Metadata-Version: 2.1
Name: clusterking
Version: 0.9.dev5
Summary: Cluster sets of histograms/curves, in particular kinematic distributions in high energy physics.
Home-page: https://github.com/clusterking/clusterking
License: MIT
Project-URL: Bug Tracker, https://github.com/clusterking/clusterking/issues
Project-URL: Source Code, https://github.com/clusterking/clusterking/
Project-URL: Documentation, https://clusterking.readthedocs.io/
Description: .. note: Always use full path to image, because it won't render on
           pypi and others otherwise
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/logo/logo.png
            :align: right
        
        Clustering of Kinematic Graphs
        ==============================
        
        |Build Status| |Coveralls| |Doc Status| |Pypi status| |Binder| |Chat| |License|
        
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           :target: https://travis-ci.org/clusterking/clusterking
        
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           :alt: Documentation Status
        
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            :alt: Pypi status
        
        .. |Binder| image:: https://mybinder.org/badge_logo.svg
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           :target: https://gitter.im/clusterking/community
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           :target: https://github.com/clusterking/clusterking/blob/master/LICENSE.txt
           :alt: License
        
        .. start-body
        
        Description
        -----------
        
        This package provides a flexible yet easy to use framework to cluster sets of histograms (or other higher dimensional data) and to select benchmark points representing each cluster. The package particularly focuses on use cases in high energy physics.
        
        Physics Case
        ------------
        
        While most of this package is very general and can be applied to a broad variety of use cases, we have been focusing on applications in high energy physics (particle physics) so far and provide additional convenience methods for this use case. In particular, most of the current tutorials are in this context.
        
        Though very successful, the Standard Model of Particle Physics is believed to be uncomplete, prompting the search for New Physics (NP).
        The phenomenology of NP models typically depends on a number of free parameters, sometimes strongly influencing the shape of distributions of kinematic variables. Besides being an obvious challenge when presenting exclusion limits on such models, this also is an issue for experimental analyses that need to make assumptions on kinematic distributions in order to extract features of interest, but still want to publish their results in a very general way.
        
        By clustering the NP parameter space based on a metric that quantifies the similarity of the resulting kinematic distributions, a small number of NP benchmark points can be chosen in such a way that they can together represent the whole parameter space. Experiments (and theorists) can then report exclusion limits and measurements for these benchmark points without sacrificing generality.  
        
        Installation
        ------------
        
        ``clusterking`` can be installed with the python package installer:
        
        .. code:: sh
        
            pip3 install clusterking
        
        For a local installation, you might want to use the ``--user`` switch of ``pip``.
        You can also update your current installation with ``pip3 install --upgrade clusterking``.  
        
        For the latest development version type:
        
        .. code:: sh
        
            git clone https://github.com/clusterking/clusterking/
            cd clusterking
            pip3 install --user .
        
        Usage and Documentation
        -----------------------
        
        Good starting point: **Jupyter notebooks** in the ``examples/jupyter_notebook`` directory (|binder2|_).
        
        .. |binder2| replace:: **run online using binder**
        .. _binder2: https://mybinder.org/v2/gh/clusterking/clusterking/master?filepath=examples%2Fjupyter_notebooks
        
        .. _run online using binder: https://mybinder.org/v2/gh/clusterking/clusterking/master?filepath=examples%2Fjupyter_notebooks
        
        For a documentation of the classes and functions in this package, **read the docs on** |readthedocs.io|_.
        
        .. |readthedocs.io| replace:: **readthedocs.io**
        .. _readthedocs.io: https://clusterking.readthedocs.io/
        
        Example
        -------
        
        Sample and cluster
        ~~~~~~~~~~~~~~~~~~
        
        Being a condensed version of the basic tutorial, the following code is all that is needed to cluster the shape of the ``q^2`` distribution of ``B-> D* tau nu`` in the space of Wilson coefficients:
        
        .. code:: python
        
           import flavio
           import numpy as np
           import clusterking as ck
        
           s = ck.scan.WilsonScanner()
           d = ck.DataWithErrors()
        
           # Set up kinematic function
        
           def dBrdq2(w, q):
             return flavio.np_prediction("dBR/dq2(B+->Dtaunu)", w, q)
        
           s.set_dfunction(
             dBrdq2,
             binning=np.linspace(3.2, 11.6, 10),
             normalize=True
           )
        
           # Set sampling points in Wilson space
        
           s.set_spoints_equidist(
             {
                 "CVL_bctaunutau": (-1, 1, 10),
                 "CSL_bctaunutau": (-1, 1, 10),
                 "CT_bctaunutau": (-1, 1, 10)
             },
             scale=5,
             eft='WET',
             basis='flavio'
           )
        
           s.run(d)
        
           # Use hierarchical clustering
        
           c = ck.cluster.HierarchyCluster(d)
           c.set_metric()         # Use default metric (Euclidean)
           c.build_hierarchy()    # Build up clustering hierarchy
           c.cluster(max_d=0.15)  # "Cut off" hierarchy
           c.write()              # Write results to d
        
        Benchmark points
        ~~~~~~~~~~~~~~~~
        
        .. code:: python
        
           b = ck.Benchmark(d)
           b.set_metric()      # Use default metric (Euclidean)
           b.select_bpoints()  # Select benchmark points based on metric
           b.write()           # Write results to d
        
        Plotting
        ~~~~~~~~
        
        .. code:: python
        
            d.plot_clusters_scatter(
                ['CVL_bctaunutau', 'CSL_bctaunutau', 'CT_bctaunutau'],
                clusters=[1,2]  # Only plot 2 clusters for better visibility
            )
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/scatter_3d_02.png
         
        .. code:: python
        
            d.plot_clusters_fill(['CVL_bctaunutau', 'CSL_bctaunutau'])
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/fill_2d.png
        
        Plotting all benchmark points:
        
        .. code:: python
        
            bp.plot_dist()
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/all_bcurves.png
        
        Plotting minima and maxima of bin contents for all histograms in a cluster (+benchmark histogram):
        
        .. code:: python
        
            bp.plot_dist_minmax(clusters=[0, 2])
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/minmax_02.png
        
        Similarly with box plots:
        
        .. code:: python
        
           bp.plot_dist_box()
        
        .. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/box_plot.png
        
        License & Contributing
        ----------------------
        
        This project is ongoing work and questions_, comments, `bug reports`_ or `pull requests`_ are most welcome. You can also use the chat room on gitter_ or contact us via email_.  We are also working on a paper, so please make sure to cite us once we publish.
        
        .. _email: mailto:clusterkinematics@gmail.com
        .. _gitter: https://gitter.im/clusterking/community
        .. _questions: https://github.com/clusterking/clusterking/issues
        .. _bug reports: https://github.com/clusterking/clusterking/issues
        .. _pull requests: https://github.com/clusterking/clusterking/pulls
        
        This software is lienced under the `MIT license`_.
        
        .. _MIT  license: https://github.com/clusterking/clusterking/blob/master/LICENSE.txt
        
        .. end-body
        
Keywords: clustering,cluster,kinematics,cluster-analysis,machine-learning,ml,hep,hep-ml,hep-ex,hep-ph,wilson
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Description-Content-Type: text/x-rst
