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@wong2 wong2/cloudpickle.py
Created Feb 28, 2014

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"""
This class is defined to override standard pickle functionality
The goals of it follow:
-Serialize lambdas and nested functions to compiled byte code
-Deal with main module correctly
-Deal with other non-serializable objects
It does not include an unpickler, as standard python unpickling suffices.
This module was extracted from the `cloud` package, developed by `PiCloud, Inc.
<http://www.picloud.com>`_.
Copyright (c) 2012, Regents of the University of California.
Copyright (c) 2009 `PiCloud, Inc. <http://www.picloud.com>`_.
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
* Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
* 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.
* Neither the name of the University of California, Berkeley 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.
"""
import operator
import os
import pickle
import struct
import sys
import types
from functools import partial
import itertools
from copy_reg import _extension_registry, _inverted_registry, _extension_cache
import new
import dis
import traceback
#relevant opcodes
STORE_GLOBAL = chr(dis.opname.index('STORE_GLOBAL'))
DELETE_GLOBAL = chr(dis.opname.index('DELETE_GLOBAL'))
LOAD_GLOBAL = chr(dis.opname.index('LOAD_GLOBAL'))
GLOBAL_OPS = [STORE_GLOBAL, DELETE_GLOBAL, LOAD_GLOBAL]
HAVE_ARGUMENT = chr(dis.HAVE_ARGUMENT)
EXTENDED_ARG = chr(dis.EXTENDED_ARG)
import logging
cloudLog = logging.getLogger("Cloud.Transport")
try:
import ctypes
except (MemoryError, ImportError):
logging.warning('Exception raised on importing ctypes. Likely python bug.. some functionality will be disabled', exc_info = True)
ctypes = None
PyObject_HEAD = None
else:
# for reading internal structures
PyObject_HEAD = [
('ob_refcnt', ctypes.c_size_t),
('ob_type', ctypes.c_void_p),
]
try:
from cStringIO import StringIO
except ImportError:
from StringIO import StringIO
# These helper functions were copied from PiCloud's util module.
def islambda(func):
return getattr(func,'func_name') == '<lambda>'
def xrange_params(xrangeobj):
"""Returns a 3 element tuple describing the xrange start, step, and len
respectively
Note: Only guarentees that elements of xrange are the same. parameters may
be different.
e.g. xrange(1,1) is interpretted as xrange(0,0); both behave the same
though w/ iteration
"""
xrange_len = len(xrangeobj)
if not xrange_len: #empty
return (0,1,0)
start = xrangeobj[0]
if xrange_len == 1: #one element
return start, 1, 1
return (start, xrangeobj[1] - xrangeobj[0], xrange_len)
#debug variables intended for developer use:
printSerialization = False
printMemoization = False
useForcedImports = True #Should I use forced imports for tracking?
class CloudPickler(pickle.Pickler):
dispatch = pickle.Pickler.dispatch.copy()
savedForceImports = False
savedDjangoEnv = False #hack tro transport django environment
def __init__(self, file, protocol=None, min_size_to_save= 0):
pickle.Pickler.__init__(self,file,protocol)
self.modules = set() #set of modules needed to depickle
self.globals_ref = {} # map ids to dictionary. used to ensure that functions can share global env
def dump(self, obj):
# note: not thread safe
# minimal side-effects, so not fixing
recurse_limit = 3000
base_recurse = sys.getrecursionlimit()
if base_recurse < recurse_limit:
sys.setrecursionlimit(recurse_limit)
self.inject_addons()
try:
return pickle.Pickler.dump(self, obj)
except RuntimeError, e:
if 'recursion' in e.args[0]:
msg = """Could not pickle object as excessively deep recursion required.
