pip upload works #65

uzhdag · Jul 13, 2018 · 946be69 · 946be69
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diff --git a/Makefile b/Makefile
@@ -59,7 +59,13 @@ test-all: ## run tests on every Python version with tox
 	pytest --runslow 
 
 coverage: ## check code coverage quickly with the default Python
-	pytest --cov pathpy --runslow --latex --cov-report html --cov-report term --doctest-modules
+	pytest --cov pathpy \
+		--runslow \
+		--latex \
+		--networkx \
+		--cov-report html \
+		--cov-report term \
+		--doctest-modules
 
 docs: ## generate Sphinx HTML documentation, including API docs
 	rm -f docs/pathpy.rst

diff --git a/README.rst b/README.rst
@@ -1,71 +1,131 @@
-<img src="https://github.com/IngoScholtes/pathpy/blob/master/pathpy_logo.png" width="300" alt="pathpy logo" />
-
-# Introduction
-
-`pathpy` is an OpenSource python package for the analysis of time series data on networks using **higher-order** and **multi-order** graphical models.
-
-The package is specifically tailored to analyze temporal networks as well as sequential data that capture multiple short, independent paths observed in an underlying graph topology. 
-Examples for data that can be analysed with `pathpy` include time-stamped social networks, user click streams in information networks, biological pathways, or traces of information propagating in social media. 
-Unifying the analysis of pathways and temporal networks, `pathpy` provides various methods to extract time-respecting paths from time-stamped network data. 
-It extends (and will eventually supersede) the package [`pyTempnets`](https://github.com/IngoScholtes/pyTempNets).
-
-`pathpy` facilitates the analysis of temporal correlations in time series data on networks.
-It uses a principled model selection technique to infer higher-order graphical representations that capture both topological and temporal characteristics. 
-It specifically allows to answer the question when a network abstraction of time series data is justified and when higher-order network representations are needed.
-
-The theoretical foundation of this package, **higher-order network models**, was developed in the following research works:
-
-1. I Scholtes: [When is a network a network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](http://dl.acm.org/citation.cfm?id=3098145), In KDD'17 - Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Halifax, Nova Scotia, Canada, August 13-17, 2017
-2. I Scholtes, N Wider, A Garas: [Higher-Order Aggregate Networks in the Analysis of Temporal Networks: Path structures and centralities](http://dx.doi.org/10.1140/epjb/e2016-60663-0), The European Physical Journal B, 89:61, March 2016
-3. I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer: [Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks](http://www.nature.com/ncomms/2014/140924/ncomms6024/full/ncomms6024.html), Nature Communications, 5, September 2014
-4. R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer: [Betweenness preference: Quantifying correlations in the topological dynamics of temporal networks](http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.198701), Phys Rev Lett, 110(19), 198701, May 2013
-
-`pathpy` extends this approach towards **multi-layer graphical models** that capture temporal correlations at multiple length scales simultaneously. 
-An illustrative example for a collection of paths (left) and a multi-order graphical representation is shown below. 
-All mathematical details of the framework can be found in this [recent research paper](http://dl.acm.org/citation.cfm?id=3098145).
-
-[![Watch promotional video](https://img.youtube.com/vi/CxJkVrD2ZlM/0.jpg)](https://www.youtube.com/watch?v=CxJkVrD2ZlM)
-
-<img src="https://github.com/IngoScholtes/pathpy/blob/master/multiorder.png" width="500" alt="Illustration of Multi-Order Model" />
-
-# Download and installation
-
-`pathpy` is pure python code. It has no platform-specific dependencies and should thus work on all platforms. 
-It builds on `numpy` and `scipy`. 
-The latest version of `pathpy` can be installed by typing:
-
-`> pip install git+git://github.com/IngoScholtes/pathpy.git`
-
-# Tutorial
-
-A [comprehensive educational tutorial](https://ingoscholtes.github.io/pathpy/tutorial.html) which shows how you can use `pathpy` to analyze data on pathways and temporal networks is [available online](https://ingoscholtes.github.io/pathpy/tutorial.html).
-Moreover, a tutorial which illustrates the abstraction of **higher-order networks** in the modeling of dynamical processes in temporal networks is [available here](https://www.sg.ethz.ch/team/people/ischoltes/research-insights/temporal-networks-demo/). 
-The latter tutorial is based on the predecessor library [`pyTempNets`](https://github.com/IngoScholtes/pyTempNets). Most of its features have been ported to `pathpy`.
-
-# Documentation
-
-The code is fully documented via docstrings which are accessible through python's built-in help system. Just type `help(SYMBOL_NAME)` to see the documentation of a class or method. A [reference manual is available here](https://ingoscholtes.github.io/pathpy/hierarchy.html).
-
-# Releases and Versioning
-
-The first public beta release of pathpy (released February 17 2017) is [v1.0-beta](https://github.com/IngoScholtes/pathpy/releases/tag/v1.0-beta.1). 
-Following versions are named MAJOR.MINOR.PATCH according to [semantic versioning](http://semver.org/). 
-The date of each release is encoded in the PATCH version.
-
-# Acknowledgements
-
-The research behind this data analysis framework was funded by the Swiss State Secretariat for Education, Research and Innovation [(Grant C14.0036)](https://www.sg.ethz.ch/projects/seri-information-spaces/). 
-The development of this package was generously supported by the [MTEC Foundation](http://www.mtec.ethz.ch/research/support/MTECFoundation.html) in the context of the project [The Influence of Interaction Patterns on Success in Socio-Technical Systems: From Theory to Practice](https://www.sg.ethz.ch/projects/mtec-interaction-patterns/).
-
-# Contributors
-
