Source code for openquake.baselib.general

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

# Copyright (C) 2014-2017 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 <>.

Utility functions of general interest.
from __future__ import division, print_function
import os
import sys
import imp
import copy
import math
import operator
import warnings
import tempfile
import importlib
import itertools
import subprocess
import collections

import numpy
from decorator import decorator
from openquake.baselib.python3compat import decode

F64 = numpy.float64

[docs]class WeightedSequence(collections.MutableSequence): """ A wrapper over a sequence of weighted items with a total weight attribute. Adding items automatically increases the weight. """ @classmethod
[docs] def merge(cls, ws_list): """ Merge a set of WeightedSequence objects. :param ws_list: a sequence of :class: `openquake.baselib.general.WeightedSequence` instances :returns: a :class:`openquake.baselib.general.WeightedSequence` instance """ return sum(ws_list, cls())
def __init__(self, seq=()): """ param seq: a finite sequence of pairs (item, weight) """ self._seq = [] self.weight = 0 self.extend(seq) def __getitem__(self, sliceobj): """ Return an item or a slice """ return self._seq[sliceobj] def __setitem__(self, i, v): """ Modify the sequence """ self._seq[i] = v def __delitem__(self, sliceobj): """ Remove an item from the sequence """ del self._seq[sliceobj] def __len__(self): """ The length of the sequence """ return len(self._seq) def __add__(self, other): """ Add two weighted sequences and return a new WeightedSequence with weight equal to the sum of the weights. """ new = self.__class__() new._seq.extend(self._seq) new._seq.extend(other._seq) new.weight = self.weight + other.weight return new
[docs] def insert(self, i, item_weight): """ Insert an item with the given weight in the sequence """ item, weight = item_weight self._seq.insert(i, item) self.weight += weight
def __lt__(self, other): """ Ensure ordering by weight """ return self.weight < other.weight def __eq__(self, other): """ Compare for equality the items contained in self """ return all(x == y for x, y in zip(self, other)) def __repr__(self): """ String representation of the sequence, including the weight """ return '<%s %s, weight=%s>' % (self.__class__.__name__, self._seq, self.weight)
[docs]def distinct(keys): """ Return the distinct keys in order. """ known = set() outlist = [] for key in keys: if key not in known: outlist.append(key) known.add(key) return outlist
[docs]def ceil(a, b): """ Divide a / b and return the biggest integer close to the quotient. :param a: a number :param b: a positive number :returns: the biggest integer close to the quotient """ assert b > 0, b return int(math.ceil(float(a) / b))
[docs]def nokey(item): """ Dummy function to apply to items without a key """ return 'Unspecified'
[docs]def block_splitter(items, max_weight, weight=lambda item: 1, kind=nokey): """ :param items: an iterator over items :param max_weight: the max weight to split on :param weight: a function returning the weigth of a given item :param kind: a function returning the kind of a given item Group together items of the same kind until the total weight exceeds the `max_weight` and yield `WeightedSequence` instances. Items with weight zero are ignored. For instance >>> items = 'ABCDE' >>> list(block_splitter(items, 3)) [<WeightedSequence ['A', 'B', 'C'], weight=3>, <WeightedSequence ['D', 'E'], weight=2>] The default weight is 1 for all items. """ if max_weight <= 0: raise ValueError('max_weight=%s' % max_weight) ws = WeightedSequence([]) prev_kind = 'Unspecified' for item in items: w = weight(item) k = kind(item) if w < 0: # error raise ValueError('The item %r got a negative weight %s!' % (item, w)) elif w == 0: # ignore items with 0 weight pass elif ws.weight + w > max_weight or k != prev_kind: new_ws = WeightedSequence([(item, w)]) if ws: yield ws ws = new_ws else: ws.append((item, w)) prev_kind = k if ws: yield ws
