536 lines
15 KiB
Python
536 lines
15 KiB
Python
# 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 operations common to all high-level objects (File, etc.).
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"""
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from collections.abc import (
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Mapping, MutableMapping, KeysView, ValuesView, ItemsView
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)
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import os
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import posixpath
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import numpy as np
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# The high-level interface is serialized; every public API function & method
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# is wrapped in a lock. We re-use the low-level lock because (1) it's fast,
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# and (2) it eliminates the possibility of deadlocks due to out-of-order
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# lock acquisition.
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from .._objects import phil, with_phil
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from .. import h5d, h5i, h5r, h5p, h5f, h5t, h5s
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from .compat import fspath, filename_encode
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def is_hdf5(fname):
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""" Determine if a file is valid HDF5 (False if it doesn't exist). """
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with phil:
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fname = os.path.abspath(fspath(fname))
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if os.path.isfile(fname):
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return h5f.is_hdf5(filename_encode(fname))
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return False
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def find_item_type(data):
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"""Find the item type of a simple object or collection of objects.
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E.g. [[['a']]] -> str
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The focus is on collections where all items have the same type; we'll return
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None if that's not the case.
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The aim is to treat numpy arrays of Python objects like normal Python
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collections, while treating arrays with specific dtypes differently.
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We're also only interested in array-like collections - lists and tuples,
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possibly nested - not things like sets or dicts.
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"""
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if isinstance(data, np.ndarray):
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if (
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data.dtype.kind == 'O'
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and not h5t.check_string_dtype(data.dtype)
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and not h5t.check_vlen_dtype(data.dtype)
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):
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item_types = {type(e) for e in data.flat}
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else:
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return None
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elif isinstance(data, (list, tuple)):
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item_types = {find_item_type(e) for e in data}
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else:
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return type(data)
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if len(item_types) != 1:
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return None
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return item_types.pop()
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def guess_dtype(data):
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""" Attempt to guess an appropriate dtype for the object, returning None
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if nothing is appropriate (or if it should be left up the the array
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constructor to figure out)
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"""
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with phil:
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if isinstance(data, h5r.RegionReference):
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return h5t.regionref_dtype
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if isinstance(data, h5r.Reference):
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return h5t.ref_dtype
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item_type = find_item_type(data)
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if item_type is bytes:
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return h5t.string_dtype(encoding='ascii')
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if item_type is str:
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return h5t.string_dtype()
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return None
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def is_float16_dtype(dt):
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if dt is None:
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return False
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dt = np.dtype(dt) # normalize strings -> np.dtype objects
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return dt.kind == 'f' and dt.itemsize == 2
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def array_for_new_object(data, specified_dtype=None):
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"""Prepare an array from data used to create a new dataset or attribute"""
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# We mostly let HDF5 convert data as necessary when it's written.
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# But if we are going to a float16 datatype, pre-convert in python
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# to workaround a bug in the conversion.
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# https://github.com/h5py/h5py/issues/819
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if is_float16_dtype(specified_dtype):
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as_dtype = specified_dtype
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elif not isinstance(data, np.ndarray) and (specified_dtype is not None):
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# If we need to convert e.g. a list to an array, don't leave numpy
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# to guess a dtype we already know.
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as_dtype = specified_dtype
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else:
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as_dtype = guess_dtype(data)
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data = np.asarray(data, order="C", dtype=as_dtype)
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# In most cases, this does nothing. But if data was already an array,
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# and as_dtype is a tagged h5py dtype (e.g. for an object array of strings),
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# asarray() doesn't replace its dtype object. This gives it the tagged dtype:
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if as_dtype is not None:
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data = data.view(dtype=as_dtype)
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return data
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def default_lapl():
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""" Default link access property list """
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lapl = h5p.create(h5p.LINK_ACCESS)
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fapl = h5p.create(h5p.FILE_ACCESS)
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fapl.set_fclose_degree(h5f.CLOSE_STRONG)
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lapl.set_elink_fapl(fapl)
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return lapl
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def default_lcpl():
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""" Default link creation property list """
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lcpl = h5p.create(h5p.LINK_CREATE)
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lcpl.set_create_intermediate_group(True)
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return lcpl
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dlapl = default_lapl()
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dlcpl = default_lcpl()
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def is_empty_dataspace(obj):
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""" Check if an object's dataspace is empty """
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if obj.get_space().get_simple_extent_type() == h5s.NULL:
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return True
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return False
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class CommonStateObject:
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"""
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Mixin class that allows sharing information between objects which
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reside in the same HDF5 file. Requires that the host class have
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a ".id" attribute which returns a low-level ObjectID subclass.
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Also implements Unicode operations.
