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."""
from __future__ import annotations

import json
import os
import warnings
from functools import cached_property
from pathlib import Path
from typing import Any, Literal

import h5py
import numpy as np
from docstring_parser import DocstringStyle

from .. import utils as uvutils
from ..docstrings import copy_replace_short_description
from .uvdata import UVData, _future_array_shapes_warning

__all__ = ["UVH5", "FastUVH5Meta"]


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

hdf5plugin_present = True
try:
    import hdf5plugin  # noqa: F401
except ImportError as error:
    hdf5plugin_present = False
    hdf5plugin_error = error


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)
    # dset is indexed in native dtype, but is upcast upon assignment

    if dtype_out == np.complex64:
        compound_dtype = [("r", "f4"), ("i", "f4")]
    else:
        compound_dtype = [("r", "f8"), ("i", "f8")]

    output_array.view(compound_dtype)[:, :] = uvutils._index_dset(dset, 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

    if data.dtype == np.complex64:
        compound_dtype = [("r", "f4"), ("i", "f4")]
    else:
        compound_dtype = [("r", "f8"), ("i", "f8")]

    # 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
        dset[indices[0], indices[1], indices[2]] = data.view(compound_dtype).astype(
            dtype_out, copy=False
        )
    else:
        dset[indices[0], np.s_[:], indices[1], indices[2]] = data.view(
            compound_dtype
        ).astype(dtype_out, copy=False)

    return


def _get_compression(compression):
    """
    Get the HDF5 compression and compression options to use.

    Parameters
    ----------
    compression : str
        HDF5 compression specification or "bitshuffle".

    Returns
    -------
    compression_use : str
        HDF5 compression specification
    compression_opts : tuple
        HDF5 compression options
    """
    if compression == "bitshuffle":
        if not hdf5plugin_present:  # pragma: no cover
            raise ImportError(
                "The hdf5plugin package is not installed but is required to use "
                "bitshuffle compression."
            ) from hdf5plugin_error

        compression_use = 32008
        compression_opts = (0, 2)
    else:
        compression_use = compression
        compression_opts = None

