# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2020 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 io
import os
import re
import gzip
import getpass
import itertools
import collections
import numpy
import h5py
import pandas
from openquake.baselib import hdf5, config, performance, python3compat, general
CALC_REGEX = r'(calc|cache)_(\d+)\.hdf5'
[docs]def get_datadir():
"""
Extracts the path of the directory where the openquake data are stored
from the environment ($OQ_DATADIR) or from the shared_dir in the
configuration file.
"""
datadir = os.environ.get('OQ_DATADIR')
if not datadir:
shared_dir = config.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')
return datadir
[docs]def get_calc_ids(datadir=None):
"""
Extract the available calculation IDs from the datadir, in order.
"""
datadir = datadir or get_datadir()
if not os.path.exists(datadir):
return []
calc_ids = set()
for f in os.listdir(datadir):
mo = re.match(CALC_REGEX, f)
if mo:
calc_ids.add(int(mo.group(2)))
return sorted(calc_ids)
[docs]def get_last_calc_id(datadir=None):
"""
Extract the latest calculation ID from the given directory.
If none is found, return 0.
"""
datadir = datadir or get_datadir()
calcs = get_calc_ids(datadir)
if not calcs:
return 0
return calcs[-1]
[docs]def hdf5new(datadir=None):
"""
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.
"""
datadir = datadir or get_datadir()
if not os.path.exists(datadir):
os.makedirs(datadir)
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
performance.init_performance(new)
return new
[docs]def read(calc_id, mode='r', datadir=None):
"""
:param calc_id: calculation ID or filename
: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.
"""
datadir = datadir or get_datadir()
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:
dstore.parent = read(hc_id, datadir=os.path.dirname(dstore.filename))
return dstore
def _range(value):
if hasattr(value, '__len__'):
return list(value)
else:
return list(range(value))
[docs]def sel(dset, filterdict):
"""
Select a dataset with shape_descr. For instance
dstore.sel('hcurves', imt='PGA', sid=2)
"""
assert 'shape_descr' in dset.attrs, 'Missing %s.shape_descr' % dset.name
lst = []
for dim in python3compat.decode(dset.attrs['shape_descr']):
if dim in filterdict:
val = filterdict[dim]
values = _range(dset.attrs[dim])
idx = values.index(val)
lst.append(slice(idx, idx + 1))
else:
lst.append(slice(None))
return dset[tuple(lst)]
[docs]def dset2df(dset, index, filterdict):
"""
Converts an HDF5 dataset with an attribute shape_descr into a Pandas
dataframe. NB: this is very slow for large datasets.
"""
arr = sel(dset, filterdict)
shape_descr = python3compat.decode(dset.attrs['shape_descr'])
out = []
tags = []
idxs = []
dtlist = []
for dim in shape_descr:
values = _range(dset.attrs[dim])
if dim in filterdict:
val = filterdict[dim]
idx = values.index(val)
idxs.append([idx])
values = [val]
else:
idxs.append(range(len(values)))
if isinstance(values[0], str): # like the loss_type
dt = '<S16'
else:
dt = type(values[0])
dtlist.append((dim, dt))
tags.append(values)
dtlist.append(('value', dset.dtype))
for idx, vals in zip(itertools.product(*idxs), itertools.product(*tags)):
out.append(vals + (arr[idx],))
return pandas.DataFrame.from_records(numpy.array(out, dtlist), index)
[docs]class DataStore(collections.abc.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'] = 42
>>> print(ds['example'][()])
42
>>> 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`.
"""
[docs] class EmptyDataset(ValueError):
"""Raised when reading an empty dataset"""
def __init__(self, calc_id=None, datadir=None, params=(), mode=None):
datadir = datadir or get_datadir()
if isinstance(calc_id, str): # passed a real path
self.filename = calc_id
self.calc_id, datadir = extract_calc_id_datadir(calc_id, datadir)
else:
if calc_id is None: # use a new datastore
self.calc_id = get_last_calc_id(datadir) + 1
elif 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.filename = os.path.join(
datadir, 'calc_%s.hdf5' % self.calc_id)
self.tempname = self.filename[:-5] + '_tmp.hdf5'
if not os.path.exists(datadir):
os.makedirs(datadir)
self.params = params
self.parent = () # can be set later
self.datadir = datadir
self.mode = mode or ('r+' if os.path.exists(self.filename) else 'w')
if self.mode == 'r' and not os.path.exists(self.filename):
raise IOError('File not found: %s' % self.filename)
self.hdf5 = () # so that `key in self.hdf5` is valid
self.open(self.mode)
[docs] def open(self, mode):
"""
Open the underlying .hdf5 file and the parent, if any
"""
if self.hdf5 == (): # not already open
try:
self.hdf5 = hdf5.File(self.filename, mode)
except OSError as exc:
raise OSError('%s in %s' % (exc, self.filename))
@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__
"""
try:
return h5py.File.__getitem__(self.hdf5, name)
except KeyError:
if self.parent != ():
return self.parent.getitem(name)
else:
raise
[docs] def swmr_on(self):
"""
Enable the SWMR mode on the underlying HDF5 file
"""
self.close() # flush everything
