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Michael Dorner michaeldorner Blekinge Institute of Technology Sweden www.michaeldorner.de git push origin main --force

InnerSourceCommons/InnerSourcePatterns 540

Proven approaches that can guide you through applying open source best practices within your organization

michaeldorner/BeeTee 198

Demo application for Bluetooth device scanning using the iOS private framework "BluetoothManager"

michaeldorner/DecisionTrees 101

Seminar work "Decision Trees - An Introduction" with presentation, seminar paper, and Python implementation

michaeldorner/ByteBackpacker 30

ByteBackpacker is a small utility written in Swift to pack value types into a Byte array and unpack them back.

michaeldorner/CEP-Systems 17

An overview of Complex Event Processing Systems

michaeldorner/instanceofBenchmark 13

Bechmarking Java's instanceof and its alternatives

michaeldorner/only-time-will-tell 3

Only Time Will Tell: Replication package

michaeldorner/highsierramediakeyenabler 1

MacOS High Sierra Media Key Enabler for iTunes

michaeldorner/Introduction-to-Complex-Data-Analysis 1

Course material for Introduction to Complex Data Analysis with Python

michaeldorner/leadsheets 1

A LaTeX package for creating leadsheets and songbooks

startedjkbren/matplotlib-multilayer-network

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Michael Dorner

commit sha ce7aa81f29218bf06d64cac1fe1061475d06b116

Update visualization.ipynb

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Xiangyu Xu

commit sha f010fe36c71e8a9163e43966254dc1c680662d21

fix typo (#5008) * fix typo * fix pyyaml typo * Apply changes to other relevant __getattrs__. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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Ross Barnowski

commit sha 906bf82ab7edf0ad4cea067b3be5a4e1cba356a3

Minor docstring cleanup to fix sphinx warning. (#5010)

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Matt Schwennesen

commit sha 87a52639ff89e56f6fbd50878e1a71cbd249bf7a

updated contributor file for GSoC (#5020) * updated contributor file * Fix minor rst link issue. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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davenpi

commit sha f33a11d149b21a04e76f91cf548a86dadd03de9d

update docs to match implementation of geographical threshold graph (#5011) * update docs to match implementation of geographical threshold graph * make changes from review and update description of what p(r) (formerly h(r)) actually does * rollback renaming commit and fix spacing issue * keep p_dist everywhere and update its description