    return compression_use, compression_opts


[docs]class FastUVH5Meta: """ A fast read-only interface to UVH5 file metadata that makes some assumptions. This class is just a really thin wrapper over a UVH5 file that makes it easier to read in parts of the metadata at a time. This makes it much faster to perform small tasks where simple metadata is required, rather than reading in the whole header. All metadata is available as attributes, through ``__getattr__`` magic. Thus, accessing eg. ``obj.freq_array`` will go and get the frequencies directly from the file, and store them in memory. However, some attributes are made faster than the default, by assumptions on the data shape -- in particular, times and baselines. Anything that is read in is stored in memory so the second access is much faster. However, the memory can be released simply by deleting the attribute (it can be accessed again, and the data will be re-read). Parameters ---------- filename : str or Path The filename to read from. blt_order : tuple of str or "determine", optional The order of the baseline-time axis. This can be determined, or read directly from file, however since it has been optional in the past, many existing files do not contain it in the metadata. Some reading operations are significantly faster if this is known, so providing it here can provide a speedup. Default is to try and read it from file, and if not there, just leave it as None. Set to "determine" to auto-detect the blt_order from the metadata (takes extra time to do so). blts_are_rectangular : bool, optional Whether the baseline-time axis is rectangular. This can be read from metadata in new files, but many old files do not contain it. If not provided, the rectangularity will be determined from the data. This is a non-negligible operation, so if you know it, it can be provided here to speed up reading. time_axis_faster_than_bls : bool, optional If blts are rectangular, this variable specifies whether the time axis is the fastest-moving virtual axis. Various reading functions benefit from knowing this, so if it is known, it can be provided here to speed up reading. It will be determined from the data if not provided. recompute_nbls : bool, optional Whether to recompute the number of unique baselines from the data. Before v1.2 of the UVH5 spec, it was possible to have an incorrect number of baselines in the header without error, so this provides an opportunity to rectify it. Old HERA files (< March 2023) may have this issue, but in this case the correct number of baselines can be computed more quickly than by fully re=computing, and so we do this. astrometry_library : str Library used for calculating the LSTs. Allowed options are 'erfa' (which uses the pyERFA), 'novas' (which uses the python-novas library), and 'astropy' (which uses the astropy utilities). Default is erfa unless the telescope_location frame is MCMF (on the moon), in which case the default is astropy. Notes ----- To check if a particular attribute is available, use ``hasattr(obj, attr)``. Many attributes will not show up dynamically in an interpreter, because they are gotten dynamically from the file. """ _string_attrs = frozenset( { "history", "instrument", "object_name", "x_orientation", "telescope_name", "rdate", "timesys", "eq_coeffs_convention", "phase_type", "phase_center_frame", "version", } ) _defaults = {"x_orientation": None, "flex_spw": False} _int_attrs = frozenset( { "Nblts", "Ntimes", "Npols", "Nspws", "Nfreqs", "uvplane_reference_time", "Nphase", "Nants_data", "Nants_telescope", } ) _float_attrs = frozenset( { "dut1", "earth_omega", "gst0", "phase_center_ra", "phase_center_dec", "phase_center_epoch", } ) _bool_attrs = frozenset(("flex_spw",)) def __init__( self, path: str | Path | h5py.File | h5py.Group, blt_order: Literal["determine"] | tuple[str] | None = None, blts_are_rectangular: bool | None = None, time_axis_faster_than_bls: bool | None = None, recompute_nbls: bool | None = None, astrometry_library: str | None = None, ): self.__file = None if isinstance(path, h5py.File): self.path = Path(path.filename) self.__file = path self.__header = path["/Header"] self.__datagrp = path["/Data"] elif isinstance(path, h5py.Group): self.path = Path(path.file.filename) self.__file = path.file self.__header = path self.__datagrp = self.__file["/Data"] elif isinstance(path, (str, Path)): self.path = Path(path) self.__blts_are_rectangular = blts_are_rectangular self.__time_first = time_axis_faster_than_bls self.__blt_order = blt_order self._recompute_nbls = recompute_nbls self._astrometry_library = astrometry_library
[docs] def is_open(self) -> bool: """Whether the file is open.""" return bool(self.__file)
