

        try:
            inv_mask = np.ones(self.Nblts, dtype=bool)
            inv_mask[select_mask] = False
        except IndexError:
            raise IndexError(
                "select_mask must be an array-like, either of ints with shape (Nblts), "
                "or  of ints within the range (-Nblts, Nblts)."
            )
        # Now that we know nthat all the inputs are sensible, lets make sure that
        # the select_mask choice is sensible
        cat_id = self.phase_center_catalog[cat_name]["cat_id"]

        # If we have selected any entries that don't correspond to the cat_id
        # in question, either downselect or raise an error.
        if np.any(cat_id != self.phase_center_id_array[select_mask]):
            if downselect:
                select_mask = np.logical_and(
                    ~inv_mask, cat_id == self.phase_center_id_array
                )
                inv_mask = ~select_mask
            else:
                raise ValueError(
                    "Data selected with select_mask includes that which has not been "
                    "phased to %s. You can fix this by either revising select_mask or "
                    "setting downselect=True." % cat_name
                )

        # Now check for no(-ish) ops
        if np.all(inv_mask):
            # You didn't actually select anything we could change
            warnings.warn(
                "No relevant data selected - %s not added to the data set" % new_name
            )
        elif not np.any(cat_id == self.phase_center_id_array[inv_mask]):
            # No matching catalog IDs found outside the range, so this is really a
            # replace more than a split.
            warnings.warn(
                "All data for %s selected - using rename_phase_center instead of a "
                "split_phase_center." % cat_name
            )
            self.rename_phase_center(cat_name, new_name)
        else:
            temp_dict = self.phase_center_catalog[cat_name]
            cat_id = self._add_phase_center(
                new_name,
                temp_dict["cat_type"],
                cat_lon=temp_dict.get("cat_lon"),
                cat_lat=temp_dict.get("cat_lat"),
                cat_frame=temp_dict.get("cat_frame"),
                cat_epoch=temp_dict.get("cat_epoch"),
                cat_times=temp_dict.get("cat_times"),
                cat_pm_ra=temp_dict.get("cat_pm_ra"),
                cat_pm_dec=temp_dict.get("cat_pm_dec"),
                cat_dist=temp_dict.get("cat_dist"),
                cat_vrad=temp_dict.get("cat_vrad"),
            )
            self.phase_center_id_array[select_mask] = cat_id

    def merge_phase_centers(self, catname1, catname2, force_merge=False):
        """
        Merge two differently named objects into one within a mutli-phase-ctr data set.

        Recombines two different objects into a single catalog entry -- useful if
        having previously used `split_phase_center` or when multiple objects with
        different names share the same source parameters.

        Parameters
        ----------
        catname1 : str
            String containing the name of the first phase center. Note that this name
            will be preserved in the UVData object.
        catname2 : str
            String containing the name of the second phase center, which will be merged
            into the first phase center. Note that once the merge is complete, all
            information about this phase center is removed.
        force_merge : bool
            Normally, the method will throw an error if the phase center properties
            differ for `catname1` and `catname2`. This can be overriden by setting this
            to True. Default is False.

        Raises
        ------
        ValueError
            If catname1 or catname2 are not found in the UVData object, of if their
            properties differ (and `force_merge` is not set to True).
        UserWarning
            If forcing the merge of two objects with different properties.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use merge_phase_centers on a non-multi phase center data set."
            )
        if catname1 not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % catname1)
        if catname2 not in self.phase_center_catalog.keys():
            raise ValueError("No entry by the name %s in the catalog." % catname2)
        temp_dict = self.phase_center_catalog[catname2]
        # First, let's check and see if the dict entries are identical
        cat_id, cat_diffs = self._look_in_catalog(
            catname1,
            cat_type=temp_dict["cat_type"],
            cat_lon=temp_dict.get("cat_lon"),
            cat_lat=temp_dict.get("cat_lat"),
            cat_frame=temp_dict.get("cat_frame"),
            cat_epoch=temp_dict.get("cat_epoch"),
            cat_times=None,
            cat_pm_ra=None,
            cat_pm_dec=None,
            cat_dist=None,
            cat_vrad=None,
        )
        if cat_diffs != 0:
            if force_merge:
                warnings.warn(
                    "Forcing %s and %s together, even though their attributes "
                    "differ" % (catname1, catname2)
                )
            else:
                raise ValueError(
                    "Attributes of %s and %s differ in phase_center_catalog, which "
                    "means that they are likely not referring to the same position in "
                    "the sky. You can ignore this error and force merge_phase_centers "
                    "to complete by setting force_merge=True, but this should be done "
                    "with substantial caution." % (catname1, catname2)
                )

        old_cat_id = self.phase_center_catalog[catname2]["cat_id"]
        self.phase_center_id_array[self.phase_center_id_array == old_cat_id] = cat_id
        self._remove_phase_center(catname2)

    def print_phase_center_info(
        self, cat_name=None, hms_format=None, return_str=False, print_table=True
    ):
        """
        Print out the details of objects in a mutli-phase-ctr data set.

        Prints out an ASCII table that contains the details of the
        `phase_center_catalog` attribute, which acts as the internal source catalog
        for UVData objects.

        Parameters
        ----------
        cat_name : str
            Optional parameter which, if provided, will cause the method to only return
            information on the phase center with the matching name. Default is to print
            out information on all catalog entries.
        hms_format : bool
            Optional parameter, which if selected, can be used to force coordinates to
            be printed out in Hours-Min-Sec (if set to True) or Deg-Min-Sec (if set to
            False) format. Default is to print out in HMS if all the objects have
            coordinate frames of icrs, gcrs, fk5, fk4, and top; otherwise, DMS format
            is used.
        return_str: bool
            If set to True, the method returns an ASCII string which contains all the
            table infrmation. Default is False.
        print_table : bool
            If set to True, prints the table to the terminal window. Default is True.

        Returns
        -------
        table_str : bool
            If return_str=True, an ASCII string containing the entire table text

        Raises
        ------
        ValueError
            If `cat_name` matches no keys in `phase_center_catalog`.
        """
        r2d = 180.0 / np.pi
        r2m = 60.0 * 180.0 / np.pi
        r2s = 3600.0 * 180.0 / np.pi
        ra_frames = ["icrs", "gcrs", "fk5", "fk4", "topo"]

        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use print_phase_center_info on a "
                "non-multi phase center data set."
            )
        if cat_name is None:
            name_list = list(self.phase_center_catalog.keys())
            dict_list = [self.phase_center_catalog[name] for name in name_list]
        elif cat_name in self.phase_center_catalog.keys():
            name_list = [cat_name]
            dict_list = [self.phase_center_catalog[cat_name]]
        else:
            raise ValueError("No entry by the name %s in the catalog." % cat_name)

        # We want to check and actually see which fields we need to
        # print
        any_lon = any_lat = any_frame = any_epoch = any_times = False
        any_pm_ra = any_pm_dec = any_dist = any_vrad = False

        cat_id_list = []
        for indv_dict in dict_list:
            cat_id_list.append(indv_dict["cat_id"])
            any_lon = any_lon or indv_dict.get("cat_lon") is not None
            any_lat = any_lat or indv_dict.get("cat_lat") is not None
            any_frame = any_frame or indv_dict.get("cat_frame") is not None
            any_epoch = any_epoch or indv_dict.get("cat_epoch") is not None
            any_times = any_times or indv_dict.get("cat_times") is not None
            any_pm_ra = any_pm_ra or indv_dict.get("cat_pm_ra") is not None
            any_pm_dec = any_pm_dec or indv_dict.get("cat_pm_dec") is not None
            any_dist = any_dist or indv_dict.get("cat_dist") is not None
            any_vrad = any_vrad or indv_dict.get("cat_vrad") is not None

            if any_lon and (hms_format is None):
                cat_frame = indv_dict.get("cat_frame")
                cat_type = indv_dict["cat_type"]
                if (cat_frame not in ra_frames) or (cat_type == "driftscan"):
                    hms_format = False

        if hms_format is None:
            hms_format = True

        col_list = []
        col_list.append(
            {"hdr": ("ID", "#"), "fmt": "% 4i", "field": " %4s ", "name": "cat_id"}
        )
        col_list.append(
            {
                "hdr": ("Cat Entry", "Name"),
                "fmt": "%12s",
                "field": " %12s ",
                "name": "cat_name",
            }
        )
        col_list.append(
            {"hdr": ("Type", ""), "fmt": "%9s", "field": " %9s ", "name": "cat_type"}
        )

        if any_lon:
            col_list.append(
                {
                    "hdr": ("Az/Lon/RA", "hours" if hms_format else "deg"),
                    "fmt": "% 3i:%02i:%05.2f",
                    "field": (" %12s " if hms_format else " %13s "),
                    "name": "cat_lon",
                }
            )
        if any_lat:
            col_list.append(
                {
                    "hdr": ("El/Lat/Dec", "deg"),
                    "fmt": "%1s%2i:%02i:%05.2f",
                    "field": " %12s ",
                    "name": "cat_lat",
                }
            )
        if any_frame:
            col_list.append(
                {
                    "hdr": ("Frame", ""),
                    "fmt": "%5s",
                    "field": " %5s ",
                    "name": "cat_frame",
                }
            )
        if any_epoch:
            col_list.append(
                {
                    "hdr": ("Epoch", ""),
                    "fmt": "%7s",
                    "field": " %7s ",
                    "name": "cat_epoch",
                }
            )
        if any_times:
            col_list.append(
                {
                    "hdr": ("   Ephem Range   ", "Start-MJD    End-MJD"),
                    "fmt": " %8.2f  % 8.2f",
                    "field": " %20s ",
                    "name": "cat_times",
                }
            )
        if any_pm_ra:
            col_list.append(
                {
                    "hdr": ("PM-Ra", "mas/yr"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_pm_ra",
                }
            )
        if any_pm_dec:
            col_list.append(
                {
                    "hdr": ("PM-Dec", "mas/yr"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_pm_dec",
                }
            )
        if any_dist:
            col_list.append(
                {
                    "hdr": ("Dist", "pc"),
                    "fmt": "%.1e",
                    "field": " %7s ",
                    "name": "cat_dist",
                }
            )
        if any_vrad:
            col_list.append(
                {
                    "hdr": ("V_rad", "km/s"),
                    "fmt": "%.4g",
                    "field": " %6s ",
                    "name": "cat_vrad",
                }
            )

        top_str = ""
        bot_str = ""
        for col in col_list:
            top_str += col["field"] % col["hdr"][0]
            bot_str += col["field"] % col["hdr"][1]

        info_str = ""

        info_str += top_str + "\n"
        info_str += bot_str + "\n"
        info_str += ("-" * len(bot_str)) + "\n"
        # We want to print in the order of cat_id
        for idx in np.argsort(cat_id_list):
            tbl_str = ""
            for col in col_list:
                # If we have a "special" field that needs extra handling,
                # take care of that up front
                if col["name"] == "cat_name":
                    temp_val = name_list[idx]
                else:
                    temp_val = dict_list[idx][col["name"]]
                if temp_val is None:
                    temp_str = ""
                elif col["name"] == "cat_lon":
                    temp_val = np.median(temp_val)
                    temp_val /= 15.0 if hms_format else 1.0
                    coord_tuple = (
                        np.mod(temp_val * r2d, 360.0),
                        np.mod(temp_val * r2m, 60.0),
                        np.mod(temp_val * r2s, 60.0),
                    )
                    temp_str = col["fmt"] % coord_tuple
                elif col["name"] == "cat_lat":
                    temp_val = np.median(temp_val)
                    coord_tuple = (
                        "-" if temp_val < 0.0 else "+",
                        np.mod(np.abs(temp_val) * r2d, 360.0),
                        np.mod(np.abs(temp_val) * r2m, 60.0),
                        np.mod(np.abs(temp_val) * r2s, 60.0),
                    )
                    temp_str = col["fmt"] % coord_tuple
                elif col["name"] == "cat_epoch":
                    use_byrs = dict_list[idx]["cat_frame"] in ["fk4", "fk4noeterms"]
                    temp_val = ("B%6.1f" if use_byrs else "J%6.1f") % temp_val
                    temp_str = col["fmt"] % temp_val
                elif col["name"] == "cat_times":
                    time_tuple = (
                        np.min(temp_val) - 2400000.5,
                        np.max(temp_val) - 2400000.5,
                    )
                    temp_str = col["fmt"] % time_tuple
                elif (col["name"] == "cat_dist") or (col["name"] == "cat_vrad"):
                    temp_val = np.median(temp_val)
                    temp_str = col["fmt"] % temp_val
                else:
                    temp_str = col["fmt"] % temp_val
                tbl_str += col["field"] % temp_str
            info_str += tbl_str + "\n"

        if print_table:
            # We need this extra bit of code to handle trailing whitespace, since
            # otherwise some checks (e.g., doc check on tutorials) will balk
            print(
                "\n".join([line.rstrip() for line in info_str.split("\n")]), end=""
            )  # pragma: nocover
        if return_str:
            return info_str

    def _update_phase_center_id(self, cat_name, new_cat_id=None, reserved_ids=None):
        """
        Update a phase center with a new catalog ID number.

        Parameters
        ----------
        cat_name : str
            Name of the phase center, which corresponds to a key in the attribute
            `phase_center_catalog`.
        new_cat_id : int
            Optional argument. If supplied, then the method will attempt to use the
            provided value as the new catalog ID, provided that an existing catalog
            entry is not already using the same value. If not supplied, then the
            method will automatically assign a value.
        reserved_ids : array-like in int
            Optional argument. An array-like of ints that denotes which ID numbers
            are already reserved. Useful for when combining two separate catalogs.

        Raises
        ------
        ValueError
            If not using the method on a multi-phase-ctr data set, if there's no entry
            that matches `cat_name`, or of the value `new_cat_id` is already taken.
        """
        if not self.multi_phase_center:
            raise ValueError(
                "Cannot use _update_phase_center_id on a "
                "non-multi phase center data set."
            )

        if cat_name not in self.phase_center_catalog.keys():
            raise ValueError(
                "Cannot run _update_phase_center_id: no entry with name %s." % cat_name
            )

        old_cat_id = self.phase_center_catalog[cat_name]["cat_id"]

        used_cat_ids = [] if (reserved_ids is None) else reserved_ids.copy()
        for name in self.phase_center_catalog.keys():
            if name != cat_name:
                used_cat_ids.append(self.phase_center_catalog[name]["cat_id"])
        if new_cat_id is None:
            # If the old ID is in the reserved list, then we'll need to update it
            if old_cat_id not in used_cat_ids:
                # Don't need to actually update anything
                return
            else:
                new_cat_id = np.arange(len(used_cat_ids) + 1)[
                    ~np.isin(np.arange(len(used_cat_ids) + 1), used_cat_ids)
                ][0]
        else:
            if new_cat_id in used_cat_ids:
                raise ValueError("Catalog ID supplied already taken by another source.")

        self.phase_center_id_array[
            self.phase_center_id_array == old_cat_id
        ] = new_cat_id
        self.phase_center_catalog[cat_name]["cat_id"] = new_cat_id

    def _set_multi_phase_center(self, preserve_phase_center_info=False):
        """
        Set multi_phase_center to True, and adjust required paramteres.

        This method is typically not be called directly by users; instead it is called
        by the file-reading methods to indicate that an object has multiple phase
        centers with in the same data set.

        Parameters
        ----------
        preserve_phase_center_info : bool
            Preserve the source information located in `object_name`, and for phased
            data sets, also `phase_center_ra`, `phase_center_dec`, `phase_center_epoch`
            and `phase_center_frame`. Default is False. Note that setting this to
            False will mean that some required attributes will NOT be correctly set,
            e.g., `phase_center_id_array` -- these will need to be set after calling
            `preserve_phase_center_info` in order for the UVData object to be viable.

        Raises
        ------
        ValueError
            if the telescope_name is not in known telescopes
        """
        # If you have already set this, don't do anything
        if self.multi_phase_center:
            return

        # All multi phase center objects have phase_type="phased", even if they are
        # unphased.
        if self.phase_type == "phased":
            cat_type = "sidereal"
        else:
            self._set_phased()
            cat_type = "unphased"

        self.multi_phase_center = True

        # Mark once-option arrays as now required
        self._phase_center_id_array.required = True
        self._Nphase.required = True
        self._phase_center_catalog.required = True

        # This should technically be required for any phased data set, but for now,
        # we are only gonna make it mandatory for mutli-phase-ctr data sets.
        self._phase_center_app_ra.required = True
        self._phase_center_app_dec.required = True
        self._phase_center_frame_pa.required = True
        self.Nphase = 0
        self.phase_center_catalog = {}
        cat_name = self.object_name
        self.object_name = "multi"

        if preserve_phase_center_info:
            cat_id = self._add_phase_center(
                cat_name,
                cat_type=cat_type,
                cat_lon=self.phase_center_ra,
                cat_lat=self.phase_center_dec,
                cat_frame=self.phase_center_frame,
                cat_epoch=self.phase_center_epoch,
            )
            self.phase_center_id_array = np.zeros(self.Nblts, dtype=int) + cat_id

        self.phase_center_ra = 0.0
        self.phase_center_dec = 0.0
        if self.phase_center_frame is None:
            self.phase_center_frame = "icrs"
        if self.phase_center_epoch is None:
            self.phase_center_epoch = 2000.0

        if (cat_type == "unphased") and preserve_phase_center_info:
            # If moving from unphased, then we'll fill in app_ra and app_dec in
            # the way that we normally would if this were an "unphased" object.
            self._set_app_coords_helper()

    def _set_drift(self):
        """
        Set phase_type to 'drift' and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by phasing methods and file-reading methods to indicate the object has a
        `phase_type` of "drift" and define which metadata are required.
        """
        self.phase_type = "drift"
        self._phase_center_frame.required = False
        self._phase_center_ra.required = False
        self._phase_center_dec.required = False
        self._phase_center_app_ra.required = False
        self._phase_center_app_dec.required = False
        self._phase_center_frame_pa.required = False

    def _set_phased(self):
        """
        Set phase_type to 'phased' and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by phasing methods and file-reading methods to indicate the object has a
        `phase_type` of "phased" and define which metadata are required.
        """
        self.phase_type = "phased"
        self._phase_center_frame.required = True
        self._phase_center_ra.required = True
        self._phase_center_dec.required = True
        self._phase_center_app_ra.required = True
        self._phase_center_app_dec.required = True
        self._phase_center_frame_pa.required = True

    @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):
        """Iterate 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 _set_future_array_shapes(self):
        """
        Set future_array_shapes to True and adjust required parameters.

        This method should not be called directly by users; instead it is called
        by file-reading methods and `use_future_array_shapes` to indicate the
        `future_array_shapes` is True and define expected parameter shapes.

        """
        self.future_array_shapes = True
        self._freq_array.form = ("Nfreqs",)
        self._channel_width.form = ("Nfreqs",)
        for param_name in self._data_params:
            getattr(self, "_" + param_name).form = ("Nblts", "Nfreqs", "Npols")

    def use_future_array_shapes(self):
        """
        Change the array shapes of this object to match the planned future shapes.

        This method sets allows users to convert to the planned array shapes changes
        before the changes go into effect. This method sets the `future_array_shapes`
        parameter on this object to True.

        """
        self._set_future_array_shapes()
        if not self.metadata_only:
            # remove the length-1 spw axis for all data-like parameters
            for param_name in self._data_params:
                setattr(self, param_name, (getattr(self, param_name))[:, 0, :, :])

        # remove the length-1 spw axis for the freq_array
        self.freq_array = self.freq_array[0, :]

        if not self.flex_spw:
            # make channel_width be an array of length Nfreqs rather than a single value
            # (not needed with flexible spws because this is already done in that case)
            self.channel_width = (
                np.zeros(self.Nfreqs, dtype=np.float64) + self.channel_width
            )

    def use_current_array_shapes(self):
        """
        Change the array shapes of this object to match the current future shapes.

        This method sets allows users to convert back to the current array shapes.
        This method sets the `future_array_shapes` parameter on this object to False.
        """
        if not self.flex_spw:
            unique_channel_widths = np.unique(self.channel_width)
            if unique_channel_widths.size > 1:
                raise ValueError(
                    "channel_width parameter contains multiple unique values, but "
                    "only one spectral window is present. Cannot collapse "
                    "channel_width to a single value."
                )
            self._channel_width.form = ()
            self.channel_width = unique_channel_widths[0]

        self.future_array_shapes = False
        for param_name in self._data_params:
            getattr(self, "_" + param_name).form = ("Nblts", 1, "Nfreqs", "Npols")
        if not self.metadata_only:
            for param_name in self._data_params:
                setattr(
                    self, param_name, (getattr(self, param_name))[:, np.newaxis, :, :]
                )

        self._freq_array.form = (
            1,
            "Nfreqs",
        )
        self.freq_array = self.freq_array[np.newaxis, :]

    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"
                        # this parameter is as of this comment most likely a float
                        # since only diameters and antenna positions will probably
                        # trigger this else statement
                        # assign float64 as the type of the array
                        array_val = (
                            np.zeros(self_shape, dtype=np.float64,)
                            + 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(
                f"Telescope {self.telescope_name} is not in known_telescopes."
            )

    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 _set_lsts_helper(self):
        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]
        return

    def _set_app_coords_helper(self, pa_only=False):
        """
        Set values for the apparent coordinate arrays.

        This is an internal helper function, which is not designed to be called by
        users, but rather individual read/write functions for the UVData object.
        Users should use the phase() method for updating/adjusting coordinate values.

        Parameters
        ----------
        pa_only : bool, False
            Skip the calculation of the apparent RA/Dec, and only calculate the
            position angle between `phase_center_frame` and the apparent coordinate
            system. Useful for reading in data formats that do not calculate a PA.
        """
        if self.phase_type != "phased":
            # Uhhh... what do you want me to do? If the dataset isn't phased, there
            # isn't an apparent position to calculate. Time to bail, I guess...
            return
        if pa_only:
            app_ra = self.phase_center_app_ra
            app_dec = self.phase_center_app_dec
        elif self.multi_phase_center:
            app_ra = np.zeros(self.Nblts, dtype=float)
            app_dec = np.zeros(self.Nblts, dtype=float)
            for name in self.phase_center_catalog.keys():
                temp_dict = self.phase_center_catalog[name]
                select_mask = self.phase_center_id_array == temp_dict["cat_id"]
                cat_type = temp_dict["cat_type"]
                lon_val = temp_dict.get("cat_lon")
                lat_val = temp_dict.get("cat_lat")
                epoch = temp_dict.get("cat_epoch")
                frame = temp_dict.get("cat_frame")
                pm_ra = temp_dict.get("cat_pm_ra")
                pm_dec = temp_dict.get("cat_pm_dec")
                vrad = temp_dict.get("vrad")
                dist = temp_dict.get("cat_dist")

                app_ra[select_mask], app_dec[select_mask] = uvutils.calc_app_coords(
                    lon_val,
                    lat_val,
                    frame,
                    coord_epoch=epoch,
                    pm_ra=pm_ra,
                    pm_dec=pm_dec,
                    vrad=vrad,
                    dist=dist,
                    time_array=self.time_array[select_mask],
                    lst_array=self.lst_array[select_mask],
                    telescope_loc=self.telescope_location_lat_lon_alt,
                    coord_type=cat_type,
                )
        else:
            # So this is actually the easier of the two cases -- just use the object
            # properties to fill in the relevant data
            app_ra, app_dec = uvutils.calc_app_coords(
                self.phase_center_ra,
                self.phase_center_dec,
                self.phase_center_frame,
                coord_epoch=self.phase_center_epoch,
                time_array=self.time_array,
                lst_array=self.lst_array,
                telescope_loc=self.telescope_location_lat_lon_alt,
                coord_type="sidereal",
            )

        # Now that we have the apparent coordinates sorted out, we can figure out what
        # it is we want to do with the position angle
        frame_pa = uvutils.calc_frame_pos_angle(
            self.time_array,
            app_ra,
            app_dec,
            self.telescope_location_lat_lon_alt,
            self.phase_center_frame,
            ref_epoch=self.phase_center_epoch,
        )
        self.phase_center_app_ra = app_ra
        self.phase_center_app_dec = app_dec
        self.phase_center_frame_pa = frame_pa

    def set_lsts_from_time_array(self, background=False):
        """Set the lst_array based from the time_array.

        Parameters
        ----------
        background : bool, False
            When set to True, start the calculation on a threading.Thread in the
            background and return the thread to the user.

        Returns
        -------
        proc : None or threading.Thread instance
            When background is set to True, a thread is returned which must be
            joined before the lst_array exists on the UVData object.

        """
        if not background:
            self._set_lsts_helper()
            return
        else:
            proc = threading.Thread(target=self._set_lsts_helper)
            proc.start()
            return proc

    def _check_flex_spw_contiguous(self):
        """
        Check if the spectral windows are contiguous for flex_spw datasets.

        This checks the flex_spw_id_array to make sure that all channels for each
        spectral window are together in one block, versus being interspersed (e.g.,
        channel #1 and #3 is in spw #1, channels #2 and #4 are in spw #2). In theory,
        UVH5 and UVData objects can handle this, but MIRIAD, MIR, UVFITS, and MS file
        formats cannot, so we just consider it forbidden.
        """
        if self.flex_spw:
            exp_spw_ids = np.unique(self.spw_array)
            # This is an internal consistency check to make sure that the indexes match
            # up as expected -- this shouldn't error unless someone is mucking with
            # settings they shouldn't be.
            assert np.all(np.unique(self.flex_spw_id_array) == exp_spw_ids)

            n_breaks = np.sum(self.flex_spw_id_array[1:] != self.flex_spw_id_array[:-1])
            if (n_breaks + 1) != self.Nspws:
                raise ValueError(
                    "Channels from different spectral windows are interspersed with "
                    "one another, rather than being grouped together along the "
                    "frequency axis. Most file formats do not support such "
                    "non-grouping of data."
                )
        else:
            # If this isn't a flex_spw data set, then there is only 1 spectral window,
            # which means that the check always passes
            pass
        return True

    def _check_freq_spacing(self, raise_errors=True):
        """
        Check if frequencies are evenly spaced and separated by their channel width.

        This is a requirement for writing uvfits & miriad files.

        Parameters
        ----------
        raise_errors : bool
            Option to raise errors if the various checks do not pass.

        Returns
        -------
        spacing_error : bool
            Flag that channel spacings or channel widths are not equal.
        chanwidth_error : bool
            Flag that channel spacing does not match channel width.

        """
        spacing_error = False
        chanwidth_error = False
        if self.future_array_shapes:
            freq_spacing = np.diff(self.freq_array)
            freq_array_use = self.freq_array
        else:
            freq_spacing = np.diff(self.freq_array[0])
            freq_array_use = self.freq_array[0]
        if self.Nfreqs == 1:
            # Skip all of this if there is only 1 channel
            pass
        elif self.flex_spw:
            # Check to make sure that the flexible spectral window has indicies set up
            # correctly (grouped together) for this check
            self._check_flex_spw_contiguous()
            diff_chanwidth = np.diff(self.channel_width)
            freq_dir = []
            # We want to grab unique spw IDs, in the order that they appear in the data
            select_mask = np.append((np.diff(self.flex_spw_id_array) != 0), True)
            for idx in self.flex_spw_id_array[select_mask]:
                chan_mask = self.flex_spw_id_array == idx
                freq_dir += [
                    np.sign(np.mean(np.diff(freq_array_use[chan_mask])))
                ] * np.sum(chan_mask)

            # Pop off the first entry, since the above arrays are diff'd
            # (and thus one element shorter)
            freq_dir = np.array(freq_dir[1:])
            # Ignore cases where looking at the boundaries of spectral windows
            bypass_check = self.flex_spw_id_array[1:] != self.flex_spw_id_array[:-1]
            if not np.all(
                np.logical_or(
                    bypass_check,
                    np.isclose(
                        diff_chanwidth,
                        0.0,
                        rtol=self._freq_array.tols[0],
                        atol=self._freq_array.tols[1],
                    ),
                )
            ):
                spacing_error = True
            if not np.all(
                np.logical_or(
                    bypass_check,
                    np.isclose(
                        freq_spacing,
                        self.channel_width[1:] * freq_dir,
                        rtol=self._freq_array.tols[0],
                        atol=self._freq_array.tols[1],
                    ),
                )
            ):
                chanwidth_error = True
        else:
            freq_dir = np.sign(np.mean(freq_spacing))
            if not np.isclose(
                np.min(freq_spacing),
                np.max(freq_spacing),
                rtol=self._freq_array.tols[0],
                atol=self._freq_array.tols[1],
            ):
                spacing_error = True
            if self.future_array_shapes:
                if not np.isclose(
                    np.min(self.channel_width),
                    np.max(self.channel_width),
                    rtol=self._freq_array.tols[0],
                    atol=self._freq_array.tols[1],
                ):
                    spacing_error = True
                else:
                    if not np.isclose(
                        np.mean(freq_spacing),
                        np.mean(self.channel_width) * freq_dir,
                        rtol=self._channel_width.tols[0],
                        atol=self._channel_width.tols[1],
                    ):
                        chanwidth_error = True
            else:
                if not np.isclose(
                    np.mean(freq_spacing),
                    self.channel_width * freq_dir,
                    rtol=self._channel_width.tols[0],
                    atol=self._channel_width.tols[1],
                ):
                    chanwidth_error = True
        if raise_errors and spacing_error:
            raise ValueError(
                "The frequencies are not evenly spaced (probably "
                "because of a select operation) or has differing "
                "values of channel widths. Some file formats "
                "(e.g. uvfits, miriad) and methods (frequency_average) "
                "do not support unevenly spaced frequencies."
            )
        if raise_errors and chanwidth_error:
            raise ValueError(
                "The frequencies are separated by more than their "
                "channel width (probably because of a select operation). "
                "Some file formats (e.g. uvfits, miriad) and "
                "methods (frequency_average) do not support "
                "frequencies that are spaced by more than their "
                "channel width."
            )

        return spacing_error, chanwidth_error

    def _calc_nants_data(self):
        """Calculate the number of antennas from ant_1_array and ant_2_array arrays."""
        return int(np.union1d(self.ant_1_array, self.ant_2_array).size)

    def check(
        self,
        check_extra=True,
        run_check_acceptability=True,
        check_freq_spacing=False,
        strict_uvw_antpos_check=False,
        allow_flip_conj=False,
    ):
        """
        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.
        check_freq_spacing :  bool
            Option to check if frequencies are evenly spaced and the spacing is
            equal to their channel_width. This is not required for UVData
            objects in general but is required to write to uvfits and miriad files.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        allow_flip_conj : bool
            If set to True, and the UVW coordinates do not match antenna positions,
            check and see if flipping the conjugation of the baselines (i.e, multiplying
            the UVWs by -1) resolves  the apparent discrepancy -- and if it does, fix
            the apparent conjugation error in `uvw_array` and `data_array`. Default is
            False.

