1143 lines
41 KiB
Python
1143 lines
41 KiB
Python
|
# This file is part of h5py, a Python interface to the HDF5 library.
|
||
|
|
#
|
||
|
|
# http://www.h5py.org
|
||
|
|
#
|
||
|
|
# Copyright 2008-2020 Andrew Collette and contributors
|
||
|
|
#
|
||
|
|
# License: Standard 3-clause BSD; see "license.txt" for full license terms
|
||
|
|
# and contributor agreement.
|
||
|
|
|
||
|
|
"""
|
||
|
|
Implements support for high-level dataset access.
|
||
|
|
"""
|
||
|
|
|
||
|
|
import posixpath as pp
|
||
|
|
import sys
|
||
|
|
|
||
|
|
import numpy
|
||
|
|
|
||
|
|
from .. import h5, h5s, h5t, h5r, h5d, h5p, h5fd, h5ds, _selector
|
||
|
|
from .base import (
|
||
|
|
array_for_new_object, cached_property, Empty, find_item_type, HLObject,
|
||
|
|
phil, product, with_phil,
|
||
|
|
)
|
||
|
|
from . import filters
|
||
|
|
from . import selections as sel
|
||
|
|
from . import selections2 as sel2
|
||
|
|
from .datatype import Datatype
|
||
|
|
from .compat import filename_decode
|
||
|
|
from .vds import VDSmap, vds_support
|
||
|
|
|
||
|
|
_LEGACY_GZIP_COMPRESSION_VALS = frozenset(range(10))
|
||
|
|
MPI = h5.get_config().mpi
|
||
|
|
|
||
|
|
|
||
|
|
def make_new_dset(parent, shape=None, dtype=None, data=None, name=None,
|
||
|
|
chunks=None, compression=None, shuffle=None,
|
||
|
|
fletcher32=None, maxshape=None, compression_opts=None,
|
||
|
|
fillvalue=None, scaleoffset=None, track_times=False,
|
||
|
|
external=None, track_order=None, dcpl=None, dapl=None,
|
||
|
|
efile_prefix=None, virtual_prefix=None, allow_unknown_filter=False,
|
||
|
|
rdcc_nslots=None, rdcc_nbytes=None, rdcc_w0=None):
|
||
|
|
""" Return a new low-level dataset identifier """
|
||
|
|
|
||
|
|
# Convert data to a C-contiguous ndarray
|
||
|
|
if data is not None and not isinstance(data, Empty):
|
||
|
|
data = array_for_new_object(data, specified_dtype=dtype)
|
||
|
|
|
||
|
|
# Validate shape
|
||
|
|
if shape is None:
|
||
|
|
if data is None:
|
||
|
|
if dtype is None:
|
||
|
|
raise TypeError("One of data, shape or dtype must be specified")
|
||
|
|
data = Empty(dtype)
|
||
|
|
shape = data.shape
|
||
|
|
else:
|
||
|
|
shape = (shape,) if isinstance(shape, int) else tuple(shape)
|
||
|
|
if data is not None and (product(shape) != product(data.shape)):
|
||
|
|
raise ValueError("Shape tuple is incompatible with data")
|
||
|
|
|
||
|
|
if isinstance(maxshape, int):
|
||
|
|
maxshape = (maxshape,)
|
||
|
|
tmp_shape = maxshape if maxshape is not None else shape
|
||
|
|
|
||
|
|
# Validate chunk shape
|
||
|
|
if isinstance(chunks, int) and not isinstance(chunks, bool):
|
||
|
|
chunks = (chunks,)
|
||
|
|
if isinstance(chunks, tuple) and any(
|
||
|
|
chunk > dim for dim, chunk in zip(tmp_shape, chunks) if dim is not None
|
||
|
|
):
|
||
|
|
errmsg = "Chunk shape must not be greater than data shape in any dimension. "\
|
||
|
|
"{} is not compatible with {}".format(chunks, shape)
|
||
|
|
raise ValueError(errmsg)
|
||
|
|
|
||
|
|
if isinstance(dtype, Datatype):
|
||
|
|
# Named types are used as-is
|
||
|
|
tid = dtype.id
|
||
|
|
dtype = tid.dtype # Following code needs this
|
||
|
|
else:
|
||
|
|
# Validate dtype
|
||
|
|
if dtype is None and data is None:
|
||
|
|
dtype = numpy.dtype("=f4")
|
||
|
|
elif dtype is None and data is not None:
|
||
|
|
dtype = data.dtype
|
||
|
|
else:
|
||
|
|
dtype = numpy.dtype(dtype)
|
||
|
|
tid = h5t.py_create(dtype, logical=1)
|
||
|
|
|
||
|
|
# Legacy
|
||
|
|
if any((compression, shuffle, fletcher32, maxshape, scaleoffset)) and chunks is False:
|
||
|
|
raise ValueError("Chunked format required for given storage options")
|
||
|
|
|
||
|
|
# Legacy
|
||
|
|
if compression is True:
|
||
|
|
if compression_opts is None:
|
||
|
|
compression_opts = 4
|
||
|
|
compression = 'gzip'
|
||
|
|
|
||
|
|
# Legacy
|
||
|
|
if compression in _LEGACY_GZIP_COMPRESSION_VALS:
|
||
|
|
if compression_opts is not None:
|
||
|
|
raise TypeError("Conflict in compression options")
|
||
|
|
compression_opts = compression
|
||
|
|
compression = 'gzip'
|
||
|
|
dcpl = filters.fill_dcpl(
|
||
|
|
dcpl or h5p.create(h5p.DATASET_CREATE), shape, dtype,
|
||
|
|
chunks, compression, compression_opts, shuffle, fletcher32,
|
||
|
|
maxshape, scaleoffset, external, allow_unknown_filter)
|
||
|
|
|
||
|
|
if fillvalue is not None:
|
||
|
|
# prepare string-type dtypes for fillvalue
|
||
|
|
string_info = h5t.check_string_dtype(dtype)
|
||
|
|
if string_info is not None:
|
||
|
|
# fake vlen dtype for fixed len string fillvalue
|
||
|
|
# to not trigger unwanted encoding
|
||
|
|
dtype = h5t.string_dtype(string_info.encoding)
|
||
|
|
fillvalue = numpy.array(fillvalue, dtype=dtype)
|
||
|
|
else:
|
||
|
|
fillvalue = numpy.array(fillvalue)
|
||
|
|
dcpl.set_fill_value(fillvalue)
|
||
|
|
|
||
|
|
if track_times is None:
|
||
|
|
# In case someone explicitly passes None for the default
|
||
|
|
track_times = False
|
||
|
|
if track_times in (True, False):
|
||
|
|
dcpl.set_obj_track_times(track_times)
|
||
|
|
else:
|
||
|
|
raise TypeError("track_times must be either True or False")
|
||
|
|
if track_order is True:
|
||
|
|
