├── .gitattributes ├── .gitignore ├── DESCRIPTION.rst ├── LICENSE.txt ├── README.md ├── _config.yml ├── docs ├── Doxyfile ├── _config.yml ├── annotated.html ├── bc_s.png ├── bdwn.png ├── classes.html ├── classpathpy_1_1HigherOrderNetwork_1_1EmptySCCError.html ├── classpathpy_1_1HigherOrderNetwork_1_1EmptySCCError.png ├── classpathpy_1_1HigherOrderNetwork_1_1HigherOrderNetwork-members.html ├── classpathpy_1_1HigherOrderNetwork_1_1HigherOrderNetwork.html ├── classpathpy_1_1Log_1_1Log-members.html ├── classpathpy_1_1Log_1_1Log.html ├── classpathpy_1_1Log_1_1Severity-members.html ├── classpathpy_1_1Log_1_1Severity.html ├── classpathpy_1_1Log_1_1Severity.png ├── classpathpy_1_1MarkovSequence_1_1MarkovSequence-members.html ├── classpathpy_1_1MarkovSequence_1_1MarkovSequence.html ├── classpathpy_1_1MultiOrderModel_1_1MultiOrderModel-members.html ├── classpathpy_1_1MultiOrderModel_1_1MultiOrderModel.html ├── classpathpy_1_1Paths_1_1Paths-members.html ├── classpathpy_1_1Paths_1_1Paths.html ├── classpathpy_1_1TemporalNetwork_1_1TemporalNetwork-members.html ├── classpathpy_1_1TemporalNetwork_1_1TemporalNetwork.html ├── closed.png ├── dir_03abf54a0648869b4c214957834b2a86.html ├── doxygen.css ├── doxygen.png ├── dynsections.js ├── ftv2blank.png ├── ftv2cl.png ├── ftv2doc.png ├── ftv2folderclosed.png ├── ftv2folderopen.png ├── ftv2lastnode.png ├── ftv2link.png ├── ftv2mlastnode.png ├── ftv2mnode.png ├── ftv2mo.png ├── ftv2node.png ├── ftv2ns.png ├── ftv2plastnode.png ├── ftv2pnode.png ├── ftv2splitbar.png ├── ftv2vertline.png ├── functions.html ├── functions_func.html ├── functions_vars.html ├── hierarchy.html ├── index.html ├── jquery.js ├── nav_f.png ├── nav_g.png ├── nav_h.png ├── open.png ├── search │ ├── all_0.html │ ├── all_0.js │ ├── all_1.html │ ├── all_1.js │ ├── all_10.html │ ├── all_10.js │ ├── all_11.html │ ├── all_11.js │ ├── all_12.html │ ├── all_12.js │ ├── all_13.html │ ├── all_13.js │ ├── all_2.html │ ├── all_2.js │ ├── all_3.html │ ├── all_3.js │ ├── all_4.html │ ├── all_4.js │ ├── all_5.html │ ├── all_5.js │ ├── all_6.html │ ├── all_6.js │ ├── all_7.html │ ├── all_7.js │ ├── all_8.html │ ├── all_8.js │ ├── all_9.html │ ├── all_9.js │ ├── all_a.html │ ├── all_a.js │ ├── all_b.html │ ├── all_b.js │ ├── all_c.html │ ├── all_c.js │ ├── all_d.html │ ├── all_d.js │ ├── all_e.html │ ├── all_e.js │ ├── all_f.html │ ├── all_f.js │ ├── classes_0.html │ ├── classes_0.js │ ├── classes_1.html │ ├── classes_1.js │ ├── classes_2.html │ ├── classes_2.js │ ├── classes_3.html │ ├── classes_3.js │ ├── classes_4.html │ ├── classes_4.js │ ├── classes_5.html │ ├── classes_5.js │ ├── classes_6.html │ ├── classes_6.js │ ├── close.png │ ├── functions_0.html │ ├── functions_0.js │ ├── functions_1.html │ ├── functions_1.js │ ├── functions_2.html │ ├── functions_2.js │ ├── functions_3.html │ ├── functions_3.js │ ├── functions_4.html │ ├── functions_4.js │ ├── functions_5.html │ ├── functions_5.js │ ├── functions_6.html │ ├── functions_6.js │ ├── functions_7.html │ ├── functions_7.js │ ├── functions_8.html │ ├── functions_8.js │ ├── functions_9.html │ ├── functions_9.js │ ├── functions_a.html │ ├── functions_a.js │ ├── functions_b.html │ ├── functions_b.js │ ├── functions_c.html │ ├── functions_c.js │ ├── functions_d.html │ ├── functions_d.js │ ├── functions_e.html │ ├── functions_e.js │ ├── functions_f.html │ ├── functions_f.js │ ├── mag_sel.png │ ├── nomatches.html │ ├── search.css │ ├── search.js │ ├── search_l.png │ ├── search_m.png │ ├── search_r.png │ ├── variables_0.html │ ├── variables_0.js │ ├── variables_1.html │ ├── variables_1.js │ ├── variables_2.html │ ├── variables_2.js │ ├── variables_3.html │ ├── variables_3.js │ ├── variables_4.html │ ├── variables_4.js │ ├── variables_5.html │ ├── variables_5.js │ ├── variables_6.html │ ├── variables_6.js │ ├── variables_7.html │ ├── variables_7.js │ ├── variables_8.html │ ├── variables_8.js │ ├── variables_9.html │ ├── variables_9.js │ ├── variables_a.html │ ├── variables_a.js │ ├── variables_b.html │ └── variables_b.js ├── sync_off.png ├── sync_on.png ├── tab_a.png ├── tab_b.png ├── tab_h.png ├── tab_s.png ├── tabs.css ├── tutorial.html └── tutorial.ipynb ├── multiorder.png ├── pathpy.pyproj ├── pathpy.sln ├── pathpy ├── HigherOrderNetwork.py ├── Log.py ├── MarkovSequence.py ├── MultiOrderModel.py ├── Paths.py ├── TemporalNetwork.py └── __init__.py ├── pathpy_logo.png ├── setup.py └── tests ├── README.md ├── conftest.py ├── test_Path.py ├── test_TemporalNetwork.py ├── test_data ├── edge_frequency.edge ├── example_int.tedges ├── example_timestamp.tedges └── ngram_simple.ngram └── test_estimation.py /.gitattributes: -------------------------------------------------------------------------------- 1 | # Auto detect text files and perform LF normalization 2 | * text=auto 3 | 4 | # Custom for Visual Studio 5 | *.cs diff=csharp 6 | 7 | # Standard to msysgit 8 | *.doc diff=astextplain 9 | *.DOC diff=astextplain 10 | *.docx diff=astextplain 11 | *.DOCX diff=astextplain 12 | *.dot diff=astextplain 13 | *.DOT diff=astextplain 14 | *.pdf diff=astextplain 15 | *.PDF diff=astextplain 16 | *.rtf diff=astextplain 17 | *.RTF diff=astextplain 18 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | # Byte-compiled / optimized / DLL files 2 | __pycache__/ 3 | *.py[cod] 4 | 5 | # C extensions 6 | *.so 7 | 8 | # Distribution / packaging 9 | .Python 10 | env/ 11 | build/ 12 | develop-eggs/ 13 | dist/ 14 | downloads/ 15 | eggs/ 16 | lib/ 17 | lib64/ 18 | parts/ 19 | sdist/ 20 | var/ 21 | *.egg-info/ 22 | .installed.cfg 23 | *.egg 24 | 25 | # PyInstaller 26 | # Usually these files are written by a python script from a template 27 | # before PyInstaller builds the exe, so as to inject date/other infos into it. 28 | *.manifest 29 | *.spec 30 | 31 | # Installer logs 32 | pip-log.txt 33 | pip-delete-this-directory.txt 34 | 35 | # Unit test / coverage reports 36 | htmlcov/ 37 | .tox/ 38 | .coverage 39 | .cache 40 | nosetests.xml 41 | coverage.xml 42 | 43 | # Translations 44 | *.mo 45 | *.pot 46 | 47 | # Django stuff: 48 | *.log 49 | 50 | # PyBuilder 51 | target/ 52 | 53 | # ========================= 54 | # Operating System Files 55 | # ========================= 56 | 57 | # OSX 58 | # ========================= 59 | 60 | .DS_Store 61 | .AppleDouble 62 | .LSOverride 63 | 64 | # Thumbnails 65 | ._* 66 | 67 | # Files that might appear on external disk 68 | .Spotlight-V100 69 | .Trashes 70 | 71 | # Directories potentially created on remote AFP share 72 | .AppleDB 73 | .AppleDesktop 74 | Network Trash Folder 75 | Temporary Items 76 | .apdisk 77 | 78 | # Windows 79 | # ========================= 80 | 81 | # Windows image file caches 82 | Thumbs.db 83 | ehthumbs.db 84 | 85 | # Folder config file 86 | Desktop.ini 87 | 88 | # Recycle Bin used on file shares 89 | $RECYCLE.BIN/ 90 | 91 | # Windows Installer files 92 | *.cab 93 | *.msi 94 | *.msm 95 | *.msp 96 | 97 | # Windows shortcuts 98 | *.lnk 99 | pyTempNets.v12.suo 100 | *.png 101 | *.TMP 102 | pyTempNet.egg-info/requires.txt 103 | pyTempNet.egg-info/SOURCES.txt 104 | pyTempNet.egg-info/dependency_links.txt 105 | pyTempNet.egg-info/requires.txt 106 | *.suo 107 | pyTempNet.egg-info/top_level.txt 108 | pyTempNet.egg-info/requires.txt 109 | pyTempNet.egg-info/requires.txt 110 | pyTempNet.egg-info/dependency_links.txt 111 | pyTempNet.egg-info/SOURCES.txt 112 | .idea/ 113 | .eggs/ 114 | prof/* 115 | -------------------------------------------------------------------------------- /DESCRIPTION.rst: -------------------------------------------------------------------------------- 1 | pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models. -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | pathpy logo 2 | 3 | **Note**: This is the repository of an old version of pathpy. It will soon be replaced by [pathpy 3](https://github.com/pathpy/pathpy), which has a new [home on gitHub](https://github.com/pathpy/pathpy). 4 | 5 | # Introduction 6 | 7 | `pathpy` is an OpenSource python package for the modeling and analysis of pathways and temporal networks 8 | using **higher-order** and **multi-order** graphical models. 9 | 10 | The package is specifically tailored to analyze sequential data which capture multiple observations of short, independent paths 11 | observed in an underlying graph topology. Examples for such data include user click streams in information networks, 12 | biological pathways, or traces of information propagating in social media. Unifying the analysis of pathways and temporal networks, 13 | `pathpy` also supports the extraction of time-respecting paths from time-stamped network data. It extends (and will eventually supersede) 14 | the package [`pyTempnets`](https://github.com/IngoScholtes/pyTempNets). 15 | 16 | `pathpy` facilitates the analysis of temporal correlations in such sequential data. It uses a principled model selection 17 | technique to infer higher-order graphical representations that capture both topological and temporal 18 | characteristics of time-resolved relational data. It specifically allows to answer the question whether a (first-order) network 19 | abstraction of such data is justified, or whether higher-order network abstractions are needed. 20 | 21 | The theoretical foundation of this package, **higher-order network models**, has been developed in the following research works: 22 | 23 | 1. I Scholtes: [When is a network a network? Multi-Order Graphical Model Selection in Pathways and Temporal Networks](https://arxiv.org/abs/1702.05499), to appear in KDD'17, arXiv:1702.05499 24 | 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 25 | 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 26 | 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 27 | 28 | `pathpy` extends this approach towards **multi-layer graphical models** that capture temporal correlations in pathways at multiple length scales simultaneously. An illustrative example for 29 | a collection of pathways (left) and a multi-order graphical representation of these pathways is shown below. All mathematical details of the framework can be found in this [recent research paper](https://arxiv.org/abs/1702.05499). 30 | 31 | [![Watch promotional video](https://img.youtube.com/vi/CxJkVrD2ZlM/0.jpg)](https://www.youtube.com/watch?v=CxJkVrD2ZlM) 32 | 33 | Illustration of Multi-Order Model 34 | 35 | # Download and installation 36 | 37 | `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: 38 | 39 | `> pip install git+git://github.com/IngoScholtes/pathpy.git` 40 | 41 | `pathpy` currently requires python 3.x. We are planning to make the next version backwards compatible with python 2.x. 42 | 43 | # Tutorial 44 | 45 | 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). 46 | 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 47 | latter tutorial is based on the predecessor library [`pyTempNets`](https://github.com/IngoScholtes/pyTempNets). Most of its features have been ported to `pathpy`. 48 | 49 | # Documentation 50 | 51 | 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). 52 | 53 | # Releases and Versioning 54 | 55 | 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. 56 | 57 | # Acknowledgements 58 | 59 | 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/). 60 | 61 | # Contributors 62 | 63 | [Ingo Scholtes](http://www.ingoscholtes.net) (project lead, development) 64 | [Luca Verginer](http://www.verginer.eu/about/) (development, testing) 65 | 66 | # Past Contributors 67 | Roman Cattaneo (development) 68 | Nicolas Wider (testing) 69 | 70 | # Copyright 71 | 72 | pathpy is licensed under the [GNU Affero General Public License](https://choosealicense.com/licenses/agpl-3.0/). 73 | 74 | (c) Copyright ETH Zürich, Chair of Systems Design, 2015-2018 75 | -------------------------------------------------------------------------------- /_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-minimal -------------------------------------------------------------------------------- /docs/_config.yml: -------------------------------------------------------------------------------- 1 | theme: jekyll-theme-tactile -------------------------------------------------------------------------------- /docs/bc_s.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/bc_s.png -------------------------------------------------------------------------------- /docs/bdwn.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/bdwn.png -------------------------------------------------------------------------------- /docs/classpathpy_1_1HigherOrderNetwork_1_1EmptySCCError.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | pathpy: pathpy.HigherOrderNetwork.EmptySCCError Class Reference 8 | 9 | 10 | 11 | 12 | 13 | 16 | 17 | 18 | 19 |
