Mini Shell

#!/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
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modification, are permitted provided that the following conditions
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    - 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.

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'''