-[Ingo Scholtes](http://www.ingoscholtes.net) (project lead, development)  
-Luca Verginer (development, test suite integration)  
-Roman Cattaneo (development)  
-Nicolas Wider (testing)  
-
-# Copyright
-
-`pathpy` is licensed under the [GNU Affero General Public License](https://choosealicense.com/licenses/agpl-3.0/).
+Introduction
+============
+
+``pathpy`` is an OpenSource python package for the analysis of time
+series data on networks using **higher-order** and **multi-order**
+graphical models.
+
+The package is specifically tailored to analyze temporal networks as
+well as sequential data that capture multiple short, independent paths
+observed in an underlying graph topology. Examples for data that can be
+analysed with ``pathpy`` include time-stamped social networks, user
+click streams in information networks, biological pathways, or traces of
+information propagating in social media. Unifying the analysis of
+pathways and temporal networks, ``pathpy`` provides various methods to
+extract time-respecting paths from time-stamped network data. It extends
+(and will eventually supersede) the package
+```pyTempnets`` <https://github.com/IngoScholtes/pyTempNets>`__.
+
+``pathpy`` facilitates the analysis of temporal correlations in time
+series data on networks. It uses a principled model selection technique
+to infer higher-order graphical representations that capture both
+topological and temporal characteristics. It specifically allows to
+answer the question when a network abstraction of time series data is
+justified and when higher-order network representations are needed.
+
+The theoretical foundation of this package, **higher-order network
+models**, was developed in the following research works:
+
+1. I Scholtes: `When is a network a network? Multi-Order Graphical Model
+   Selection in Pathways and Temporal
+   Networks <http://dl.acm.org/citation.cfm?id=3098145>`__, In KDD'17 -
+   Proceedings of the 23rd ACM SIGKDD International Conference on
+   Knowledge Discovery and Data Mining, Halifax, Nova Scotia, Canada,
+   August 13-17, 2017
+2. I Scholtes, N Wider, A Garas: `Higher-Order Aggregate Networks in the
+   Analysis of Temporal Networks: Path structures and
+   centralities <http://dx.doi.org/10.1140/epjb/e2016-60663-0>`__, The
+   European Physical Journal B, 89:61, March 2016
+3. I Scholtes, N Wider, R Pfitzner, A Garas, CJ Tessone, F Schweitzer:
+   `Causality-driven slow-down and speed-up of diffusion in
+   non-Markovian temporal
+   networks <http://www.nature.com/ncomms/2014/140924/ncomms6024/full/ncomms6024.html>`__,
+   Nature Communications, 5, September 2014
+4. R Pfitzner, I Scholtes, A Garas, CJ Tessone, F Schweitzer:
+   `Betweenness preference: Quantifying correlations in the topological
+   dynamics of temporal
+   networks <http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.110.198701>`__,
+   Phys Rev Lett, 110(19), 198701, May 2013
+
+``pathpy`` extends this approach towards **multi-layer graphical
+models** that capture temporal correlations at multiple length scales
+simultaneously. An illustrative example for a collection of paths (left)
+and a multi-order graphical representation is shown below. All
+mathematical details of the framework can be found in this `recent
+research paper <http://dl.acm.org/citation.cfm?id=3098145>`__.
+
+|Watch promotional video|
+
+Download and installation
+=========================
+
+``pathpy`` is pure python code. It has no platform-specific dependencies
+and should thus work on all platforms. It builds on ``numpy`` and
+``scipy``. The latest version of ``pathpy`` can be installed by typing:
+
+``> pip install git+git://github.com/IngoScholtes/pathpy.git``
+
+Tutorial
+========
+
+A `comprehensive educational
+tutorial <https://ingoscholtes.github.io/pathpy/tutorial.html>`__ which
+shows how you can use ``pathpy`` to analyze data on pathways and
+temporal networks is `available
+online <https://ingoscholtes.github.io/pathpy/tutorial.html>`__.
+Moreover, a tutorial which illustrates the abstraction of **higher-order
+networks** in the modeling of dynamical processes in temporal networks
+is `available
+here <https://www.sg.ethz.ch/team/people/ischoltes/research-insights/temporal-networks-demo/>`__.
+The latter tutorial is based on the predecessor library
+```pyTempNets`` <https://github.com/IngoScholtes/pyTempNets>`__. Most of
+its features have been ported to ``pathpy``.
+
+Documentation
+=============
+
+The code is fully documented via docstrings which are accessible through
+python's built-in help system. Just type ``help(SYMBOL_NAME)`` to see
+the documentation of a class or method. A `reference manual is available
+here <https://ingoscholtes.github.io/pathpy/hierarchy.html>`__.
+
+Releases and Versioning
+=======================
+
+The first public beta release of pathpy (released February 17 2017) is
+`v1.0-beta <https://github.com/IngoScholtes/pathpy/releases/tag/v1.0-beta.1>`__.
+Following versions are named MAJOR.MINOR.PATCH according to `semantic
+versioning <http://semver.org/>`__. The date of each release is encoded
+in the PATCH version.
+
+Acknowledgements
+================
+
+The research behind this data analysis framework was funded by the Swiss
+State Secretariat for Education, Research and Innovation `(Grant
+C14.0036) <https://www.sg.ethz.ch/projects/seri-information-spaces/>`__.
+The development of this package was generously supported by the `MTEC
+Foundation <http://www.mtec.ethz.ch/research/support/MTECFoundation.html>`__
+in the context of the project `The Influence of Interaction Patterns on
+Success in Socio-Technical Systems: From Theory to
+Practice <https://www.sg.ethz.ch/projects/mtec-interaction-patterns/>`__.
+
+Contributors
+============
+
+| `Ingo Scholtes <http://www.ingoscholtes.net>`__ (project lead,
+  development)
+| Luca Verginer (development, test suite integration)
+| Roman Cattaneo (development)
+| Nicolas Wider (testing)
+
+Copyright
+=========
+
+``pathpy`` is licensed under the `GNU Affero General Public
+License <https://choosealicense.com/licenses/agpl-3.0/>`__.
 
