266 lines
9.2 KiB
Python
266 lines
9.2 KiB
Python
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# This file is part of h5py, a Python interface to the HDF5 library.
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#
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# http://www.h5py.org
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#
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# Copyright 2008-2013 Andrew Collette and contributors
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#
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# License: Standard 3-clause BSD; see "license.txt" for full license terms
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# and contributor agreement.
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"""
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Implements high-level operations for attributes.
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Provides the AttributeManager class, available on high-level objects
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as <obj>.attrs.
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"""
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from __future__ import absolute_import
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import numpy
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import uuid
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from .. import h5s, h5t, h5a
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from . import base
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from .base import phil, with_phil, Empty, is_empty_dataspace
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from .dataset import readtime_dtype
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from .datatype import Datatype
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class AttributeManager(base.MutableMappingHDF5, base.CommonStateObject):
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"""
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Allows dictionary-style access to an HDF5 object's attributes.
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These are created exclusively by the library and are available as
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a Python attribute at <object>.attrs
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Like Group objects, attributes provide a minimal dictionary-
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style interface. Anything which can be reasonably converted to a
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Numpy array or Numpy scalar can be stored.
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Attributes are automatically created on assignment with the
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syntax <obj>.attrs[name] = value, with the HDF5 type automatically
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deduced from the value. Existing attributes are overwritten.
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To modify an existing attribute while preserving its type, use the
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method modify(). To specify an attribute of a particular type and
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shape, use create().
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"""
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def __init__(self, parent):
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""" Private constructor.
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"""
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self._id = parent.id
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@with_phil
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def __getitem__(self, name):
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""" Read the value of an attribute.
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"""
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attr = h5a.open(self._id, self._e(name))
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if is_empty_dataspace(attr):
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return Empty(attr.dtype)
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dtype = readtime_dtype(attr.dtype, [])
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shape = attr.shape
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# Do this first, as we'll be fiddling with the dtype for top-level
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# array types
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htype = h5t.py_create(dtype)
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# NumPy doesn't support top-level array types, so we have to "fake"
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# the correct type and shape for the array. For example, consider
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# attr.shape == (5,) and attr.dtype == '(3,)f'. Then:
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if dtype.subdtype is not None:
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subdtype, subshape = dtype.subdtype
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shape = attr.shape + subshape # (5, 3)
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dtype = subdtype # 'f'
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arr = numpy.ndarray(shape, dtype=dtype, order='C')
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attr.read(arr, mtype=htype)
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if len(arr.shape) == 0:
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return arr[()]
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return arr
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@with_phil
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def __setitem__(self, name, value):
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""" Set a new attribute, overwriting any existing attribute.
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The type and shape of the attribute are determined from the data. To
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use a specific type or shape, or to preserve the type of an attribute,
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use the methods create() and modify().
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"""
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self.create(name, data=value, dtype=base.guess_dtype(value))
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@with_phil
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def __delitem__(self, name):
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""" Delete an attribute (which must already exist). """
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h5a.delete(self._id, self._e(name))
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def create(self, name, data, shape=None, dtype=None):
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""" Create a new attribute, overwriting any existing attribute.
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name
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Name of the new attribute (required)
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data
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An array to initialize the attribute (required)
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shape
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Shape of the attribute. Overrides data.shape if both are
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given, in which case the total number of points must be unchanged.
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dtype
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Data type of the attribute. Overrides data.dtype if both
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are given.
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"""
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with phil:
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# First, make sure we have a NumPy array. We leave the data
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# type conversion for HDF5 to perform.
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if not isinstance(data, Empty):
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data = numpy.asarray(data, order='C')
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if shape is None:
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shape = data.shape
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use_htype = None # If a committed type is given, we must use it
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# in the call to h5a.create.
