Source code for pyuvdata.uvdata.uvh5

# -*- mode: python; coding: utf-8 -*-
# Copyright (c) 2018 Radio Astronomy Software Group
# Licensed under the 2-clause BSD License

"""Class for reading and writing UVH5 files."""
import numpy as np
import os
import warnings
import h5py
import json

from .uvdata import UVData
from .. import utils as uvutils

__all__ = ["UVH5"]


# define HDF5 type for interpreting HERA correlator outputs (integers) as
# complex numbers
_hera_corr_dtype = np.dtype([("r", "<i4"), ("i", "<i4")])


def _check_uvh5_dtype(dtype):
    """
    Check that a specified custom datatype conforms to UVH5 standards.

    According to the UVH5 spec, the data type for the data array must be a
    compound datatype with an "r" field and an "i" field. Additionally, both
    datatypes must be the same (e.g., "<i4", "<r8", etc.).

    Parameters
    ----------
    dtype : numpy dtype
        A numpy dtype object with an "r" field and an "i" field.

    Returns
    -------
    None

    Raises
    ------
    ValueError
        This is raised if dtype is not a numpy dtype, if the dtype does not have
        an "r" field and an "i" field, or if the "r" field and "i" fields have
        different types.
    """
    if not isinstance(dtype, np.dtype):
        raise ValueError("dtype in a uvh5 file must be a numpy dtype")
    if "r" not in dtype.names or "i" not in dtype.names:
        raise ValueError(
            "dtype must be a compound datatype with an 'r' field and an 'i' field"
        )
    rkind = dtype["r"].kind
    ikind = dtype["i"].kind
    if rkind != ikind:
        raise ValueError(
            "dtype must have the same kind ('i4', 'r8', etc.) for both real "
            "and imaginary fields"
        )
    return


def _read_complex_astype(dset, indices, dtype_out=np.complex64):
    """
    Read the given data set of a specified type to floating point complex data.

    Parameters
    ----------
    dset : h5py dataset
        A reference to an HDF5 dataset on disk.
    indices : tuple
        The indices to extract. Should be either lists of indices or numpy
        slice objects.
    dtype_out : str or numpy dtype
        The datatype of the output array. One of (complex, np.complex64,
        np.complex128). Default is np.complex64 (single-precision real and
        imaginary floats).

    Returns
    -------
    output_array : ndarray
        The array referenced in the dataset cast to complex values.

    Raises
    ------
    ValueError
        This is raised if dtype_out is not an acceptable value.
    """
    if dtype_out not in (complex, np.complex64, np.complex128):
        raise ValueError(
            "output datatype must be one of (complex, np.complex64, np.complex128)"
        )
    dset_shape, indices = uvutils._get_dset_shape(dset, indices)
    output_array = np.empty(dset_shape, dtype=dtype_out)
    dtype_in = dset.dtype
    with dset.astype(dtype_in):
        # dset is indexed in native dtype, but is upcast upon assignment
        output_array.real = uvutils._index_dset(dset["r"], indices)
        output_array.imag = uvutils._index_dset(dset["i"], indices)

    return output_array


def _write_complex_astype(data, dset, indices):
    """
    Write floating point complex data as a specified type.

    Parameters
    ----------
    data : ndarray
        The data array to write out. Should be a complex-valued array that
        supports the .real and .imag attributes for accessing real and imaginary
        components.
    dset : h5py dataset
        A reference to an HDF5 dataset on disk.
    indices : tuple
        A 3-tuple representing indices to write data to. Should be either lists
        of indices or numpy slice objects. For data arrays with 4 dimensions, the second
        dimension (the old spw axis) should not be included because it can only be
        length one.

    Returns
    -------
    None
    """
    # get datatype from dataset
    dtype_out = dset.dtype
    # make doubly sure dtype is valid; should be unless user is pathological
    _check_uvh5_dtype(dtype_out)
    if len(dset.shape) == 3:
        # this is the future array shape
        with dset.astype(dtype_out):
            dset[indices[0], indices[1], indices[2], "r"] = data.real
            dset[indices[0], indices[1], indices[2], "i"] = data.imag
    else:
        with dset.astype(dtype_out):
            dset[indices[0], np.s_[:], indices[1], indices[2], "r"] = data.real
            dset[indices[0], np.s_[:], indices[1], indices[2], "i"] = data.imag
    return


