/usr/lib/python2.7/dist-packages/h5py/_hl/dataset.py is in python-h5py 2.6.0-1.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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#
# http://www.h5py.org
#
# Copyright 2008-2013 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.
"""
from __future__ import absolute_import
import posixpath as pp
import sys
import six
from six.moves import xrange # pylint: disable=redefined-builtin
import numpy
from .. import h5, h5s, h5t, h5r, h5d, h5p, h5fd
from .base import HLObject, phil, with_phil
from . import filters
from . import selections as sel
from . import selections2 as sel2
from .datatype import Datatype
_LEGACY_GZIP_COMPRESSION_VALS = frozenset(range(10))
MPI = h5.get_config().mpi
def readtime_dtype(basetype, names):
""" Make a NumPy dtype appropriate for reading """
if len(names) == 0: # Not compound, or we want all fields
return basetype
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 not name 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])
def make_new_dset(parent, shape=None, dtype=None, data=None,
chunks=None, compression=None, shuffle=None,
fletcher32=None, maxshape=None, compression_opts=None,
fillvalue=None, scaleoffset=None, track_times=None):
""" Return a new low-level dataset identifier
Only creates anonymous datasets.
"""
# Convert data to a C-contiguous ndarray
if data is not None:
from . import base
data = numpy.asarray(data, order="C", dtype=base.guess_dtype(data))
# Validate shape
if shape is None:
if data is None:
raise TypeError("Either data or shape must be specified")
shape = data.shape
else:
shape = tuple(shape)
if data is not None and (numpy.product(shape) != numpy.product(data.shape)):
raise ValueError("Shape tuple is incompatible with data")
tmp_shape = maxshape if maxshape is not None else shape
# Validate chunk shape
if isinstance(chunks, tuple) and (-numpy.array([ i>=j for i,j in zip(tmp_shape,chunks) if i is not None])).any():
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.generate_dcpl(shape, dtype, chunks, compression, compression_opts,
shuffle, fletcher32, maxshape, scaleoffset)
if fillvalue is not None:
fillvalue = numpy.array(fillvalue)
dcpl.set_fill_value(fillvalue)
if track_times in (True, False):
dcpl.set_obj_track_times(track_times)
elif track_times is not None:
raise TypeError("track_times 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)
sid = h5s.create_simple(shape, maxshape)
dset_id = h5d.create(parent.id, None, tid, sid, dcpl=dcpl)
if data is not None:
dset_id.write(h5s.ALL, h5s.ALL, data)
return dset_id
class AstypeContext(object):
"""
Context manager which allows changing the type read from a dataset.
"""
def __init__(self, dset, dtype):
self._dset = dset
self._dtype = numpy.dtype(dtype)
def __enter__(self):
# pylint: disable=protected-access
self._dset._local.astype = self._dtype
def __exit__(self, *args):
# pylint: disable=protected-access
self._dset._local.astype = None
if MPI:
class CollectiveContext(object):
""" 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 Dataset(HLObject):
"""
Represents an HDF5 dataset
"""
def astype(self, dtype):
""" Get a context manager allowing you to perform reads to a
different destination type, e.g.:
>>> with dataset.astype('f8'):
... double_precision = dataset[0:100:2]
"""
return AstypeContext(self, dtype)
if MPI:
@property
@with_phil
def collective(self):
""" Context manager for MPI collective reads & writes """
return CollectiveContext(self)
@property
@with_phil
def dims(self):
""" Access dimension scales attached to this dataset. """
from .dims import DimensionManager
return DimensionManager(self)
@property
@with_phil
def ndim(self):
"""Numpy-style attribute giving the number of dimensions"""
return self.id.rank
@property
@with_phil
def shape(self):
"""Numpy-style shape tuple giving dataset dimensions"""
return self.id.shape
@shape.setter
@with_phil
def shape(self, shape):
# pylint: disable=missing-docstring
self.resize(shape)
@property
@with_phil
def size(self):
"""Numpy-style attribute giving the total dataset size"""
return numpy.prod(self.shape)
@property
@with_phil
def dtype(self):
"""Numpy dtype representing the datatype"""
return self.id.dtype
@property
@with_phil
def value(self):
""" Alias for dataset[()] """
DeprecationWarning("dataset.value has been deprecated. "
"Use dataset[()] instead.")
return self[()]
@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 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)
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.ndarray((1,), dtype=self.dtype)
self._dcpl.get_fill_value(arr)
return arr[0]
@with_phil
def __init__(self, bind):
""" Create a new Dataset object by binding to a low-level DatasetID.
