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# encoding: utf-8
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"""Pickle related utilities. Perhaps this should be called 'can'."""
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# Copyright (c) IPython Development Team.
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# Distributed under the terms of the Modified BSD License.
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import copy
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import logging
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import sys
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from types import FunctionType
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try:
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import cPickle as pickle
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except ImportError:
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import pickle
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from . import codeutil # This registers a hook when it's imported
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from . import py3compat
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from .importstring import import_item
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from .py3compat import string_types, iteritems, buffer_to_bytes_py2
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from IPython.config import Application
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from IPython.utils.log import get_logger
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if py3compat.PY3:
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buffer = memoryview
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class_type = type
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else:
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from types import ClassType
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class_type = (type, ClassType)
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try:
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PICKLE_PROTOCOL = pickle.DEFAULT_PROTOCOL
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except AttributeError:
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PICKLE_PROTOCOL = pickle.HIGHEST_PROTOCOL
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def _get_cell_type(a=None):
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"""the type of a closure cell doesn't seem to be importable,
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so just create one
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"""
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def inner():
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return a
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return type(py3compat.get_closure(inner)[0])
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cell_type = _get_cell_type()
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#-------------------------------------------------------------------------------
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# Functions
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#-------------------------------------------------------------------------------
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def use_dill():
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"""use dill to expand serialization support
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adds support for object methods and closures to serialization.
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"""
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# import dill causes most of the magic
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import dill
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# dill doesn't work with cPickle,
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# tell the two relevant modules to use plain pickle
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global pickle
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pickle = dill
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try:
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from IPython.kernel.zmq import serialize
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except ImportError:
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pass
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else:
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serialize.pickle = dill
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# disable special function handling, let dill take care of it
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can_map.pop(FunctionType, None)
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def use_cloudpickle():
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"""use cloudpickle to expand serialization support
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adds support for object methods and closures to serialization.
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"""
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from cloud.serialization import cloudpickle
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global pickle
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pickle = cloudpickle
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try:
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from IPython.kernel.zmq import serialize
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except ImportError:
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pass
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else:
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serialize.pickle = cloudpickle
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# disable special function handling, let cloudpickle take care of it
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can_map.pop(FunctionType, None)
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#-------------------------------------------------------------------------------
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# Classes
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#-------------------------------------------------------------------------------
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class CannedObject(object):
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def __init__(self, obj, keys=[], hook=None):
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"""can an object for safe pickling
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Parameters
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==========
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obj:
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The object to be canned
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keys: list (optional)
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list of attribute names that will be explicitly canned / uncanned
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hook: callable (optional)
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An optional extra callable,
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which can do additional processing of the uncanned object.
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large data may be offloaded into the buffers list,
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used for zero-copy transfers.
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"""
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self.keys = keys
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self.obj = copy.copy(obj)
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self.hook = can(hook)
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for key in keys:
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setattr(self.obj, key, can(getattr(obj, key)))
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self.buffers = []
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def get_object(self, g=None):
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if g is None:
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g = {}
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obj = self.obj
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for key in self.keys:
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setattr(obj, key, uncan(getattr(obj, key), g))
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if self.hook:
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self.hook = uncan(self.hook, g)
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self.hook(obj, g)
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return self.obj
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class Reference(CannedObject):
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"""object for wrapping a remote reference by name."""
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def __init__(self, name):
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if not isinstance(name, string_types):
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raise TypeError("illegal name: %r"%name)
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self.name = name
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self.buffers = []
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def __repr__(self):
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return "<Reference: %r>"%self.name
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def get_object(self, g=None):
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if g is None:
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g = {}
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return eval(self.name, g)
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class CannedCell(CannedObject):
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"""Can a closure cell"""
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def __init__(self, cell):
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self.cell_contents = can(cell.cell_contents)
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def get_object(self, g=None):
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cell_contents = uncan(self.cell_contents, g)
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def inner():
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return cell_contents
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return py3compat.get_closure(inner)[0]
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class CannedFunction(CannedObject):
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def __init__(self, f):
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self._check_type(f)
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self.code = f.__code__
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if f.__defaults__:
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self.defaults = [ can(fd) for fd in f.__defaults__ ]
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else:
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self.defaults = None
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closure = py3compat.get_closure(f)
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if closure:
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self.closure = tuple( can(cell) for cell in closure )
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else:
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self.closure = None
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self.module = f.__module__ or '__main__'
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self.__name__ = f.__name__
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self.buffers = []
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def _check_type(self, obj):
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assert isinstance(obj, FunctionType), "Not a function type"
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def get_object(self, g=None):
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# try to load function back into its module:
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if not self.module.startswith('__'):
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__import__(self.module)
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g = sys.modules[self.module].__dict__
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if g is None:
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g = {}
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if self.defaults:
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defaults = tuple(uncan(cfd, g) for cfd in self.defaults)
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else:
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defaults = None
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if self.closure:
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closure = tuple(uncan(cell, g) for cell in self.closure)
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else:
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closure = None
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newFunc = FunctionType(self.code, g, self.__name__, defaults, closure)
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return newFunc
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class CannedClass(CannedObject):
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def __init__(self, cls):
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self._check_type(cls)
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self.name = cls.__name__
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self.old_style = not isinstance(cls, type)
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self._canned_dict = {}