def __del__(self): """Close the file when the object is deleted.""" if self.__file: self.__file.close() def __getstate__(self): """Get the state of the object.""" return { k: v for k, v in self.__dict__.items() if k not in ( "_FastUVH5Meta__file", "_FastUVH5Meta__header", "_FastUVH5Meta__datagrp", "header", "datagrp", ) } def __setstate__(self, state): """Set the state of the object.""" self.__dict__.update(state) self.__file = None def __eq__(self, other): """Check equality of two FastUVH5Meta objects.""" if not isinstance(other, FastUVH5Meta): return False return ( self.path == other.path and self.__blts_are_rectangular == other.__blts_are_rectangular and ( self.__time_first == other.__time_first or self.__time_first is None or other.__time_first is None ) and self.__blt_order == other.__blt_order and self.Nbls == other.Nbls ) def __hash__(self): """Get a unique hash for the object.""" return hash(self.path)
[docs] def close(self): """Close the file.""" self.__header = None self.__datagrp = None try: del self.header # need to refresh these except AttributeError: pass try: del self.datagrp except AttributeError: pass if self.__file: self.__file.close() self.__file = None
[docs] def open(self): # noqa: A003 """Open the file.""" if not self.__file: self.__file = h5py.File(self.path, "r") self.__header = self.__file["/Header"] self.__datagrp = self.__file["/Data"]
@cached_property def header(self) -> h5py.Group: """Get the header group.""" if not self.__file: self.open() return self.__header @cached_property def datagrp(self) -> h5py.Group: """Get the header group.""" if not self.__file: self.open() return self.__datagrp
[docs] def get_transactional(self, item: str, cache: bool = True) -> Any: """Get an attribute from the metadata but close the file object afterwards. Using this method is safer than direct attribute access when dealing with many files. Parameters ---------- item The attribute to get. cache Whether to cache the attribute in the object so that the next access is faster. """ try: val = getattr(self, item) finally: self.close() if not cache: if item in self.__dict__: del self.__dict__[item] return val
def __getattr__(self, name: str) -> Any: """Get attribute directly from header group.""" try: x = self.header[name][()] if name in self._string_attrs: x = bytes(x).decode("utf8") elif name in self._int_attrs: x = int(x) elif name in self._float_attrs: x = float(x) self.__dict__[name] = x return x except KeyError: try: return self._defaults[name] except KeyError as e: raise AttributeError(f"{name} not found in {self.path}") from e @cached_property def Nbls(self) -> int: # noqa: N802 """The number of unique baselines.""" if self._recompute_nbls: if self.__blts_are_rectangular: return self.Nblts // self.Ntimes else: return len(np.unique(self.baseline_array)) else: nbls = int(self.header["Nbls"][()]) if self._recompute_nbls is None: # Test if this is an "old" HERA file. If so, the Nbls is wrong # in the header, and equal to Nblts == Nbls*Ntimes. if ( self.telescope_name == "HERA" and nbls == self.Nblts and (self.__blts_are_rectangular or self.Nblts % self.Ntimes == 0) ): return self.Nblts // self.Ntimes else: return nbls else: return nbls
[docs] def get_blt_order(self) -> tuple[str]: """Get the blt order from analysing metadata.""" return uvutils.determine_blt_order( time_array=self.time_array, ant_1_array=self.ant_1_array, ant_2_array=self.ant_2_array, baseline_array=self.baseline_array, Nbls=self.Nbls, Ntimes=self.Ntimes, )
@cached_property def blt_order(self) -> tuple[str]: """Tuple defining order of blts axis.""" if self.__blt_order not in (None, "determine"): return self.__blt_order h = self.header if "blt_order" in h: blt_order_str = bytes(h["blt_order"][()]).decode("utf8") return tuple(blt_order_str.split(", ")) else: if self.__blt_order == "determine": return self.get_blt_order() else: return None @cached_property def blts_are_rectangular(self) -> bool: """Whether blts axis is rectangular. That is, this is true if the blts can be reshaped to ``(Ntimes, Nbls)`` OR ``(Nbls, Ntimes)``. This requires each baseline to have the same number of times associated, AND the blt ordering to either be (time, baseline) or (baseline,time). """ if self.__blts_are_rectangular is not None: return self.__blts_are_rectangular h = self.header if "blts_are_rectangular" in h: return bool(h["blts_are_rectangular"][()]) if self.Nblts == self.Ntimes * self.Nbls and self.blt_order in ( ("time", "baseline"), ("baseline", "time"), ): return True is_rect, self.__time_first = uvutils.determine_rectangularity( time_array=self.time_array, baseline_array=self.baseline_array, nbls=self.Nbls, ntimes=self.Ntimes, ) return is_rect @cached_property def time_axis_faster_than_bls(self) -> bool: """Whether times move first in the blt axis.""" # first hit the blts_are_rectangular property to set the time_first property if not self.blts_are_rectangular: return False if self.__time_first is not None: return self.