[docs]class UVH5(UVData): """ A class for UVH5 file objects. This class defines an HDF5-specific subclass of UVData for reading and writing UVH5 files. This class should not be interacted with directly, instead use the read_uvh5 and write_uvh5 methods on the UVData class. """ def _read_header( self, header, filename, run_check_acceptability=True, background_lsts=True ): """ Read header information from a UVH5 file. This is an internal function called by the user-space methods. Properties of the UVData object are updated as the file is processed. Parameters ---------- header : h5py datagroup A reference to an h5py data group that contains the header information. Should be "/Header" for UVH5 files conforming to spec. run_check_acceptability : bool Option to check acceptable range of the values of parameters after reading in the file. background_lsts : bool When set to True, the lst_array is calculated in a background thread. Returns ------- None """ # get telescope information latitude = header["latitude"][()] longitude = header["longitude"][()] altitude = header["altitude"][()] self.telescope_location_lat_lon_alt_degrees = (latitude, longitude, altitude) self.instrument = bytes(header["instrument"][()]).decode("utf8") self.telescope_name = bytes(header["telescope_name"][()]).decode("utf8") # set history appropriately self.history = bytes(header["history"][()]).decode("utf8") if not uvutils._check_history_version(self.history, self.pyuvdata_version_str): self.history += self.pyuvdata_version_str # check for vis_units if "vis_units" in header: self.vis_units = bytes(header["vis_units"][()]).decode("utf8") # Added here because older files allowed for both upper and lowercase # formats, although since the attribute is case sensitive, we want to # correct for this here. if self.vis_units == "UNCALIB": self.vis_units = "uncalib" else: # default to uncalibrated data self.vis_units = "uncalib" # check for optional values if "dut1" in header: self.dut1 = float(header["dut1"][()]) if "earth_omega" in header: self.earth_omega = float(header["earth_omega"][()]) if "gst0" in header: self.gst0 = float(header["gst0"][()]) if "rdate" in header: self.rdate = bytes(header["rdate"][()]).decode("utf8") if "timesys" in header: self.timesys = bytes(header["timesys"][()]).decode("utf8") if "x_orientation" in header: self.x_orientation = bytes(header["x_orientation"][()]).decode("utf8") if "blt_order" in header: blt_order_str = bytes(header["blt_order"][()]).decode("utf8") self.blt_order = tuple(blt_order_str.split(", ")) if self.blt_order == ("bda",): self._blt_order.form = (1,) if "antenna_diameters" in header: self.antenna_diameters = header["antenna_diameters"][()] if "uvplane_reference_time" in header: self.uvplane_reference_time = int(header["uvplane_reference_time"][()]) if "eq_coeffs" in header: self.eq_coeffs = header["eq_coeffs"][()] if "eq_coeffs_convention" in header: self.eq_coeffs_convention = bytes( header["eq_coeffs_convention"][()] ).decode("utf8") # We've added a few new keywords that did not exist before, so check to see if # any of them are in the header, and if not, mark the data set as being # "regular" (e.g., not a flexible spectral window setup, single source only). if "flex_spw" in header: if bool(header["flex_spw"][()]): self._set_flex_spw() if "flex_spw_id_array" in header: self.flex_spw_id_array = header["flex_spw_id_array"][:] if "multi_phase_center" in header: if bool(header["multi_phase_center"][()]): self._set_phased() self._set_multi_phase_center(preserve_phase_center_info=False) if "Nphase" in header: self.Nphase = int(header["Nphase"][()]) self.phase_type = bytes(header["phase_type"][()]).decode("utf8") self.object_name = bytes(header["object_name"][()]).decode("utf8") # Here is where we start handing phase center information. If we have a multi # phase center dataset, this information is going to be handled a bit # differently, since all of these entries are expected to be required arrays. if self.multi_phase_center: self.phase_center_id_array = header["phase_center_id_array"][:] # check for phasing information if self.phase_type == "phased": self.phase_center_ra = float(header["phase_center_ra"][()]) self.phase_center_dec = float(header["phase_center_dec"][()]) if "phase_center_epoch" in header: self.phase_center_epoch = float(header["phase_center_epoch"][()]) self._set_phased() if "phase_center_app_ra" in header and "phase_center_app_dec" in header: self.phase_center_app_ra = header["phase_center_app_ra"][:] self.phase_center_app_dec = header["phase_center_app_dec"][:] if "phase_center_frame_pa" in header: self.phase_center_frame_pa = header["phase_center_frame_pa"][:] else: if self.phase_type != "drift": warnings.warn( "Unknown phase types are no longer supported, marking this " 'object as phase_type="drift" by default. If this was a phased ' "object, you can set the phase type correctly by running the " "_set_phased() method." ) self._set_drift() # Here is where we collect the other optional source/phasing info if "phase_center_catalog" in header: self.phase_center_catalog = {} for key in header["phase_center_catalog"].keys(): self.phase_center_catalog[key] = json.loads( bytes(header["phase_center_catalog"][key][()]).decode("utf8") ) if "phase_center_frame" in header: self.phase_center_frame = bytes(header["phase_center_frame"][()]).decode( "utf8" ) # get antenna arrays # cast to native python int type self.Nants_data = int(header["Nants_data"][()]) self.Nants_telescope = int(header["Nants_telescope"][()]) self.ant_1_array = header["ant_1_array"][:] self.ant_2_array = header["ant_2_array"][:] self.antenna_names = [ bytes(n).decode("utf8") for n in header["antenna_names"][:] ] self.antenna_numbers = header["antenna_numbers"][:] self.antenna_positions = header["antenna_positions"][:] # set telescope params try: self.set_telescope_params() except ValueError as ve: warnings.warn(str(ve)) # get baseline array self.baseline_array = self.antnums_to_baseline( self.ant_1_array, self.ant_2_array ) self.Nbls = len(np.unique(self.baseline_array)) # get uvw array self.uvw_array = header["uvw_array"][:, :] # get time information self.time_array = header["time_array"][:] integration_time = header["integration_time"] self.integration_time = integration_time[:] proc = None if "lst_array" in header: self.lst_array = header["lst_array"][:] # check that lst_array in file is self-consistent if run_check_acceptability: ( latitude, longitude, altitude, ) = self.telescope_location_lat_lon_alt_degrees lst_array = uvutils.get_lst_for_time( self.time_array, latitude, longitude, altitude, ) if not np.all( np.isclose( self.lst_array, lst_array, rtol=self._lst_array.tols[0], atol=self._lst_array.tols[1], ) ): warnings.warn( "LST values stored in {file} are not self-consistent " "with time_array and telescope