[docs]def split_in_slices(number, num_slices): """ :param number: a positive number to split in slices :param num_slices: the number of slices to return (at most) :returns: a list of slices >>> split_in_slices(4, 2) [slice(0, 2, None), slice(2, 4, None)] >>> split_in_slices(5, 1) [slice(0, 5, None)] >>> split_in_slices(5, 2) [slice(0, 3, None), slice(3, 5, None)] >>> split_in_slices(2, 4) [slice(0, 1, None), slice(1, 2, None)] """ assert number > 0, number assert num_slices > 0, num_slices blocksize = int(math.ceil(number / num_slices)) slices = [] start = 0 while True: stop = min(start + blocksize, number) slices.append(slice(start, stop)) if stop == number: break start += blocksize return slices
[docs]def split_in_blocks(sequence, hint, weight=lambda item: 1, key=nokey): """ Split the `sequence` in a number of WeightedSequences close to `hint`. :param sequence: a finite sequence of items :param hint: an integer suggesting the number of subsequences to generate :param weight: a function returning the weigth of a given item :param key: a function returning the key of a given item The WeightedSequences are of homogeneous key and they try to be balanced in weight. For instance >>> items = 'ABCDE' >>> list(split_in_blocks(items, 3)) [<WeightedSequence ['A', 'B'], weight=2>, <WeightedSequence ['C', 'D'], weight=2>, <WeightedSequence ['E'], weight=1>] """ if isinstance(sequence, int): return split_in_slices(sequence, hint) if hint == 0: # do not split return sequence items = list(sequence) if key is nokey else sorted(sequence, key=key) assert hint > 0, hint assert len(items) > 0, len(items) total_weight = float(sum(weight(item) for item in items)) return block_splitter(items, math.ceil(total_weight / hint), weight, key)
[docs]def assert_close(a, b, rtol=1e-07, atol=0, context=None): """ Compare for equality up to a given precision two composite objects which may contain floats. NB: if the objects are or contain generators, they are exhausted. :param a: an object :param b: another object :param rtol: relative tolerance :param atol: absolute tolerance """ if isinstance(a, float) or isinstance(a, numpy.ndarray) and a.shape: # shortcut numpy.testing.assert_allclose(a, b, rtol, atol) return if isinstance(a, (str, bytes, int)): # another shortcut assert a == b return if hasattr(a, '_slots_'): # record-like objects assert a._slots_ == b._slots_ for x in a._slots_: assert_close(getattr(a, x), getattr(b, x), rtol, atol, x) return if hasattr(a, 'keys'): # dict-like objects assert a.keys() == b.keys() for x in a: assert_close(a[x], b[x], rtol, atol, x) return if hasattr(a, '__dict__'): # objects with an attribute dictionary assert_close(vars(a), vars(b), context=a) return if hasattr(a, '__iter__'): # iterable objects xs, ys = list(a), list(b) assert len(xs) == len(ys), ('Lists of different lenghts: %d != %d' % (len(xs), len(ys))) for x, y in zip(xs, ys): assert_close(x, y, rtol, atol, x) return if a == b: # last attempt to avoid raising the exception return ctx = '' if context is None else 'in context ' + repr(context) raise AssertionError('%r != %r %s' % (a, b, ctx))
[docs]def writetmp(content=None, dir=None, prefix="tmp", suffix="tmp"): """Create temporary file with the given content. Please note: the temporary file must be deleted by the caller. :param string content: the content to write to the temporary file. :param string dir: directory where the file should be created :param string prefix: file name prefix :param string suffix: file name suffix :returns: a string with the path to the temporary file """ if dir is not None: if not os.path.exists(dir): os.makedirs(dir) fh, path = tempfile.mkstemp(dir=dir, prefix=prefix, suffix=suffix) if content: fh = os.fdopen(fh, "wb") if hasattr(content, 'encode'): content = content.encode('utf8') fh.write(content) fh.close() return path
[docs]def git_suffix(fname): """ :returns: `<short git hash>` if Git repository found """ try: gh = subprocess.check_output( ['git', 'rev-parse', '--short', 'HEAD'], stderr=open(os.devnull, 'w'), cwd=os.path.dirname(fname)).strip() gh = "-git" + decode(gh) if gh else '' return gh except: # trapping everything on purpose; git may not be installed or it # may not work properly return ''