        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:
            raise ValueError('Phase type must be either "phased" or "drift"')

        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.
        if self.Nants_data != self._calc_nants_data():
            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 set(np.unique(self.ant_1_array)).issubset(self.antenna_numbers):
            raise ValueError("All antennas in ant_1_array must be in antenna_numbers.")
        if not set(np.unique(self.ant_2_array)).issubset(self.antenna_numbers):
            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)
                )

        if run_check_acceptability:
            # check that the uvws make sense given the antenna positions
            # make a metadata only copy of this object to properly calculate uvws
            temp_obj = self.copy(metadata_only=True)

            if temp_obj.phase_center_frame is not None:
                output_phase_frame = temp_obj.phase_center_frame
            else:
                output_phase_frame = "icrs"

            with warnings.catch_warnings():
                warnings.simplefilter("ignore")
                temp_obj.set_uvws_from_antenna_positions(
                    allow_phasing=True, output_phase_frame=output_phase_frame,
                )

            if not np.allclose(temp_obj.uvw_array, self.uvw_array, atol=1):
                max_diff = np.max(np.abs(temp_obj.uvw_array - self.uvw_array))
                if allow_flip_conj and np.allclose(
                    -temp_obj.uvw_array, self.uvw_array, atol=1
                ):
                    warnings.warn(
                        "UVW orientation appears to be flipped, attempting to "
                        "fix by changing conjugation of baselines."
                    )
                    self.uvw_array *= -1
                    self.data_array = np.conj(self.data_array)
                elif not strict_uvw_antpos_check:
                    warnings.warn(
                        "The uvw_array does not match the expected values given "
                        "the antenna positions. The largest discrepancy is "
                        f"{max_diff} meters. This is a fairly common situation "
                        "but might indicate an error in the antenna positions, "
                        "the uvws or the phasing."
                    )
                else:
                    raise ValueError(
                        "The uvw_array does not match the expected values given "
                        "the antenna positions. The largest discrepancy is "
                        f"{max_diff} meters."
                    )

            # 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(
                    # this line used to use np.linalg.norm but it turns out
                    # squaring and sqrt is slightly more efficient unless the array
                    # is "very large".
                    np.sqrt(
                        self.uvw_array[~autos, 0] ** 2
                        + self.uvw_array[~autos, 1] ** 2
                        + self.uvw_array[~autos, 2] ** 2
                    ),
                    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."
                )

        if check_freq_spacing:
            self._check_freq_spacing()

        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
        -------
        UVData
            Copy of self.
        """
        if not metadata_only:
            return super(UVData, self).copy()
        else:
            uv = UVData()
            # include all attributes, not just UVParameter ones.
            for attr in self.__iter__(uvparams_only=False):
                # skip properties
                if isinstance(getattr(type(self), attr, None), property):
                    continue

                # skip data like parameters
                # parameter names have a leading underscore we want to ignore
                if attr.lstrip("_") in self._data_params:
                    continue
                setattr(uv, attr, copy.deepcopy(getattr(self, attr)))

            if uv.future_array_shapes:
                for param_name in uv._data_params:
                    getattr(uv, "_" + param_name).form = ("Nblts", "Nfreqs", "Npols")

            return uv

    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 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
            its 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 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(
                                f"Baseline {key} not found for polarization "
                                "array in data."
                            )
                        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(
                            f"Baseline {key} not found for polarization array in data."
                        )
                    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
    ):
        """
        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]:
                    if self.future_array_shapes:
                        out = data[
                            blt_start:blt_stop:dblt, :, p_start[0] : p_stop[0] : dp[0]
                        ]
                    else:
                        out = data[
                            blt_start:blt_stop:dblt,
                            :,
                            :,
                            p_start[0] : p_stop[0] : dp[0],
                        ]
                else:
                    if self.future_array_shapes:
                        out = data[blt_start:blt_stop:dblt, :, indp[0]]
                    else:
                        out = data[blt_start:blt_stop:dblt, :, :, indp[0]]
            else:
                out = data[ind1]
                if p_reg_spaced[0]:
                    if self.future_array_shapes:
                        out = out[:, :, p_start[0] : p_stop[0] : dp[0]]
                    else:
                        out = out[:, :, :, p_start[0] : p_stop[0] : dp[0]]
                else:
                    if self.future_array_shapes:
                        out = out[:, :, indp[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]:
                    if self.future_array_shapes:
                        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,
                                :,
                                :,
                                p_start[1] : p_stop[1] : dp[1],
                            ]
                        )
                else:
                    if self.future_array_shapes:
                        out = np.conj(data[blt_start:blt_stop:dblt, :, indp[1]])
                    else:
                        out = np.conj(data[blt_start:blt_stop:dblt, :, :, indp[1]])
            else:
                out = data[ind2]
                if p_reg_spaced[1]:
                    if self.future_array_shapes:
                        out = np.conj(out[:, :, p_start[1] : p_stop[1] : dp[1]])
                    else:
                        out = np.conj(out[:, :, :, p_start[1] : p_stop[1] : dp[1]])
                else:
                    if self.future_array_shapes:
                        out = np.conj(out[:, :, indp[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]:
                if self.future_array_shapes:
                    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][:, :, :, p_start[0] : p_stop[0] : dp[0]],
                        out[1][:, :, :, p_start[1] : p_stop[1] : dp[1]],
                        axis=0,
                    )
            else:
                if self.future_array_shapes:
                    out = np.append(
                        out[0][:, :, indp[0]], out[1][:, :, indp[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 self.future_array_shapes:
                if out.shape[2] == 1:
                    # one polarization dimension
                    out = np.squeeze(out, axis=2)
            else:
                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_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_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 list(zip(*self.baseline_to_antnums(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 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)
        # When we select conjugated baselines, there is a call to np.conj()
        # inside of _smart_slicing to correct the data array. This has the
        # unintended consequence of promoting the dtype of an array of np.bool_
        # to np.int8. Rather than having a bunch of special handling for this
        # ~corner case, we instead explicitly cast back to np.bool_ before we
        # hand back to the user.
        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 get_lsts(self, key1, key2=None, key3=None):
        """
        Get the LSTs 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
            LSTs from the lst_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.lst_array[np.append(inds1, inds2)]

    def get_ENU_antpos(self, center=False, pick_data_ants=False):
        """
        Get 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 = np.in1d(telescope_ants, data_ants)
            antpos = antpos[select, :]
            ants = telescope_ants[select]

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

        return antpos, ants

    def _set_method_helper(self, dshape, key1, key2=None, key3=None):
        """
        Extract the indices for setting data, flags, or nsample arrays.

        This is a helper method designed to work with set_data, set_flags, and
        set_nsamples. Given the shape of the data-like array and the keys
        corresponding to where the data should end up, it finds the indices
        that are needed for the `_index_dset` method.

        Parameters
        ----------
        dshape : tuple of int
            The shape of the data-like array. This is used to ensure the array
            is compatible with the indices selected.
        key1, key2, key3 : int or tuple of ints
            Identifier of which flags to set, 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, set all flags for
                    that pol.
                else:
                    interpreted as a baseline number, set all flags for that baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                set all flags for that baseline, pol. pol may be a string or int.

        Returns
        -------
        inds : tuple of int
            The indices in the data-like array to slice into.

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        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)
        if len(ind2) != 0:
            raise ValueError(
                "the requested key is present on the object, but conjugated. Please "
                "conjugate data and keys appropriately and try again"
            )

        if self.future_array_shapes:
            expected_shape = (len(ind1), self.Nfreqs, len(indp[0]))
        else:
            expected_shape = (len(ind1), 1, self.Nfreqs, len(indp[0]))
        if dshape != expected_shape:
            raise ValueError(
                "the input array is not compatible with the shape of the destination. "
                f"Input array shape is {dshape}, expected shape is {expected_shape}."
            )

        blt_slices, blt_sliceable = uvutils._convert_to_slices(
            ind1, max_nslice_frac=0.1
        )
        pol_slices, pol_sliceable = uvutils._convert_to_slices(
            indp[0], max_nslice_frac=0.5
        )

        if self.future_array_shapes:
            inds = [ind1, np.s_[:], indp[0]]
        else:
            inds = [ind1, np.s_[:], np.s_[:], indp[0]]
        if blt_sliceable:
            inds[0] = blt_slices
        if pol_sliceable:
            inds[-1] = pol_slices

        return tuple(inds)

    def set_data(self, data, key1, key2=None, key3=None):
        """
        Set the data array to some values provided by the user.

        Parameters
        ----------
        data : ndarray of complex
            The data to overwrite into the data_array. Must be the same shape as
            the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which data to set, 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.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = data.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.data_array, inds, data)

        return

    def set_flags(self, flags, key1, key2=None, key3=None):
        """
        Set the flag array to some values provided by the user.

        Parameters
        ----------
        flag : ndarray of boolean
            The flags to overwrite into the fkag_array. Must be the same shape
            as the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which flags to set, 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, set all flags for
                    that pol.
                else:
                    interpreted as a baseline number, set all flags for that baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),
                set all flags for that baseline, pol. pol may be a string or int.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = flags.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.flag_array, inds, flags)

        return

    def set_nsamples(self, nsamples, key1, key2=None, key3=None):
        """
        Set the nsamples array to some values provided by the user.

        Parameters
        ----------
        nsamples : ndarray of float
            The nsamples to overwrite into the nsample_array. Must be the same
            shape as the target indices.
        key1, key2, key3 : int or tuple of ints
            Identifier of which nsamples to set, 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, set all data for
                    that pol.
                else:
                    interpreted as a baseline number, set all nsamples for that
                    baseline.

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

            if key is length 3: interpreted as antenna pair and pol (ant1, ant2,
                pol), set all nsamples for that baseline, pol. pol may be a
                string or int.

        Returns
        -------
        None

        Raises
        ------
        ValueError:
            If more than 3 keys are passed, if the requested indices are
            conjugated in the data, if the data array shape is not compatible
            with the indices.

        """
        dshape = nsamples.shape
        inds = self._set_method_helper(dshape, key1, key2, key3)
        uvutils._index_dset(self.nsample_array, inds, nsamples)

        return

    def antpairpol_iter(self, squeeze="default"):
        """
        Iterate 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 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

            if not self.metadata_only:
                orig_data_array = copy.copy(self.data_array)
                for pol_ind in np.arange(self.Npols):
                    if self.future_array_shapes:
                        self.data_array[
                            index_array, :, new_pol_inds[pol_ind]
                        ] = np.conj(orig_data_array[index_array, :, pol_ind])
                    else:
                        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,
        strict_uvw_antpos_check=False,
    ):
        """
        Rearrange polarizations in the event they are not uvfits compatible.

        Parameters
        ----------
        order : str
            Either a string specifying a canonical 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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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]
        if not self.metadata_only:
            # data array is special and large, take is faster here
            if self.future_array_shapes:
                self.data_array = np.take(self.data_array, index_array, axis=2)
                self.nsample_array = self.nsample_array[:, :, index_array]
                self.flag_array = self.flag_array[:, :, index_array]
            else:
                self.data_array = np.take(self.data_array, index_array, axis=3)
                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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    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,
        strict_uvw_antpos_check=False,
    ):
        """
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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 self.phase_center_app_ra is not None:
            self.phase_center_app_ra = self.phase_center_app_ra[index_array]
        if self.phase_center_app_dec is not None:
            self.phase_center_app_dec = self.phase_center_app_dec[index_array]
        if self.phase_center_frame_pa is not None:
            self.phase_center_frame_pa = self.phase_center_frame_pa[index_array]
        if self.multi_phase_center:
            self.phase_center_id_array = self.phase_center_id_array[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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    def reorder_freqs(
        self,
        spw_order=None,
        channel_order=None,
        select_spw=None,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Arrange frequency axis according to desired order.

        Can be applied across the entire frequency axis, or just a subset.

        Parameters
        ----------
        spw_order : str or array_like of int
            A string describing the desired order of spectral windows along the
            frequecy axis. Allowed strings include `number` (sort on spectral window
            number) and `freq` (sort on median frequency). A '-' can be appended
            to signify descending order instead of the default ascending order,
            e.g., if you have SPW #1 and 2, and wanted them ordered as [2, 1],
            you would specify `-number`. Alternatively, one can supply an array
            of length Nspws that specifies the new order, with values matched to
            the specral window number given in `spw_array`. Default is to apply no
            sorting of spectral windows.
        channel_order : str or array_like of int
            A string describing the desired order of frequency channels within a
            spectral window. Allowed strings include `freq`, which will sort channels
            within a spectral window by frequency. A '-' can be optionally appended
            to signify descending order instead of the default ascending order.
            Alternatively, one can supply an index array of length Nfreqs that
            specifies the new order. Default is to apply no sorting of channels
            within a single spectral window. Note that proving an array_like of ints
            will cause the values given to `spw_order` and `select_spw` to be ignored.
        select_spw : int or array_like of int
            An int or array_like of ints which specifies which spectral windows to
            apply sorting. Note that setting this argument will cause the value
            given to `spw_order` to be ignored.
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Returns
        -------
        None

        Raises
        ------
        UserWarning
            Raised if providing arguments to select_spw and freq_screen (the latter
            overrides the former).
        ValueError
            Raised if select_spw contains values not in spw_array, or if freq_screen
            is not the same length as freq_array.

        """
        if (spw_order is None) and (channel_order is None):
            warnings.warn(
                "Not specifying either spw_order or channel_order causes "
                "no sorting actions to be applied. Returning object unchanged."
            )
            return

        # Check to see if there are arguments we should be ignoring
        if isinstance(channel_order, (np.ndarray, list, tuple)):
            if select_spw is not None:
                warnings.warn(
                    "The select_spw argument is ignored when providing an "
                    "array_like of int for channel_order"
                )
            if spw_order is not None:
                warnings.warn(
                    "The spw_order argument is ignored when providing an "
                    "array_like of int for channel_order"
                )
            if not np.all(np.sort(channel_order) == np.arange(self.Nfreqs)):
                raise ValueError(
                    "Index array for channel_order must contain all indicies for "
                    "the frequency axis, without duplicates."
                )
            index_array = channel_order
        else:
            index_array = np.arange(self.Nfreqs)
            # Multipy by 1.0 here to make a cheap copy of the array to manipulate
            temp_freqs = 1.0 * (
                self.freq_array if self.future_array_shapes else self.freq_array[0, :]
            )
            # Same trick for ints -- add 0 to make a cheap copy
            temp_spws = 0 + (
                self.flex_spw_id_array
                if self.flex_spw
                else (np.zeros(self.Nfreqs) + self.spw_array)
            )

            # Check whether or not we need to sort the channels in individual windows
            sort_spw = {idx: channel_order is not None for idx in self.spw_array}
            if select_spw is not None:
                if spw_order is not None:
                    warnings.warn(
                        "The spw_order argument is ignored when providing an "
                        "argument for select_spw"
                    )
                if channel_order is None:
                    warnings.warn(
                        "Specifying select_spw without providing channel_order causes "
                        "no sorting actions to be applied. Returning object unchanged."
                    )
                    return
                if isinstance(select_spw, (np.ndarray, list, tuple)):
                    sort_spw = {idx: idx in select_spw for idx in self.spw_array}
                else:
                    sort_spw = {idx: idx == select_spw for idx in self.spw_array}
            elif spw_order is not None:
                if isinstance(spw_order, (np.ndarray, list, tuple)):
                    if not np.all(np.sort(spw_order) == np.sort(self.spw_array)):
                        raise ValueError(
                            "Index array for spw_order must contain all indicies for "
                            "the frequency axis, without duplicates."
                        )
                elif spw_order not in ["number", "freq", "-number", "-freq", None]:
                    raise ValueError(
                        "spw_order can only be one of 'number', '-number', "
                        "'freq', '-freq', or None"
                    )
                elif self.Nspws > 1:
                    # Only need to do this step if we actually have multiple spws.

                    # If the string starts with a '-', then we will flip the order at
                    # the end of the operation
                    flip_spws = spw_order[0] == "-"

                    if "number" in spw_order:
                        spw_order = np.sort(self.spw_array)
                    elif "freq" in spw_order:
                        spw_order = self.spw_array[
                            np.argsort(
                                [
                                    np.median(temp_freqs[temp_spws == idx])
                                    for idx in self.spw_array
                                ]
                            )
                        ]
                    if flip_spws:
                        spw_order = np.flip(spw_order)
                # Now that we know the spw order, we can apply the first sort
                index_array = np.concatenate(
                    [index_array[temp_spws == idx] for idx in spw_order]
                )
                temp_freqs = temp_freqs[index_array]
                temp_spws = temp_spws[index_array]
            # Spectral windows are assumed sorted at this point
            if channel_order is not None:
                if channel_order not in ["freq", "-freq"]:
                    raise ValueError(
                        "channel_order can only be one of 'freq' or '-freq'"
                    )
                for idx in self.spw_array:
                    if sort_spw[idx]:
                        select_mask = temp_spws == idx
                        subsort_order = index_array[select_mask]
                        subsort_order = subsort_order[
                            np.argsort(temp_freqs[select_mask])
                        ]
                        index_array[select_mask] = (
                            np.flip(subsort_order)
                            if channel_order[0] == "-"
                            else subsort_order
                        )

        if np.all(index_array[1:] > index_array[:-1]):
            # Nothing to do - the data are already sorted!
            return

        # Now update all of the arrays.
        if self.future_array_shapes:
            self.freq_array = self.freq_array[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, :]
        else:
            self.freq_array = self.freq_array[:, 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, :]
        if self.flex_spw:
            self.flex_spw_id_array = self.flex_spw_id_array[index_array]
            self.channel_width = self.channel_width[index_array]
            # Reorder the spw-axis items based on their first appearance in the data
            unique_index = np.sort(
                np.unique(self.flex_spw_id_array, return_index=True)[1]
            )
            self.spw_array = self.flex_spw_id_array[unique_index]

        if self.eq_coeffs is not None:
            self.eq_coeffs = self.eq_coeffs[:, index_array]
        # check if object is self-consistent
        if run_check:
            self.check(
                check_extra=check_extra,
                run_check_acceptability=run_check_acceptability,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

    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] *= eq_coeff1 * eq_coeff2
            else:
                self.data_array[blt_inds] /= eq_coeff1 * eq_coeff2

        return

    def _apply_w_proj(self, new_w_vals, old_w_vals, select_mask=None):
        """
        Apply corrections based on changes to w-coord.

        Adjusts the data to account for a change along the w-axis of a baseline.

        Parameters
        ----------
        new_w_vals: float or ndarray of float
            New w-coordinates for the baselines, in units of meters. Can either be a
            solitary float (helpful for unphasing data, where new_w_vals can be set to
            0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).
        old_w_vals: float or ndarray of float
            Old w-coordinates for the baselines, in units of meters. Can either be a
            solitary float (helpful for unphasing data, where new_w_vals can be set to
            0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).
        select_mask: ndarray of bool
            Array is of shape (Nblts,), where the sum of all enties marked True is
            equal to Nselect (mentioned above).

        Raises
        ------
        IndexError
            If the length of new_w_vals or old_w_vals isn't compatible with
            select_mask, or if select mask isn't the right length.
        """
        # If we only have metadata, then we have no work to do. W00t!
        if self.metadata_only or (self.data_array is None):
            return

        if select_mask is None:
            select_len = self.Nblts
        else:
            try:
                inv_mask = np.ones(self.Nblts, dtype=bool)
                inv_mask[select_mask] = False
                select_mask = ~inv_mask
                select_len = np.sum(select_mask)
            except IndexError:
                raise IndexError(
                    "select_mask must be an array-like, either of ints with shape "
                    "(Nblts), or of ints within the range (-Nblts, Nblts)."
                )

        # Promote everything to float64 ndarrays if they aren't already
        old_w_vals = np.array(old_w_vals, dtype=np.float64)
        old_w_vals.shape += (1,) if (old_w_vals.ndim == 0) else ()
        new_w_vals = np.array(new_w_vals, dtype=np.float64)
        new_w_vals.shape += (1,) if (new_w_vals.ndim == 0) else ()

        # Make sure the lengths of everything make sense
        new_val_len = len(new_w_vals)
        old_val_len = len(old_w_vals)

        if new_val_len not in [1, select_len]:
            raise IndexError(
                "The length of new_w_vals is wrong (expected 1 or %i, got %i)!"
                % (select_len, new_val_len)
            )
        if old_val_len not in [1, select_len]:
            raise IndexError(
                "The length of old_w_vals is wrong (expected 1 or %i, got %i)!"
                % (select_len, old_val_len)
            )

        # Calculate the difference in w terms as a function of freq. Note that the
        # 1/c is there to speed of processing (faster to multiply than divide)
        delta_w_lambda = (
            (new_w_vals - old_w_vals).reshape(-1, 1)
            * (1.0 / const.c.to("m/s").value)
            * self.freq_array.reshape(1, self.Nfreqs)
        )
        if select_mask is None or np.all(select_mask):
            # If all the w values are changing, it turns out to be twice as fast
            # to ditch any sort of selection mask and just do the full multiply.
            if self.future_array_shapes:
                self.data_array *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, :, None]
                )
            else:
                self.data_array *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, None, :, None]
                )
        elif np.any(select_mask):
            # In the case we are _not_ doing all baselines, use a selection mask to
            # only update the values we need. In the worse case, it slows down the
            # processing by ~2x, but it can save a lot on time and memory if only
            # needing to update a select number of baselines.
            if self.future_array_shapes:
                self.data_array[select_mask] *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, :, None]
                )
            else:
                self.data_array[select_mask] *= np.exp(
                    (-1j * 2 * np.pi) * delta_w_lambda[:, None, :, None]
                )

    def unphase_to_drift(
        self, phase_frame=None, use_ant_pos=True, use_old_proj=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. Default is True.
        use_old_proj : bool
            If True, uses the 'old' way of calculating baseline projections.
            Default is False.

        Raises
        ------
        ValueError
            If the phase_type is not 'phased'
        """
        if self.phase_type == "phased":
            pass
        else:
            raise ValueError(
                "The data is already drift scanning; can only unphase phased data."
            )

        if not use_old_proj:
            # Check to make sure that these attributes are actually filled. Otherwise,
            # you probably want to use the old phase method.
            if (
                (not use_ant_pos)
                and (self.phase_center_app_ra is None)
                or (self.phase_center_app_dec is None)
            ):
                raise AttributeError(
                    "Object missing phase_center_ra_app or phase_center_dec_app, "
                    "which implies that the data were phased using the 'old' "
                    "method for phasing (which is not compatible with the new "
                    "version of the code). Please run unphase_to_drift with "
                    "use_old_proj=True to continue."
                )

            telescope_location = self.telescope_location_lat_lon_alt

            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.
            select_mask = ~self._check_for_unphased()

            new_uvw = uvutils.calc_uvw(
                lst_array=self.lst_array,
                use_ant_pos=use_ant_pos,
                uvw_array=self.uvw_array,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=self.ant_1_array,
                ant_2_array=self.ant_2_array,
                old_app_ra=self.phase_center_app_ra,
                old_app_dec=self.phase_center_app_dec,
                old_frame_pa=self.phase_center_frame_pa,
                telescope_lat=telescope_location[0],
                telescope_lon=telescope_location[1],
                to_enu=True,
            )

            self._apply_w_proj(0.0, self.uvw_array[select_mask, 2], select_mask)
            self.uvw_array = new_uvw

            # remove/update phase center
            if self.multi_phase_center:
                self.phase_center_id_array[:] = self._add_phase_center(
                    "unphased", "unphased"
                )
                self.phase_center_app_ra = self.lst_array.copy()
                self.phase_center_app_dec[:] = (
                    np.zeros(self.Nblts) + self.telescope_location[0]
                )
                self.phase_center_frame_pa = np.zeros(self.Nblts)
            else:
                self.phase_center_frame = None
                self.phase_center_ra = None
                self.phase_center_dec = None
                self.phase_center_epoch = None
                self.phase_center_app_ra = None
                self.phase_center_app_dec = None
                self.phase_center_frame_pa = None
                self._set_drift()
            return

        # If you are a multi phase center data set, there's no valid reason to be going
        # back to the old phase method. Time to bail!
        if self.multi_phase_center:
            raise ValueError(
                "Multi phase center data sets are not compatible with the old phasing "
                "method, please set use_old_proj=False."
            )

        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)
            )
            if self.future_array_shapes:
                phs = np.exp(-1j * 2 * np.pi * (-1) * w_lambda[:, :, None])
            else:
                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)

        telescope_location = EarthLocation.from_geocentric(
            *self.telescope_location, unit=units.m
        )
        obs_times = Time(unique_times, format="jd")
        itrs_telescope_locations = telescope_location.get_itrs(obstime=obs_times)
        itrs_telescope_locations = SkyCoord(itrs_telescope_locations)
        # just calling transform_to(coord.GCRS) will delete the obstime information
        # need to re-add obstimes for a GCRS transformation
        if phase_frame == "gcrs":
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())(obstime=obs_times)
            )
        else:
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())
            )

        frame_telescope_locations.representation_type = "cartesian"

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

            obs_time = obs_times[ind]

            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,
                )
                # instead of looping through every ind, find the spot in antenna number
                # array where ant_num <= ant1 < ant_number and similarly for ant2
                # for all baselines in inds
                # then find the uvw coordinate for all at the same time

                # antenna_numbers does not necessarily need to be in order on the object
                # but needs to be in order for the searchsorted to work.
                # ant1_index and ant2_index arrays will preserve the order of blts
                ant_sort = np.argsort(self.antenna_numbers)
                ant1_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_1_array[inds]
                )
                ant2_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_2_array[inds]
                )
                self.uvw_array[inds] = (
                    ant_uvw[ant_sort][ant2_index, :] - ant_uvw[ant_sort][ant1_index, :]
                )

            else:
                frame_telescope_location = frame_telescope_locations[ind]
                itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

                uvws_use = self.uvw_array[inds, :]

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

                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,
                    representation_type="cartesian",
                )

                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_dict_helper(
        self,
        ra,
        dec,
        epoch,
        phase_frame,
        ephem_times,
        cat_type,
        pm_ra,
        pm_dec,
        dist,
        vrad,
        cat_name,
        lookup_name,
        select_mask,
        time_array,
    ):
        """
        Supplies a dictionary with parametrs for the phase method to use.

        This method should not be called directly by users; it is instead a function
        called by the `phase` method, which packages up phase center information
        into a single dictionary to allow for consistent behavior between different
        instantiations of `UVData` objects.
        """
        cat_id = None
        info_source = "user"
        if self.multi_phase_center:
            name_list = list(self.phase_center_catalog.keys())
        else:
            name_list = [self.object_name]

        # We only want to use the JPL-Horizons service if using a non-mutli-phase-ctr
        # instance of a UVData object.
        if lookup_name and (cat_name not in name_list) and self.multi_phase_center:
            if (cat_type is None) or (cat_type == "ephem"):
                [
                    cat_times,
                    cat_lon,
                    cat_lat,
                    cat_dist,
                    cat_vrad,
                ] = uvutils.lookup_jplhorizons(
                    cat_name,
                    time_array,
                    telescope_loc=self.telescope_location_lat_lon_alt,
                )
                cat_type = "ephem"
                cat_pm_ra = cat_pm_dec = None
                cat_epoch = 2000.0
                cat_frame = "icrs"
                info_source = "jplh"
            else:
                raise ValueError(
                    "Unable to find %s in among the existing sources "
                    "recorded in the catalog. Please supply source "
                    "information (e.g., RA and Dec coordinates) and "
                    "set lookup_name=False." % cat_name
                )
        elif (cat_name in name_list) and self.multi_phase_center:
            # If the name of the source matches, then verify that all of its
            # properties are the same as what is stored in phase_center_catalog.
            if lookup_name:
                cat_id = self.phase_center_catalog[cat_name]["cat_id"]
                cat_diffs = 0
            else:
                cat_id, cat_diffs = self._look_in_catalog(
                    cat_name,
                    cat_type=cat_type,
                    cat_lon=ra,
                    cat_lat=dec,
                    cat_frame=phase_frame,
                    cat_epoch=epoch,
                    cat_times=ephem_times,
                    cat_pm_ra=pm_ra,
                    cat_pm_dec=pm_dec,
                    cat_dist=dist,
                    cat_vrad=vrad,
                )
            # If cat_diffs > 0, it means that the catalog entries dont match
            if cat_diffs != 0:
                # Last chance here -- if we have selected all of the data phased
                # to this phase center, then we are still okay.
                if select_mask is None:
                    # We have selected all data, so we're good
                    pass
                elif np.all(
                    np.not_equal(
                        self.phase_center_id_array[~select_mask],
                        self.phase_center_catalog[cat_name]["cat_id"],
                    )
                ):
                    # We have selected a subset of the data that contains
                    # everything that was phased to the object
                    pass
                else:
                    raise ValueError(
                        "The entry name %s is not unique, but arguments to phase "
                        "do not match that stored in phase_center_catalog. Try using a "
                        "different name, using select_mask to select all data "
                        "phased to this phase center, or using the existing phase "
                        "center information by setting lookup_name=True." % cat_name
                    )
                cat_type = "sidereal" if cat_type is None else cat_type
                cat_lon = ra
                cat_lat = dec
                cat_frame = phase_frame
                cat_epoch = epoch
                cat_times = ephem_times
                cat_pm_ra = pm_ra
                cat_pm_dec = pm_dec
                cat_dist = dist
                cat_vrad = vrad
            else:
                temp_dict = self.phase_center_catalog[cat_name]
                cat_id = temp_dict["cat_id"]
                cat_type = temp_dict["cat_type"]
                info_source = temp_dict["info_source"]
                # Get here will return None if no key found, which we want
                cat_lon = temp_dict.get("cat_lon")
                cat_lat = temp_dict.get("cat_lat")
                cat_frame = temp_dict.get("cat_frame")
                cat_epoch = temp_dict.get("cat_epoch")
                cat_times = temp_dict.get("cat_times")
                cat_pm_ra = temp_dict.get("cat_pm_ra")
                cat_pm_dec = temp_dict.get("cat_pm_dec")
                cat_dist = temp_dict.get("cat_dist")
                cat_vrad = temp_dict.get("cat_vrad")
        else:
            # Either this is not a multi phase center data set, or the name of the
            # source is unique!
            cat_type = "sidereal" if cat_type is None else cat_type
            cat_lon = ra
            cat_lat = dec
            cat_frame = phase_frame
            cat_epoch = epoch
            cat_times = ephem_times
            cat_pm_ra = pm_ra
            cat_pm_dec = pm_dec
            cat_dist = dist
            cat_vrad = vrad

        if cat_epoch is None:
            cat_epoch = 1950.0 if (cat_frame in ["fk4", "fk4noeterms"]) else 2000.0
        if isinstance(cat_epoch, str) or isinstance(cat_epoch, Time):
            cat_epoch = Time(cat_epoch).to_value(
                "byear" if cat_frame in ["fk4", "fk4noeterms"] else "jyear"
            )

        # One last check - if we have an ephem phase center, lets make sure that the
        # time range of the ephemeris encapsulates the entire range of time_array
        check_ephem = False
        if cat_type == "ephem":
            # Take advantage of this to make sure that lat, lon, and times are all
            # ndarray types
            cat_lon = np.array(cat_lon, dtype=float)
            cat_lat = np.array(cat_lat, dtype=float)
            cat_times = np.array(cat_times, dtype=float)
            cat_lon.shape += (1,) if (cat_lon.ndim == 0) else ()
            cat_lat.shape += (1,) if (cat_lat.ndim == 0) else ()
            cat_times.shape += (1,) if (cat_times.ndim == 0) else ()
            check_ephem = np.min(time_array) < np.min(cat_times)
            check_ephem = check_ephem or (np.max(time_array) > np.max(cat_times))
            # If the ephem was supplied by JPL-Horizons, then we can easily expand
            # it to cover the requested range.
            if check_ephem and (info_source == "jplh"):
                # Concat the two time ranges to make sure that we cover both the
                # requested time range _and_ the original time range.
                [
                    cat_times,
                    cat_lon,
                    cat_lat,
                    cat_dist,
                    cat_vrad,
                ] = uvutils.lookup_jplhorizons(
                    cat_name,
                    np.concatenate((np.reshape(time_array, -1), cat_times)),
                    telescope_loc=self.telescope_location_lat_lon_alt,
                )
            elif check_ephem:
                # The ephem was user-supplied during the call to the phase method,
                # raise an error to ask for more ephem data.
                raise ValueError(
                    "Ephemeris data does not cover the entirety of the time range "
                    "attempted to be phased. Please supply additional ephem data "
                    "(and if used, set lookup_name=False)."
                )
        # Time to repackage everything into a dict
        phase_dict = {
            "cat_name": cat_name,
            "cat_type": cat_type,
            "cat_lon": cat_lon,
            "cat_lat": cat_lat,
            "cat_frame": cat_frame,
            "cat_epoch": cat_epoch,
            "cat_times": cat_times,
            "cat_pm_ra": cat_pm_ra,
            "cat_pm_dec": cat_pm_dec,
            "cat_dist": cat_dist,
            "cat_vrad": cat_vrad,
            "info_source": info_source,
            "cat_id": cat_id,
        }

        # Finally, make sure everything is a float or an ndarray of floats
        for key in phase_dict.keys():
            if isinstance(phase_dict[key], np.ndarray):
                phase_dict[key] = phase_dict[key].astype(float)
            elif (key == "cat_id") and (phase_dict[key] is not None):
                # If this is the cat_id, make it an int
                phase_dict[key] == int(phase_dict[key])
            elif not ((phase_dict[key] is None) or isinstance(phase_dict[key], str)):
                phase_dict[key] = float(phase_dict[key])

        return phase_dict

    def phase(
        self,
        ra,
        dec,
        epoch="J2000",
        phase_frame="icrs",
        cat_type=None,
        ephem_times=None,
        pm_ra=None,
        pm_dec=None,
        dist=None,
        vrad=None,
        cat_name=None,
        lookup_name=False,
        use_ant_pos=True,
        allow_rephase=True,
        orig_phase_frame=None,
        select_mask=None,
        cleanup_old_sources=True,
        use_old_proj=False,
        fix_old_proj=True,
    ):
        """
        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.

        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 transformed 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.
        cat_type : str
            Type of phase center to be added. Must be one of:
            "sidereal" (fixed RA/Dec), "ephem" (RA/Dec that moves with time),
            "driftscan" (fixed az/el position). Default is "sidereal", other selections
            are only permissible if `multi_phase_center=True`.
        ephem_times : ndarray of float
            Only used when `cat_type="ephem"`. Describes the time for which the values
            of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).
        pm_ra : float
            Proper motion in RA, in units of mas/year. Only used for sidereal phase
            centers.
        pm_dec : float
            Proper motion in Dec, in units of mas/year. Only used for sidereal phase
            centers.
        dist : float or ndarray of float
            Distance of the source, in units of pc. Only used for sidereal and ephem
            phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        vrad : float or ndarray of float
            Radial velocity of the source, in units of km/s. Only used for sidereal and
            ephem phase centers. Expected to be a float for sidereal and driftscan phase
            centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.
        cat_name :str
            Name of the phase center being phased to. Required if
            `multi_phase_center=True`, otherwise `object_name` set to this value.
        lookup_name : bool
            Only used if `multi_phase_center=True`, allows the user to lookup phase
            center infomation in `phase_center_catalog` (for the entry matching
            `cat_name`). Setting this to `True` will ignore the values supplied to the
            `ra`, `dec`, `epoch`, `phase_frame`, `pm_ra`, `pm_dec`, `dist`, `vrad`.
        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.
        select_mask : ndarray of bool
            Optional mask for selecting which data to operate on along the blt-axis,
            only used if with multi phase center data sets (i.e.,
            `multi_phase_center=True`). Shape is (Nblts,).
        use_old_proj : bool
            If True, use the "old" method for calculating baseline uvw-coordinates,
            which involved using astropy to move antenna positions (in ITRF) into
            the requested reference frame (either GCRS or ICRS). Default is False.
        fix_old_proj : bool
            If True, the method will convert a data set with coordinates calculated
            using the "old" method, correct them, and then produce new coordinates
            using the "new" method.