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if isinstance(dtype, Datatype):
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use_htype = dtype.id
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dtype = dtype.dtype
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elif dtype is None:
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dtype = data.dtype
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else:
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dtype = numpy.dtype(dtype) # In case a string, e.g. 'i8' is passed
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original_dtype = dtype # We'll need this for top-level array types
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# Where a top-level array type is requested, we have to do some
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# fiddling around to present the data as a smaller array of
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# subarrays.
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if dtype.subdtype is not None:
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subdtype, subshape = dtype.subdtype
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# Make sure the subshape matches the last N axes' sizes.
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if shape[-len(subshape):] != subshape:
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raise ValueError("Array dtype shape %s is incompatible with data shape %s" % (subshape, shape))
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# New "advertised" shape and dtype
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shape = shape[0:len(shape)-len(subshape)]
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dtype = subdtype
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# Not an array type; make sure to check the number of elements
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# is compatible, and reshape if needed.
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else:
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if shape is not None and numpy.product(shape) != numpy.product(data.shape):
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raise ValueError("Shape of new attribute conflicts with shape of data")
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if shape != data.shape:
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data = data.reshape(shape)
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# We need this to handle special string types.
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if not isinstance(data, Empty):
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data = numpy.asarray(data, dtype=dtype)
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# Make HDF5 datatype and dataspace for the H5A calls
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if use_htype is None:
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htype = h5t.py_create(original_dtype, logical=True)
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htype2 = h5t.py_create(original_dtype) # Must be bit-for-bit representation rather than logical
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else:
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htype = use_htype
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htype2 = None
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if isinstance(data, Empty):
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space = h5s.create(h5s.NULL)
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else:
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space = h5s.create_simple(shape)
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# This mess exists because you can't overwrite attributes in HDF5.
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# So we write to a temporary attribute first, and then rename.
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tempname = uuid.uuid4().hex
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try:
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attr = h5a.create(self._id, self._e(tempname), htype, space)
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except:
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raise
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else:
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try:
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if not isinstance(data, Empty):
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attr.write(data, mtype=htype2)
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except:
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attr.close()
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h5a.delete(self._id, self._e(tempname))
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raise
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else:
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try:
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# No atomic rename in HDF5 :(
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if h5a.exists(self._id, self._e(name)):
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h5a.delete(self._id, self._e(name))
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h5a.rename(self._id, self._e(tempname), self._e(name))
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except:
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attr.close()
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h5a.delete(self._id, self._e(tempname))
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raise
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def modify(self, name, value):
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""" Change the value of an attribute while preserving its type.
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Differs from __setitem__ in that if the attribute already exists, its
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type is preserved. This can be very useful for interacting with
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externally generated files.
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If the attribute doesn't exist, it will be automatically created.
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"""
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with phil:
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if not name in self:
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self[name] = value
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else:
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value = numpy.asarray(value, order='C')
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attr = h5a.open(self._id, self._e(name))
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if is_empty_dataspace(attr):
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raise IOError("Empty attributes can't be modified")
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# Allow the case of () <-> (1,)
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if (value.shape != attr.shape) and not \
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(numpy.product(value.shape) == 1 and numpy.product(attr.shape) == 1):
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raise TypeError("Shape of data is incompatible with existing attribute")
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attr.write(value)
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@with_phil
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def __len__(self):
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""" Number of attributes attached to the object. """
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# I expect we will not have more than 2**32 attributes
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return h5a.get_num_attrs(self._id)
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def __iter__(self):
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""" Iterate over the names of attributes. """
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with phil:
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attrlist = []
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def iter_cb(name, *args):
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""" Callback to gather attribute names """
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attrlist.append(self._d(name))
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h5a.iterate(self._id, iter_cb)
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for name in attrlist:
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yield name
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@with_phil
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def __contains__(self, name):
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""" Determine if an attribute exists, by name. """
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return h5a.exists(self._id, self._e(name))
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@with_phil
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def __repr__(self):
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if not self._id:
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return "<Attributes of closed HDF5 object>"
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return "<Attributes of HDF5 object at %s>" % id(self._id)
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