# -*- 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
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero 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 <http://www.gnu.org/licenses/>.
import os
import re
import getpass
import collections
import numpy
import h5py
from openquake.baselib.python3compat import pickle
from openquake.baselib import hdf5
from openquake.commonlib import config
from openquake.commonlib.writers import write_csv
DATADIR = os.environ.get('OQ_DATADIR')
if not DATADIR:
shared_dir = config.get('directory', 'shared_dir')
if shared_dir:
DATADIR = os.path.join(shared_dir, getpass.getuser(), 'oqdata')
else: # use the home of the user
DATADIR = os.path.join(os.path.expanduser('~'), 'oqdata')
[docs]def get_calc_ids(datadir=DATADIR):
"""
Extract the available calculation IDs from the datadir, in order.
"""
if not os.path.exists(datadir):
return []
calc_ids = []
for f in os.listdir(datadir):
mo = re.match(r'calc_(\d+)\.hdf5', f)
if mo:
calc_ids.append(int(mo.group(1)))
return sorted(calc_ids)
[docs]def get_last_calc_id(datadir=DATADIR):
"""
Extract the latest calculation ID from the given directory.
If none is found, return 0.
"""
calcs = get_calc_ids(datadir)
if not calcs:
return 0
return calcs[-1]
[docs]def hdf5new(datadir=DATADIR):
"""
Return a new `hdf5.File by` instance with name determined by the last
calculation in the datadir (plus one). Set the .path attribute to the
generated filename.
"""
calc_id = get_last_calc_id(datadir) + 1
fname = os.path.join(datadir, 'calc_%d.hdf5' % calc_id)
new = hdf5.File(fname, 'w')
new.path = fname
return new
[docs]def read(calc_id, mode='r', datadir=DATADIR):
"""
:param calc_id: calculation ID or hdf5path
:param mode: 'r' or 'w'
:param datadir: the directory where to look
:returns: the corresponding DataStore instance
Read the datastore, if it exists and it is accessible.
"""
dstore = DataStore(calc_id, datadir, mode=mode)
try:
hc_id = dstore['oqparam'].hazard_calculation_id
except KeyError: # no oqparam
hc_id = None
if hc_id:
# TODO: we will need to store the parent directory to be able
# to use hazard calculations generated by another user;
# for the moment we assume that the datadir is the same for
# parent and child calculations
dstore.parent = read(hc_id, datadir=datadir)
return dstore
[docs]class DataStore(collections.MutableMapping):
"""
DataStore class to store the inputs/outputs of a calculation on the
filesystem.
Here is a minimal example of usage:
>>> ds = DataStore()
>>> ds['example'] = 'hello world'
>>> print(ds['example'])
hello world
>>> ds.clear()
When reading the items, the DataStore will return a generator. The
items will be ordered lexicographically according to their name.
There is a serialization protocol to store objects in the datastore.
An object is serializable if it has a method `__toh5__` returning
an array and a dictionary, and a method `__fromh5__` taking an array
and a dictionary and populating the object.
For an example of use see :class:`openquake.hazardlib.site.SiteCollection`.
"""
def __init__(self, calc_id=None, datadir=DATADIR,
params=(), mode=None):
calc_id, datadir = extract_calc_id_datadir(calc_id, datadir)
if not os.path.exists(datadir):
os.makedirs(datadir)
if calc_id < 0: # use an old datastore
calc_ids = get_calc_ids(datadir)
try:
self.calc_id = calc_ids[calc_id]
except IndexError:
raise IndexError('There are %d old calculations, cannot '
'retrieve the %s' % (len(calc_ids), calc_id))
else: # use the given datastore
self.calc_id = calc_id
self.params = params
self.mode = mode
self.parent = () # can be set later
self.datadir = datadir
self.calc_dir = os.path.join(datadir, 'calc_%s' % self.calc_id)
self.hdf5path = self.calc_dir + '.hdf5'
if mode == 'r' and not os.path.exists(self.hdf5path):
raise IOError('File not found: %s' % self.hdf5path)
self.hdf5 = None
self.open()
[docs] def open(self):
"""
Open the underlying .hdf5 file and the parent, if any
"""
if self.hdf5 is None: # not already open
mode = self.mode or 'r+' if os.path.exists(self.hdf5path) else 'w'
self.hdf5 = hdf5.File(self.hdf5path, mode, libver='latest')
if self.parent != () and self.parent.hdf5 is None:
self.parent.open()
@property
def export_dir(self):
"""
Return the underlying export directory
"""
try:
return self._export_dir
except AttributeError:
self._export_dir = self['oqparam'].export_dir
return self._export_dir
@export_dir.setter
def export_dir(self, value):
"""
Set the export directory
"""
self._export_dir = value
@property
def export_dir(self):
"""
Return the underlying export directory
"""
edir = getattr(self, '_export_dir', None) or self['oqparam'].export_dir
return edir
@export_dir.setter
def export_dir(self, value):
"""
Set the export directory
"""
self._export_dir = value
[docs] def getitem(self, name):
"""
Return a dataset by using h5py.File.__getitem__
"""
return h5py.File.__getitem__(self.hdf5, name)
[docs] def set_nbytes(self, key, nbytes=None):
"""
Set the `nbytes` attribute on the HDF5 object identified by `key`.
