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    "info": {
        "author": "NetworkUnit authors and contributors",
        "author_email": "r.gutzen@fz-juelich.de",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
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            "Programming Language :: Python :: 2",
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        "description": "===========\nNetworkUnit\n===========\n\nA SciUnit_ library for validation testing of spiking networks.\n\n.. _SciUnit: https://github.com/scidash/sciunit\n\n.. image:: https://mybinder.org/badge.svg\n   :target: https://mybinder.org/v2/gh/INM-6/NetworkUnit/master?filepath=examples%2Findex.ipynb\n   :alt: Binder Link\n\n.. role:: py(code)\n   :language: python\n\nInstallation\n------------\n\n.. code:: bash\n\n    pip install networkunit\n\nConcept\n-------\nThe NetworkUnit module builds upon the formalized validation scheme of the SciUnit package,\nwhich enables the validation of *models* against experimental data (or other models) via *tests*.\nA test is matched to the model by *capabilities* and quantitatively evaluated by a *score*.\nThe following figure illustrates a typical test design within NetworkUnit.\nThe blue boxes indicate the components of the implementation of the validation test, i.e.,\nclasses, class instances, data sets, and parameters.\nThe relation between the boxes are indicated by annotated arrows.The basic functionality is\nshown by green arrows.  The difference in the test design for comparing against experimental\ndata (validation) and  another  simulation  (substantiation)  is  indicated  by  yellow  and\nred  arrows,  respectively.  The  relevant  functionality  of  some  components  for  the\ncomputation  of  test  score  is  indicated  by  pseudo-code.  The  capability\nclass :py:`ProducesProperty` contains  the  function :py:`calc_property()`. The test :py:`XYTest` has a function\n:py:`generate_prediction()` which makes use of this capability, inherited by the model class,\nto generate a model prediction. The initialized test instance :py:`XYTest_paramZ` makes use of its\n:py:`judge()` function to evaluate this model prediction and compute the score :py:`TestScore`.\nThe :py:`XYTest` can inherit from multiple abstract test classes (:py:`BaseTest`),\nwhich is for example used with the :py:`M2MTest` to add the functionality of evaluating multiple model classes.\nTo make the test executable it has to be linked to a ScoreType and all free parameters need to be set\n(by a :py:`Params` dict) to ensure a reproducible result.\n\n.. image:: https://raw.githubusercontent.com/INM-6/NetworkUnit/master/figures/NetworkUnit_Flowchart_X2M_M2M.png\n   :width: 500\n   :alt: NetworkUnit Flowchart\n\nShowcase examples on how to use NetworkUnit can be found `in this repository`_ and interactive reveal.js slides are\naccessible via the launch-binder button at the top.\n\n.. _`in this repository`: https://web.gin.g-node.org/INM-6/network_validation\n\nOverview of tests\n-----------------\n===================================     =======================     ===================================================\nClass name                              Parent class                Prediction measure\n===================================     =======================     ===================================================\ntwo_sample_test                         \\-                          \\-\ncorrelation_test                        two_sample_test             \\-\ncorrelation_dist_test                   correlation_test            correlation coefficients\ncorrelation_matrix_test                 correlation_test            correlation coefficient matrix\ngeneralized_correlation_matrix_test     correlation_matrix_test     matrix of derived cross-correlation measures\neigenvalue_test                         correlation_test            eigenvalues of the correlation coefficient matrix\ncovariance_test                         two_sample_test             covariances\nfiring_rate_test                        two_sample_test             firing rates\nisi_variation_test                      two_sample_test             inter-spike-intervals, their CV, or LV\ngraph_centrality_helperclass            sciunit.Test                graph centrality measures of given adjacency matrix\n===================================     =======================     ===================================================\n\nInheritance order in case of multiple inheritance for derived test classes:\n\n.. code:: python\n\n    class new_test(sciunit.TestM2M, graph_centrality_helperclass, <base_test_class>)\n\n\nOverview of scores\n------------------\n\n================    ===============================     ===================\nClass name          Test name                           Comparison measure\n================    ===============================     ===================\nstudents_t          Student't test                      sample mean\nks_distance         Kolmogorov-Smirnov test             sample distribution\nkl_divergence       Kullback-Leibler divergence         sample entropy\nmwu_statistic       Mann-Whitney U test                 rank sum\nlevene_score        Levene's test                       sample variance\neffect_size         Effect size                         standardized mean\nbest_effect_size    Bayesian estimation effect size     standardized mean\n================    ===============================     ===================\n\nOverview of model classes\n-------------------------\n\n===================     ===================     ===============     ==================================\nModel name              Capability              Parent class        Purpose\n===================     ===================     ===============     ==================================\nloaded_data             \\-                      sciunit.Model       loading simulated data\nspiketrain_data         ProducesSpikeTrains     simulation_data     loading simulated spiking data\nstochastic_activity     ProducesSpikeTrains     sciunit.Model       generating stochastic spiking data\n===================     ===================     ===============     ==================================\n\nOther validation test repositories\n----------------------------------\n\n- NeuronUnit_\n- HippoUnit_\n- BasalUnit_\n- MorphoUnit_\n- CerebellumUnit_\n\n.. _NeuronUnit: https://github.com/BlueBrain/neuronunit\n.. _HippoUnit: https://github.com/apdavison/hippounit\n.. _BasalUnit: https://github.com/appukuttan-shailesh/basalunit\n.. _MorphoUnit: https://github.com/appukuttan-shailesh/morphounit\n.. _CerebellumUnit: https://github.com/lungsi/cerebellum-unit\n\n\nAcknowledgments\n---------------\nThis open source software code was developed in part or in whole in the Human Brain Project, funded from the European Union\u2019s Horizon 2020 Framework Programme for Research and Innovation under Specific Grant Agreements No. 720270 and No. 785907 (Human Brain Project SGA1 and SGA2).\n\n\n",
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