Source code for openquake.baselib.hdf5

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4

# Copyright (C) 2015-2017 GEM Foundation

# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.

# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.

# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake.  If not, see <>.

import os
import sys
import ast
import logging
import operator
import tempfile
import importlib
import itertools
try:  # with Python 3
    from urllib.parse import quote_plus, unquote_plus
except ImportError:  # with Python 2
    from urllib import quote_plus, unquote_plus
import collections
import numpy
import h5py
from openquake.baselib.python3compat import pickle, decode

vbytes = h5py.special_dtype(vlen=bytes)
vstr = h5py.special_dtype(vlen=str)
vuint32 = h5py.special_dtype(vlen=numpy.uint32)

[docs]def create(hdf5, name, dtype, shape=(None,), compression=None, fillvalue=0, attrs=None): """ :param hdf5: a h5py.File object :param name: an hdf5 key string :param dtype: dtype of the dataset (usually composite) :param shape: shape of the dataset (can be extendable) :param compression: None or 'gzip' are recommended :param attrs: dictionary of attributes of the dataset :returns: a HDF5 dataset """ if shape[0] is None: # extendable dataset dset = hdf5.create_dataset( name, (0,) + shape[1:], dtype, chunks=True, maxshape=shape, compression=compression) else: # fixed-shape dataset dset = hdf5.create_dataset(name, shape, dtype, fillvalue=fillvalue, compression=compression) if attrs: for k, v in attrs.items(): dset.attrs[k] = v return dset
[docs]def extend(dset, array): """ Extend an extensible dataset with an array of a compatible dtype. :param dset: an h5py dataset :param array: an array of length L :returns: the total length of the dataset (i.e. initial length + L) """ length = len(dset) newlength = length + len(array) dset.resize((newlength,) + array.shape[1:]) dset[length:newlength] = array return newlength
[docs]def extend3(hdf5path, key, array, **attrs): """ Extend an HDF5 file dataset with the given array """ with h5py.File(hdf5path) as h5: try: dset = h5[key] except KeyError: dset = create(h5, key, array.dtype, shape=(None,) + array.shape[1:]) length = extend(dset, array) for key, val in attrs.items(): dset.attrs[key] = val h5.flush() return length
[docs]class LiteralAttrs(object): """ A class to serialize a set of parameters in HDF5 format. The goal is to store simple parameters as an HDF5 table in a readable way. Each parameter can be retrieved as an attribute, given its name. The implementation treats specially dictionary attributes, by storing them as `attrname.keyname` strings, see the example below: >>> class Ser(LiteralAttrs): ... def __init__(self, a, b): ... self.a = a ... self.b = b >>> ser = Ser(1, dict(x='xxx', y='yyy')) >>> arr, attrs = ser.__toh5__() >>> for k, v in arr: ... print('%s=%s' % (k, v)) a=1 b.x='xxx' b.y='yyy' >>> s = object.__new__(Ser) >>> s.__fromh5__(arr, attrs) >>> s.a 1 >>> s.b['x'] 'xxx' The implementation is not recursive, i.e. there will be at most one dot in the serialized names (in the example here `a`, `b.x`, `b.y`). """ def __toh5__(self): info_dt = numpy.dtype([('par_name', vbytes), ('par_value', vbytes)]) attrnames = sorted(a for a in vars(self) if not a.startswith('_')) lst = [] for attr in attrnames: value = getattr(self, attr) if isinstance(value, dict): for k, v in sorted(value.items()): key = '%s.%s' % (attr, k) lst.append((key, repr(v))) else: lst.append((attr, repr(value))) return numpy.array(lst, info_dt), {} def __fromh5__(self, array, attrs): dd = collections.defaultdict(dict) for (name_, literal_) in array: name = decode(name_) literal = decode(literal_) if '.' in name: k1, k2 = name.split('.', 1) dd[k1][k2] = ast.literal_eval(literal) else: dd[name] = ast.literal_eval(literal) vars(self).update(dd) def __repr__(self): names = sorted(n for n in vars(self) if not n.startswith('_')) nameval = ', '.join('%s=%r' % (n, getattr(self, n)) for n in names) return '<%s %s>' % (self.__class__.__name__, nameval)