dcpl.set_attr_creation_order(
|
||
|
|
h5p.CRT_ORDER_TRACKED | h5p.CRT_ORDER_INDEXED)
|
||
|
|
elif track_order is False:
|
||
|
|
dcpl.set_attr_creation_order(0)
|
||
|
|
elif track_order is not None:
|
||
|
|
raise TypeError("track_order must be either True or False")
|
||
|
|
|
||
|
|
if maxshape is not None:
|
||
|
|
maxshape = tuple(m if m is not None else h5s.UNLIMITED for m in maxshape)
|
||
|
|
|
||
|
|
if any([efile_prefix, virtual_prefix, rdcc_nbytes, rdcc_nslots, rdcc_w0]):
|
||
|
|
dapl = dapl or h5p.create(h5p.DATASET_ACCESS)
|
||
|
|
|
||
|
|
if efile_prefix is not None:
|
||
|
|
dapl.set_efile_prefix(efile_prefix)
|
||
|
|
|
||
|
|
if virtual_prefix is not None:
|
||
|
|
dapl.set_virtual_prefix(virtual_prefix)
|
||
|
|
|
||
|
|
if rdcc_nbytes or rdcc_nslots or rdcc_w0:
|
||
|
|
cache_settings = list(dapl.get_chunk_cache())
|
||
|
|
if rdcc_nslots is not None:
|
||
|
|
cache_settings[0] = rdcc_nslots
|
||
|
|
if rdcc_nbytes is not None:
|
||
|
|
cache_settings[1] = rdcc_nbytes
|
||
|
|
if rdcc_w0 is not None:
|
||
|
|
cache_settings[2] = rdcc_w0
|
||
|
|
dapl.set_chunk_cache(*cache_settings)
|
||
|
|
|
||
|
|
if isinstance(data, Empty):
|
||
|
|
sid = h5s.create(h5s.NULL)
|
||
|
|
else:
|
||
|
|
sid = h5s.create_simple(shape, maxshape)
|
||
|
|
|
||
|
|
dset_id = h5d.create(parent.id, name, tid, sid, dcpl=dcpl, dapl=dapl)
|
||
|
|
|
||
|
|
if (data is not None) and (not isinstance(data, Empty)):
|
||
|
|
dset_id.write(h5s.ALL, h5s.ALL, data)
|
||
|
|
|
||
|
|
return dset_id
|
||
|
|
|
||
|
|
|
||
|
|
def open_dset(parent, name, dapl=None, efile_prefix=None, virtual_prefix=None,
|
||
|
|
rdcc_nslots=None, rdcc_nbytes=None, rdcc_w0=None, **kwds):
|
||
|
|
""" Return an existing low-level dataset identifier """
|
||
|
|
|
||
|
|
if any([efile_prefix, virtual_prefix, rdcc_nbytes, rdcc_nslots, rdcc_w0]):
|
||
|
|
dapl = dapl or h5p.create(h5p.DATASET_ACCESS)
|
||
|
|
|
||
|
|
if efile_prefix is not None:
|
||
|
|
dapl.set_efile_prefix(efile_prefix)
|
||
|
|
|
||
|
|
if virtual_prefix is not None:
|
||
|
|
dapl.set_virtual_prefix(virtual_prefix)
|
||
|
|
|
||
|
|
if rdcc_nbytes or rdcc_nslots or rdcc_w0:
|
||
|
|
cache_settings = list(dapl.get_chunk_cache())
|
||
|
|
if rdcc_nslots is not None:
|
||
|
|
cache_settings[0] = rdcc_nslots
|
||
|
|
if rdcc_nbytes is not None:
|
||
|
|
cache_settings[1] = rdcc_nbytes
|
||
|
|
if rdcc_w0 is not None:
|
||
|
|
cache_settings[2] = rdcc_w0
|
||
|
|
dapl.set_chunk_cache(*cache_settings)
|
||
|
|
|
||
|
|
dset_id = h5d.open(parent.id, name, dapl=dapl)
|
||
|
|
|
||
|
|
return dset_id
|
||
|
|
|
||
|
|
|
||
|
|
class AstypeWrapper:
|
||
|
|
"""Wrapper to convert data on reading from a dataset.
|
||
|
|
"""
|
||
|
|
def __init__(self, dset, dtype):
|
||
|
|
self._dset = dset
|
||
|
|
self._dtype = numpy.dtype(dtype)
|
||
|
|
|
||
|
|
def __getitem__(self, args):
|
||
|
|
return self._dset.__getitem__(args, new_dtype=self._dtype)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
""" Get the length of the underlying dataset
|
||
|
|
|
||
|
|
>>> length = len(dataset.astype('f8'))
|
||
|
|
"""
|
||
|
|
return len(self._dset)
|
||
|
|
|
||
|
|
def __array__(self, dtype=None):
|
||
|
|
data = self[:]
|
||
|
|
if dtype is not None:
|
||
|
|
data = data.astype(dtype)
|
||
|
|
return data
|
||
|
|
|
||
|
|
|
||
|
|
class AsStrWrapper:
|
||
|
|
"""Wrapper to decode strings on reading the dataset"""
|
||
|
|
def __init__(self, dset, encoding, errors='strict'):
|
||
|
|
self._dset = dset
|
||
|
|
if encoding is None:
|
||
|
|
encoding = h5t.check_string_dtype(dset.dtype).encoding
|
||
|
|
self.encoding = encoding
|
||
|
|
self.errors = errors
|
||
|
|
|
||
|
|
def __getitem__(self, args):
|
||
|
|
bytes_arr = self._dset[args]
|
||
|
|
# numpy.char.decode() seems like the obvious thing to use. But it only
|
||
|
|
# accepts numpy string arrays, not object arrays of bytes (which we
|
||
|
|
# return from HDF5 variable-length strings). And the numpy
|
||
|
|
# implementation is not faster than doing it with a loop; in fact, by
|
||
|
|
# not converting the result to a numpy unicode array, the
|
||
|
|
# naive way can be faster! (Comparing with numpy 1.18.4, June 2020)
|
||
|
|
if numpy.isscalar(bytes_arr):
|
||
|
|
return bytes_arr.decode(self.encoding, self.errors)
|
||
|
|
|
||
|
|
return numpy.array([
|
||
|
|
b.decode(self.encoding, self.errors) for b in bytes_arr.flat
|
||
|
|
], dtype=object).reshape(bytes_arr.shape)
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
""" Get the length of the underlying dataset
|
||
|
|
|
||
|
|
>>> length = len(dataset.asstr())
|
||
|
|
"""
|
||
|
|
return len(self._dset)
|
||
|
|
|
||
|
|
def __array__(self):
|
||
|
|
return numpy.array([
|
||
|
|
b.decode(self.encoding, self.errors) for b in self._dset
|
||
|
|
], dtype=object).reshape(self._dset.shape)
|
||
|
|
|
||
|
|
|
||
|
|
class FieldsWrapper:
|
||
|
|
"""Wrapper to extract named fields from a dataset with a struct dtype"""
|
||
|
|
extract_field = None
|
||
|
|
|
||
|
|
def __init__(self, dset, prior_dtype, names):
|
||
|
|
self._dset = dset
|
||
|
|
if isinstance(names, str):
|
||
|
|