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Dan Schult

commit sha a30814db084d754f07e5c26720c8dfc4c175a835

Style changes (#5022) * Add greedy algorithm for solving TSP Many problems of Combinational Optimization can be represented as graphs. These problems have enormous significance in many aspects of science, but there are not any algorithms to solve some of them in polynomial time. However many, heuristic and metaheuristic algorithms have been published over the past years in order to solve / approximate the solutions to these problems. The purpose of this commit is to add implementation of such algorithms for solve one of the most famous problems of Combinational Optimizations, Travelling Salesman Problem (TSP). A greedy algorithm has been implemented at the moment for this reason. "applications" package has been created which include modules that represent a problem. Each module contains several algorithms for solving the specific problem. At this commit, tsp.py module is added which contains greedy_tsp() function; a implementation of a greedy algorithm. * Fix example error * Trivial changes List of changes: Removal of unnesecary _is_weighted() function Improvements on documentation * Add applications package to setup.py file * Change output of greedy algorithm Algorithm's output is a list of nodes now * Add simulated annealing algorithm Add a metaheuristic local search algorithm for solving TSP * Minor changes * Fix example doc errors * Compatible with python 3 * Move tsp module to algorithms package * Code improvements * Handle small graphs and fix doc examples * Documentation changes and rename variables * Adds Threshold Accepting algorithm for TSP * Implemented maximal matching of minimal weight and created test suite. * Removed useless print * Implemented Christofides. * Coding was missing * Add more general traveling_salesman_problem using christofides Also reconfigure import structure and remove min_weight_matching from module since it is now in matching.py * Add new functions to the docs and minor typos * pep8 fixes * fix pep8 and change .gitignore * Add tests of the approximation namespace update docs in approximation/__init__.py * Fix is_matching to check if edges in G. Other tweaks: doc changes and put not_implemented_for on find_matching functions * Improve is_matching selfloop handling and expand tests * Move tsp to approximation directory. Apply black. * Move tsp tests to approximation tests folder * Attempt to bring tsp up to current code. * commit pep8 that my black didnt change, but pep8speaks did find. ?? * tweak a few things and run black * combine #4083 and #3585 into traveling_salesman.py * Match chistofides output to other tsp functions and adjust calling syntax of tests tweak docs tweak see also section * Put big-O complexity in in-line math env. Prevents sphinx from trying to do variable substitution between pipes. * Minor touchups to christofides docstring. * RST touchups to tsp module docstring. * Rm extra string from tsp module. * Docstring touchups for traveling_salesman_problem. * rst fixups for greedy_tsp docstring. * rst formatting for simulated annealing docstring. * More math in-lining for simulated annealing docstring. * rst and minor grammatical fixes to TA docstring. * Fix path-finding and test all methods for tsp function * the refactoring was incomplete. Now maybe is - Add tests of TSP with all methods. - Refactor tests to match simulated_annealing tests and threshold tests. - Unify treatment of weight so unweighted edges use default weight 1. weight now defaults to "weight" with a default value of 1. - Rename tolerance to max_iterations (tolerance is used for error bound) - Rename iterations to N_inner (each iteration takes this many inner loops) - Introduce idioms like `pairwise` and `cycle.copy()` (over cycle[:]) - Allow passthrough of method kwargs for traveling_salesman_problem Still need to: - add test of case where path is more than one edge less that cycle (incomplete_graph) - require cycle input (maybe make default list(G)??) - consider the complexity claims in the doc_strings * More api changes to TSP functions - `chritofides` now allows (and ignores) selfloops - `move` can be a function as well as "1-1" and "1-0" - `method` for traveling_salesman_problem must have 2 arguments instead of passing kwargs. User must "curry" to set parameters - changed doc_string typos in matching.py * Add test to check that cycle=False can remove many edges * Change init_cycle api to require input from user The idea is to make the user specify the initial cycle to start from rather than relying on the programmers default of a greedy algorithm. To easy usage, I check for a string "greedy" as a shortcut. * Update docs with more correct complexity info. * Check for complete graph now more efficient and selfloops ignored * merge is_matching changes * New Networkx changes * Stub for Asadpour. Needed to create GSoC PR * Update to integrate changes from main * Added function stubs and draft docstrings for the Asadpour algorithm * Skeleton classes and methods for tree iterators * Attempting to set up basic tests for MST of a partition * testing * I'm not entirly sure how the commit hook works... * Moved iterators into the correct files to maintain proper codebase visibility * Including Black reformat * Revert "Merge branch 'networkx:main' into main" This reverts commit 0616a2331adfcc02976d305937aa52272ed48266, reversing changes made to 1ea769371f54c4c6f9a51f860caf4a60aef7d094. * Trying to merge again * Attempting to merge (4) * Now passes all tests except test_namespace_alias in /tests/test_import.py * Everything should FINALLY pass (I wipped my networkx dir and re-download from upstream) * reinstall the pre-commit hook * Grabbing black reformats * Working on debugging ascent method plus black reformats * Ascent method terminating, but at non-optimal solution * minor edits * Fixed termination condition, still given non-optimal result * Minor bugfix, still non-optimal result * Fixed subtle bug in find_epsilon() * Cleaned code and tried something which didn't work * Modified the ArborescenceIterator to accept init partition * Black formats * Branch and bound returning optimal solution * Working Ascent method, code needs cleaning * black formatting changes * Performance tweaks and testing fractional answers * Fixed test bug, I hope * Asadpour output for ascent method * Fixed numpy imports crashing pypi tests * Removed branch and bound method. One unit test misbehaving * Added asymmetric fractional test for the ascent method * Removed printn statements and tweaked final test to be more asymmetric * Draft of spanning_tree_distribution * Black changes * Changed HK to only report on the support of the answer * Fixed contraction bug by changing to MultiGraph. Problem with prob > 1 * Black reformats * Fixed pypi test error * Further testing of dist fix * Can sample spanning trees * Developing test for sampling spanning tree * Changed sample_spanning_tree test to Chi squared test * Tweaked signifiance level * Found true minimum sample size * fixed typo * untested implementation of asadpour_tsp * Fixed issue reading flow_dict * Fixed runtime errors in asadpour_tsp * black reformats * Adding test cases * documentation update * Fixed rounding error with tests * One new test and check * Documentation update for the iterators * Attempting to fix class documentation * Pull out the style changes into a separate branch * fix mixed history * more Co-authored-by: Thodoris Sotiropoulos <theosotr@windowslive.com> Co-authored-by: Luca Cappelletti <cappelletti.luca94@gmail.com> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: mjschwenne <mjschwenne@gmail.com>

