https://github.com/RadioAstronomySoftwareGroup/pyuvdata
Tip revision: 7d3804fe8cf404221981b483d0bdc7503c969e37 authored by Matthew Kolopanis on 29 March 2024, 20:07:46 UTC
update min versions tests
update min versions tests
Tip revision: 7d3804f
uvh5.py
# -*- 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
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
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)
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
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
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
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)))
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
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
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
@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
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
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
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