"""
return self.hdf5.set_nbytes(key, nbytes)
[docs] def set_attrs(self, key, **kw):
"""
Set the HDF5 attributes of the given key
"""
attrs = h5py.File.__getitem__(self.hdf5, key).attrs
for k, v in kw.items():
attrs[k] = v
[docs] def get_attr(self, key, name, default=None):
"""
:param key: dataset path
:param name: name of the attribute
:param default: value to return if the attribute is missing
"""
try:
obj = h5py.File.__getitem__(self.hdf5, key)
except KeyError:
if self.parent != ():
return self.parent.get_attr(key, name, default)
else:
raise
try:
return obj.attrs[name]
except KeyError:
if default is None:
raise
return default
[docs] def create_dset(self, key, dtype, shape=(None,), compression=None,
fillvalue=0, attrs=None):
"""
Create a one-dimensional HDF5 dataset.
:param key: name of the dataset
:param dtype: dtype of the dataset (usually composite)
:param shape: shape of the dataset, possibly extendable
:param compression: the kind of HDF5 compression to use
:param attrs: dictionary of attributes of the dataset
:returns: a HDF5 dataset
"""
return hdf5.create(
self.hdf5, key, dtype, shape, compression, fillvalue, attrs)
[docs] def extend(self, key, array, **attrs):
"""
Extend the dataset associated to the given key; create it if needed
:param key: name of the dataset
:param array: array to store
:param attrs: a dictionary of attributes
"""
try:
dset = self.hdf5[key]
except KeyError:
dset = hdf5.create(self.hdf5, key, array.dtype,
shape=(None,) + array.shape[1:])
hdf5.extend(dset, array)
for k, v in attrs.items():
dset.attrs[k] = v
return dset
[docs] def save(self, key, kw):
"""
Update the object associated to `key` with the `kw` dictionary;
works for LiteralAttrs objects and automatically flushes.
"""
if key not in self:
obj = hdf5.LiteralAttrs()
else:
obj = self[key]
vars(obj).update(kw)
self[key] = obj
self.flush()
[docs] def export_path(self, relname, export_dir=None):
"""
Return the path of the exported file by adding the export_dir in
front, the calculation ID at the end.
:param relname: relative file name
:param export_dir: export directory (if None use .export_dir)
"""
assert not os.path.dirname(relname), relname
name, ext = relname.rsplit('.', 1)
newname = '%s_%s.%s' % (name, self.calc_id, ext)
if export_dir is None:
export_dir = self.export_dir
return os.path.join(export_dir, newname)
[docs] def build_fname(self, prefix, postfix, fmt, export_dir=None):
"""
Build a file name from a realization, by using prefix and extension.
:param prefix: the prefix to use
:param postfix: the postfix to use (can be a realization object)
:param fmt: the extension ('csv', 'xml', etc)
:param export_dir: export directory (if None use .export_dir)
:returns: relative pathname including the extension
"""
if hasattr(postfix, 'sm_lt_path'): # is a realization
fname = '%s-rlz-%03d.%s' % (prefix, postfix.ordinal, fmt)
else:
fname = '%s-%s.%s' % (prefix, postfix, fmt)
return self.export_path(fname, export_dir)
[docs] def export_csv(self, key):
"""
Generic csv exporter
"""
return write_csv(self.export_path(key, 'csv'), self[key])
[docs] def flush(self):
"""Flush the underlying hdf5 file"""
if self.parent != ():
self.parent.flush()
if self.hdf5: # is open
self.hdf5.flush()
[docs] def close(self):
"""Close the underlying hdf5 file"""
if self.parent != ():
self.parent.flush()
self.parent.close()
if self.hdf5: # is open
self.hdf5.flush()
self.hdf5.close()
self.hdf5 = None
[docs] def clear(self):
"""Remove the datastore from the file system"""
self.close()
os.remove(self.hdf5path)
[docs] def getsize(self, key=None):
"""
Return the size in byte of the output associated to the given key.