self.extract_field = names
|
||
|
|
names = [names]
|
||
|
|
self.read_dtype = readtime_dtype(prior_dtype, names)
|
||
|
|
|
||
|
|
def __array__(self, dtype=None):
|
||
|
|
data = self[:]
|
||
|
|
if dtype is not None:
|
||
|
|
data = data.astype(dtype)
|
||
|
|
return data
|
||
|
|
|
||
|
|
def __getitem__(self, args):
|
||
|
|
data = self._dset.__getitem__(args, new_dtype=self.read_dtype)
|
||
|
|
if self.extract_field is not None:
|
||
|
|
data = data[self.extract_field]
|
||
|
|
return data
|
||
|
|
|
||
|
|
def __len__(self):
|
||
|
|
""" Get the length of the underlying dataset
|
||
|
|
|
||
|
|
>>> length = len(dataset.fields(['x', 'y']))
|
||
|
|
"""
|
||
|
|
return len(self._dset)
|
||
|
|
|
||
|
|
|
||
|
|
def readtime_dtype(basetype, names):
|
||
|
|
"""Make a NumPy compound dtype with a subset of available fields"""
|
||
|
|
if basetype.names is None: # Names provided, but not compound
|
||
|
|
raise ValueError("Field names only allowed for compound types")
|
||
|
|
|
||
|
|
for name in names: # Check all names are legal
|
||
|
|
if name not in basetype.names:
|
||
|
|
raise ValueError("Field %s does not appear in this type." % name)
|
||
|
|
|
||
|
|
return numpy.dtype([(name, basetype.fields[name][0]) for name in names])
|
||
|
|
|
||
|
|
|
||
|
|
if MPI:
|
||
|
|
class CollectiveContext:
|
||
|
|
|
||
|
|
""" Manages collective I/O in MPI mode """
|
||
|
|
|
||
|
|
# We don't bother with _local as threads are forbidden in MPI mode
|
||
|
|
|
||
|
|
def __init__(self, dset):
|
||
|
|
self._dset = dset
|
||
|
|
|
||
|
|
def __enter__(self):
|
||
|
|
# pylint: disable=protected-access
|
||
|
|
self._dset._dxpl.set_dxpl_mpio(h5fd.MPIO_COLLECTIVE)
|
||
|
|
|
||
|
|
def __exit__(self, *args):
|
||
|
|
# pylint: disable=protected-access
|
||
|
|
self._dset._dxpl.set_dxpl_mpio(h5fd.MPIO_INDEPENDENT)
|
||
|
|
|
||
|
|
|
||
|
|
class ChunkIterator:
|
||
|
|
"""
|
||
|
|
Class to iterate through list of chunks of a given dataset
|
||
|
|
"""
|
||
|
|
def __init__(self, dset, source_sel=None):
|
||
|
|
self._shape = dset.shape
|
||
|
|
rank = len(dset.shape)
|
||
|
|
|
||
|
|
if not dset.chunks:
|
||
|
|
# can only use with chunked datasets
|
||
|
|
raise TypeError("Chunked dataset required")
|
||
|
|
|
||
|
|
self._layout = dset.chunks
|
||
|
|
if source_sel is None:
|
||
|
|
# select over entire dataset
|
||
|
|
self._sel = tuple(
|
||
|
|
slice(0, self._shape[dim])
|
||
|
|
for dim in range(rank)
|
||
|
|
)
|
||
|
|
else:
|
||
|
|
if isinstance(source_sel, slice):
|
||
|
|
self._sel = (source_sel,)
|
||
|
|
else:
|
||
|
|
self._sel = source_sel
|
||
|
|
if len(self._sel) != rank:
|
||
|
|
raise ValueError("Invalid selection - selection region must have same rank as dataset")
|
||
|
|
self._chunk_index = []
|
||
|
|
for dim in range(rank):
|
||
|
|
s = self._sel[dim]
|
||
|
|
if s.start < 0 or s.stop > self._shape[dim] or s.stop <= s.start:
|
||
|
|
raise ValueError("Invalid selection - selection region must be within dataset space")
|
||
|
|
index = s.start // self._layout[dim]
|
||
|
|
self._chunk_index.append(index)
|
||
|
|
|
||
|
|
def __iter__(self):
|
||
|
|
return self
|
||
|
|
|
||
|
|
def __next__(self):
|
||
|
|
rank = len(self._shape)
|
||
|
|
slices = []
|
||
|
|
if rank == 0 or self._chunk_index[0] * self._layout[0] >= self._sel[0].stop:
|
||
|
|
# ran past the last chunk, end iteration
|
||
|
|
raise StopIteration()
|
||
|
|
|
||
|
|
for dim in range(rank):
|
||
|
|
s = self._sel[dim]
|
||
|
|
start = self._chunk_index[dim] * self._layout[dim]
|
||
|
|
stop = (self._chunk_index[dim] + 1) * self._layout[dim]
|
||
|
|
# adjust the start if this is an edge chunk
|
||
|
|
if start < s.start:
|
||
|
|
start = s.start
|
||
|
|
if stop > s.stop:
|
||
|
|
stop = s.stop # trim to end of the selection
|
||
|
|
s = slice(start, stop, 1)
|
||
|
|
slices.append(s)
|
||
|
|
|
||
|
|
# bump up the last index and carry forward if we run outside the selection
|
||
|
|
dim = rank - 1
|
||
|
|
while dim >= 0:
|
||
|
|
s = self._sel[dim]
|
||
|
|
self._chunk_index[dim] += 1
|
||
|
|
|
||
|
|
chunk_end = self._chunk_index[dim] * self._layout[dim]
|
||
|
|
if chunk_end < s.stop:
|
||
|
|
# we still have room to extend along this dimensions
|
||
|
|
return tuple(slices)
|
||
|
|
|
||
|
|
if dim > 0:
|
||
|
|
# reset to the start and continue iterating with higher dimension
|
||
|
|
self._chunk_index[dim] = s.start // self._layout[dim]
|
||
|
|
dim -= 1
|
||
|
|
return tuple(slices)
|
||
|
|
|
||
|
|
|
||
|
|
class Dataset(HLObject):
|
||
|
|
|
||
|
|
"""
|
||
|
|
Represents an HDF5 dataset
|
||
|
|
"""
|
||
|
|
|
||
|
|
def astype(self, dtype):
|
||
|
|
""" Get a wrapper allowing you to perform reads to a
|
||
|
|
different destination type, e.g.:
|
||
|
|
|
||
|
|
>>> double_precision = dataset.astype('f8')[0:100:2]
|
||
|
|
"""
|
||
|
|
return AstypeWrapper(self, dtype)
|
||
|
|
|
||
|
|
def asstr(self, encoding=None, errors='strict'):
|
||
|
|
"""Get a wrapper to read string data as Python strings:
|
||
|
|
|
||
|
|
>>> str_array = dataset.asstr()[:]
|
||
|
|
|
||
|
|
The parameters have the same meaning as in ``bytes.decode()``.