        Raises
        ------
        ValueError
            If the phase_type is 'phased' and allow_rephase is False
        """
        # Non-multi phase center datasets don't (yet) have a way of recording the
        # 'extra' source properties, or selection mask, so make sure that these aren't
        # using any of those if looking at a single object.
        if not self.multi_phase_center:
            if select_mask is not None:
                raise ValueError(
                    "Cannot apply a selection mask if multi_phase_center=False. "
                    "Remove the select_mask argument to continue."
                )

            check_params = [pm_ra, pm_dec, dist, vrad]
            check_names = ["pm_ra", "pm_dec", "dist", "vrad"]
            for name, value in zip(check_names, check_params):
                if value not in [0, None]:
                    raise ValueError(
                        "Non-zero values of %s not supported when "
                        "multi_phase_center=False." % name
                    )

            if (cat_type != "sidereal") and (cat_type is not None):
                raise ValueError(
                    "Only sidereal sources are supported when multi_phase_center=False"
                )
            if lookup_name:
                raise ValueError(
                    "Object name lookup is not supported when multi_phase_center=False"
                )
        else:
            if cat_name is None:
                raise ValueError(
                    "Must supply a unique name for cat_name when phasing a "
                    "multi phase center data set."
                )

        # If you are a multi phase center data set, there's no valid reason to be going
        # back to the old phase method. Time to bail!
        if self.multi_phase_center and use_old_proj:
            raise NotImplementedError(
                "Multi phase center data sets are not compatible with the old phasing "
                "method, please set use_old_proj=False."
            )

        if not allow_rephase and (self.phase_type == "phased"):
            raise ValueError(
                "The data is already phased; set allow_rephase"
                " to True to unphase and rephase."
            )

        # Right up front, we're gonna split off the piece of the code that
        # does the phasing using the "new" method, since its a lot more flexible
        # and because I think at some point, everything outside of this loop
        # can be deprecated
        if not use_old_proj:
            needs_fix = (
                (not use_ant_pos)
                and (self.phase_type == "phased")
                and (
                    self.phase_center_app_ra is None
                    or self.phase_center_app_dec is None
                )
            )
            if needs_fix:
                if fix_old_proj:
                    # So to fix the 'old' projection, we use the unphase_to_drift
                    # method with the 'old' projection to bring the data set back
                    # to ENU, and then we can move from there. Of course, none of
                    # this is actually neccessary if calculating the coordinates
                    # from antenna positions, so you do you, puvudataset.
                    self.unphase_to_drift(
                        phase_frame=orig_phase_frame,
                        use_old_proj=True,
                        use_ant_pos=use_ant_pos,
                    )
                else:
                    raise AttributeError(
                        "Data missing phase_center_ra_app or phase_center_dec_app, "
                        "which implies that the data were phased using the 'old' "
                        "method for phasing (which is not compatible with the new "
                        "version of the code). You can fix this by calling the "
                        "phase method with fix_old_proj=True, or can otherwise "
                        "proceed by using the 'old' projection method by setting "
                        "use_old_proj=True."
                    )

            # Grab all the meta-data we need for the rotations
            time_array = self.time_array
            lst_array = self.lst_array
            uvw_array = self.uvw_array
            ant_1_array = self.ant_1_array
            ant_2_array = self.ant_2_array
            old_w_vals = self.uvw_array[:, 2].copy()
            old_w_vals[self._check_for_unphased()] = 0.0
            old_app_ra = self.phase_center_app_ra
            old_app_dec = self.phase_center_app_dec
            old_frame_pa = self.phase_center_frame_pa
            # Check and see if we have any unphased objects, in which case
            # their w-values should be zeroed out.

            if select_mask is not None:
                if len(select_mask) != self.Nblts:
                    raise IndexError("Selection mask must be of length Nblts.")
                time_array = time_array[select_mask]
                lst_array = lst_array[select_mask]
                uvw_array = uvw_array[select_mask, :]
                ant_1_array = ant_1_array[select_mask]
                ant_2_array = ant_2_array[select_mask]
                if isinstance(old_w_vals, np.ndarray):
                    old_w_vals = old_w_vals[select_mask]

            # Before moving forward with the heavy calculations, we need to do some
            # basic housekeeping to make sure that we've got the coordinate data that
            # we need in order to proceed.
            phase_dict = self._phase_dict_helper(
                ra,
                dec,
                epoch,
                phase_frame,
                ephem_times,
                cat_type,
                pm_ra,
                pm_dec,
                dist,
                vrad,
                cat_name,
                lookup_name,
                select_mask,
                time_array,
            )

            # We got the meta-data, now handle calculating the apparent coordinates.
            # First, check if we need to look up the phase center in question
            new_app_ra, new_app_dec = uvutils.calc_app_coords(
                phase_dict["cat_lon"],
                phase_dict["cat_lat"],
                coord_frame=phase_dict["cat_frame"],
                coord_epoch=phase_dict["cat_epoch"],
                coord_times=phase_dict["cat_times"],
                coord_type=phase_dict["cat_type"],
                time_array=time_array,
                lst_array=lst_array,
                pm_ra=phase_dict["cat_pm_ra"],
                pm_dec=phase_dict["cat_pm_dec"],
                vrad=phase_dict["cat_vrad"],
                dist=phase_dict["cat_dist"],
                telescope_loc=self.telescope_location_lat_lon_alt,
            )

            # Now calculate position angles. If this is a single phase center data set,
            # the ref frame is always equal to the source coordinate frame. In a multi
            # phase center data set, those two components are allowed to be decoupled.
            new_frame_pa = uvutils.calc_frame_pos_angle(
                time_array,
                new_app_ra,
                new_app_dec,
                self.telescope_location_lat_lon_alt,
                self.phase_center_frame if self.multi_phase_center else phase_frame,
                ref_epoch=self.phase_center_epoch if self.multi_phase_center else epoch,
            )

            # Now its time to do some rotations and calculate the new coordinates
            new_uvw = uvutils.calc_uvw(
                app_ra=new_app_ra,
                app_dec=new_app_dec,
                frame_pa=new_frame_pa,
                lst_array=lst_array,
                use_ant_pos=use_ant_pos,
                uvw_array=uvw_array,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=ant_1_array,
                ant_2_array=ant_2_array,
                old_app_ra=old_app_ra,
                old_app_dec=old_app_dec,
                old_frame_pa=old_frame_pa,
                telescope_lat=self.telescope_location_lat_lon_alt[0],
                telescope_lon=self.telescope_location_lat_lon_alt[1],
                from_enu=(self.phase_type == "drift"),
            )

            # With all operations complete, we now start manipulating the UVData object
            if self.multi_phase_center:
                cat_id = self._add_phase_center(
                    phase_dict["cat_name"],
                    phase_dict["cat_type"],
                    cat_lon=phase_dict["cat_lon"],
                    cat_lat=phase_dict["cat_lat"],
                    cat_frame=phase_dict["cat_frame"],
                    cat_epoch=phase_dict["cat_epoch"],
                    cat_times=phase_dict["cat_times"],
                    cat_pm_ra=phase_dict["cat_pm_ra"],
                    cat_pm_dec=phase_dict["cat_pm_dec"],
                    cat_dist=phase_dict["cat_dist"],
                    cat_vrad=phase_dict["cat_vrad"],
                    info_source=phase_dict["info_source"],
                    cat_id=phase_dict["cat_id"],
                    force_update=True,
                )

            # Now its time to update the raw data. This will return empty if
            # metadata_only is set to True. Note that cat_type is only allowed
            # to be unphased if this is a multi_phase_center data set.
            new_w_vals = 0.0 if (cat_type == "unphased") else new_uvw[:, 2]
            self._apply_w_proj(new_w_vals, old_w_vals, select_mask=select_mask)

            # Finally, we now take it upon ourselves to update some metadata. What we
            # do here will depend a little bit on whether or not we have a selection
            # mask active, since most everything is affected by that.
            if select_mask is not None:
                self.uvw_array[select_mask] = new_uvw
                self.phase_center_app_ra[select_mask] = new_app_ra
                self.phase_center_app_dec[select_mask] = new_app_dec
                self.phase_center_frame_pa[select_mask] = new_frame_pa
                if self.multi_phase_center:
                    self.phase_center_id_array[select_mask] = cat_id
            else:
                self.uvw_array = new_uvw
                self.phase_center_app_ra = new_app_ra
                self.phase_center_app_dec = new_app_dec
                self.phase_center_frame_pa = new_frame_pa
                if self.multi_phase_center:
                    self.phase_center_id_array[:] = cat_id

            # If not multi phase center, make sure to update the ra/dec values, since
            # otherwise we'll have no record of source properties.
            if not self.multi_phase_center:
                # Make sure this is actually marked as a phased dataset now
                self._set_phased()

                # Update the phase center properties
                self.phase_center_ra = phase_dict["cat_lon"]
                self.phase_center_dec = phase_dict["cat_lat"]
                self.phase_center_epoch = phase_dict["cat_epoch"]
                self.phase_center_frame = phase_dict["cat_frame"]
                if cat_name is not None:
                    self.object_name = cat_name
            else:
                self.phase_center_ra = 0.0
                self.phase_center_dec = 0.0
                self.phase_center_epoch = 2000.0
                if cleanup_old_sources:
                    self._clear_unused_phase_centers()
            # All done w/ the new phase method
            return
        warnings.warn(
            "The original `phase` method is deprecated, and will be removed in "
            "pyuvdata v3.0 (although `fix_phase` will remain for longer). "
            "Note that the old and new phase methods are NOT compatible with one "
            "another, so if you have phased using the old method, you should call "
            "the phase method with fix_old_proj=True, or otherwise can use the "
            "unphase_to_drift method with use_old_proj=True to undo the old "
            "corrections before using the new version of the phase method.",
            DeprecationWarning,
        )
        if self.phase_type == "drift":
            pass
        elif self.phase_type == "phased":
            # To get to this point, allow_rephase has to be true
            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,
                    use_old_proj=True,
                )
        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
        self.phase_center_app_ra = None
        self.phase_center_app_dec = None
        self.phase_center_frame_pa = None

        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)

        telescope_location = EarthLocation.from_geocentric(
            *self.telescope_location, unit=units.m
        )
        obs_times = Time(unique_times, format="jd")

        itrs_telescope_locations = telescope_location.get_itrs(obstime=obs_times)
        itrs_telescope_locations = SkyCoord(itrs_telescope_locations)
        # just calling transform_to(coord.GCRS) will delete the obstime information
        # need to re-add obstimes for a GCRS transformation
        if phase_frame == "gcrs":
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())(obstime=obs_times)
            )
        else:
            frame_telescope_locations = itrs_telescope_locations.transform_to(
                getattr(coord, f"{phase_frame}".upper())
            )
        # set the representation_type to cartensian to get xyz later
        frame_telescope_locations.representation_type = "cartesian"

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

            obs_time = obs_times[ind]

            itrs_lat_lon_alt = self.telescope_location_lat_lon_alt

            frame_telescope_location = frame_telescope_locations[ind]

            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
                )
                # instead of looping through every ind, find the spot in antenna number
                # array where ant_num <= ant1 < ant_number and similarly for ant2
                # for all baselines in inds
                # then find the uvw coordinate for all at the same time

                # antenna_numbers does not necessarily need to be in order on the object
                # but needs to be in order for the searchsorted to work.
                # ant1_index and ant2_index arrays will preserve the order of blts
                ant_sort = np.argsort(self.antenna_numbers)
                ant1_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_1_array[inds]
                )
                ant2_index = np.searchsorted(
                    self.antenna_numbers[ant_sort], self.ant_2_array[inds]
                )
                self.uvw_array[inds] = (
                    frame_ant_uvw[ant_sort][ant2_index, :]
                    - frame_ant_uvw[ant_sort][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)
            )
            if self.future_array_shapes:
                phs = np.exp(-1j * 2 * np.pi * w_lambda[:, :, None])
            else:
                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=True,
        use_old_proj=False,
        allow_rephase=True,
        orig_phase_frame=None,
        select_mask=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.
        select_mask : array_like
            Selection mask for which data should be rephased, only applicable if
            `multi_phase_center=True`. Any array-like able to be used as an index
            is suitable -- the most typical is an array of bool with length `Nblts`,
            or an array of ints within the range (-Nblts, Nblts).

        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.floating)):
            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.rad
        zenith_dec = obs_zenith_coord.dec.rad

        self.phase(
            zenith_ra,
            zenith_dec,
            epoch="J2000",
            phase_frame=phase_frame,
            use_ant_pos=use_ant_pos,
            use_old_proj=use_old_proj,
            allow_rephase=allow_rephase,
            orig_phase_frame=orig_phase_frame,
            select_mask=select_mask,
            cat_name=("zenith_at_jd%f" % self.time_array[0])
            if self.multi_phase_center
            else None,
        )

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

        Parameters
        ----------
        allow_phasing : bool
            Option for phased data. If data is phased and allow_phasing=True,
            UVWs will be calculated and the visibilities will be rephased. Default
            is False.
        require_phasing : bool
            Option for phased data. If data is phased and require_phasing=True, then
            the method will throw an error unless allow_phasing=True, otherwise if
            `require_phasing=False` and `allow_phasing=False`, the UVWs will be
            recalculated but the data will NOT be rephased. This feature should only be
            used in limited circumstances (e.g., when certain metadata like exact time
            are not trusted), as misuse can significantly corrupt data.
        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 and use_old_proj
            is True.
        output_phase_frame : str
            The astropy frame to phase to. Either 'icrs' or 'gcrs'. Only used if
            allow_phasing is True, and use_old_proj is True.
        use_old_proj : bool
            If set to True, uses the 'old' method of calculating baseline vectors.
            Default is False, which will instead use the 'new' method.

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

        Warns
        -----
        UserWarning
            If the phase_type is 'phased'

        """
        if not use_old_proj and not (
            self.phase_center_app_ra is None or self.phase_center_app_dec is None
        ):
            if (self.phase_type == "phased") and (
                not (allow_phasing) and require_phasing
            ):
                raise ValueError(
                    "UVW recalculation requires either unphased data or the ability "
                    "to rephase data. Use unphase_to_drift or set allow_phasing=True."
                )

            telescope_location = self.telescope_location_lat_lon_alt
            new_uvw = uvutils.calc_uvw(
                app_ra=self.phase_center_app_ra,
                app_dec=self.phase_center_app_dec,
                frame_pa=self.phase_center_frame_pa,
                lst_array=self.lst_array,
                use_ant_pos=True,
                antenna_positions=self.antenna_positions,
                antenna_numbers=self.antenna_numbers,
                ant_1_array=self.ant_1_array,
                ant_2_array=self.ant_2_array,
                telescope_lat=telescope_location[0],
                telescope_lon=telescope_location[1],
                from_enu=(self.phase_type != "phased"),
                to_enu=(self.phase_type != "phased"),
            )
            if self.phase_type == "phased":
                if allow_phasing:
                    old_w_vals = self.uvw_array[:, 2].copy()
                    old_w_vals[self._check_for_unphased()] = 0.0
                    self._apply_w_proj(new_uvw[:, 2], old_w_vals)
                else:
                    warnings.warn(
                        "Recalculating uvw_array without adjusting visibility phases "
                        "-- this can introduce significant errors if used incorrectly."
                    )
            # If the data are phased, we've already adjusted the phases. Now we just
            # need to update the uvw's and we are home free.
            self.uvw_array = new_uvw
            return

        # mutli-phase-ctr datasets should never use the 'old' uvw calculation method
        if self.multi_phase_center:
            raise NotImplementedError(
                "Multi phase center data sets are not compatible with the old uvw "
                "calculation  method, please set use_old_proj=False."
            )

        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, use_old_proj=True,
                )
            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)
        # this code used to loop through every bl in the unique,
        # find the index into self.antenna_array of ant1 and ant2
        # and fill out the self.uvw_array for all matching bls.

        # instead, find the indices and reverse inds from the unique,
        # create the unique ant1 and ant2 arrays
        # use searchsorted to find the index of the antenna numbers into ant1 and ant2
        # create the unique uvw array then broadcast to self.uvw_array
        bls, unique_inds, reverse_inds = np.unique(
            self.baseline_array, return_index=True, return_inverse=True
        )

        # antenna_numbers does not necessarily need to be in order on the object
        # but needs to be in order for the searchsorted to work.
        # ant1_index and ant2_index arrays will preserve the order of blts
        ant_sort = np.argsort(self.antenna_numbers)
        ant1_index = np.searchsorted(
            self.antenna_numbers[ant_sort], self.ant_1_array[unique_inds],
        )
        ant2_index = np.searchsorted(
            self.antenna_numbers[ant_sort], self.ant_2_array[unique_inds],
        )
        _uvw_array = np.zeros((bls.size, 3))
        _uvw_array = (
            antenna_locs_ENU[ant_sort][ant2_index, :]
            - antenna_locs_ENU[ant_sort][ant1_index, :]
        )
        self.uvw_array = _uvw_array[reverse_inds]
        if phase_type == "phased":
            self.phase(
                phase_center_ra,
                phase_center_dec,
                phase_center_epoch,
                phase_frame=output_phase_frame,
                use_old_proj=use_old_proj,
            )

    def fix_phase(
        self, use_ant_pos=True,
    ):
        """
        Fix the data to be consistent with the new phasing method.

        This is a simple utility function for updating UVW coordinates calculated using
        the 'old' phasing algorithm with those calculated by the 'new' algorithm. Note
        that this step is required for using the new methods with data phased using the
        `phase` methiod prior to pyuvdata v2.2.

        Parameters
        ----------
        use_ant_pos : bool
            Use the antenna positions for determining UVW coordinates. Default is True.
        """
        # If we are missing apparent coordinates, we should calculate those now
        if (self.phase_center_app_ra is None) or (self.phase_center_app_dec is None):
            self._set_app_coords_helper()

        # If we are just using the antenna positions, we don't actually need to do
        # anything, since the new baseline vectors will be unaffected by the prior
        # phasing method, and the delta_w values already get correctly corrected for.
        if use_ant_pos:
            self.set_uvws_from_antenna_positions(
                allow_phasing=True, use_old_proj=False,
            )
        elif self.multi_phase_center:
            raise ValueError(
                "Cannot run fix_phase on a mutli-phase-ctr dataset without using the "
                "antenna positions. Please set use_ant_pos=True."
            )
        else:
            # Record the old values
            phase_center_ra = self.phase_center_ra
            phase_center_dec = self.phase_center_dec
            phase_center_frame = self.phase_center_frame
            phase_center_epoch = self.phase_center_epoch
            cat_name = self.object_name

            # Bring the UVWs back to ENU/unphased
            self.unphase_to_drift(
                phase_frame=self.phase_center_frame,
                use_ant_pos=False,
                use_old_proj=True,
            )

            # Check for any autos, since their uvws get potentially corrupted
            # by the above operation
            auto_mask = self.ant_1_array == self.ant_2_array
            if any(auto_mask):
                self.uvw_array[auto_mask, :] = 0.0

            # And rephase the data using the new algorithm
            self.phase(
                phase_center_ra,
                phase_center_dec,
                phase_frame=phase_center_frame,
                epoch=phase_center_epoch,
                cat_name=cat_name,
                use_ant_pos=False,
            )

    def __add__(
        self,
        other,
        inplace=False,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        orig_phase_frame=None,
        use_ant_pos=True,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        make_multi_phase=False,
        ignore_name=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.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            two objects are different, the combined object will keep all the history of
            both input objects (if many objects are combined in succession this can
            lead to very long histories). If False and if the histories for the two
            objects are different, the combined object will have the history of the
            first object and only the parts of the second object history that are unique
            (this is done word by word and can result in hard to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. Setting this to true will allow for two
            UVData objects to be combined, even if the phase center properties do not
            agree (so long as the names are unique for each phase center). Default is
            False.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.


        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 is 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 = self.copy()

        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )

        # Check to make sure that both objects are consistent w/ use of flex_spw
        if this.flex_spw != other.flex_spw:
            raise ValueError(
                "To combine these data, flex_spw must be set to the same "
                "value (True or False) for both objects."
            )

        # check that both objects have the same array shapes
        if this.future_array_shapes != other.future_array_shapes:
            raise ValueError(
                "Both objects must have the same `future_array_shapes` parameter. "
                "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                "methods to convert them."
            )

        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",
            "_telescope_name",
            "_instrument",
            "_telescope_location",
            "_phase_type",
            "_Nants_telescope",
            "_antenna_names",
            "_antenna_numbers",
            "_antenna_positions",
            "_phase_center_frame",
            "_phase_center_epoch",
        ]
        if not this.future_array_shapes and not this.flex_spw:
            compatibility_params.append("_channel_width")

        multi_obj_check = False
        if this.multi_phase_center == other.multi_phase_center:
            # If the names are different and we are making a mutli-phase-ctr data set,
            # then we can skip the step of checking the ra and dec, otherwise we need to
            # check it
            multi_obj_check = make_multi_phase or this.multi_phase_center
            if not ((this.object_name != other.object_name) and multi_obj_check):
                compatibility_params.append("_phase_center_ra")
                compatibility_params.append("_phase_center_dec")

            # Also, if we are not supposed to ignore the name, then make sure that its
            # one of the parameters we check for compatibility.
            if not (ignore_name or multi_obj_check):
                compatibility_params.append("_object_name")
        elif not (this.multi_phase_center or make_multi_phase):
            raise ValueError(
                "To combine these data, please run the add operation with the UVData "
                "object with multi_phase_center set to True as the first object in the "
                "add operation."
            )

        # 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
        )

        # If we have a flexible spectral window, the handling here becomes a bit funky,
        # because we are allowed to have channels with the same frequency *if* they
        # belong to different spectral windows (one real-life example: you might want
        # to preserve guard bands in the correlator, which can have overlaping RF
        # frequency channels)
        if this.flex_spw:
            this_freq_ind = np.array([], dtype=np.int64)
            other_freq_ind = np.array([], dtype=np.int64)
            both_freq = np.array([], dtype=float)
            both_spw = np.intersect1d(this.spw_array, other.spw_array)
            for idx in both_spw:
                this_mask = np.where(this.flex_spw_id_array == idx)[0]
                other_mask = np.where(other.flex_spw_id_array == idx)[0]
                if this.future_array_shapes:
                    both_spw_freq, this_spw_ind, other_spw_ind = np.intersect1d(
                        this.freq_array[this_mask],
                        other.freq_array[other_mask],
                        return_indices=True,
                    )
                else:
                    both_spw_freq, this_spw_ind, other_spw_ind = np.intersect1d(
                        this.freq_array[0, this_mask],
                        other.freq_array[0, other_mask],
                        return_indices=True,
                    )
                this_freq_ind = np.append(this_freq_ind, this_mask[this_spw_ind])
                other_freq_ind = np.append(other_freq_ind, other_mask[other_spw_ind])
                both_freq = np.append(both_freq, both_spw_freq)
        else:
            if this.future_array_shapes:
                both_freq, this_freq_ind, other_freq_ind = np.intersect1d(
                    this.freq_array, other.freq_array, return_indices=True
                )
            else:
                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
            if this.future_array_shapes:
                this_inds = np.ravel_multi_index(
                    (
                        this_blts_ind[:, np.newaxis, np.newaxis],
                        this_freq_ind[np.newaxis, :, np.newaxis],
                        this_pol_ind[np.newaxis, np.newaxis, :],
                    ),
                    this.data_array.shape,
                ).flatten()
                other_inds = np.ravel_multi_index(
                    (
                        other_blts_ind[:, np.newaxis, np.newaxis],
                        other_freq_ind[np.newaxis, :, np.newaxis],
                        other_pol_ind[np.newaxis, np.newaxis, :],
                    ),
                    other.data_array.shape,
                ).flatten()
            else:
                this_inds = np.ravel_multi_index(
                    (
                        this_blts_ind[:, np.newaxis, np.newaxis, np.newaxis],
                        np.zeros((1, 1, 1, 1), dtype=np.int64),
                        this_freq_ind[np.newaxis, np.newaxis, :, np.newaxis],
                        this_pol_ind[np.newaxis, np.newaxis, np.newaxis, :],
                    ),
                    this.data_array.shape,
                ).flatten()
                other_inds = np.ravel_multi_index(
                    (
                        other_blts_ind[:, np.newaxis, np.newaxis, np.newaxis],
                        np.zeros((1, 1, 1, 1), dtype=np.int64),
                        other_freq_ind[np.newaxis, np.newaxis, :, np.newaxis],
                        other_pol_ind[np.newaxis, np.newaxis, np.newaxis, :],
                    ),
                    other.data_array.shape,
                ).flatten()
            this_all_zero = np.all(this.data_array.flatten()[this_inds] == 0)
            this_all_flag = np.all(this.flag_array.flatten()[this_inds])
            other_all_zero = np.all(other.data_array.flatten()[other_inds] == 0)
            other_all_flag = np.all(other.flag_array.flatten()[other_inds])

            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 = ([], [])

        # if there's any overlap in blts, check extra params
        temp = np.nonzero(np.in1d(other_blts, this_blts))[0]
        if len(temp) > 0:
            # add metadata to be checked to compatibility params
            extra_params = [
                "_integration_time",
                "_uvw_array",
                "_lst_array",
                "_phase_center_app_ra",
                "_phase_center_app_dec",
                "_phase_center_frame_pa",
                "_phase_center_id_array",
                "_phase_center_catalog",
                "_Nphase",
            ]
            compatibility_params.extend(extra_params)
            if not ignore_name and ("_object_name" not in compatibility_params):
                compatibility_params.append("_object_name")

        # TODO: Add handling for what happens when you have two different source
        # catalogs that you want to combine

        # find the freq indices in "other" but not in "this"
        if self.flex_spw:
            other_mask = np.ones_like(other.flex_spw_id_array, dtype=bool)
            for idx in np.intersect1d(this.spw_array, other.spw_array):
                if this.future_array_shapes:
                    other_mask[other.flex_spw_id_array == idx] = np.isin(
                        other.freq_array[other.flex_spw_id_array == idx],
                        this.freq_array[this.flex_spw_id_array == idx],
                        invert=True,
                    )
                else:
                    other_mask[other.flex_spw_id_array == idx] = np.isin(
                        other.freq_array[0, other.flex_spw_id_array == idx],
                        this.freq_array[0, this.flex_spw_id_array == idx],
                        invert=True,
                    )
            temp = np.where(other_mask)[0]
        else:
            if this.future_array_shapes:
                temp = np.nonzero(~np.in1d(other.freq_array, this.freq_array))[0]
            else:
                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 = []

        # if channel width is an array and there's any overlap in freqs,
        # check extra params
        if this.future_array_shapes or this.flex_spw:
            if this.future_array_shapes:
                temp = np.nonzero(np.in1d(other.freq_array, this.freq_array))[0]
            else:
                temp = np.nonzero(
                    np.in1d(other.freq_array[0, :], this.freq_array[0, :])
                )[0]
            if len(temp) > 0:
                # add metadata to be checked to compatibility params
                extra_params = ["_channel_width"]
                compatibility_params.extend(extra_params)

        # 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 cp in compatibility_params:
            if cp == "_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 cp == "_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 cp == "_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],
                )
            elif cp == "_channel_width" and this.future_array_shapes or this.flex_spw:
                # only check that overlapping freq indices match
                params_match = np.allclose(
                    this.channel_width[this_freq_ind],
                    other.channel_width[other_freq_ind],
                    rtol=this._channel_width.tols[0],
                    atol=this._channel_width.tols[1],
                )
            elif (cp == "_phase_center_app_ra") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_app_ra[this_blts_ind],
                    other.phase_center_app_ra[other_blts_ind],
                    rtol=this._phase_center_app_ra.tols[0],
                    atol=this._phase_center_app_ra.tols[1],
                )
            elif (cp == "_phase_center_app_dec") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_app_dec[this_blts_ind],
                    other.phase_center_app_dec[other_blts_ind],
                    rtol=this._phase_center_app_dec.tols[0],
                    atol=this._phase_center_app_dec.tols[1],
                )
            elif (cp == "_phase_center_frame_pa") and (this.phase_type == "phased"):
                # only check that overlapping blt indices match
                params_match = np.allclose(
                    this.phase_center_frame_pa[this_blts_ind],
                    other.phase_center_frame_pa[other_blts_ind],
                    rtol=this._phase_center_frame_pa.tols[0],
                    atol=this._phase_center_frame_pa.tols[1],
                )
            else:
                params_match = getattr(this, cp) == getattr(other, cp)
            if not params_match:
                msg = (
                    "UVParameter " + cp[1:] + " does not match. Cannot combine objects."
                )
                if cp[1:] == "object_name":
                    msg += (
                        " This can potentially be remedied by setting "
                        "ignore_name=True, or by allowing the creation of a "
                        "mutli-phase-ctr dataset (by setting make_multi_phase=True)."
                    )
                raise ValueError(msg)

        # At this point, we are assuming that the two data sets _mostly_ compatible.
        # Last thing we need to check is if these are mutli-phase-ctr data sets, whether
        # or not they are compatible.
        if this.multi_phase_center or make_multi_phase:
            if other.multi_phase_center:
                other_names = list(other.phase_center_catalog.keys())
                other_cat = other.phase_center_catalog
            else:
                other_names = [other.object_name]
                other_cat = {
                    other_names[0]: {
                        "cat_type": "sidereal",
                        "cat_lon": other.phase_center_ra,
                        "cat_lat": other.phase_center_dec,
                        "cat_frame": other.phase_center_frame,
                        "cat_epoch": other.phase_center_epoch,
                    },
                }

            for name in other_names:
                cat_id, cat_diffs = this._look_in_catalog(
                    name, phase_dict=other_cat[name]
                )
                if (cat_id is not None) and (cat_diffs != 0):
                    # We have a name conflict, raise an error now
                    raise ValueError(
                        "There exists a target named %s in both objects in the "
                        "sum, but their properties are different. Use the rename_"
                        "phase_center method in order to rename it in one object."
                        % name
                    )

        # Begin manipulating the objects.
        if make_multi_phase and (not this.multi_phase_center):
            this._set_multi_phase_center(preserve_phase_center_info=True)
        if other.multi_phase_center:
            # This to get adding stuff to the catalog
            reserved_ids = [
                other.phase_center_catalog[name]["cat_id"]
                for name in other.phase_center_catalog.keys()
            ]
            # First loop, we want to look at the sources that are in this, but not
            # other, since we need to choose catalog IDs that won't collide with the
            # catalog that exists.
            for name in this.phase_center_catalog.keys():
                if name not in other.phase_center_catalog.keys():
                    this._update_phase_center_id(name, reserved_ids=reserved_ids)
            # Next loop, we want to update the IDs of sources that are in both
            for name in this.phase_center_catalog.keys():
                if name in other.phase_center_catalog.keys():
                    this._update_phase_center_id(
                        name, new_cat_id=other.phase_center_catalog[name]["cat_id"],
                    )
            # Finally, add those other objects not found in this
            for name in other.phase_center_catalog.keys():
                if name not in this.phase_center_catalog.keys():
                    this._add_phase_center(
                        name,
                        cat_type=other.phase_center_catalog[name]["cat_type"],
                        cat_lon=other.phase_center_catalog[name]["cat_lon"],
                        cat_lat=other.phase_center_catalog[name]["cat_lat"],
                        cat_frame=other.phase_center_catalog[name]["cat_frame"],
                        cat_epoch=other.phase_center_catalog[name]["cat_epoch"],
                        cat_times=other.phase_center_catalog[name]["cat_times"],
                        cat_pm_ra=other.phase_center_catalog[name]["cat_pm_ra"],
                        cat_pm_dec=other.phase_center_catalog[name]["cat_pm_dec"],
                        cat_dist=other.phase_center_catalog[name]["cat_dist"],
                        cat_vrad=other.phase_center_catalog[name]["cat_vrad"],
                        info_source=other.phase_center_catalog[name]["info_source"],
                        cat_id=other.phase_center_catalog[name]["cat_id"],
                    )
        elif this.multi_phase_center:
            # If other is not multi phase center, then we'll go ahead and add the object
            # information here.
            other_cat_id = this._add_phase_center(
                other.object_name,
                cat_type="sidereal",
                cat_lon=other.phase_center_ra,
                cat_lat=other.phase_center_dec,
                cat_frame=other.phase_center_frame,
                cat_epoch=other.phase_center_epoch,
            )

        # 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:
                if this.future_array_shapes:
                    zero_pad = np.zeros((len(bnew_inds), this.Nfreqs, this.Npols))
                else:
                    zero_pad = np.zeros((len(bnew_inds), 1, 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 this.phase_type == "phased":
                this.phase_center_app_ra = np.concatenate(
                    [this.phase_center_app_ra, other.phase_center_app_ra[bnew_inds]]
                )[blt_order]
                this.phase_center_app_dec = np.concatenate(
                    [this.phase_center_app_dec, other.phase_center_app_dec[bnew_inds]]
                )[blt_order]
                this.phase_center_frame_pa = np.concatenate(
                    [this.phase_center_frame_pa, other.phase_center_frame_pa[bnew_inds]]
                )[blt_order]
            if this.multi_phase_center:
                if other.multi_phase_center:
                    this.phase_center_id_array = np.concatenate(
                        [
                            this.phase_center_id_array,
                            other.phase_center_id_array[bnew_inds],
                        ]
                    )[blt_order]
                else:
                    this.phase_center_id_array = np.concatenate(
                        [this.phase_center_id_array, [other_cat_id] * len(bnew_inds)]
                    )[blt_order]

        if len(fnew_inds) > 0:
            if this.future_array_shapes:
                this.freq_array = np.concatenate(
                    [this.freq_array, other.freq_array[fnew_inds]]
                )
            else:
                this.freq_array = np.concatenate(
                    [this.freq_array, other.freq_array[:, fnew_inds]], axis=1
                )

            if this.flex_spw or this.future_array_shapes:
                this.channel_width = np.concatenate(
                    [this.channel_width, other.channel_width[fnew_inds]]
                )

            if this.flex_spw:
                this.flex_spw_id_array = np.concatenate(
                    [this.flex_spw_id_array, other.flex_spw_id_array[fnew_inds]]
                )
                this.spw_array = np.concatenate([this.spw_array, other.spw_array])
                # We want to preserve per-spw information based on first appearance
                # in the concatenated array.
                unique_index = np.sort(
                    np.unique(this.flex_spw_id_array, return_index=True)[1]
                )
                this.spw_array = this.flex_spw_id_array[unique_index]
                this.Nspws = len(this.spw_array)

            # If we have a flex/multi-spw data set, need to sort out the order of the
            # individual windows first.
            if this.flex_spw:
                f_order = np.concatenate(
                    [
                        np.where(this.flex_spw_id_array == idx)[0]
                        for idx in sorted(this.spw_array)
                    ]
                )

                # With spectral windows sorted, check and see if channels within
                # windows need sorting. If they are ordered in ascending or descending
                # fashion, leave them be. If not, sort in ascending order
                for idx in this.spw_array:
                    select_mask = this.flex_spw_id_array[f_order] == idx
                    check_freqs = (
                        this.freq_array[f_order[select_mask]]
                        if this.future_array_shapes
                        else this.freq_array[0, f_order[select_mask]]
                    )
                    if (not np.all(check_freqs[1:] > check_freqs[:-1])) and (
                        not np.all(check_freqs[1:] < check_freqs[:-1])
                    ):
                        subsort_order = f_order[select_mask]
                        f_order[select_mask] = subsort_order[np.argsort(check_freqs)]
            else:
                if this.future_array_shapes:
                    f_order = np.argsort(this.freq_array)
                else:
                    f_order = np.argsort(this.freq_array[0, :])

            if not self.metadata_only:
                if this.future_array_shapes:
                    zero_pad = np.zeros(
                        (this.data_array.shape[0], len(fnew_inds), this.Npols)
                    )
                    this.data_array = np.concatenate(
                        [this.data_array, zero_pad], axis=1
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array, zero_pad], axis=1
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array, 1 - zero_pad], axis=1
                    ).astype(np.bool_)
                else:
                    zero_pad = np.zeros(
                        (this.data_array.shape[0], 1, 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:
                if this.future_array_shapes:
                    zero_pad = np.zeros(
                        (
                            this.data_array.shape[0],
                            this.data_array.shape[1],
                            len(pnew_inds),
                        )
                    )
                    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_)
                else:
                    zero_pad = np.zeros(
                        (
                            this.data_array.shape[0],
                            1,
                            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]
        if this.future_array_shapes:
            freq_t2o = np.nonzero(np.in1d(this.freq_array, other.freq_array))[0]
        else:
            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:
            if this.future_array_shapes:
                this.data_array[np.ix_(blt_t2o, freq_t2o, pol_t2o)] = other.data_array
                this.nsample_array[
                    np.ix_(blt_t2o, freq_t2o, pol_t2o)
                ] = other.nsample_array
                this.flag_array[np.ix_(blt_t2o, freq_t2o, pol_t2o)] = other.flag_array
            else:
                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 this.future_array_shapes:
                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])
            else:
                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:
            if this.future_array_shapes:
                this.freq_array = this.freq_array[f_order]
            else:
                this.freq_array = this.freq_array[:, f_order]
            if this.flex_spw or this.future_array_shapes:
                this.channel_width = this.channel_width[f_order]
            if this.flex_spw:
                this.flex_spw_id_array = this.flex_spw_id_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.size
        this.Npols = this.polarization_array.shape[0]
        this.Nants_data = this._calc_nants_data()

        # Update filename parameter
        this.filename = uvutils._combine_filenames(this.filename, other.filename)
        if this.filename is not None:
            this._filename.form = (len(this.filename),)

        # Check specific requirements
        if this.Nfreqs > 1:
            spacing_error, chanwidth_error = this._check_freq_spacing(
                raise_errors=False
            )

            if spacing_error:
                warnings.warn(
                    "Combined frequencies are not evenly spaced or have differing "
                    "values of channel widths. This will make it impossible to write "
                    "this data out to some file types."
                )
            elif chanwidth_error:
                warnings.warn(
                    "Combined frequencies are separated by more than their "
                    "channel width. This will make it impossible to write this data "
                    "out to some file types."
                )

        if n_axes > 0:
            history_update_string += " axis using pyuvdata."

            histories_match = uvutils._check_histories(this.history, other.history)

            this.history += history_update_string
            if not histories_match:
                if verbose_history:
                    this.history += " Next object history follows. " + other.history
                else:
                    extra_history = uvutils._combine_history_addition(
                        this.history, other.history
                    )
                    if extra_history is not None:
                        this.history += (
                            " Unique part of next object history follows. "
                            + extra_history
                        )

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

        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=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. Setting this to true will allow for two
            UVData objects to be combined, even if the phase center properties do not
            agree (so long as the names are unique for each phase center). Default is
            False.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.


