Source code for openquake.baselib.hdf5

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

# Copyright (C) 2015-2021 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 ast
import csv
import inspect
import tempfile
import warnings
import importlib
import itertools
from urllib.parse import quote_plus, unquote_plus
import collections
import json
import toml
import pandas
import numpy
import h5py
from openquake.baselib import InvalidFile
from openquake.baselib.python3compat import encode, decode

vbytes = h5py.special_dtype(vlen=bytes)
vstr = h5py.special_dtype(vlen=str)
vuint8 = h5py.special_dtype(vlen=numpy.uint8)
vuint16 = h5py.special_dtype(vlen=numpy.uint16)
vuint32 = h5py.special_dtype(vlen=numpy.uint32)
vfloat32 = h5py.special_dtype(vlen=numpy.float32)
vfloat64 = h5py.special_dtype(vlen=numpy.float64)

FLOAT = (float, numpy.float32, numpy.float64)
INT = (int, numpy.int32, numpy.uint32, numpy.int64, numpy.uint64)

[docs]def maybe_encode(value): """ If value is a sequence of strings, encode it """ if isinstance(value, bytes): return numpy.void(value) if isinstance(value, (list, tuple)): if value and isinstance(value[0], str): return encode(value) return value
[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] = maybe_encode(v) return dset
[docs]def preshape(obj): """ :returns: the shape of obj, except the last dimension """ if hasattr(obj, 'shape'): # array return obj.shape[:-1] return ()
[docs]def extend(dset, array, **attrs): """ 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) if len(array) == 0: return length newlength = length + len(array) if == 'object': # vlen array shape = (newlength,) + preshape(array[0]) else: shape = (newlength,) + array.shape[1:] dset.resize(shape) dset[length:newlength] = array for key, val in attrs.items(): dset.attrs[key] = val return newlength
[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)
[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, 'dtype'): # 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 Group( """ A mock for a h5py group object """ def __init__(self, items, attrs): self.dic = {quote_plus(k): v for k, v in items} self.attrs = attrs def __getitem__(self, key): return self.dic[key] def __setitem__(self, key, value): self.dic[key] = value def __iter__(self): yield from self.dic def __len__(self): return len(self.dic)
[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'][()] 1 >>> dic['b'][()] 3 >>> f.close() """ def __init__(self, name, mode='r', driver=None, libver='latest', userblock_size=None, swmr=True, rdcc_nslots=None, rdcc_nbytes=None, rdcc_w0=None, track_order=None, **kwds): super().__init__(name, mode, driver, libver, userblock_size, swmr, rdcc_nslots, rdcc_nbytes, rdcc_w0, track_order, **kwds)
[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
[docs] def save_vlen(self, key, data): # used in SourceWriterTestCase """ Save a sequence of variable-length arrays :param key: name of the dataset :param data: data to store as a list of arrays """ shape = (None,) + data[0].shape[:-1] try: dset = self[key] except KeyError: vdt = h5py.special_dtype(vlen=data[0].dtype) dset = create(self, key, vdt, shape, fillvalue=None) length = len(dset) dset.resize((length + len(data),) + shape[1:]) dset[length:length + len(data)] = data
[docs] def save_attrs(self, path, attrs, **kw): items = list(attrs.items()) + list(kw.items()) if items: a = super().__getitem__(path).attrs for k, v in sorted(items): try: a[k] = maybe_encode(v) except Exception as exc: raise TypeError( 'Could not store attribute %s=%s: %s' % (k, v, exc))
