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        "author": "Matthieu Marinangeli",
        "author_email": "matthieu.marinangeli@epfl.ch",
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            "Intended Audience :: Science/Research",
            "Operating System :: MacOS",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "lauztat\n^^^^^^^\n\n.. image:: https://travis-ci.org/marinang/lauztat.svg?branch=master\n    :target: https://travis-ci.org/marinang/lauztat\n\n.. image:: https://dev.azure.com/matthieumarinangeli/matthieumarinangeli/_apis/build/status/marinang.lauztat?branchName=master\n    :alt: Build Status\n    :target: https://dev.azure.com/matthieumarinangeli/matthieumarinangeli/_build?definitionId=1\n\n.. image:: https://img.shields.io/azure-devops/tests/matthieumarinangeli/matthieumarinangeli/1.svg?compact_message\n    :alt: Test Status\n    :target: https://dev.azure.com/matthieumarinangeli/matthieumarinangeli/_build?definitionId=1\n\n.. image:: https://img.shields.io/coveralls/github/marinang/lauztat.svg\n    :alt: Coveralls github\n    :target: https://coveralls.io/github/marinang/lauztat?branch=master\n\n.. image:: https://api.codacy.com/project/badge/Grade/f78242fbdbd34ef8a21a9f9055b6c898\n    :alt: Codacy Badge\n    :target: https://app.codacy.com/app/marinang/lauztat?utm_source=github.com&utm_medium=referral&utm_content=marinang/lauztat&utm_campaign=Badge_Grade_Dashboard\n\n.. image:: https://mybinder.org/badge_logo.svg\n    :target: https://mybinder.org/v2/gh/marinang/lauztat/master?filepath=examples%2Fnotebooks%2F\n\n.. image:: https://img.shields.io/pypi/v/lauztat.svg\n    :alt: PyPI\n    :target: https://pypi.org/project/lauztat/\n\n.. image:: https://img.shields.io/pypi/pyversions/lauztat.svg\n    :alt: PyPI - Python Version\n    :target: https://pypi.org/project/lauztat/\n\n.. image:: https://zenodo.org/badge/DOI/10.5281/zenodo.2593789.svg\n    :target: https://doi.org/10.5281/zenodo.2593789\n\n\n\nPure python statistics tools for high energy physics using `zfit <https://github.com/zfit/zfit>`__ as\na backend for maximum likelihood fits.\n\nTests for discovery, upper limits and confidence intervals are provided based on likelihood ratios\nin a frequentist approach (using pseudo-experiments) or using asymptotic formulae from\n\"Asymptotic formulae for likelihood-based tests of new physics\" `[arxiv:1007.1727] <https://arxiv.org/abs/1007.1727>`__.\n\nlauztat has been developed at EPFL, Lausanne Switzerland (laus' or lauz is how the cool kids call Lausanne).\n\nInstallation\n------------\n\nInstall ``lauztat`` like any other Python package:\n\n.. code-block:: bash\n\n    pip install lauztat                       # maybe with sudo or --user, or in virtualenv\n\nDependencies\n------------\n\n- `Numpy <https://scipy.org/install.html>`__\n- `zfit <https://github.com/zfit/zfit>`__\n- `matplotlib <https://matplotlib.org/users/installing.html>`__ (optionnal)\n\nGetting started\n---------------\n\nUsual HEP results can be recast in terms of hypothesis testing where you have to\nchoose a null H\\ :sub:`0` and an alternative H\\ :sub:`1` hypothesis, H\\ :sub:`0`\nbeing the one you want to disprove.\nTo do a test you will need your data (and weights), a model, a loss function builder\nand a minimizer as input to a calculator (*FrequentistCalculator* or *AsymptoticCalculator*).\n\nDiscovery:\n==========\n\nif you do a measurement to find signals S in a dataset and you find an excess, this\ntest answers \"is the data compatible with the background only ?\" with:\n\n- H\\ :sub:`0`: background only (S = 0)\n- H\\ :sub:`1`: presence of a signal (S \u2260 0)\n\nThe test return a p-value or a significance Z. If Z \u2265 3 there is an evidence\nand if Z \u2265 5 a discovery of a signal.\n\nExamples of significance computations for a gaussian peak over an exponential background are\nprovided for the `asymptotic calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/discovery_zfit_asy.ipynb>`__\nand the `frequentist calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/discovery_zfit_freq.ipynb>`__\nand can be ran in `mybinder <https://mybinder.org/v2/gh/marinang/lauztat/master?filepath=examples%2Fnotebooks%2F>`__.\n\nUpper limit:\n============\n\nif you find a small signal excess in a dataset, but not enough to claim\nan evidence or a discovery, you can exclude large signal yields S:\n\n- H\\ :sub:`0`: background + some signal (S = S\\ :sub:`0`)\n- H\\ :sub:`1`: S < S\\ :sub:`0`\n\nS\\ :sub:`0` is adjusted to a predefined p-value, typically 5%. S\\ :sub:`0` is the upper\nlimit on the signal yield S with 95 % confidence level\n(CL = 1 - p ; p = 5 % \u27fa CL = 95%).\n\nExamples of `CLs <https://iopscience.iop.org/article/10.1088/0954-3899/28/10/313/meta>`__ upper limits on the signal yield\nfor a gaussian peak over an exponential background are\nprovided for the `asymptotic calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/upper_limit_zfit_asy.ipynb>`__\nand the `frequentist calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/upper_limit_zfit_freq.ipynb>`__\nand can be ran in `mybinder <https://mybinder.org/v2/gh/marinang/lauztat/master?filepath=examples%2Fnotebooks%2F>`__.\n\nConfidence interval:\n====================\n\nif you do a measurement of a parameter \u03b1 with an estimator \u1fb0, given an observation\n\u1fb0\\ :sub:`obs` what value of \u03b1 are not rejected at a certain confidence level (typically 68%)?\n\n- H\\ :sub:`0`: \u03b1 \u2264 \u03b1 \\ :sub:`down` or \u03b1 \u2265 \u03b1\\ :sub:`up`\n- H\\ :sub:`1`: \u03b1\\ :sub:`down` < \u03b1 < \u03b1\\ :sub:`up`\n\n\u03b1\\ :sub:`down` and \u03b1\\ :sub:`up` are adjusted such the test returns a p-value of 32%.\n\nExamples of confidence intervals on the mean of a gaussian peak are\nprovided for the `asymptotic calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/confidence_interval_zfit_asy.ipynb>`__\nand the `frequentist calculator <https://github.com/marinang/lauztat/blob/master/examples/notebooks/confidence_interval_zfit_freq.ipynb>`__\n(Feldman and Cousins confidence interval `[arxiv:9711021] <https://arxiv.org/abs/physics/9711021>`__)\nand can be ran in `mybinder <https://mybinder.org/v2/gh/marinang/lauztat/master?filepath=examples%2Fnotebooks%2F>`__.",
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