location. Consider " "recomputing with utils.get_lst_for_time.".format(file=filename) ) else: # compute lst_array from time_array and telescope location proc = self.set_lsts_from_time_array(background=background_lsts) # get frequency information self.freq_array = header["freq_array"][:] self.spw_array = header["spw_array"][:] if self.freq_array.ndim == 1: arr_shape_msg = ( "The size of arrays in this file are not internally consistent, " "which should not happen. Please file an issue in our GitHub issue " "log so that we can fix it." ) assert ( np.asarray(header["channel_width"]).size == self.freq_array.size ), arr_shape_msg self._set_future_array_shapes() # Pull in the channel_width parameter as either an array or as a single float, # depending on whether or not the data is stored with a flexible spw. if self.flex_spw or np.asarray(header["channel_width"]).ndim == 1: self.channel_width = header["channel_width"][:] else: self.channel_width = float(header["channel_width"][()]) # get polarization information self.polarization_array = header["polarization_array"][:] # get data shapes self.Nfreqs = int(header["Nfreqs"][()]) self.Npols = int(header["Npols"][()]) self.Ntimes = int(header["Ntimes"][()]) self.Nblts = int(header["Nblts"][()]) self.Nspws = int(header["Nspws"][()]) # get extra_keywords if "extra_keywords" in header: self.extra_keywords = {} for key in header["extra_keywords"].keys(): if header["extra_keywords"][key].dtype.type in (np.string_, np.object_): self.extra_keywords[key] = bytes( header["extra_keywords"][key][()] ).decode("utf8") else: # special handling for empty datasets == python `None` type if header["extra_keywords"][key].shape is None: self.extra_keywords[key] = None else: self.extra_keywords[key] = header["extra_keywords"][key][()] if proc is not None: # if lsts are in the background wait for them to return proc.join() return def _get_data( self, dgrp, antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, data_array_dtype, keep_all_metadata, multidim_index, run_check, check_extra, run_check_acceptability, strict_uvw_antpos_check, fix_old_proj, fix_use_ant_pos, ): """ Read the data-size arrays (data, flags, nsamples) from a file. This is an internal function to read just the visibility, flag, and nsample data of the UVH5 file. This is separated from full read so that header/metadata and data can be read independently. See the documentation of `read_uvh5` for a full description of most of the descriptions of parameters. Below we only include a description of args unique to this function. Parameters ---------- dgrp : h5py datagroup The HDF5 datagroup containing the datasets. Should be "/Data" for UVH5 files conforming to spec. Returns ------- None Raises ------ ValueError This is raised if the data array read from the file is not a complex datatype (np.complex64 or np.complex128). """ # figure out what data to read in blt_inds, freq_inds, pol_inds, history_update_string = self._select_preprocess( antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, ) # figure out which axis is the most selective if blt_inds is not None: blt_frac = len(blt_inds) / float(self.Nblts) else: blt_frac = 1 if freq_inds is not None: freq_frac = len(freq_inds) / float(self.Nfreqs) else: freq_frac = 1 if pol_inds is not None: pol_frac = len(pol_inds) / float(self.Npols) else: pol_frac = 1 min_frac = np.min([blt_frac, freq_frac, pol_frac]) arr_shape_msg = ( "The size of arrays in this file are not internally consistent, " "which should not happen. Please file an issue in our GitHub issue " "log so that we can fix it." ) if dgrp["visdata"].ndim == 3: assert self.freq_array.ndim == 1, arr_shape_msg assert self.channel_width.size == self.freq_array.size, arr_shape_msg self._set_future_array_shapes() # get the fundamental datatype of the visdata; if integers, we need to # cast to floats visdata_dtype = dgrp["visdata"].dtype if visdata_dtype not in ("complex64", "complex128"): _check_uvh5_dtype(visdata_dtype) if data_array_dtype not in (np.complex64, np.complex128): raise ValueError( "data_array_dtype must be np.complex64 or np.complex128" ) custom_dtype = True else: custom_dtype = False if min_frac == 1: # no select, read in all the data inds = (np.s_[:], np.s_[:], np.s_[:]) if custom_dtype: self.data_array = _read_complex_astype( dgrp["visdata"], inds, data_array_dtype ) else: self.data_array = uvutils._index_dset(dgrp["visdata"], inds) self.flag_array = uvutils._index_dset(dgrp["flags"], inds) self.nsample_array = uvutils._index_dset(dgrp["nsamples"], inds) else: # do select operations on everything except data_array, flag_array # and nsample_array self._select_metadata( blt_inds, freq_inds, pol_inds, history_update_string, keep_all_metadata ) # determine which axes can be sliced, rather than fancy indexed # max_nslice_frac of 0.1 yields slice speedup over fancy index for HERA data # See pyuvdata PR #805 if blt_inds is not None: blt_slices, blt_sliceable = uvutils._convert_to_slices( blt_inds, max_nslice_frac=0.1 ) else: blt_inds, blt_slices = np.s_[:], np.s_[:] blt_sliceable = True if freq_inds is not None: freq_slices, freq_sliceable = uvutils._convert_to_slices( freq_inds, max_nslice_frac=0.1 ) else: freq_inds, freq_slices = np.s_[:], np.s_[:] freq_sliceable = True if pol_inds is not None: pol_slices, pol_sliceable = uvutils._convert_to_slices( pol_inds, max_nslice_frac=0.5 ) else: pol_inds, pol_slices = np.s_[:], np.s_[:] pol_sliceable = True # open references to datasets visdata_dset = dgrp["visdata"] flags_dset = dgrp["flags"] nsamples_dset = dgrp["nsamples"] # check that multidim_index is appropriate if multidim_index: # if more than one dim is not sliceable, then not appropriate if sum([blt_sliceable, freq_sliceable, pol_sliceable]) < 2: multidim_index = False # just read in the right portions of the data and flag arrays if blt_frac == min_frac: # construct inds list given simultaneous sliceability inds = [blt_inds, np.s_[:], np.s_[:]] if blt_sliceable: inds[0] = blt_slices if multidim_index: if freq_sliceable: inds[1] = freq_slices else: inds[1] = freq_inds if multidim_index: if pol_sliceable: inds[2] = pol_slices else: inds[2] = pol_inds inds = tuple(inds) # index datasets if custom_dtype: visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype) else: visdata = uvutils._index_dset(visdata_dset, inds) flags = uvutils._index_dset(flags_dset, inds) nsamples = uvutils._index_dset(nsamples_dset, inds) # down select on other dimensions if necessary # use indices not slices here: generally not the bottleneck if not multidim_index and freq_frac < 1: if self.future_array_shapes: visdata = visdata[:, freq_inds, :] flags = flags[:, freq_inds, :] nsamples = nsamples[:, freq_inds, :] else: visdata = visdata[:, :, freq_inds, :] flags = flags[:, :, freq_inds, :] nsamples = nsamples[:, :, freq_inds, :] if