# the implementation below stores a dataset per each object; it would be nicer # to store an array, however I am not able to do that with the current version # of h5py; the best I could do is to store an array of variable length ASCII # strings, but then I would have to use the ASCII format of pickle, which is # the least efficient. The current solution looks like a decent compromise.
[docs]class PickleableSequence(collections.Sequence): """ An immutable sequence of pickleable objects that can be serialized in HDF5 format. Here is an example, using the LiteralAttrs class defined in this module, but any pickleable class would do: >>> seq = PickleableSequence([LiteralAttrs(), LiteralAttrs()]) >>> with File('/tmp/x.h5', 'w') as f: ... f['data'] = seq >>> with File('/tmp/x.h5') as f: ... f['data'] (<LiteralAttrs >, <LiteralAttrs >) """ def __init__(self, objects): self._objects = tuple(objects) def __getitem__(self, i): return self._objects[i] def __len__(self): return len(self._objects) def __repr__(self): return repr(self._objects) def __toh5__(self): dic = {} nbytes = 0 for i, obj in enumerate(self._objects): pik = pickle.dumps(obj, pickle.HIGHEST_PROTOCOL) dic['%06d' % i] = numpy.array(pik) nbytes += len(pik) return dic, dict(nbytes=nbytes) def __fromh5__(self, dic, attrs): self._objects = tuple(pickle.loads(dic[k].value) for k in sorted(dic)) vars(self).update(attrs)
[docs]def cls2dotname(cls): """ The full Python name (i.e. `pkg.subpkg.mod.cls`) of a class """ return '%s.%s' % (cls.__module__, cls.__name__)
[docs]def dotname2cls(dotname): """ The class associated to the given dotname (i.e. `pkg.subpkg.mod.cls`) """ modname, clsname = dotname.rsplit('.', 1) return getattr(importlib.import_module(modname), clsname)
[docs]def get_nbytes(dset): """ If the dataset has an attribute 'nbytes', return it. Otherwise get the size of the underlying array. Returns None if the dataset is actually a group. """ if 'nbytes' in dset.attrs: # look if the dataset has an attribute nbytes return dset.attrs['nbytes'] elif hasattr(dset, 'value'): # else extract nbytes from the underlying array return dset.size * numpy.zeros(1, dset.dtype).nbytes
[docs]class ByteCounter(object): """ A visitor used to measure the dimensions of a HDF5 dataset or group. Use it as ByteCounter.get_nbytes(dset_or_group). """
[docs] @classmethod def get_nbytes(cls, dset): nbytes = get_nbytes(dset) if nbytes is not None: return nbytes # else dip in the tree self = cls() dset.visititems(self) return self.nbytes
def __init__(self, nbytes=0): self.nbytes = nbytes def __call__(self, name, dset_or_group): nbytes = get_nbytes(dset_or_group) if nbytes: self.nbytes += nbytes
[docs]class File(h5py.File): """ Subclass of :class:`h5py.File` able to store and retrieve objects conforming to the HDF5 protocol used by the OpenQuake software. It works recursively also for dictionaries of the form name->obj. >>> f = File('/tmp/x.h5', 'w') >>> f['dic'] = dict(a=dict(x=1, y=2), b=3) >>> dic = f['dic'] >>> dic['a']['x'].value 1 >>> dic['b'].value 3 >>> f.close() """
[docs] @classmethod def temporary(cls): """ Returns a temporary hdf5 file, open for writing. The temporary name is stored in the .path attribute. It is the user responsability to remove the file when closed. """ fh, path = tempfile.mkstemp(suffix='.hdf5') os.close(fh) self = cls(path, 'w') self.path = path return self