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"""
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@property
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def _lapl(self):
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""" Fetch the link access property list appropriate for this object
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"""
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return dlapl
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@property
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def _lcpl(self):
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""" Fetch the link creation property list appropriate for this object
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"""
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return dlcpl
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def _e(self, name, lcpl=None):
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""" Encode a name according to the current file settings.
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Returns name, or 2-tuple (name, lcpl) if lcpl is True
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- Binary strings are always passed as-is, h5t.CSET_ASCII
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- Unicode strings are encoded utf8, h5t.CSET_UTF8
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If name is None, returns either None or (None, None) appropriately.
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"""
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def get_lcpl(coding):
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""" Create an appropriate link creation property list """
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lcpl = self._lcpl.copy()
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lcpl.set_char_encoding(coding)
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return lcpl
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if name is None:
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return (None, None) if lcpl else None
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if isinstance(name, bytes):
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coding = h5t.CSET_ASCII
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else:
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try:
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name = name.encode('ascii')
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coding = h5t.CSET_ASCII
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except UnicodeEncodeError:
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name = name.encode('utf8')
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coding = h5t.CSET_UTF8
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if lcpl:
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return name, get_lcpl(coding)
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return name
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def _d(self, name):
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""" Decode a name according to the current file settings.
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- Try to decode utf8
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- Failing that, return the byte string
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If name is None, returns None.
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"""
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if name is None:
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return None
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try:
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return name.decode('utf8')
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except UnicodeDecodeError:
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pass
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return name
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class _RegionProxy:
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"""
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Proxy object which handles region references.
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To create a new region reference (datasets only), use slicing syntax:
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>>> newref = obj.regionref[0:10:2]
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To determine the target dataset shape from an existing reference:
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>>> shape = obj.regionref.shape(existingref)
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where <obj> may be any object in the file. To determine the shape of
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the selection in use on the target dataset:
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>>> selection_shape = obj.regionref.selection(existingref)
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"""
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def __init__(self, obj):
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self.obj = obj
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self.id = obj.id
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def __getitem__(self, args):
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if not isinstance(self.id, h5d.DatasetID):
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raise TypeError("Region references can only be made to datasets")
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from . import selections
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with phil:
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selection = selections.select(self.id.shape, args, dataset=self.obj)
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return h5r.create(self.id, b'.', h5r.DATASET_REGION, selection.id)
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def shape(self, ref):
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""" Get the shape of the target dataspace referred to by *ref*. """
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with phil:
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sid = h5r.get_region(ref, self.id)
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return sid.shape
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def selection(self, ref):
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""" Get the shape of the target dataspace selection referred to by *ref*
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"""
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from . import selections
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with phil:
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sid = h5r.get_region(ref, self.id)
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return selections.guess_shape(sid)
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class HLObject(CommonStateObject):
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"""
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Base class for high-level interface objects.
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"""
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@property
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def file(self):
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""" Return a File instance associated with this object """
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from . import files
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with phil:
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return files.File(self.id)
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@property
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@with_phil
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def name(self):
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""" Return the full name of this object. None if anonymous. """
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return self._d(h5i.get_name(self.id))
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@property
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@with_phil
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def parent(self):
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"""Return the parent group of this object.
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This is always equivalent to obj.file[posixpath.dirname(obj.name)].
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ValueError if this object is anonymous.
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"""
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if self.name is None:
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raise ValueError("Parent of an anonymous object is undefined")
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return self.file[posixpath.dirname(self.name)]
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@property
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@with_phil
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def id(self):
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""" Low-level identifier appropriate for this object """
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return self._id
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@property
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@with_phil
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def ref(self):
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""" An (opaque) HDF5 reference to this object """
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return h5r.create(self.id, b'.', h5r.OBJECT)
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@property
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@with_phil
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def regionref(self):
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"""Create a region reference (Datasets only).
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The syntax is regionref[<slices>]. For example, dset.regionref[...]
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creates a region reference in which the whole dataset is selected.
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Can also be used to determine the shape of the referenced dataset
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(via .shape property), or the shape of the selection (via the
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.selection property).
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"""
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return _RegionProxy(self)
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@property
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def attrs(self):
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""" Attributes attached to this object """
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from . import attrs
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with phil:
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return attrs.AttributeManager(self)
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@with_phil
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def __init__(self, oid):
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""" Setup this object, given its low-level identifier """
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self._id = oid
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@with_phil
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def __hash__(self):
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return hash(self.id)
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@with_phil
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def __eq__(self, other):
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if hasattr(other, 'id'):
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return self.id == other.id
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return NotImplemented
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def __bool__(self):
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with phil:
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return bool(self.id)
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__nonzero__ = __bool__
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def __getnewargs__(self):
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"""Disable pickle.