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for k,v in cls.__dict__.items():
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if k not in ('__weakref__', '__dict__'):
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self._canned_dict[k] = can(v)
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if self.old_style:
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mro = []
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else:
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mro = cls.mro()
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self.parents = [ can(c) for c in mro[1:] ]
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self.buffers = []
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def _check_type(self, obj):
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assert isinstance(obj, class_type), "Not a class type"
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def get_object(self, g=None):
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parents = tuple(uncan(p, g) for p in self.parents)
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return type(self.name, parents, uncan_dict(self._canned_dict, g=g))
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class CannedArray(CannedObject):
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def __init__(self, obj):
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from numpy import ascontiguousarray
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self.shape = obj.shape
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self.dtype = obj.dtype.descr if obj.dtype.fields else obj.dtype.str
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self.pickled = False
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if sum(obj.shape) == 0:
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self.pickled = True
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elif obj.dtype == 'O':
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# can't handle object dtype with buffer approach
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self.pickled = True
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elif obj.dtype.fields and any(dt == 'O' for dt,sz in obj.dtype.fields.values()):
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self.pickled = True
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if self.pickled:
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# just pickle it
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self.buffers = [pickle.dumps(obj, PICKLE_PROTOCOL)]
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else:
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# ensure contiguous
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obj = ascontiguousarray(obj, dtype=None)
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self.buffers = [buffer(obj)]
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def get_object(self, g=None):
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from numpy import frombuffer
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data = self.buffers[0]
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if self.pickled:
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# we just pickled it
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return pickle.loads(buffer_to_bytes_py2(data))
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else:
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return frombuffer(data, dtype=self.dtype).reshape(self.shape)
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class CannedBytes(CannedObject):
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wrap = bytes
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def __init__(self, obj):
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self.buffers = [obj]
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def get_object(self, g=None):
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data = self.buffers[0]
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return self.wrap(data)
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def CannedBuffer(CannedBytes):
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wrap = buffer
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#-------------------------------------------------------------------------------
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# Functions
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#-------------------------------------------------------------------------------
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def _import_mapping(mapping, original=None):
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"""import any string-keys in a type mapping
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"""
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log = get_logger()
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log.debug("Importing canning map")
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for key,value in list(mapping.items()):
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if isinstance(key, string_types):
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try:
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cls = import_item(key)
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except Exception:
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if original and key not in original:
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# only message on user-added classes
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log.error("canning class not importable: %r", key, exc_info=True)
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mapping.pop(key)
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else:
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mapping[cls] = mapping.pop(key)
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def istype(obj, check):
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"""like isinstance(obj, check), but strict
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This won't catch subclasses.
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"""
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if isinstance(check, tuple):
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for cls in check:
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if type(obj) is cls:
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return True
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return False
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else:
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return type(obj) is check
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def can(obj):
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"""prepare an object for pickling"""
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import_needed = False
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for cls,canner in iteritems(can_map):
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if isinstance(cls, string_types):
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import_needed = True
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break
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elif istype(obj, cls):
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return canner(obj)
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if import_needed:
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# perform can_map imports, then try again
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# this will usually only happen once
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_import_mapping(can_map, _original_can_map)
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return can(obj)
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return obj
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def can_class(obj):
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if isinstance(obj, class_type) and obj.__module__ == '__main__':
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return CannedClass(obj)
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else:
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return obj
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def can_dict(obj):
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"""can the *values* of a dict"""
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if istype(obj, dict):
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newobj = {}
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for k, v in iteritems(obj):
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newobj[k] = can(v)
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return newobj
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else:
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return obj
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sequence_types = (list, tuple, set)
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def can_sequence(obj):
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"""can the elements of a sequence"""
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if istype(obj, sequence_types):
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t = type(obj)
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return t([can(i) for i in obj])
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else:
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return obj
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def uncan(obj, g=None):
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"""invert canning"""
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import_needed = False
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for cls,uncanner in iteritems(uncan_map):
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if isinstance(cls, string_types):
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import_needed = True
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break
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elif isinstance(obj, cls):
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return uncanner(obj, g)
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if import_needed:
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# perform uncan_map imports, then try again
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# this will usually only happen once
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_import_mapping(uncan_map, _original_uncan_map)
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return uncan(obj, g)
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return obj
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def uncan_dict(obj, g=None):
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if istype(obj, dict):
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newobj = {}
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for k, v in iteritems(obj):
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newobj[k] = uncan(v,g)
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return newobj
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else:
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return obj
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def uncan_sequence(obj, g=None):
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if istype(obj, sequence_types):
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t = type(obj)
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return t([uncan(i,g) for i in obj])
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else:
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return obj
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def _uncan_dependent_hook(dep, g=None):
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dep.check_dependency()
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def can_dependent(obj):
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return CannedObject(obj, keys=('f', 'df'), hook=_uncan_dependent_hook)
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#-------------------------------------------------------------------------------
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# API dictionaries
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#-------------------------------------------------------------------------------
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# These dicts can be extended for custom serialization of new objects
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can_map = {
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'IPython.parallel.dependent' : can_dependent,
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'numpy.ndarray' : CannedArray,
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FunctionType : CannedFunction,
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bytes : CannedBytes,
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buffer : CannedBuffer,
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cell_type : CannedCell,
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class_type : can_class,
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}
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uncan_map = {
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CannedObject : lambda obj, g: obj.get_object(g),
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}
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# for use in _import_mapping:
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_original_can_map = can_map.copy()
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_original_uncan_map = uncan_map.copy()
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