not multidim_index and pol_frac < 1: if self.future_array_shapes: visdata = visdata[:, :, pol_inds] flags = flags[:, :, pol_inds] nsamples = nsamples[:, :, pol_inds] else: visdata = visdata[:, :, :, pol_inds] flags = flags[:, :, :, pol_inds] nsamples = nsamples[:, :, :, pol_inds] elif freq_frac == min_frac: # construct inds list given simultaneous sliceability inds = [np.s_[:], freq_inds, np.s_[:]] if freq_sliceable: inds[1] = freq_slices if multidim_index: if blt_sliceable: inds[0] = blt_slices else: inds[0] = blt_inds if multidim_index: if pol_sliceable: inds[2] = pol_slices else: inds[2] = pol_inds inds = tuple(inds) # index datasets if custom_dtype: visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype) else: visdata = uvutils._index_dset(visdata_dset, inds) flags = uvutils._index_dset(flags_dset, inds) nsamples = uvutils._index_dset(nsamples_dset, inds) # down select on other dimensions if necessary # use indices not slices here: generally not the bottleneck if not multidim_index and blt_frac < 1: visdata = visdata[blt_inds] flags = flags[blt_inds] nsamples = nsamples[blt_inds] if not multidim_index and pol_frac < 1: if self.future_array_shapes: visdata = visdata[:, :, pol_inds] flags = flags[:, :, pol_inds] nsamples = nsamples[:, :, pol_inds] else: visdata = visdata[:, :, :, pol_inds] flags = flags[:, :, :, pol_inds] nsamples = nsamples[:, :, :, pol_inds] else: # construct inds list given simultaneous sliceability inds = [np.s_[:], np.s_[:], pol_inds] if pol_sliceable: inds[2] = pol_slices if multidim_index: if blt_sliceable: inds[0] = blt_slices else: inds[0] = blt_inds if multidim_index: if freq_sliceable: inds[1] = freq_slices else: inds[1] = freq_inds inds = tuple(inds) # index datasets if custom_dtype: visdata = _read_complex_astype(visdata_dset, inds, data_array_dtype) else: visdata = uvutils._index_dset(visdata_dset, inds) flags = uvutils._index_dset(flags_dset, inds) nsamples = uvutils._index_dset(nsamples_dset, inds) # down select on other dimensions if necessary # use indices not slices here: generally not the bottleneck if not multidim_index and blt_frac < 1: visdata = visdata[blt_inds] flags = flags[blt_inds] nsamples = nsamples[blt_inds] if not multidim_index and freq_frac < 1: if self.future_array_shapes: visdata = visdata[:, freq_inds, :] flags = flags[:, freq_inds, :] nsamples = nsamples[:, freq_inds, :] else: visdata = visdata[:, :, freq_inds, :] flags = flags[:, :, freq_inds, :] nsamples = nsamples[:, :, freq_inds, :] # save arrays in object self.data_array = visdata self.flag_array = flags self.nsample_array = nsamples if self.data_array.ndim == 3: assert self.freq_array.ndim == 1, arr_shape_msg assert self.channel_width.size == self.freq_array.size, arr_shape_msg self._set_future_array_shapes() # Finally, backfill the apparent coords if they aren't in the original datafile if self.phase_type == "phased" and ( (self.phase_center_app_ra is None) or (self.phase_center_app_dec is None) or (self.phase_center_frame_pa is None) ): self._set_app_coords_helper() # Default behavior for UVH5 is to fix phasing if the problem is detected, # since the absence of the app coord attributes is the most clear indicator # of the old phasing algorithm being used. Double-check the mutli-phase-ctr # attribute just to be extra safe. if (not self.multi_phase_center) and ( ((fix_old_proj) or (fix_old_proj is None)) ): self.fix_phase(use_ant_pos=fix_use_ant_pos) else: warnings.warn( "This data appears to have been phased-up using the old `phase` " "method, which is incompatible with the current sent of methods. " "Please run the `fix_phase` method (or set fix_old_proj=True when " "loading the dataset) to address this issue." ) # check if object has all required UVParameters set if run_check: self.check( check_extra=check_extra, run_check_acceptability=run_check_acceptability, strict_uvw_antpos_check=strict_uvw_antpos_check, allow_flip_conj=True, ) return
[docs] def read_uvh5( self, filename, antenna_nums=None, antenna_names=None, ant_str=None, bls=None, frequencies=None, freq_chans=None, times=None, time_range=None, 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 in data from a UVH5 file. Parameters ---------- filename : str The UVH5 file to read from. antenna_nums : array_like of int, optional The antennas numbers to include when reading data into the object (antenna positions and names for the removed antennas will be retained unless `keep_all_metadata` is False). This cannot be provided if `antenna_names` is also provided. Ignored if read_data is False. antenna_names : array_like of str, optional The antennas names to include when reading data into the object (antenna positions and names for the removed antennas will be retained unless `keep_all_metadata` is False). This cannot be provided if `antenna_nums` is also provided. Ignored if read_data is False. bls : list of tuple, optional A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines to include when reading data into the object. For length-2 tuples, the ordering of the numbers within the tuple does not matter. For length-3 tuples, the polarization string is in the order of the two antennas. If length-3 tuples are provided, `polarizations` must be None. Ignored if read_data is False. ant_str : str, optional A string containing information about what antenna numbers and polarizations to include when reading data into the object. Can be 'auto', 'cross', 'all', or combinations of antenna numbers and polarizations (e.g. '1', '1_2', '1x_2y'). See tutorial for more examples of valid strings and the behavior of different forms for ant_str. If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will be kept for both baselines (1, 2) and (2, 3) to return a valid pyuvdata object. An ant_str cannot be passed in addition to any of `antenna_nums`, `antenna_names`, `bls` args or the `polarizations` parameters, if it is a ValueError will be raised. Ignored if read_data is False. frequencies : array_like of float, optional The frequencies to include when reading data into the object, each value passed here should exist in the freq_array. Ignored if read_data is False. freq_chans : array_like of int, optional The frequency channel numbers to include when reading data into the object. Ignored if read_data is False. times : array_like of float, optional The times to include when reading data into the object, each value passed here should exist in the time_array. Cannot be used with `time_range`. time_range : array_like of float, optional The time range in Julian Date to keep in the object, must be length 2. Some of the times in the object should fall between the first and last elements. Cannot be used with `times`. 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, 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. 