Try _fast_serialization=2 or contact PiCloud support"""
raise pickle.PicklingError(msg)
finally:
new_recurse = sys.getrecursionlimit()
if new_recurse == recurse_limit:
sys.setrecursionlimit(base_recurse)
def save_buffer(self, obj):
"""Fallback to save_string"""
pickle.Pickler.save_string(self,str(obj))
dispatch[buffer] = save_buffer
#block broken objects
def save_unsupported(self, obj, pack=None):
raise pickle.PicklingError("Cannot pickle objects of type %s" % type(obj))
dispatch[types.GeneratorType] = save_unsupported
#python2.6+ supports slice pickling. some py2.5 extensions might as well. We just test it
try:
slice(0,1).__reduce__()
except TypeError: #can't pickle -
dispatch[slice] = save_unsupported
#itertools objects do not pickle!
for v in itertools.__dict__.values():
if type(v) is type:
dispatch[v] = save_unsupported
def save_dict(self, obj):
"""hack fix
If the dict is a global, deal with it in a special way
"""
#print 'saving', obj
if obj is __builtins__:
self.save_reduce(_get_module_builtins, (), obj=obj)
else:
pickle.Pickler.save_dict(self, obj)
dispatch[pickle.DictionaryType] = save_dict
def save_module(self, obj, pack=struct.pack):
"""
Save a module as an import
"""
#print 'try save import', obj.__name__
self.modules.add(obj)
self.save_reduce(subimport,(obj.__name__,), obj=obj)
dispatch[types.ModuleType] = save_module #new type
def save_codeobject(self, obj, pack=struct.pack):
"""
Save a code object
"""
#print 'try to save codeobj: ', obj
args = (
obj.co_argcount, obj.co_nlocals, obj.co_stacksize, obj.co_flags, obj.co_code,
obj.co_consts, obj.co_names, obj.co_varnames, obj.co_filename, obj.co_name,
obj.co_firstlineno, obj.co_lnotab, obj.co_freevars, obj.co_cellvars
)
self.save_reduce(types.CodeType, args, obj=obj)
dispatch[types.CodeType] = save_codeobject #new type
def save_function(self, obj, name=None, pack=struct.pack):
""" Registered with the dispatch to handle all function types.
Determines what kind of function obj is (e.g. lambda, defined at
interactive prompt, etc) and handles the pickling appropriately.
"""
write = self.write
name = obj.__name__
modname = pickle.whichmodule(obj, name)
#print 'which gives %s %s %s' % (modname, obj, name)
try:
themodule = sys.modules[modname]
except KeyError: # eval'd items such as namedtuple give invalid items for their function __module__
modname = '__main__'
if modname == '__main__':
themodule = None
if themodule:
self.modules.add(themodule)
if not self.savedDjangoEnv:
#hack for django - if we detect the settings module, we transport it
django_settings = os.environ.get('DJANGO_SETTINGS_MODULE', '')
if django_settings:
django_mod = sys.modules.get(django_settings)
if django_mod:
cloudLog.debug('Transporting django settings %s during save of %s', django_mod, name)
self.savedDjangoEnv = True
self.modules.add(django_mod)
write(pickle.MARK)
self.save_reduce(django_settings_load, (django_mod.__name__,), obj=django_mod)
write(pickle.POP_MARK)
# if func is lambda, def'ed at prompt, is in main, or is nested, then
# we'll pickle the actual function object rather than simply saving a
# reference (as is done in default pickler), via save_function_tuple.
if islambda(obj) or obj.func_code.co_filename == '<stdin>' or themodule == None:
#Force server to import modules that have been imported in main
modList = None
if themodule == None and not self.savedForceImports:
mainmod = sys.modules['__main__']
if useForcedImports and hasattr(mainmod,'___pyc_forcedImports__'):
modList = list(mainmod.___pyc_forcedImports__)
self.savedForceImports = True
self.save_function_tuple(obj, modList)
return
else: # func is nested
klass = getattr(themodule, name, None)
if klass is None or klass is not obj:
self.save_function_tuple(obj, [themodule])
return
if obj.__dict__:
# essentially save_reduce, but workaround needed to avoid recursion
self.save(_restore_attr)
write(pickle.MARK + pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
self.save(obj.__dict__)
write(pickle.TUPLE + pickle.REDUCE)
else:
write(pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
dispatch[types.FunctionType] = save_function
def save_function_tuple(self, func, forced_imports):
""" Pickles an actual func object.
A func comprises: code, globals, defaults, closure, and dict. We
extract and save these, injecting reducing functions at certain points
to recreate the func object. Keep in mind that some of these pieces
can contain a ref to the func itself. Thus, a naive save on these
pieces could trigger an infinite loop of save's. To get around that,
we first create a skeleton func object using just the code (this is
safe, since this won't contain a ref to the func), and memoize it as
soon as it's created. The other stuff can then be filled in later.