self.open('a')
try:
self.hdf5.swmr_mode = True
except ValueError: # already set
pass
[docs] def set_attrs(self, key, **kw):
"""
Set the HDF5 attributes of the given key
"""
self.hdf5.save_attrs(key, kw)
[docs] def set_shape_attrs(self, key, **kw):
"""
Set shape attributes
"""
hdf5.set_shape_attrs(self.hdf5, key, kw)
[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 get_attrs(self, key):
"""
:param key: dataset path
:returns: dictionary of attributes for that path
"""
try:
dset = h5py.File.__getitem__(self.hdf5, key)
except KeyError:
if self.parent != ():
dset = h5py.File.__getitem__(self.parent.hdf5, key)
else:
raise
return dict(dset.attrs)
[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 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)
"""
# removing inner slashed to avoid creating intermediate directories
name, ext = relname.replace('/', '-').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 = prefix + ('-%s' % postfix if postfix else '') + '.' + fmt
return self.export_path(fname, export_dir)
[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 = ()
[docs] def clear(self):
"""Remove the datastore from the file system"""
self.close()
os.remove(self.filename)
[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.filename)
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
[docs] def store_files(self, fnames, where='input/'):
"""
:param fnames: a set of full pathnames
"""
prefix = len(os.path.commonprefix(fnames))
for fname in fnames:
data = gzip.compress(open(fname, 'rb').read())
self[where + fname[prefix:]] = numpy.void(data)
[docs] def retrieve_files(self, prefix='input'):
"""
:yields: pairs (relative path, data)
"""
for k, v in self[prefix].items():
if hasattr(v, 'items'):
yield from self.retrieve_files(prefix + '/' + k)
else:
yield prefix + '/' + k, gzip.decompress(
bytes(numpy.asarray(v[()])))
[docs] def get_file(self, key):
"""
:returns: a BytesIO object
"""
data = bytes(numpy.asarray(self[key][()]))
return io.BytesIO(gzip.decompress(data))
[docs] def read_df(self, key, index=None, sel=()):
"""
:param key: name of the structured dataset
:param index: if given, name of the "primary key" field
:param sel: dictionary used to select subsets of the dataset
:returns: pandas DataFrame associated to the dataset
"""
dset = self.getitem(key)
if len(dset) == 0:
raise self.EmptyDataset('Dataset %s is empty' % key)
elif 'shape_descr' in dset.attrs:
return dset2df(dset, index, sel)
elif '__pdcolumns__' in dset.attrs:
columns = dset.attrs['__pdcolumns__'].split()
dic = {col: dset[col][:] for col in columns if col != index}
if index is not None:
index = dset[index][:]
return pandas.DataFrame(dic, index=index)
dtlist = []
for name in dset.dtype.names:
dt = dset.dtype[name]
if dt.shape: # vector field
templ = name + '_%d' * len(dt.shape)
for i, _ in numpy.ndenumerate(numpy.zeros(dt.shape)):
dtlist.append((templ % i, dt.base))
else: # scalar field
dtlist.append((name, dt))
data = numpy.zeros(len(dset), dtlist)
for name in dset.dtype.names:
arr = dset[name]
dt = dset.dtype[name]
if dt.shape: # vector field
templ = name + '_%d' * len(dt.shape)
for i, _ in numpy.ndenumerate(numpy.zeros(dt.shape)):
data[templ % i] = arr[(slice(None),) + i]
else: # scalar field
data[name] = arr
return pandas.DataFrame.from_records(data, index=index)
[docs] def read_unique(self, key, field):
"""
:param key: key to a dataset containing a structured array
:param field: a field in the structured array
:returns: sorted, unique values
Works with chunks of 1M records
"""
unique = set()
dset = self.getitem(key)
for slc in general.gen_slices(0, len(dset), 10_000_000):
arr = numpy.unique(dset[slc][field])
unique.update(arr)
return sorted(unique)
[docs] def sel(self, key, **kw):
"""
Select a dataset with shape_descr. For instance
dstore.sel('hcurves', imt='PGA', sid=2)
"""
return sel(self.getitem(key), kw)
@property
def metadata(self):
"""
:returns: datastore metadata version, date, checksum as a dictionary
"""
a = self.hdf5.attrs
return dict(generated_by='OpenQuake engine %s' % a['engine_version'],
start_date=a['date'], checksum=a['checksum32'])
def __getitem__(self, key):
if self.hdf5 == (): # the datastore is closed
raise ValueError('Cannot find %s in %s' % (key, self))
try:
val = self.hdf5[key]
except KeyError:
if self.parent != ():
self.parent.open('r')
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))
return val
def __setitem__(self, key, val):
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.filename))
def __delitem__(self, key):
del self.hdf5[key]
def __enter__(self):
self.was_close = self.hdf5 == ()
if self.was_close:
self.open(self.mode)
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=(),
filename=self.filename)
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):
if self.hdf5 == (): # closed
return 1
return sum(1 for f in self.hdf5)
def __hash__(self):
return self.calc_id
def __repr__(self):
status = 'open' if self.hdf5 else 'closed'
return '<%s %s %s>' % (self.__class__.__name__, self.filename, status)