def __setitem__(self, path, obj): cls = obj.__class__ if hasattr(obj, '__toh5__'): obj, attrs = obj.__toh5__() pyclass = cls2dotname(cls) else: pyclass = '' if isinstance(obj, dict): for k, v in sorted(obj.items()): key = '%s/%s' % (path, quote_plus(k)) self[key] = v else: super(File, self).__setitem__(path, obj) if pyclass: self.flush() # make sure it is fully saved a = super(File, self).__getitem__(path).attrs a['__pyclass__'] = pyclass for k, v in sorted(attrs.items()): a[k] = v def __getitem__(self, path): h5obj = super(File, self).__getitem__(path) h5attrs = h5obj.attrs if '__pyclass__' in h5attrs: # NB: the `decode` below is needed for Python 3 cls = dotname2cls(decode(h5attrs['__pyclass__'])) obj = cls.__new__(cls) if hasattr(h5obj, 'items'): # is group h5obj = {unquote_plus(k): self['%s/%s' % (path, k)] for k, v in h5obj.items()} elif hasattr(h5obj, 'value'): h5obj = h5obj.value obj.__fromh5__(h5obj, h5attrs) return obj else: return h5obj
[docs] def set_nbytes(self, key, nbytes=None): """ Set the `nbytes` attribute on the HDF5 object identified by `key`. """ obj = super(File, self).__getitem__(key) if nbytes is not None: # size set from outside obj.attrs['nbytes'] = nbytes else: # recursively determine the size of the datagroup obj.attrs['nbytes'] = nbytes = ByteCounter.get_nbytes(obj) return nbytes
[docs] def save(self, nodedict, root=''): """ Save a node dictionary in the .hdf5 file, starting from the root dataset. A common application is to convert XML files into .hdf5 files, see the usage in :mod:`openquake.commands.to_hdf5`. :param nodedict: a dictionary with keys 'tag', 'attrib', 'text', 'nodes' """ setitem = super(File, self).__setitem__ getitem = super(File, self).__getitem__ tag = nodedict['tag'] text = nodedict.get('text', None) if hasattr(text, 'strip'): text = text.strip() attrib = nodedict.get('attrib', {}) path = '/'.join([root, tag]) nodes = nodedict.get('nodes', []) if text not in ('', None): # text=0 is stored try: setitem(path, text) except Exception as exc: sys.stderr.write('%s: %s\n' % (path, exc)) raise elif attrib and not nodes: setitem(path, numpy.nan) for subdict in _resolve_duplicates(nodes):, path) if attrib: dset = getitem(path) for k, v in attrib.items(): dset.attrs[k] = v
def _resolve_duplicates(dicts): # when node dictionaries with duplicated tags are passed (for instance # [{'tag': 'SourceGroup', ...}, {'tag': 'SourceGroup', ...}]) # add an incremental number to the tag, preceded by a semicolon: # [{'tag': 'SourceGroup;1', ...}, {'tag': 'SourceGroup;2', ...}]) # in this way the dictionaries can be saved in HDF5 format without # name conflicts and by respecting the ordering for tag, grp in itertools.groupby(dicts, operator.itemgetter('tag')): group = list(grp) if len(group) > 1: # there are duplicate tags for i, dic in enumerate(group, 1): dic['tag'] += ';%d' % i return dicts
[docs]def array_of_vstr(lst): """ :param lst: a list of strings or bytes :returns: an array of variable length ASCII strings """ ls = [] for el in lst: try: ls.append(el.encode('utf-8')) except AttributeError: ls.append(el) return numpy.array(ls, vstr)
[docs]def save(path, items, **extra): """ :param path: an .hdf5 pathname :param items: a generator of pairs (key, array-like) :param extra: extra attributes to be saved in the file """ with File(path, 'w') as f: for key, val in items: try: f[key] = val except ValueError as err: if 'Object header message is too large' in str(err): logging.error(str(err)) for k, v in extra.items(): f.attrs[k] = v
[docs]class ArrayWrapper(object): """ A pickleable and serializable wrapper over an array, HDF5 dataset or group """ def __init__(self, array, attrs): vars(self).update(attrs) self.array = array def __iter__(self): return iter(self.array) def __len__(self): return len(self.array) def __getitem__(self, idx): return self.array[idx] def __toh5__(self): return (self.array, {k: v for k, v in vars(self).items() if k != 'array' and not k.startswith('_')}) def __fromh5__(self, array, attrs): self.__init__(array, attrs) @property def dtype(self): """dtype of the underlying array""" return self.array.dtype @property def shape(self): """shape of the underlying array""" return self.array.shape