|
||
|
|
If ``encoding`` is unspecified, it will use the encoding in the HDF5
|
||
|
|
datatype (either ascii or utf-8).
|
||
|
|
"""
|
||
|
|
string_info = h5t.check_string_dtype(self.dtype)
|
||
|
|
if string_info is None:
|
||
|
|
raise TypeError(
|
||
|
|
"dset.asstr() can only be used on datasets with "
|
||
|
|
"an HDF5 string datatype"
|
||
|
|
)
|
||
|
|
if encoding is None:
|
||
|
|
encoding = string_info.encoding
|
||
|
|
return AsStrWrapper(self, encoding, errors=errors)
|
||
|
|
|
||
|
|
def fields(self, names, *, _prior_dtype=None):
|
||
|
|
"""Get a wrapper to read a subset of fields from a compound data type:
|
||
|
|
|
||
|
|
>>> 2d_coords = dataset.fields(['x', 'y'])[:]
|
||
|
|
|
||
|
|
If names is a string, a single field is extracted, and the resulting
|
||
|
|
arrays will have that dtype. Otherwise, it should be an iterable,
|
||
|
|
and the read data will have a compound dtype.
|
||
|
|
"""
|
||
|
|
if _prior_dtype is None:
|
||
|
|
_prior_dtype = self.dtype
|
||
|
|
return FieldsWrapper(self, _prior_dtype, names)
|
||
|
|
|
||
|
|
if MPI:
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def collective(self):
|
||
|
|
""" Context manager for MPI collective reads & writes """
|
||
|
|
return CollectiveContext(self)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def dims(self):
|
||
|
|
""" Access dimension scales attached to this dataset. """
|
||
|
|
from .dims import DimensionManager
|
||
|
|
with phil:
|
||
|
|
return DimensionManager(self)
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def ndim(self):
|
||
|
|
"""Numpy-style attribute giving the number of dimensions"""
|
||
|
|
return self.id.rank
|
||
|
|
|
||
|
|
@property
|
||
|
|
def shape(self):
|
||
|
|
"""Numpy-style shape tuple giving dataset dimensions"""
|
||
|
|
if 'shape' in self._cache_props:
|
||
|
|
return self._cache_props['shape']
|
||
|
|
|
||
|
|
with phil:
|
||
|
|
shape = self.id.shape
|
||
|
|
|
||
|
|
# If the file is read-only, cache the shape to speed-up future uses.
|
||
|
|
# This cache is invalidated by .refresh() when using SWMR.
|
||
|
|
if self._readonly:
|
||
|
|
self._cache_props['shape'] = shape
|
||
|
|
return shape
|
||
|
|
|
||
|
|
@shape.setter
|
||
|
|
@with_phil
|
||
|
|
def shape(self, shape):
|
||
|
|
# pylint: disable=missing-docstring
|
||
|
|
self.resize(shape)
|
||
|
|
|
||
|
|
@property
|
||
|
|
def size(self):
|
||
|
|
"""Numpy-style attribute giving the total dataset size"""
|
||
|
|
if 'size' in self._cache_props:
|
||
|
|
return self._cache_props['size']
|
||
|
|
|
||
|
|
if self._is_empty:
|
||
|
|
size = None
|
||
|
|
else:
|
||
|
|
size = product(self.shape)
|
||
|
|
|
||
|
|
# If the file is read-only, cache the size to speed-up future uses.
|
||
|
|
# This cache is invalidated by .refresh() when using SWMR.
|
||
|
|
if self._readonly:
|
||
|
|
self._cache_props['size'] = size
|
||
|
|
return size
|
||
|
|
|
||
|
|
@property
|
||
|
|
def nbytes(self):
|
||
|
|
"""Numpy-style attribute giving the raw dataset size as the number of bytes"""
|
||
|
|
size = self.size
|
||
|
|
if size is None: # if we are an empty 0-D array, then there are no bytes in the dataset
|
||
|
|
return 0
|
||
|
|
return self.dtype.itemsize * size
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _selector(self):
|
||
|
|
"""Internal object for optimised selection of data"""
|
||
|
|
if '_selector' in self._cache_props:
|
||
|
|
return self._cache_props['_selector']
|
||
|
|
|
||
|
|
slr = _selector.Selector(self.id.get_space())
|
||
|
|
|
||
|
|
# If the file is read-only, cache the reader to speed up future uses.
|
||
|
|
# This cache is invalidated by .refresh() when using SWMR.
|
||
|
|
if self._readonly:
|
||
|
|
self._cache_props['_selector'] = slr
|
||
|
|
return slr
|
||
|
|
|
||
|
|
@property
|
||
|
|
def _fast_reader(self):
|
||
|
|
"""Internal object for optimised reading of data"""
|
||
|
|
if '_fast_reader' in self._cache_props:
|
||
|
|
return self._cache_props['_fast_reader']
|
||
|
|
|
||
|
|
rdr = _selector.Reader(self.id)
|
||
|
|
|
||
|
|
# If the file is read-only, cache the reader to speed up future uses.
|
||
|
|
# This cache is invalidated by .refresh() when using SWMR.
|
||
|
|
if self._readonly:
|
||
|
|
self._cache_props['_fast_reader'] = rdr
|
||
|
|
return rdr
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def dtype(self):
|
||
|
|
"""Numpy dtype representing the datatype"""
|
||
|
|
return self.id.dtype
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def chunks(self):
|
||
|
|
"""Dataset chunks (or None)"""
|
||
|
|
dcpl = self._dcpl
|
||
|
|
if dcpl.get_layout() == h5d.CHUNKED:
|
||
|
|
return dcpl.get_chunk()
|
||
|
|
return None
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def compression(self):
|
||
|
|
"""Compression strategy (or None)"""
|
||
|
|
for x in ('gzip','lzf','szip'):
|
||
|
|
if x in self._filters:
|
||
|
|
return x
|
||
|
|
return None
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def compression_opts(self):
|
||
|
|
""" Compression setting. Int(0-9) for gzip, 2-tuple for szip. """
|
||
|
|
return self._filters.get(self.compression, None)
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def shuffle(self):
|
||
|
|
"""Shuffle filter present (T/F)"""
|
||
|
|
return 'shuffle' in self._filters
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def fletcher32(self):
|
||
|
|
"""Fletcher32 filter is present (T/F)"""
|
||
|
|
return 'fletcher32' in self._filters
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def scaleoffset(self):
|
||
|
|
"""Scale/offset filter settings. For integer data types, this is
|
||
|
|
the number of bits stored, or 0 for auto-detected. For floating
|
||
|
|
point data types, this is the number of decimal places retained.
|
||
|
|
If the scale/offset filter is not in use, this is None."""
|
||
|
|
try:
|
||
|
|
return self._filters['scaleoffset'][1]
|
||
|
|
except KeyError:
|
||
|
|
return None
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def external(self):
|
||
|
|
"""External file settings. Returns a list of tuples of
|
||
|
|
(name, offset, size) for each external file entry, or returns None
|
||
|
|
if no external files are used."""