__time_first if "time_axis_faster_than_bls" in self.header: return bool(self.header["time_axis_faster_than_bls"][()]) if self.Ntimes == 1: return False if self.Ntimes == self.Nblts: return True return self.header["time_array"][1] != self.header["time_array"][0] @cached_property def phase_center_catalog(self) -> dict | None: """Dictionary of phase centers.""" header = self.header if "phase_center_catalog" not in header: return None phase_center_catalog = {} key0 = next(iter(header["phase_center_catalog"].keys())) if isinstance(header["phase_center_catalog"][key0], h5py.Group): # This is the new, correct way for pc, pc_dict in header["phase_center_catalog"].items(): pc_id = int(pc) phase_center_catalog[pc_id] = {} for key, dset in pc_dict.items(): if issubclass(dset.dtype.type, np.bytes_): phase_center_catalog[pc_id][key] = bytes(dset[()]).decode( "utf8" ) elif dset.shape is None: phase_center_catalog[pc_id][key] = None else: phase_center_catalog[pc_id][key] = dset[()] else: # This is the old way this was written for key in header["phase_center_catalog"].keys(): pc_dict = json.loads( bytes(header["phase_center_catalog"][key][()]).decode("utf8") ) pc_dict["cat_name"] = key pc_id = pc_dict.pop("cat_id") phase_center_catalog[pc_id] = pc_dict return phase_center_catalog @cached_property def phase_type(self) -> str: """The phase type of the data.""" h = self.header if self.phase_center_catalog is not None: if all( pc["cat_type"] == "unprojected" for pc in self.phase_center_catalog.values() ): return "drift" else: return "phased" phs = bytes(h["phase_type"][()]).decode("utf8") if phs in ("drift", "phased"): return phs warnings.warn( "Unknown phase types are no longer supported, marking this " "object as unprojected (unphased) by default." ) return "drift" @cached_property def phase_center_id_array(self): """Array of phase center IDs.""" if self.phase_center_catalog: return self.header["phase_center_id_array"][:] else: return None @cached_property def times(self) -> np.ndarray: """The unique times in the file.""" h = self.header if self.blts_are_rectangular: if self.time_axis_faster_than_bls: return h["time_array"][: self.Ntimes] else: return h["time_array"][:: self.Nbls] else: return np.unique(self.time_array) @cached_property def lsts(self) -> np.ndarray: """The unique LSTs in the file.""" h = self.header if "lst_array" in h and self.blts_are_rectangular: if self.time_axis_faster_than_bls: return h["lst_array"][: self.Ntimes] else: return h["lst_array"][:: self.Nbls] else: return np.unique(self.lst_array) @cached_property def lst_array(self) -> np.ndarray: """The LSTs corresponding to each baseline-time.""" h = self.header if "lst_array" in h: return h["lst_array"][:] else: lat, lon, alt = self.telescope_location_lat_lon_alt_degrees lst_array = uvutils.get_lst_for_time( jd_array=self.time_array, latitude=lat, longitude=lon, altitude=alt, astrometry_library=self._astrometry_library, frame=self.telescope_frame, ) return lst_array @cached_property def channel_width(self) -> float: """The width of each frequency channel in Hz.""" # 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. h = self.header if self.flex_spw or np.asarray(h["channel_width"]).ndim == 1: return h["channel_width"][:] else: return float(h["channel_width"][()]) @cached_property def extra_keywords(self) -> dict: """The extra_keywords from the file.""" header = self.header if "extra_keywords" not in header: return {} extra_keywords = {} for key in header["extra_keywords"].keys(): if header["extra_keywords"][key].dtype.type in (np.string_, np.object_): 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: extra_keywords[key] = None else: extra_keywords[key] = header["extra_keywords"][key][()] return extra_keywords @cached_property def unique_antpair_1_array(self) -> np.ndarray: """The unique antenna 1 indices in the file.""" h = self.header if self.blts_are_rectangular: if self.time_axis_faster_than_bls: return h["ant_1_array"][:: self.Ntimes] else: return h["ant_1_array"][: self.Nbls] else: return np.array([x for x, y in self.antpairs]) @cached_property def unique_antpair_2_array(self) -> np.ndarray: """The unique antenna 2 indices in the file.""" h = self.header if self.blts_are_rectangular: if self.time_axis_faster_than_bls: return h["ant_2_array"][:: self.Ntimes] else: return h["ant_2_array"][: self.Nbls] else: return np.array([y for x, y in self.antpairs]) @cached_property def unique_ants(self) -> set: """The unique antennas in the file.""" return set( np.unique( np.concatenate( (self.unique_antpair_1_array, self.unique_antpair_2_array) ) ) ) @cached_property def baseline_array(self) -> np.ndarray: """The baselines in the file, as unique integers.""" return uvutils.antnums_to_baseline( self.ant_1_array, self.ant_2_array, self.Nants_telescope ) @cached_property def unique_baseline_array(self) -> np.ndarray: """The unique baselines in the file, as unique integers.""" return uvutils.antnums_to_baseline( self.unique_antpair_1_array, self.unique_antpair_2_array, self.Nants_telescope, ) @cached_property def antenna_names(self) -> list[str]: """The antenna names in the file.""" return [bytes(name).decode("utf8") for name in self.header["antenna_names"][:]] @cached_property def antpairs(self) -> list[tuple[int, int]]: """Get the unique antenna pairs in the file.""" if self.blts_are_rectangular: return list(zip(self.unique_antpair_1_array, self.unique_antpair_2_array)) else: return list(set(zip(self.ant_1_array, self.ant_2_array)))