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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pathpy.HigherOrderNetwork.EmptySCCError Class Reference
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94 | Inheritance diagram for pathpy.HigherOrderNetwork.EmptySCCError:
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Detailed Description

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This exception is thrown whenever a non-empty strongly 
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The documentation for this class was generated from the following file: 107 |
108 | 109 | 114 | 115 | 116 | -------------------------------------------------------------------------------- /docs/classpathpy_1_1HigherOrderNetwork_1_1EmptySCCError.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/classpathpy_1_1HigherOrderNetwork_1_1EmptySCCError.png -------------------------------------------------------------------------------- /docs/classpathpy_1_1Log_1_1Log-members.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 7 | pathpy: Member List 8 | 9 | 10 | 11 | 12 | 13 | 16 | 17 | 18 | 19 |
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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addpathpy.Log.Logstatic
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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pathpy Directory Reference
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file  __init__.py
 
file  HigherOrderNetwork.py
 
file  Log.py
 
file  MarkovSequence.py
 
file  MultiOrderModel.py
 
file  Paths.py
 
file  TemporalNetwork.py
 
file  test.py
 
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106 | 107 | 112 | 113 | 114 | -------------------------------------------------------------------------------- /docs/doxygen.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/doxygen.png -------------------------------------------------------------------------------- /docs/dynsections.js: -------------------------------------------------------------------------------- 1 | function toggleVisibility(linkObj) 2 | { 3 | var base = $(linkObj).attr('id'); 4 | var summary = $('#'+base+'-summary'); 5 | var content = $('#'+base+'-content'); 6 | var trigger = $('#'+base+'-trigger'); 7 | var src=$(trigger).attr('src'); 8 | if (content.is(':visible')===true) { 9 | content.hide(); 10 | summary.show(); 11 | $(linkObj).addClass('closed').removeClass('opened'); 12 | $(trigger).attr('src',src.substring(0,src.length-8)+'closed.png'); 13 | } else { 14 | content.show(); 15 | summary.hide(); 16 | $(linkObj).removeClass('closed').addClass('opened'); 17 | $(trigger).attr('src',src.substring(0,src.length-10)+'open.png'); 18 | } 19 | return false; 20 | } 21 | 22 | function updateStripes() 23 | { 24 | $('table.directory tr'). 25 | removeClass('even').filter(':visible:even').addClass('even'); 26 | } 27 | function toggleLevel(level) 28 | { 29 | $('table.directory tr').each(function(){ 30 | var l = this.id.split('_').length-1; 31 | var i = $('#img'+this.id.substring(3)); 32 | var a = $('#arr'+this.id.substring(3)); 33 | if (l 2 | 3 | 4 | 5 | 6 | 7 | pathpy: Class Hierarchy 8 | 9 | 10 | 11 | 12 | 13 | 16 | 17 | 18 | 19 |
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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pathpy 26 |  1.0 27 |
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pathpy is an OpenSource python package for the modeling and analysis of pathways and temporal networks using higher-order and multi-order graphical models
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25 | 26 | 27 | -------------------------------------------------------------------------------- /docs/search/variables_b.js: -------------------------------------------------------------------------------- 1 | var searchData= 2 | [ 3 | ['warning',['WARNING',['../classpathpy_1_1Log_1_1Severity.html#ae4a13b6d9d1d2485bceb5843df6e3f40',1,'pathpy::Log::Severity']]] 4 | ]; 5 | -------------------------------------------------------------------------------- /docs/sync_off.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/sync_off.png -------------------------------------------------------------------------------- /docs/sync_on.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/sync_on.png -------------------------------------------------------------------------------- /docs/tab_a.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/tab_a.png -------------------------------------------------------------------------------- /docs/tab_b.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/tab_b.png -------------------------------------------------------------------------------- /docs/tab_h.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/tab_h.png -------------------------------------------------------------------------------- /docs/tab_s.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/docs/tab_s.png -------------------------------------------------------------------------------- /docs/tabs.css: -------------------------------------------------------------------------------- 1 | .tabs, .tabs2, .tabs3 { 2 | background-image: url('tab_b.png'); 3 | width: 100%; 4 | z-index: 101; 5 | font-size: 13px; 6 | font-family: 'Lucida Grande',Geneva,Helvetica,Arial,sans-serif; 7 | } 8 | 9 | .tabs2 { 10 | font-size: 10px; 11 | } 12 | .tabs3 { 13 | font-size: 9px; 14 | } 15 | 16 | .tablist { 17 | margin: 0; 18 | padding: 0; 19 | display: table; 20 | } 21 | 22 | .tablist li { 23 | float: left; 24 | display: table-cell; 25 | background-image: url('tab_b.png'); 26 | line-height: 36px; 27 | list-style: none; 28 | } 29 | 30 | .tablist a { 31 | display: block; 32 | padding: 0 20px; 33 | font-weight: bold; 34 | background-image:url('tab_s.png'); 35 | background-repeat:no-repeat; 36 | background-position:right; 37 | color: #283A5D; 38 | text-shadow: 0px 1px 1px rgba(255, 255, 255, 0.9); 39 | text-decoration: none; 40 | outline: none; 41 | } 42 | 43 | .tabs3 .tablist a { 44 | padding: 0 10px; 45 | } 46 | 47 | .tablist a:hover { 48 | background-image: url('tab_h.png'); 49 | background-repeat:repeat-x; 50 | color: #fff; 51 | text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); 52 | text-decoration: none; 53 | } 54 | 55 | .tablist li.current a { 56 | background-image: url('tab_a.png'); 57 | background-repeat:repeat-x; 58 | color: #fff; 59 | text-shadow: 0px 1px 1px rgba(0, 0, 0, 1.0); 60 | } 61 | -------------------------------------------------------------------------------- /multiorder.