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 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. Returns ------- None Raises ------ IOError If filename doesn't exist. ValueError If the data_array_dtype is not a complex dtype. If incompatible select keywords are set (e.g. `ant_str` with other antenna selectors, `times` and `time_range`) or select keywords exclude all data or if keywords are set to the wrong type. """ if not os.path.exists(filename): raise IOError(filename + " not found") # update filename attribute basename = os.path.basename(filename) self.filename = [basename] self._filename.form = (1,) # open hdf5 file for reading with h5py.File(filename, "r") as f: # extract header information header = f["/Header"] self._read_header( header, filename, run_check_acceptability=run_check_acceptability, background_lsts=background_lsts, ) if not read_data: # don't read in the data. This means the object is incomplete, # but that may not matter for many purposes. # run a check if desired before returning if run_check: self.check( check_extra=check_extra, run_check_acceptability=run_check_acceptability, strict_uvw_antpos_check=strict_uvw_antpos_check, ) return # Now read in the data dgrp = f["/Data"] self._get_data( dgrp, antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, data_array_dtype, keep_all_metadata, multidim_index, run_check, check_extra, run_check_acceptability, strict_uvw_antpos_check, fix_old_proj, fix_use_ant_pos, ) # For now, always use current shapes when data is read in, even if the file # has the future shapes. if self.future_array_shapes: self.use_current_array_shapes() return
def _write_header(self, header): """ Write data to the header datagroup of a UVH5 file. Parameters ---------- header : h5py datagroup The datagroup to write the header information to. For a UVH5 file conforming to the spec, it should be "/Header" Returns ------- None """ # write out UVH5 version information if self.multi_phase_center: # this is Version 1.1! header["version"] = np.string_("1.1") elif self.future_array_shapes: # this is Version 1.0 header["version"] = np.string_("1.0") else: header["version"] = np.string_("0.1") # write out telescope and source information header["latitude"] = self.telescope_location_lat_lon_alt_degrees[0] header["longitude"] = self.telescope_location_lat_lon_alt_degrees[1] header["altitude"] = self.telescope_location_lat_lon_alt_degrees[2] header["telescope_name"] = np.string_(self.telescope_name) header["instrument"] = np.string_(self.instrument) header["object_name"] = np.string_(self.object_name) # write out required UVParameters header["Nants_data"] = self.Nants_data header["Nants_telescope"] = self.Nants_telescope header["Nbls"] = self.Nbls header["Nblts"] = self.Nblts header["Nfreqs"] = self.Nfreqs header["Npols"] = self.Npols header["Nspws"] = self.Nspws header["Ntimes"] = self.Ntimes header["antenna_numbers"] = self.antenna_numbers header["uvw_array"] = self.uvw_array header["vis_units"] = np.string_(self.vis_units) header["channel_width"] = self.channel_width header["time_array"] = self.time_array header["freq_array"] = self.freq_array header["integration_time"] = self.integration_time header["lst_array"] = self.lst_array header["polarization_array"] = self.polarization_array header["spw_array"] = self.spw_array header["ant_1_array"] = self.ant_1_array header["ant_2_array"] = self.ant_2_array header["antenna_positions"] = self.antenna_positions header["flex_spw"] = self.flex_spw header["multi_phase_center"] = self.multi_phase_center # handle antenna_names; works for lists or arrays header["antenna_names"] = np.asarray(self.antenna_names, dtype="bytes") # write out phasing information header["phase_type"] = np.string_(self.phase_type) if self.phase_center_ra is not None: header["phase_center_ra"] = self.phase_center_ra if self.phase_center_app_ra is not None: header["phase_center_app_ra"] = self.phase_center_app_ra if self.phase_center_dec is not None: header["phase_center_dec"] = self.phase_center_dec if self.phase_center_app_dec is not None: header["phase_center_app_dec"] = self.phase_center_app_dec if self.phase_center_frame_pa is not None: header["phase_center_frame_pa"] = self.phase_center_frame_pa if self.phase_center_epoch is not None: header["phase_center_epoch"] = self.phase_center_epoch if self.phase_center_frame is not None: header["phase_center_frame"] = np.string_(self.phase_center_frame) # write out optional parameters if self.dut1 is not None: header["dut1"] = self.dut1 if self.earth_omega is not None: header["earth_omega"] = self.earth_omega if self.gst0 is not None: header["gst0"] = self.gst0 if self.rdate is not None: header["rdate"] = np.string_(self.rdate) if self.timesys is not None: header["timesys"] = np.string_(self.timesys) if self.x_orientation is not None: header["x_orientation"] = np.string_(self.x_orientation) if self.blt_order is not None: header["blt_order"] = np.string_(", ".join(self.blt_order)) if self.antenna_diameters is not None: header["antenna_diameters"] = self.antenna_diameters if self.uvplane_reference_time is not None: header["uvplane_reference_time"] = self.uvplane_reference_time if self.eq_coeffs is not None: header["eq_coeffs"] = self.eq_coeffs if self.eq_coeffs_convention is not None: header["eq_coeffs_convention"] = np.string_(self.eq_coeffs_convention) if self.flex_spw_id_array is not None: header["flex_spw_id_array"] = self.flex_spw_id_array if self.phase_center_id_array is not None: header["phase_center_id_array"] = self.phase_center_id_array if self.Nphase is not None: header["Nphase"] = self.Nphase # Write out the catalog, if available if self.phase_center_catalog: phase_dict = header.create_group("phase_center_catalog") for k in self.phase_center_catalog.keys(): # Dictionary entries can be written out as JSON-formatted strings. phase_dict[k] = np.string_(json.dumps(self.phase_center_catalog[k])) # write out extra keywords if it exists and has elements if self.extra_keywords: extra_keywords = header.create_group("extra_keywords") for k in self.extra_keywords.keys(): if isinstance(self.extra_keywords[k], str): extra_keywords[k] = np.string_(self.extra_keywords[k]) elif self.extra_keywords[k] is None: # save as empty/null dataset extra_keywords[k] = h5py.Empty("f") else: extra_keywords[k] = self.extra_keywords[k] # write out history header["history"] = np.string_(self.history) return