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, Group)) and obj: for k, v in obj.items(): # NB: there was a line sorted(obj.items()) here # it was removed because it caused the absurd issue # # for an exposure with more than 65536 assets if isinstance(k, tuple): # multikey k = '-'.join(k) key = '%s/%s' % (path, quote_plus(k)) self[key] = v if isinstance(obj, Group): self.save_attrs( path, obj.attrs, __pyclass__=cls2dotname(Group)) elif (isinstance(obj, numpy.ndarray) and obj.shape and len(obj) and isinstance(obj[0], str)): self.create_dataset(path, obj.shape, vstr)[:] = obj elif isinstance(obj, numpy.ndarray) and obj.shape: d = self.create_dataset(path, obj.shape, obj.dtype, fillvalue=None) d[:] = obj elif (isinstance(obj, numpy.ndarray) and'bytes')): self._set(path, numpy.void(bytes(obj))) elif isinstance(obj, list) and len(obj) and isinstance( obj[0], numpy.ndarray): self.save_vlen(path, obj) elif isinstance(obj, bytes): self._set(path, numpy.void(obj)) else: self._set(path, obj) if pyclass: self.flush() # make sure it is fully saved self.save_attrs(path, attrs, __pyclass__=pyclass) def _set(self, path, obj): try: super().__setitem__(path, obj) except Exception as exc: raise exc.__class__('Could not set %s=%r' % (path, obj)) def __getitem__(self, path): h5obj = super().__getitem__(path) h5attrs = h5obj.attrs if '__pyclass__' in h5attrs: cls = dotname2cls(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, 'shape'): h5obj = h5obj[()] if hasattr(obj, '__fromh5__'): obj.__fromh5__(h5obj, h5attrs) else: # Group object obj.dic = h5obj obj.attrs = h5attrs return obj else: return h5obj def __getstate__(self): # make the file pickleable return {'_id': 0}
[docs] def getitem(self, name): """ Return a dataset by using h5py.File.__getitem__ """ return h5py.File.__getitem__(self, name)
[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 dumps(dic): """ Dump in json """ new = {} for k, v in dic.items(): if k.startswith('_') or v is None: pass elif isinstance(v, (list, tuple)) and v: if isinstance(v[0], INT): new[k] = [int(x) for x in v] elif isinstance(v[0], FLOAT): new[k] = [float(x) for x in v] else: new[k] = json.dumps(v) elif isinstance(v, FLOAT): new[k] = float(v) elif isinstance(v, INT): new[k] = int(v) elif hasattr(v, 'tolist'): lst = v.tolist() if lst and isinstance(lst[0], bytes): new[k] = json.dumps(decode_array(v)) else: new[k] = json.dumps(lst) elif hasattr(v, '__dict__'): new[k] = {cls2dotname(v.__class__): dumps(vars(v))} elif isinstance(v, dict): new[k] = dumps(v) else: new[k] = json.dumps(v) return "{%s}" % ','.join('\n"%s": %s' % it for it in new.items())
[docs]def set_shape_descr(hdf5file, dsetname, kw): """ Set shape attributes on a dataset (and possibly other attributes) """ dset = hdf5file[dsetname] S = len(dset.shape) if len(kw) < S: raise ValueError('The dataset %s has %d dimensions but you passed %d' ' axis' % (dsetname, S, len(kw))) keys = list(kw) fields, extra = keys[:S], keys[S:] dic = dict(shape_descr=fields) for f in fields: dic[f] = kw[f] dset.attrs['json'] = dumps(dic) for e in extra: dset.attrs[e] = kw[e]
[docs]def get_shape_descr(json_string): """ :param json_string: JSON string containing the shape_descr :returns: a dictionary field -> values extracted from the shape_descr """ dic = json.loads(json_string) for field in dic['shape_descr']: val = dic[field] if isinstance(val, INT): dic[field] = range(val) return dic
[docs]class ArrayWrapper(object): """ A pickleable and serializable wrapper over an array, HDF5 dataset or group :param array: an array (or the empty tuple) :param attrs: metadata of the array (or dictionary of arrays) """
[docs] @classmethod def from_(cls, obj, extra='value'): if isinstance(obj, cls): # it is already an ArrayWrapper return obj elif inspect.isgenerator(obj): array, attrs = (), dict(obj) elif hasattr(obj, '__toh5__'): return obj elif hasattr(obj, 'attrs'): # is a dataset array, attrs = obj[()], dict(obj.attrs) if 'json' in attrs: attrs.update(get_shape_descr(attrs.pop('json'))) else: # assume obj is an array array, attrs = obj, {} return cls(array, attrs, (extra,))