        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 is 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.

        """
        self.__add__(
            other,
            inplace=True,
            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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            make_multi_phase=make_multi_phase,
            ignore_name=ignore_name,
        )
        return self

    def fast_concat(
        self,
        other,
        axis,
        inplace=False,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        orig_phase_frame=None,
        use_ant_pos=True,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        ignore_name=False,
    ):
        """
        Concatenate two UVData objects along specified axis with almost no checking.

        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 or list of UVData objects
            UVData object or list of UVData objects 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'.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            objects are different, the combined object will keep all the history of
            all input objects (if many objects are combined this can lead to very long
            histories). If False and if the histories for the objects are different,
            the combined object will have the history of the first object and only the
            parts of the other object histories that are unique (this is done word by
            word and can result in hard to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        ignore_name : bool
            Option to ignore the name of the phase center (`cat_name` in
            `phase_center_catalog` when `multi_phase_center=True`, otherwise
            `object_name`) when combining two UVData objects. Doing so effectively
            adopts the name found in the first UVData object in the sum. Default is
            False.

        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 = self.copy()
        if not isinstance(other, (list, tuple, np.ndarray)):
            # if this is a UVData object already, stick it in a list
            other = [other]
        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        for obj in other:
            if not issubclass(obj.__class__, this.__class__):
                if not issubclass(this.__class__, obj.__class__):
                    raise ValueError(
                        "Only UVData (or subclass) objects can be "
                        "added to a UVData (or subclass) object"
                    )
            obj.check(
                check_extra=check_extra,
                run_check_acceptability=run_check_acceptability,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

        # check that all objects have the same array shapes
        for obj in other:
            if this.future_array_shapes != obj.future_array_shapes:
                raise ValueError(
                    "All objects must have the same `future_array_shapes` parameter. "
                    "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                    "methods to convert them."
                )

        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
                )

            for obj in other:
                if obj.phase_type != "drift":
                    warnings.warn("Unphasing other UVData object to drift")
                    obj.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.
            for obj in other:
                if obj.phase_type == "drift" or (
                    not np.isclose(
                        obj.phase_center_ra,
                        phase_center_radec[0],
                        rtol=obj._phase_center_ra.tols[0],
                        atol=obj._phase_center_ra.tols[1],
                    )
                    or not np.isclose(
                        obj.phase_center_dec,
                        phase_center_radec[1],
                        rtol=obj._phase_center_dec.tols[0],
                        atol=obj._phase_center_dec.tols[1],
                    )
                ):
                    warnings.warn("Phasing other UVData object to phase_center_radec")
                    obj.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",
            "_telescope_name",
            "_instrument",
            "_telescope_location",
            "_phase_type",
            "_Nants_telescope",
            "_antenna_names",
            "_antenna_numbers",
            "_antenna_positions",
            "_phase_center_ra",
            "_phase_center_dec",
            "_phase_center_epoch",
            "_multi_phase_center",
            "_phase_center_catalog",
            "_Nphase",
        ]
        if not this.future_array_shapes and not this.flex_spw:
            compatibility_params.append("_channel_width")

        if not (this.multi_phase_center or ignore_name):
            compatibility_params += ["_object_name"]

        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",
                "_phase_center_id_array",
            ]
        elif axis == "polarization":
            history_update_string += "polarization"
            compatibility_params += [
                "_freq_array",
                "_ant_1_array",
                "_ant_2_array",
                "_integration_time",
                "_uvw_array",
                "_lst_array",
                "_phase_center_id_array",
            ]
        elif axis == "blt":
            history_update_string += "baseline-time"
            compatibility_params += ["_freq_array", "_polarization_array"]

        history_update_string += " axis using pyuvdata."

        histories_match = []
        for obj in other:
            histories_match.append(uvutils._check_histories(this.history, obj.history))

        this.history += history_update_string
        for obj_num, obj in enumerate(other):
            if not histories_match[obj_num]:
                if verbose_history:
                    this.history += " Next object history follows. " + obj.history
                else:
                    extra_history = uvutils._combine_history_addition(
                        this.history, obj.history
                    )
                    if extra_history is not None:
                        this.history += (
                            " Unique part of next object history follows. "
                            + extra_history
                        )

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

        if axis == "freq":
            this.Nfreqs = sum([this.Nfreqs] + [obj.Nfreqs for obj in other])
            if this.future_array_shapes:
                this.freq_array = np.concatenate(
                    [this.freq_array] + [obj.freq_array for obj in other]
                )
            else:
                this.freq_array = np.concatenate(
                    [this.freq_array] + [obj.freq_array for obj in other], axis=1
                )
            if this.flex_spw or this.future_array_shapes:
                this.channel_width = np.concatenate(
                    [this.channel_width] + [obj.channel_width for obj in other]
                )
            if this.flex_spw:
                this.flex_spw_id_array = np.concatenate(
                    [this.flex_spw_id_array] + [obj.flex_spw_id_array for obj in other]
                )
                this.spw_array = np.concatenate(
                    [this.spw_array] + [obj.spw_array for obj in other]
                )
                # We want to preserve per-spw information based on first appearance
                # in the concatenated array.
                unique_index = np.sort(
                    np.unique(this.flex_spw_id_array, return_index=True)[1]
                )
                this.spw_array = this.flex_spw_id_array[unique_index]

                this.Nspws = len(this.spw_array)

            spacing_error, chanwidth_error = this._check_freq_spacing(
                raise_errors=False
            )
            if spacing_error:
                warnings.warn(
                    "Combined frequencies are not evenly spaced or have differing "
                    "values of channel widths. This will make it impossible to write "
                    "this data out to some file types."
                )
            elif chanwidth_error:
                warnings.warn(
                    "Combined frequencies are separated by more than their "
                    "channel width. This will make it impossible to write this data "
                    "out to some file types."
                )

            if not self.metadata_only:
                if this.future_array_shapes:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=1,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=1,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=1,
                    )
                else:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=2,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=2,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=2,
                    )
        elif axis == "polarization":
            this.polarization_array = np.concatenate(
                [this.polarization_array] + [obj.polarization_array for obj in other]
            )
            this.Npols = sum([this.Npols] + [obj.Npols for obj in other])

            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:
                if this.future_array_shapes:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=2,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=2,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=2,
                    )
                else:
                    this.data_array = np.concatenate(
                        [this.data_array] + [obj.data_array for obj in other], axis=3,
                    )
                    this.nsample_array = np.concatenate(
                        [this.nsample_array] + [obj.nsample_array for obj in other],
                        axis=3,
                    )
                    this.flag_array = np.concatenate(
                        [this.flag_array] + [obj.flag_array for obj in other], axis=3,
                    )
        elif axis == "blt":
            this.Nblts = sum([this.Nblts] + [obj.Nblts for obj in other])
            this.ant_1_array = np.concatenate(
                [this.ant_1_array] + [obj.ant_1_array for obj in other]
            )
            this.ant_2_array = np.concatenate(
                [this.ant_2_array] + [obj.ant_2_array for obj in other]
            )
            this.Nants_data = this._calc_nants_data()
            this.uvw_array = np.concatenate(
                [this.uvw_array] + [obj.uvw_array for obj in other], axis=0
            )
            this.time_array = np.concatenate(
                [this.time_array] + [obj.time_array for obj in other]
            )
            this.Ntimes = len(np.unique(this.time_array))
            this.lst_array = np.concatenate(
                [this.lst_array] + [obj.lst_array for obj in other]
            )
            this.baseline_array = np.concatenate(
                [this.baseline_array] + [obj.baseline_array for obj in other]
            )
            this.Nbls = len(np.unique(this.baseline_array))
            this.integration_time = np.concatenate(
                [this.integration_time] + [obj.integration_time for obj in other]
            )
            if not self.metadata_only:
                this.data_array = np.concatenate(
                    [this.data_array] + [obj.data_array for obj in other], axis=0,
                )
                this.nsample_array = np.concatenate(
                    [this.nsample_array] + [obj.nsample_array for obj in other], axis=0,
                )
                this.flag_array = np.concatenate(
                    [this.flag_array] + [obj.flag_array for obj in other], axis=0,
                )
            if this.phase_type == "phased":
                this.phase_center_app_ra = np.concatenate(
                    [this.phase_center_app_ra]
                    + [obj.phase_center_app_ra for obj in other]
                )
                this.phase_center_app_dec = np.concatenate(
                    [this.phase_center_app_dec]
                    + [obj.phase_center_app_dec for obj in other]
                )
                this.phase_center_frame_pa = np.concatenate(
                    [this.phase_center_frame_pa]
                    + [obj.phase_center_frame_pa for obj in other]
                )
            if this.multi_phase_center:
                this.phase_center_id_array = np.concatenate(
                    [this.phase_center_id_array]
                    + [obj.phase_center_id_array for obj in other]
                )

        # update filename attribute
        for obj in other:
            this.filename = uvutils._combine_filenames(this.filename, obj.filename)
        if this.filename is not None:
            this._filename.form = len(this.filename)

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

        if not inplace:
            return this

    def sum_vis(
        self,
        other,
        inplace=False,
        difference=False,
        verbose_history=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        override_params=None,
    ):
        """
        Sum visibilities between two UVData objects.

        By default requires that all UVParameters are the same on the two objects
        except for `history`, `data_array`, `object_name`, and `extra_keywords`.
        The `object_name` values are concatenated if they are different. If keys
        in `extra_keywords` have different values the values from the first
        object are taken.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        difference : bool
            If True, differences the visibilities of the two UVData objects
            rather than summing them.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        verbose_history : bool
            Option to allow more verbose history. If True and if the histories for the
            two objects are different, the combined object will keep all the history of
            both input objects (this can lead to long histories). If False and if the
            histories for the two objects are different, the combined object will have
            the history of the first object and only the parts of the second object
            history that are unique (this is done word by word and can result in hard
            to interpret histories).
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        override_params : array_like of strings
            List of object UVParameters to omit from compatibility check. Overridden
            parameters will not be compared between the objects, and the values
            for these parameters will be taken from the first object.

        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 = self.copy()

        # Check that both objects are UVData and valid
        this.check(
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )

        # check that both objects have the same array shapes
        if this.future_array_shapes != other.future_array_shapes:
            raise ValueError(
                "Both objects must have the same `future_array_shapes` parameter. "
                "Use the `use_future_array_shapes` or `use_current_array_shapes` "
                "methods to convert them."
            )

        compatibility_params = list(this.__iter__())
        remove_params = ["_history", "_data_array", "_object_name", "_extra_keywords"]

        # Add underscores to override_params to match list from __iter__()
        # Add to parameters to be removed
        if override_params and all(isinstance(param, str) for param in override_params):
            for param in override_params:
                if param[0] != "_":
                    param = "_" + param
                if param not in compatibility_params:
                    msg = (
                        "Provided parameter " + param[1:] + " is not a recognizable "
                        "UVParameter."
                    )
                    raise ValueError(msg)
                remove_params.append(param)

        # compatibility_params should define the parameters that need to
        # be the same for objects to be summed or diffed
        compatibility_params = list(set(compatibility_params) - set(remove_params))

        # Check each UVParameter in compatibility_params
        for param in compatibility_params:
            params_match = getattr(this, param) == getattr(other, param)
            if not params_match:
                msg = (
                    "UVParameter " + param[1:] + " does not match. Cannot "
                    "combine objects."
                )
                raise ValueError(msg)

        # Merge extra keywords and object_name
        for intersection in set(this.extra_keywords.keys()) & set(
            other.extra_keywords.keys()
        ):
            if this.extra_keywords[intersection] != other.extra_keywords[intersection]:
                warnings.warn(
                    "Keyword " + intersection + " in _extra_keywords is different "
                    "in the two objects. Taking the first object's entry."
                )

        # Merge extra_keywords lists, taking values from the first object
        this.extra_keywords = dict(
            list(other.extra_keywords.items()) + list(this.extra_keywords.items())
        )

        # Merge object_name if different.
        if this.object_name != other.object_name:
            this.object_name = this.object_name + "-" + other.object_name

        # 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."

        histories_match = uvutils._check_histories(this.history, other.history)

        this.history += history_update_string
        if not histories_match:
            if verbose_history:
                this.history += " Second object history follows. " + other.history
            else:
                extra_history = uvutils._combine_history_addition(
                    this.history, other.history
                )
                if extra_history is not None:
                    this.history += (
                        " Unique part of second object history follows. "
                        + extra_history
                    )

        # merge file names
        this.filename = uvutils._combine_filenames(this.filename, other.filename)

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

        if not inplace:
            return this

    def diff_vis(
        self,
        other,
        inplace=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        override_params=None,
    ):
        """
        Difference visibilities between two UVData objects.

        By default requires that all UVParameters are the same on the two objects
        except for `history`, `data_array`, `object_name`, and `extra_keywords`.
        The `object_name` values are concatenated if they are different. If keys
        in `extra_keywords` have different values the values from the first
        object are taken.

        Parameters
        ----------
        other : UVData object
            Another UVData object which will be added to self.
        inplace : bool
            If True, overwrite self as we go, otherwise create a third object
            as the sum of the two.
        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.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        override_params : array_like of strings
            List of object UVParameters to omit from compatibility check. Overridden
            parameters will not be compared between the objects, and the values
            for these parameters will be taken from the first object.

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

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

        Used to support 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.

        """
        return uvutils.parse_ants(
            uv=self,
            ant_str=ant_str,
            print_toggle=print_toggle,
            x_orientation=self.x_orientation,
        )

    def _select_preprocess(
        self,
        antenna_nums,
        antenna_names,
        ant_str,
        bls,
        frequencies,
        freq_chans,
        times,
        time_range,
        lsts,
        lst_range,
        polarizations,
        blt_inds,
    ):
        """
        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 or list of int, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]), a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]), or a list of
            baseline numbers (e.g. [67599, 71699, 73743]) 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`, `lsts`, or
            `lst_array`.
        time_range : array_like of float, optional
            The time range in Julian Date to keep in the object, must be length
            2. Some of the times in the object should fall between the first and
            last elements. Cannot be used with `times`, `lsts`, or `lst_array`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int or str, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array. If passing strings, the
            canonical polarization strings (e.g. "xx", "rr") are supported and if the
            `x_orientation` attribute is set, the physical dipole strings
            (e.g. "nn", "ee") are also supported.
        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)
                if bls is not None and len(bls) == 0:
                    raise ValueError(
                        f"There is no data matching ant_str={ant_str} in this object."
                    )

        # 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
            # Check to make sure that we actually have these antenna nums in the data
            ant_check = np.logical_or(
                np.isin(antenna_nums, self.ant_1_array),
                np.isin(antenna_nums, self.ant_2_array),
            )
            if not np.all(ant_check):
                raise ValueError(
                    "Antenna number % i is not present in the ant_1_array or "
                    "ant_2_array" % antenna_nums[~ant_check][0]
                )
            ant_blt_inds = np.where(
                np.logical_and(
                    np.isin(self.ant_1_array, antenna_nums),
                    np.isin(self.ant_2_array, antenna_nums),
                )
            )[0]
        else:
            ant_blt_inds = None

        if bls is not None:
            if isinstance(bls, list) and all(
                isinstance(bl_ind, (int, np.integer,),) for bl_ind in bls
            ):
                for bl_ind in bls:
                    if not (bl_ind in self.baseline_array):
                        raise ValueError(
                            "Baseline number {i} is not present in the "
                            "baseline_array".format(i=bl_ind)
                        )
                bls = list(zip(*self.baseline_to_antnums(bls)))
            elif 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) or a list of baseline numbers."
                )
            if not all(
                [isinstance(item[0], (int, np.integer,),) for item in bls]
                + [isinstance(item[1], (int, np.integer,),) for item in bls]
            ):
                raise ValueError(
                    "bls must be a list of tuples of antenna numbers "
                    "(optionally with polarization) or a list of baseline numbers."
                )
            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.int64)
            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:
                        # find conjugate polarization
                        bl_pols.add(uvutils.conj_pol(bl[2]))
                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.int64
                )
            else:
                blt_inds = ant_blt_inds

        have_times = times is not None
        have_time_range = time_range is not None
        have_lsts = lsts is not None
        have_lst_range = lst_range is not None
        if (
            np.count_nonzero([have_times, have_time_range, have_lsts, have_lst_range])
            > 1
        ):
            raise ValueError(
                "Only one of [times, time_range, lsts, lst_range] may be "
                "specified per selection operation."
            )

        if times is not None:
            times = uvutils._get_iterable(times)
            if np.array(times).ndim > 1:
                times = np.array(times).flatten()

            time_blt_inds = np.zeros(0, dtype=np.int64)
            for jd in times:
                if np.any(
                    np.isclose(
                        self.time_array,
                        jd,
                        rtol=self._time_array.tols[0],
                        atol=self._time_array.tols[1],
                    )
                ):
                    time_blt_inds = np.append(
                        time_blt_inds,
                        np.where(
                            np.isclose(
                                self.time_array,
                                jd,
                                rtol=self._time_array.tols[0],
                                atol=self._time_array.tols[1],
                            )
                        )[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(
                    f"No elements in time range between {time_range[0]} and "
                    f"{time_range[1]}."
                )

        if lsts is not None:
            if np.any(np.asarray(lsts) > 2 * np.pi):
                warnings.warn(
                    "The lsts parameter contained a value greater than 2*pi. "
                    "LST values are assumed to be in radians, not hours."
                )
            lsts = uvutils._get_iterable(lsts)
            if np.array(lsts).ndim > 1:
                lsts = np.array(lsts).flatten()

            time_blt_inds = np.zeros(0, dtype=np.int64)
            for lst in lsts:
                if np.any(
                    np.isclose(
                        self.lst_array,
                        lst,
                        rtol=self._lst_array.tols[0],
                        atol=self._lst_array.tols[1],
                    )
                ):
                    time_blt_inds = np.append(
                        time_blt_inds,
                        np.where(
                            np.isclose(
                                self.lst_array,
                                lst,
                                rtol=self._lst_array.tols[0],
                                atol=self._lst_array.tols[1],
                            )
                        )[0],
                    )
                else:
                    raise ValueError(f"LST {lst} is not present in the lst_array")

        if lst_range is not None:
            if np.size(lst_range) != 2:
                raise ValueError("lst_range must be length 2.")
            if np.any(np.asarray(lst_range) > 2 * np.pi):
                warnings.warn(
                    "The lst_range contained a value greater than 2*pi. "
                    "LST values are assumed to be in radians, not hours."
                )
            if lst_range[1] < lst_range[0]:
                # we're wrapping around LST = 2*pi = 0
                lst_range_1 = [lst_range[0], 2 * np.pi]
                lst_range_2 = [0, lst_range[1]]
                time_blt_inds1 = np.nonzero(
                    (self.lst_array <= lst_range_1[1])
                    & (self.lst_array >= lst_range_1[0])
                )[0]
                time_blt_inds2 = np.nonzero(
                    (self.lst_array <= lst_range_2[1])
                    & (self.lst_array >= lst_range_2[0])
                )[0]
                time_blt_inds = np.union1d(time_blt_inds1, time_blt_inds2)
            else:
                time_blt_inds = np.nonzero(
                    (self.lst_array <= lst_range[1]) & (self.lst_array >= lst_range[0])
                )[0]
            if time_blt_inds.size == 0:
                raise ValueError(
                    f"No elements in LST range between {lst_range[0]} and "
                    f"{lst_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.int64
                )
            else:
                blt_inds = time_blt_inds

        if lsts is not None or lst_range is not None:
            if n_selects > 0:
                history_update_string += ", lsts"
            else:
                history_update_string += "lsts"
            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.int64
                )
            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 = sorted(set(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:
                if self.future_array_shapes:
                    frequencies = self.freq_array[freq_chans]
                else:
                    frequencies = self.freq_array[0, freq_chans]
            else:
                frequencies = uvutils._get_iterable(frequencies)
                if self.future_array_shapes:
                    frequencies = np.sort(
                        list(set(frequencies) | set(self.freq_array[freq_chans]))
                    )
                else:
                    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

            if self.future_array_shapes:
                freq_arr_use = self.freq_array
            else:
                freq_arr_use = self.freq_array[0, :]
            # Check and see that all requested freqs are available
            freq_check = np.isin(frequencies, freq_arr_use)
            if not np.all(freq_check):
                raise ValueError(
                    "Frequency %g is not present in the freq_array"
                    % frequencies[np.where(~freq_check)[0][0]]
                )
            freq_inds = np.where(np.isin(freq_arr_use, frequencies))[0]

            if len(frequencies) > 1:
                freq_ind_separation = freq_inds[1:] - freq_inds[:-1]
                if self.flex_spw:
                    freq_ind_separation = freq_ind_separation[
                        np.diff(self.flex_spw_id_array[freq_inds]) == 0
                    ]
                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 = sorted(set(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.int64)
            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 = sorted(set(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,
    ):
        """
        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 = self._calc_nants_data()

            if self.phase_center_app_ra is not None:
                self.phase_center_app_ra = self.phase_center_app_ra[blt_inds]
            if self.phase_center_app_dec is not None:
                self.phase_center_app_dec = self.phase_center_app_dec[blt_inds]
            if self.phase_center_frame_pa is not None:
                self.phase_center_frame_pa = self.phase_center_frame_pa[blt_inds]
            if self.multi_phase_center:
                self.phase_center_id_array = self.phase_center_id_array[blt_inds]

            self.Ntimes = len(np.unique(self.time_array))
            if not keep_all_metadata:
                ants_to_keep = set(np.unique(self.ant_1_array)).union(
                    np.unique(self.ant_2_array)
                )

                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)
            if self.future_array_shapes:
                self.freq_array = self.freq_array[freq_inds]
            else:
                self.freq_array = self.freq_array[:, freq_inds]
            if self.flex_spw or self.future_array_shapes:
                self.channel_width = self.channel_width[freq_inds]
            if self.flex_spw:
                self.flex_spw_id_array = self.flex_spw_id_array[freq_inds]
                # Use the spw ID array to check and see which SPWs are left
                self.spw_array = self.spw_array[
                    np.isin(self.spw_array, self.flex_spw_id_array)
                ]
                self.Nspws = len(self.spw_array)

        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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        inplace=True,
        keep_all_metadata=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int or str, optional
            The polarizations numbers to keep in the object, each value passed
            here should exist in the polarization_array. If passing strings, the
            canonical polarization strings (e.g. "xx", "rr") are supported and if the
            `x_orientation` attribute is set, the physical dipole strings
            (e.g. "nn", "ee") are also supported.
        blt_inds : array_like of int, optional
            The baseline-time indices to keep in the object. This is
            not commonly used.
        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).
        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.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

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

        (
            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,
            lsts,
            lst_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:
            if self.future_array_shapes:
                for param_name, param in zip(
                    self._data_params, uv_object.data_like_parameters
                ):
                    setattr(uv_object, param_name, param[:, freq_inds, :])
            else:
                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:
            if self.future_array_shapes:
                for param_name, param in zip(
                    self._data_params, uv_object.data_like_parameters
                ):
                    setattr(uv_object, param_name, param[:, :, pol_inds])
            else:
                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,
                strict_uvw_antpos_check=strict_uvw_antpos_check,
            )

        if not inplace:
            return uv_object

    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))

        # update app source coords to new times
        self._set_app_coords_helper()

        # 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.int64)
        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:
            if self.future_array_shapes:
                temp_data = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols),
                    dtype=self.nsample_array.dtype,
                )
            else:
                temp_data = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, 1, 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=None,
        n_times_to_avg=None,
        blt_order="time",
        minor_order="baseline",
        keep_ragged=True,
        summing_correlator_mode=False,
        allow_drift=False,
    ):
        """
        Average to a longer integration time.

        This method will average a UVData object either by an integer factor
        (by setting `n_times_to_avg`) or by a factor that can differ by
        baseline-time sample such that after averaging, the samples have an
        integration time greater than or equal to the `min_int_time` (up to the
        tolerance on the integration_time).

        Note that if the integrations for a baseline do not divide evenly by the
        `n_times_to_avg` or into the specified `min_int_time`, the final
        integrations for that baseline may have integration times less than
        `min_int_time` or be composed of fewer input integrations than `n_times_to_avg`.
        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 average the UVData integration_time to
            in seconds.
        n_times_to_avg : int
            Number of time integrations to average together.
        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,
            or that have a number of integrations that do not evenly divide by
            n_times_to_avg, keep_ragged controls whether to keep the (averaged)
            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 averaging 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 averaging in drift mode may result in more decoherence.

        Returns
        -------
        None

        """
        if min_int_time is None and n_times_to_avg is None:
            raise ValueError("Either min_int_time or n_times_to_avg must be set.")

        if min_int_time is not None and n_times_to_avg is not None:
            raise ValueError("Only one of min_int_time or n_times_to_avg can be set.")

        if self.Ntimes == 1:
            raise ValueError("Only one time in this object, cannot downsample.")

        if min_int_time is not 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 "
                    "longer than the value specified; doing nothing."
                )
                return
        else:
            if not isinstance(n_times_to_avg, (int, np.integer)):
                raise ValueError("n_times_to_avg must be an integer.")
        # 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()

        if min_int_time is not None:
            # 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],
                )
            )
            bls_to_downsample = np.unique(self.baseline_array[inds_to_downsample])
        else:
            bls_to_downsample = np.unique(self.baseline_array)

        # figure out how many baseline times we'll end up with at the end
        n_new_samples = 0
        for bl in bls_to_downsample:
            bl_inds = np.nonzero(self.baseline_array == bl)[0]
            int_times = self.integration_time[bl_inds]

            if min_int_time is not None:
                running_int_time = 0.0
                for itime, int_time in enumerate(int_times):
                    running_int_time += int_time
                    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
                        n_new_samples += 1
                        running_int_time = 0.0
            else:
                n_bl_times = self.time_array[bl_inds].size
                nsample_temp = np.sum(n_bl_times / n_times_to_avg)
                if keep_ragged and not np.isclose(nsample_temp, np.floor(nsample_temp)):
                    n_new_samples += np.ceil(nsample_temp).astype(int)
                else:
                    n_new_samples += np.floor(nsample_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 = int_times
            if len(np.unique(int_times)) == 1:
                # this baseline has all the same integration times
                if len(np.unique(dtime)) > 1 and not np.isclose(
                    np.max(dtime),
                    np.min(dtime),
                    rtol=self._integration_time.tols[0],
                    atol=self._integration_time.tols[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],
                    rtol=self._integration_time.tols[0],
                    atol=self._integration_time.tols[1],
                ):
                    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.int64)
        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:
            if self.future_array_shapes:
                temp_data = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols), dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, self.Nfreqs, self.Npols),
                    dtype=self.nsample_array.dtype,
                )
            else:
                temp_data = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.data_array.dtype,
                )
                temp_flag = np.zeros(
                    (temp_Nblts, 1, self.Nfreqs, self.Npols),
                    dtype=self.flag_array.dtype,
                )
                temp_nsample = np.zeros(
                    (temp_Nblts, 1, 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
                if min_int_time is not None:
                    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],
                    )
                else:
                    over_min_int_time = n_sum >= n_times_to_avg
                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():
                            if self.future_array_shapes:
                                ax1_inds, ax2_inds = np.nonzero(temp_flag[temp_idx])
                                mask[:, ax1_inds, ax2_inds] = False
                            else:
                                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
                        )

                        # nsample array is the fraction of data that we actually kept,
                        # relative to the amount that went into the sum or average
                        nsample_dtype = self.nsample_array.dtype.type
                        # promote nsample dtype if half-precision
                        if nsample_dtype is np.float16:
                            masked_nsample_dtype = np.float32
                        else:
                            masked_nsample_dtype = nsample_dtype
                        masked_nsample = np.ma.masked_array(
                            self.nsample_array[averaging_idx],
                            mask=mask,
                            dtype=masked_nsample_dtype,
                        )

                        if self.future_array_shapes:
                            int_time_arr = self.integration_time[
                                averaging_idx, np.newaxis, np.newaxis
                            ]
                        else:
                            int_time_arr = self.integration_time[
                                averaging_idx, np.newaxis, np.newaxis, np.newaxis
                            ]
                        masked_int_time = np.ma.masked_array(
                            np.ones_like(
                                self.data_array[averaging_idx],
                                dtype=self.integration_time.dtype,
                            )
                            * int_time_arr,
                            mask=mask,
                        )
                        if summing_correlator_mode:
                            temp_data[temp_idx] = np.sum(masked_data, axis=0)
                        else:
                            # take potential non-uniformity of integration_time
                            # and nsamples into account
                            weights = masked_nsample * masked_int_time
                            weighted_data = masked_data * weights
                            temp_data[temp_idx] = np.sum(
                                weighted_data, axis=0
                            ) / np.sum(weights, axis=0)

                        # output of masked array calculation should be coerced
                        # to the datatype of temp_nsample (which has the same
                        # precision as the original nsample_array)
                        temp_nsample[temp_idx] = np.sum(
                            masked_nsample * masked_int_time, axis=0
                        ) / np.sum(self.integration_time[averaging_idx])
                    # 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
        if min_int_time is not None:
            bls_not_downsampled = set(self.baseline_array) - set(bls_to_downsample)
            inds_to_keep = []
            for bl in bls_not_downsampled:
                inds_to_keep += np.nonzero(self.baseline_array == bl)[0].tolist()
            inds_to_keep = np.array(inds_to_keep, dtype=np.int64)
        else:
            inds_to_keep = np.array([], dtype=bool)
        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
        if min_int_time is not None:
            history_update_string = (
                " Downsampled data to {:f} second integration "
                "time using pyuvdata.".format(min_int_time)
            )
        else:
            history_update_string = (
                " Downsampled data by a factor of {} in "
                "time using pyuvdata.".format(n_times_to_avg)
            )
        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 not downsample and not upsample:
            warnings.warn(
                "No resampling will be done because target time is not "
                "a factor of 2 or more off from integration_time. To "
                "force resampling set only_upsample or only_downsample "
                "keywords or call upsample_in_time or downsample_in_time."
            )
            return

        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 frequency_average(
        self, n_chan_to_avg, summing_correlator_mode=False, propagate_flags=False
    ):
        """
        Average in frequency.