"""
from threading import local
if not isinstance(bind, h5d.DatasetID):
raise ValueError("%s is not a DatasetID" % bind)
HLObject.__init__(self, bind)
self._dcpl = self.id.get_create_plist()
self._dxpl = h5p.create(h5p.DATASET_XFER)
self._filters = filters.get_filters(self._dcpl)
self._local = local()
self._local.astype = None
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 xrange(shape[0]):
yield self[i]
@with_phil
def __getitem__(self, args):
""" 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,)
# Sort field indices from the rest of the args.
names = tuple(x for x in args if isinstance(x, six.string_types))
args = tuple(x for x in args if not isinstance(x, six.string_types))
if not six.PY3:
names = tuple(x.encode('utf-8') if isinstance(x, six.text_type) else x for x in names)
new_dtype = getattr(self._local, 'astype', None)
if new_dtype is not None:
new_dtype = readtime_dtype(new_dtype, names)
else:
# This is necessary because in the case of array types, NumPy
# discards the array information at the top level.
new_dtype = readtime_dtype(self.id.dtype, names)
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:
return numpy.array((0,), dtype=new_dtype)
if numpy.product(mshape) == 0:
return numpy.array(mshape, dtype=new_dtype)
out = numpy.empty(mshape, dtype=new_dtype)
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 numpy.product(self.shape) == 0:
# These are the only access methods NumPy allows for such objects
if args == (Ellipsis,) or args == tuple():
return numpy.empty(self.shape, dtype=new_dtype)
# === Scalar dataspaces =================
if self.shape == ():
fspace = self.id.get_space()
selection = sel2.select_read(fspace, args)
arr = numpy.ndarray(selection.mshape, dtype=new_dtype)
for mspace, fspace in selection:
self.id.read(mspace, fspace, arr, mtype)
if len(names) == 1:
arr = arr[names[0]]
if selection.mshape is None:
return arr[()]
return arr
# === Everything else ===================
# Perform the dataspace selection.
selection = sel.select(self.shape, args, dsid=self.id)
if selection.nselect == 0:
return numpy.ndarray(selection.mshape, dtype=new_dtype)
# Up-converting to (1,) so that numpy.ndarray correctly creates
# np.void rows in case of multi-field dtype. (issue 135)
single_element = selection.mshape == ()
mshape = (1,) if single_element else selection.mshape
arr = numpy.ndarray(mshape, new_dtype, order='C')
# HDF5 has a bug where if the memory shape has a different rank
# than the dataset, the read is very slow
if len(mshape) < len(self.shape):
# pad with ones
mshape = (1,)*(len(self.shape)-len(mshape)) + mshape
# Perfom the actual read
mspace = h5s.create_simple(mshape)
fspace = selection.id
self.id.read(mspace, fspace, arr, mtype, dxpl=self._dxpl)
# Patch up the output for NumPy
if len(names) == 1:
arr = arr[names[0]] # Single-field recarray convention
if arr.shape == ():
arr = numpy.asscalar(arr)
if single_element:
arr = arr[0]
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, six.string_types))
args = tuple(x for x in args if not isinstance(x, six.string_types))
if not six.PY3:
names = tuple(x.encode('utf-8') if isinstance(x, six.text_type) else x for x in names)
# Generally we try to avoid converting the arrays on the Python
# side. However, for compound literals this is unavoidable.
vlen = h5t.check_dtype(vlen=self.dtype)
if vlen is not None and vlen not in (bytes, six.text_type):
try:
val = numpy.asarray(val, dtype=vlen)
except ValueError:
try:
val = numpy.array([numpy.array(x, dtype=vlen)
for x in val], dtype=self.dtype)
except ValueError:
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(
(numpy.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 == 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)])
else:
val = numpy.asarray(val, order='C')
# 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, dsid=self.id)
if selection.nselect == 0:
return
# Broadcast scalars if necessary.
if mshape == () and selection.mshape != ():
if self.dtype.subdtype is not None:
raise TypeError("Scalar broadcasting is not supported for array dtypes")
val2 = numpy.empty(selection.mshape[-1], dtype=val.dtype)
val2[...] = val
val = val2
mshape = val.shape
# Perform the write, with broadcasting
# Be careful to pad memory shape with ones to avoid HDF5 chunking
# glitch, which kicks in for mismatched memory/file selections
if len(mshape) < len(self.shape):
mshape_pad = (1,)*(len(self.shape)-len(mshape)) + mshape
else:
mshape_pad = mshape
mspace = h5s.create_simple(mshape_pad, (h5s.UNLIMITED,)*len(mshape_pad))
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 source_sel is None:
source_sel = sel.SimpleSelection(self.shape)
else:
source_sel = sel.select(self.shape, source_sel, self.id) # 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, self.id)
for mspace in dest_sel.broadcast(source_sel.mshape):
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 source_sel is None:
source_sel = sel.SimpleSelection(source.shape)
else:
source_sel = sel.select(source.shape, source_sel, self.id) # 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.id)
for fspace in dest_sel.broadcast(source_sel.mshape):
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 INTERCHANGABLE WITH ARRAYS. For one thing,
you have to read the whole dataset everytime this method is called.
"""
arr = numpy.empty(self.shape, dtype=self.dtype if dtype is None else dtype)
# Special case for (0,)*-shape datasets
if numpy.product(self.shape) == 0:
return arr
self.read_direct(arr)
return arr
@with_phil
def __repr__(self):
if not self:
r = six.u('<Closed HDF5 dataset>')
else:
if self.name is None:
namestr = six.u('("anonymous")')
else:
name = pp.basename(pp.normpath(self.name))
namestr = six.u('"%s"') % (
name if name != six.u('') else six.u('/'))
r = six.u('<HDF5 dataset %s: shape %s, type "%s">') % \
(namestr, self.shape, self.dtype.str)
if six.PY3:
return r
return r.encode('utf8')
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
librarary version >=1.9.178
"""
self._id.refresh()
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
librarary version >=1.9.178
"""
self._id.flush()
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