[docs] def has_key(self, key: tuple[int, int] | tuple[int, int, str]) -> bool: """Check if the file has a given antpair or antpair-pol key.""" if len(key) == 2 and key in self.antpairs or (key[1], key[0]) in self.antpairs: return True elif len(key) == 3 and ( (key[:2] in self.antpairs and key[2] in self.pols) or ((key[1], key[0]) in self.antpairs and key[2][::-1] in self.pols) ): return True else: return False
@cached_property def pols(self) -> list[str]: """The polarizations in the file, as standardized strings, eg. 'xx' or 'ee'.""" return [ uvutils.polnum2str(p, x_orientation=self.x_orientation) for p in self.polarization_array ] @cached_property def antpos_enu(self) -> np.ndarray: """The antenna positions in ENU coordinates, in meters.""" return uvutils.ENU_from_ECEF( self.antenna_positions + self.telescope_location, *self.telescope_location_lat_lon_alt, frame="itrs", ) @cached_property def telescope_location(self): """The telescope location in ECEF coordinates, in meters.""" return uvutils.XYZ_from_LatLonAlt( *self.telescope_location_lat_lon_alt, frame=self.telescope_frame ) @property def telescope_location_lat_lon_alt(self) -> tuple[float, float, float]: """The telescope location in latitude, longitude, and altitude, in degrees.""" return self.latitude * np.pi / 180, self.longitude * np.pi / 180, self.altitude @property def telescope_location_lat_lon_alt_degrees(self) -> tuple[float, float, float]: """The telescope location in latitude, longitude, and altitude, in degrees.""" return self.latitude, self.longitude, self.altitude @property def telescope_frame(self) -> str: """The telescope frame.""" h = self.header if "telescope_frame" in h: telescope_frame = bytes(h["telescope_frame"][()]).decode("utf8") if telescope_frame not in ["itrs", "mcmf"]: raise ValueError( f"Telescope frame in file is {telescope_frame}. " "Only 'itrs' and 'mcmf' are currently supported." ) return telescope_frame else: # default to ITRS return "itrs" @property def ellipsoid(self) -> str: """The reference ellipsoid to use for lunar coordinates.""" h = self.header if self.telescope_frame == "mcmf": if "ellipsoid" in h: return bytes(h["ellipsoid"][()]).decode("utf8") else: return "SPHERE" else: return None @cached_property def vis_units(self) -> str: """The visibility units in the file, as a string.""" # check for vis_units h = self.header if "vis_units" in h: vis_units = bytes(h["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 vis_units == "UNCALIB": vis_units = "uncalib" else: # default to uncalibrated data vis_units = "uncalib" return vis_units
[docs] def to_uvdata( self, check_lsts: bool = False, astrometry_library: str | None = None ) -> UVData: """Convert the file to a UVData object. The object will be metadata-only. Parameters ---------- check_lsts : bool Option to check that the LSTs match the expected values for the telescope location and times. astrometry_library : str Library used for calculating the LSTs. Allowed options are 'erfa' (which uses the pyERFA), 'novas' (which uses the python-novas library), and 'astropy' (which uses the astropy utilities). Default is erfa unless the telescope_location frame is MCMF (on the moon), in which case the default is astropy. """ uvd = UVH5() uvd.read_uvh5( self, read_data=False, run_check_acceptability=check_lsts, use_future_array_shapes=True, astrometry_library=astrometry_library, ) return uvd
[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_with_fast_meta( self, filename: str | Path | FastUVH5Meta, run_check_acceptability: bool = True, blt_order: tuple[str] | None | Literal["determine"] = None, blts_are_rectangular: bool | None = None, time_axis_faster_than_bls: bool | None = None, background_lsts: bool = True, recompute_nbls: bool | None = None, astrometry_library: str | None = None, ): if not isinstance(filename, FastUVH5Meta): obj = FastUVH5Meta( filename, blt_order=blt_order, blts_are_rectangular=blts_are_rectangular, time_axis_faster_than_bls=time_axis_faster_than_bls, recompute_nbls=recompute_nbls, astrometry_library=astrometry_library, ) else: obj = filename # First, get the things relevant for setting LSTs, so that can be run in the # background if desired. self.time_array = obj.time_array # must set the frame before setting the location using lat/lon/alt self._telescope_location.frame = obj.telescope_frame if self._telescope_location.frame == "mcmf": self._telescope_location.ellipsoid = obj.ellipsoid self.telescope_location_lat_lon_alt_degrees = ( obj.telescope_location_lat_lon_alt_degrees ) if "lst_array" in obj.header: self.lst_array = obj.header["lst_array"][:] proc = None else: proc = self.set_lsts_from_time_array( background=background_lsts, astrometry_library=astrometry_library ) # This only checks the LSTs, which is not necessary if they are being # computed now run_check_acceptability = False if run_check_acceptability: lat, lon, alt = self.telescope_location_lat_lon_alt_degrees