# Copyright (c) 2015, Ecole Polytechnique Federale de Lausanne, Blue Brain Project
# All rights reserved.
#
# This file is part of NeuroM <https://github.com/BlueBrain/NeuroM>
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# 2. Redistributions in binary form must reproduce the above copyright
# notice, this list of conditions and the following disclaimer in the
# documentation and/or other materials provided with the distribution.
# 3. Neither the name of the copyright holder nor the names of
# its contributors may be used to endorse or promote products
# derived from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
# ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
# WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY
# DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
# (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
# LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
# ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
"""NeuroM helper utilities."""
import json
import warnings
from enum import Enum
from functools import partial, update_wrapper, wraps
import numpy as np
class memoize:
"""cache the return value of a method.
This class is meant to be used as a decorator of methods. The return value
from a given method invocation will be cached on the instance whose method
was invoked. All arguments passed to a method decorated with memoize must
be hashable.
If a memoized method is invoked directly on its class the result will not
be cached. Instead the method will be invoked like a static method::
class Obj:
@memoize
def add_to(self, arg):
return self + arg
Obj.add_to(1) # not enough arguments
Obj.add_to(1, 2) # returns 3, result is not cached
"""
def __init__(self, func):
"""Initialize a memoize object."""
self.func = func
update_wrapper(self, func)
def __get__(self, obj, objtype=None):
"""Get the attribute from the object."""
return partial(self, obj)
def __call__(self, *args, **kw):
"""Callable for decorator."""
obj = args[0]
try:
cache = obj.__cache # pylint: disable=protected-access
except AttributeError:
cache = obj.__cache = {}
key = (self.func, args[1:], frozenset(kw.items()))
try:
res = cache[key]
except KeyError:
res = cache[key] = self.func(*args, **kw)
return res
def _warn_deprecated(msg):
"""Issue a deprecation warning."""
warnings.simplefilter('always', DeprecationWarning)
warnings.warn(msg, category=DeprecationWarning, stacklevel=2)
warnings.simplefilter('default', DeprecationWarning)
[docs]def deprecated(fun_name=None, msg=""):
"""Issue a deprecation warning for a function."""
def _deprecated(fun):
"""Issue a deprecation warning for a function."""
@wraps(fun)
def _wrapper(*args, **kwargs):
"""Issue deprecation warning and forward arguments to fun."""
name = fun_name if fun_name is not None else fun.__name__
_warn_deprecated('Call to deprecated function %s. %s' % (name, msg))
return fun(*args, **kwargs)
return _wrapper
return _deprecated
def deprecated_module(mod_name, msg=""):
"""Issue a deprecation warning for a module."""
_warn_deprecated('Module %s is deprecated. %s' % (mod_name, msg))
class NeuromJSON(json.JSONEncoder):
"""JSON encoder that handles numpy types.
In python3, numpy.dtypes don't serialize to correctly, so a custom
converter is needed.
"""
def default(self, o): # pylint: disable=method-hidden
"""Override default method for numpy types."""
if isinstance(o, np.floating):
return float(o)
if isinstance(o, np.integer):
return int(o)
if isinstance(o, np.ndarray):
return o.tolist()
return json.JSONEncoder.default(self, o)
# pylint: disable=comparison-with-callable
[docs]class OrderedEnum(Enum):
"""An ordered enum class.
Implementation taken here https://docs.python.org/3/library/enum.html#orderedenum
Fixes https://github.com/BlueBrain/NeuroM/issues/697
"""
def __ge__(self, other):
"""Check greater than or equal to."""
if self.__class__ is other.__class__:
return self.value >= other.value
raise NotImplementedError
def __gt__(self, other):
"""Check greater than."""
if self.__class__ is other.__class__:
return self.value > other.value
raise NotImplementedError
def __le__(self, other):
"""Check less than or equal to."""
if self.__class__ is other.__class__:
return self.value <= other.value
raise NotImplementedError
def __lt__(self, other):
"""Check less than."""
if self.__class__ is other.__class__:
return self.value < other.value
raise NotImplementedError