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Ross Barnowski

commit sha 2d1bf7072c1b71429442822eda69bd2c3c0065e1

Fix automatic documenting of class methods via autosummary (#5021) * WIP: Autogenerate stubs for class methods. Modify default autosummary/classes template to auto-generate method stubs. * WIP: preserve special-casing of __init__. __init__ method of classes is embedded in main class doc and thus should be ignored in method autosummary. * Suppress sphinx warnings. * Remove manual autosummary of ismags methods * Escape some rst characters in argmap.signature docstring.

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Vadim

commit sha 5641ea5d0b05800630b90c7bea558144c7ddc1de

[GSoC contribution] Improving functions `descendants` and `ancestors` in dag.py (#5017) * Improved functions `ancestors` and `descendants` * `descendants` and `ancestors` work correctly and are well-defined for the Graph

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Andrew Eckart

commit sha 850a2dcbc26253184378a6d304f6b0275ecabefe

Add unit tests and examples / doctests for nx.descendants_at_distance (#5029) * Add unit tests and examples / doctests for nx.descendants_at_distance * Add unit test for exception; minor refactor (fix unreachable line)

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Vadim

commit sha 5f17cc4afddfe30d8ce5322b4f9f0107941d6d2e

[GSoC contribution] Adding examples to docstrings in dag.py (#5019) * Adding examples to docstrings * Examples rewritten according to comments * Update linebreaking + wording in is_aperiodic examples. * style-nit: lengthen lines in transitive_closure examples. Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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Martha Frysztacki

commit sha 5867cbae81ca5e95e1b624a197c8cd6ddcb3fef8

modularity_max: breaking the loop when given community size is reached (#4950) * modularity_max: allow input of desired number of communities * import warnings * format * format * improvements according to discussion * try to manually merge main + resolve conflicts * add test for n_communities parameter using circular ladder graph * style of test

