Edit: __info__.py
#!/usr/bin/env python # # Author: Mike McKerns (mmckerns @caltech and @uqfoundation) # Copyright (c) 2023 The Uncertainty Quantification Foundation. # License: 3-clause BSD. The full license text is available at: # - https://github.com/uqfoundation/dill/blob/master/LICENSE ''' ----------------------------- dill: serialize all of Python ----------------------------- About Dill ========== ``dill`` extends Python's ``pickle`` module for serializing and de-serializing Python objects to the majority of the built-in Python types. Serialization is the process of converting an object to a byte stream, and the inverse of which is converting a byte stream back to a Python object hierarchy. ``dill`` provides the user the same interface as the ``pickle`` module, and also includes some additional features. In addition to pickling Python objects, ``dill`` provides the ability to save the state of an interpreter session in a single command. Hence, it would be feasible to save an interpreter session, close the interpreter, ship the pickled file to another computer, open a new interpreter, unpickle the session and thus continue from the 'saved' state of the original interpreter session. ``dill`` can be used to store Python objects to a file, but the primary usage is to send Python objects across the network as a byte stream. ``dill`` is quite flexible, and allows arbitrary user defined classes and functions to be serialized. Thus ``dill`` is not intended to be secure against erroneously or maliciously constructed data. It is left to the user to decide whether the data they unpickle is from a trustworthy source. ``dill`` is part of ``pathos``, a Python framework for heterogeneous computing. ``dill`` is in active development, so any user feedback, bug reports, comments, or suggestions are highly appreciated. A list of issues is located at https://github.com/uqfoundation/dill/issues, with a legacy list maintained at https://uqfoundation.github.io/project/pathos/query. Major Features ============== ``dill`` can pickle the following standard types: - none, type, bool, int, float, complex, bytes, str, - tuple, list, dict, file, buffer, builtin, - Python classes, namedtuples, dataclasses, metaclasses, - instances of classes, - set, frozenset, array, functions, exceptions ``dill`` can also pickle more 'exotic' standard types: - functions with yields, nested functions, lambdas, - cell, method, unboundmethod, module, code, methodwrapper, - methoddescriptor, getsetdescriptor, memberdescriptor, wrapperdescriptor, - dictproxy, slice, notimplemented, ellipsis, quit ``dill`` cannot yet pickle these standard types: - frame, generator, traceback ``dill`` also provides the capability to: - save and load Python interpreter sessions - save and extract the source code from functions and classes - interactively diagnose pickling errors Current Release =============== The latest released version of ``dill`` is available from: https://pypi.org/project/dill ``dill`` is distributed under a 3-clause BSD license. Development Version =================== You can get the latest development version with all the shiny new features at: https://github.com/uqfoundation If you have a new contribution, please submit a pull request. Installation ============ ``dill`` can be installed with ``pip``:: $ pip install dill To optionally include the ``objgraph`` diagnostic tool in the install:: $ pip install dill[graph] For windows users, to optionally install session history tools:: $ pip install dill[readline] Requirements ============ ``dill`` requires: - ``python`` (or ``pypy``), **>=3.7** - ``setuptools``, **>=42** Optional requirements: - ``objgraph``, **>=1.7.2** - ``pyreadline``, **>=1.7.1** (on windows) Basic Usage =========== ``dill`` is a drop-in replacement for ``pickle``. Existing code can be updated to allow complete pickling using:: >>> import dill as pickle or:: >>> from dill import dumps, loads ``dumps`` converts the object to a unique byte string, and ``loads`` performs the inverse operation:: >>> squared = lambda x: x**2 >>> loads(dumps(squared))(3) 9 There are a number of options to control serialization which are provided as keyword arguments to several ``dill`` functions: * with *protocol*, the pickle protocol level can be set. This uses the same value as the ``pickle`` module, *DEFAULT_PROTOCOL*. * with *byref=True*, ``dill`` to behave a lot more like pickle with certain objects (like modules) pickled by reference as opposed to attempting to