[docs] def write_uvh5( self, filename, clobber=False, chunks=True, data_compression=None, flags_compression="lzf", nsample_compression="lzf", data_write_dtype=None, add_to_history=None, run_check=True, check_extra=True, run_check_acceptability=True, strict_uvw_antpos_check=False, ): """ Write an 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 (no filter/compression). Dataset must be chunked. flags_compression : str HDF5 filter to apply when writing the flags_array. Default is the LZF filter. Dataset must be chunked. nsample_compression : str HDF5 filter to apply when writing the nsample_array. Default is the LZF filter. Dataset must be chunked. data_write_dtype : numpy dtype The datatype of output visibility data. If 'None', then the same datatype as data_array will be used. The user may specify 'c8' for single-precision floats or 'c16' for double-presicion. Otherwise, a numpy dtype object must be specified with an 'r' field and an 'i' field for real and imaginary parts, respectively. See uvh5.py for an example of defining such a datatype. 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. Returns ------- None Raises ------ IOError If the file located at `filename` already exists and clobber=False, an IOError is raised. Notes ----- The HDF5 library allows for the application of "filters" when writing data, which can provide moderate to significant levels of compression for the datasets in question. Testing has shown that for some typical cases of UVData objects (empty/sparse flag_array objects, and/or uniform nsample_arrays), the built-in LZF filter provides significant compression for minimal computational overhead. Note that for typical HERA data files written after mid-2020, the bitshuffle filter was applied to the data_array. Because of the lack of portability, it is not included as an option here; in the future, it may be added. Note that as long as bitshuffle is installed on the system in a way that h5py can find it, no action needs to be taken to _read_ a data_array encoded with bitshuffle (or an error will be raised). """ if run_check: self.check( check_extra=check_extra, run_check_acceptability=run_check_acceptability, strict_uvw_antpos_check=strict_uvw_antpos_check, ) if os.path.exists(filename): if clobber: print("File exists; clobbering") else: raise IOError("File exists; skipping") revert_fas = False if self.multi_phase_center: # We force using future array shapes here multi_phase_center is v1.1, but # future_array_shapes is v1.0 (but a UVData object can have the # multi_phase_center enabled, but not use future_array_shapes). We capture # the current state so that it can be reverted later if needed. if not self.future_array_shapes: revert_fas = True self.use_future_array_shapes() # open file for writing with h5py.File(filename, "w") as f: # write header header = f.create_group("Header") self._write_header(header) # write out data, flags, and nsample arrays dgrp = f.create_group("Data") if data_write_dtype is None: if self.data_array.dtype == "complex64": data_write_dtype = "c8" else: data_write_dtype = "c16" if data_write_dtype not in ("c8", "c16"): _check_uvh5_dtype(data_write_dtype) visdata = dgrp.create_dataset( "visdata", self.data_array.shape, chunks=chunks, compression=data_compression, dtype=data_write_dtype, ) indices = (np.s_[:], np.s_[:], np.s_[:]) _write_complex_astype(self.data_array, visdata, indices) else: visdata = dgrp.create_dataset( "visdata", chunks=chunks, data=self.data_array, compression=data_compression, dtype=data_write_dtype, ) dgrp.create_dataset( "flags", chunks=chunks, data=self.flag_array, compression=flags_compression, ) dgrp.create_dataset( "nsamples", chunks=chunks, data=self.nsample_array.astype(np.float32), compression=nsample_compression, ) if revert_fas: self.use_current_array_shapes() return
[docs] 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 to be written to in parts. 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 (no filter/compression). Dataset must be chunked. flags_compression : str HDF5 filter to apply when writing the flags_array. Default is the LZF filter. Dataset must be chunked. nsample_compression : str HDF5 filter to apply when writing the nsample_array. Default is the LZF filter. Dataset must be chunked. data_write_dtype : str or numpy dtype The datatype of output visibility data. If 'None', then double- precision floats will be used. The user may specify 'c8' for single-precision floats or 'c16' for double-presicion. Otherwise, a numpy dtype object must be specified with an 'r' field and an 'i' field for real and imaginary parts, respectively. See uvh5.py for an example of defining such a datatype. Returns ------- None Raises ------ IOError If the file located at `filename` already exists and clobber=False, an IOError is raised. Notes ----- When partially writing out data, this function should be called first to initialize the file on disk. The data is then actually written by calling the write_uvh5_part method, with the same filename as the one specified in this function. See the tutorial for a worked example. The HDF5 library allows for the application of "filters" when writing data, which can provide moderate to significant levels of compression for the datasets in question. Testing has shown that for some typical cases of UVData objects (empty/sparse flag_array objects, and/or uniform nsample_arrays), the built-in LZF filter provides significant compression for minimal computational overhead. Note that for typical HERA data files written after mid-2018, the bitshuffle filter was applied to the data_array. Because of the lack of portability, it is not included as an option here; in the future, it may be added. Note that as long as bitshuffle is installed on the system in a way that h5py can find it, no action needs to be taken to _read_ a data_array encoded with bitshuffle (or an error will be raised). """ if os.path.exists(filename): if clobber: print("File exists; clobbering") else: raise IOError("File exists; skipping") # write header and empty arrays to file with h5py.File(filename, "w") as f: # write header header = f.create_group("Header") self._write_header(header) # initialize the data groups on disk if self.future_array_shapes: data_size = (self.Nblts, self.Nfreqs, self.Npols) else: data_size = (self.Nblts, 1, self.Nfreqs, self.Npols) dgrp = f.create_group("Data") if data_write_dtype is None: # we don't know what kind of data we'll get--default to double-precision data_write_dtype = "c16" if data_write_dtype not in ("c8", "c16"): # make sure the data type is correct _check_uvh5_dtype(data_write_dtype) dgrp.create_dataset( "visdata", data_size, chunks=chunks, dtype=data_write_dtype, compression=data_compression, ) dgrp.create_dataset( "flags", data_size, chunks=chunks, dtype="b1", compression=flags_compression, ) dgrp.create_dataset( "nsamples", data_size, chunks=chunks, dtype="f4", compression=nsample_compression, ) return
