/usr/lib/python3/dist-packages/h5py/_hl/attrs.py is in python3-h5py 2.7.0-1.
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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 high-level operations for attributes.
Provides the AttributeManager class, available on high-level objects
as <obj>.attrs.
"""
from __future__ import absolute_import
import numpy
from .. import h5s, h5t, h5a
from . import base
from .base import phil, with_phil, Empty, is_empty_dataspace
from .dataset import readtime_dtype
from .datatype import Datatype
class AttributeManager(base.MutableMappingHDF5, base.CommonStateObject):
"""
Allows dictionary-style access to an HDF5 object's attributes.
These are created exclusively by the library and are available as
a Python attribute at <object>.attrs
Like Group objects, attributes provide a minimal dictionary-
style interface. Anything which can be reasonably converted to a
Numpy array or Numpy scalar can be stored.
Attributes are automatically created on assignment with the
syntax <obj>.attrs[name] = value, with the HDF5 type automatically
deduced from the value. Existing attributes are overwritten.
To modify an existing attribute while preserving its type, use the
method modify(). To specify an attribute of a particular type and
shape, use create().
"""
def __init__(self, parent):
""" Private constructor.
"""
self._id = parent.id
@with_phil
def __getitem__(self, name):
""" Read the value of an attribute.
"""
attr = h5a.open(self._id, self._e(name))
if is_empty_dataspace(attr):
return Empty(attr.dtype)
dtype = readtime_dtype(attr.dtype, [])
shape = attr.shape
# Do this first, as we'll be fiddling with the dtype for top-level
# array types
htype = h5t.py_create(dtype)
# NumPy doesn't support top-level array types, so we have to "fake"
# the correct type and shape for the array. For example, consider
# attr.shape == (5,) and attr.dtype == '(3,)f'. Then:
if dtype.subdtype is not None:
subdtype, subshape = dtype.subdtype
shape = attr.shape + subshape # (5, 3)
dtype = subdtype # 'f'
arr = numpy.ndarray(shape, dtype=dtype, order='C')
attr.read(arr, mtype=htype)
if len(arr.shape) == 0:
return arr[()]
return arr
@with_phil
def __setitem__(self, name, value):
""" Set a new attribute, overwriting any existing attribute.
The type and shape of the attribute are determined from the data. To
use a specific type or shape, or to preserve the type of an attribute,
use the methods create() and modify().
"""
self.create(name, data=value, dtype=base.guess_dtype(value))
@with_phil
def __delitem__(self, name):
""" Delete an attribute (which must already exist). """
h5a.delete(self._id, self._e(name))
def create(self, name, data, shape=None, dtype=None):
""" Create a new attribute, overwriting any existing attribute.
name
Name of the new attribute (required)
data
An array to initialize the attribute (required)
shape
Shape of the attribute. Overrides data.shape if both are
given, in which case the total number of points must be unchanged.
dtype
Data type of the attribute. Overrides data.dtype if both
are given.
"""
import uuid
with phil:
# First, make sure we have a NumPy array. We leave the data
# type conversion for HDF5 to perform.
if not isinstance(data, Empty):
data = numpy.asarray(data, order='C')
if shape is None:
shape = data.shape
use_htype = None # If a committed type is given, we must use it
# in the call to h5a.create.
if isinstance(dtype, Datatype):
use_htype = dtype.id
dtype = dtype.dtype
elif dtype is None:
dtype = data.dtype
else:
dtype = numpy.dtype(dtype) # In case a string, e.g. 'i8' is passed
original_dtype = dtype # We'll need this for top-level array types
# Where a top-level array type is requested, we have to do some
# fiddling around to present the data as a smaller array of
# subarrays.
if dtype.subdtype is not None:
subdtype, subshape = dtype.subdtype
# Make sure the subshape matches the last N axes' sizes.
if shape[-len(subshape):] != subshape:
raise ValueError("Array dtype shape %s is incompatible with data shape %s" % (subshape, shape))
# New "advertised" shape and dtype
shape = shape[0:len(shape)-len(subshape)]
dtype = subdtype
# Not an array type; make sure to check the number of elements
# is compatible, and reshape if needed.
else:
if shape is not None and numpy.product(shape) != numpy.product(data.shape):
raise ValueError("Shape of new attribute conflicts with shape of data")
if shape != data.shape:
data = data.reshape(shape)
# We need this to handle special string types.
if not isinstance(data, Empty):
data = numpy.asarray(data, dtype=dtype)
# Make HDF5 datatype and dataspace for the H5A calls
if use_htype is None:
htype = h5t.py_create(original_dtype, logical=True)
htype2 = h5t.py_create(original_dtype) # Must be bit-for-bit representation rather than logical
else:
htype = use_htype
htype2 = None
if isinstance(data, Empty):
space = h5s.create(h5s.NULL)
else:
space = h5s.create_simple(shape)
# This mess exists because you can't overwrite attributes in HDF5.
# So we write to a temporary attribute first, and then rename.
tempname = uuid.uuid4().hex
try:
attr = h5a.create(self._id, self._e(tempname), htype, space)
except:
raise
else:
try:
if not isinstance(data, Empty):
attr.write(data, mtype=htype2)
except:
attr.close()
h5a.delete(self._id, self._e(tempname))
raise
else:
try:
# No atomic rename in HDF5 :(
if h5a.exists(self._id, self._e(name)):
h5a.delete(self._id, self._e(name))
h5a.rename(self._id, self._e(tempname), self._e(name))
except:
attr.close()
h5a.delete(self._id, self._e(tempname))
raise
def modify(self, name, value):
""" Change the value of an attribute while preserving its type.
Differs from __setitem__ in that if the attribute already exists, its
type is preserved. This can be very useful for interacting with
externally generated files.
If the attribute doesn't exist, it will be automatically created.
"""
with phil:
if not name in self:
self[name] = value
else:
value = numpy.asarray(value, order='C')
attr = h5a.open(self._id, self._e(name))
if is_empty_dataspace(attr):
raise IOError("Empty attributes can't be modified")
# Allow the case of () <-> (1,)
if (value.shape != attr.shape) and not \
(numpy.product(value.shape) == 1 and numpy.product(attr.shape) == 1):
raise TypeError("Shape of data is incompatible with existing attribute")
attr.write(value)
@with_phil
def __len__(self):
""" Number of attributes attached to the object. """
# I expect we will not have more than 2**32 attributes
return h5a.get_num_attrs(self._id)
def __iter__(self):
""" Iterate over the names of attributes. """
with phil:
attrlist = []
def iter_cb(name, *args):
""" Callback to gather attribute names """
attrlist.append(self._d(name))
h5a.iterate(self._id, iter_cb)
for name in attrlist:
yield name
@with_phil
def __contains__(self, name):
""" Determine if an attribute exists, by name. """
return h5a.exists(self._id, self._e(name))
@with_phil
def __repr__(self):
if not self._id:
return "<Attributes of closed HDF5 object>"
return "<Attributes of HDF5 object at %s>" % id(self._id)
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