        Does a simple average over an integer number of input channels, leaving
        flagged samples out of the average.

        In the future, this method will support non-equally spaced channels
        and varying channel widths. It will also support setting the frequency
        to the true mean of the averaged non-flagged frequencies rather than
        the simple mean of the input channel frequencies. For now it does not.

        Parameters
        ----------
        n_chan_to_avg : int
            Number of channels to average together. If Nfreqs does not divide
            evenly by this number, the frequencies at the end of the freq_array
            will be dropped to make it evenly divisable. To control which
            frequencies are removed, use select before calling this 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).
        propagate_flags: bool
            Option to flag an averaged entry even if some of its contributors
            are not flagged. The averaged result will still leave the flagged
            samples out of the average, except when all contributors are
            flagged.
        """
        if self.flex_spw:
            raise NotImplementedError(
                "Frequency averaging not (yet) available for flexible spectral windows"
            )
        self._check_freq_spacing()

        n_final_chan = int(np.floor(self.Nfreqs / n_chan_to_avg))
        nfreq_mod_navg = self.Nfreqs % n_chan_to_avg
        if nfreq_mod_navg != 0:
            # not an even number of final channels
            warnings.warn(
                "Nfreqs does not divide by `n_chan_to_avg` evenly. "
                "The final {} frequencies will be excluded, to "
                "control which frequencies to exclude, use a "
                "select to control.".format(nfreq_mod_navg)
            )
            chan_to_keep = np.arange(n_final_chan * n_chan_to_avg)
            self.select(freq_chans=chan_to_keep)

        if self.future_array_shapes:
            self.freq_array = self.freq_array.reshape(
                (n_final_chan, n_chan_to_avg)
            ).mean(axis=1)
            self.channel_width = self.channel_width.reshape(
                (n_final_chan, n_chan_to_avg)
            ).sum(axis=1)
        else:
            self.freq_array = self.freq_array.reshape(
                (1, n_final_chan, n_chan_to_avg)
            ).mean(axis=2)
            self.channel_width = self.channel_width * n_chan_to_avg
        self.Nfreqs = n_final_chan

        if self.eq_coeffs is not None:
            eq_coeff_diff = np.diff(self.eq_coeffs, axis=1)
            if np.abs(np.max(eq_coeff_diff)) > 0:
                warnings.warn(
                    "eq_coeffs vary by frequency. They should be "
                    "applied to the data using `remove_eq_coeffs` "
                    "before frequency averaging."
                )
            self.eq_coeffs = self.eq_coeffs.reshape(
                (self.Nants_telescope, n_final_chan, n_chan_to_avg)
            ).mean(axis=2)

        if not self.metadata_only:
            if self.future_array_shapes:
                shape_tuple = (
                    self.Nblts,
                    n_final_chan,
                    n_chan_to_avg,
                    self.Npols,
                )
            else:
                shape_tuple = (
                    self.Nblts,
                    1,
                    n_final_chan,
                    n_chan_to_avg,
                    self.Npols,
                )

            mask = self.flag_array.reshape(shape_tuple)

            if propagate_flags:
                # if any contributors are flagged, the result should be flagged
                if self.future_array_shapes:
                    self.flag_array = np.any(
                        self.flag_array.reshape(shape_tuple), axis=2
                    )
                else:
                    self.flag_array = np.any(
                        self.flag_array.reshape(shape_tuple), axis=3
                    )
            else:
                # 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
                if self.future_array_shapes:
                    self.flag_array = (
                        np.sum(~self.flag_array.reshape(shape_tuple), axis=2) == 0
                    )
                else:
                    self.flag_array = (
                        np.sum(~self.flag_array.reshape(shape_tuple), axis=3) == 0
                    )

            # need to update mask if a downsampled visibility will be flagged
            # so that we don't set it to zero
            for n_chan in np.arange(n_final_chan):
                if self.future_array_shapes:
                    if (self.flag_array[:, n_chan]).any():
                        ax0_inds, ax2_inds = np.nonzero(self.flag_array[:, n_chan, :])
                        # Only if all entries are masked
                        # May not happen due to propagate_flags keyword
                        # mask should be left alone otherwise
                        if np.all(mask[ax0_inds, n_chan, :, ax2_inds]):
                            mask[ax0_inds, n_chan, :, ax2_inds] = False
                else:
                    if (self.flag_array[:, :, n_chan]).any():
                        ax0_inds, ax1_inds, ax3_inds = np.nonzero(
                            self.flag_array[:, :, n_chan, :]
                        )
                        # Only if all entries are masked
                        # May not happen due to propagate_flags keyword
                        # mask should be left alone otherwise
                        if np.all(mask[ax0_inds, ax1_inds, n_chan, :, ax3_inds]):
                            mask[ax0_inds, ax1_inds, n_chan, :, ax3_inds] = False

            masked_data = np.ma.masked_array(
                self.data_array.reshape(shape_tuple), mask=mask
            )

            self.nsample_array = self.nsample_array.reshape(shape_tuple)
            # promote nsample dtype if half-precision
            nsample_dtype = self.nsample_array.dtype.type
            if nsample_dtype is np.float16:
                masked_nsample_dtype = np.float32
            else:
                masked_nsample_dtype = nsample_dtype
            masked_nsample = np.ma.masked_array(
                self.nsample_array, mask=mask, dtype=masked_nsample_dtype
            )

            if summing_correlator_mode:
                if self.future_array_shapes:
                    self.data_array = np.sum(masked_data, axis=2).data
                else:
                    self.data_array = np.sum(masked_data, axis=3).data
            else:
                # need to weight by the nsample_array
                if self.future_array_shapes:
                    self.data_array = (
                        np.sum(masked_data * masked_nsample, axis=2)
                        / np.sum(masked_nsample, axis=2)
                    ).data
                else:
                    self.data_array = (
                        np.sum(masked_data * masked_nsample, axis=3)
                        / np.sum(masked_nsample, axis=3)
                    ).data

            # nsample array is the fraction of data that we actually kept,
            # relative to the amount that went into the sum or average.
            # Need to take care to return precision back to original value.
            if self.future_array_shapes:
                self.nsample_array = (
                    np.sum(masked_nsample, axis=2) / float(n_chan_to_avg)
                ).data.astype(nsample_dtype)
            else:
                self.nsample_array = (
                    np.sum(masked_nsample, axis=3) / float(n_chan_to_avg)
                ).data.astype(nsample_dtype)

    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 compress_by_redundancy(
        self, method="select", tol=1.0, inplace=True, keep_all_metadata=True
    ):
        """
        Downselect or average to only have one baseline per redundant group.

        Either select the first baseline in the redundant group or average over
        the baselines in the redundant group.

        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.
        method : str
            Options are "select", which just keeps the first baseline in each
            redundant group or "average" which averages over the baselines in each
            redundant group and assigns the average to the first baseline in the group.
        inplace : bool
            Option to do selection on current object.
        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

        """
        allowed_methods = ["select", "average"]
        if method not in allowed_methods:
            raise ValueError(f"method must be one of {allowed_methods}")

        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]

        if method == "average":
            # do a metadata only select to get all the metadata right
            new_obj = self.copy(metadata_only=True)
            new_obj.select(bls=bl_ants, keep_all_metadata=keep_all_metadata)

            if not self.metadata_only:
                # initalize the data like arrays
                if new_obj.future_array_shapes:
                    temp_data_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.data_array.dtype,
                    )
                    temp_nsample_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.nsample_array.dtype,
                    )
                    temp_flag_array = np.zeros(
                        (new_obj.Nblts, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.flag_array.dtype,
                    )
                else:
                    temp_data_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.data_array.dtype,
                    )
                    temp_nsample_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.nsample_array.dtype,
                    )
                    temp_flag_array = np.zeros(
                        (new_obj.Nblts, 1, new_obj.Nfreqs, new_obj.Npols),
                        dtype=self.flag_array.dtype,
                    )
            for grp_ind, group in enumerate(red_gps):
                if len(conjugates) > 0:
                    conj_group = set(group).intersection(conjugates)
                    reg_group = list(set(group) - conj_group)
                    conj_group = list(conj_group)
                else:
                    reg_group = group
                    conj_group = []
                group_times = []
                group_inds = []
                conj_group_inds = []
                conj_group_times = []
                for bl in reg_group:
                    bl_inds = np.where(self.baseline_array == bl)[0]
                    group_inds.extend(bl_inds)
                    group_times.extend(self.time_array[bl_inds])
                for bl in conj_group:
                    bl_inds = np.where(self.baseline_array == bl)[0]
                    conj_group_inds.extend(bl_inds)
                    conj_group_times.extend(self.time_array[bl_inds])

                group_inds = np.array(group_inds, dtype=np.int64)
                conj_group_inds = np.array(conj_group_inds, dtype=np.int64)
                # now we have to figure out which times are the same to a tolerance
                # so we can average over them.
                time_inds = np.arange(len(group_times + conj_group_times))
                time_gps = uvutils.find_clusters(
                    time_inds,
                    np.array(group_times + conj_group_times),
                    self._time_array.tols[1],
                )

                # average over the same times
                obj_bl = bl_ants[grp_ind]
                obj_inds = new_obj._key2inds(obj_bl)[0]
                obj_times = new_obj.time_array[obj_inds]

                for gp in time_gps:
                    # Note that this average time is just used for identifying the
                    # index to use for the blt axis on the averaged data set.
                    # We do not update the actual time on that data set because it can
                    # result in confusing behavior -- small numerical rounding errors
                    # can result in many more unique times in the final data set than
                    # in the initial data set.
                    avg_time = np.average(np.array(group_times + conj_group_times)[gp])

                    obj_time_ind = np.where(
                        np.abs(obj_times - avg_time) < self._time_array.tols[1]
                    )[0]

                    if obj_time_ind.size == 1:
                        this_obj_ind = obj_inds[obj_time_ind[0]]
                    else:
                        warnings.warn(
                            "Index baseline in the redundant group does not "
                            "have all the times, compressed object will be "
                            "missing those times."
                        )
                        continue

                    # time_ind contains indices for both regular and conjugated bls
                    # because we needed to group them together in time.
                    # The regular ones are first and extend the length of group_times,
                    # so we use that to split them back up.
                    regular_orientation = np.array(
                        [time_ind for time_ind in gp if time_ind < len(group_times)],
                        dtype=np.int64,
                    )
                    regular_inds = group_inds[np.array(regular_orientation)]
                    conj_orientation = np.array(
                        [
                            time_ind - len(group_times)
                            for time_ind in gp
                            if time_ind >= len(group_times)
                        ],
                        dtype=np.int64,
                    )
                    conj_inds = conj_group_inds[np.array(conj_orientation)]
                    # check that the integration times are all the same
                    int_times = np.concatenate(
                        (
                            self.integration_time[regular_inds],
                            self.integration_time[conj_inds],
                        )
                    )
                    if not np.all(
                        np.abs(int_times - new_obj.integration_time[obj_time_ind])
                        < new_obj._integration_time.tols[1]
                    ):
                        warnings.warn(
                            "Integrations times are not identical in a redundant "
                            "group. Averaging anyway but this may cause unexpected "
                            "behavior."
                        )

                    if not self.metadata_only:
                        vis_to_avg = np.concatenate(
                            (
                                self.data_array[regular_inds],
                                np.conj(self.data_array[conj_inds]),
                            )
                        )
                        nsample_to_avg = np.concatenate(
                            (
                                self.nsample_array[regular_inds],
                                self.nsample_array[conj_inds],
                            )
                        )
                        flags_to_avg = np.concatenate(
                            (self.flag_array[regular_inds], self.flag_array[conj_inds],)
                        )
                        # if all data is flagged, average it all as if it were not
                        if np.all(flags_to_avg):
                            mask = np.zeros_like(flags_to_avg)
                        else:
                            mask = flags_to_avg

                        vis_to_avg = np.ma.masked_array(vis_to_avg, mask=mask)

                        nsample_to_avg = np.ma.masked_array(nsample_to_avg, mask=mask)

                        avg_vis = np.ma.average(
                            vis_to_avg, weights=nsample_to_avg, axis=0
                        )
                        avg_nsample = np.sum(nsample_to_avg, axis=0)
                        avg_flag = np.all(flags_to_avg, axis=0)

                        temp_data_array[this_obj_ind] = avg_vis
                        temp_nsample_array[this_obj_ind] = avg_nsample
                        temp_flag_array[this_obj_ind] = avg_flag

            if inplace:
                self.select(bls=bl_ants, keep_all_metadata=keep_all_metadata)
                if not self.metadata_only:
                    self.data_array = temp_data_array
                    self.nsample_array = temp_nsample_array
                    self.flag_array = temp_flag_array
                self.check()
                return
            else:
                if not self.metadata_only:
                    new_obj.data_array = temp_data_array
                    new_obj.nsample_array = temp_nsample_array
                    new_obj.flag_array = temp_flag_array
                new_obj.check()
                return new_obj
        else:
            return self.select(
                bls=bl_ants, inplace=inplace, 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 = self._calc_nants_data()
        self.Nbls = np.unique(self.baseline_array).size
        self.Nblts = Nblts_full

        if self.phase_center_app_ra is not None:
            self.phase_center_app_ra = self.phase_center_app_ra[blt_map]
        if self.phase_center_app_dec is not None:
            self.phase_center_app_dec = self.phase_center_app_dec[blt_map]
        if self.phase_center_frame_pa is not None:
            self.phase_center_frame_pa = self.phase_center_frame_pa[blt_map]
        if self.multi_phase_center:
            self.phase_center_id_array = self.phase_center_id_array[blt_map]

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

        self.check()

    def _convert_from_filetype(self, other):
        """
        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):
        """
        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', 'mir', 'ms'

        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()
        elif filetype == "mir":
            from . import mir

            other_obj = mir.Mir()
        elif filetype == "ms":
            from . import ms

            other_obj = ms.MS()
        else:
            raise ValueError("filetype must be uvfits, mir, miriad, ms, fhd, or uvh5")
        for p in self:
            param = getattr(self, p)
            setattr(other_obj, p, param)
        return other_obj

    def read_fhd(
        self,
        filelist,
        use_model=False,
        axis=None,
        read_data=True,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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, a layout file and a flag file.
            An obs file is also required if `read_data` is False.
        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.
        read_data : bool
            Read in the visibility, nsample and flag data. If set to False, only
            the metadata will be read in. Setting read_data to False results in
            a metadata only object. If read_data is False, an obs file must be
            included in the filelist.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        fhd_obj = fhd.FHD()
        fhd_obj.read_fhd(
            filelist,
            use_model=use_model,
            background_lsts=background_lsts,
            read_data=read_data,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        self._convert_from_filetype(fhd_obj)
        del fhd_obj

    def read_mir(
        self,
        filepath,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
    ):
        """
        Read in data from an SMA MIR file.

        Note that with the exception of filepath, the reset of the parameters are
        used to sub-select a range of data that matches the limitations of the current
        instantiation of pyuvdata  -- namely 1 spectral window, 1 source. These could
        be dropped in the future, as pyuvdata capabilities grow.

        Parameters
        ----------
        filepath : str
             The file path to the MIR folder to read from.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values. Default is false.
        """
        from . import mir

        mir_obj = mir.Mir()
        mir_obj.read_mir(
            filepath,
            isource=isource,
            irec=irec,
            isb=isb,
            corrchunk=corrchunk,
            pseudo_cont=pseudo_cont,
        )
        self._convert_from_filetype(mir_obj)
        del mir_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,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        calc_lst=True,
        fix_old_proj=False,
        fix_use_ant_pos=True,
    ):
        """
        Read in data from a miriad file.

        Parameters
        ----------
        filepath : str
            The miriad root directory to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to read into the object.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `bls` or `polarizations` parameters, if it is a ValueError will be raised.
        polarizations : array_like of int or str, optional
            List of polarization integers or strings to read-in. e.g. ['xx', 'yy', ...]
        time_range : list of float, optional
            len-2 list containing min and max range of times in Julian Date to
            include when reading data into the object. e.g. [2458115.20, 2458115.40]
        read_data : bool
            Read in the visibility and flag data. If set to false,
            only the metadata will be read in. Setting read_data to False
            results in an incompletely defined object (check will not pass).
        phase_type : str, optional
            Option to specify the phasing status of the data. Options are 'drift',
            'phased' or None. 'drift' means the data are zenith drift data,
            'phased' means the data are phased to a single RA/Dec. Default is None
            meaning it will be guessed at based on the file contents.
        correct_lat_lon : bool
            Option to update the latitude and longitude from the known_telescopes
            list if the altitude is missing.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        miriad_obj = miriad.Miriad()
        miriad_obj.read_miriad(
            filepath,
            correct_lat_lon=correct_lat_lon,
            read_data=read_data,
            phase_type=phase_type,
            antenna_nums=antenna_nums,
            ant_str=ant_str,
            bls=bls,
            polarizations=polarizations,
            time_range=time_range,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            calc_lst=calc_lst,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(miriad_obj)
        del miriad_obj

    def read_ms(
        self,
        filepath,
        axis=None,
        data_column="DATA",
        pol_order="AIPS",
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        ignore_single_chan=True,
        raise_error=True,
        read_weights=True,
    ):
        """
        Read in data from a measurement set.

        Parameters
        ----------
        filepath : str
            The measurement set root directory to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        data_column : str
            name of CASA data column to read into data_array. Options are:
            'DATA', 'MODEL', or 'CORRECTED_DATA'
        pol_order : str
            Option to specify polarizations order convention, options are
            'CASA' or 'AIPS'.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        ignore_single_chan : bool
            Some measurement sets (e.g., those from ALMA) use single channel spectral
            windows for recording pseudo-continuum channels or storing other metadata
            in the track when the telescopes are not on source. Because of the way
            the object is strutured (where all spectral windows are assumed to be
            simultaneously recorded), this can significantly inflate the size and memory
            footprint of UVData objects. By default, single channel windows are ignored
            to avoid this issue, although they can be included if setting this parameter
            equal to True.
        raise_error : bool
            The measurement set format allows for different spectral windows and
            polarizations to have different metdata for the same time-baseline
            combination, but UVData objects do not. If detected, by default the reader
            will throw an error. However, if set to False, the reader will simply give
            a warning, and will use the first value read in the file as the "correct"
            metadata in the UVData object.
        read_weights : bool
            Read in the weights from the MS file, default is True. If false, the method
            will set the `nsamples_array` to the same uniform value (namely 1.0).

        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.

        """
        if isinstance(filepath, (list, tuple, np.ndarray)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        from . import ms

        ms_obj = ms.MS()
        ms_obj.read_ms(
            filepath,
            data_column=data_column,
            pol_order=pol_order,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            ignore_single_chan=ignore_single_chan,
            raise_error=raise_error,
            read_weights=read_weights,
        )
        self._convert_from_filetype(ms_obj)
        del ms_obj

    def read_mwa_corr_fits(
        self,
        filelist,
        axis=None,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        read_data=True,
        data_array_dtype=np.complex64,
        nsample_array_dtype=np.float32,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Read in MWA correlator gpu box files.

        The default settings remove some of the instrumental effects in the bandpass
        by dividing out the digital gains and the coarse band shape.
        If the desired output is raw correlator data, set remove_dig_gains=False,
        remove_coarse_band=False, correct_cable_len=False, and
        phase_to_pointing_center=False.

        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.
        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_aoflagger_flags : bool
            Option to use aoflagger mwaf flag files. Defaults to true if aoflagger
            flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Option to divide out digital gains.
        remove_coarse_band : bool
            Option to divide out coarse band shape.
        correct_cable_len : bool
            Option to apply a cable delay correction.
        correct_van_vleck : bool
            Option to apply a van vleck correction.
        cheby_approx : bool
            Only used if correct_van_vleck is True. Option to implement the van
            vleck correction with a chebyshev polynomial approximation.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna.
        flag_small_sig_ants : bool
            Being replaced with flag_small_auto_ants and will be removed in v2.4.
        propagate_coarse_flags : bool
            Option to propagate flags for missing coarse channel integrations
            across frequency.
        flag_init: bool
            Set to True in order to do routine flagging of coarse channel edges,
            start or end integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if flag_init is True. Set to the width to flag on the edge
            of each coarse channel, in hz. Errors if not equal to integer
            multiple of channel_width. Set to 0 for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if flag_init is True. Set to the number of seconds to flag
            at the end of the observation. Set to 0 for no flagging. Errors if
            not an integer multiple of the integration time.
        flag_dc_offset: bool
            Only used if flag_init is True. Set to True to flag the center fine
            channel of each coarse channel. Only used if file_type is
            'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
        phase_to_pointing_center : bool
            Option to phase to the observation pointing center.
        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.
        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).
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3).
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run).
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        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

        if isinstance(filelist[0], (list, tuple, np.ndarray)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )
        if use_cotter_flags is not None:
            use_aoflagger_flags = use_cotter_flags
            warnings.warn(
                "Use `use_aoflagger_flags` instead of `use_cotter_flags`."
                "`use_cotter_flags` is deprecated, and will be removed in "
                "pyuvdata v2.4.",
                DeprecationWarning,
            )
        if flag_small_sig_ants is not None:
            flag_small_auto_ants = flag_small_sig_ants
            warnings.warn(
                "Use `flag_small_auto_ants` instead of `flag_small_sig_ants`."
                "`flag_small_sig_ants` is deprecated, and will be removed in "
                "pyuvdata v2.4.",
                DeprecationWarning,
            )

        corr_obj = mwa_corr_fits.MWACorrFITS()
        corr_obj.read_mwa_corr_fits(
            filelist,
            use_aoflagger_flags=use_aoflagger_flags,
            remove_dig_gains=remove_dig_gains,
            remove_coarse_band=remove_coarse_band,
            correct_cable_len=correct_cable_len,
            correct_van_vleck=correct_van_vleck,
            cheby_approx=cheby_approx,
            flag_small_auto_ants=flag_small_auto_ants,
            propagate_coarse_flags=propagate_coarse_flags,
            flag_init=flag_init,
            edge_width=edge_width,
            start_flag=start_flag,
            end_flag=end_flag,
            flag_dc_offset=flag_dc_offset,
            remove_flagged_ants=remove_flagged_ants,
            phase_to_pointing_center=phase_to_pointing_center,
            read_data=read_data,
            data_array_dtype=data_array_dtype,
            nsample_array_dtype=nsample_array_dtype,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        self._convert_from_filetype(corr_obj)
        del corr_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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        keep_all_metadata=True,
        read_data=True,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        fix_old_proj=None,
        fix_use_ant_pos=True,
    ):
        """
        Read in header, metadata and data from a single uvfits file.

        Parameters
        ----------
        filename : str
            The uvfits file to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_names` is also provided. Ignored if read_data is False.
        antenna_names : array_like of str, optional
            The antennas names to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_nums` is also provided. Ignored if read_data is False.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None. Ignored if read_data is False.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `antenna_names`, `bls` args or the `polarizations` parameters,
            if it is a ValueError will be raised. Ignored if read_data is False.
        frequencies : array_like of float, optional
            The frequencies to include when reading data into the object, each
            value passed here should exist in the freq_array. Ignored if
            read_data is False.
        freq_chans : array_like of int, optional
            The frequency channel numbers to include when reading data into the
            object. Ignored if read_data is False.
        times : array_like of float, optional
            The times to include when reading data into the object, each value
            passed here should exist in the time_array 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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        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.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        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,
            lsts=lsts,
            lst_range=lst_range,
            polarizations=polarizations,
            blt_inds=blt_inds,
            keep_all_metadata=keep_all_metadata,
            read_data=read_data,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(uvfits_obj)
        del uvfits_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,
        lsts=None,
        lst_range=None,
        polarizations=None,
        blt_inds=None,
        keep_all_metadata=True,
        read_data=True,
        data_array_dtype=np.complex128,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        fix_old_proj=None,
        fix_use_ant_pos=True,
    ):
        """
        Read a UVH5 file.

        Parameters
        ----------
        filename : str
             The UVH5 file to read from.
        axis : str
            Axis to concatenate files along. This enables fast concatenation
            along the specified axis without the normal checking that all other
            metadata agrees. This method does not guarantee correct resulting
            objects. Please see the docstring for fast_concat for details.
            Allowed values are: 'blt', 'freq', 'polarization'. Only used if
            multiple files are passed.
        antenna_nums : array_like of int, optional
            The antennas numbers to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_names` is also provided. Ignored if read_data is False.
        antenna_names : array_like of str, optional
            The antennas names to include when reading data into the object
            (antenna positions and names for the removed antennas will be retained
            unless `keep_all_metadata` is False). This cannot be provided if
            `antenna_nums` is also provided. Ignored if read_data is False.
        bls : list of tuple, optional
            A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of
            baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines
            to include when reading data into the object. For length-2 tuples,
            the ordering of the numbers within the tuple does not matter. For
            length-3 tuples, the polarization string is in the order of the two
            antennas. If length-3 tuples are provided, `polarizations` must be
            None. Ignored if read_data is False.
        ant_str : str, optional
            A string containing information about what antenna numbers
            and polarizations to include when reading data into the object.
            Can be 'auto', 'cross', 'all', or combinations of antenna numbers
            and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more
            examples of valid strings and the behavior of different forms for ant_str.
            If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will
            be kept for both baselines (1, 2) and (2, 3) to return a valid
            pyuvdata object.
            An ant_str cannot be passed in addition to any of `antenna_nums`,
            `antenna_names`, `bls` args or the `polarizations` parameters,
            if it is a ValueError will be raised. Ignored if read_data is False.
        frequencies : array_like of float, optional
            The frequencies to include when reading data into the object, each
            value passed here should exist in the freq_array. Ignored if
            read_data is False.
        freq_chans : array_like of int, optional
            The frequency channel numbers to include when reading data into the
            object. Ignored if read_data is False.
        times : array_like of float, optional
            The times to include when reading data into the object, each value
            passed here should exist in the time_array 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`.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        polarizations : array_like of int, optional
            The polarizations numbers to include when reading data into the
            object, each value passed here should exist in the polarization_array.
            Ignored if read_data is False.
        blt_inds : array_like of int, optional
            The baseline-time indices to include when reading data into the
            object. This is not commonly used. Ignored if read_data is False.
        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'.
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            to apply the correction if the attributes `phase_center_app_ra` and
            `phase_center_app_dec` are missing (as they were introduced alongside the
            new phasing method).
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.

        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)):
            raise ValueError(
                "Reading multiple files from class specific "
                "read functions is no longer supported. "
                "Use the generic `uvdata.read` function instead."
            )

        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,
            lsts=lsts,
            lst_range=lst_range,
            polarizations=polarizations,
            blt_inds=blt_inds,
            data_array_dtype=data_array_dtype,
            keep_all_metadata=keep_all_metadata,
            read_data=read_data,
            multidim_index=multidim_index,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
        )
        self._convert_from_filetype(uvh5_obj)
        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",
        phase_epoch=None,
        orig_phase_frame=None,
        phase_use_ant_pos=True,
        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_data=True,
        phase_type=None,
        correct_lat_lon=True,
        use_model=False,
        data_column="DATA",
        pol_order="AIPS",
        data_array_dtype=np.complex128,
        nsample_array_dtype=np.float32,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        skip_bad_files=False,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
        lsts=None,
        lst_range=None,
        calc_lst=True,
        fix_old_proj=None,
        fix_use_ant_pos=True,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        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_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' or
            'mwa_corr_fits'.
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3). Only used if
            file_type is 'mwa_corr_fits'.
        use_aoflagger_flags : bool
            Option to use aoflagger mwaf flag files. Defaults to true if aoflagger
            flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out digital
            gains.
        remove_coarse_band : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse
            band shape.
        correct_cable_len : bool
            Flag to apply cable length correction. Only used if file_type is
            'mwa_corr_fits'.
        correct_van_vleck : bool
            Flag to apply a van vleck correction. Only used if file_type is
            'mwa_corr_fits'.
        cheby_approx : bool
            Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.
            Option to implement the van vleck correction with a chebyshev polynomial
            approximation. Set to False to run the integral version of the correction.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna. Only used if file_type is
            'mwa_corr_fits'.
        flag_small_sig_ants : bool
            Being replaced by flag_small_auto_ants and will be removed in v2.4.
        propogate_coarse_flags : bool
            Option to propogate flags for missing coarse channel integrations
            across frequency. Only used if file_type is 'mwa_corr_fits'.
        flag_init: bool
            Only used if file_type is 'mwa_corr_fits'. Set to True in order to
            do routine flagging of coarse channel edges, start or end
            integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the width to flag on the edge of each coarse channel, in hz.
            Errors if not equal to integer multiple of channel_width. Set to 0
            for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the number of seconds to flag at the end of the observation. Set
            to 0 for no flagging. Errors if not an integer multiple of the
            integration time.
        flag_dc_offset: bool
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to True to flag the center fine channel of each coarse channel. Only
            used if file_type is 'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
            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.
        skip_bad_files : bool
            Option when reading multiple files to catch read errors such that
            the read continues even if one or more files are corrupted. Files
            that produce errors will be printed. Default is False (files will
            not be skipped).
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values in MIR dataset. Default is false.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True. Only applies to MIRIAD files.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False, unless reading a UVH5 file that is missing the `phase_center_app_ra`
            and `phase_center_app_dec` attributes (as these were introduced at the same
            time as the new `phase` method), in which case the default is True.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. By default, this is only done if reading
            in a file with multiple sources.
        ignore_name : bool
            Only relevant when reading in multiple files, which are concatenated into a
            single UVData object. Option to ignore the name of the phase center when
            combining multiple files, which would otherwise result in an error being
            raised because of attributes not matching. Doing so effectively adopts the
            name found in the first file read in. Default 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, mir, 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"
                elif os.path.exists(os.path.join(file_test, "sch_read")):
                    # It's Submillimeter Array mir format.
                    file_type = "mir"

            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:

            file_num = 0
            file_warnings = ""
            unread = True
            f = filename[file_num]
            while unread and file_num < len(filename):
                try:
                    self.read(
                        filename[file_num],
                        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_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,
                        nsample_array_dtype=nsample_array_dtype,
                        skip_bad_files=skip_bad_files,
                        background_lsts=background_lsts,
                        run_check=run_check,
                        check_extra=check_extra,
                        run_check_acceptability=run_check_acceptability,
                        strict_uvw_antpos_check=strict_uvw_antpos_check,
                        isource=None,
                        irec=irec,
                        isb=isb,
                        corrchunk=corrchunk,
                        pseudo_cont=pseudo_cont,
                        calc_lst=calc_lst,
                        fix_old_proj=fix_old_proj,
                        fix_use_ant_pos=fix_use_ant_pos,
                        make_multi_phase=make_multi_phase,
                    )
                    unread = False
                except KeyError as err:
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to KeyError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
                except ValueError as err:
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to ValueError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
                except OSError as err:  # pragma: nocover
                    file_warnings = (
                        file_warnings + f"Failed to read {f} due to OSError: {err}\n"
                    )
                    file_num += 1
                    if skip_bad_files is False:
                        raise
            if (
                allow_rephase
                and phase_center_radec is None
                and not unphase_to_drift
                and self.phase_type == "phased"
                and not self.multi_phase_center
                and not make_multi_phase
            ):
                # set the phase center to be the phase center of the first file
                phase_center_radec = [self.phase_center_ra, self.phase_center_dec]
                phase_frame = self.phase_center_frame
                phase_epoch = self.phase_center_epoch

            uv_list = []
            if len(filename) > file_num + 1:
                for f in filename[file_num + 1 :]:
                    uv2 = UVData()
                    try:
                        uv2.read(
                            f,
                            file_type=file_type,
                            phase_center_radec=phase_center_radec,
                            phase_frame=phase_frame,
                            phase_epoch=phase_epoch,
                            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_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,
                            nsample_array_dtype=nsample_array_dtype,
                            skip_bad_files=skip_bad_files,
                            background_lsts=background_lsts,
                            run_check=run_check,
                            check_extra=check_extra,
                            run_check_acceptability=run_check_acceptability,
                            strict_uvw_antpos_check=strict_uvw_antpos_check,
                            isource=None,
                            irec=irec,
                            isb=isb,
                            corrchunk=corrchunk,
                            pseudo_cont=pseudo_cont,
                            calc_lst=calc_lst,
                            fix_old_proj=fix_old_proj,
                            fix_use_ant_pos=fix_use_ant_pos,
                            make_multi_phase=make_multi_phase,
                        )
                        uv_list.append(uv2)
                    except KeyError as err:
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to KeyError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
                    except ValueError as err:
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to ValueError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
                    except OSError as err:  # pragma: nocover
                        file_warnings = (
                            file_warnings
                            + f"Failed to read {f} due to OSError: {err}\n"
                        )
                        if skip_bad_files:
                            continue
                        else:
                            raise
            if unread is True:
                warnings.warn(
                    "########################################################\n"
                    "ALL FILES FAILED ON READ - NO READABLE FILES IN FILENAME\n"
                    "########################################################"
                )
            elif len(file_warnings) > 0:
                warnings.warn(file_warnings)

