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refactor setup.py:
* use entry_points * build universal wheels * use setuptools_scm for version tracking * eliminate readme duplication by dropping README.md * use manifest.in * add .gitignore
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‎.gitignore

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# Created by https://www.gitignore.io
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### Python ###
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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env/
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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*.egg-info/
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.installed.cfg
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*.egg
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*,cover
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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target/
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dill/LICENSE
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dill/README.rst
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dill/_version.py

‎MANIFEST

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‎MANIFEST.in

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include DEV_NOTES
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include README.rst
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include LICENSE

‎README.md ‎README.rst

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1-
dill
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====
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serialize all of python
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About Dill
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----------
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Dill extends python's 'pickle' module for serializing and de-serializing
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python objects to the majority of the built-in python types. Serialization
9-
is the process of converting an object to a byte stream, and the inverse
10-
of which is converting a byte stream back to on python object hierarchy.
11-
12-
Dill provides the user the same interface as the 'pickle' module, and
13-
also includes some additional features. In addition to pickling python
14-
objects, dill provides the ability to save the state of an interpreter
15-
session in a single command. Hence, it would be feasable to save a
16-
interpreter session, close the interpreter, ship the pickled file to
17-
another computer, open a new interpreter, unpickle the session and
18-
thus continue from the 'saved' state of the original interpreter
19-
session.
20-
21-
Dill can be used to store python objects to a file, but the primary
22-
usage is to send python objects across the network as a byte stream.
23-
Dill is quite flexible, and allows arbitrary user defined classes
24-
and funcitons to be serialized. Thus dill is not intended to be
25-
secure against erroneously or maliciously constructed data. It is
26-
left to the user to decide whether the data they unpickle is from
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a trustworthy source.
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29-
Dill is part of pathos, a python framework for heterogeneous computing.
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Dill is in active development, so any user feedback, bug reports, comments,
31-
or suggestions are highly appreciated. A list of known issues is maintained
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at http://trac.mystic.cacr.caltech.edu/project/pathos/query, with a public
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ticket list at https://github.com/uqfoundation/dill/issues.
34-
35-
36-
Major Features
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--------------
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Dill can pickle the following standard types::
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40-
* none, type, bool, int, long, float, complex, str, unicode,
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* tuple, list, dict, file, buffer, builtin,
42-
* both old and new style classes,
43-
* instances of old and new style classes,
44-
* set, frozenset, array, functions, exceptions
45-
46-
Dill can also pickle more 'exotic' standard types::
47-
48-
* functions with yields, nested functions, lambdas
49-
* cell, method, unboundmethod, module, code, methodwrapper,
50-
* dictproxy, methoddescriptor, getsetdescriptor, memberdescriptor,
51-
* wrapperdescriptor, xrange, slice,
52-
* notimplemented, ellipsis, quit
53-
54-
Dill cannot yet pickle these standard types::
55-
56-
* frame, generator, traceback
57-
58-
Dill also provides the capability to::
59-
60-
* save and load python interpreter sessions
61-
* save and extract the source code from functions and classes
62-
* interactively diagnose pickling errors
63-
64-
65-
Current Release
66-
---------------
67-
The latest stable release of dill is available from::
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http://trac.mystic.cacr.caltech.edu/project/pathos
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70-
or::
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https://github.com/uqfoundation/dill/releases
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73-
or also::
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https://pypi.python.org/pypi/dill
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76-
Dill is distributed under a 3-clause BSD license.
77-
78-
79-
Development Version
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-------------------
81-
You can get the latest development version with all the shiny new features at::
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https://github.com/uqfoundation
83-
84-
Feel free to fork the github mirror of our svn trunk. If you have a new
85-
contribution, please submit a pull request.
86-
87-
88-
More Information
89-
----------------
90-
Probably the best way to get started is to look at the tests that are
91-
provide within dill. See `dill.tests` for a set of scripts that demonstrate
92-
dill's ability to serialize different python objects. Since dill conforms
93-
to the 'pickle' interface, the examples and documentation at
94-
http://docs.python.org/library/pickle.html also apply to dill if one will
95-
`import dill as pickle`. The source code is also generally well documented,
96-
so further questions may be resolved by inspecting the code itself. Please
97-
also feel free to submit a ticket on github, or ask a question on
98-
stackoverflow (@Mike McKerns).
99-
100-
Dill is an active research tool. There are a growing number of publications
101-
and presentations that discuss real-world examples and new features of dill
102-
in greater detail than presented in the user's guide. If you would like to
103-
share how you use dill in your work, please post a link or send an email
104-
(to mmckerns at caltech dot edu).
105-
106-
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Citation