uvutils.check_lsts_against_times( jd_array=self.time_array, lst_array=self.lst_array, latitude=lat, longitude=lon, altitude=alt, lst_tols=(0, uvutils.LST_RAD_TOL), frame=self._telescope_location.frame, ellipsoid=self._telescope_location.ellipsoid, ) # Required parameters for attr in [ "instrument", "telescope_name", "history", "vis_units", "Nfreqs", "Npols", "Nspws", "Ntimes", "Nblts", "Nbls", "Nants_data", "Nants_telescope", "antenna_names", "antenna_numbers", "antenna_positions", "ant_1_array", "ant_2_array", "phase_center_id_array", "baseline_array", "integration_time", "freq_array", "spw_array", "channel_width", "polarization_array", "uvw_array", "channel_width", "phase_center_catalog", ]: try: setattr(self, attr, getattr(obj, attr)) except AttributeError as e: raise KeyError(str(e)) from e # check this as soon as we have the inputs 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(self.channel_width).size == self.freq_array.size ), arr_shape_msg self._set_future_array_shapes() # For now, only set the rectangularity parameters if they exist in the header of # the file. These could be set automatically later on, but for now we'll leave # that up to the user dealing with the UVData object. if "blts_are_rectangular" in obj.header: self.blts_are_rectangular = obj.blts_are_rectangular if "time_axis_faster_than_bls" in obj.header: self.time_axis_faster_than_bls = obj.time_axis_faster_than_bls if not uvutils._check_history_version(self.history, self.pyuvdata_version_str): self.history += self.pyuvdata_version_str # Optional parameters for attr in [ "dut1", "earth_omega", "gst0", "rdate", "timesys", "x_orientation", "blt_order", "antenna_diameters", "uvplane_reference_time", "eq_coeffs", "eq_coeffs_convention", "flex_spw_id_array", "flex_spw_polarization_array", "phase_center_app_ra", "phase_center_app_dec", "phase_center_frame_pa", "extra_keywords", ]: try: setattr(self, attr, getattr(obj, attr)) except AttributeError: pass if self.blt_order is not None: self._blt_order.form = (len(self.blt_order),) # 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 obj.flex_spw: self._set_flex_spw() if self.flex_spw_id_array is None and not self.flex_spw: self.flex_spw_id_array = np.full(self.Nfreqs, self.spw_array[0], dtype=int) # Here is where we start handling phase center information. If we have a # multi phase center dataset, we need to get different header items if self.phase_center_catalog is not None: self.Nphase = obj.Nphase else: cat_name = getattr(obj, "object_name", None) if obj.phase_type == "drift": if cat_name is None: cat_name = "unprojected" cat_id = self._add_phase_center(cat_name, cat_type="unprojected") else: cat_id = self._add_phase_center( cat_name, cat_type="sidereal", cat_lon=obj.phase_center_ra, cat_lat=obj.phase_center_dec, cat_frame=obj.phase_center_frame, cat_epoch=obj.phase_center_epoch, ) self.phase_center_id_array = np.zeros(self.Nblts, dtype=int) + cat_id # set telescope params try: self.set_telescope_params() except ValueError as ve: warnings.warn(str(ve)) # wait for the LST computation if needed if proc is not None: proc.join() def _read_header( self, filename: str | Path | FastUVH5Meta | h5py.File | h5py.Group, **kwargs ): """ 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. Other Parameters ---------------- All other parameters passed through to :func:`_read_header_with_fast_meta`. Returns ------- None """ self._read_header_with_fast_meta(filename, **kwargs) def _get_data( self, dgrp, antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, phase_center_ids, catalog_names, data_array_dtype, keep_all_metadata, multidim_index, ): """ 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). """ # check for bitshuffle data; bitshuffle filter number is 32008 # TODO should we check for any other filters? if "32008" in dgrp["visdata"]._filters: if not hdf5plugin_present: # pragma: no cover raise ImportError( "hdf5plugin is not installed but is required to read this dataset" ) from hdf5plugin_error # 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, phase_center_ids, catalog_names, ) # 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_by_index( 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() return
[docs] @copy_replace_short_description( UVData.read_mwa_corr_fits, style=DocstringStyle.NUMPYDOC ) 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, phase_center_ids=None, catalog_names=None, keep_all_metadata=True, read_data=True, data_array_dtype=np.complex128, multidim_index=False, remove_flex_pol=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, check_autos=True, fix_autos=True, use_future_array_shapes=False, blt_order: tuple[str] | Literal["determine"] | None = None, blts_are_rectangular: bool | None = None, time_axis_faster_than_bls: bool | None = None, recompute_nbls: bool | None = None, astrometry_library: str | None = None, ): """Read in data from a UVH5 file.""" if isinstance(filename, FastUVH5Meta): meta = filename filename = str(meta.path) close_meta = False else: close_meta = True meta = FastUVH5Meta( filename, blt_order=blt_order, blts_are_rectangular=blts_are_rectangular, time_axis_faster_than_bls=time_axis_faster_than_bls, recompute_nbls=recompute_nbls, ) # update filename attribute basename = os.path.basename(filename) self.filename = [basename] self._filename.form = (1,) # open hdf5 file for reading self._read_header( meta, run_check_acceptability=run_check_acceptability, background_lsts=background_lsts, astrometry_library=astrometry_library, ) if read_data: # Now read in the data self._get_data( meta.datagrp, antenna_nums, antenna_names, ant_str, bls, frequencies, freq_chans, times, time_range, lsts, lst_range, polarizations, blt_inds, phase_center_ids, catalog_names, data_array_dtype, keep_all_metadata, multidim_index, ) if close_meta: meta.close() # Finally, backfill the apparent coords if they aren't in the original datafile add_app_coords = ( self.phase_center_app_ra is None or (self.phase_center_app_dec is None) or (self.phase_center_frame_pa is None) ) if add_app_coords: 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 multi-phase-ctr attribute just to be extra safe. old_phase_compatible, _ = self._old_phase_attributes_compatible() if np.any(~self._check_for_cat_type("unprojected")) and old_phase_compatible: if (fix_old_proj) or (fix_old_proj is None and add_app_coords): self.fix_phase(use_ant_pos=fix_use_ant_pos) elif add_app_coords: warnings.warn( "This data appears to have been phased-up using the old " "`phase` method, which is incompatible with the current set of " "methods. Please run the `fix_phase` method (or set " "`fix_old_proj=True` when loading the dataset) to address this " "issue." ) if remove_flex_pol: self.remove_flex_pol() if use_future_array_shapes: if not self.future_array_shapes: self.use_future_array_shapes() else: warnings.warn(_future_array_shapes_warning, DeprecationWarning) if self.future_array_shapes: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="This method will be removed in version 3.0 when the " "current array shapes are no longer supported.", ) self.use_current_array_shapes() # 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, check_autos=check_autos, fix_autos=fix_autos, ) 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 assert_err_msg = ( "This is a bug, please make an issue in our issue log at " "https://github.com/RadioAstronomySoftwareGroup/pyuvdata/issues" ) assert self.future_array_shapes, assert_err_msg header["version"] = np.string_("1.2") # write out telescope and source information header["telescope_frame"] = np.string_(self._telescope_location.frame) if self._telescope_location.frame == "mcmf": header["ellipsoid"] = self._telescope_location.ellipsoid 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) # 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 # handle antenna_names; works for lists or arrays header["antenna_names"] = np.asarray(self.antenna_names, dtype="bytes") # write out phasing information # Write out the catalog, if available header["phase_center_id_array"] = self.phase_center_id_array header["Nphase"] = self.Nphase # this is a dict of dicts. Top level key is the phase_center_id, # next level keys give details for each phase center. pc_group = header.create_group("phase_center_catalog") for pc, pc_dict in self.phase_center_catalog.items(): this_group = pc_group.create_group(str(pc)) for key, value in pc_dict.items(): if isinstance(value, str): this_group[key] = np.bytes_(value) elif value is None: this_group[key] = h5py.Empty("f") else: this_group[key] = value header["phase_center_app_ra"] = self.phase_center_app_ra header["phase_center_app_dec"] = self.phase_center_app_dec header["phase_center_frame_pa"] = self.phase_center_frame_pa # 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.flex_spw_polarization_array is not None: header["flex_spw_polarization_array"] = self.flex_spw_polarization_array if self.blts_are_rectangular is not None: header["blts_are_rectangular"] = self.blts_are_rectangular if self.time_axis_faster_than_bls is not None: header["time_axis_faster_than_bls"] = self.time_axis_faster_than_bls # 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, check_autos=True, fix_autos=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). In addition to the normal HDF5 filter values, the user may specify "bitshuffle" which will set the compression to `32008` for bitshuffle and will set the `compression_opts` to `(0, 2)` to allow bitshuffle to automatically determine the block size and to use the LZF filter after bitshuffle. Using `bitshuffle` requires having the `hdf5plugin` package installed. Dataset must be chunked to use compression. 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. check_autos : bool Check whether any auto-correlations have non-zero imaginary values in data_array (which should not mathematically exist). Default is True. fix_autos : bool If auto-correlations with imaginary values are found, fix those values so that they are real-only in data_array. Default is False. 