def _check_header( self, filename, run_check_acceptability=True, background_lsts=True ): """ Check that the metadata in a file header matches the object's metadata. Parameters ---------- header : h5py datagroup A reference to an h5py data group that contains the header information. For UVH5 files conforming to the spec, this should be "/Header". run_check_acceptability : bool Option to check acceptable range of the values of parameters after reading in the file. background_lsts : bool When set to True, the lst_array is calculated in a background thread. Returns ------- None Notes ----- This function creates a new UVData object an reads in the header information saved on disk to compare with the object in memory. Note that this adds some small memory overhead, but this amount is typically much smaller than the size of the data. """ uvd_file = UVH5() with h5py.File(filename, "r") as f: header = f["/Header"] uvd_file._read_header( header, filename, run_check_acceptability=run_check_acceptability, background_lsts=background_lsts, ) # temporarily remove data, flag, and nsample arrays, so we only check metadata if self.data_array is not None: data_array = self.data_array self.data_array = None replace_data = True else: replace_data = False if self.flag_array is not None: flag_array = self.flag_array self.flag_array = None replace_flags = True else: replace_flags = False if self.nsample_array is not None: nsample_array = self.nsample_array self.nsample_array = None replace_nsamples = True else: replace_nsamples = False # also ignore filename attribute uvd_file.filename = self.filename uvd_file._filename.form = self._filename.form if self != uvd_file: raise AssertionError( "The object metadata in memory and metadata on disk are different" ) else: # clean up after ourselves if replace_data: self.data_array = data_array if replace_flags: self.flag_array = flag_array if replace_nsamples: self.nsample_array = nsample_array del uvd_file return
[docs] def write_uvh5_part( self, filename, data_array, flag_array, nsample_array, check_header=True, antenna_nums=None, antenna_names=None, ant_str=None, bls=None, frequencies=None, freq_chans=None, times=None, time_range=None, lsts=None, lst_range=None, polarizations=None, blt_inds=None, run_check_acceptability=True, add_to_history=None, ): """ Write out a part of a UVH5 file that has been previously initialized. Parameters ---------- filename : str The file on disk to write data to. It must already exist, and is assumed to have been initialized with initialize_uvh5_file. data_array : array of float The data to write to disk. A check is done to ensure that the dimensions of the data passed in conform to the ones specified by the "selection" arguments. flag_array : array of bool The flags array to write to disk. A check is done to ensure that the dimensions of the data passed in conform to the ones specified by the "selection" arguments. nsample_array : array of float The nsample array to write to disk. A check is done to ensure that the dimensions fo the data passed in conform to the ones specified by the "selection" arguments. check_header : bool Option to check that the metadata present in the header on disk matches that in the object. run_check_acceptability : bool If check_header, additional option to check acceptable range of the values of parameters after reading in the file. antenna_nums : array_like of int, optional The antennas numbers to include when writing data into the object (antenna positions and names for the excluded antennas will be retained). This cannot be provided if antenna_names is also provided. antenna_names : array_like of str, optional The antennas names to include when writing data into the object (antenna positions and names for the excluded antennas will be retained). This cannot be provided if antenna_nums is also provided. bls : list of tuples, optional A list of antenna number tuples (e.g. [(0, 1), (3, 2)]) or a list of baseline 3-tuples (e.g. [(0, 1, 'xx'), (2, 3, 'yy')]) specifying baselines to write to the file. For length-2 tuples, the ordering of the numbers within the tuple does not matter. For length-3 tuples, the polarization string is in the order of the two antennas. If length-3 tuples are provided, the polarizations argument below must be None. ant_str : str, optional A string containing information about what antenna numbers and polarizations to include when writing data into the object. Can be 'auto', 'cross', 'all', or combinations of antenna numbers and polarizations (e.g. '1', '1_2', '1x_2y'). See tutorial for more examples of valid strings and the behavior of different forms for ant_str. If '1x_2y,2y_3y' is passed, both polarizations 'xy' and 'yy' will be written for both baselines (1, 2) and (2, 3) to reflect a valid pyuvdata object. An ant_str cannot be passed in addition to any of the above antenna args or the polarizations arg. frequencies : array_like of float, optional The frequencies to include when writing data to the file. freq_chans : array_like of int, optional The frequency channel numbers to include when writing data to the file. times : array_like of float, optional The times in Julian Day to include when writing data to the file. time_range : array_like of float, optional The time range in Julian Date to include when writing data to the file, must be length 2. Some of the times in the object should fall between the first and last elements. Cannot be used with `times`. 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 to include when writing data to the file. blt_inds : array_like of int, optional The baseline-time indices to include when writing data to the file. This is not commonly used. add_to_history : str String to append to history before write out. Default is no appending. Returns ------- None Raises ------ AssertionError An AsserionError is raised if: (1) the location specified by `filename` does not exist; (2) the data_array, flag_array, and nsample_array do not all have the same shape; (3) the shape of the data arrays do not correspond to the sizes specified by the properties to write out. Notes ----- When partially writing out data, this function should be called after calling initialize_uvh5_file. The same filename is passed in, with an optional check to ensure that the object's metadata in-memory matches the header on-disk. See the tutorial for a worked example. """ # check that the file already exists if not os.path.exists(filename): raise AssertionError( "{0} does not exists; please first initialize it with " "initialize_uvh5_file".format(filename) ) if check_header: self._check_header( filename, run_check_acceptability=run_check_acceptability ) # figure out which "full file" indices to write data to blt_inds, freq_inds, pol_inds, _ = self._select_preprocess( antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, ) # make sure that the dimensions of the data to write are correct if data_array.shape != flag_array.shape: raise AssertionError("data_array and flag_array must have the same shape") if data_array.shape != nsample_array.shape: raise AssertionError( "data_array