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/multiorder.png -------------------------------------------------------------------------------- /pathpy.pyproj: -------------------------------------------------------------------------------- 1 |  2 | 3 | 4 | Debug 5 | 2.0 6 | {896991fc-0289-4bae-b680-88e19508c91f} 7 | 8 | 9 | 10 | 11 | . 12 | . 13 | 14 | 15 | 16 | 17 | 10.0 18 | $(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion) 19 | pathpy 20 | 21 | 22 | 23 | 24 | $(MSBuildExtensionsPath32)\Microsoft\VisualStudio\v$(VisualStudioVersion)\Python Tools\Microsoft.PythonTools.targets 25 | 26 | 27 | 28 | Code 29 | 30 | 31 | Code 32 | 33 | 34 | Code 35 | 36 | 37 | Code 38 | 39 | 40 | Code 41 | 42 | 43 | 44 | 45 | 46 | 47 | 48 | 49 | 50 | 51 | 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | 63 | 64 | 65 | 66 | 67 | 68 | 69 | 70 | 71 | 72 | 73 | -------------------------------------------------------------------------------- /pathpy.sln: -------------------------------------------------------------------------------- 1 |  2 | Microsoft Visual Studio Solution File, Format Version 12.00 3 | # Visual Studio 14 4 | VisualStudioVersion = 14.0.25420.1 5 | MinimumVisualStudioVersion = 10.0.40219.1 6 | Project("{888888A0-9F3D-457C-B088-3A5042F75D52}") = "pathpy", "pathpy.pyproj", "{896991FC-0289-4BAE-B680-88E19508C91F}" 7 | EndProject 8 | Global 9 | GlobalSection(SolutionConfigurationPlatforms) = preSolution 10 | Debug|Any CPU = Debug|Any CPU 11 | Release|Any CPU = Release|Any CPU 12 | EndGlobalSection 13 | GlobalSection(ProjectConfigurationPlatforms) = postSolution 14 | {896991FC-0289-4BAE-B680-88E19508C91F}.Debug|Any CPU.ActiveCfg = Debug|Any CPU 15 | {896991FC-0289-4BAE-B680-88E19508C91F}.Release|Any CPU.ActiveCfg = Release|Any CPU 16 | EndGlobalSection 17 | GlobalSection(SolutionProperties) = preSolution 18 | HideSolutionNode = FALSE 19 | EndGlobalSection 20 | EndGlobal 21 | -------------------------------------------------------------------------------- /pathpy/Log.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | pathpy is an OpenSource python package for the analysis of sequential data 4 | on pathways and temporal networks using higher- and multi order graphical models 5 | 6 | Copyright (C) 2016-2017 Ingo Scholtes, ETH Zürich 7 | 8 | This program is free software: you can redistribute it and/or modify 9 | it under the terms of the GNU Affero General Public License as published 10 | by the Free Software Foundation, either version 3 of the License, or 11 | (at your option) any later version. 12 | 13 | This program is distributed in the hope that it will be useful, 14 | but WITHOUT ANY WARRANTY; without even the implied warranty of 15 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 16 | GNU Affero General Public License for more details. 17 | 18 | You should have received a copy of the GNU Affero General Public License 19 | along with this program. If not, see . 20 | 21 | Contact the developer: 22 | 23 | E-mail: ischoltes@ethz.ch 24 | Web: http://www.ingoscholtes.net 25 | """ 26 | 27 | import enum 28 | import time 29 | import io 30 | import sys 31 | 32 | class Severity(enum.IntEnum): 33 | """ An enumeration that can be used to indicate 34 | the severity of log messages, and which can be 35 | used tpo filter messages based on severities. 36 | """ 37 | 38 | ## Error messages 39 | ERROR = 4 40 | 41 | ## Warning messages 42 | WARNING = 3 43 | 44 | ## Informational messages (default minimum level) 45 | INFO = 2 46 | 47 | ## Messages regarding timing and performance 48 | TIMING = 1 49 | 50 | ## Debug messages (really verbose) 51 | DEBUG = 0 52 | 53 | 54 | class Log: 55 | """ A simple logging class, that allows to select what messages should 56 | be recorded in the output, and where these message should be directed. 57 | """ 58 | 59 | ## the output stream to which log entries will be written 60 | output_stream = sys.stdout 61 | 62 | ## The minimum severity level of messages to be logged 63 | min_severity = Severity.INFO 64 | 65 | 66 | @staticmethod 67 | def setMinSeverity(severity): 68 | """ Sets the minimum sveerity level a message 69 | needs to have in order to be recorded in the output stream. 70 | By default, any message which has a severity of at least 71 | Severity.INFO will be written to the output stream. All messages 72 | with lower priority will be surpressed. 73 | """ 74 | Log.min_severity = severity 75 | 76 | 77 | @staticmethod 78 | def setOutputStream(stream): 79 | """ Sets the output stream to which all messages will be 80 | written. By default, this is sys.stdout, but it can be 81 | changed in order to redirect the log to a logfile. 82 | """ 83 | output_stream = stream 84 | 85 | 86 | @staticmethod 87 | def add(msg, severity=Severity.INFO): 88 | """ Adds a message with the given severity to the log. This message will be written 89 | to the log output stream, which by default is sys.stdout. A newline character 90 | will be added to the message by default. 91 | """ 92 | if severity >= Log.min_severity: 93 | ts = time.strftime("%Y-%m-%d %H:%M:%S", time.gmtime()) 94 | Log.output_stream.write(ts + ' [' + str(severity) + ']\t' + msg + '\n') 95 | Log.output_stream.flush() 96 | -------------------------------------------------------------------------------- /pathpy/MarkovSequence.