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"description": ".. note: Always use full path to image, from https://raw.githubusercontent.com/\n because it won't render on pypi and others otherwise if you use the relative\n path from this repo :(\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/logo/logo.png\n :align: right\n\nClustering of Kinematic Graphs\n==============================\n\n|Build Status| |Coveralls| |Doc Status| |Pypi status| |Binder| |Chat| |License| |Black|\n\n.. |Build Status| image:: https://travis-ci.org/clusterking/clusterking.svg?branch=master\n :target: https://travis-ci.org/clusterking/clusterking\n :alt: CI\n\n.. |Coveralls| image:: https://coveralls.io/repos/github/clusterking/clusterking/badge.svg?branch=master\n :target: https://coveralls.io/github/clusterking/clusterking?branch=master\n\n.. |Doc Status| image:: https://readthedocs.org/projects/clusterking/badge/?version=latest\n :target: https://clusterking.readthedocs.io/\n :alt: Documentation\n\n.. |Pypi Status| image:: https://badge.fury.io/py/clusterking.svg\n :target: https://pypi.org/project/clusterking/\n :alt: Pypi\n\n.. |Binder| image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/badges/png/binder.png\n :target: https://mybinder.org/v2/gh/clusterking/clusterking/master?filepath=examples%2Fjupyter_notebooks\n :alt: Binder\n\n.. |Chat| image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/badges/png/gitter.png\n :target: https://gitter.im/clusterking/community\n :alt: Gitter\n\n.. |License| image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/badges/png/license.png\n :target: https://github.com/clusterking/clusterking/blob/master/LICENSE.txt\n :alt: License\n\n.. |Black| image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/badges/png/black.png\n :target: https://github.com/python/black\n :alt: Black\n\n.. start-body\n\nDescription\n-----------\n\nThis package provides a flexible yet easy to use framework to cluster sets of\nhistograms (or other higher dimensional data) and to select benchmark points\nrepresenting each cluster. The package particularly focuses on use cases in\nhigh energy physics.\n\nPhysics Case\n------------\n\nWhile most of this package is very general and can be applied to a broad variety\nof use cases, we have been focusing on applications in high energy physics\n(particle physics) so far and provide additional convenience methods for this\nuse case. In particular, most of the current tutorials are in this context.\n\nThough very successful, the Standard Model of Particle Physics is believed to\nbe uncomplete, prompting the search for New Physics (NP). The phenomenology\nof NP models typically depends on a number of free parameters, sometimes\nstrongly influencing the shape of distributions of kinematic variables.\nBesides being an obvious challenge when presenting exclusion limits on such\nmodels, this also is an issue for experimental analyses that need to make\nassumptions on kinematic distributions in order to extract features of\ninterest, but still want to publish their results in a very general way.\n\nBy clustering the NP parameter space based on a metric that quantifies the\nsimilarity of the resulting kinematic distributions, a small number of NP\nbenchmark points can be chosen in such a way that they can together represent\nthe whole parameter space. Experiments (and theorists) can then report\nexclusion limits and measurements for these benchmark points without\nsacrificing generality.\n\nInstallation\n------------\n\n``clusterking`` can be installed/upgraded with the `python package installer `_:\n\n.. code:: sh\n\n pip3 install --user --upgrade \"clusterking[plotting]\"\n\nIf you do not require plotting, you can remove ``[plotting]``.\n\nMore options and troubleshooting advice is given in the `documentation `_.\n\nUsage and Documentation\n-----------------------\n\nGood starting point: **Jupyter notebooks** in the ``examples/jupyter_notebook`` directory.