pickle the object itself. * with *recurse=True*, objects referred to in the global dictionary are recursively traced and pickled, instead of the default behavior of attempting to store the entire global dictionary. * with *fmode*, the contents of the file can be pickled along with the file handle, which is useful if the object is being sent over the wire to a remote system which does not have the original file on disk. Options are *HANDLE_FMODE* for just the handle, *CONTENTS_FMODE* for the file content and *FILE_FMODE* for content and handle. * with *ignore=False*, objects reconstructed with types defined in the top-level script environment use the existing type in the environment rather than a possibly different reconstructed type. The default serialization can also be set globally in *dill.settings*. Thus, we can modify how ``dill`` handles references to the global dictionary locally or globally:: >>> import dill.settings >>> dumps(absolute) == dumps(absolute, recurse=True) False >>> dill.settings['recurse'] = True >>> dumps(absolute) == dumps(absolute, recurse=True) True ``dill`` also includes source code inspection, as an alternate to pickling:: >>> import dill.source >>> print(dill.source.getsource(squared)) squared = lambda x:x**2 To aid in debugging pickling issues, use *dill.detect* which provides tools like pickle tracing:: >>> import dill.detect >>> with dill.detect.trace(): >>> dumps(squared) ┬ F1: <function <lambda> at 0x7fe074f8c280> ├┬ F2: <function _create_function at 0x7fe074c49c10> │└ # F2 [34 B] ├┬ Co: <code object <lambda> at 0x7fe07501eb30, file "<stdin>", line 1> │├┬ F2: <function _create_code at 0x7fe074c49ca0> ││└ # F2 [19 B] │└ # Co [87 B] ├┬ D1: <dict object at 0x7fe0750d4680> │└ # D1 [22 B] ├┬ D2: <dict object at 0x7fe074c5a1c0> │└ # D2 [2 B] ├┬ D2: <dict object at 0x7fe074f903c0> │├┬ D2: <dict object at 0x7fe074f8ebc0> ││└ # D2 [2 B] │└ # D2 [23 B] └ # F1 [180 B] With trace, we see how ``dill`` stored the lambda (``F1``) by first storing ``_create_function``, the underlying code object (``Co``) and ``_create_code`` (which is used to handle code objects), then we handle the reference to the global dict (``D2``) plus other dictionaries (``D1`` and ``D2``) that save the lambda object's state. A ``#`` marks when the object is actually stored. More Information ================ Probably the best way to get started is to look at the documentation at http://dill.rtfd.io. Also see ``dill.tests`` for a set of scripts that demonstrate how ``dill`` can serialize different Python objects. You can run the test suite with ``python -m dill.tests``. The contents of any pickle file can be examined with ``undill``. As ``dill`` conforms to the ``pickle`` interface, the examples and documentation found at http://docs.python.org/library/pickle.html also apply to ``dill`` if one will ``import dill as pickle``. The source code is also generally well documented, so further questions may be resolved by inspecting the code itself. Please feel free to submit a ticket on github, or ask a question on stackoverflow (**@Mike McKerns**). If you would like to share how you use ``dill`` in your work, please send an email (to **mmckerns at uqfoundation dot org**). Citation ======== If you use ``dill`` to do research that leads to publication, we ask that you acknowledge use of ``dill`` by citing the following in your publication:: M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis, "Building a framework for predictive science", Proceedings of the 10th Python in Science Conference, 2011; http://arxiv.org/pdf/1202.1056 Michael McKerns and Michael Aivazis, "pathos: a framework for heterogeneous computing", 2010- ; https://uqfoundation.github.io/project/pathos Please see https://uqfoundation.github.io/project/pathos or http://arxiv.org/pdf/1202.1056 for further information. ''' __version__ = '0.3.7' __author__ = 'Mike McKerns' __license__ = ''' Copyright (c) 2004-2016 California Institute of Technology. Copyright (c) 2016-2023 The Uncertainty Quantification Foundation. All rights reserved. This software is available subject to the conditions and terms laid out below. By downloading and using this software you are agreeing to the following conditions. 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 names of the copyright holders nor the names of any of the 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 OWNER 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. '''
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