"""
save = self.save
write = self.write
# save the modules (if any)
if forced_imports:
write(pickle.MARK)
save(_modules_to_main)
#print 'forced imports are', forced_imports
forced_names = map(lambda m: m.__name__, forced_imports)
save((forced_names,))
#save((forced_imports,))
write(pickle.REDUCE)
write(pickle.POP_MARK)
code, f_globals, defaults, closure, dct, base_globals = self.extract_func_data(func)
save(_fill_function) # skeleton function updater
write(pickle.MARK) # beginning of tuple that _fill_function expects
# create a skeleton function object and memoize it
save(_make_skel_func)
save((code, len(closure), base_globals))
write(pickle.REDUCE)
self.memoize(func)
# save the rest of the func data needed by _fill_function
save(f_globals)
save(defaults)
save(closure)
save(dct)
write(pickle.TUPLE)
write(pickle.REDUCE) # applies _fill_function on the tuple
@staticmethod
def extract_code_globals(co):
"""
Find all globals names read or written to by codeblock co
"""
code = co.co_code
names = co.co_names
out_names = set()
n = len(code)
i = 0
extended_arg = 0
while i < n:
op = code[i]
i = i+1
if op >= HAVE_ARGUMENT:
oparg = ord(code[i]) + ord(code[i+1])*256 + extended_arg
extended_arg = 0
i = i+2
if op == EXTENDED_ARG:
extended_arg = oparg*65536L
if op in GLOBAL_OPS:
out_names.add(names[oparg])
#print 'extracted', out_names, ' from ', names
return out_names
def extract_func_data(self, func):
"""
Turn the function into a tuple of data necessary to recreate it:
code, globals, defaults, closure, dict
"""
code = func.func_code
# extract all global ref's
func_global_refs = CloudPickler.extract_code_globals(code)
if code.co_consts: # see if nested function have any global refs
for const in code.co_consts:
if type(const) is types.CodeType and const.co_names:
func_global_refs = func_global_refs.union( CloudPickler.extract_code_globals(const))
# process all variables referenced by global environment
f_globals = {}
for var in func_global_refs:
#Some names, such as class functions are not global - we don't need them
if func.func_globals.has_key(var):
f_globals[var] = func.func_globals[var]
# defaults requires no processing
defaults = func.func_defaults
def get_contents(cell):
try:
return cell.cell_contents
except ValueError, e: #cell is empty error on not yet assigned
raise pickle.PicklingError('Function to be pickled has free variables that are referenced before assignment in enclosing scope')
# process closure
if func.func_closure:
closure = map(get_contents, func.func_closure)
else:
closure = []
# save the dict
dct = func.func_dict
if printSerialization:
outvars = ['code: ' + str(code) ]
outvars.append('globals: ' + str(f_globals))
outvars.append('defaults: ' + str(defaults))
outvars.append('closure: ' + str(closure))
print 'function ', func, 'is extracted to: ', ', '.join(outvars)
base_globals = self.globals_ref.get(id(func.func_globals), {})
self.globals_ref[id(func.func_globals)] = base_globals
return (code, f_globals, defaults, closure, dct, base_globals)
def save_global(self, obj, name=None, pack=struct.pack):
write = self.write
memo = self.memo
if name is None:
name = obj.__name__
modname = getattr(obj, "__module__", None)
if modname is None:
modname = pickle.whichmodule(obj, name)
try:
__import__(modname)
themodule = sys.modules[modname]
except (ImportError, KeyError, AttributeError): #should never occur
raise pickle.PicklingError(
"Can't pickle %r: Module %s cannot be found" %
(obj, modname))
if modname == '__main__':
themodule = None
if themodule:
self.modules.add(themodule)
sendRef = True
typ = type(obj)
#print 'saving', obj, typ
try:
try: #Deal with case when getattribute fails with exceptions
klass = getattr(themodule, name)
except (AttributeError):
if modname == '__builtin__': #new.* are misrepeported
modname = 'new'
__import__(modname)
themodule = sys.modules[modname]
try:
klass = getattr(themodule, name)
except AttributeError, a:
#print themodule, name, obj, type(obj)
raise pickle.PicklingError("Can't pickle builtin %s" % obj)
else:
raise
except (ImportError, KeyError, AttributeError):
if typ == types.TypeType or typ == types.ClassType:
sendRef = False
else: #we can't deal with this
raise
else:
if klass is not obj and (typ == types.TypeType or typ == types.ClassType):
sendRef = False
if not sendRef:
#note: Third party types might crash this - add better checks!