[docs]def run_in_process(code, *args): """ Run in an external process the given Python code and return the output as a Python object. If there are arguments, then code is taken as a template and traditional string interpolation is performed. :param code: string or template describing Python code :param args: arguments to be used for interpolation :returns: the output of the process, as a Python object """ if args: code %= args try: out = subprocess.check_output([sys.executable, '-c', code]) except subprocess.CalledProcessError as exc: print(exc.cmd[-1], file=sys.stderr) raise if out: return eval(out, {}, {})
[docs]class CodeDependencyError(Exception): pass
[docs]def import_all(module_or_package): """ If `module_or_package` is a module, just import it; if it is a package, recursively imports all the modules it contains. Returns the names of the modules that were imported as a set. The set can be empty if the modules were already in sys.modules. """ already_imported = set(sys.modules) mod_or_pkg = importlib.import_module(module_or_package) if not hasattr(mod_or_pkg, '__path__'): # is a simple module return set(sys.modules) - already_imported # else import all modules contained in the package [pkg_path] = mod_or_pkg.__path__ n = len(pkg_path) for cwd, dirs, files in os.walk(pkg_path): if all(os.path.basename(f) != '' for f in files): # the current working directory is not a subpackage continue for f in files: if f.endswith('.py'): # convert PKGPATH/subpackage/ -> subpackage.module # works at any level of nesting modname = (module_or_package + cwd[n:].replace(os.sep, '.') + '.' + os.path.basename(f[:-3])) try: importlib.import_module(modname) except Exception as exc: print('Could not import %s: %s: %s' % ( modname, exc.__class__.__name__, exc), file=sys.stderr) return set(sys.modules) - already_imported
[docs]def assert_independent(package, *packages): """ :param package: Python name of a module/package :param packages: Python names of modules/packages Make sure the `package` does not depend from the `packages`. """ assert packages, 'At least one package must be specified' import_package = 'from openquake.baselib.general import import_all\n' \ 'print(import_all("%s"))' % package imported_modules = run_in_process(import_package) for mod in imported_modules: for pkg in packages: if mod.startswith(pkg): raise CodeDependencyError('%s depends on %s' % (package, pkg))
[docs]def search_module(module, syspath=sys.path): """ Given a module name (possibly with dots) returns the corresponding filepath, or None, if the module cannot be found. :param module: (dotted) name of the Python module to look for :param syspath: a list of directories to search (default sys.path) """ lst = module.split(".") pkg, submodule = lst[0], ".".join(lst[1:]) try: fileobj, filepath, descr = imp.find_module(pkg, syspath) except ImportError: return if submodule: # recursive search return search_module(submodule, [filepath]) return filepath
[docs]class CallableDict(collections.OrderedDict): r""" A callable object built on top of a dictionary of functions, used as a smart registry or as a poor man generic function dispatching on the first argument. It is typically used to implement converters. Here is an example: >>> format_attrs = CallableDict() # dict of functions (fmt, obj) -> str >>> @format_attrs.add('csv') # implementation for csv ... def format_attrs_csv(fmt, obj): ... items = sorted(vars(obj).items()) ... return '\n'.join('%s,%s' % item for item in items) >>> @format_attrs.add('json') # implementation for json ... def format_attrs_json(fmt, obj): ... return json.dumps(vars(obj)) `format_attrs(fmt, obj)` calls the correct underlying function depending on the `fmt` key. If the format is unknown a `KeyError` is raised. It is also possible to set a `keymissing` function to specify what to return if the key is missing. For a more practical example see the implementation of the exporters in openquake.calculators.export """ def __init__(self, keyfunc=lambda key: key, keymissing=None): super(CallableDict, self).__init__() self.keyfunc = keyfunc self.keymissing = keymissing