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Matt Schwennesen

commit sha e098dccd8be4e08497117fa39d66800907c2a932

GSoC Asadpour ATSP Implementation Pull Request (#4740) * Add greedy algorithm for solving TSP Many problems of Combinational Optimization can be represented as graphs. These problems have enormous significance in many aspects of science, but there are not any algorithms to solve some of them in polynomial time. However many, heuristic and metaheuristic algorithms have been published over the past years in order to solve / approximate the solutions to these problems. The purpose of this commit is to add implementation of such algorithms for solve one of the most famous problems of Combinational Optimizations, Travelling Salesman Problem (TSP). A greedy algorithm has been implemented at the moment for this reason. "applications" package has been created which include modules that represent a problem. Each module contains several algorithms for solving the specific problem. At this commit, tsp.py module is added which contains greedy_tsp() function; a implementation of a greedy algorithm. * Fix example error * Trivial changes List of changes: Removal of unnesecary _is_weighted() function Improvements on documentation * Add applications package to setup.py file * Change output of greedy algorithm Algorithm's output is a list of nodes now * Add simulated annealing algorithm Add a metaheuristic local search algorithm for solving TSP * Minor changes * Fix example doc errors * Compatible with python 3 * Move tsp module to algorithms package * Code improvements * Handle small graphs and fix doc examples * Documentation changes and rename variables * Adds Threshold Accepting algorithm for TSP * Implemented maximal matching of minimal weight and created test suite. * Removed useless print * Implemented Christofides. * Coding was missing * Add more general traveling_salesman_problem using christofides Also reconfigure import structure and remove min_weight_matching from module since it is now in matching.py * Add new functions to the docs and minor typos * pep8 fixes * fix pep8 and change .gitignore * Add tests of the approximation namespace update docs in approximation/__init__.py * Fix is_matching to check if edges in G. Other tweaks: doc changes and put not_implemented_for on find_matching functions * Improve is_matching selfloop handling and expand tests * Move tsp to approximation directory. Apply black. * Move tsp tests to approximation tests folder * Attempt to bring tsp up to current code. * commit pep8 that my black didnt change, but pep8speaks did find. ?? * tweak a few things and run black * combine #4083 and #3585 into traveling_salesman.py * Match chistofides output to other tsp functions and adjust calling syntax of tests tweak docs tweak see also section * Put big-O complexity in in-line math env. Prevents sphinx from trying to do variable substitution between pipes. * Minor touchups to christofides docstring. * RST touchups to tsp module docstring. * Rm extra string from tsp module. * Docstring touchups for traveling_salesman_problem. * rst fixups for greedy_tsp docstring. * rst formatting for simulated annealing docstring. * More math in-lining for simulated annealing docstring. * rst and minor grammatical fixes to TA docstring. * Fix path-finding and test all methods for tsp function * the refactoring was incomplete. Now maybe is - Add tests of TSP with all methods. - Refactor tests to match simulated_annealing tests and threshold tests. - Unify treatment of weight so unweighted edges use default weight 1. weight now defaults to "weight" with a default value of 1. - Rename tolerance to max_iterations (tolerance is used for error bound) - Rename iterations to N_inner (each iteration takes this many inner loops) - Introduce idioms like `pairwise` and `cycle.copy()` (over cycle[:]) - Allow passthrough of method kwargs for traveling_salesman_problem Still need to: - add test of case where path is more than one edge less that cycle (incomplete_graph) - require cycle input (maybe make default list(G)??) - consider the complexity claims in the doc_strings * More api changes to TSP functions - `chritofides` now allows (and ignores) selfloops - `move` can be a function as well as "1-1" and "1-0" - `method` for traveling_salesman_problem must have 2 arguments instead of passing kwargs. User must "curry" to set parameters - changed doc_string typos in matching.py * Add test to check that cycle=False can remove many edges * Change init_cycle api to require input from user The idea is to make the user specify the initial cycle to start from rather than relying on the programmers default of a greedy algorithm. To easy usage, I check for a string "greedy" as a shortcut. * Update docs with more correct complexity info. * Check for complete graph now more efficient and selfloops ignored * merge is_matching changes * Stub for Asadpour. Needed to create GSoC PR * Update to integrate changes from main * Added function stubs and draft docstrings for the Asadpour algorithm * testing * I'm not entirly sure how the commit hook works... * Moved iterators into the correct files to maintain proper codebase visibility * Including Black reformat * Grabbing black reformats * Working on debugging ascent method plus black reformats * Ascent method terminating, but at non-optimal solution * minor edits * Fixed termination condition, still given non-optimal result * Minor bugfix, still non-optimal result * Fixed subtle bug in find_epsilon() * Cleaned code and tried something which didn't work * Modified the ArborescenceIterator to accept init partition * Black formats * Branch and bound returning optimal solution * Working Ascent method, code needs cleaning * black formatting changes * Performance tweaks and testing fractional answers * Fixed test bug, I hope * Asadpour output for ascent method * Fixed numpy imports crashing pypi tests * Removed branch and bound method. One unit test misbehaving * Added asymmetric fractional test for the ascent method * Removed printn statements and tweaked final test to be more asymmetric * Draft of spanning_tree_distribution * Black changes * Changed HK to only report on the support of the answer * Fixed contraction bug by changing to MultiGraph. Problem with prob > 1 * Black reformats * Fixed pypi test error * Further testing of dist fix * Can sample spanning trees * Developing test for sampling spanning tree * Changed sample_spanning_tree test to Chi squared test * Tweaked signifiance level * Found true minimum sample size * fixed typo * untested implementation of asadpour_tsp * Fixed issue reading flow_dict * Fixed runtime errors in asadpour_tsp * black reformats * Adding test cases * documentation update * Fixed rounding error with tests * One new test and check * Documentation update for the iterators * Attempting to fix class documentation * Reventing documentation changes * Update mst.py to accept suggestion Co-authored-by: Dan Schult <dschult@colgate.edu> * Update branchings.py accept doc string edit Co-authored-by: Dan Schult <dschult@colgate.edu> * Review suggestions from dshult * Cleaned code, merged functions if possible and opened partition functionality to all * Fixed pypi test error * Implemented review suggestions from rossbar * review edits added SpanningTreeIterator to algorithms/__init__.py * Update __init__.py ack, hasty / stupid change was meant to be a draft; github isn't letting me make a new branch to PR into this one * fixed misspelling of Kirchhoff * Implement suggestions from boothby Co-authored-by: Thodoris Sotiropoulos <theosotr@windowslive.com> Co-authored-by: Luca Cappelletti <cappelletti.luca94@gmail.com> Co-authored-by: Dan Schult <dschult@colgate.edu> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Kelly Boothby <kelly.r.boothby@gmail.com> Co-authored-by: Kelly Boothby <boothby@dwavesys.com>