d = dict(obj.__dict__) #copy dict proxy to a dict
if not isinstance(d.get('__dict__', None), property): # don't extract dict that are properties
d.pop('__dict__',None)
d.pop('__weakref__',None)
# hack as __new__ is stored differently in the __dict__
new_override = d.get('__new__', None)
if new_override:
d['__new__'] = obj.__new__
self.save_reduce(type(obj),(obj.__name__,obj.__bases__,
d),obj=obj)
#print 'internal reduce dask %s %s' % (obj, d)
return
if self.proto >= 2:
code = _extension_registry.get((modname, name))
if code:
assert code > 0
if code <= 0xff:
write(pickle.EXT1 + chr(code))
elif code <= 0xffff:
write("%c%c%c" % (pickle.EXT2, code&0xff, code>>8))
else:
write(pickle.EXT4 + pack("<i", code))
return
write(pickle.GLOBAL + modname + '\n' + name + '\n')
self.memoize(obj)
dispatch[types.ClassType] = save_global
dispatch[types.BuiltinFunctionType] = save_global
dispatch[types.TypeType] = save_global
def save_instancemethod(self, obj):
#Memoization rarely is ever useful due to python bounding
self.save_reduce(types.MethodType, (obj.im_func, obj.im_self,obj.im_class), obj=obj)
dispatch[types.MethodType] = save_instancemethod
def save_inst_logic(self, obj):
"""Inner logic to save instance. Based off pickle.save_inst
Supports __transient__"""
cls = obj.__class__
memo = self.memo
write = self.write
save = self.save
if hasattr(obj, '__getinitargs__'):
args = obj.__getinitargs__()
len(args) # XXX Assert it's a sequence
pickle._keep_alive(args, memo)
else:
args = ()
write(pickle.MARK)
if self.bin:
save(cls)
for arg in args:
save(arg)
write(pickle.OBJ)
else:
for arg in args:
save(arg)
write(pickle.INST + cls.__module__ + '\n' + cls.__name__ + '\n')
self.memoize(obj)
try:
getstate = obj.__getstate__
except AttributeError:
stuff = obj.__dict__
#remove items if transient
if hasattr(obj, '__transient__'):
transient = obj.__transient__
stuff = stuff.copy()
for k in list(stuff.keys()):
if k in transient:
del stuff[k]
else:
stuff = getstate()
pickle._keep_alive(stuff, memo)
save(stuff)
write(pickle.BUILD)
def save_inst(self, obj):
# Hack to detect PIL Image instances without importing Imaging
# PIL can be loaded with multiple names, so we don't check sys.modules for it
if hasattr(obj,'im') and hasattr(obj,'palette') and 'Image' in obj.__module__:
self.save_image(obj)
else:
self.save_inst_logic(obj)
dispatch[types.InstanceType] = save_inst
def save_property(self, obj):
# properties not correctly saved in python
self.save_reduce(property, (obj.fget, obj.fset, obj.fdel, obj.__doc__), obj=obj)
dispatch[property] = save_property
def save_itemgetter(self, obj):
"""itemgetter serializer (needed for namedtuple support)
a bit of a pain as we need to read ctypes internals"""
class ItemGetterType(ctypes.Structure):
_fields_ = PyObject_HEAD + [
('nitems', ctypes.c_size_t),
('item', ctypes.py_object)
]
itemgetter_obj = ctypes.cast(ctypes.c_void_p(id(obj)), ctypes.POINTER(ItemGetterType)).contents
return self.save_reduce(operator.itemgetter, (itemgetter_obj.item,))
if PyObject_HEAD:
dispatch[operator.itemgetter] = save_itemgetter
def save_reduce(self, func, args, state=None,
listitems=None, dictitems=None, obj=None):
"""Modified to support __transient__ on new objects
Change only affects protocol level 2 (which is always used by PiCloud"""
# Assert that args is a tuple or None
if not isinstance(args, types.TupleType):
raise pickle.PicklingError("args from reduce() should be a tuple")
# Assert that func is callable
if not hasattr(func, '__call__'):
raise pickle.PicklingError("func from reduce should be callable")
save = self.save
write = self.write
# Protocol 2 special case: if func's name is __newobj__, use NEWOBJ
if self.proto >= 2 and getattr(func, "__name__", "") == "__newobj__":
#Added fix to allow transient
cls = args[0]
if not hasattr(cls, "__new__"):
raise pickle.PicklingError(
"args[0] from __newobj__ args has no __new__")
if obj is not None and cls is not obj.__class__:
raise pickle.PicklingError(
"args[0] from __newobj__ args has the wrong class")
args = args[1:]
save(cls)
#Don't pickle transient entries
if hasattr(obj, '__transient__'):
transient = obj.__transient__
state = state.copy()
for k in list(state.keys()):
if k in transient:
del state[k]
save(args)
write(pickle.NEWOBJ)
else:
save(func)
save(args)
write(pickle.REDUCE)
if obj is not None:
self.memoize(obj)