|
||
|
|
count = self._dcpl.get_external_count()
|
||
|
|
if count<=0:
|
||
|
|
return None
|
||
|
|
ext_list = list()
|
||
|
|
for x in range(count):
|
||
|
|
(name, offset, size) = self._dcpl.get_external(x)
|
||
|
|
ext_list.append( (filename_decode(name), offset, size) )
|
||
|
|
return ext_list
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def maxshape(self):
|
||
|
|
"""Shape up to which this dataset can be resized. Axes with value
|
||
|
|
None have no resize limit. """
|
||
|
|
space = self.id.get_space()
|
||
|
|
dims = space.get_simple_extent_dims(True)
|
||
|
|
if dims is None:
|
||
|
|
return None
|
||
|
|
|
||
|
|
return tuple(x if x != h5s.UNLIMITED else None for x in dims)
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def fillvalue(self):
|
||
|
|
"""Fill value for this dataset (0 by default)"""
|
||
|
|
arr = numpy.zeros((1,), dtype=self.dtype)
|
||
|
|
self._dcpl.get_fill_value(arr)
|
||
|
|
return arr[0]
|
||
|
|
|
||
|
|
@cached_property
|
||
|
|
@with_phil
|
||
|
|
def _extent_type(self):
|
||
|
|
"""Get extent type for this dataset - SIMPLE, SCALAR or NULL"""
|
||
|
|
return self.id.get_space().get_simple_extent_type()
|
||
|
|
|
||
|
|
@cached_property
|
||
|
|
def _is_empty(self):
|
||
|
|
"""Check if extent type is empty"""
|
||
|
|
return self._extent_type == h5s.NULL
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __init__(self, bind, *, readonly=False):
|
||
|
|
""" Create a new Dataset object by binding to a low-level DatasetID.
|
||
|
|
"""
|
||
|
|
if not isinstance(bind, h5d.DatasetID):
|
||
|
|
raise ValueError("%s is not a DatasetID" % bind)
|
||
|
|
super().__init__(bind)
|
||
|
|
|
||
|
|
self._dcpl = self.id.get_create_plist()
|
||
|
|
self._dxpl = h5p.create(h5p.DATASET_XFER)
|
||
|
|
self._filters = filters.get_filters(self._dcpl)
|
||
|
|
self._readonly = readonly
|
||
|
|
self._cache_props = {}
|
||
|
|
|
||
|
|
def resize(self, size, axis=None):
|
||
|
|
""" Resize the dataset, or the specified axis.
|
||
|
|
|
||
|
|
The dataset must be stored in chunked format; it can be resized up to
|
||
|
|
the "maximum shape" (keyword maxshape) specified at creation time.
|
||
|
|
The rank of the dataset cannot be changed.
|
||
|
|
|
||
|
|
"Size" should be a shape tuple, or if an axis is specified, an integer.
|
||
|
|
|
||
|
|
BEWARE: This functions differently than the NumPy resize() method!
|
||
|
|
The data is not "reshuffled" to fit in the new shape; each axis is
|
||
|
|
grown or shrunk independently. The coordinates of existing data are
|
||
|
|
fixed.
|
||
|
|
"""
|
||
|
|
with phil:
|
||
|
|
if self.chunks is None:
|
||
|
|
raise TypeError("Only chunked datasets can be resized")
|
||
|
|
|
||
|
|
if axis is not None:
|
||
|
|
if not (axis >=0 and axis < self.id.rank):
|
||
|
|
raise ValueError("Invalid axis (0 to %s allowed)" % (self.id.rank-1))
|
||
|
|
try:
|
||
|
|
newlen = int(size)
|
||
|
|
except TypeError:
|
||
|
|
raise TypeError("Argument must be a single int if axis is specified")
|
||
|
|
size = list(self.shape)
|
||
|
|
size[axis] = newlen
|
||
|
|
|
||
|
|
size = tuple(size)
|
||
|
|
self.id.set_extent(size)
|
||
|
|
#h5f.flush(self.id) # THG recommends
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __len__(self):
|
||
|
|
""" The size of the first axis. TypeError if scalar.
|
||
|
|
|
||
|
|
Limited to 2**32 on 32-bit systems; Dataset.len() is preferred.
|
||
|
|
"""
|
||
|
|
size = self.len()
|
||
|
|
if size > sys.maxsize:
|
||
|
|
raise OverflowError("Value too big for Python's __len__; use Dataset.len() instead.")
|
||
|
|
return size
|
||
|
|
|
||
|
|
def len(self):
|
||
|
|
""" The size of the first axis. TypeError if scalar.
|
||
|
|
|
||
|
|
Use of this method is preferred to len(dset), as Python's built-in
|
||
|
|
len() cannot handle values greater then 2**32 on 32-bit systems.
|
||
|
|
"""
|
||
|
|
with phil:
|
||
|
|
shape = self.shape
|
||
|
|
if len(shape) == 0:
|
||
|
|
raise TypeError("Attempt to take len() of scalar dataset")
|
||
|
|
return shape[0]
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __iter__(self):
|
||
|
|
""" Iterate over the first axis. TypeError if scalar.
|
||
|
|
|
||
|
|
BEWARE: Modifications to the yielded data are *NOT* written to file.
|
||
|
|
"""
|
||
|
|
shape = self.shape
|
||
|
|
if len(shape) == 0:
|
||
|
|
raise TypeError("Can't iterate over a scalar dataset")
|
||
|
|
for i in range(shape[0]):
|
||
|
|
yield self[i]
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def iter_chunks(self, sel=None):
|
||
|
|
""" Return chunk iterator. If set, the sel argument is a slice or
|
||
|
|
tuple of slices that defines the region to be used. If not set, the
|
||
|
|
entire dataspace will be used for the iterator.
|
||
|
|
|
||
|
|
For each chunk within the given region, the iterator yields a tuple of
|
||
|
|
slices that gives the intersection of the given chunk with the
|
||
|
|
selection area.
|
||
|
|
|
||
|
|
A TypeError will be raised if the dataset is not chunked.
|
||
|
|
|
||
|
|
A ValueError will be raised if the selection region is invalid.
|
||
|
|
|
||
|
|
"""
|
||
|
|
return ChunkIterator(self, sel)
|
||
|
|
|
||
|
|
@cached_property
|
||
|
|
def _fast_read_ok(self):
|
||
|
|
"""Is this dataset suitable for simple reading"""
|
||
|
|
return (
|
||
|
|
self._extent_type == h5s.SIMPLE
|
||
|
|
and isinstance(self.id.get_type(), (h5t.TypeIntegerID, h5t.TypeFloatID))
|
||
|
|
)
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __getitem__(self, args, new_dtype=None):
|
||
|
|
""" Read a slice from the HDF5 dataset.
|
||
|
|
|
||
|
|
Takes slices and recarray-style field names (more than one is
|
||
|
|
allowed!) in any order. Obeys basic NumPy rules, including
|
||
|
|
broadcasting.