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Handles for HDF5 objects can't be reliably deserialised, because the
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recipient may not have access to the same files. So we do this to
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fail early.
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If you really want to pickle h5py objects and can live with some
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limitations, look at the h5pickle project on PyPI.
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"""
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raise TypeError("h5py objects cannot be pickled")
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def __getstate__(self):
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# Pickle protocols 0 and 1 use this instead of __getnewargs__
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raise TypeError("h5py objects cannot be pickled")
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# --- Dictionary-style interface ----------------------------------------------
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# To implement the dictionary-style interface from groups and attributes,
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# we inherit from the appropriate abstract base classes in collections.
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#
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# All locking is taken care of by the subclasses.
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# We have to override ValuesView and ItemsView here because Group and
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# AttributeManager can only test for key names.
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class KeysViewHDF5(KeysView):
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def __str__(self):
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return "<KeysViewHDF5 {}>".format(list(self))
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def __reversed__(self):
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yield from reversed(self._mapping)
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__repr__ = __str__
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class ValuesViewHDF5(ValuesView):
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"""
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Wraps e.g. a Group or AttributeManager to provide a value view.
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Note that __contains__ will have poor performance as it has
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to scan all the links or attributes.
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"""
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def __contains__(self, value):
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with phil:
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for key in self._mapping:
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if value == self._mapping.get(key):
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return True
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return False
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def __iter__(self):
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with phil:
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for key in self._mapping:
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yield self._mapping.get(key)
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def __reversed__(self):
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with phil:
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for key in reversed(self._mapping):
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yield self._mapping.get(key)
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class ItemsViewHDF5(ItemsView):
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"""
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Wraps e.g. a Group or AttributeManager to provide an items view.
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"""
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def __contains__(self, item):
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with phil:
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key, val = item
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if key in self._mapping:
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return val == self._mapping.get(key)
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return False
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def __iter__(self):
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with phil:
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for key in self._mapping:
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yield (key, self._mapping.get(key))
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def __reversed__(self):
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with phil:
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for key in reversed(self._mapping):
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yield (key, self._mapping.get(key))
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class MappingHDF5(Mapping):
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"""
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Wraps a Group, AttributeManager or DimensionManager object to provide
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an immutable mapping interface.
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We don't inherit directly from MutableMapping because certain
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subclasses, for example DimensionManager, are read-only.
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"""
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def keys(self):
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""" Get a view object on member names """
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return KeysViewHDF5(self)
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def values(self):
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""" Get a view object on member objects """
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return ValuesViewHDF5(self)
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def items(self):
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""" Get a view object on member items """
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return ItemsViewHDF5(self)
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def _ipython_key_completions_(self):
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""" Custom tab completions for __getitem__ in IPython >=5.0. """
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return sorted(self.keys())
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class MutableMappingHDF5(MappingHDF5, MutableMapping):
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"""
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Wraps a Group or AttributeManager object to provide a mutable
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mapping interface, in contrast to the read-only mapping of
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MappingHDF5.
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"""
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pass
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class Empty:
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"""
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Proxy object to represent empty/null dataspaces (a.k.a H5S_NULL).
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This can have an associated dtype, but has no shape or data. This is not
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the same as an array with shape (0,).
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"""
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shape = None
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size = None
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def __init__(self, dtype):
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self.dtype = np.dtype(dtype)
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def __eq__(self, other):
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if isinstance(other, Empty) and self.dtype == other.dtype:
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return True
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return False
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def __repr__(self):
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return "Empty(dtype={0!r})".format(self.dtype)
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def product(nums):
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"""Calculate a numeric product
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For small amounts of data (e.g. shape tuples), this simple code is much
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faster than calling numpy.prod().
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"""
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prod = 1
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for n in nums:
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prod *= n
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return prod
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# Simple variant of cached_property:
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# Unlike functools, this has no locking, so we don't have to worry about
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# deadlocks with phil (see issue gh-2064). Unlike cached-property on PyPI, it
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# doesn't try to import asyncio (which can be ~100 extra modules).
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# Many projects seem to have similar variants of this, often without attribution,
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# but to be cautious, this code comes from cached-property (Copyright (c) 2015,
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# Daniel Greenfeld, BSD license), where it is attributed to bottle (Copyright
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# (c) 2009-2022, Marcel Hellkamp, MIT license).
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class cached_property(object):
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def __init__(self, func):
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self.__doc__ = getattr(func, "__doc__")
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self.func = func
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def __get__(self, obj, cls):
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if obj is None:
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return self
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value = obj.__dict__[self.func.__name__] = self.func(obj)
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return value
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