# More new special cases (that work with older protocols as
# well): when __reduce__ returns a tuple with 4 or 5 items,
# the 4th and 5th item should be iterators that provide list
# items and dict items (as (key, value) tuples), or None.
if listitems is not None:
self._batch_appends(listitems)
if dictitems is not None:
self._batch_setitems(dictitems)
if state is not None:
#print 'obj %s has state %s' % (obj, state)
save(state)
write(pickle.BUILD)
def save_xrange(self, obj):
"""Save an xrange object in python 2.5
Python 2.6 supports this natively
"""
range_params = xrange_params(obj)
self.save_reduce(_build_xrange,range_params)
#python2.6+ supports xrange pickling. some py2.5 extensions might as well. We just test it
try:
xrange(0).__reduce__()
except TypeError: #can't pickle -- use PiCloud pickler
dispatch[xrange] = save_xrange
def save_partial(self, obj):
"""Partial objects do not serialize correctly in python2.x -- this fixes the bugs"""
self.save_reduce(_genpartial, (obj.func, obj.args, obj.keywords))
if sys.version_info < (2,7): #2.7 supports partial pickling
dispatch[partial] = save_partial
def save_file(self, obj):
"""Save a file"""
import StringIO as pystringIO #we can't use cStringIO as it lacks the name attribute
from ..transport.adapter import SerializingAdapter
if not hasattr(obj, 'name') or not hasattr(obj, 'mode'):
raise pickle.PicklingError("Cannot pickle files that do not map to an actual file")
if obj.name == '<stdout>':
return self.save_reduce(getattr, (sys,'stdout'), obj=obj)
if obj.name == '<stderr>':
return self.save_reduce(getattr, (sys,'stderr'), obj=obj)
if obj.name == '<stdin>':
raise pickle.PicklingError("Cannot pickle standard input")
if hasattr(obj, 'isatty') and obj.isatty():
raise pickle.PicklingError("Cannot pickle files that map to tty objects")
if 'r' not in obj.mode:
raise pickle.PicklingError("Cannot pickle files that are not opened for reading")
name = obj.name
try:
fsize = os.stat(name).st_size
except OSError:
raise pickle.PicklingError("Cannot pickle file %s as it cannot be stat" % name)
if obj.closed:
#create an empty closed string io
retval = pystringIO.StringIO("")
retval.close()
elif not fsize: #empty file
retval = pystringIO.StringIO("")
try:
tmpfile = file(name)
tst = tmpfile.read(1)
except IOError:
raise pickle.PicklingError("Cannot pickle file %s as it cannot be read" % name)
tmpfile.close()
if tst != '':
raise pickle.PicklingError("Cannot pickle file %s as it does not appear to map to a physical, real file" % name)
elif fsize > SerializingAdapter.max_transmit_data:
raise pickle.PicklingError("Cannot pickle file %s as it exceeds cloudconf.py's max_transmit_data of %d" %
(name,SerializingAdapter.max_transmit_data))
else:
try:
tmpfile = file(name)
contents = tmpfile.read(SerializingAdapter.max_transmit_data)
tmpfile.close()
except IOError:
raise pickle.PicklingError("Cannot pickle file %s as it cannot be read" % name)
retval = pystringIO.StringIO(contents)
curloc = obj.tell()
retval.seek(curloc)
retval.name = name
self.save(retval) #save stringIO
self.memoize(obj)
dispatch[file] = save_file
"""Special functions for Add-on libraries"""
def inject_numpy(self):
numpy = sys.modules.get('numpy')
if not numpy or not hasattr(numpy, 'ufunc'):
return
self.dispatch[numpy.ufunc] = self.__class__.save_ufunc
numpy_tst_mods = ['numpy', 'scipy.special']
def save_ufunc(self, obj):
"""Hack function for saving numpy ufunc objects"""
name = obj.__name__
for tst_mod_name in self.numpy_tst_mods:
tst_mod = sys.modules.get(tst_mod_name, None)
if tst_mod:
if name in tst_mod.__dict__:
self.save_reduce(_getobject, (tst_mod_name, name))
return
raise pickle.PicklingError('cannot save %s. Cannot resolve what module it is defined in' % str(obj))
def inject_timeseries(self):
"""Handle bugs with pickling scikits timeseries"""
tseries = sys.modules.get('scikits.timeseries.tseries')
if not tseries or not hasattr(tseries, 'Timeseries'):
return
self.dispatch[tseries.Timeseries] = self.__class__.save_timeseries
def save_timeseries(self, obj):
import scikits.timeseries.tseries as ts
func, reduce_args, state = obj.__reduce__()
if func != ts._tsreconstruct:
raise pickle.PicklingError('timeseries using unexpected reconstruction function %s' % str(func))
state = (1,
obj.shape,
obj.dtype,
obj.flags.fnc,
obj._data.tostring(),
ts.getmaskarray(obj).tostring(),
obj._fill_value,
obj._dates.shape,
obj._dates.__array__().tostring(),
obj._dates.dtype, #added -- preserve type
obj.freq,
obj._optinfo,
)
return self.save_reduce(_genTimeSeries, (reduce_args, state))
def inject_email(self):
"""Block email LazyImporters from being saved"""
email = sys.modules.get('email')
if not email:
return
self.dispatch[email.LazyImporter] = self.__class__.save_unsupported
def inject_addons(self):
"""Plug in system. Register additional pickling functions if modules already loaded"""
self.inject_numpy()
self.inject_timeseries()
self.inject_email()
"""Python Imaging Library"""
def save_image(self, obj):
if not obj.im and obj.fp and 'r' in obj.fp.mode and obj.fp.name \
and not obj.fp.closed and (not hasattr(obj, 'isatty') or not obj.isatty()):
#if image not loaded yet -- lazy load
self.save_reduce(_lazyloadImage,(obj.fp,), obj=obj)
else:
#image is loaded - just transmit it over
self.save_reduce(_generateImage, (obj.size, obj.mode, obj.tostring()), obj=obj)
"""
def memoize(self, obj):
pickle.Pickler.memoize(self, obj)
if printMemoization:
print 'memoizing ' + str(obj)
"""
# Shorthands for legacy support
def dump(obj, file, protocol=2):
CloudPickler(file, protocol).dump(obj)
def dumps(obj, protocol=2):
file = StringIO()
cp = CloudPickler(file,protocol)
cp.dump(obj)
#print 'cloud dumped', str(obj), str(cp.modules)
return file.getvalue()
#hack for __import__ not working as desired
def subimport(name):
__import__(name)
return sys.modules[name]
#hack to load django settings:
def django_settings_load(name):
modified_env = False
if 'DJANGO_SETTINGS_MODULE' not in os.environ:
os.environ['DJANGO_SETTINGS_MODULE'] = name # must set name first due to circular deps
modified_env = True
try:
module = subimport(name)
except Exception, i:
print >> sys.stderr, 'Cloud not import django settings %s:' % (name)
print_exec(sys.stderr)
if modified_env:
del os.environ['DJANGO_SETTINGS_MODULE']
else:
#add project directory to sys,path:
if hasattr(module,'__file__'):
dirname = os.path.split(module.__file__)[0] + '/'
sys.path.append(dirname)
# restores function attributes
def _restore_attr(obj, attr):
for key, val in attr.items():
setattr(obj, key, val)
return obj
def _get_module_builtins():
return pickle.__builtins__
def print_exec(stream):
ei = sys.exc_info()
traceback.print_exception(ei[0], ei[1], ei[2], None, stream)
def _modules_to_main(modList):
"""Force every module in modList to be placed into main"""
if not modList:
return
main = sys.modules['__main__']
for modname in modList:
if type(modname) is str:
try:
mod = __import__(modname)
except Exception, i: #catch all...