|
||
|
|
|
||
|
|
Also supports:
|
||
|
|
|
||
|
|
* Boolean "mask" array indexing
|
||
|
|
"""
|
||
|
|
args = args if isinstance(args, tuple) else (args,)
|
||
|
|
|
||
|
|
if self._fast_read_ok and (new_dtype is None):
|
||
|
|
try:
|
||
|
|
return self._fast_reader.read(args)
|
||
|
|
except TypeError:
|
||
|
|
pass # Fall back to Python read pathway below
|
||
|
|
|
||
|
|
if self._is_empty:
|
||
|
|
# Check 'is Ellipsis' to avoid equality comparison with an array:
|
||
|
|
# array equality returns an array, not a boolean.
|
||
|
|
if args == () or (len(args) == 1 and args[0] is Ellipsis):
|
||
|
|
return Empty(self.dtype)
|
||
|
|
raise ValueError("Empty datasets cannot be sliced")
|
||
|
|
|
||
|
|
# Sort field names from the rest of the args.
|
||
|
|
names = tuple(x for x in args if isinstance(x, str))
|
||
|
|
|
||
|
|
if names:
|
||
|
|
# Read a subset of the fields in this structured dtype
|
||
|
|
if len(names) == 1:
|
||
|
|
names = names[0] # Read with simpler dtype of this field
|
||
|
|
args = tuple(x for x in args if not isinstance(x, str))
|
||
|
|
return self.fields(names, _prior_dtype=new_dtype)[args]
|
||
|
|
|
||
|
|
if new_dtype is None:
|
||
|
|
new_dtype = self.dtype
|
||
|
|
mtype = h5t.py_create(new_dtype)
|
||
|
|
|
||
|
|
# === Special-case region references ====
|
||
|
|
|
||
|
|
if len(args) == 1 and isinstance(args[0], h5r.RegionReference):
|
||
|
|
|
||
|
|
obj = h5r.dereference(args[0], self.id)
|
||
|
|
if obj != self.id:
|
||
|
|
raise ValueError("Region reference must point to this dataset")
|
||
|
|
|
||
|
|
sid = h5r.get_region(args[0], self.id)
|
||
|
|
mshape = sel.guess_shape(sid)
|
||
|
|
if mshape is None:
|
||
|
|
# 0D with no data (NULL or deselected SCALAR)
|
||
|
|
return Empty(new_dtype)
|
||
|
|
out = numpy.zeros(mshape, dtype=new_dtype)
|
||
|
|
if out.size == 0:
|
||
|
|
return out
|
||
|
|
|
||
|
|
sid_out = h5s.create_simple(mshape)
|
||
|
|
sid_out.select_all()
|
||
|
|
self.id.read(sid_out, sid, out, mtype)
|
||
|
|
return out
|
||
|
|
|
||
|
|
# === Check for zero-sized datasets =====
|
||
|
|
|
||
|
|
if self.size == 0:
|
||
|
|
# Check 'is Ellipsis' to avoid equality comparison with an array:
|
||
|
|
# array equality returns an array, not a boolean.
|
||
|
|
if args == () or (len(args) == 1 and args[0] is Ellipsis):
|
||
|
|
return numpy.zeros(self.shape, dtype=new_dtype)
|
||
|
|
|
||
|
|
# === Scalar dataspaces =================
|
||
|
|
|
||
|
|
if self.shape == ():
|
||
|
|
fspace = self.id.get_space()
|
||
|
|
selection = sel2.select_read(fspace, args)
|
||
|
|
if selection.mshape is None:
|
||
|
|
arr = numpy.zeros((), dtype=new_dtype)
|
||
|
|
else:
|
||
|
|
arr = numpy.zeros(selection.mshape, dtype=new_dtype)
|
||
|
|
for mspace, fspace in selection:
|
||
|
|
self.id.read(mspace, fspace, arr, mtype)
|
||
|
|
if selection.mshape is None:
|
||
|
|
return arr[()]
|
||
|
|
return arr
|
||
|
|
|
||
|
|
# === Everything else ===================
|
||
|
|
|
||
|
|
# Perform the dataspace selection.
|
||
|
|
selection = sel.select(self.shape, args, dataset=self)
|
||
|
|
|
||
|
|
if selection.nselect == 0:
|
||
|
|
return numpy.zeros(selection.array_shape, dtype=new_dtype)
|
||
|
|
|
||
|
|
arr = numpy.zeros(selection.array_shape, new_dtype, order='C')
|
||
|
|
|
||
|
|
# Perform the actual read
|
||
|
|
mspace = h5s.create_simple(selection.mshape)
|
||
|
|
fspace = selection.id
|
||
|
|
self.id.read(mspace, fspace, arr, mtype, dxpl=self._dxpl)
|
||
|
|
|
||
|
|
# Patch up the output for NumPy
|
||
|
|
if arr.shape == ():
|
||
|
|
return arr[()] # 0 dim array -> numpy scalar
|
||
|
|
return arr
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __setitem__(self, args, val):
|
||
|
|
""" Write to the HDF5 dataset from a Numpy array.
|
||
|
|
|
||
|
|
NumPy's broadcasting rules are honored, for "simple" indexing
|
||
|
|
(slices and integers). For advanced indexing, the shapes must
|
||
|
|
match.
|
||
|
|
"""
|
||
|
|
args = args if isinstance(args, tuple) else (args,)
|
||
|
|
|
||
|
|
# Sort field indices from the slicing
|
||
|
|
names = tuple(x for x in args if isinstance(x, str))
|
||
|
|
args = tuple(x for x in args if not isinstance(x, str))