def __init__(self, array, attrs, extra=('value',)): vars(self).update(attrs) self._extra = tuple(extra) if len(array): self.array = array def __iter__(self): if hasattr(self, 'array'): return iter(self.array) else: return iter(vars(self).items()) def __len__(self): if hasattr(self, 'array'): return len(self.array) else: return len(vars(self)) def __getitem__(self, idx): if isinstance(idx, str) and idx in self.__dict__: return getattr(self, idx) return self.array[idx] def __setitem__(self, idx, val): if isinstance(idx, str) and idx in self.__dict__: setattr(self, idx, val) else: self.array[idx] = val def __toh5__(self): arr = getattr(self, 'array', ()) if len(arr): return arr, self.to_dict() return self.to_dict(), {} def __fromh5__(self, array, attrs): self.__init__(array, attrs) def __repr__(self): if hasattr(self, 'shape_descr'): assert len(self.shape) == len(self.shape_descr), ( self.shape_descr, self.shape) lst = ['%s=%d' % (descr, size) for descr, size in zip(self.shape_descr, self.shape)] return '<%s(%s)>' % (self.__class__.__name__, ', '.join(lst)) return '<%s%s>' % (self.__class__.__name__, self.shape) @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 if hasattr(self, 'array') else ()
[docs] def toml(self): """ :returns: a TOML string representation of the ArrayWrapper """ if self.shape: return toml.dumps(self.array) dic = {} for k, v in vars(self).items(): if k.startswith('_'): continue elif k == 'json': dic.update(json.loads(bytes(v))) else: dic[k] = v return toml.dumps(dic)
[docs] def to_dframe(self): """ Convert an ArrayWrapper with shape (D1, ..., DN) and attributes T1, ..., TN which are list of tags of lenghts D1, ... DN into a DataFrame with rows (tag1, ... tagN, extra1, ... extraM) of maximum length D1 * ... * DN. Zero values are discarded. >>> from pprint import pprint >>> dic = dict(shape_descr=['taxonomy', 'occupancy'], ... taxonomy=['RC', 'WOOD'], ... occupancy=['RES', 'IND', 'COM']) >>> arr = numpy.zeros((2, 3)) >>> arr[0, 0] = 2000 >>> arr[0, 1] = 5000 >>> arr[1, 0] = 500 >>> aw = ArrayWrapper(arr, dic) >>> pprint(aw.to_dframe()) taxonomy occupancy value 0 RC RES 2000.0 1 RC IND 5000.0 2 WOOD RES 500.0 """ if hasattr(self, 'array'): names = self.array.dtype.names if names: # wrapper over a structured array return pandas.DataFrame({n: self[n] for n in names}) if hasattr(self, 'json'): vars(self).update(json.loads(self.json)) shape = self.shape tup = len(self._extra) > 1 if tup: if shape[-1] != len(self._extra): raise ValueError( 'There are %d extra-fields but %d dimensions in %s' % (len(self._extra), shape[-1], self)) shape_descr = tuple(decode(d) for d in self.shape_descr) fields = shape_descr + self._extra out = [] tags = [] idxs = [] for i, tagname in enumerate(shape_descr): values = getattr(self, tagname) if len(values) != shape[i]: raise ValueError( 'The tag %s with %d values is inconsistent with %s' % (tagname, len(values), self)) tags.append(decode_array(values)) idxs.append(range(len(values))) for idx, values in zip(itertools.product(*idxs), itertools.product(*tags)): val = self.array[idx] if isinstance(val, numpy.ndarray): if val.sum(): out.append(values + tuple(val)) elif val: # is a scalar out.append(values + (val,)) return pandas.DataFrame(out, columns=fields)
[docs] def to_dict(self): """ Convert the public attributes into a dictionary """ return {k: v for k, v in vars(self).items() if k != 'array' and not k.startswith('_')}
[docs]def decode_array(values): """ Decode the values which are bytestrings. """ out = [] for val in values: try: out.append(val.decode('utf8')) except AttributeError: out.append(val) return out
[docs]def parse_comment(comment): """ Parse a comment of the form `investigation_time=50.0, imt="PGA", ...` and returns it as pairs of strings: >>> parse_comment('''path=['b1'], time=50.0, imt="PGA"''') [('path', ['b1']), ('time', 50.0), ('imt', 'PGA')] """ if comment[0] == '"' and comment[-1] == '"': comment = comment[1:-1] try: dic = toml.loads('{%s}' % comment.replace('""', '"')) except toml.TomlDecodeError as err: raise ValueError('%s in %s' % (err, comment)) return list(dic.items())
[docs]def build_dt(dtypedict, names): """ Build a composite dtype for a list of names and dictionary name -> dtype with a None entry corresponding to the default dtype. """ lst = [] for name in names: try: dt = dtypedict[name] except KeyError: if None in dtypedict: dt = dtypedict[None] else: raise KeyError('Missing dtype for field %r' % name) lst.append((name, vstr if dt is str else dt)) return numpy.dtype(lst)