and nsample_array must have the same shape" ) # check what part of each dimension to grab # we can use numpy slice objects to index the h5py indices if blt_inds is not None: Nblts = len(blt_inds) # test if blts are regularly spaced if len(set(np.ediff1d(blt_inds))) <= 1: blt_reg_spaced = True blt_start = blt_inds[0] blt_end = blt_inds[-1] + 1 if len(blt_inds) == 1: d_blt = 1 else: d_blt = blt_inds[1] - blt_inds[0] blt_inds = np.s_[blt_start:blt_end:d_blt] else: blt_reg_spaced = False else: Nblts = self.Nblts blt_reg_spaced = True blt_inds = np.s_[:] if freq_inds is not None: Nfreqs = len(freq_inds) # test if frequencies are regularly spaced if len(set(np.ediff1d(freq_inds))) <= 1: freq_reg_spaced = True freq_start = freq_inds[0] freq_end = freq_inds[-1] + 1 if len(freq_inds) == 1: d_freq = 1 else: d_freq = freq_inds[1] - freq_inds[0] freq_inds = np.s_[freq_start:freq_end:d_freq] else: freq_reg_spaced = False else: Nfreqs = self.Nfreqs freq_reg_spaced = True freq_inds = np.s_[:] if pol_inds is not None: Npols = len(pol_inds) # test if pols are regularly spaced if len(set(np.ediff1d(pol_inds))) <= 1: pol_reg_spaced = True pol_start = pol_inds[0] pol_end = pol_inds[-1] + 1 if len(pol_inds) == 1: d_pol = 1 else: d_pol = pol_inds[1] - pol_inds[0] pol_inds = np.s_[pol_start:pol_end:d_pol] else: pol_reg_spaced = False else: Npols = self.Npols pol_reg_spaced = True pol_inds = np.s_[:] # check for proper size of input arrays if self.future_array_shapes: proper_shape = (Nblts, Nfreqs, Npols) else: proper_shape = (Nblts, 1, Nfreqs, Npols) if data_array.shape != proper_shape: raise AssertionError( "data_array has shape {0}; was expecting {1}".format( data_array.shape, proper_shape ) ) # actually write the data with h5py.File(filename, "r+") as f: dgrp = f["/Data"] visdata_dset = dgrp["visdata"] flags_dset = dgrp["flags"] nsamples_dset = dgrp["nsamples"] visdata_dtype = visdata_dset.dtype if visdata_dtype not in ("complex64", "complex128"): custom_dtype = True else: custom_dtype = False # check if we can do fancy indexing # as long as at least 2 out of 3 axes can be written as slices, # we can be fancy n_reg_spaced = np.count_nonzero( [blt_reg_spaced, freq_reg_spaced, pol_reg_spaced] ) if n_reg_spaced >= 2: if custom_dtype: indices = (blt_inds, freq_inds, pol_inds) _write_complex_astype(data_array, visdata_dset, indices) else: if self.future_array_shapes: visdata_dset[blt_inds, freq_inds, pol_inds] = data_array else: visdata_dset[blt_inds, :, freq_inds, pol_inds] = data_array if self.future_array_shapes: flags_dset[blt_inds, freq_inds, pol_inds] = flag_array nsamples_dset[blt_inds, freq_inds, pol_inds] = nsample_array else: flags_dset[blt_inds, :, freq_inds, pol_inds] = flag_array nsamples_dset[blt_inds, :, freq_inds, pol_inds] = nsample_array elif n_reg_spaced == 1: # figure out which axis is regularly spaced if blt_reg_spaced: for ifreq, freq_idx in enumerate(freq_inds): for ipol, pol_idx in enumerate(pol_inds): if custom_dtype: indices = (blt_inds, freq_idx, pol_idx) if self.future_array_shapes: _write_complex_astype( data_array[:, ifreq, ipol], visdata_dset, indices, ) else: _write_complex_astype( data_array[:, :, ifreq, ipol], visdata_dset, indices, ) else: if self.future_array_shapes: visdata_dset[ blt_inds, freq_idx, pol_idx ] = data_array[:, ifreq, ipol] else: visdata_dset[ blt_inds, :, freq_idx, pol_idx ] = data_array[:, :, ifreq, ipol] if self.future_array_shapes: flags_dset[blt_inds, freq_idx, pol_idx] = flag_array[ :, ifreq, ipol ] nsamples_dset[ blt_inds, freq_idx, pol_idx ] = nsample_array[:, ifreq, ipol] else: flags_dset[blt_inds, :, freq_idx, pol_idx] = flag_array[ :, :, ifreq, ipol ] nsamples_dset[ blt_inds, :, freq_idx, pol_idx ] = nsample_array[:, :, ifreq, ipol] elif freq_reg_spaced: for iblt, blt_idx in enumerate(blt_inds): for ipol, pol_idx in enumerate(pol_inds): if custom_dtype: indices = (blt_idx, freq_inds, pol_idx) if self.future_array_shapes: _write_complex_astype( data_array[iblt, :, ipol], visdata_dset, indices ) else: _write_complex_astype( data_array[iblt, :, :, ipol], visdata_dset, indices, ) else: if self.future_array_shapes: visdata_dset[ blt_idx, freq_inds, pol_idx ] = data_array[iblt, :, ipol] else: visdata_dset[ blt_idx, :, freq_inds, pol_idx ] = data_array[iblt, :, :, ipol] if self.future_array_shapes: flags_dset[blt_idx, freq_inds, pol_idx] = flag_array[ iblt, :, ipol ] nsamples_dset[ blt_idx, freq_inds, pol_idx ] = nsample_array[iblt, :, ipol] else: flags_dset[blt_idx, :, freq_inds, pol_idx] = flag_array[ iblt, :, :, ipol ] nsamples_dset[ blt_idx, :, freq_inds, pol_idx ] = nsample_array[iblt, :, :, ipol] else: # pol_reg_spaced for iblt, blt_idx in enumerate(blt_inds): for ifreq, freq_idx in enumerate(freq_inds): if custom_dtype: indices = (blt_idx, freq_idx, pol_inds) if self.future_array_shapes: _write_complex_astype( data_array[iblt, ifreq, :], visdata_dset, indices, ) else: _write_complex_astype( data_array[iblt, :, ifreq, :], visdata_dset, indices, ) else: if self.future_array_shapes: visdata_dset[ blt_idx, freq_idx, pol_inds ] = data_array[iblt, ifreq, :] else: visdata_dset[ blt_idx, :, freq_idx, pol_inds ] = data_array[iblt, :, ifreq, :] if self.future_array_shapes: flags_dset[blt_idx, freq_idx, pol_inds] = flag_array[ iblt, ifreq, : ] nsamples_dset[ blt_idx, freq_idx, pol_inds ] = nsample_array[iblt, ifreq, :] else: flags_dset[blt_idx, :, freq_idx, pol_inds] = flag_array[ iblt, :, ifreq, : ] nsamples_dset[ blt_idx, :, freq_idx, pol_inds ] = nsample_array[iblt, :, ifreq, :] else: # all axes irregularly spaced # perform a triple loop -- probably very slow! for iblt, blt_idx in enumerate(blt_inds): for ifreq, freq_idx in enumerate(freq_inds): for ipol, pol_idx in enumerate(pol_inds): if custom_dtype: indices = (blt_idx, freq_idx, pol_idx) if self.future_array_shapes: _write_complex_astype( data_array[iblt, ifreq, ipol], visdata_dset, indices, ) else: _write_complex_astype( data_array[iblt, :, ifreq, ipol], visdata_dset, indices, ) else: if self.future_array_shapes: visdata_dset[ blt_idx, freq_idx, pol_idx ] = data_array[iblt, ifreq, ipol] else: visdata_dset[ blt_idx, :, freq_idx, pol_idx ] = data_array[iblt, :, ifreq, ipol] if self.future_array_shapes: flags_dset[blt_idx, freq_idx, pol_idx] = flag_array[ iblt, ifreq, ipol ] nsamples_dset[ blt_idx, freq_idx, pol_idx ] = nsample_array[iblt, ifreq, ipol] else: flags_dset[blt_idx, :, freq_idx, pol_idx] = flag_array[ iblt, :, ifreq, ipol ] nsamples_dset[ blt_idx, :, freq_idx, pol_idx ] = nsample_array[iblt, :, ifreq, ipol] # append to history if desired if add_to_history is not None: history = np.string_(self.history) + np.string_(add_to_history) if "history" in f["Header"]: # erase dataset first b/c it has fixed-length string datatype del f["Header"]["history"] f["Header"]["history"] = np.string_(history) return