|
||
|
|
|
||
|
|
# Generally we try to avoid converting the arrays on the Python
|
||
|
|
# side. However, for compound literals this is unavoidable.
|
||
|
|
vlen = h5t.check_vlen_dtype(self.dtype)
|
||
|
|
if vlen is not None and vlen not in (bytes, str):
|
||
|
|
try:
|
||
|
|
val = numpy.asarray(val, dtype=vlen)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
try:
|
||
|
|
val = numpy.array([numpy.array(x, dtype=vlen)
|
||
|
|
for x in val], dtype=self.dtype)
|
||
|
|
except (ValueError, TypeError):
|
||
|
|
pass
|
||
|
|
if vlen == val.dtype:
|
||
|
|
if val.ndim > 1:
|
||
|
|
tmp = numpy.empty(shape=val.shape[:-1], dtype=object)
|
||
|
|
tmp.ravel()[:] = [i for i in val.reshape(
|
||
|
|
(product(val.shape[:-1]), val.shape[-1])
|
||
|
|
)]
|
||
|
|
else:
|
||
|
|
tmp = numpy.array([None], dtype=object)
|
||
|
|
tmp[0] = val
|
||
|
|
val = tmp
|
||
|
|
elif self.dtype.kind == "O" or \
|
||
|
|
(self.dtype.kind == 'V' and \
|
||
|
|
(not isinstance(val, numpy.ndarray) or val.dtype.kind != 'V') and \
|
||
|
|
(self.dtype.subdtype is None)):
|
||
|
|
if len(names) == 1 and self.dtype.fields is not None:
|
||
|
|
# Single field selected for write, from a non-array source
|
||
|
|
if not names[0] in self.dtype.fields:
|
||
|
|
raise ValueError("No such field for indexing: %s" % names[0])
|
||
|
|
dtype = self.dtype.fields[names[0]][0]
|
||
|
|
cast_compound = True
|
||
|
|
else:
|
||
|
|
dtype = self.dtype
|
||
|
|
cast_compound = False
|
||
|
|
|
||
|
|
val = numpy.asarray(val, dtype=dtype.base, order='C')
|
||
|
|
if cast_compound:
|
||
|
|
val = val.view(numpy.dtype([(names[0], dtype)]))
|
||
|
|
val = val.reshape(val.shape[:len(val.shape) - len(dtype.shape)])
|
||
|
|
elif (self.dtype.kind == 'S'
|
||
|
|
and (h5t.check_string_dtype(self.dtype).encoding == 'utf-8')
|
||
|
|
and (find_item_type(val) is str)
|
||
|
|
):
|
||
|
|
# Writing str objects to a fixed-length UTF-8 string dataset.
|
||
|
|
# Numpy's normal conversion only handles ASCII characters, but
|
||
|
|
# when the destination is UTF-8, we want to allow any unicode.
|
||
|
|
# This *doesn't* handle numpy fixed-length unicode data ('U' dtype),
|
||
|
|
# as HDF5 has no equivalent, and converting fixed length UTF-32
|
||
|
|
# to variable length UTF-8 would obscure what's going on.
|
||
|
|
str_array = numpy.asarray(val, order='C', dtype=object)
|
||
|
|
val = numpy.array([
|
||
|
|
s.encode('utf-8') for s in str_array.flat
|
||
|
|
], dtype=self.dtype).reshape(str_array.shape)
|
||
|
|
else:
|
||
|
|
# If the input data is already an array, let HDF5 do the conversion.
|
||
|
|
# If it's a list or similar, don't make numpy guess a dtype for it.
|
||
|
|
dt = None if isinstance(val, numpy.ndarray) else self.dtype.base
|
||
|
|
val = numpy.asarray(val, order='C', dtype=dt)
|
||
|
|
|
||
|
|
# Check for array dtype compatibility and convert
|
||
|
|
if self.dtype.subdtype is not None:
|
||
|
|
shp = self.dtype.subdtype[1]
|
||
|
|
valshp = val.shape[-len(shp):]
|
||
|
|
if valshp != shp: # Last dimension has to match
|
||
|
|
raise TypeError("When writing to array types, last N dimensions have to match (got %s, but should be %s)" % (valshp, shp,))
|
||
|
|
mtype = h5t.py_create(numpy.dtype((val.dtype, shp)))
|
||
|
|
mshape = val.shape[0:len(val.shape)-len(shp)]
|
||
|
|
|
||
|
|
# Make a compound memory type if field-name slicing is required
|
||
|
|
elif len(names) != 0:
|
||
|
|
|
||
|
|
mshape = val.shape
|
||
|
|
|
||
|
|
# Catch common errors
|
||
|
|
if self.dtype.fields is None:
|
||
|
|
raise TypeError("Illegal slicing argument (not a compound dataset)")
|
||
|
|
mismatch = [x for x in names if x not in self.dtype.fields]
|
||
|
|
if len(mismatch) != 0:
|
||
|
|
mismatch = ", ".join('"%s"'%x for x in mismatch)
|
||
|
|
raise ValueError("Illegal slicing argument (fields %s not in dataset type)" % mismatch)
|
||
|
|
|
||
|
|
# Write non-compound source into a single dataset field
|
||
|
|
if len(names) == 1 and val.dtype.fields is None:
|
||
|
|
subtype = h5t.py_create(val.dtype)
|
||
|
|
mtype = h5t.create(h5t.COMPOUND, subtype.get_size())
|
||
|
|
mtype.insert(self._e(names[0]), 0, subtype)
|
||
|
|
|
||
|
|
# Make a new source type keeping only the requested fields
|
||
|
|
else:
|
||
|
|
fieldnames = [x for x in val.dtype.names if x in names] # Keep source order
|
||
|
|
mtype = h5t.create(h5t.COMPOUND, val.dtype.itemsize)
|
||
|
|
for fieldname in fieldnames:
|
||
|
|
subtype = h5t.py_create(val.dtype.fields[fieldname][0])
|
||
|
|
offset = val.dtype.fields[fieldname][1]
|
||
|
|
mtype.insert(self._e(fieldname), offset, subtype)
|
||
|
|
|
||
|
|
# Use mtype derived from array (let DatasetID.write figure it out)
|
||
|
|
else:
|
||
|
|
mshape = val.shape
|
||
|
|
mtype = None
|
||
|
|
|
||
|
|
# Perform the dataspace selection
|
||
|
|
selection = sel.select(self.shape, args, dataset=self)
|
||
|
|
|
||
|
|
if selection.nselect == 0:
|
||
|
|
return
|
||
|
|
|
||
|
|
# Broadcast scalars if necessary.
|
||
|
|
# In order to avoid slow broadcasting filling the destination by
|
||
|
|
# the scalar value, we create an intermediate array of the same
|
||
|
|
# size as the destination buffer provided that size is reasonable.
|
||
|
|
# We assume as reasonable a size smaller or equal as the used dataset
|
||
|
|
# chunk size if any.
|
||
|
|
# In case of dealing with a non-chunked destination dataset or with
|
||
|
|
# a selection whose size is larger than the dataset chunk size we fall
|
||
|
|
# back to using an intermediate array of size equal to the last dimension
|
||
|
|
# of the destination buffer.
|
||
|
|
# The reasoning behind is that it makes sense to assume the creator of
|
||
|
|
# the dataset used an appropriate chunk size according the available
|
||
|
|
# memory. In any case, if we cannot afford to create an intermediate
|
||
|
|
# array of the same size as the dataset chunk size, the user program has
|
||
|
|
# little hope to go much further. Solves h5py issue #1067
|
||
|
|
if mshape == () and selection.array_shape != ():
|
||
|
|
if self.dtype.subdtype is not None:
|
||
|
|
raise TypeError("Scalar broadcasting is not supported for array dtypes")
|
||
|
|
if self.chunks and (product(self.chunks) >= product(selection.array_shape)):
|
||
|
|
val2 = numpy.empty(selection.array_shape, dtype=val.dtype)
|
||
|
|
else:
|
||
|
|
val2 = numpy.empty(selection.array_shape[-1], dtype=val.dtype)
|
||
|
|
val2[...] = val
|
||
|
|
val = val2
|
||
|
|
mshape = val.shape
|
||
|
|
|
||
|
|
# Perform the write, with broadcasting
|
||
|
|
mspace = h5s.create_simple(selection.expand_shape(mshape))
|
||
|
|
for fspace in selection.broadcast(mshape):
|
||
|
|
self.id.write(mspace, fspace, val, mtype, dxpl=self._dxpl)
|
||
|
|
|
||
|
|
def read_direct(self, dest, source_sel=None, dest_sel=None):
|
||
|
|
""" Read data directly from HDF5 into an existing NumPy array.