def _read_csv(fileobj, compositedt): itemsize = [0] * len(compositedt) dt = [] for i, name in enumerate(compositedt.names): dt.append(compositedt[name]) if compositedt[name].kind == 'S': # limit of the length of byte-fields itemsize[i] = compositedt[name].itemsize rows = [] for lineno, row in enumerate(csv.reader(fileobj), 3): cols = [] for i, col in enumerate(row): if itemsize[i] and len(col) > itemsize[i]: raise ValueError( 'line %d: %s=%r has length %d > %d' % (lineno, compositedt.names[i], col, len(col), itemsize[i])) if dt[i].kind == 'b': # boolean cols.append(int(col)) else: cols.append(col) rows.append(tuple(cols)) return numpy.array(rows, compositedt) # NB: it would be nice to use numpy.loadtxt( # f, build_dt(dtypedict, header), delimiter=sep, ndmin=1, comments=None) # however numpy does not support quoting, and "foo,bar" would be split :-(
[docs]def read_csv(fname, dtypedict={None: float}, renamedict={}, sep=',', index=None): """ :param fname: a CSV file with an header and float fields :param dtypedict: a dictionary fieldname -> dtype, None -> default :param renamedict: aliases for the fields to rename :param sep: separator (default comma) :param index: if not None, returns a pandas DataFrame :returns: an ArrayWrapper, unless there is an index """ attrs = {} with open(fname, encoding='utf-8-sig') as f: while True: first = next(f) if first.startswith('#'): attrs = dict(parse_comment(first.strip('#,\n '))) continue break header = first.strip().split(sep) if isinstance(dtypedict, dict): dt = build_dt(dtypedict, header) else: # in test_recompute dt is already a composite dtype dt = dtypedict try: arr = _read_csv(f, dt) except KeyError: raise KeyError('Missing None -> default in dtypedict') except Exception as exc: raise InvalidFile('%s: %s' % (fname, exc)) if renamedict: newnames = [] for name in arr.dtype.names: new = renamedict.get(name, name) newnames.append(new) arr.dtype.names = newnames if index: df = pandas.DataFrame.from_records(arr, index) vars(df).update(attrs) return df return ArrayWrapper(arr, attrs)
def _fix_array(arr, key): """ :param arr: array or array-like object :param key: string associated to the error (appear in the error message) If `arr` is a numpy array with dtype object containing strings, convert it into a numpy array containing bytes, unless it has more than 2 dimensions or contains non-strings (these are errors). Return `arr` unchanged in the other cases. """ if arr is None: return () if not isinstance(arr, numpy.ndarray): return arr if arr.dtype.names: # for extract_assets d[0] is the pair # ('id', ('|S50', {'h5py_encoding': 'ascii'})) # this is a horrible workaround for the h5py 2.10.0 issue # dtlist = [] for i, n in enumerate(arr.dtype.names): if isinstance(arr.dtype.descr[i][1], tuple): dtlist.append((n, str(arr.dtype[n]))) else: dtlist.append((n, arr.dtype[n])) arr.dtype = dtlist return arr
[docs]def save_npz(obj, path): """ :param obj: object to serialize :param path: an .npz pathname """ a = {} for key, val in vars(obj).items(): if key.startswith('_'): continue elif isinstance(val, str): # without this oq extract would fail a[key] = val.encode('utf-8') else: a[key] = _fix_array(val, key) # turn into an error with warnings.catch_warnings(): warnings.filterwarnings("error", category=UserWarning) numpy.savez_compressed(path, **a)
# #################### obj <-> json ##################### #
[docs]def obj_to_json(obj): """ :param obj: a Python object with a .__dict__ :returns: a JSON string """ return dumps({cls2dotname(obj.__class__): vars(obj)})
[docs]def json_to_obj(js): """ :param js: a JSON string with the form {"cls": {"arg1": ...}} :returns: an instance cls(arg1, ...) """ [(dotname, attrs)] = json.loads(js).items() cls = dotname2cls(dotname) obj = cls.__new__(cls) vars(obj).update(attrs) return obj