185 lines
5.2 KiB
Python
185 lines
5.2 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 support for HDF5 dimension scales.
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"""
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from __future__ import absolute_import
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from .. import h5ds
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from . import base
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from .base import phil, with_phil
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from .dataset import Dataset
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class DimensionProxy(base.CommonStateObject):
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"""
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Represents an HDF5 "dimension".
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"""
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@property
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@with_phil
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def label(self):
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""" Get or set the dimension scale label """
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#return h5ds.get_label(self._id, self._dimension)
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# Produces a segfault for a non-existent label (Fixed in hdf5-1.8.8).
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# Here is a workaround:
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try:
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dset = Dataset(self._id)
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return self._d(dset.attrs['DIMENSION_LABELS'][self._dimension])
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except (KeyError, IndexError):
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return ''
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@label.setter
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@with_phil
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def label(self, val):
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# pylint: disable=missing-docstring
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h5ds.set_label(self._id, self._dimension, self._e(val))
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@with_phil
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def __init__(self, id_, dimension):
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self._id = id_
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self._dimension = dimension
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@with_phil
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def __hash__(self):
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return hash((type(self), self._id, self._dimension))
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@with_phil
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def __eq__(self, other):
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return hash(self) == hash(other)
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@with_phil
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def __iter__(self):
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for k in self.keys():
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yield k
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@with_phil
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def __len__(self):
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return h5ds.get_num_scales(self._id, self._dimension)
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@with_phil
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def __getitem__(self, item):
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if isinstance(item, int):
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scales = []
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h5ds.iterate(self._id, self._dimension, scales.append, 0)
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return Dataset(scales[item])
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else:
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def f(dsid):
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""" Iterate over scales to find a matching name """
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if h5ds.get_scale_name(dsid) == self._e(item):
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return dsid
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res = h5ds.iterate(self._id, self._dimension, f, 0)
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if res is None:
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raise KeyError(item)
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return Dataset(res)
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def attach_scale(self, dset):
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""" Attach a scale to this dimension.
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Provide the Dataset of the scale you would like to attach.
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"""
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with phil:
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h5ds.attach_scale(self._id, dset.id, self._dimension)
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def detach_scale(self, dset):
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""" Remove a scale from this dimension.
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Provide the Dataset of the scale you would like to remove.
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"""
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with phil:
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h5ds.detach_scale(self._id, dset.id, self._dimension)
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def items(self):
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""" Get a list of (name, Dataset) pairs with all scales on this
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dimension.
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"""
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with phil:
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scales = []
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# H5DSiterate raises an error if there are no dimension scales,
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# rather than iterating 0 times. See #483.
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if len(self) > 0:
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h5ds.iterate(self._id, self._dimension, scales.append, 0)
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return [
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(self._d(h5ds.get_scale_name(x)), Dataset(x))
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for x in scales
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]
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def keys(self):
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""" Get a list of names for the scales on this dimension. """
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with phil:
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return [key for (key, _) in self.items()]
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def values(self):
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""" Get a list of Dataset for scales on this dimension. """
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with phil:
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return [val for (_, val) in self.items()]
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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 "<Dimension of closed HDF5 dataset>"
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return ('<"%s" dimension %d of HDF5 dataset at %s>'
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% (self.label, self._dimension, id(self._id)))
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class DimensionManager(base.MappingHDF5, base.CommonStateObject):
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"""
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Represents a collection of dimension associated with a dataset.
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Like AttributeManager, an instance of this class is returned when
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accessing the ".dims" property on a Dataset.
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"""
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@with_phil
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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, index):
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""" Return a Dimension object
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"""
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if index > len(self) - 1:
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raise IndexError('Index out of range')
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return DimensionProxy(self._id, index)
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@with_phil
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def __len__(self):
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""" Number of dimensions associated with the dataset. """
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return len(Dataset(self._id).shape)
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@with_phil
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def __iter__(self):
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""" Iterate over the dimensions. """
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for i in range(len(self)):
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yield self[i]
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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 "<Dimensions of closed HDF5 dataset>"
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return "<Dimensions of HDF5 object at %s>" % id(self._id)
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def create_scale(self, dset, name=''):
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""" Create a new dimension, from an initial scale.
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Provide the dataset and a name for the scale.
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"""
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with phil:
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h5ds.set_scale(dset.id, self._e(name))
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