|
||
|
|
|
||
|
|
The destination array must be C-contiguous and writable.
|
||
|
|
Selections must be the output of numpy.s_[<args>].
|
||
|
|
|
||
|
|
Broadcasting is supported for simple indexing.
|
||
|
|
"""
|
||
|
|
with phil:
|
||
|
|
if self._is_empty:
|
||
|
|
raise TypeError("Empty datasets have no numpy representation")
|
||
|
|
if source_sel is None:
|
||
|
|
source_sel = sel.SimpleSelection(self.shape)
|
||
|
|
else:
|
||
|
|
source_sel = sel.select(self.shape, source_sel, self) # for numpy.s_
|
||
|
|
fspace = source_sel.id
|
||
|
|
|
||
|
|
if dest_sel is None:
|
||
|
|
dest_sel = sel.SimpleSelection(dest.shape)
|
||
|
|
else:
|
||
|
|
dest_sel = sel.select(dest.shape, dest_sel)
|
||
|
|
|
||
|
|
for mspace in dest_sel.broadcast(source_sel.array_shape):
|
||
|
|
self.id.read(mspace, fspace, dest, dxpl=self._dxpl)
|
||
|
|
|
||
|
|
def write_direct(self, source, source_sel=None, dest_sel=None):
|
||
|
|
""" Write data directly to HDF5 from a NumPy array.
|
||
|
|
|
||
|
|
The source array must be C-contiguous. Selections must be
|
||
|
|
the output of numpy.s_[<args>].
|
||
|
|
|
||
|
|
Broadcasting is supported for simple indexing.
|
||
|
|
"""
|
||
|
|
with phil:
|
||
|
|
if self._is_empty:
|
||
|
|
raise TypeError("Empty datasets cannot be written to")
|
||
|
|
if source_sel is None:
|
||
|
|
source_sel = sel.SimpleSelection(source.shape)
|
||
|
|
else:
|
||
|
|
source_sel = sel.select(source.shape, source_sel) # for numpy.s_
|
||
|
|
mspace = source_sel.id
|
||
|
|
|
||
|
|
if dest_sel is None:
|
||
|
|
dest_sel = sel.SimpleSelection(self.shape)
|
||
|
|
else:
|
||
|
|
dest_sel = sel.select(self.shape, dest_sel, self)
|
||
|
|
|
||
|
|
for fspace in dest_sel.broadcast(source_sel.array_shape):
|
||
|
|
self.id.write(mspace, fspace, source, dxpl=self._dxpl)
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __array__(self, dtype=None):
|
||
|
|
""" Create a Numpy array containing the whole dataset. DON'T THINK
|
||
|
|
THIS MEANS DATASETS ARE INTERCHANGEABLE WITH ARRAYS. For one thing,
|
||
|
|
you have to read the whole dataset every time this method is called.
|
||
|
|
"""
|
||
|
|
arr = numpy.zeros(self.shape, dtype=self.dtype if dtype is None else dtype)
|
||
|
|
|
||
|
|
# Special case for (0,)*-shape datasets
|
||
|
|
if self.size == 0:
|
||
|
|
return arr
|
||
|
|
|
||
|
|
self.read_direct(arr)
|
||
|
|
return arr
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def __repr__(self):
|
||
|
|
if not self:
|
||
|
|
r = '<Closed HDF5 dataset>'
|
||
|
|
else:
|
||
|
|
if self.name is None:
|
||
|
|
namestr = '("anonymous")'
|
||
|
|
else:
|
||
|
|
name = pp.basename(pp.normpath(self.name))
|
||
|
|
namestr = '"%s"' % (name if name != '' else '/')
|
||
|
|
r = '<HDF5 dataset %s: shape %s, type "%s">' % (
|
||
|
|
namestr, self.shape, self.dtype.str
|
||
|
|
)
|
||
|
|
return r
|
||
|
|
|
||
|
|
if hasattr(h5d.DatasetID, "refresh"):
|
||
|
|
@with_phil
|
||
|
|
def refresh(self):
|
||
|
|
""" Refresh the dataset metadata by reloading from the file.
|
||
|
|
|
||
|
|
This is part of the SWMR features and only exist when the HDF5
|
||
|
|
library version >=1.9.178
|
||
|
|
"""
|
||
|
|
self._id.refresh()
|
||
|
|
self._cache_props.clear()
|
||
|
|
|
||
|
|
if hasattr(h5d.DatasetID, "flush"):
|
||
|
|
@with_phil
|
||
|
|
def flush(self):
|
||
|
|
""" Flush the dataset data and metadata to the file.
|
||
|
|
If the dataset is chunked, raw data chunks are written to the file.
|
||
|
|
|
||
|
|
This is part of the SWMR features and only exist when the HDF5
|
||
|
|
library version >=1.9.178
|
||
|
|
"""
|
||
|
|
self._id.flush()
|
||
|
|
|
||
|
|
if vds_support:
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def is_virtual(self):
|
||
|
|
"""Check if this is a virtual dataset"""
|
||
|
|
return self._dcpl.get_layout() == h5d.VIRTUAL
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def virtual_sources(self):
|
||
|
|
"""Get a list of the data mappings for a virtual dataset"""
|
||
|
|
if not self.is_virtual:
|
||
|
|
raise RuntimeError("Not a virtual dataset")
|
||
|
|
dcpl = self._dcpl
|
||
|
|
return [
|
||
|
|
VDSmap(dcpl.get_virtual_vspace(j),
|
||
|
|
dcpl.get_virtual_filename(j),
|
||
|
|
dcpl.get_virtual_dsetname(j),
|
||
|
|
dcpl.get_virtual_srcspace(j))
|
||
|
|
for j in range(dcpl.get_virtual_count())]
|
||
|
|
|
||
|
|
@with_phil
|
||
|
|
def make_scale(self, name=''):
|
||
|
|
"""Make this dataset an HDF5 dimension scale.
|
||
|
|
|
||
|
|
You can then attach it to dimensions of other datasets like this::
|
||
|
|
|
||
|
|
other_ds.dims[0].attach_scale(ds)
|
||
|
|
|
||
|
|
You can optionally pass a name to associate with this scale.
|
||
|
|
"""
|
||
|
|
h5ds.set_scale(self._id, self._e(name))
|
||
|
|
|
||
|
|
@property
|
||
|
|
@with_phil
|
||
|
|
def is_scale(self):
|
||
|
|
"""Return ``True`` if this dataset is also a dimension scale.
|
||
|
|
|
||
|
|
Return ``False`` otherwise.
|
||
|
|
"""
|
||
|
|
return h5ds.is_scale(self._id)
|