 (c) Copyright ETH Zürich, Chair of Systems Design, 2015-2017
+
+.. |Watch promotional video| image:: https://img.youtube.com/vi/CxJkVrD2ZlM/0.jpg
+   :target: https://www.youtube.com/watch?v=CxJkVrD2ZlM
diff --git a/pathpy/__init__.py b/pathpy/__init__.py
@@ -2,7 +2,7 @@
 
 __author__ = """Ingo Scholtes"""
 __email__ = 'ischoltes@ethz.ch'
-__version__ = '2.0.0'
+__version__ = '2.0.0.dev1'
 
 from .classes import DAG
 from .classes import Paths

diff --git a/setup.cfg b/setup.cfg
@@ -1,16 +1,8 @@
 [bumpversion]
-current_version = 1.2.1
+current_version = 2.0.0
 commit = True
 tag = True
 
-[bumpversion:file:setup.py]
-search = version='{current_version}'
-replace = version='{new_version}'
-
-[bumpversion:file:pathpy/__init__.py]
-search = __version__ = '{current_version}'
-replace = __version__ = '{new_version}'
-
 [bdist_wheel]
 universal = 1
 

diff --git a/setup.py b/setup.py
@@ -2,44 +2,44 @@
 # -*- coding: utf-8 -*-
 
 """The setup script."""
+
 from setuptools import setup, find_packages
+from pathpy import __version__
 
-with open('README.rst') as readme_file:
-    readme = readme_file.read()
 
-with open('HISTORY.rst') as history_file:
+with open('README.rst') as readme_file, open('HISTORY.rst') as history_file:
+    readme = readme_file.read()
     history = history_file.read()
 
-requirements = ["numpy", "scipy"]
+install_requirements = ['numpy', 'scipy']
 
 setup_requirements = ['pytest-runner']
 
-test_requirements = ['pytest']
-
 setup(
     author="Ingo Scholtes",
     author_email='ischoltes@ethz.ch',
+    license='AGPL-3.0+',
     classifiers=[
         'Development Status :: 4 - Beta',
         'Intended Audience :: Science/Research',
         'Topic :: Scientific/Engineering :: Information Analysis',
-        'License :: OSI Approved :: AGPL-3.0+ License',
+        'License :: OSI Approved :: GNU Affero General Public License v3 or later (AGPLv3+)',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.4',
         'Programming Language :: Python :: 3.5',
+        'Programming Language :: Python :: 3.6',
     ],
-    description="A python package for the analysis of sequential data on pathways and temporal networks from the perspective of higher-order network models.",
-    install_requires=requirements,
+    description="A python package for the analysis of sequential data on pathways and "
+                "temporal networks from the perspective of higher-order network models.",
+    install_requires=install_requirements,
+    setup_requires=setup_requirements,
     long_description=readme + '\n\n' + history,
     include_package_data=True,
-    keywords='pathpy',
+    python_requires='>=3.5',
+    keywords='network analysis temporal networks pathways sequence modeling graph mining',
     name='pathpy',
-    packages=find_packages(include=['pathpy']),
-    setup_requires=setup_requirements,
+    packages=find_packages(),
     test_suite='tests',
-    tests_require=test_requirements,
     url='https://github.com/IngoScholtes/pathpy',
-    version='2.0.0',
-    zip_safe=False,
-    data_files=[('.', ['DESCRIPTION.rst', 'README.rst', 'HISTORY.rst'])]
+    version=__version__,
+    zip_safe=False
 )