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--------
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If you use dill to do research that leads to publication, we ask that you
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acknowledge use of dill by citing the following in your publication::
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M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
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"Building a framework for predictive science", Proceedings of
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the 10th Python in Science Conference, 2011;
115-
http://arxiv.org/pdf/1202.1056
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Michael McKerns and Michael Aivazis,
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"pathos: a framework for heterogeneous computing", 2010- ;
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http://dev.danse.us/trac/pathos
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121-
Please see http://trac.mystic.cacr.caltech.edu/project/pathos or
122-
http://arxiv.org/pdf/1202.1056 for further information.
123-
1+
-----------------------------
2+
dill: serialize all of python
3+
-----------------------------
4+
5+
About Dill
6+
==========
7+
8+
Dill extends python's 'pickle' module for serializing and de-serializing
9+
python objects to the majority of the built-in python types. Serialization
10+
is the process of converting an object to a byte stream, and the inverse
11+
of which is converting a byte stream back to on python object hierarchy.
12+
13+
Dill provides the user the same interface as the 'pickle' module, and
14+
also includes some additional features. In addition to pickling python
15+
objects, dill provides the ability to save the state of an interpreter
16+
session in a single command. Hence, it would be feasable to save a
17+
interpreter session, close the interpreter, ship the pickled file to
18+
another computer, open a new interpreter, unpickle the session and
19+
thus continue from the 'saved' state of the original interpreter
20+
session.
21+
22+
Dill can be used to store python objects to a file, but the primary
23+
usage is to send python objects across the network as a byte stream.
24+
Dill is quite flexible, and allows arbitrary user defined classes
25+
and funcitons to be serialized. Thus dill is not intended to be
26+
secure against erroneously or maliciously constructed data. It is
27+
left to the user to decide whether the data they unpickle is from
28+
a trustworthy source.
29+
30+
Dill is part of pathos, a python framework for heterogeneous computing.
31+
Dill is in active development, so any user feedback, bug reports, comments,
32+
or suggestions are highly appreciated. A list of known issues is maintained
33+
at http://trac.mystic.cacr.caltech.edu/project/pathos/query, with a public
34+
ticket list at https://github.com/uqfoundation/dill/issues.
35+
36+
37+
Major Features
38+
==============
39+
40+
Dill can pickle the following standard types::
41+
42+
- none, type, bool, int, long, float, complex, str, unicode,
43+
- tuple, list, dict, file, buffer, builtin,
44+
- both old and new style classes,
45+
- instances of old and new style classes,
46+
- set, frozenset, array, functions, exceptions
47+
48+
Dill can also pickle more 'exotic' standard types::
49+
50+
- functions with yields, nested functions, lambdas,
51+
- cell, method, unboundmethod, module, code, methodwrapper,
52+
- dictproxy, methoddescriptor, getsetdescriptor, memberdescriptor,
53+
- wrapperdescriptor, xrange, slice,
54+
- notimplemented, ellipsis, quit
55+
56+
Dill cannot yet pickle these standard types::
57+
58+
- frame, generator, traceback
59+
60+
Dill also provides the capability to::
61+
62+
- save and load python interpreter sessions
63+
- save and extract the source code from functions and classes
64+
- interactively diagnose pickling errors
65+
66+
67+
Current Release
68+
===============
69+
70+
The latest stable version of dill is available from::
71+
72+
https://pypi.python.org/pypi/dill
73+
74+
or::
75+
76+
https://github.com/uqfoundation/dill/releases
77+
78+
Dill is distributed under a 3-clause BSD license.
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80+
>>> import dill
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>>> print (dill.license())
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83+
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Development Version
85+
===================
86+
87+
You can get the latest development version with all the shiny new features at::
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89+
https://github.com/uqfoundation
90+
91+
Feel free to fork the github mirror of our svn trunk. If you have a new
92+
contribution, please submit a pull request.
93+
94+
95+
Requirements
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============
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98+
Dill requires::
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- python2, version >= 2.5 *or* python3, version >= 3.1
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Optional requirements::
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- numpy, version >= 1.6
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- objgraph, version >= 1.7.2
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107+
108+
More Information
109+
================
110+
111+
Probably the best way to get started is to look at the tests that are
112+
provided within dill. See `dill.tests` for a set of scripts that demonstrate
113+
dill's ability to serialize different python objects. Since dill conforms
114+
to the 'pickle' interface, the examples and documentation at
115+
http://docs.python.org/library/pickle.html also apply to dill if one will
116+
`import dill as pickle`. The source code is also generally well
117+
documented, so further questions may be resolved by inspecting the code
118+
itself. Please also feel free to submit a ticket on github, or ask a
119+
question on stackoverflow (@Mike McKerns).
120+
121+
Dill is an active research tool. There are a growing number of publications
122+
and presentations that discuss real-world examples and new features of dill
123+
in greater detail than presented in the user's guide. If you would like to
124+
share how you use dill in your work, please post a link or send an email
125+
(to mmckerns at caltech dot edu).
126+
127+
128+
Citation
129+
========
130+
131+
If you use dill to do research that leads to publication, we ask that you
132+
acknowledge use of dill by citing the following in your publication::
133+
134+
M.M. McKerns, L. Strand, T. Sullivan, A. Fang, M.A.G. Aivazis,
135+
"Building a framework for predictive science", Proceedings of
136+
the 10th Python in Science Conference, 2011;
137+
http://arxiv.org/pdf/1202.1056
138+
139+
Michael McKerns and Michael Aivazis,
140+
"pathos: a framework for heterogeneous computing", 2010- ;
141+
http://trac.mystic.cacr.caltech.edu/project/pathos
142+
143+
Please see http://trac.mystic.cacr.caltech.edu/project/pathos or
144+
http://arxiv.org/pdf/1202.1056 for further information.
145+

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