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | pathpy is an OpenSource python package for the analysis of sequential data on pathways and temporal networks using higher- and multi order graphical models 4 | 5 | Copyright (C) 2016-2017 Ingo Scholtes, ETH Zürich 6 | 7 | This program is free software: you can redistribute it and/or modify 8 | it under the terms of the GNU Affero General Public License as published 9 | by the Free Software Foundation, either version 3 of the License, or 10 | (at your option) any later version. 11 | 12 | This program is distributed in the hope that it will be useful, 13 | but WITHOUT ANY WARRANTY; without even the implied warranty of 14 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 15 | GNU Affero General Public License for more details. 16 | 17 | You should have received a copy of the GNU Affero General Public License 18 | along with this program. If not, see . 19 | 20 | Contact the developer: 21 | 22 | E-mail: ischoltes@ethz.ch 23 | Web: http://www.ingoscholtes.net 24 | """ 25 | 26 | import numpy as _np 27 | import collections as _co 28 | import bisect as _bs 29 | import itertools as _iter 30 | 31 | import scipy.sparse as _sparse 32 | import scipy.misc as _misc 33 | import scipy.sparse.linalg as _sla 34 | import scipy.linalg as _la 35 | 36 | from scipy.stats import chi2 37 | 38 | from pathpy.Log import Log 39 | from pathpy.Log import Severity 40 | 41 | _np.seterr(all='warn') 42 | 43 | 44 | class MarkovSequence: 45 | """ Instances of this class can be used to fit 46 | standard higher-order Markov models for 47 | sequences generated from concatenated paths """ 48 | 49 | def __init__(self, sequence): 50 | """ 51 | Generates a Markov model for a sequence, given 52 | as a single list of strings 53 | """ 54 | 55 | ## The sequence to be modeled 56 | self.sequence = sequence 57 | 58 | ## The transition probabilities of higher-order Markov chains 59 | self.P = {} 60 | 61 | ## the set of states of higher-order Markov chains 62 | self.states = {} 63 | self.states[1] = set(sequence) 64 | 65 | 66 | def fitMarkovModel(self, k=1): 67 | """ Generates a k-th order Markov model 68 | for the underlying sequence 69 | """ 70 | 71 | # TODO: Add support for k=0 72 | 73 | assert len(self.sequence)>0, "Error: Empty sequence" 74 | 75 | # MLE fit of transition probabilities 76 | self.P[k] = _co.defaultdict( lambda: _co.defaultdict( lambda: 0.0 ) ) 77 | 78 | Log.add('Fitting Markov model with order k = ' + str(k)) 79 | 80 | # Generate initial memory prefix 81 | mem = (()) 82 | for s in self.sequence[:k]: 83 | mem += (s,) 84 | 85 | # count state transitions 86 | for s in self.sequence[k:]: 87 | self.P[k][mem][s] += 1.0 88 | 89 | # shift memory by one element 90 | mem = mem[1:] + (s,) 91 | 92 | # normalize transitions 93 | for m in self.P[k]: 94 | S = float(sum(self.P[k][m].values())) 95 | for s in self.P[k][m]: 96 | self.P[k][m][s] /= S 97 | Log.add('finished.') 98 | 99 | 100 | def getLikelihood(self, k=1, log=True): 101 | """ 102 | Returns the likelihood of the sequence 103 | assuming a k-th order Markov model 104 | """ 105 | 106 | if k not in self.P: 107 | self.fitMarkovModel(k) 108 | 109 | L = 0 110 | 111 | # Generate initial prefix 112 | mem = (()) 113 | for s in self.sequence[:k]: 114 | mem += (s,) 115 | 116 | for s in self.sequence[k:]: 117 | L += _np.log(self.P[k][mem][s]) 118 | 119 | # shift memory by one element 120 | mem = mem[1:] + (s,) 121 | 122 | if log: 123 | return L 124 | else: 125 | return _np.exp(L) 126 | 127 | 128 | def getBIC(self, k=1, m=1): 129 | """ Returns the Bayesian Information Criterion 130 | assuming a k-th order Markov model """ 131 | 132 | if k not in self.P: 133 | self.fitMarkovModel(k) 134 | 135 | if m not in self.P: 136 | self.fitMarkovModel(m) 137 | 138 | L_k = self.getLikelihood(k, log=True) 139 | L_m = self.getLikelihood(m, log=True) 140 | 141 | s = len(self.states[1]) 142 | n = len(self.sequence)-k 143 | 144 | # the transition matrix of a first-order model with s states has s**2 entries, subject to the 145 | # constraint that entries in each row must sum up to one (thus effectively reducing 146 | # the degrees of freedom by a factor of s, i.e. we have s**2-s**1. Generalizing this to order k, 147 | # we arrive at s**k * (s-1) = s**(k+1) - s**k derees of freedom 148 | bic = _np.log(n) * (s**k - s**m) * (s-1) - 2.0 * (L_k-L_m) 149 | 150 | return bic 151 | 152 | 153 | def getAIC(self, k=1, m=1): 154 | """ Returns the Aikake Information Criterion 155 | assuming a k-th order Markov model """ 156 | 157 | if k not in self.P: 158 | self.fitMarkovModel(k) 159 | 160 | if m not in self.P: 161 | self.fitMarkovModel(m) 162 | 163 | L_k = self.getLikelihood(k, log=True) 164 | L_m = self.getLikelihood(m, log=True) 165 | 166 | s = len(self.states[1]) 167 | n = len(self.sequence) 168 | 169 | aic = 2 * (s**k - s**m) * (s-1) - 2.0 * (L_k - L_m) 170 | 171 | return aic 172 | 173 | 174 | def estimateOrder(self, maxOrder, method='BIC'): 175 | """ Estimates the optimal order of a Markov model 176 | based on Likelihood, BIC or AIC """ 177 | 178 | assert method == 'BIC' or method == 'AIC' or method == 'Likelihood', "Error: Expecting method 'AIC', 'BIC' or 'Likelihood'" 179 | 180 | values = [] 181 | orders = [] 182 | 183 | # We need k < m for the BIC and AIC calculation, which 184 | # is why we only test up to maxOrder - 1 185 | for k in range(1, maxOrder): 186 | if k not in self.P: 187 | self.fitMarkovModel(k) 188 | 189 | orders.append(k) 190 | 191 | if method == 'AIC': 192 | values.append(self.getAIC(k, maxOrder)) 193 | elif method == 'BIC': 194 | values.append(self.getBIC(k, maxOrder)) 195 | elif method == 'Likelihood': 196 | values.append(self.getLikelihood(k, log=True)) 197 | 198 | if method == 'Likelihood': 199 | values.append(self.getLikelihood(maxOrder, log=True)) 200 | orders.append(maxOrder) 201 | 202 | # return order at which likelihood is maximized 203 | return orders[_np.argmax(values)] 204 | else: 205 | # return order at which BIC/AIC are minimized 206 | return orders[_np.argmin(values)] 207 | -------------------------------------------------------------------------------- /pathpy/__init__.py: -------------------------------------------------------------------------------- 1 | from .Log import * 2 | from .TemporalNetwork import * 3 | from .Paths import * 4 | from .HigherOrderNetwork import * 5 | from .MultiOrderModel import * 6 | from .MarkovSequence import * 7 | 8 | import pathpy.Log as Log -------------------------------------------------------------------------------- /pathpy_logo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/IngoScholtes/pathpy/857f97d1b5a7eec8466775adfed29c28f32f87aa/pathpy_logo.png -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | """A python package for the analysis of sequential data on pathways and temporal networks from the perspective of higher-order network models. 