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Dimitrios Papageorgiou

commit sha 278bb078ba18f820c547fdceb8680094b0acf20e

Louvain Community Detection Algorithm (#4929) * Initial implementation of Louvain Community Detection algorithm * calculate degrees once at the beginning of level * size stays the same and minor improvements on calculating degrees * remove walrus operator because of CIs fail * find neighbors of each node once * improve gen_graph function * add test for partition validation * Add seed on louvain_communities and test for quality and partition * Add a generator for the dendrogram and only return the last partition * decrease quality limit to fix CI test * improve docstring * add py_random_state decorator on generate_dendrogram * handle weights * fix weight param issue and add a test for None * Remove deepcopy and improve docstrings * use seed in weight test and check for a different name * writing documentation is good * change in the modularity gain formula and make total_weights a list * make docstring raw for latex formula * minor performance improvements * add support for MultiGraphs * remove .get() from _convert_multigraph * add resolution parameter, build docs and improve docstrings * change resolution test * Add support for directed graphs * Add support for MultiDiGraphs and quality tests * Update generate_dendrogram docstring Co-authored-by: Dan Schult <dschult@colgate.edu> * Update _one_level params in docstring Co-authored-by: Dan Schult <dschult@colgate.edu> * rename generate_dendrogram to louvain_partitions * Formalize parameters's docs for _neighbor_weights function * Remove randint() from tests * Explain how ordering happens * Explain better how second phase works * Add a See Also section * perform at least on step every time * Add description of Louvain Algorithm * add doi links and reference Co-authored-by: Dan Schult <dschult@colgate.edu>

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Joakim Skarding

commit sha f27522c9717ea5455d4634f9ed71d3f5e48e5528

Fix disconnected graph bug in link prediction CCPA and add tests (#4924) * Fix disconnected graph bug in link prediction CCPA and add tests for CCPA * Fix multiple components bug in CCPA and raise exception for self links * Fix CCPA, remove alpha restriction and add suggested values to docs Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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Andrew Eckart

commit sha 7090340356943548ea5c7691ed2a676a63a1ed46

Store `G.adj` as a local variable to speed up `complement_edges(G)` (#5032)

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Ross Barnowski

commit sha 2e61dacc1ffcdcf44edb5fd68dca5f51e09db219

Mark two atsp tests as slow. (#5040)

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Ross Barnowski

commit sha a8b907df38d9d36f77d75c65d9e6a5518b849c6b

Fix bug in selfloop drawing in draw_networkx_edges (#5048) * Add test case for bug. * Make sure selfloops are included in orig edgelist b4 drawing. This fixes the bug where selfloops were drawn no matter what, even if they were explicitly excluded via the edgelist parameter.