            # Concatenate once at end
            if axis is not None:
                # Rewrote fast_concat to operate on lists
                self.fast_concat(
                    uv_list,
                    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,
                    ignore_name=ignore_name,
                )
            else:
                # Too much work to rewrite __add__ to operate on lists
                # of files, so instead doing a binary tree merge
                uv_list = [self] + uv_list
                while len(uv_list) > 1:
                    for uv1, uv2 in zip(uv_list[0::2], uv_list[1::2]):
                        uv1.__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,
                            ignore_name=ignore_name,
                        )
                    uv_list = uv_list[0::2]
                # Because self was at the beginning of the list,
                # everything is merged into it at the end of this loop

        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,
                    lsts=lsts,
                    lst_range=lst_range,
                    polarizations=polarizations,
                    blt_inds=blt_inds,
                    read_data=read_data,
                    keep_all_metadata=keep_all_metadata,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )

            elif file_type == "mir":
                self.read_mir(
                    filename,
                    isource=isource,
                    irec=irec,
                    isb=isb,
                    corrchunk=corrchunk,
                    pseudo_cont=pseudo_cont,
                )
                select = False

            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,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    calc_lst=calc_lst,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )

            elif file_type == "mwa_corr_fits":
                self.read_mwa_corr_fits(
                    filename,
                    use_aoflagger_flags=use_aoflagger_flags,
                    use_cotter_flags=use_cotter_flags,
                    remove_dig_gains=remove_dig_gains,
                    remove_coarse_band=remove_coarse_band,
                    correct_cable_len=correct_cable_len,
                    correct_van_vleck=correct_van_vleck,
                    cheby_approx=cheby_approx,
                    flag_small_auto_ants=flag_small_auto_ants,
                    flag_small_sig_ants=flag_small_sig_ants,
                    propagate_coarse_flags=propagate_coarse_flags,
                    flag_init=flag_init,
                    edge_width=edge_width,
                    start_flag=start_flag,
                    end_flag=end_flag,
                    flag_dc_offset=True,
                    remove_flagged_ants=remove_flagged_ants,
                    phase_to_pointing_center=phase_to_pointing_center,
                    read_data=read_data,
                    data_array_dtype=data_array_dtype,
                    nsample_array_dtype=nsample_array_dtype,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            elif file_type == "fhd":
                self.read_fhd(
                    filename,
                    use_model=use_model,
                    background_lsts=background_lsts,
                    read_data=read_data,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            elif file_type == "ms":
                self.read_ms(
                    filename,
                    data_column=data_column,
                    pol_order=pol_order,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            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,
                    lsts=lsts,
                    lst_range=lst_range,
                    polarizations=polarizations,
                    blt_inds=blt_inds,
                    read_data=read_data,
                    data_array_dtype=data_array_dtype,
                    keep_all_metadata=keep_all_metadata,
                    multidim_index=multidim_index,
                    background_lsts=background_lsts,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                    fix_old_proj=fix_old_proj,
                    fix_use_ant_pos=fix_use_ant_pos,
                )
                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,
                    keep_all_metadata=keep_all_metadata,
                    run_check=run_check,
                    check_extra=check_extra,
                    run_check_acceptability=run_check_acceptability,
                    strict_uvw_antpos_check=strict_uvw_antpos_check,
                )

            if make_multi_phase:
                self._set_multi_phase_center(preserve_phase_center_info=True)

            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],
                    )
                    or (self.phase_center_frame != phase_frame)
                    or (self.phase_center_epoch != phase_epoch)
                ):
                    warnings.warn("Phasing this UVData object to phase_center_radec")
                    self.phase(
                        phase_center_radec[0],
                        phase_center_radec[1],
                        epoch=phase_epoch,
                        phase_frame=phase_frame,
                        orig_phase_frame=orig_phase_frame,
                        use_ant_pos=phase_use_ant_pos,
                        allow_rephase=True,
                    )

    @classmethod
    def from_file(
        cls,
        filename,
        axis=None,
        file_type=None,
        allow_rephase=True,
        phase_center_radec=None,
        unphase_to_drift=False,
        phase_frame="icrs",
        phase_epoch=None,
        orig_phase_frame=None,
        phase_use_ant_pos=True,
        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_data=True,
        phase_type=None,
        correct_lat_lon=True,
        use_model=False,
        data_column="DATA",
        pol_order="AIPS",
        data_array_dtype=np.complex128,
        nsample_array_dtype=np.float32,
        use_aoflagger_flags=None,
        use_cotter_flags=None,
        remove_dig_gains=True,
        remove_coarse_band=True,
        correct_cable_len=False,
        correct_van_vleck=False,
        cheby_approx=True,
        flag_small_auto_ants=True,
        flag_small_sig_ants=None,
        propagate_coarse_flags=True,
        flag_init=True,
        edge_width=80e3,
        start_flag="goodtime",
        end_flag=0.0,
        flag_dc_offset=True,
        remove_flagged_ants=True,
        phase_to_pointing_center=False,
        skip_bad_files=False,
        multidim_index=False,
        background_lsts=True,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
        isource=None,
        irec=None,
        isb=None,
        corrchunk=None,
        pseudo_cont=False,
        lsts=None,
        lst_range=None,
        calc_lst=True,
        fix_old_proj=None,
        fix_use_ant_pos=True,
        make_multi_phase=False,
        ignore_name=False,
    ):
        """
        Initialize a new UVData object by reading the input file.

        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_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' or
            'mwa_corr_fits'.
        nsample_array_dtype : numpy dtype
            Datatype to store the output nsample_array as. Must be either
            np.float64 (double-precision), np.float32 (single-precision), or
            np.float16 (half-precision). Half-precision is only recommended for
            cases where no sampling or averaging of baselines will occur,
            because round-off errors can be quite large (~1e-3). Only used if
            file_type is 'mwa_corr_fits'.
        use_aoflagger_flags : bool
            Only used if file_type is 'mwa_corr_fits'. Option to use aoflagger mwaf
            flag files. Defaults to true if aoflagger flag files are submitted.
        use_cotter_flags : bool
            Being replaced by use_aoflagger_flags and will be removed in v2.4.
        remove_dig_gains : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out digital
            gains.
        remove_coarse_band : bool
            Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse
            band shape.
        correct_cable_len : bool
            Flag to apply cable length correction. Only used if file_type is
            'mwa_corr_fits'.
        correct_van_vleck : bool
            Flag to apply a van vleck correction. Only used if file_type is
            'mwa_corr_fits'.
        cheby_approx : bool
            Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.
            Option to implement the van vleck correction with a chebyshev polynomial
            approximation. Set to False to run the integral version of the correction.
        flag_small_auto_ants : bool
            Only used if correct_van_vleck is True. Option to completely flag any
            antenna for which the autocorrelation falls below a threshold found by
            the Van Vleck correction to indicate bad data. Specifically, the
            threshold used is 0.5 * integration_time * channel_width. If set to False,
            only the times and frequencies at which the auto is below the
            threshold will be flagged for the antenna. Only used if file_type is
            'mwa_corr_fits'.
        flag_small_sig_ants : bool
            Being replaced by flag_small_auto_ants and will be removed in v2.4.
        propogate_coarse_flags : bool
            Option to propogate flags for missing coarse channel integrations
            across frequency. Only used if file_type is 'mwa_corr_fits'.
        flag_init: bool
            Only used if file_type is 'mwa_corr_fits'. Set to True in order to
            do routine flagging of coarse channel edges, start or end
            integrations, or the center fine channel of each coarse
            channel. See associated keywords.
        edge_width: float
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the width to flag on the edge of each coarse channel, in hz.
            Errors if not equal to integer multiple of channel_width. Set to 0
            for no edge flagging.
        start_flag: float or str
            Only used if flag_init is True. The number of seconds to flag at the
            beginning of the observation. Set to 0 for no flagging. Default is
            'goodtime', which uses information in the metafits file to determine
            the length of time that should be flagged. Errors if input is not a
            float or 'goodtime'. Errors if float input is not equal to an
            integer multiple of the integration time.
        end_flag: floats
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to the number of seconds to flag at the end of the observation. Set
            to 0 for no flagging. Errors if not an integer multiple of the
            integration time.
        flag_dc_offset: bool
            Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set
            to True to flag the center fine channel of each coarse channel. Only
            used if file_type is 'mwa_corr_fits'.
        remove_flagged_ants : bool
            Option to perform a select to remove antennas flagged in the metafits
            file. If correct_van_vleck and flag_small_auto_ants are both True then
            antennas flagged by the Van Vleck correction are also removed.
            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.
        skip_bad_files : bool
            Option when reading multiple files to catch read errors such that
            the read continues even if one or more files are corrupted. Files
            that produce errors will be printed. Default is False (files will
            not be skipped).
        multidim_index : bool
            [Only for HDF5] If True, attempt to index the HDF5 dataset
            simultaneously along all data axes. Otherwise index one axis at-a-time.
            This only works if data selection is sliceable along all but one axis.
            If indices are not well-matched to data chunks, this can be slow.
        background_lsts : bool
            When set to True, the lst_array is calculated in a background thread.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            after after reading in the file (the default is True,
            meaning the check will be run). Ignored if read_data is False.
        check_extra : bool
            Option to check optional parameters as well as required ones (the
            default is True, meaning the optional parameters will be checked).
            Ignored if read_data is False.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters after
            reading in the file (the default is True, meaning the acceptable
            range check will be done). Ignored if read_data is False.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        isource : int
            Source code for MIR dataset
        irec : int
            Receiver code for MIR dataset
        isb : int
            Sideband code for MIR dataset
        corrchunk : int
            Correlator chunk code for MIR dataset
        pseudo_cont : boolean
            Read in only pseudo-continuuum values in MIR dataset. Default is false.
        lsts : array_like of float, optional
            The local sidereal times (LSTs) to keep in the object, each value
            passed here should exist in the lst_array. Cannot be used with
            `times`, `time_range`, or `lst_range`.
        lst_range : array_like of float, optional
            The local sidereal time (LST) range in radians to keep in the
            object, must be of length 2. Some of the LSTs in the object should
            fall between the first and last elements. If the second value is
            smaller than the first, the LSTs are treated as having phase-wrapped
            around LST = 2*pi = 0, and the LSTs kept on the object will run from
            the larger value, through 0, and end at the smaller value.
        calc_lst : bool
            Recalculate the LST values that are present within the file, useful in
            cases where the "online" calculate values have precision or value errors.
            Default is True. Only applies to MIRIAD files.
        fix_old_proj : bool
            Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`
            method was used prior to writing the data, which had errors of the order of
            one part in 1e4 - 1e5. See the phasing memo for more details. Default is
            False, unless reading a UVH5 file that is missing the `phase_center_app_ra`
            and `phase_center_app_dec` attributes (as these were introduced at the same
            time as the new `phase` method), in which case the default is True.
        fix_use_ant_pos : bool
            If setting `fix_old_proj` to True, use the antenna positions to derive the
            correct uvw-coordinates rather than using the baseline vectors. Default is
            True.
        make_multi_phase : bool
            Option to make the output a multi phase center dataset, capable of holding
            data on multiple phase centers. By default, this is only done if reading
            in a file with multiple sources.
        ignore_name : bool
            Only relevant when reading in multiple files, which are concatenated into a
            single UVData object. Option to ignore the name of the phase center when
            combining multiple files, which would otherwise result in an error being
            raised because of attributes not matching. Doing so effectively adopts the
            name found in the first file read in. Default 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.

        """
        uvd = cls()
        uvd.read(
            filename,
            axis=axis,
            file_type=file_type,
            allow_rephase=allow_rephase,
            phase_center_radec=phase_center_radec,
            unphase_to_drift=unphase_to_drift,
            phase_frame=phase_frame,
            phase_epoch=phase_epoch,
            orig_phase_frame=orig_phase_frame,
            phase_use_ant_pos=phase_use_ant_pos,
            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_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,
            nsample_array_dtype=nsample_array_dtype,
            use_aoflagger_flags=use_aoflagger_flags,
            use_cotter_flags=use_cotter_flags,
            remove_dig_gains=remove_dig_gains,
            remove_coarse_band=remove_coarse_band,
            correct_cable_len=correct_cable_len,
            correct_van_vleck=correct_van_vleck,
            cheby_approx=cheby_approx,
            flag_small_auto_ants=flag_small_auto_ants,
            flag_small_sig_ants=flag_small_sig_ants,
            propagate_coarse_flags=propagate_coarse_flags,
            flag_init=flag_init,
            edge_width=edge_width,
            start_flag=start_flag,
            end_flag=end_flag,
            flag_dc_offset=flag_dc_offset,
            remove_flagged_ants=remove_flagged_ants,
            phase_to_pointing_center=phase_to_pointing_center,
            skip_bad_files=skip_bad_files,
            multidim_index=multidim_index,
            background_lsts=background_lsts,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            isource=isource,
            irec=irec,
            isb=isb,
            corrchunk=corrchunk,
            pseudo_cont=pseudo_cont,
            lsts=lsts,
            lst_range=lst_range,
            calc_lst=calc_lst,
            fix_old_proj=fix_old_proj,
            fix_use_ant_pos=fix_use_ant_pos,
            make_multi_phase=make_multi_phase,
            ignore_name=ignore_name,
        )
        return uvd

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

        Parameters
        ----------
        filename : str
            The miriad root directory to write to.
        clobber : bool
            Option to overwrite the filename if the file already exists.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.
        no_antnums : bool
            Option to not write the antnums variable to the file.
            Should only be used for testing purposes.
        calc_lst : bool
            Recalculate the LST values upon writing the file. This is done to perform
            higher-precision accounting for the difference in MIRAD timestamps vs
            pyuvdata (the former marks the beginning of an integration, the latter
            marks the midpoint). Default is False, which instead uses a simple formula
            for correcting the LSTs, expected to be accurate to approximately 0.1 µsec
            precision.

        Raises
        ------
        ValueError
            If the frequencies are not evenly spaced or are separated by more
            than their channel width or if the UVData object is a metadata only object.
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a miriad file.")

        miriad_obj = self._convert_to_filetype("miriad")
        miriad_obj.write_miriad(
            filepath,
            clobber=clobber,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
            no_antnums=no_antnums,
            calc_lst=calc_lst,
        )
        del miriad_obj

    def write_mir(
        self, filepath,
    ):
        """
        Write the data to a mir file.

        Parameters
        ----------
        filename : str
            The mir root directory to write to.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.
        NotImplementedError
            Method is not fully implemented yet, and thus will raise an error

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a mir file.")

        mir_obj = self._convert_to_filetype("mir")
        mir_obj.write_mir(filepath,)
        del mir_obj

    def write_ms(
        self,
        filename,
        force_phase=False,
        clobber=False,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        Write a CASA measurement set (MS).

        Parameters
        ----------
        filename : str
            The measurement set file path to write to (a measurement set is really
            a folder with many files).
        force_phase : bool
            Option to automatically phase drift scan data to zenith of the first
            timestamp.
        clobber : bool
            Option to overwrite the file if it already exists.
        run_check : bool
            Option to check for the existence and proper shapes of parameters
            before writing the file.
        check_extra : bool
            Option to check optional parameters as well as required ones.
        run_check_acceptability : bool
            Option to check acceptable range of the values of parameters before
            writing the file.
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.

        """
        if self.metadata_only:
            raise ValueError(
                "Cannot write out metadata only objects to a measurement set file."
            )

        ms_obj = self._convert_to_filetype("ms")
        ms_obj.write_ms(
            filename,
            force_phase=force_phase,
            clobber=clobber,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del ms_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,
        strict_uvw_antpos_check=False,
    ):
        """
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            The `phase_type` of the object is "drift" and the `force_phase`
            keyword is not set.
            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".
            If the UVData object is a metadata only object.
        TypeError
            If any entry in extra_keywords is not a single string or number.

        """
        if self.metadata_only:
            raise ValueError("Cannot write out metadata only objects to a uvfits file.")

        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,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del uvfits_obj

    def write_uvh5(
        self,
        filename,
        clobber=False,
        chunks=True,
        data_compression=None,
        flags_compression="lzf",
        nsample_compression="lzf",
        data_write_dtype=None,
        run_check=True,
        check_extra=True,
        run_check_acceptability=True,
        strict_uvw_antpos_check=False,
    ):
        """
        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.
        chunks : tuple or bool
            h5py.create_dataset chunks keyword. Tuple for chunk shape,
            True for auto-chunking, None for no chunking. Default is True.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression. Dataset must be chunked.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        Raises
        ------
        ValueError
            If the UVData object is a metadata only object.

        """
        if self.metadata_only:
            raise ValueError(
                "Cannot write out metadata only objects to a uvh5 file. To initialize "
                "a uvh5 file for partial writing, use the `initialize_uvh5_file` "
                "method."
            )

        uvh5_obj = self._convert_to_filetype("uvh5")
        uvh5_obj.write_uvh5(
            filename,
            clobber=clobber,
            chunks=chunks,
            data_compression=data_compression,
            flags_compression=flags_compression,
            nsample_compression=nsample_compression,
            data_write_dtype=data_write_dtype,
            run_check=run_check,
            check_extra=check_extra,
            run_check_acceptability=run_check_acceptability,
            strict_uvw_antpos_check=strict_uvw_antpos_check,
        )
        del uvh5_obj

    def initialize_uvh5_file(
        self,
        filename,
        clobber=False,
        chunks=True,
        data_compression=None,
        flags_compression="lzf",
        nsample_compression="lzf",
        data_write_dtype=None,
    ):
        """
        Initialize a UVH5 file on disk 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.
        chunks : tuple or bool
            h5py.create_dataset chunks keyword. Tuple for chunk shape,
            True for auto-chunking, None for no chunking. Default is True.
        data_compression : str
            HDF5 filter to apply when writing the data_array. Default is
            None meaning no filter or compression. Dataset must be chunked.
        flags_compression : str
            HDF5 filter to apply when writing the flags_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        nsample_compression : str
            HDF5 filter to apply when writing the nsample_array. Default is "lzf"
            for the LZF filter. Dataset must be chunked.
        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,
            chunks=chunks,
            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,
        add_to_history=None,
        run_check_acceptability=True,
    ):
        """
        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.
        add_to_history : str
            String to append to history before write out. Default is no appending.
        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).
        strict_uvw_antpos_check : bool
            Option to raise an error rather than a warning if the check that
            uvws match antenna positions does not pass.