sys.stderr.write('warning: could not import %s\n. Your function may unexpectedly error due to this import failing; \
A version mismatch is likely. Specific error was:\n' % modname)
print_exec(sys.stderr)
else:
setattr(main,mod.__name__, mod)
else:
#REVERSE COMPATIBILITY FOR CLOUD CLIENT 1.5 (WITH EPD)
#In old version actual module was sent
setattr(main,modname.__name__, modname)
#object generators:
def _build_xrange(start, step, len):
"""Built xrange explicitly"""
return xrange(start, start + step*len, step)
def _genpartial(func, args, kwds):
if not args:
args = ()
if not kwds:
kwds = {}
return partial(func, *args, **kwds)
def _fill_function(func, globals, defaults, closure, dict):
""" Fills in the rest of function data into the skeleton function object
that were created via _make_skel_func().
"""
func.func_globals.update(globals)
func.func_defaults = defaults
func.func_dict = dict
if len(closure) != len(func.func_closure):
raise pickle.UnpicklingError("closure lengths don't match up")
for i in range(len(closure)):
_change_cell_value(func.func_closure[i], closure[i])
return func
def _make_skel_func(code, num_closures, base_globals = None):
""" Creates a skeleton function object that contains just the provided
code and the correct number of cells in func_closure. All other
func attributes (e.g. func_globals) are empty.
"""
#build closure (cells):
if not ctypes:
raise Exception('ctypes failed to import; cannot build function')
cellnew = ctypes.pythonapi.PyCell_New
cellnew.restype = ctypes.py_object
cellnew.argtypes = (ctypes.py_object,)
dummy_closure = tuple(map(lambda i: cellnew(None), range(num_closures)))
if base_globals is None:
base_globals = {}
base_globals['__builtins__'] = __builtins__
return types.FunctionType(code, base_globals,
None, None, dummy_closure)
# this piece of opaque code is needed below to modify 'cell' contents
cell_changer_code = new.code(
1, 1, 2, 0,
''.join([
chr(dis.opmap['LOAD_FAST']), '\x00\x00',
chr(dis.opmap['DUP_TOP']),
chr(dis.opmap['STORE_DEREF']), '\x00\x00',
chr(dis.opmap['RETURN_VALUE'])
]),
(), (), ('newval',), '<nowhere>', 'cell_changer', 1, '', ('c',), ()
)
def _change_cell_value(cell, newval):
""" Changes the contents of 'cell' object to newval """
return new.function(cell_changer_code, {}, None, (), (cell,))(newval)
"""Constructors for 3rd party libraries
Note: These can never be renamed due to client compatibility issues"""
def _getobject(modname, attribute):
mod = __import__(modname)
return mod.__dict__[attribute]
def _generateImage(size, mode, str_rep):
"""Generate image from string representation"""
import Image
i = Image.new(mode, size)
i.fromstring(str_rep)
return i
def _lazyloadImage(fp):
import Image
fp.seek(0) #works in almost any case
return Image.open(fp)
"""Timeseries"""
def _genTimeSeries(reduce_args, state):
import scikits.timeseries.tseries as ts
from numpy import ndarray
from numpy.ma import MaskedArray
time_series = ts._tsreconstruct(*reduce_args)
#from setstate modified
(ver, shp, typ, isf, raw, msk, flv, dsh, dtm, dtyp, frq, infodict) = state
#print 'regenerating %s' % dtyp
MaskedArray.__setstate__(time_series, (ver, shp, typ, isf, raw, msk, flv))
_dates = time_series._dates
#_dates.__setstate__((ver, dsh, typ, isf, dtm, frq)) #use remote typ
ndarray.__setstate__(_dates,(dsh,dtyp, isf, dtm))
_dates.freq = frq
_dates._cachedinfo.update(dict(full=None, hasdups=None, steps=None,
toobj=None, toord=None, tostr=None))
# Update the _optinfo dictionary
time_series._optinfo.update(infodict)
return time_series
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