\nYou can also try running them online right now (without any installation required) using\n|binder2|_ (just note that this is somewhat unstable, slow and takes some time\nto start up).\n\n.. |binder2| replace:: binder\n.. _binder2: https://mybinder.org/v2/gh/clusterking/clusterking/master?filepath=examples%2Fjupyter_notebooks\n\n.. _run online using binder: https://mybinder.org/v2/gh/clusterking/clusterking/master?filepath=examples%2Fjupyter_notebooks\n\nFor a documentation of the classes and functions in this package, **read the docs on** |readthedocs.io|_.\n\n.. |readthedocs.io| replace:: **readthedocs.io**\n.. _readthedocs.io: https://clusterking.readthedocs.io/\n\nFor additional examples, presentations and more, you can also head to our `other repositories`_.\n\n.. _other repositories: https://github.com/clusterking\n\nExample\n-------\n\nSample\n~~~~~~\n\nThe following code (taken from ``examples/jupyter_notebook/010_basic_tutorial.ipynb``) is all that\nis needed to cluster the shape of the ``q^2`` distribution of ``B -> D tau nu``\nin the space of Wilson coefficients:\n\n.. code:: python\n\n import flavio\n import numpy as np\n import clusterking as ck\n\n s = ck.scan.WilsonScanner(scale=5, eft='WET', basis='flavio')\n\n # Set up kinematic function\n\n def dBrdq2(w, q):\n return flavio.np_prediction(\"dBR/dq2(B+->Dtaunu)\", w, q)\n\n s.set_dfunction(\n dBrdq2,\n binning=np.linspace(3.2, 11.6, 10),\n normalize=True\n )\n\n # Set sampling points in Wilson space\n\n s.set_spoints_equidist({\n \"CVL_bctaunutau\": (-1, 1, 10),\n \"CSL_bctaunutau\": (-1, 1, 10),\n \"CT_bctaunutau\": (-1, 1, 10)\n })\n\n # Create data object to write to and run\n\n d = ck.DataWithErrors()\n r = s.run(d)\n r.write() # Write results back to data object\n\nCluster\n~~~~~~~\n\nUsing hierarchical clustering:\n\n.. code:: python\n\n c = ck.cluster.HierarchyCluster() # Initialize worker class\n c.set_metric(\"euclidean\")\n c.set_max_d(0.15) # \"Cut off\" value for hierarchy\n r = c.run(d) # Run clustering on d\n r.write() # Write results to d\n\nBenchmark points\n~~~~~~~~~~~~~~~~\n\n.. code:: python\n\n b = ck.Benchmark() # Initialize worker class\n b.set_metric(\"euclidean\")\n r = b.run(d) # Select benchmark points based on metric\n r.write() # Write results back to d\n\nPlotting\n~~~~~~~~\n\n.. code:: python\n\n d.plot_clusters_scatter(\n ['CVL_bctaunutau', 'CSL_bctaunutau', 'CT_bctaunutau'],\n clusters=[1,2] # Only plot 2 clusters for better visibility\n )\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/scatter_3d_02.png\n\n.. code:: python\n\n d.plot_clusters_fill(['CVL_bctaunutau', 'CSL_bctaunutau'])\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/fill_2d.png\n\nPlotting all benchmark points:\n\n.. code:: python\n\n d.plot_dist()\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/all_bcurves.png\n\nPlotting minima and maxima of bin contents for all histograms in a cluster (+benchmark histogram):\n\n.. code:: python\n\n d.plot_dist_minmax(clusters=[0, 2])\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/minmax_02.png\n\nSimilarly with box plots:\n\n.. code:: python\n\n d.plot_dist_box()\n\n.. image:: https://raw.githubusercontent.com/clusterking/clusterking/master/readme_assets/plots/box_plot.png\n\nLicense & Contributing\n----------------------\n\nThis project is ongoing work and questions_, comments,\n`bug reports`_ or `pull requests`_ are most welcome. You can also use the chat\nroom on gitter_ or contact us via email_.\nWe are also working on a paper, so please make sure to cite us once we publish.\n\n.. _email: mailto:clusterkinematics@gmail.com\n.. _gitter: https://gitter.im/clusterking/community\n.. _questions: https://github.com/clusterking/clusterking/issues\n.. _bug reports: https://github.com/clusterking/clusterking/issues\n.. _pull requests: https://github.com/clusterking/clusterking/pulls\n\nThis software is licenced under the `MIT license`_.\n\n.. _MIT license: https://github.com/clusterking/clusterking/blob/master/LICENSE.txt\n\n.. end-body\n\n\n",
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