        """
        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,
            add_to_history=add_to_history,
            run_check_acceptability=run_check_acceptability,
        )
        del uvh5_obj
# -*- 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. 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. Create a new UVData object. # add the UVParameters to the class # standard angle tolerance: 1 mas in radians. # TODO: Spw axis to be collapsed in future release # 1 ms in days # this dimensionality of freq_array does not allow for different spws # to have different dimensions # TODO: Spw axis to be collapsed in future release # mHz # 1 ms # 1 mHz # --- multi phase center handling --- # --- flexible spectral window information --- # --- phasing information --- # The tolerance here is set by the fact that is is calculated using an arctan, # the limiting precision of which happens around values of 1. # --- antenna information ---- # 1 mm # -------- extra, non-required parameters ---------- # 1 mm # --- other stuff --- # the below are copied from AIPS memo 117, but could be revised to # merge with other sources of data. Set flex_spw to True, and adjust required parameters.          This method should not be called directly by users; instead it is called         by the file-reading methods to indicate that an object has multiple spectral         windows concatenated together across the frequency axis. # Mark once-optional arrays as now required # Now make sure that chan_width is set to be an array Set scan numbers by grouping consecutive integrations on the same phase center.          This approach mimics the definition of scan number in measurement sets and is         especially helpful for distinguishing between repeated visits to multiple         phase centers.          Parameters         ----------         override : bool             When True, will redefine existing scan numbers. Default is False. # We are grouping based on integrations on a phase center. # If this isn't defined, we cannot define scan numbers in this way # and default to a single "scan". # This loops over phase centers, finds contiguous integrations with # ndimage.label, and then finds the slices to return those contiguous # integrations with nd.find_objects. # Sort by start integration number, which we can extract from # the start of each slice in the list. # Incrementally increase the scan number with each group in # slice_list_ord Check the catalog to see if an existing entry matches provided data.          This is a helper function for verifying if an entry already exists within         the catalog, contained within the attribute `phase_center_catalog`.          Parameters         ----------         cat_name : str             Name of the phase center, which should match a key in             `phase_center_catalog`.         phase_dict : dict             Instead of providing individual parameters, one may provide a dict which             matches that format used within `phase_center_catalog` for checking for             existing entries. If used, all other parameters (save for `ignore_name` and             `cat_name`) are disregarded.         cat_type : str             Type of phase center of the entry. Must be one of:                 "sidereal" (fixed RA/Dec),                 "ephem" (RA/Dec that moves with time),                 "driftscan" (fixed az/el position),                 "unphased" (no w-projection, equivalent to `phase_type` == "drift").         cat_lon : float or ndarray             Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.             No default, not used when `cat_type="unphased"`. Expected to be a float for             sidereal and driftscan phase centers, and an ndarray of floats of shape             (Npts,) for ephem phase centers.         cat_lat : float or ndarray             Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.             No default, not used when `cat_type="unphased"`. Expected to be a float for             sidereal and driftscan phase centers, and an ndarray of floats of shape             (Npts,) for ephem phase centers.         cat_frame : str             Coordinate frame that cat_lon and cat_lat are given in. Only used for             sidereal and ephem phase centers. Can be any of the several supported frames             in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).         cat_epoch : str or float             Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given             in unites of fractional years, either as a float or as a string with the             epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).         cat_times : ndarray of floats             Only used when `cat_type="ephem"`. Describes the time for which the values             of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).         cat_pm_ra : float             Proper motion in RA, in units of mas/year. Only used for sidereal phase             centers.         cat_pm_dec : float             Proper motion in Dec, in units of mas/year. Only used for sidereal phase             centers.         cat_dist : float or ndarray of float             Distance of the source, in units of pc. Only used for sidereal and ephem             phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         cat_vrad : float or ndarray of float             Radial velocity of the source, in units of km/s. Only used for sidereal and             ephem phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         ignore_name : bool             Nominally, `_look_in_catalog` will only look at entries where `cat_name`             matches the name of an entry in the catalog. However, by setting this to             True, the method will search all entries in the catalog and see if any             match all of the provided data (excluding `cat_name`).          Returns         -------         cat_id : int or None             The unique ID number for the phase center added to the internal catalog.             This value is used in the `phase_center_id_array` attribute to denote which             source a given baseline-time corresponds to. If no catalog entry matches,             then None is returned.         cat_diffs : int             The number of differences between the information provided and the catalog             entry contained within `phase_center_catalog`. If everything matches, then             `cat_diffs=0`. # 1 marcsec tols # Emulate the defaults that are set if None is detected for # unphased and driftscan types. # If no tolerance specified, expect attributes to be identical # Numpy will throw a Value error if you have two arrays # of different shape, which we can catch to flag that # the two arrays are actually not within tolerance. Add an entry to the internal object/source catalog.          This is a helper function for adding a source to the internal         catalog, contained within the attribute `phase_center_catalog`.          Parameters         ----------         cat_name : str             Name of the phase center to be added, must be unique (i.e., not contained             as a key in the UVData attribute `phase_center_catalog`).         cat_type : str             Type of phase center to be added. Must be one of:                 "sidereal" (fixed RA/Dec),                 "ephem" (RA/Dec that moves with time),                 "driftscan" (fixed az/el position),                 "unphased" (no w-projection, equivalent to `phase_type` == "drift").         cat_lon : float or ndarray             Value of the longitudinal coordinate (e.g., RA, Az, l) of the phase center.             No default, not used when `cat_type="unphased"`. Expected to be a float for             sidereal and driftscan phase centers, and an ndarray of floats of shape             (Npts,) for ephem phase centers.         cat_lat : float or ndarray             Value of the latitudinal coordinate (e.g., Dec, El, b) of the phase center.             No default, not used when `cat_type="unphased"`. Expected to be a float for             sidereal and driftscan phase centers, and an ndarray of floats of shape             (Npts,) for ephem phase centers.         cat_frame : str             Coordinate frame that cat_lon and cat_lat are given in. Only used             for sidereal and ephem targets. Can be any of the several supported frames             in astropy (a limited list: fk4, fk5, icrs, gcrs, cirs, galactic).         cat_epoch : str or float             Epoch of the coordinates, only used when cat_frame = fk4 or fk5. Given             in unites of fractional years, either as a float or as a string with the             epoch abbreviation (e.g, Julian epoch 2000.0 would be J2000.0).         cat_times : ndarray of floats             Only used when `cat_type="ephem"`. Describes the time for which the values             of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).         cat_pm_ra : float             Proper motion in RA, in units of mas/year. Only used for sidereal phase             centers.         cat_pm_dec : float             Proper motion in Dec, in units of mas/year. Only used for sidereal phase             centers.         cat_dist : float or ndarray of float             Distance of the source, in units of pc. Only used for sidereal and ephem             phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         cat_vrad : float or ndarray of float             Radial velocity of the source, in units of km/s. Only used for sidereal and             ephem phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         info_source : str             Optional string describing the source of the information provided. Used             primarily in UVData to denote when an ephemeris has been supplied by the             JPL-Horizons system, user-supplied, or read in by one of the various file             interpreters. Default is 'user'.         force_update : bool             Normally, `_add_phase_center` will throw an error if there already exists an             identically named phase center with different properties. However, if one             sets `force_update=True`, the method will overwrite the existing entry in             `phase_center_catalog` with the paramters supplied, preserving only the             parameters `cat_id` and `cat_name`. Note that doing this will _not_ update             other atributes of the `UVData` object. Default is False.         cat_id : int             An integer signifying the ID number for the phase center, used in the             `phase_center_id_array` attribute. The default is for the method to assign             this value automatically.          Returns         -------         cat_id : int             The unique ID number for the phase center added to the internal catalog.             This value is used in the `phase_center_id_array` attribute to denote which             source a given baseline-time corresponds to.          Raises         ------         ValueError             If attempting to add a non-unique source name, attempting to use the method             w/ a UVData object where multi_phase_center=False, or if adding a sidereal             source without coordinates. # Check whether we should actually be doing this in the first place # The catalog name "unphased" is used internally whenever we have to make a # block of data as unphased in a data set. To avoid naming collisions, check # that someone hasn't tried to use it for any other purpose. # We currently only have 4 supported types -- make sure the user supplied # one of those # Both proper motion parameters need to be set together # If left unset, unphased and driftscan defaulted to Az, El = (0, 90) # Let's check some case-specific things and make sure all the entires are value # Names serve as dict keys, so we need to make sure that they're unique # If the source does have the same name, check to see if all the # atributes match. If so, no problem, go about your business # Everything matches, return the catalog ID of the matching entry # We want to create a unique ID for each source, for use in indexing arrays. # The logic below ensures that we pick the lowest positive integer that is # not currently being used by another source # If source is unique, begin creating a dictionary for it Remove an entry from the internal object/source catalog.          Removes an entry from the attribute `phase_center_catalog`. Only allowed when         the UVData object in question is a multi phase center data set (i.e.,         `multi_phase_center=True`).          Parameters         ----------         defunct_name : str             Name of the source to be removed          Raises         ------         ValueError             If multi_phase_center is not set to True         IndexError             If the name provided is not found as a key in `phase_center_catalog` Remove objects dictionaries and names that are no longer in use.          Goes through the `phase_center_catalog` attribute in of a UVData object and         clears out entries that are no longer being used, and appropriately updates         `phase_center_id_array` accordingly. This function is not typically called         by users, but instead is used by other methods.          Raises         ------         ValueError             If attempting to call the method when multi_phase_center=False. # Check the number of "good" sources we have -- if we haven't dropped any, # then we are free to bail, otherwise update the Nphase attribute # Time to kill the entries that are no longer in the source stack Check which Nblts are unphased in a multi phase center dataset.          This convenience method returns back a boolean mask to identify which data         along the Blt axis contains unphased objects (which is only applicable when         multi_phase_center=True)          Returns         -------         blt_mask : ndarray of bool             A boolean mask for identifying which elements contain unphased objects # Check and see if we have any unphased objects, in which case # their w-values should be zeroed out. # Use dict to construct a bool array # If not multi phase center, we just need to check the phase type Rename a phase center/catalog entry within a multi phase center data set.          Parameters         ----------         old_name : str             Phase center name for the data to be renamed.         new_name : str             New name for the phase center.          Raises         ------         ValueError             If attempting to run the method on a non multi phase center data set, if             `old_name` is not found as a key in `phase_center_catalog`, if `new_name`             already exists as a key in `phase_center_catalog`, or if attempting to             name a source "unphased" (which is reserved).         TypeError             If `new_name` is not actually a string. # This is basically just a no-op, so return to user Rename the phase center (but preserve other properties) of a subset of data.          Allows you to rename a subset of the data phased to a particular phase center,         marked by a different name than the original. Useful when you want to phase to         one position, but want to differentiate different groups of data (e.g., marking         every other integration to make jackknifing easier).          Parameters         ----------         cat_name : str             Name of the phase center to be split.         new_name : str             New name for the object.         select_mask : array_like             Selection mask for which data should be identified as belonging to the phase             center labeled by `new_name`. Any array-like able to be used as an index             is suitable -- the most typical is an array of bool with length `Nblts`,             or an array of ints within the range (-Nblts, Nblts).         downselect : bool             If selecting data that is not marked as belonging to `cat_name`,             normally an error is thrown. By setting this to True, `select_mask` will             be modified to exclude data not marked as belonging to `cat_name`.          Raises         ------         ValueError             If attempting to run the method on a non multi phase center data set, if             `old_name` is not found as a key in `phase_center_catalog`, if `new_name`             already exists as a key in `phase_center_catalog`, or if attempting to             name a source "unphased" (which is reserved). Also raised if `select_mask`             contains data that doesn't belong to `cat_name`, unless setting             `downselect` to True.         IndexError             If select_mask is not a valid indexing array.         UserWarning             If all data for `cat_name` was selected (in which case `rename_phase_center`             is called instead), or if no valid data was selected. # Check to make sure that everything lines up with # Now that we know nthat all the inputs are sensible, lets make sure that # the select_mask choice is sensible # If we have selected any entries that don't correspond to the cat_id # in question, either downselect or raise an error. # Now check for no(-ish) ops # You didn't actually select anything we could change # No matching catalog IDs found outside the range, so this is really a # replace more than a split. Merge two differently named objects into one within a mutli-phase-ctr data set.          Recombines two different objects into a single catalog entry -- useful if         having previously used `split_phase_center` or when multiple objects with         different names share the same source parameters.          Parameters         ----------         catname1 : str             String containing the name of the first phase center. Note that this name             will be preserved in the UVData object.         catname2 : str             String containing the name of the second phase center, which will be merged             into the first phase center. Note that once the merge is complete, all             information about this phase center is removed.         force_merge : bool             Normally, the method will throw an error if the phase center properties             differ for `catname1` and `catname2`. This can be overriden by setting this             to True. Default is False.          Raises         ------         ValueError             If catname1 or catname2 are not found in the UVData object, of if their             properties differ (and `force_merge` is not set to True).         UserWarning             If forcing the merge of two objects with different properties. # First, let's check and see if the dict entries are identical Print out the details of objects in a mutli-phase-ctr data set.          Prints out an ASCII table that contains the details of the         `phase_center_catalog` attribute, which acts as the internal source catalog         for UVData objects.          Parameters         ----------         cat_name : str             Optional parameter which, if provided, will cause the method to only return             information on the phase center with the matching name. Default is to print             out information on all catalog entries.         hms_format : bool             Optional parameter, which if selected, can be used to force coordinates to             be printed out in Hours-Min-Sec (if set to True) or Deg-Min-Sec (if set to             False) format. Default is to print out in HMS if all the objects have             coordinate frames of icrs, gcrs, fk5, fk4, and top; otherwise, DMS format             is used.         return_str: bool             If set to True, the method returns an ASCII string which contains all the             table infrmation. Default is False.         print_table : bool             If set to True, prints the table to the terminal window. Default is True.          Returns         -------         table_str : bool             If return_str=True, an ASCII string containing the entire table text          Raises         ------         ValueError             If `cat_name` matches no keys in `phase_center_catalog`. # We want to check and actually see which fields we need to # print # We want to print in the order of cat_id # If we have a "special" field that needs extra handling, # take care of that up front # We need this extra bit of code to handle trailing whitespace, since # otherwise some checks (e.g., doc check on tutorials) will balk # pragma: nocover Update a phase center with a new catalog ID number.          Parameters         ----------         cat_name : str             Name of the phase center, which corresponds to a key in the attribute             `phase_center_catalog`.         new_cat_id : int             Optional argument. If supplied, then the method will attempt to use the             provided value as the new catalog ID, provided that an existing catalog             entry is not already using the same value. If not supplied, then the             method will automatically assign a value.         reserved_ids : array-like in int             Optional argument. An array-like of ints that denotes which ID numbers             are already reserved. Useful for when combining two separate catalogs.          Raises         ------         ValueError             If not using the method on a multi-phase-ctr data set, if there's no entry             that matches `cat_name`, or of the value `new_cat_id` is already taken. # If the old ID is in the reserved list, then we'll need to update it # Don't need to actually update anything Set multi_phase_center to True, and adjust required paramteres.          This method is typically not be called directly by users; instead it is called         by the file-reading methods to indicate that an object has multiple phase         centers with in the same data set.          Parameters         ----------         preserve_phase_center_info : bool             Preserve the source information located in `object_name`, and for phased             data sets, also `phase_center_ra`, `phase_center_dec`, `phase_center_epoch`             and `phase_center_frame`. Default is False. Note that setting this to             False will mean that some required attributes will NOT be correctly set,             e.g., `phase_center_id_array` -- these will need to be set after calling             `preserve_phase_center_info` in order for the UVData object to be viable.          Raises         ------         ValueError             if the telescope_name is not in known telescopes # If you have already set this, don't do anything # All multi phase center objects have phase_type="phased", even if they are # unphased. # Mark once-option arrays as now required # This should technically be required for any phased data set, but for now, # we are only gonna make it mandatory for mutli-phase-ctr data sets. # If moving from unphased, then we'll fill in app_ra and app_dec in # the way that we normally would if this were an "unphased" object. Set phase_type to 'drift' and adjust required parameters.          This method should not be called directly by users; instead it is called         by phasing methods and file-reading methods to indicate the object has a         `phase_type` of "drift" and define which metadata are required. Set phase_type to 'phased' and adjust required parameters.          This method should not be called directly by users; instead it is called         by phasing methods and file-reading methods to indicate the object has a         `phase_type` of "phased" and define which metadata are required. List of strings giving the data-like parameters. Iterate defined parameters which are data-like (not metadata-like). 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. Set future_array_shapes to True and adjust required parameters.          This method should not be called directly by users; instead it is called         by file-reading methods and `use_future_array_shapes` to indicate the         `future_array_shapes` is True and define expected parameter shapes. Change the array shapes of this object to match the planned future shapes.          This method sets allows users to convert to the planned array shapes changes         before the changes go into effect. This method sets the `future_array_shapes`         parameter on this object to True. # remove the length-1 spw axis for all data-like parameters # remove the length-1 spw axis for the freq_array # make channel_width be an array of length Nfreqs rather than a single value # (not needed with flexible spws because this is already done in that case) Change the array shapes of this object to match the current future shapes.          This method sets allows users to convert back to the current array shapes.         This method sets the `future_array_shapes` parameter on this object to False. 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 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 # 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 # this parameter is as of this comment most likely a float # since only diameters and antenna positions will probably # trigger this else statement # assign float64 as the type of the array 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. Set values for the apparent coordinate arrays.          This is an internal helper function, which is not designed to be called by         users, but rather individual read/write functions for the UVData object.         Users should use the phase() method for updating/adjusting coordinate values.          Parameters         ----------         pa_only : bool, False             Skip the calculation of the apparent RA/Dec, and only calculate the             position angle between `phase_center_frame` and the apparent coordinate             system. Useful for reading in data formats that do not calculate a PA. # Uhhh... what do you want me to do? If the dataset isn't phased, there # isn't an apparent position to calculate. Time to bail, I guess... # So this is actually the easier of the two cases -- just use the object # properties to fill in the relevant data # Now that we have the apparent coordinates sorted out, we can figure out what # it is we want to do with the position angle Set the lst_array based from the time_array.          Parameters         ----------         background : bool, False             When set to True, start the calculation on a threading.Thread in the             background and return the thread to the user.          Returns         -------         proc : None or threading.Thread instance             When background is set to True, a thread is returned which must be             joined before the lst_array exists on the UVData object. Check if the spectral windows are contiguous for flex_spw datasets.          This checks the flex_spw_id_array to make sure that all channels for each         spectral window are together in one block, versus being interspersed (e.g.,         channel #1 and #3 is in spw #1, channels #2 and #4 are in spw #2). In theory,         UVH5 and UVData objects can handle this, but MIRIAD, MIR, UVFITS, and MS file         formats cannot, so we just consider it forbidden. # This is an internal consistency check to make sure that the indexes match # up as expected -- this shouldn't error unless someone is mucking with # settings they shouldn't be. # If this isn't a flex_spw data set, then there is only 1 spectral window, # which means that the check always passes Check if frequencies are evenly spaced and separated by their channel width.          This is a requirement for writing uvfits & miriad files.          Parameters         ----------         raise_errors : bool             Option to raise errors if the various checks do not pass.          Returns         -------         spacing_error : bool             Flag that channel spacings or channel widths are not equal.         chanwidth_error : bool             Flag that channel spacing does not match channel width. # Skip all of this if there is only 1 channel # Check to make sure that the flexible spectral window has indicies set up # correctly (grouped together) for this check # We want to grab unique spw IDs, in the order that they appear in the data # Pop off the first entry, since the above arrays are diff'd # (and thus one element shorter) # Ignore cases where looking at the boundaries of spectral windows Calculate the number of antennas from ant_1_array and ant_2_array arrays. 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.         check_freq_spacing :  bool             Option to check if frequencies are evenly spaced and the spacing is             equal to their channel_width. This is not required for UVData             objects in general but is required to write to uvfits and miriad files.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         allow_flip_conj : bool             If set to True, and the UVW coordinates do not match antenna positions,             check and see if flipping the conjugation of the baselines (i.e, multiplying             the UVWs by -1) resolves  the apparent discrepancy -- and if it does, fix             the apparent conjugation error in `uvw_array` and `data_array`. Default is             False.          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 # Check internal consistency of numbers which don't explicitly correspond # to the shape of another array. # require that all entries in ant_1_array and ant_2_array exist in # antenna_numbers # issue warning if extra_keywords keys are longer than 8 characters # issue warning if extra_keywords values are lists, arrays or dicts # check that the uvws make sense given the antenna positions # make a metadata only copy of this object to properly calculate uvws # check auto and cross-corrs have sensible uvws # this line used to use np.linalg.norm but it turns out # squaring and sqrt is slightly more efficient unless the array # is "very large". Make and return a copy of the UVData object.          Parameters         ----------         metadata_only : bool             If True, only copy the metadata of the object.          Returns         -------         UVData             Copy of self. # include all attributes, not just UVParameter ones. # skip properties # skip data like parameters # parameter names have a leading underscore we want to ignore 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) 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. 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             its conjugate.          Returns         -------         inds : ndarray of int-64             indices of the antpair along the baseline-time axis. # check for expanded antpair or key # if getting auto-corr, ordered must be True # get indices Interpret user specified key as 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 # Single string given, assume it is polarization # For simplicity # Nested tuple. Call function again. # Small number, assume it is a polarization number a la AIPS memo # Larger number, assume it is a baseline number # Key is an antenna pair # Key is an antenna pair + pol # pol is str # polarization number a la AIPS memo # Catch autos 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 # only unconjugated baselines # only conjugated baselines # both conjugated and unconjugated baselines # one polarization dimension # one polarization dimension # one spw dimension # if out is a view rather than a copy, make it read-only Get the unique antennas that have data associated with them.          Returns         -------         ndarray of int             Array of unique antennas with data associated with them. Get the unique baselines that have data associated with them.          Returns         -------         ndarray of int             Array of unique baselines with data associated with them. 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. Get the polarizations in the data.          Returns         -------         list of str             list of polarizations (as strings) in the data. 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. 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 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. 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. # When we select conjugated baselines, there is a call to np.conj() # inside of _smart_slicing to correct the data array. This has the # unintended consequence of promoting the dtype of an array of np.bool_ # to np.int8. Rather than having a bunch of special handling for this # ~corner case, we instead explicitly cast back to np.bool_ before we # hand back to the user. 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. 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. Get the LSTs 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             LSTs from the lst_array for the given antpair or baseline. Get 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,) Extract the indices for setting data, flags, or nsample arrays.          This is a helper method designed to work with set_data, set_flags, and         set_nsamples. Given the shape of the data-like array and the keys         corresponding to where the data should end up, it finds the indices         that are needed for the `_index_dset` method.          Parameters         ----------         dshape : tuple of int             The shape of the data-like array. This is used to ensure the array             is compatible with the indices selected.         key1, key2, key3 : int or tuple of ints             Identifier of which flags to set, 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, set all flags for                     that pol.                 else:                     interpreted as a baseline number, set all flags for that baseline.              if key is length 2: interpreted as an antenna pair, set all flags                 for that baseline.              if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),                 set all flags for that baseline, pol. pol may be a string or int.          Returns         -------         inds : tuple of int             The indices in the data-like array to slice into.          Raises         ------         ValueError:             If more than 3 keys are passed, if the requested indices are             conjugated in the data, if the data array shape is not compatible             with the indices. Set the data array to some values provided by the user.          Parameters         ----------         data : ndarray of complex             The data to overwrite into the data_array. Must be the same shape as             the target indices.         key1, key2, key3 : int or tuple of ints             Identifier of which data to set, 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.          Returns         -------         None          Raises         ------         ValueError:             If more than 3 keys are passed, if the requested indices are             conjugated in the data, if the data array shape is not compatible             with the indices. Set the flag array to some values provided by the user.          Parameters         ----------         flag : ndarray of boolean             The flags to overwrite into the fkag_array. Must be the same shape             as the target indices.         key1, key2, key3 : int or tuple of ints             Identifier of which flags to set, 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, set all flags for                     that pol.                 else:                     interpreted as a baseline number, set all flags for that baseline.              if key is length 2: interpreted as an antenna pair, set all flags                 for that baseline.              if key is length 3: interpreted as antenna pair and pol (ant1, ant2, pol),                 set all flags for that baseline, pol. pol may be a string or int.          Returns         -------         None          Raises         ------         ValueError:             If more than 3 keys are passed, if the requested indices are             conjugated in the data, if the data array shape is not compatible             with the indices. Set the nsamples array to some values provided by the user.          Parameters         ----------         nsamples : ndarray of float             The nsamples to overwrite into the nsample_array. Must be the same             shape as the target indices.         key1, key2, key3 : int or tuple of ints             Identifier of which nsamples to set, 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, set all data for                     that pol.                 else:                     interpreted as a baseline number, set all nsamples for that                     baseline.              if key is length 2: interpreted as an antenna pair, set all nsamples                 for that baseline.              if key is length 3: interpreted as antenna pair and pol (ant1, ant2,                 pol), set all nsamples for that baseline, pol. pol may be a                 string or int.          Returns         -------         None          Raises         ------         ValueError:             If more than 3 keys are passed, if the requested indices are             conjugated in the data, if the data array shape is not compatible             with the indices. Iterate 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 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. Rearrange polarizations in the event they are not uvfits compatible.          Parameters         ----------         order : str             Either a string specifying a canonical 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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Raises         ------         ValueError             If the order is not one of the allowed values. # data array is special and large, take is faster here # check if object is self-consistent 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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Raises         ------         ValueError             If parameter values are inappropriate # set it back to the right shape in case it was set differently before # Use lexsort to sort along different arrays in defined order. # minor_order is baseline # minor_order is baseline # minor_order is baseline # only allowed minor order is time # only allowed minor order is time # lexsort uses the listed arrays from last to first # (so the primary sort is on the last one) # actually do the reordering # check if object is self-consistent Arrange frequency axis according to desired order.          Can be applied across the entire frequency axis, or just a subset.          Parameters         ----------         spw_order : str or array_like of int             A string describing the desired order of spectral windows along the             frequecy axis. Allowed strings include `number` (sort on spectral window             number) and `freq` (sort on median frequency). A '-' can be appended             to signify descending order instead of the default ascending order,             e.g., if you have SPW #1 and 2, and wanted them ordered as [2, 1],             you would specify `-number`. Alternatively, one can supply an array             of length Nspws that specifies the new order, with values matched to             the specral window number given in `spw_array`. Default is to apply no             sorting of spectral windows.         channel_order : str or array_like of int             A string describing the desired order of frequency channels within a             spectral window. Allowed strings include `freq`, which will sort channels             within a spectral window by frequency. A '-' can be optionally appended             to signify descending order instead of the default ascending order.             Alternatively, one can supply an index array of length Nfreqs that             specifies the new order. Default is to apply no sorting of channels             within a single spectral window. Note that proving an array_like of ints             will cause the values given to `spw_order` and `select_spw` to be ignored.         select_spw : int or array_like of int             An int or array_like of ints which specifies which spectral windows to             apply sorting. Note that setting this argument will cause the value             given to `spw_order` to be ignored.         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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Returns         -------         None          Raises         ------         UserWarning             Raised if providing arguments to select_spw and freq_screen (the latter             overrides the former).         ValueError             Raised if select_spw contains values not in spw_array, or if freq_screen             is not the same length as freq_array. # Check to see if there are arguments we should be ignoring # Multipy by 1.0 here to make a cheap copy of the array to manipulate # Same trick for ints -- add 0 to make a cheap copy # Check whether or not we need to sort the channels in individual windows # Only need to do this step if we actually have multiple spws. # If the string starts with a '-', then we will flip the order at # the end of the operation # Now that we know the spw order, we can apply the first sort # Spectral windows are assumed sorted at this point # Nothing to do - the data are already sorted! # Now update all of the arrays. # Reorder the spw-axis items based on their first appearance in the data # check if object is self-consistent 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". # apply coefficients for each baseline # get indices for this key # make sure coefficients are the right size to broadcast Apply corrections based on changes to w-coord.          Adjusts the data to account for a change along the w-axis of a baseline.          Parameters         ----------         new_w_vals: float or ndarray of float             New w-coordinates for the baselines, in units of meters. Can either be a             solitary float (helpful for unphasing data, where new_w_vals can be set to             0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).         old_w_vals: float or ndarray of float             Old w-coordinates for the baselines, in units of meters. Can either be a             solitary float (helpful for unphasing data, where new_w_vals can be set to             0.0) or an array of shape (Nselect,) (which is Nblts if select_mask=None).         select_mask: ndarray of bool             Array is of shape (Nblts,), where the sum of all enties marked True is             equal to Nselect (mentioned above).          Raises         ------         IndexError             If the length of new_w_vals or old_w_vals isn't compatible with             select_mask, or if select mask isn't the right length. # If we only have metadata, then we have no work to do. W00t! # Promote everything to float64 ndarrays if they aren't already # Make sure the lengths of everything make sense # Calculate the difference in w terms as a function of freq. Note that the # 1/c is there to speed of processing (faster to multiply than divide) # If all the w values are changing, it turns out to be twice as fast # to ditch any sort of selection mask and just do the full multiply. # In the case we are _not_ doing all baselines, use a selection mask to # only update the values we need. In the worse case, it slows down the # processing by ~2x, but it can save a lot on time and memory if only # needing to update a select number of baselines. 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. Default is True.         use_old_proj : bool             If True, uses the 'old' way of calculating baseline projections.             Default is False.          Raises         ------         ValueError             If the phase_type is not 'phased' # Check to make sure that these attributes are actually filled. Otherwise, # you probably want to use the old phase method. # Check and see if we have any unphased objects, in which case # their w-values should be zeroed out. # remove/update phase center # If you are a multi phase center data set, there's no valid reason to be going # back to the old phase method. Time to bail! # use center of observation for obstime for 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 # apply -w phasor # just calling transform_to(coord.GCRS) will delete the obstime information # need to re-add obstimes for a GCRS transformation # instead of looping through every ind, find the spot in antenna number # array where ant_num <= ant1 < ant_number and similarly for ant2 # for all baselines in inds # then find the uvw coordinate for all at the same time # antenna_numbers does not necessarily need to be in order on the object # but needs to be in order for the searchsorted to work. # ant1_index and ant2_index arrays will preserve the order of blts # now convert them to ENU, which is the space uvws are in # remove phase center Supplies a dictionary with parametrs for the phase method to use.          This method should not be called directly by users; it is instead a function         called by the `phase` method, which packages up phase center information         into a single dictionary to allow for consistent behavior between different         instantiations of `UVData` objects. # We only want to use the JPL-Horizons service if using a non-mutli-phase-ctr # instance of a UVData object. # If the name of the source matches, then verify that all of its # properties are the same as what is stored in phase_center_catalog. # If cat_diffs > 0, it means that the catalog entries dont match # Last chance here -- if we have selected all of the data phased # to this phase center, then we are still okay. # We have selected all data, so we're good # We have selected a subset of the data that contains # everything that was phased to the object # Get here will return None if no key found, which we want # Either this is not a multi phase center data set, or the name of the # source is unique! # One last check - if we have an ephem phase center, lets make sure that the # time range of the ephemeris encapsulates the entire range of time_array # Take advantage of this to make sure that lat, lon, and times are all # ndarray types # If the ephem was supplied by JPL-Horizons, then we can easily expand # it to cover the requested range. # Concat the two time ranges to make sure that we cover both the # requested time range _and_ the original time range. # The ephem was user-supplied during the call to the phase method, # raise an error to ask for more ephem data. # Time to repackage everything into a dict # Finally, make sure everything is a float or an ndarray of floats # If this is the cat_id, make it an int 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.          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 transformed 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.         cat_type : str             Type of phase center to be added. Must be one of:             "sidereal" (fixed RA/Dec), "ephem" (RA/Dec that moves with time),             "driftscan" (fixed az/el position). Default is "sidereal", other selections             are only permissible if `multi_phase_center=True`.         ephem_times : ndarray of float             Only used when `cat_type="ephem"`. Describes the time for which the values             of `cat_lon` and `cat_lat` are caclulated, in units of JD. Shape is (Npts,).         pm_ra : float             Proper motion in RA, in units of mas/year. Only used for sidereal phase             centers.         pm_dec : float             Proper motion in Dec, in units of mas/year. Only used for sidereal phase             centers.         dist : float or ndarray of float             Distance of the source, in units of pc. Only used for sidereal and ephem             phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         vrad : float or ndarray of float             Radial velocity of the source, in units of km/s. Only used for sidereal and             ephem phase centers. Expected to be a float for sidereal and driftscan phase             centers, and an ndarray of floats of shape (Npts,) for ephem phase centers.         cat_name :str             Name of the phase center being phased to. Required if             `multi_phase_center=True`, otherwise `object_name` set to this value.         lookup_name : bool             Only used if `multi_phase_center=True`, allows the user to lookup phase             center infomation in `phase_center_catalog` (for the entry matching             `cat_name`). Setting this to `True` will ignore the values supplied to the             `ra`, `dec`, `epoch`, `phase_frame`, `pm_ra`, `pm_dec`, `dist`, `vrad`.         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.         select_mask : ndarray of bool             Optional mask for selecting which data to operate on along the blt-axis,             only used if with multi phase center data sets (i.e.,             `multi_phase_center=True`). Shape is (Nblts,).         use_old_proj : bool             If True, use the "old" method for calculating baseline uvw-coordinates,             which involved using astropy to move antenna positions (in ITRF) into             the requested reference frame (either GCRS or ICRS). Default is False.         fix_old_proj : bool             If True, the method will convert a data set with coordinates calculated             using the "old" method, correct them, and then produce new coordinates             using the "new" method.          Raises         ------         ValueError             If the phase_type is 'phased' and allow_rephase is False # Non-multi phase center datasets don't (yet) have a way of recording the # 'extra' source properties, or selection mask, so make sure that these aren't # using any of those if looking at a single object. # If you are a multi phase center data set, there's no valid reason to be going # back to the old phase method. Time to bail! # Right up front, we're gonna split off the piece of the code that # does the phasing using the "new" method, since its a lot more flexible # and because I think at some point, everything outside of this loop # can be deprecated # So to fix the 'old' projection, we use the unphase_to_drift # method with the 'old' projection to bring the data set back # to ENU, and then we can move from there. Of course, none of # this is actually neccessary if calculating the coordinates # from antenna positions, so you do you, puvudataset. # Grab all the meta-data we need for the rotations # Check and see if we have any unphased objects, in which case # their w-values should be zeroed out. # Before moving forward with the heavy calculations, we need to do some # basic housekeeping to make sure that we've got the coordinate data that # we need in order to proceed. # We got the meta-data, now handle calculating the apparent coordinates. # First, check if we need to look up the phase center in question # Now calculate position angles. If this is a single phase center data set, # the ref frame is always equal to the source coordinate frame. In a multi # phase center data set, those two components are allowed to be decoupled. # Now its time to do some rotations and calculate the new coordinates # With all operations complete, we now start manipulating the UVData object # Now its time to update the raw data. This will return empty if # metadata_only is set to True. Note that cat_type is only allowed # to be unphased if this is a multi_phase_center data set. # Finally, we now take it upon ourselves to update some metadata. What we # do here will depend a little bit on whether or not we have a selection # mask active, since most everything is affected by that. # If not multi phase center, make sure to update the ra/dec values, since # otherwise we'll have no record of source properties. # Make sure this is actually marked as a phased dataset now # Update the phase center properties # All done w/ the new phase method # To get to this point, allow_rephase has to be true # convert to icrs (i.e. J2000) to write to object # use center of observation for obstime for gcrs # This promotion is REQUIRED to get the right answer when we # add in the telescope location for ICRS # just calling transform_to(coord.GCRS) will delete the obstime information # need to re-add obstimes for a GCRS transformation # set the representation_type to cartensian to get xyz later # This promotion is REQUIRED to get the right answer when we # add in the telescope location for ICRS # instead of looping through every ind, find the spot in antenna number # array where ant_num <= ant1 < ant_number and similarly for ant2 # for all baselines in inds # then find the uvw coordinate for all at the same time # antenna_numbers does not necessarily need to be in order on the object # but needs to be in order for the searchsorted to work. # ant1_index and ant2_index arrays will preserve the order of blts # Also, uvws should be thought of like ENU, not ECEF (or rotated ECEF) # convert them to ECEF to transform between frames # this takes out the telescope location in the new frame, # so these are vectors again # calculate data and apply phasor 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.         select_mask : array_like             Selection mask for which data should be rephased, only applicable if             `multi_phase_center=True`. Any array-like able to be used as an index             is suitable -- the most typical is an array of bool with length `Nblts`,             or an array of ints within the range (-Nblts, Nblts).          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 # Generate ra/dec of zenith at time in the phase_frame coordinate # system to use for phasing Calculate UVWs based on antenna_positions.          Parameters         ----------         allow_phasing : bool             Option for phased data. If data is phased and allow_phasing=True,             UVWs will be calculated and the visibilities will be rephased. Default             is False.         require_phasing : bool             Option for phased data. If data is phased and require_phasing=True, then             the method will throw an error unless allow_phasing=True, otherwise if             `require_phasing=False` and `allow_phasing=False`, the UVWs will be             recalculated but the data will NOT be rephased. This feature should only be             used in limited circumstances (e.g., when certain metadata like exact time             are not trusted), as misuse can significantly corrupt data.         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 and use_old_proj             is True.         output_phase_frame : str             The astropy frame to phase to. Either 'icrs' or 'gcrs'. Only used if             allow_phasing is True, and use_old_proj is True.         use_old_proj : bool             If set to True, uses the 'old' method of calculating baseline vectors.             Default is False, which will instead use the 'new' method.          Raises         ------         ValueError             If data is phased and allow_phasing is False.          Warns         -----         UserWarning             If the phase_type is 'phased' # If the data are phased, we've already adjusted the phases. Now we just # need to update the uvw's and we are home free. # mutli-phase-ctr datasets should never use the 'old' uvw calculation method # this code used to loop through every bl in the unique, # find the index into self.antenna_array of ant1 and ant2 # and fill out the self.uvw_array for all matching bls. # instead, find the indices and reverse inds from the unique, # create the unique ant1 and ant2 arrays # use searchsorted to find the index of the antenna numbers into ant1 and ant2 # create the unique uvw array then broadcast to self.uvw_array # antenna_numbers does not necessarily need to be in order on the object # but needs to be in order for the searchsorted to work. # ant1_index and ant2_index arrays will preserve the order of blts Fix the data to be consistent with the new phasing method.          This is a simple utility function for updating UVW coordinates calculated using         the 'old' phasing algorithm with those calculated by the 'new' algorithm. Note         that this step is required for using the new methods with data phased using the         `phase` methiod prior to pyuvdata v2.2.          Parameters         ----------         use_ant_pos : bool             Use the antenna positions for determining UVW coordinates. Default is True. # If we are missing apparent coordinates, we should calculate those now # If we are just using the antenna positions, we don't actually need to do # anything, since the new baseline vectors will be unaffected by the prior # phasing method, and the delta_w values already get correctly corrected for. # Record the old values # Bring the UVWs back to ENU/unphased # Check for any autos, since their uvws get potentially corrupted # by the above operation # And rephase the data using the new algorithm Combine two UVData objects along frequency, polarization and/or baseline-time.          Parameters         ----------         other : UVData object             Another UVData object which will be added to self.         inplace : bool             If True, overwrite self as we go, otherwise create a third object             as the sum of the two.         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.         verbose_history : bool             Option to allow more verbose history. If True and if the histories for the             two objects are different, the combined object will keep all the history of             both input objects (if many objects are combined in succession this can             lead to very long histories). If False and if the histories for the two             objects are different, the combined object will have the history of the             first object and only the parts of the second object history that are unique             (this is done word by word and can result in hard to interpret histories).         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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         make_multi_phase : bool             Option to make the output a multi phase center dataset, capable of holding             data on multiple phase centers. Setting this to true will allow for two             UVData objects to be combined, even if the phase center properties do not             agree (so long as the names are unique for each phase center). Default is             False.         ignore_name : bool             Option to ignore the name of the phase center (`cat_name` in             `phase_center_catalog` when `multi_phase_center=True`, otherwise             `object_name`) when combining two UVData objects. Doing so effectively             adopts the name found in the first UVData object in the sum. Default is             False.           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 is 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. # Check that both objects are UVData and valid # Check to make sure that both objects are consistent w/ use of flex_spw # check that both objects have the same array shapes # 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 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. # Define parameters that must be the same to add objects # But phase_center should be the same, even if in drift (empty parameters) # If the names are different and we are making a mutli-phase-ctr data set, # then we can skip the step of checking the ra and dec, otherwise we need to # check it # Also, if we are not supposed to ignore the name, then make sure that its # one of the parameters we check for compatibility. # Build up history string # Create blt arrays for convenience # Check we don't have overlapping data # If we have a flexible spectral window, the handling here becomes a bit funky, # because we are allowed to have channels with the same frequency *if* they # belong to different spectral windows (one real-life example: you might want # to preserve guard bands in the correlator, which can have overlaping RF # frequency channels) # check that overlapping data is not valid # we're fine to overwrite; update history accordingly # find the blt indices in "other" but not in "this" # if there's any overlap in blts, check extra params # add metadata to be checked to compatibility params # TODO: Add handling for what happens when you have two different source # catalogs that you want to combine # find the freq indices in "other" but not in "this" # if channel width is an array and there's any overlap in freqs, # check extra params # add metadata to be checked to compatibility params # find the pol indices in "other" but not in "this" # Actually check compatibility parameters # only check that overlapping blt indices match # only check that overlapping blt indices match # only check that overlapping blt indices match # only check that overlapping freq indices match # only check that overlapping blt indices match # only check that overlapping blt indices match # only check that overlapping blt indices match # At this point, we are assuming that the two data sets _mostly_ compatible. # Last thing we need to check is if these are mutli-phase-ctr data sets, whether # or not they are compatible. # We have a name conflict, raise an error now # Begin manipulating the objects. # This to get adding stuff to the catalog # First loop, we want to look at the sources that are in this, but not # other, since we need to choose catalog IDs that won't collide with the # catalog that exists. # Next loop, we want to update the IDs of sources that are in both # Finally, add those other objects not found in this # If other is not multi phase center, then we'll go ahead and add the object # information here. # Pad out self to accommodate new data # We want to preserve per-spw information based on first appearance # in the concatenated array. # If we have a flex/multi-spw data set, need to sort out the order of the # individual windows first. # With spectral windows sorted, check and see if channels within # windows need sorting. If they are ordered in ascending or descending # fashion, leave them be. If not, sort in ascending order # Now populate the data # Update N parameters (e.g. Npols) # Update filename parameter # Check specific requirements # Check final object is self-consistent 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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         make_multi_phase : bool             Option to make the output a multi phase center dataset, capable of holding             data on multiple phase centers. Setting this to true will allow for two             UVData objects to be combined, even if the phase center properties do not             agree (so long as the names are unique for each phase center). Default is             False.         ignore_name : bool             Option to ignore the name of the phase center (`cat_name` in             `phase_center_catalog` when `multi_phase_center=True`, otherwise             `object_name`) when combining two UVData objects. Doing so effectively             adopts the name found in the first UVData object in the sum. Default is             False.           