[docs] def add(self, *keys): """ Return a decorator registering a new implementation for the CallableDict for the given keys. """ def decorator(func): for key in keys: self[key] = func return func return decorator
def __call__(self, obj, *args, **kw): key = self.keyfunc(obj) return self[key](obj, *args, **kw) def __missing__(self, key): if callable(self.keymissing): return self.keymissing raise KeyError(key)
[docs]class AccumDict(dict): """ An accumulating dictionary, useful to accumulate variables:: >> acc = AccumDict() >> acc += {'a': 1} >> acc += {'a': 1, 'b': 1} >> acc {'a': 2, 'b': 1} >> {'a': 1} + acc {'a': 3, 'b': 1} >> acc + 1 {'a': 3, 'b': 2} >> 1 - acc {'a': -1, 'b': 0} >> acc - 1 {'a': 1, 'b': 0} Also the multiplication has been defined:: >> prob1 = AccumDict(a=0.4, b=0.5) >> prob2 = AccumDict(b=0.5) >> prob1 * prob2 {'a': 0.4, 'b': 0.25} >> prob1 * 1.2 {'a': 0.48, 'b': 0.6} >> 1.2 * prob1 {'a': 0.48, 'b': 0.6} It is very common to use an AccumDict of accumulators; here is an example using the empty list as accumulator: >>> acc = AccumDict(accum=[]) >>> acc['a'] += [1] >>> acc['b'] += [2] >>> sorted(acc.items()) [('a', [1]), ('b', [2])] The implementation is smart enough to make (deep) copies of the accumulator, therefore each key has a different accumulator, which initially is the empty list (in this case). """ def __init__(self, dic=None, accum=None, **kw): if dic: self.update(dic) self.update(kw) self.accum = accum def __iadd__(self, other): if hasattr(other, 'items'): for k, v in other.items(): try: self[k] = self[k] + v except KeyError: self[k] = v else: # add other to all elements for k in self: self[k] = self[k] + other return self def __add__(self, other): new = self.__class__(self) new += other return new __radd__ = __add__ def __isub__(self, other): if hasattr(other, 'items'): for k, v in other.items(): try: self[k] = self[k] - v except KeyError: self[k] = v else: # subtract other to all elements for k in self: self[k] = self[k] - other return self def __sub__(self, other): new = self.__class__(self) new -= other return new def __rsub__(self, other): return - self.__sub__(other) def __neg__(self): return self.__class__({k: -v for k, v in self.items()}) def __imul__(self, other): if hasattr(other, 'items'): for k, v in other.items(): try: self[k] = self[k] * v except KeyError: self[k] = v else: # add other to all elements for k in self: self[k] = self[k] * other return self def __mul__(self, other): new = self.__class__(self) new *= other return new __rmul__ = __mul__ def __truediv__(self, other): return self * (1. / other) def __missing__(self, key): if self.accum is None: # no accumulator, accessing a missing key is an error raise KeyError(key) val = self[key] = copy.deepcopy(self.accum) return val
[docs] def apply(self, func, *extras): """ >> a = AccumDict({'a': 1, 'b': 2}) >> a.apply(lambda x, y: 2 * x + y, 1) {'a': 3, 'b': 5} """ return self.__class__({key: func(value, *extras) for key, value in self.items()})
# return a dict imt -> slice and the total number of levels def _slicedict_n(imt_dt): n = 0 slicedic = {} for imt in imt_dt.names: shp = imt_dt[imt].shape n1 = n + (shp[0] if shp else 1) slicedic[imt] = slice(n, n1) n = n1 return slicedic, n
[docs]class DictArray(collections.Mapping): """ A small wrapper over a dictionary of arrays serializable to HDF5: >>> d = DictArray({'PGA': [0.01, 0.02, 0.04], 'PGV': [0.1, 0.2]}) >>> from openquake.baselib import hdf5 >>> with hdf5.File('/tmp/x.h5', 'w') as f: ... f['d'] = d ... f['d'] <DictArray PGA: [ 0.01 0.02 0.04] PGV: [ 0.1 0.2]> The DictArray maintains the lexicographic order of the keys. """ def __init__(self, imtls): self.dt = dt = numpy.dtype( [(str(imt), F64, len(imls) if hasattr(imls, '__len__') else 1) for imt, imls in sorted(imtls.items())]) self.slicedic, num_levels = _slicedict_n(dt) self.array = numpy.zeros(num_levels, F64) for imt, imls in imtls.items(): self[imt] = imls