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pinselimo

commit sha 96831f99a01fcfb39ac5d85f7e814afa5f8c4c9a

Add multigraph betweenness (#4976) * Betweenness: Use lowest weight of parallel edges * Betweenness: Add edge keys for edge betweenness centrality * betweenness: Test multigraph edge betweenness centrality * betweenness: Add missing test docstrings * betweenness: Test distinct weighted shortest paths * betweenness: Create dict with edge keys for multigraphs * betweenness: Fix reference in docstring for weight

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Thanasis Mattas

commit sha 3a44d3ec7593358778a2336a5ec2faf5afe49da9

greedy_modularity_communities with digraphs and multi(di)graphs (#5007) (#5007) * refactor N & m calculation @ greedy_modularity_communities() * add Newman 'Analysis of weighted networks' @ References * extend greedy_modularity_communities to DiGraph's * separate data structures init to a new function * remove unused 'merges' list * add @not_implemented_for('directed', 'multigraph') above naive_greedy_modularity_communities() * add tests for greed_modularity_communities() with directed & directed+weighted * use nx.all_neighbors() to access successors as well as predecessors at DiGraph's * extend greedy_modularity_communities() to MultiGraph's * extend greedy_modularity_communities() to MultiDiGraph's * refactor: remove encoder/decoder dicts (node-labels are already hashable) * b pulls data from in_degree isntead of out_degree * match the sequence of the return values with the docstring reference * test: modify existing Graphs instead of creating new ones * dq correction for multi-edges explanation & other minor edits * CNM -> Clauset-Newman-Moore & isinstance(G, (nx.MultiGraph, nx.MultiDiGraph)) -> G.is_MultiGraph() * amend @not_implemented_for decorator @ naive_greedy_modularity_communities()

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WillShardlow

commit sha 59dc28a1297ec330d9bdaa0e018537615bb6750a

Fix typo smallworld.omega (#5066)

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Dan Schult

commit sha c9060dc933b7280597c3ba6d382784ed0ed45697

Allow greedy_modularity_communities to use floating point weights or resolution (#5065) * revise mapped_queue to separate priority from element * update max_modularity to use new mapped_queue * change attribute names h, d to heap, position in MappedQueue * clean up initialization of data structures and handling of q0 * change i,j,k notation to u,v,w (no indexes since gh-5007) * Update networkx/utils/mapped_queue.py Co-authored-by: Ross Barnowski <rossbar@berkeley.edu> Co-authored-by: Ross Barnowski <rossbar@berkeley.edu>

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clean up

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Michael Dorner

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use pathlib instead of os

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Michael Dorner

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Michael Dorner

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major performance improvements

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issue openedpnnl/HyperNetX

No edge or node properties in static hypergraphs

I would like to store some edge properties. For hypernetx > 1.1 this should work.

For

H = hnx.Hypergraph()
H.add_edge(hnx.Entity('e1', {1, 2}, p='a property'))
H.edges['e1'].properties['p']

I get what I expect. But for static hypergraphs, there is no properties:

H_static = H.convert_to_static(name='static')
H_static.edges.props

Am I doing something wrong?

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issue openedpnnl/HyperNetX

Dependencies are not correct

For pip install hypernetx, the packages igraph and celluloid are not installed by default. One has to do it manually. This is also the reason why the tutorial fails (#73). What about adding them to setup.py?

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issue commentpgf-tikz/pgfplots

Implement violin charts

Violin plots in pgfplots would be wonderful. Why? Because they are so more expressive than boxplots:

distribution plots

(taken from https://www.autodesk.com/research/publications/same-stats-different-graphs)

jrmoserbaltimore

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merged results into one csv

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Tikz reporting

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startedsethvargo/go-retry

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Create CITATION.CFF

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