If no key is given, returns the total size of all files.
"""
if key is None:
return os.path.getsize(self.hdf5path)
return hdf5.ByteCounter.get_nbytes(
h5py.File.__getitem__(self.hdf5, key))
[docs] def get(self, key, default):
"""
:returns: the value associated to the datastore key, or the default
"""
try:
return self[key]
except KeyError:
return default
def __getitem__(self, key):
try:
val = self.hdf5[key]
except KeyError:
if self.parent != ():
try:
val = self.parent[key]
except KeyError:
raise KeyError(
'No %r found in %s and ancestors' % (key, self))
else:
raise KeyError('No %r found in %s' % (key, self))
try:
shape = val.shape
except AttributeError: # val is a group
return val
if not shape:
val = pickle.loads(val.value)
return val
def __setitem__(self, key, value):
if isinstance(value, dict) or hasattr(value, '__toh5__'):
val = value
elif (not isinstance(value, numpy.ndarray) or
value.dtype is numpy.dtype(object)):
val = numpy.array(pickle.dumps(value, pickle.HIGHEST_PROTOCOL))
else:
val = value
if key in self.hdf5:
# there is a bug in the current version of HDF5 for composite
# arrays: is impossible to save twice the same key; so we remove
# the key first, then it is possible to save it again
del self[key]
try:
self.hdf5[key] = val
except RuntimeError as exc:
raise RuntimeError('Could not save %s: %s in %s' %
(key, exc, self.hdf5path))
def __delitem__(self, key):
del self.hdf5[key]
def __enter__(self):
self.was_close = self.hdf5 is None
if self.was_close:
self.open()
return self
def __exit__(self, etype, exc, tb):
if self.was_close: # and has been opened in __enter__, close it
self.close()
del self.was_close
def __getstate__(self):
# make the datastore pickleable
return dict(mode='r',
parent=self.parent,
calc_id=self.calc_id,
hdf5=None,
hdf5path=self.hdf5path)
def __iter__(self):
if not self.hdf5:
raise RuntimeError('%s is closed' % self)
for path in sorted(self.hdf5):
yield path
def __contains__(self, key):
return key in self.hdf5 or self.parent and key in self.parent.hdf5
def __len__(self):
return sum(1 for f in self)
def __repr__(self):
return '<%s %d>' % (self.__class__.__name__, self.calc_id)
[docs]def persistent_attribute(key):
"""
Persistent attributes are persisted to the datastore and cached.
Modifications to mutable objects are not automagically persisted.
If you have a huge object that does not fit in memory use the datastore
directory (for instance, open a HDF5 file to create an empty array, then
populate it). Notice that you can use any dict-like data structure in
place of the datastore, provided you can set attributes on it.
Here is an example:
>>> class Datastore(dict):
... "A fake datastore"
>>> class Store(object):
... a = persistent_attribute('a')
... def __init__(self, a):
... self.datastore = Datastore()
... self.a = a # this assegnation will store the attribute
>>> store = Store([1])
>>> store.a # this retrieves the attribute
[1]
>>> store.a.append(2)
>>> store.a = store.a # remember to store the modified attribute!
:param key: the name of the attribute to be made persistent
:returns: a property to be added to a class with a .datastore attribute
"""
privatekey = '_' + key
def getter(self):
# Try to get the value from the privatekey attribute (i.e. from
# the cache of the datastore); if not possible, get the value
# from the datastore and set the cache; if not possible, get the
# value from the parent and set the cache. If the value cannot
# be retrieved, raise an AttributeError.
try:
return getattr(self.datastore, privatekey)
except AttributeError:
value = self.datastore[key]
setattr(self.datastore, privatekey, value)
return value
def setter(self, value):
# Update the datastore and the private key
self.datastore[key] = value
setattr(self.datastore, privatekey, value)
return property(getter, setter)