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 is 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. Concatenate two UVData objects along specified axis with almost no checking.          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 or list of UVData objects             UVData object or list of UVData objects 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'.         inplace : bool             If True, overwrite self as we go, otherwise create a third object             as the sum of the two.         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.         verbose_history : bool             Option to allow more verbose history. If True and if the histories for the             objects are different, the combined object will keep all the history of             all input objects (if many objects are combined this can lead to very long             histories). If False and if the histories for the objects are different,             the combined object will have the history of the first object and only the             parts of the other object histories that are unique (this is done word by             word and can result in hard to interpret histories).         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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         ignore_name : bool             Option to ignore the name of the phase center (`cat_name` in             `phase_center_catalog` when `multi_phase_center=True`, otherwise             `object_name`) when combining two UVData objects. Doing so effectively             adopts the name found in the first UVData object in the sum. Default is             False.          Raises         ------         ValueError             If other is not a UVData object, axis is not an allowed value or if             self and other are not compatible. # if this is a UVData object already, stick it in a list # Check that both objects are UVData and valid # check that all objects have the same array shapes # 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 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. # Actually check compatibility parameters # We want to preserve per-spw information based on first appearance # in the concatenated array. # update filename attribute # Check final object is self-consistent Sum visibilities between two UVData objects.          By default requires that all UVParameters are the same on the two objects         except for `history`, `data_array`, `object_name`, and `extra_keywords`.         The `object_name` values are concatenated if they are different. If keys         in `extra_keywords` have different values the values from the first         object are taken.          Parameters         ----------         other : UVData object             Another UVData object which will be added to self.         difference : bool             If True, differences the visibilities of the two UVData objects             rather than summing them.         inplace : bool             If True, overwrite self as we go, otherwise create a third object             as the sum of the two.         verbose_history : bool             Option to allow more verbose history. If True and if the histories for the             two objects are different, the combined object will keep all the history of             both input objects (this can lead to long histories). If False and if the             histories for the two objects are different, the combined object will have             the history of the first object and only the parts of the second object             history that are unique (this is done word by word and can result in hard             to interpret histories).         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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         override_params : array_like of strings             List of object UVParameters to omit from compatibility check. Overridden             parameters will not be compared between the objects, and the values             for these parameters will be taken from the first object.          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. # Check that both objects are UVData and valid # check that both objects have the same array shapes # Add underscores to override_params to match list from __iter__() # Add to parameters to be removed # compatibility_params should define the parameters that need to # be the same for objects to be summed or diffed # Check each UVParameter in compatibility_params # Merge extra keywords and object_name # Merge extra_keywords lists, taking values from the first object # Merge object_name if different. # Do the summing / differencing # merge file names # Check final object is self-consistent Difference visibilities between two UVData objects.          By default requires that all UVParameters are the same on the two objects         except for `history`, `data_array`, `object_name`, and `extra_keywords`.         The `object_name` values are concatenated if they are different. If keys         in `extra_keywords` have different values the values from the first         object are taken.          Parameters         ----------         other : UVData object             Another UVData object which will be added to self.         inplace : bool             If True, overwrite self as we go, otherwise create a third object             as the sum of the two.         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.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         override_params : array_like of strings             List of object UVParameters to omit from compatibility check. Overridden             parameters will not be compared between the objects, and the values             for these parameters will be taken from the first object.          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. Get antpair and polarization from parsing an aipy-style ant string.          Used to support 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. 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 or list of int, optional             A list of antenna number tuples (e.g. [(0, 1), (3, 2)]), a list of             baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]), or a list of             baseline numbers (e.g. [67599, 71699, 73743]) 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`, `lsts`, or             `lst_array`.         time_range : array_like of float, optional             The time range in Julian Date to keep in the object, must be length             2. Some of the times in the object should fall between the first and             last elements. Cannot be used with `times`, `lsts`, or `lst_array`.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         polarizations : array_like of int or str, optional             The polarizations numbers to keep in the object, each value passed             here should exist in the polarization_array. If passing strings, the             canonical polarization strings (e.g. "xx", "rr") are supported and if the             `x_orientation` attribute is set, the physical dipole strings             (e.g. "nn", "ee") are also supported.         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 # 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 # Check to make sure that we actually have these antenna nums in the data # find conjugate polarization # Use intersection (and) to join antenna_names/nums & ant_pairs_nums # Use intersection (and) to join antenna_names/nums/ant_pairs_nums # with blt_inds # we're wrapping around LST = 2*pi = 0 # Use intesection (and) to join # antenna_names/nums/ant_pairs_nums/blt_inds with times # Use intesection (and) to join # antenna_names/nums/ant_pairs_nums/blt_inds with times # Check and see that all requested freqs are available 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. # Use the spw ID array to check and see which SPWs are left 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`.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         polarizations : array_like of int or str, optional             The polarizations numbers to keep in the object, each value passed             here should exist in the polarization_array. If passing strings, the             canonical polarization strings (e.g. "xx", "rr") are supported and if the             `x_orientation` attribute is set, the physical dipole strings             (e.g. "nn", "ee") are also supported.         blt_inds : array_like of int, optional             The baseline-time indices to keep in the object. This is             not commonly used.         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).         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.         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          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. # do select operations on everything except data_array, flag_array # and nsample_array # check if object is uv_object-consistent 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. # concatenate temp array with existing arrays # set antenna arrays from baseline_array # update metadata # update app source coords to new times # set lst array # temporarily store the metadata only to calculate UVWs correctly # properly calculate the UVWs self-consistently 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 # figure out where integration_time is longer than max_int_time # phase to RA/dec of zenith # 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. # compute the new times of the upsampled array # `offset` will be 0.5 or 1, depending on whether n_new_samples for # this baseline is even or odd. # 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. # harmonize temporary arrays with existing ones # reorganize along blt axis # check the resulting object # add to the history Average to a longer integration time.          This method will average a UVData object either by an integer factor         (by setting `n_times_to_avg`) or by a factor that can differ by         baseline-time sample such that after averaging, the samples have an         integration time greater than or equal to the `min_int_time` (up to the         tolerance on the integration_time).          Note that if the integrations for a baseline do not divide evenly by the         `n_times_to_avg` or into the specified `min_int_time`, the final         integrations for that baseline may have integration times less than         `min_int_time` or be composed of fewer input integrations than `n_times_to_avg`.         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 average the UVData integration_time to             in seconds.         n_times_to_avg : int             Number of time integrations to average together.         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,             or that have a number of integrations that do not evenly divide by             n_times_to_avg, keep_ragged controls whether to keep the (averaged)             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 averaging 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 averaging in drift mode may result in more decoherence.          Returns         -------         None # check that min_int_time is sensible given integration_time # first figure out where integration_time is shorter than min_int_time # 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. # now re-compute inds_to_downsample, in case things have changed # figure out how many baseline times we'll end up with at the end # 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. # don't do anything -- implicitly drop these integrations # 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 # this baseline has all the same integration times # varying integration times for this baseline, need to be more careful # phase to RA/dec of zenith # make temporary arrays # 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. # don't do anything -- implicitly drop these integrations # sum together that number of samples # this might be wrong if some of the constituent times are # *totally* flagged # take potential non-uniformity of integration_time into account # 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 # need to update mask if a downsampled visibility will # be flagged so that we don't set it to zero # nsample array is the fraction of data that we actually kept, # relative to the amount that went into the sum or average # promote nsample dtype if half-precision # take potential non-uniformity of integration_time # and nsamples into account # output of masked array calculation should be coerced # to the datatype of temp_nsample (which has the same # precision as the original nsample_array) # increment counters and reset values # make sure we've populated the right number of baseline-times # harmonize temporary arrays with existing ones # reorganize along blt axis # check the resulting object # add to the history 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 Average in frequency.          Does a simple average over an integer number of input channels, leaving         flagged samples out of the average.          In the future, this method will support non-equally spaced channels         and varying channel widths. It will also support setting the frequency         to the true mean of the averaged non-flagged frequencies rather than         the simple mean of the input channel frequencies. For now it does not.          Parameters         ----------         n_chan_to_avg : int             Number of channels to average together. If Nfreqs does not divide             evenly by this number, the frequencies at the end of the freq_array             will be dropped to make it evenly divisable. To control which             frequencies are removed, use select before calling this 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).         propagate_flags: bool             Option to flag an averaged entry even if some of its contributors             are not flagged. The averaged result will still leave the flagged             samples out of the average, except when all contributors are             flagged. # not an even number of final channels # if any contributors are flagged, the result should be flagged # 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 # need to update mask if a downsampled visibility will be flagged # so that we don't set it to zero # Only if all entries are masked # May not happen due to propagate_flags keyword # mask should be left alone otherwise # Only if all entries are masked # May not happen due to propagate_flags keyword # mask should be left alone otherwise # promote nsample dtype if half-precision # need to weight by the nsample_array # nsample array is the fraction of data that we actually kept, # relative to the amount that went into the sum or average. # Need to take care to return precision back to original value. 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. Downselect or average to only have one baseline per redundant group.          Either select the first baseline in the redundant group or average over         the baselines in the redundant group.          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.         method : str             Options are "select", which just keeps the first baseline in each             redundant group or "average" which averages over the baselines in each             redundant group and assigns the average to the first baseline in the group.         inplace : bool             Option to do selection on current object.         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 # do a metadata only select to get all the metadata right # initalize the data like arrays # now we have to figure out which times are the same to a tolerance # so we can average over them. # average over the same times # Note that this average time is just used for identifying the # index to use for the blt axis on the averaged data set. # We do not update the actual time on that data set because it can # result in confusing behavior -- small numerical rounding errors # can result in many more unique times in the final data set than # in the initial data set. # time_ind contains indices for both regular and conjugated bls # because we needed to group them together in time. # The regular ones are first and extend the length of group_times, # so we use that to split them back up. # check that the integration times are all the same # if all data is flagged, average it all as if it were not 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`) # Stack redundant groups into one array. # TODO should be an assert that each baseline only ends up in one group # Map group index to blt indices in the compressed array. # First baseline in the group that is also in the compressed # baseline array. # add number of blts for this group # this makes the time the fastest axis # blt_map is an index array mapping compressed blti indices to uncompressed 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 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', 'mir', 'ms'          Raises         ------         ValueError             if filetype is not a known type 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, a layout file and a flag file.             An obs file is also required if `read_data` is False.         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.         read_data : bool             Read in the visibility, nsample and flag data. If set to False, only             the metadata will be read in. Setting read_data to False results in             a metadata only object. If read_data is False, an obs file must be             included in the filelist.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run).         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          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. Read in data from an SMA MIR file.          Note that with the exception of filepath, the reset of the parameters are         used to sub-select a range of data that matches the limitations of the current         instantiation of pyuvdata  -- namely 1 spectral window, 1 source. These could         be dropped in the future, as pyuvdata capabilities grow.          Parameters         ----------         filepath : str              The file path to the MIR folder to read from.         isource : int             Source code for MIR dataset         irec : int             Receiver code for MIR dataset         isb : int             Sideband code for MIR dataset         corrchunk : int             Correlator chunk code for MIR dataset         pseudo_cont : boolean             Read in only pseudo-continuuum values. Default is false. Read in data from a miriad file.          Parameters         ----------         filepath : str             The miriad root directory to read from.         axis : str             Axis to concatenate files along. This enables fast concatenation             along the specified axis without the normal checking that all other             metadata agrees. This method does not guarantee correct resulting             objects. Please see the docstring for fast_concat for details.             Allowed values are: 'blt', 'freq', 'polarization'. Only used if             multiple files are passed.         antenna_nums : array_like of int, optional             The antennas numbers to read into the object.         bls : list of tuple, optional             A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of             baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines             to include when reading data into the object. For length-2 tuples,             the ordering of the numbers within the tuple does not matter. For             length-3 tuples, the polarization string is in the order of the two             antennas. If length-3 tuples are provided, `polarizations` must be             None.         ant_str : str, optional             A string containing information about what antenna numbers             and polarizations to include when reading data into the object.             Can be 'auto', 'cross', 'all', or combinations of antenna numbers             and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more             examples of valid strings and the behavior of different forms for ant_str.             If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will             be kept for both baselines (1, 2) and (2, 3) to return a valid             pyuvdata object.             An ant_str cannot be passed in addition to any of `antenna_nums`,             `bls` or `polarizations` parameters, if it is a ValueError will be raised.         polarizations : array_like of int or str, optional             List of polarization integers or strings to read-in. e.g. ['xx', 'yy', ...]         time_range : list of float, optional             len-2 list containing min and max range of times in Julian Date to             include when reading data into the object. e.g. [2458115.20, 2458115.40]         read_data : bool             Read in the visibility and flag data. If set to false,             only the metadata will be read in. Setting read_data to False             results in an incompletely defined object (check will not pass).         phase_type : str, optional             Option to specify the phasing status of the data. Options are 'drift',             'phased' or None. 'drift' means the data are zenith drift data,             'phased' means the data are phased to a single RA/Dec. Default is None             meaning it will be guessed at based on the file contents.         correct_lat_lon : bool             Option to update the latitude and longitude from the known_telescopes             list if the altitude is missing.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run). Ignored if read_data is False.         check_extra : bool             Option to check optional parameters as well as required ones (the             default is True, meaning the optional parameters will be checked).             Ignored if read_data is False.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters after             reading in the file (the default is True, meaning the acceptable             range check will be done). Ignored if read_data is False.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         calc_lst : bool             Recalculate the LST values that are present within the file, useful in             cases where the "online" calculate values have precision or value errors.             Default is True.         fix_old_proj : bool             Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`             method was used prior to writing the data, which had errors of the order of             one part in 1e4 - 1e5. See the phasing memo for more details. Default is             False.         fix_use_ant_pos : bool             If setting `fix_old_proj` to True, use the antenna positions to derive the             correct uvw-coordinates rather than using the baseline vectors. Default is             True.          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. Read in data from a measurement set.          Parameters         ----------         filepath : str             The measurement set root directory to read from.         axis : str             Axis to concatenate files along. This enables fast concatenation             along the specified axis without the normal checking that all other             metadata agrees. This method does not guarantee correct resulting             objects. Please see the docstring for fast_concat for details.             Allowed values are: 'blt', 'freq', 'polarization'. Only used if             multiple files are passed.         data_column : str             name of CASA data column to read into data_array. Options are:             'DATA', 'MODEL', or 'CORRECTED_DATA'         pol_order : str             Option to specify polarizations order convention, options are             'CASA' or 'AIPS'.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run).         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         ignore_single_chan : bool             Some measurement sets (e.g., those from ALMA) use single channel spectral             windows for recording pseudo-continuum channels or storing other metadata             in the track when the telescopes are not on source. Because of the way             the object is strutured (where all spectral windows are assumed to be             simultaneously recorded), this can significantly inflate the size and memory             footprint of UVData objects. By default, single channel windows are ignored             to avoid this issue, although they can be included if setting this parameter             equal to True.         raise_error : bool             The measurement set format allows for different spectral windows and             polarizations to have different metdata for the same time-baseline             combination, but UVData objects do not. If detected, by default the reader             will throw an error. However, if set to False, the reader will simply give             a warning, and will use the first value read in the file as the "correct"             metadata in the UVData object.         read_weights : bool             Read in the weights from the MS file, default is True. If false, the method             will set the `nsamples_array` to the same uniform value (namely 1.0).          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. Read in MWA correlator gpu box files.          The default settings remove some of the instrumental effects in the bandpass         by dividing out the digital gains and the coarse band shape.         If the desired output is raw correlator data, set remove_dig_gains=False,         remove_coarse_band=False, correct_cable_len=False, and         phase_to_pointing_center=False.          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.         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_aoflagger_flags : bool             Option to use aoflagger mwaf flag files. Defaults to true if aoflagger             flag files are submitted.         use_cotter_flags : bool             Being replaced by use_aoflagger_flags and will be removed in v2.4.         remove_dig_gains : bool             Option to divide out digital gains.         remove_coarse_band : bool             Option to divide out coarse band shape.         correct_cable_len : bool             Option to apply a cable delay correction.         correct_van_vleck : bool             Option to apply a van vleck correction.         cheby_approx : bool             Only used if correct_van_vleck is True. Option to implement the van             vleck correction with a chebyshev polynomial approximation.         flag_small_auto_ants : bool             Only used if correct_van_vleck is True. Option to completely flag any             antenna for which the autocorrelation falls below a threshold found by             the Van Vleck correction to indicate bad data. Specifically, the             threshold used is 0.5 * integration_time * channel_width. If set to False,             only the times and frequencies at which the auto is below the             threshold will be flagged for the antenna.         flag_small_sig_ants : bool             Being replaced with flag_small_auto_ants and will be removed in v2.4.         propagate_coarse_flags : bool             Option to propagate flags for missing coarse channel integrations             across frequency.         flag_init: bool             Set to True in order to do routine flagging of coarse channel edges,             start or end integrations, or the center fine channel of each coarse             channel. See associated keywords.         edge_width: float             Only used if flag_init is True. Set to the width to flag on the edge             of each coarse channel, in hz. Errors if not equal to integer             multiple of channel_width. Set to 0 for no edge flagging.         start_flag: float or str             Only used if flag_init is True. The number of seconds to flag at the             beginning of the observation. Set to 0 for no flagging. Default is             'goodtime', which uses information in the metafits file to determine             the length of time that should be flagged. Errors if input is not a             float or 'goodtime'. Errors if float input is not equal to an             integer multiple of the integration time.         end_flag: floats             Only used if flag_init is True. Set to the number of seconds to flag             at the end of the observation. Set to 0 for no flagging. Errors if             not an integer multiple of the integration time.         flag_dc_offset: bool             Only used if flag_init is True. Set to True to flag the center fine             channel of each coarse channel. Only used if file_type is             'mwa_corr_fits'.         remove_flagged_ants : bool             Option to perform a select to remove antennas flagged in the metafits             file. If correct_van_vleck and flag_small_auto_ants are both True then             antennas flagged by the Van Vleck correction are also removed.         phase_to_pointing_center : bool             Option to phase to the observation pointing center.         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.         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).         nsample_array_dtype : numpy dtype             Datatype to store the output nsample_array as. Must be either             np.float64 (double-precision), np.float32 (single-precision), or             np.float16 (half-precision). Half-precision is only recommended for             cases where no sampling or averaging of baselines will occur,             because round-off errors can be quite large (~1e-3).         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run).         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          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. Read in header, metadata and data from a single uvfits file.          Parameters         ----------         filename : str             The uvfits file to read from.         axis : str             Axis to concatenate files along. This enables fast concatenation             along the specified axis without the normal checking that all other             metadata agrees. This method does not guarantee correct resulting             objects. Please see the docstring for fast_concat for details.             Allowed values are: 'blt', 'freq', 'polarization'. Only used if             multiple files are passed.         antenna_nums : array_like of int, optional             The antennas numbers to include when reading data into the object             (antenna positions and names for the removed antennas will be retained             unless `keep_all_metadata` is False). This cannot be provided if             `antenna_names` is also provided. Ignored if read_data is False.         antenna_names : array_like of str, optional             The antennas names to include when reading data into the object             (antenna positions and names for the removed antennas will be retained             unless `keep_all_metadata` is False). This cannot be provided if             `antenna_nums` is also provided. Ignored if read_data is False.         bls : list of tuple, optional             A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of             baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines             to include when reading data into the object. For length-2 tuples,             the ordering of the numbers within the tuple does not matter. For             length-3 tuples, the polarization string is in the order of the two             antennas. If length-3 tuples are provided, `polarizations` must be             None. Ignored if read_data is False.         ant_str : str, optional             A string containing information about what antenna numbers             and polarizations to include when reading data into the object.             Can be 'auto', 'cross', 'all', or combinations of antenna numbers             and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more             examples of valid strings and the behavior of different forms for ant_str.             If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will             be kept for both baselines (1, 2) and (2, 3) to return a valid             pyuvdata object.             An ant_str cannot be passed in addition to any of `antenna_nums`,             `antenna_names`, `bls` args or the `polarizations` parameters,             if it is a ValueError will be raised. Ignored if read_data is False.         frequencies : array_like of float, optional             The frequencies to include when reading data into the object, each             value passed here should exist in the freq_array. Ignored if             read_data is False.         freq_chans : array_like of int, optional             The frequency channel numbers to include when reading data into the             object. Ignored if read_data is False.         times : array_like of float, optional             The times to include when reading data into the object, each value             passed here should exist in the time_array 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`.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         polarizations : array_like of int, optional             The polarizations numbers to include when reading data into the             object, each value passed here should exist in the polarization_array.             Ignored if read_data is False.         blt_inds : array_like of int, optional             The baseline-time indices to include when reading data into the             object. This is not commonly used. Ignored if read_data is False.         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.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run). Ignored if read_data is False.         check_extra : bool             Option to check optional parameters as well as required ones (the             default is True, meaning the optional parameters will be checked).             Ignored if read_data is False.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters after             reading in the file (the default is True, meaning the acceptable             range check will be done). Ignored if read_data is False.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         fix_old_proj : bool             Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`             method was used prior to writing the data, which had errors of the order of             one part in 1e4 - 1e5. See the phasing memo for more details. Default is             False.         fix_use_ant_pos : bool             If setting `fix_old_proj` to True, use the antenna positions to derive the             correct uvw-coordinates rather than using the baseline vectors. Default is             True.          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. Read a UVH5 file.          Parameters         ----------         filename : str              The UVH5 file to read from.         axis : str             Axis to concatenate files along. This enables fast concatenation             along the specified axis without the normal checking that all other             metadata agrees. This method does not guarantee correct resulting             objects. Please see the docstring for fast_concat for details.             Allowed values are: 'blt', 'freq', 'polarization'. Only used if             multiple files are passed.         antenna_nums : array_like of int, optional             The antennas numbers to include when reading data into the object             (antenna positions and names for the removed antennas will be retained             unless `keep_all_metadata` is False). This cannot be provided if             `antenna_names` is also provided. Ignored if read_data is False.         antenna_names : array_like of str, optional             The antennas names to include when reading data into the object             (antenna positions and names for the removed antennas will be retained             unless `keep_all_metadata` is False). This cannot be provided if             `antenna_nums` is also provided. Ignored if read_data is False.         bls : list of tuple, optional             A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of             baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines             to include when reading data into the object. For length-2 tuples,             the ordering of the numbers within the tuple does not matter. For             length-3 tuples, the polarization string is in the order of the two             antennas. If length-3 tuples are provided, `polarizations` must be             None. Ignored if read_data is False.         ant_str : str, optional             A string containing information about what antenna numbers             and polarizations to include when reading data into the object.             Can be 'auto', 'cross', 'all', or combinations of antenna numbers             and polarizations (e.g. '1', '1_2', '1x_2y').  See tutorial for more             examples of valid strings and the behavior of different forms for ant_str.             If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will             be kept for both baselines (1, 2) and (2, 3) to return a valid             pyuvdata object.             An ant_str cannot be passed in addition to any of `antenna_nums`,             `antenna_names`, `bls` args or the `polarizations` parameters,             if it is a ValueError will be raised. Ignored if read_data is False.         frequencies : array_like of float, optional             The frequencies to include when reading data into the object, each             value passed here should exist in the freq_array. Ignored if             read_data is False.         freq_chans : array_like of int, optional             The frequency channel numbers to include when reading data into the             object. Ignored if read_data is False.         times : array_like of float, optional             The times to include when reading data into the object, each value             passed here should exist in the time_array 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`.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         polarizations : array_like of int, optional             The polarizations numbers to include when reading data into the             object, each value passed here should exist in the polarization_array.             Ignored if read_data is False.         blt_inds : array_like of int, optional             The baseline-time indices to include when reading data into the             object. This is not commonly used. Ignored if read_data is False.         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'.         multidim_index : bool             [Only for HDF5] If True, attempt to index the HDF5 dataset             simultaneously along all data axes. Otherwise index one axis at-a-time.             This only works if data selection is sliceable along all but one axis.             If indices are not well-matched to data chunks, this can be slow.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run). Ignored if read_data is False.         check_extra : bool             Option to check optional parameters as well as required ones (the             default is True, meaning the optional parameters will be checked).             Ignored if read_data is False.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters after             reading in the file (the default is True, meaning the acceptable             range check will be done). Ignored if read_data is False.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         fix_old_proj : bool             Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`             method was used prior to writing the data, which had errors of the order of             one part in 1e4 - 1e5. See the phasing memo for more details. Default is             to apply the correction if the attributes `phase_center_app_ra` and             `phase_center_app_dec` are missing (as they were introduced alongside the             new phasing method).         fix_use_ant_pos : bool             If setting `fix_old_proj` to True, use the antenna positions to derive the             correct uvw-coordinates rather than using the baseline vectors. Default is             True.          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. 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_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' or             'mwa_corr_fits'.         nsample_array_dtype : numpy dtype             Datatype to store the output nsample_array as. Must be either             np.float64 (double-precision), np.float32 (single-precision), or             np.float16 (half-precision). Half-precision is only recommended for             cases where no sampling or averaging of baselines will occur,             because round-off errors can be quite large (~1e-3). Only used if             file_type is 'mwa_corr_fits'.         use_aoflagger_flags : bool             Option to use aoflagger mwaf flag files. Defaults to true if aoflagger             flag files are submitted.         use_cotter_flags : bool             Being replaced by use_aoflagger_flags and will be removed in v2.4.         remove_dig_gains : bool             Only used if file_type is 'mwa_corr_fits'. Option to divide out digital             gains.         remove_coarse_band : bool             Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse             band shape.         correct_cable_len : bool             Flag to apply cable length correction. Only used if file_type is             'mwa_corr_fits'.         correct_van_vleck : bool             Flag to apply a van vleck correction. Only used if file_type is             'mwa_corr_fits'.         cheby_approx : bool             Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.             Option to implement the van vleck correction with a chebyshev polynomial             approximation. Set to False to run the integral version of the correction.         flag_small_auto_ants : bool             Only used if correct_van_vleck is True. Option to completely flag any             antenna for which the autocorrelation falls below a threshold found by             the Van Vleck correction to indicate bad data. Specifically, the             threshold used is 0.5 * integration_time * channel_width. If set to False,             only the times and frequencies at which the auto is below the             threshold will be flagged for the antenna. Only used if file_type is             'mwa_corr_fits'.         flag_small_sig_ants : bool             Being replaced by flag_small_auto_ants and will be removed in v2.4.         propogate_coarse_flags : bool             Option to propogate flags for missing coarse channel integrations             across frequency. Only used if file_type is 'mwa_corr_fits'.         flag_init: bool             Only used if file_type is 'mwa_corr_fits'. Set to True in order to             do routine flagging of coarse channel edges, start or end             integrations, or the center fine channel of each coarse             channel. See associated keywords.         edge_width: float             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to the width to flag on the edge of each coarse channel, in hz.             Errors if not equal to integer multiple of channel_width. Set to 0             for no edge flagging.         start_flag: float or str             Only used if flag_init is True. The number of seconds to flag at the             beginning of the observation. Set to 0 for no flagging. Default is             'goodtime', which uses information in the metafits file to determine             the length of time that should be flagged. Errors if input is not a             float or 'goodtime'. Errors if float input is not equal to an             integer multiple of the integration time.         end_flag: floats             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to the number of seconds to flag at the end of the observation. Set             to 0 for no flagging. Errors if not an integer multiple of the             integration time.         flag_dc_offset: bool             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to True to flag the center fine channel of each coarse channel. Only             used if file_type is 'mwa_corr_fits'.         remove_flagged_ants : bool             Option to perform a select to remove antennas flagged in the metafits             file. If correct_van_vleck and flag_small_auto_ants are both True then             antennas flagged by the Van Vleck correction are also removed.             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.         skip_bad_files : bool             Option when reading multiple files to catch read errors such that             the read continues even if one or more files are corrupted. Files             that produce errors will be printed. Default is False (files will             not be skipped).         multidim_index : bool             [Only for HDF5] If True, attempt to index the HDF5 dataset             simultaneously along all data axes. Otherwise index one axis at-a-time.             This only works if data selection is sliceable along all but one axis.             If indices are not well-matched to data chunks, this can be slow.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run). Ignored if read_data is False.         check_extra : bool             Option to check optional parameters as well as required ones (the             default is True, meaning the optional parameters will be checked).             Ignored if read_data is False.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters after             reading in the file (the default is True, meaning the acceptable             range check will be done). Ignored if read_data is False.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         isource : int             Source code for MIR dataset         irec : int             Receiver code for MIR dataset         isb : int             Sideband code for MIR dataset         corrchunk : int             Correlator chunk code for MIR dataset         pseudo_cont : boolean             Read in only pseudo-continuuum values in MIR dataset. Default is false.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         calc_lst : bool             Recalculate the LST values that are present within the file, useful in             cases where the "online" calculate values have precision or value errors.             Default is True. Only applies to MIRIAD files.         fix_old_proj : bool             Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`             method was used prior to writing the data, which had errors of the order of             one part in 1e4 - 1e5. See the phasing memo for more details. Default is             False, unless reading a UVH5 file that is missing the `phase_center_app_ra`             and `phase_center_app_dec` attributes (as these were introduced at the same             time as the new `phase` method), in which case the default is True.         fix_use_ant_pos : bool             If setting `fix_old_proj` to True, use the antenna positions to derive the             correct uvw-coordinates rather than using the baseline vectors. Default is             True.         make_multi_phase : bool             Option to make the output a multi phase center dataset, capable of holding             data on multiple phase centers. By default, this is only done if reading             in a file with multiple sources.         ignore_name : bool             Only relevant when reading in multiple files, which are concatenated into a             single UVData object. Option to ignore the name of the phase center when             combining multiple files, which would otherwise result in an error being             raised because of attributes not matching. Doing so effectively adopts the             name found in the first file read in. Default 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. # this is either a list of separate files to read or a list of # FHD files or MWA correlator FITS files # this must be a list of lists of FHD or MWA correlator FITS # it's a directory, so it's either miriad, mir, or ms file type # It's miriad. # It's a measurement set. # It's Submillimeter Array mir format. # pragma: nocover # set the phase center to be the phase center of the first file # pragma: nocover #######################################################\n" #######################################################" # Concatenate once at end # Rewrote fast_concat to operate on lists # Too much work to rewrite __add__ to operate on lists # of files, so instead doing a binary tree merge # Because self was at the beginning of the list, # everything is merged into it at the end of this loop # these file types do not have select on read, so set all # select parameters # these are all done by partial read, so set to None # these aren't supported by partial read, so do it in select # reading a single "file". Call the appropriate file-type read # 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. Initialize a new UVData object by reading the input file.          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_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' or             'mwa_corr_fits'.         nsample_array_dtype : numpy dtype             Datatype to store the output nsample_array as. Must be either             np.float64 (double-precision), np.float32 (single-precision), or             np.float16 (half-precision). Half-precision is only recommended for             cases where no sampling or averaging of baselines will occur,             because round-off errors can be quite large (~1e-3). Only used if             file_type is 'mwa_corr_fits'.         use_aoflagger_flags : bool             Only used if file_type is 'mwa_corr_fits'. Option to use aoflagger mwaf             flag files. Defaults to true if aoflagger flag files are submitted.         use_cotter_flags : bool             Being replaced by use_aoflagger_flags and will be removed in v2.4.         remove_dig_gains : bool             Only used if file_type is 'mwa_corr_fits'. Option to divide out digital             gains.         remove_coarse_band : bool             Only used if file_type is 'mwa_corr_fits'. Option to divide out coarse             band shape.         correct_cable_len : bool             Flag to apply cable length correction. Only used if file_type is             'mwa_corr_fits'.         correct_van_vleck : bool             Flag to apply a van vleck correction. Only used if file_type is             'mwa_corr_fits'.         cheby_approx : bool             Only used if file_type is 'mwa_corr_fits' and correct_van_vleck is True.             Option to implement the van vleck correction with a chebyshev polynomial             approximation. Set to False to run the integral version of the correction.         flag_small_auto_ants : bool             Only used if correct_van_vleck is True. Option to completely flag any             antenna for which the autocorrelation falls below a threshold found by             the Van Vleck correction to indicate bad data. Specifically, the             threshold used is 0.5 * integration_time * channel_width. If set to False,             only the times and frequencies at which the auto is below the             threshold will be flagged for the antenna. Only used if file_type is             'mwa_corr_fits'.         flag_small_sig_ants : bool             Being replaced by flag_small_auto_ants and will be removed in v2.4.         propogate_coarse_flags : bool             Option to propogate flags for missing coarse channel integrations             across frequency. Only used if file_type is 'mwa_corr_fits'.         flag_init: bool             Only used if file_type is 'mwa_corr_fits'. Set to True in order to             do routine flagging of coarse channel edges, start or end             integrations, or the center fine channel of each coarse             channel. See associated keywords.         edge_width: float             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to the width to flag on the edge of each coarse channel, in hz.             Errors if not equal to integer multiple of channel_width. Set to 0             for no edge flagging.         start_flag: float or str             Only used if flag_init is True. The number of seconds to flag at the             beginning of the observation. Set to 0 for no flagging. Default is             'goodtime', which uses information in the metafits file to determine             the length of time that should be flagged. Errors if input is not a             float or 'goodtime'. Errors if float input is not equal to an             integer multiple of the integration time.         end_flag: floats             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to the number of seconds to flag at the end of the observation. Set             to 0 for no flagging. Errors if not an integer multiple of the             integration time.         flag_dc_offset: bool             Only used if file_type is 'mwa_corr_fits' and flag_init is True. Set             to True to flag the center fine channel of each coarse channel. Only             used if file_type is 'mwa_corr_fits'.         remove_flagged_ants : bool             Option to perform a select to remove antennas flagged in the metafits             file. If correct_van_vleck and flag_small_auto_ants are both True then             antennas flagged by the Van Vleck correction are also removed.             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.         skip_bad_files : bool             Option when reading multiple files to catch read errors such that             the read continues even if one or more files are corrupted. Files             that produce errors will be printed. Default is False (files will             not be skipped).         multidim_index : bool             [Only for HDF5] If True, attempt to index the HDF5 dataset             simultaneously along all data axes. Otherwise index one axis at-a-time.             This only works if data selection is sliceable along all but one axis.             If indices are not well-matched to data chunks, this can be slow.         background_lsts : bool             When set to True, the lst_array is calculated in a background thread.         run_check : bool             Option to check for the existence and proper shapes of parameters             after after reading in the file (the default is True,             meaning the check will be run). Ignored if read_data is False.         check_extra : bool             Option to check optional parameters as well as required ones (the             default is True, meaning the optional parameters will be checked).             Ignored if read_data is False.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters after             reading in the file (the default is True, meaning the acceptable             range check will be done). Ignored if read_data is False.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         isource : int             Source code for MIR dataset         irec : int             Receiver code for MIR dataset         isb : int             Sideband code for MIR dataset         corrchunk : int             Correlator chunk code for MIR dataset         pseudo_cont : boolean             Read in only pseudo-continuuum values in MIR dataset. Default is false.         lsts : array_like of float, optional             The local sidereal times (LSTs) to keep in the object, each value             passed here should exist in the lst_array. Cannot be used with             `times`, `time_range`, or `lst_range`.         lst_range : array_like of float, optional             The local sidereal time (LST) range in radians to keep in the             object, must be of length 2. Some of the LSTs in the object should             fall between the first and last elements. If the second value is             smaller than the first, the LSTs are treated as having phase-wrapped             around LST = 2*pi = 0, and the LSTs kept on the object will run from             the larger value, through 0, and end at the smaller value.         calc_lst : bool             Recalculate the LST values that are present within the file, useful in             cases where the "online" calculate values have precision or value errors.             Default is True. Only applies to MIRIAD files.         fix_old_proj : bool             Applies a fix to uvw-coordinates and phasing, assuming that the old `phase`             method was used prior to writing the data, which had errors of the order of             one part in 1e4 - 1e5. See the phasing memo for more details. Default is             False, unless reading a UVH5 file that is missing the `phase_center_app_ra`             and `phase_center_app_dec` attributes (as these were introduced at the same             time as the new `phase` method), in which case the default is True.         fix_use_ant_pos : bool             If setting `fix_old_proj` to True, use the antenna positions to derive the             correct uvw-coordinates rather than using the baseline vectors. Default is             True.         make_multi_phase : bool             Option to make the output a multi phase center dataset, capable of holding             data on multiple phase centers. By default, this is only done if reading             in a file with multiple sources.         ignore_name : bool             Only relevant when reading in multiple files, which are concatenated into a             single UVData object. Option to ignore the name of the phase center when             combining multiple files, which would otherwise result in an error being             raised because of attributes not matching. Doing so effectively adopts the             name found in the first file read in. Default 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. Write the data to a miriad file.          Parameters         ----------         filename : str             The miriad root directory to write to.         clobber : bool             Option to overwrite the filename if the file already exists.         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.         no_antnums : bool             Option to not write the antnums variable to the file.             Should only be used for testing purposes.         calc_lst : bool             Recalculate the LST values upon writing the file. This is done to perform             higher-precision accounting for the difference in MIRAD timestamps vs             pyuvdata (the former marks the beginning of an integration, the latter             marks the midpoint). Default is False, which instead uses a simple formula             for correcting the LSTs, expected to be accurate to approximately 0.1 µsec             precision.          Raises         ------         ValueError             If the frequencies are not evenly spaced or are separated by more             than their channel width or if the UVData object is a metadata only object.         TypeError             If any entry in extra_keywords is not a single string or number. Write the data to a mir file.          Parameters         ----------         filename : str             The mir root directory to write to.          Raises         ------         ValueError             If the UVData object is a metadata only object.         NotImplementedError             Method is not fully implemented yet, and thus will raise an error Write a CASA measurement set (MS).          Parameters         ----------         filename : str             The measurement set file path to write to (a measurement set is really             a folder with many files).         force_phase : bool             Option to automatically phase drift scan data to zenith of the first             timestamp.         clobber : bool             Option to overwrite the file if it already exists.         run_check : bool             Option to check for the existence and proper shapes of parameters             before writing the file.         check_extra : bool             Option to check optional parameters as well as required ones.         run_check_acceptability : bool             Option to check acceptable range of the values of parameters before             writing the file.         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Raises         ------         ValueError             If the UVData object is a metadata only object. 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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Raises         ------         ValueError             The `phase_type` of the object is "drift" and the `force_phase`             keyword is not set.             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".             If the UVData object is a metadata only object.         TypeError             If any entry in extra_keywords is not a single string or number. 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.         chunks : tuple or bool             h5py.create_dataset chunks keyword. Tuple for chunk shape,             True for auto-chunking, None for no chunking. Default is True.         data_compression : str             HDF5 filter to apply when writing the data_array. Default is             None meaning no filter or compression. Dataset must be chunked.         flags_compression : str             HDF5 filter to apply when writing the flags_array. Default is "lzf"             for the LZF filter. Dataset must be chunked.         nsample_compression : str             HDF5 filter to apply when writing the nsample_array. Default is "lzf"             for the LZF filter. Dataset must be chunked.         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.          Raises         ------         ValueError             If the UVData object is a metadata only object. 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.         chunks : tuple or bool             h5py.create_dataset chunks keyword. Tuple for chunk shape,             True for auto-chunking, None for no chunking. Default is True.         data_compression : str             HDF5 filter to apply when writing the data_array. Default is             None meaning no filter or compression. Dataset must be chunked.         flags_compression : str             HDF5 filter to apply when writing the flags_array. Default is "lzf"             for the LZF filter. Dataset must be chunked.         nsample_compression : str             HDF5 filter to apply when writing the nsample_array. Default is "lzf"             for the LZF filter. Dataset must be chunked.         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. 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.         add_to_history : str             String to append to history before write out. Default is no appending.         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).         strict_uvw_antpos_check : bool             Option to raise an error rather than a warning if the check that             uvws match antenna positions does not pass.