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, check_autos=check_autos, fix_autos=fix_autos, ) if os.path.exists(filename): if clobber: print("File exists; clobbering") else: raise IOError("File exists; skipping") revert_fas = False if not self.future_array_shapes: # We force using future array shapes here to always write version 1.* files. # We capture the current state so that it can be reverted later if needed. revert_fas = True self.use_future_array_shapes() data_compression, data_compression_opts = _get_compression(data_compression) # 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, compression_opts=data_compression_opts, 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, compression_opts=data_compression_opts, 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: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="This method will be removed in version 3.0 when the " "current array shapes are no longer supported.", ) 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). In addition to the normal HDF5 filter values, the user may specify "bitshuffle" which will set the compression to `32008` for bitshuffle and will set the `compression_opts` to `(0, 2)` to allow bitshuffle to automatically determine the block size and to use the LZF filter after bitshuffle. Using `bitshuffle` requires having the `hdf5plugin` package installed. Dataset must be chunked to use compression. 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") data_compression, data_compression_opts = _get_compression(data_compression) revert_fas = False if not self.future_array_shapes: # We force using future array shapes here to always write version 1.* files. # We capture the current state so that it can be reverted later if needed. revert_fas = True self.use_future_array_shapes() # write header and empty arrays to file with h5py.File(filename, "w") as f: # write header header = f.create_group("Header") self._write_header(header) # initialize the data groups on disk data_size = (self.Nblts, self.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, compression_opts=data_compression_opts, ) 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, ) if revert_fas: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="This method will be removed in version 3.0 when the " "current array shapes are no longer supported.", ) self.use_current_array_shapes() 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, 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, phase_center_ids=None, catalog_names=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. phase_center_ids : array_like of int, optional Phase center IDs to include when reading data into the object (effectively a selection on baseline-times). Cannot be used with catalog_names. catalog_names : str or array-like of str The names of the phase centers (sources) to include when reading data into the object, which should match exactly in spelling and capitalization. Cannot be used with phase_center_ids. 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) ) revert_fas = False if not self.future_array_shapes: # We force using future array shapes here to always write version 1.* files. # We capture the current state so that it can be reverted later if needed. revert_fas = True self.use_future_array_shapes() 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, phase_center_ids, catalog_names, ) # 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 proper_shape = (Nblts, Nfreqs, Npols) if data_array.shape != proper_shape: if revert_fas and data_array.shape == (Nblts, 1, Nfreqs, Npols): data_array = data_array[:, 0, :, :] flag_array = flag_array[:, 0, :, :] nsample_array = nsample_array[:, 0, :, :] else: 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: visdata_dset[blt_inds, freq_inds, pol_inds] = data_array 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) _write_complex_astype( data_array[:, ifreq, ipol], visdata_dset, indices ) else: visdata_dset[blt_inds, freq_idx, pol_idx] = data_array[ :, ifreq, ipol ] flags_dset[blt_inds, freq_idx, pol_idx] = 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) _write_complex_astype( data_array[iblt, :, ipol], visdata_dset, indices ) else: visdata_dset[blt_idx, freq_inds, pol_idx] = data_array[ iblt, :, ipol ] flags_dset[blt_idx, freq_inds, pol_idx] = 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) _write_complex_astype( data_array[iblt, ifreq, :], visdata_dset, indices ) else: visdata_dset[blt_idx, freq_idx, pol_inds] = data_array[ iblt, ifreq, : ] flags_dset[blt_idx, freq_idx, pol_inds] = 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) _write_complex_astype( data_array[iblt, ifreq, ipol], visdata_dset, indices ) else: visdata_dset[blt_idx, freq_idx, pol_idx] = data_array[ iblt, ifreq, ipol ] flags_dset[blt_idx, freq_idx, pol_idx] = 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) if revert_fas: with warnings.catch_warnings(): warnings.filterwarnings( "ignore", message="This method will be removed in version 3.0 when the " "current array shapes are no longer supported.", ) self.use_current_array_shapes() return