2 | 3 | See: https://packaging.python.org/en/latest/distributing.html 4 | """ 5 | 6 | # Always prefer setuptools over distutils 7 | from setuptools import setup, find_packages 8 | # To use a consistent encoding 9 | from codecs import open 10 | from os import path 11 | import sys 12 | 13 | here = path.abspath(path.dirname(__file__)) 14 | 15 | # Get the long description from the relevant file 16 | with open(path.join(here, 'DESCRIPTION.rst'), encoding='utf-8') as f: 17 | long_description = f.read() 18 | 19 | required = ['numpy', 'scipy'] 20 | 21 | if (sys.version_info.major, sys.version_info.minor) < (3,4): 22 | required.append('enum34') 23 | 24 | setup( 25 | name='pathpy', 26 | 27 | # Versions should comply with PEP440. For a discussion on single-sourcing 28 | # the version across setup.py and the project code, see 29 | # https://packaging.python.org/en/latest/single_source_version.html 30 | version='1.1.170316', 31 | 32 | description='A python package for the analysis of sequential data on pathways and temporal networks from the perspective of higher-order network models.', 33 | long_description=long_description, 34 | 35 | # The project's main homepage. 36 | url='https://github.com/IngoScholtes/pathpy', 37 | 38 | # Author details 39 | author='Ingo Scholtes', 40 | author_email='ischoltes@ethz.ch', 41 | 42 | # Choose your license 43 | license='AGPL-3.0+', 44 | 45 | # See https://pypi.python.org/pypi?%3Aaction=list_classifiers 46 | classifiers=[ 47 | # How mature is this project? Common values are 48 | # 3 - Alpha 49 | # 4 - Beta 50 | # 5 - Production/Stable 51 | 'Development Status :: 4 - Beta', 52 | 53 | # Indicate who your project is intended for 54 | 'Intended Audience :: Science/Research', 55 | 'Topic :: Scientific/Engineering :: Information Analysis', 56 | 57 | # Pick your license as you wish (should match "license" above) 58 | 'License :: OSI Approved :: AGPL-3.0+ License', 59 | 60 | # Specify the Python versions you support here. In particular, ensure 61 | # that you indicate whether you support Python 2, Python 3 or both. 62 | 'Programming Language :: Python :: 3', 63 | 'Programming Language :: Python :: 3.2', 64 | 'Programming Language :: Python :: 3.3', 65 | 'Programming Language :: Python :: 3.4', 66 | 'Programming Language :: Python :: 3.5', 67 | ], 68 | 69 | # What does your project relate to? 70 | keywords='network analysis temporal networks pathways sequence modeling graph mining', 71 | 72 | # You can just specify the packages manually here if your project is 73 | # simple. Or you can use find_packages(). 74 | packages=find_packages(exclude=['contrib', 'docs', 'tests*']), 75 | 76 | # List run-time dependencies here. These will be installed by pip when 77 | # your project is installed. For an analysis of "install_requires" vs pip's 78 | # requirements files see: 79 | # https://packaging.python.org/en/latest/requirements.html 80 | install_requires=required, 81 | 82 | # If there are data files included in your packages that need to be 83 | # installed, specify them here. If using Python 2.6 or less, then these 84 | # have to be included in MANIFEST.in as well. 85 | package_data={ 86 | } 87 | ) -------------------------------------------------------------------------------- /tests/README.md: -------------------------------------------------------------------------------- 1 | # Unit tests for pypath 2 | 3 | This directory contains the unit tests for methods and functions 4 | in pathpy. 5 | The testing framework [pytest](doc.pytest.org/) 6 | is required to run the tests. 7 | 8 | To run the test suite (without slow tests) run 9 | ```bash 10 | $ pytest tests 11 | ``` 12 | 13 | ## Slow functions 14 | 15 | Slow functions can be decorated with `slow` to mark them 16 | as skippable if you require only a quick check. 17 | To run all tests add the flag `--runslow`: 18 | ```bash 19 | $ pytest --runslow 20 | ``` 21 | 22 | ## Coverage report 23 | 24 | To compute a coverage report of the tests you need to install 25 | [coverage.py](https://coverage.readthedocs.io/en/coverage-4.3.4/) 26 | as well as its `pytest` integration 27 | [pytest-cov][1] 28 | ```bash 29 | $ pytest tests/ --runslow --cov=pathpy --cov-report html 30 | ``` 31 | which will create an html coverage report in the same directory. 32 | 33 | [1]: https://pypi.python.org/pypi/pytest-cov 34 | -------------------------------------------------------------------------------- /tests/conftest.py: -------------------------------------------------------------------------------- 1 | import pathpy as pp 2 | import pytest 3 | import numpy as np 4 | import os 5 | 6 | test_directory = os.path.dirname(os.path.abspath(__file__)) 7 | test_data_dir = os.path.join(test_directory, 'test_data') 8 | 9 | 10 | def pytest_addoption(parser): 11 | parser.addoption("--runslow", action="store_true", 12 | help="run slow tests") 13 | 14 | 15 | def pytest_runtest_setup(item): 16 | if 'slow' in item.keywords and not item.config.getvalue("runslow"): 17 | pytest.skip("need --runslow option to run") 18 | 19 | 20 | 21 | @pytest.fixture() 22 | def test_data_directory(): 23 | return test_data_dir 24 | 25 | 26 | @pytest.fixture() 27 | def path_from_ngram_file(): 28 | """load the example file as pypath.Path""" 29 | ngram_file_path = os.path.join(test_data_dir, 'ngram_simple.ngram') 30 | path = pp.Paths.readFile(ngram_file_path, pathFrequency=True) 31 | return path 