[docs] def new(self, array): """ Convert an array of compatible length into a DictArray: >>> d = DictArray({'PGA': [0.01, 0.02, 0.04], 'PGV': [0.1, 0.2]}) >>>, 5, 1)) # array of lenght 5 = 3 + 2 <DictArray PGA: [0 1 2] PGV: [3 4]> """ assert len(self.array) == len(array) arr = object.__new__(self.__class__) arr.dt = self.dt arr.slicedic = self.slicedic arr.array = array return arr
def __getitem__(self, imt): return self.array[self.slicedic[imt]] def __setitem__(self, imt, array): self.array[self.slicedic[imt]] = array def __iter__(self): for imt in self.dt.names: yield imt def __len__(self): return len(self.dt.names) def __toh5__(self): carray = numpy.zeros(1, self.dt) for imt in self: carray[imt] = self[imt] return carray, {} def __fromh5__(self, carray, attrs): self.array = carray[:].view(F64) self.dt = dt = numpy.dtype( [(str(imt), F64, len(carray[0][imt])) for imt in carray.dtype.names]) self.slicedic, num_levels = _slicedict_n(dt) for imt in carray.dtype.names: self[imt] = carray[0][imt] def __repr__(self): data = ['%s: %s' % (imt, self[imt]) for imt in self] return '<%s\n%s>' % (self.__class__.__name__, '\n'.join(data))
[docs]def groupby(objects, key, reducegroup=list): """ :param objects: a sequence of objects with a key value :param key: the key function to extract the key value :param reducegroup: the function to apply to each group :returns: an OrderedDict {key value: map(reducegroup, group)} >>> groupby(['A1', 'A2', 'B1', 'B2', 'B3'], lambda x: x[0], ... lambda group: ''.join(x[1] for x in group)) OrderedDict([('A', '12'), ('B', '123')]) """ kgroups = itertools.groupby(sorted(objects, key=key), key) return collections.OrderedDict((k, reducegroup(group)) for k, group in kgroups)
[docs]def groupby2(records, kfield, vfield): """ :param records: a sequence of records with positional or named fields :param kfield: the index/name/tuple specifying the field to use as a key :param vfield: the index/name/tuple specifying the field to use as a value :returns: an list of pairs of the form (key, [value, ...]). >>> groupby2(['A1', 'A2', 'B1', 'B2', 'B3'], 0, 1) [('A', ['1', '2']), ('B', ['1', '2', '3'])] Here is an example where the keyfield is a tuple of integers: >>> groupby2(['A11', 'A12', 'B11', 'B21'], (0, 1), 2) [(('A', '1'), ['1', '2']), (('B', '1'), ['1']), (('B', '2'), ['1'])] """ if isinstance(kfield, tuple): kgetter = operator.itemgetter(*kfield) else: kgetter = operator.itemgetter(kfield) if isinstance(vfield, tuple): vgetter = operator.itemgetter(*vfield) else: vgetter = operator.itemgetter(vfield) dic = groupby(records, kgetter, lambda rows: [vgetter(r) for r in rows]) return list(dic.items()) # Python3 compatible
def _reducerecords(group): records = list(group) return numpy.array(records, records[0].dtype)
[docs]def group_array(array, *kfields): """ Convert an array into an OrderedDict kfields -> array """ return groupby(array, operator.itemgetter(*kfields), _reducerecords)
[docs]def get_array(array, **kw): """ Extract a subarray by filtering on the given keyword arguments """ for name, value in kw.items(): array = array[array[name] == value] return array
[docs]def humansize(nbytes, suffixes=('B', 'KB', 'MB', 'GB', 'TB', 'PB')): """ Return file size in a human-friendly format """ if nbytes == 0: return '0 B' i = 0 while nbytes >= 1024 and i < len(suffixes) - 1: nbytes /= 1024. i += 1 f = ('%.2f' % nbytes).rstrip('0').rstrip('.') return '%s %s' % (f, suffixes[i])
# the builtin DeprecationWarning has been silenced in Python 2.7
[docs]class DeprecationWarning(UserWarning): """ Raised the first time a deprecated function is called """
[docs]def deprecated(message): """ Return a decorator to make deprecated functions. :param message: the message to print the first time the deprecated function is used. Here is an example of usage: >>> @deprecated('Use new_function instead') ... def old_function(): ... 'Do something' Notice that if the function is called several time, the deprecation warning will be displayed only the first time. """ def _deprecated(func, *args, **kw): msg = '%s.%s has been deprecated. %s' % ( func.__module__, func.__name__, message) if not hasattr(func, 'called'): warnings.warn(msg, DeprecationWarning, stacklevel=2) func.called = 0 func.called += 1 return func(*args, **kw) return decorator(_deprecated)