32 | 33 | 34 | @pytest.fixture() 35 | def path_from_edge_file(): 36 | file_path = os.path.join(test_data_dir, 'edge_frequency.edge') 37 | path = pp.Paths.readEdges(file_path, weight=True) 38 | 39 | return path 40 | 41 | 42 | @pytest.fixture() 43 | def path_from_edge_file_undirected(): 44 | file_path = os.path.join(test_data_dir, 'edge_frequency.edge') 45 | path = pp.Paths.readEdges(file_path, weight=True, undirected=True) 46 | return path 47 | 48 | 49 | def generate_random_path(size, rnd_seed): 50 | """Generate a Path with random path sequences""" 51 | import string 52 | node_set = string.ascii_lowercase 53 | 54 | def random_ngram(p_len, nodes): 55 | num_elements = len(nodes) 56 | sequence = np.random.choice(num_elements, p_len) 57 | path = [nodes[i] for i in sequence] 58 | return ','.join(path) 59 | 60 | np.random.seed(rnd_seed) 61 | paths = pp.Paths() 62 | for _ in range(size): 63 | frequency = np.random.randint(1, 4) 64 | path_length = np.random.randint(1, 10) 65 | path_to_add = random_ngram(path_length, node_set) 66 | paths.addPath(path_to_add, pathFrequency=frequency) 67 | 68 | return paths 69 | 70 | 71 | @pytest.fixture(scope='function') 72 | def random_paths(): 73 | """Generate a Path with random path sequences""" 74 | return generate_random_path 75 | 76 | 77 | @pytest.fixture() 78 | def temporal_network_object(): 79 | t = pp.TemporalNetwork() 80 | # Path of length two 81 | t.addEdge("c", "e", 1) 82 | t.addEdge("e", "f", 2) 83 | 84 | # Path of length two 85 | t.addEdge("a", "e", 3) 86 | t.addEdge("e", "g", 4) 87 | 88 | # Path of length two 89 | t.addEdge("c", "e", 5) 90 | t.addEdge("e", "f", 6) 91 | 92 | # Path of length two 93 | t.addEdge("a", "e", 7) 94 | t.addEdge("e", "g", 8) 95 | 96 | # Path of length two 97 | t.addEdge("c", "e", 9) 98 | t.addEdge("e", "f", 10) 99 | 100 | # The next two edges continue the previous path to ( c-> e-> f-> e -> b ) 101 | t.addEdge("f", "e", 11) 102 | t.addEdge("e", "b", 12) 103 | 104 | # This is an isolated edge (i.e. path of length one) 105 | t.addEdge("e", "b", 13) 106 | 107 | # Path of length two 108 | t.addEdge("c", "e", 14) 109 | t.addEdge("e", "f", 15) 110 | 111 | # Path of length two 112 | t.addEdge("b", "e", 16) 113 | t.addEdge("e", "g", 17) 114 | 115 | # Path of length two 116 | t.addEdge("c", "e", 18) 117 | t.addEdge("e", "f", 19) 118 | 119 | # Path of length two 120 | t.addEdge("c", "e", 20) 121 | t.addEdge("e", "f", 21) 122 | 123 | return t 124 | -------------------------------------------------------------------------------- /tests/test_Path.py: -------------------------------------------------------------------------------- 1 | # -*- coding: utf-8 -*- 2 | """ 3 | Created on Fri Feb 20 11:59:22 2015 4 | @author: Ingo Scholtes 5 | 6 | (c) Copyright ETH Zurich, Chair of Systems Design, 2015-2017 7 | """ 8 | import pathpy as pp 9 | import pytest 10 | 11 | 12 | slow = pytest.mark.slow 13 | 14 | 15 | def test_readfile_import(path_from_ngram_file): 16 | levels = list(path_from_ngram_file.paths.keys()) 17 | max_level = max(levels) 18 | expected = 5 19 | assert max_level == expected, \ 20 | "The nodes have not been imported correctly" 21 | 22 | assert path_from_ngram_file.getNodes() == {'a', 'b', 'c', 'd', 'e'}, \ 23 | "Wrong node labels" 24 | 25 | 26 | def test_write_file(tmpdir, random_paths): 27 | dir_path = tmpdir.mkdir("sub").join("test.edges") 28 | p = random_paths(30, 50) 29 | 30 | expected_seq = ''.join(p.getSequence()) 31 | expected_paths = sorted(expected_seq.split('|')) 32 | 33 | p.writeFile(dir_path.strpath) 34 | p2 = pp.Paths.readFile(dir_path.strpath, pathFrequency=True) 35 | 36 | read_back = ''.join(p2.getSequence()) 37 | read_back_paths = sorted(read_back.split('|')) 38 | 39 | assert expected_paths == read_back_paths 40 | 41 | 42 | def test_read_edges_import(path_from_edge_file): 43 | """test if the Paths.readEdges functions works""" 44 | levels = list(path_from_edge_file.paths.keys()) 45 | max_level = max(levels) 46 | expected_max = 1 47 | assert expected_max == max_level, \ 48 | "The nodes have not been imported correctly" 49 | 50 | assert path_from_edge_file.getNodes() == {'1', '2', '3', '5'}, \ 51 | "Nodes not imported correctly" 52 | 53 | 54 | def test_read_edges_undirected(path_from_edge_file_undirected): 55 | p = path_from_edge_file_undirected 56 | layers = list(p.paths.keys()) 57 | max_layers = max(layers) 58 | expected_layers = 1 59 | assert max_layers == expected_layers, \ 60 | "The nodes have not been imported correctly" 61 | 62 | assert p.getNodes() == {'1', '2', '3', '5'}, \ 63 | "Nodes not imported correctly" 64 | 65 | 66 | def test_get_sequence(path_from_ngram_file): 67 | from collections import Counter 68 | """Test if the Paths.getSequence function works correctly""" 69 | sequence = path_from_ngram_file.getSequence() 70 | sequence = "".join(sequence) 71 | ct = Counter(sequence.split('|')) 72 | assert dict(ct) == {'': 1, 'abcdab': 2, 'dedab': 4}, \ 73 | "Returned the wrong sequence" 74 | 75 | 76 | def test_get_unique_paths(random_paths): 77 | p = random_paths(90, 90) 78 | assert p.getUniquePaths() == 87, \ 79 | "Wrong number of paths detected" 80 | 81 | 82 | def test_observation_count_file(path_from_ngram_file): 83 | assert path_from_ngram_file.ObservationCount() == 6, \ 84 | "Wrong number of observations detected" 85 | 86 | 87 | def test_observation_count_large(random_paths): 88 | p = random_paths(90, 90) 89 | assert p.ObservationCount() == 193, \ 90 | "Wrong number of observations detected" 91 | 92 | 93 | def test_path_summary(random_paths): 94 | p = random_paths(90, 90) 95 | print(p) 96 | 97 | 98 | def test_summary_multi_order_model(random_paths): 99 | p = random_paths(90, 90) 100 | multi = pp.MultiOrderModel(paths=p, maxOrder=3) 101 | print(multi) 102 | 103 | 104 | def test_get_shortest_paths(path_from_ngram_file): 105 | path_from_ngram_file.getShortestPaths() 106 | paths_dict = path_from_ngram_file.getShortestPaths() 107 | expected_paths = {('d', 'a'): {('d', 'a')}, 108 | ('b', 'd'): {('b', 'c', 'd')}, 109 | ('d', 'e'): {('d', 'e')}, 110 | ('a', 'c'): {('a', 'b', 'c')}, 111 | ('a', 'a'): {('a',)}, 112 | ('e', 'a'): {('e', 'd', 'a')}, 113 | ('e', 'b'): {('e', 'd', 'a', 'b')}, 114 | ('e', 'e'): {('e',)}, 115 | ('a', 'b'): {('a', 'b')}, 116 | ('b', 'b'): {('b',)}, 117 | ('c', 'd'): {('c', 'd')}, 118 | ('d', 'b'): {('d', 'a', 'b')}, 119 | ('c', 'a'): {('c', 'd', 'a')}, 120 | ('b', 'a'): {('b', 'c', 'd', 'a')}, 121 | ('c', 'b'): {('c', 'd', 'a', 'b')}, 122 | ('e', 'd'): {('e', 'd')}, 123 | ('a', 'd'): {('a', 'b', 'c', 'd')}, 124 | ('d', 'd'): {('d',)}, 125 | ('c', 'c'): {('c',)}, 126 | ('b', 'c'): {('b', 'c')} 127 | } 128 | paths_to_check = dict() 129 | for k in paths_dict: 130 | for p in paths_dict[k]: 131 | paths_to_check[(k, p)] = paths_dict[k][p] 132 | assert paths_to_check == expected_paths 133 | 134 | 135 | def test_get_contained_paths(): 136 | path_to_check = ('a', 'b', 'c', 'd', 'e', 'f', 'g') 137 | node_filter = ('a', 'b', 'd', 'f', 'g') 138 | cont_paths = pp.Paths.getContainedPaths(path_to_check, node_filter) 139 | expected = [('a', 'b'), ('d',), ('f', 'g')] 140 | assert cont_paths == expected 141 | 142 | 143 | def test_filter_paths(path_from_ngram_file): 144 | from collections import Counter 145 | p = path_from_ngram_file 146 | new_paths = p.filterPaths(node_filter=['a', 'b', 'c']) 147 | expected_sequence = {'': 1, 'ab': 6, 'abc': 2} 148 | 149 | new_sequence = ''.join(new_paths.getSequence()) 150 | ct = Counter(new_sequence.split('|')) 151 | assert dict(ct) == expected_sequence 152 | 153 | 154 | def test_project_paths(path_from_ngram_file): 155 | from collections import Counter 156 | p = path_from_ngram_file 157 | mapping = {'a': 'x', 'b': 'x', 'c': 'y', 'd': 'y', 'e': 'y'} 158 | new_p = p.projectPaths(mapping=mapping) 159 | new_sequence = ''.join(new_p.getSequence()) 160 | ct = Counter(new_sequence.split('|')) 161 | expected_sequence = {'': 1, 'xxyyxx': 2, 'yyyxx': 4} 162 | assert dict(ct) == expected_sequence 163 | 164 | 165 | def test_get_nodes(random_paths): 166 | p = random_paths(3, 9) 167 | rest = p.getNodes() 168 | expected = {'b', 'o', 'u', 'v', 'w', 'y'} 169 | assert rest == expected 170 | -------------------------------------------------------------------------------- /tests/test_TemporalNetwork.py: -------------------------------------------------------------------------------- 1 | import pathpy as pp 2 | import os 3 | import numpy as np 4 | 5 | 6 | def test_read_temporal_file_int(test_data_directory,): 7 | file_path = os.path.join(test_data_directory, 'example_int.tedges') 8 | t = pp.TemporalNetwork.readFile(file_path) 9 | times = t.ordered_times 10 | expected_times = [0, 2, 4, 5, 6, 8] 11 | assert times == expected_times 12 | 13 | activities = sorted(list(t.activities.values())) 14 | expected_activities = [[], [], [], [], [0, 2, 5], [2], [4], [6], [8]] 15 | assert expected_activities == activities 16 | 17 | 18 | def test_read_temporal_file_time_stamp(test_data_directory,): 19 | file_path = os.path.join(test_data_directory, 'example_timestamp.tedges') 20 | t = pp.TemporalNetwork.readFile(file_path, timestampformat="%Y-%m-%d %H:%M") 21 | times = t.ordered_times 22 | time_diffs = [j-i for i, j in zip(times[:-1], times[1:])] 23 | expected_diffs = [10800, 15060, 264960] 24 | # TODO: The actual time number depends on local set by the user 25 | assert time_diffs == expected_diffs 26 | 27 | 28 | def test_filter_temporal_edges(temporal_network_object): 29 | t = temporal_network_object 30 | 31 | def filter_func(v, w, time): 32 | return time % 2 == 0 33 | 34 | filtered = t.filterEdges(filter_func) 35 | times = filtered.ordered_times 36 | expected = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20] 37 | assert times == expected 38 | 39 | 40 | def test_get_interpath_times(temporal_network_object): 41 | t = temporal_network_object 42 | inter_time = dict(t.getInterPathTimes()) 43 | expected = {'e': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 44 | 'b': [4, 3], 'f': [9, 5, 1] 45 | } 46 | assert inter_time == expected 47 | 48 | 49 | def test_shuffle_edges(temporal_network_object): 50 | t = temporal_network_object 51 | np.random.seed(90) 52 | t1 = t.ShuffleEdges(with_replacement=True) 53 | times1 = len(t1.tedges) 54 | expected1 = len(t.tedges) 55 | assert times1 == expected1 56 | np.random.seed(90) 57 | t2 = t.ShuffleEdges(l=4, with_replacement=False) 58 | edges2 = len(t2.tedges) 59 | expected2 = 4 60 | assert edges2 == expected2 61 | 62 | 63 | def test_inter_event_times(temporal_network_object): 64 | time_diffs = temporal_network_object.getInterEventTimes() 65 | # all time differences are 1 66 | assert (time_diffs == 1).all() 67 | 68 | 69 | def test_inter_path_times(temporal_network_object): 70 | t = temporal_network_object 71 | path_times = dict(t.getInterPathTimes()) 72 | expected = {'f': [9, 5, 1], 73 | 'e': [1, 1, 1, 1, 1, 1, 1, 1, 1, 1], 74 | 'b': [4, 3]} 75 | assert path_times == expected 76 | 77 | 78 | def test_temporal_summary(temporal_network_object): 79 | print(temporal_network_object) 80 | -------------------------------------------------------------------------------- /tests/test_data/edge_frequency.edge: -------------------------------------------------------------------------------- 1 | 1,2,45,3,3,3 2 | 1,3,2,23,2,2 3 | 1,5,5,12,5,2 4 | 3,5,2,11,45,2 5 | 5,3,4,12,2,2 6 | 5,2,1,12,4,1 7 | -------------------------------------------------------------------------------- /tests/test_data/example_int.tedges: -------------------------------------------------------------------------------- 1 | source,target,time 2 | 1,2,0 3 | 1,2,2 4 | 1,3,5 5 | 3,2,6 6 | 2,1,8 7 | 4,5,2 8 | 5,3,4 -------------------------------------------------------------------------------- /tests/test_data/example_timestamp.tedges: -------------------------------------------------------------------------------- 1 | source,target,time 2 | 1,4,2000-03-04 12:45 3 | 2,4,2000-03-04 15:45 4 | 5,2,2000-03-04 19:56 5 | 8,2,2000-03-07 21:32 -------------------------------------------------------------------------------- /tests/test_data/ngram_simple.ngram: -------------------------------------------------------------------------------- 1 | a,b,c,d,a,b,2 2 | d,e,d,a,b,4 3 | --------------------------------------------------------------------------------