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            "Development Status :: 4 - Beta",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Operating System :: MacOS",
            "Operating System :: Unix",
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        "description": "*******************************\nzfit: scalable pythonic fitting\n*******************************\n\n\n.. image:: https://zenodo.org/badge/126311570.svg\n   :target: https://zenodo.org/badge/latestdoi/126311570\n\n.. image:: https://img.shields.io/pypi/v/zfit.svg\n   :target: https://pypi.python.org/pypi/zfit\n\n.. image:: https://img.shields.io/travis/zfit/zfit.svg\n   :target: https://travis-ci.org/zfit/zfit\n\n.. image:: https://coveralls.io/repos/github/zfit/zfit/badge.svg?branch=meta_changes\n   :target: https://coveralls.io/github/zfit/zfit?branch=meta_changes\n\n.. image:: https://www.codefactor.io/repository/github/zfit/zfit/badge\n   :target: https://www.codefactor.io/repository/github/zfit/zfit\n   :alt: CodeFactor\n\n\n|\n\n\nzfit is a highly scalable and customizable model manipulation and fitting library. It uses\n`TensorFlow <https://www.tensorflow.org/>`_ as its computational backend\nand is optimised for simple and direct manipulation of probability density functions.\n\n- **Tutorials**: `Interactive IPython Tutorials <https://github.com/zfit/zfit-tutorials>`_\n- **Quick start**: `Example scripts <examples>`_\n- **Documentation**: Full documentation_ and API_\n- **Questions**: see the `FAQ <https://github.com/zfit/zfit/wiki/FAQ>`_,\n  `ask on StackOverflow <https://stackoverflow.com/questions/ask>`_ with the **zfit** tag or `contact`_ us directly.\n- **Physics/HEP**: `zfit-physics <https://github.com/zfit/zfit-physics>`_ is the place to contribute/find more HEP\n  related content\n\n\nIf you use zfit in research, please consider `citing <https://zenodo.org/badge/latestdoi/126311570>`_.\n\n*N.B.*: zfit is currently in *beta stage*, so while most core parts are established, some may still be missing and bugs may be encountered.\nIt is, however, mostly ready for production, and is being used in analyses projects.\nIf you want to use it for your project and you are not sure if all the needed functionality is there, feel free to `contact`_.\n\n\nWhy?\n====\n\nThe basic idea behind zfit is to offer a Python oriented alternative to the very successful RooFit library from the `ROOT <https://root.cern.ch/>`_ data analysis package that can integrate with the other packages that are part if the scientific Python ecosystem.\nContrary to the monolithic approach of ROOT/RooFit, the aim of zfit is to be light and flexible enough to integrate with any state-of-art tools and to allow scalability going to larger datasets.\n\nThese core ideas are supported by two basic pillars:\n\n- The skeleton and extension of the code is minimalist, simple and finite:\n  the zfit library is exclusively designed for the purpose of model fitting and sampling with no attempt to extend its functionalities to features such as statistical methods or plotting.\n\n- zfit is designed for optimal parallelisation and scalability by making use of TensorFlow as its backend.\n  The use of TensorFlow provides crucial features in the context of model fitting like taking care of the parallelisation and analytic derivatives.\n\n\n\nHow to use\n==========\n\nWhile the zfit library provides a model fitting and sampling framework for a broad list of applications,\nwe will illustrate its main features with a simple example by fitting a Gaussian distribution with an unbinned\nlikelihood fit and a parameter uncertainty estimation.\n\n\nExample in short\n----------------\n.. code-block:: python\n\n    obs = zfit.Space('x', limits=(-10, 10))\n\n    # create the model\n    mu    = zfit.Parameter(\"mu\"   , 2.4, -1, 5)\n    sigma = zfit.Parameter(\"sigma\", 1.3,  0, 5)\n    gauss = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma)\n\n    # load the data\n    data_np = np.random.normal(size=10000)\n    data = zfit.Data.from_numpy(obs=obs, array=data_np)\n\n    # build the loss\n    nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)\n\n    # minimize\n    minimizer = zfit.minimize.Minuit()\n    result = minimizer.minimize(nll)\n\n    # calculate errors\n    param_errors = result.error()\n\nThis follows the zfit workflow\n\n.. image:: docs/images/zfit_workflow_v1.png\n    :alt: zfit workflow\n\n\n\n\nFull explanation\n----------------\n\nThe default space (e.g. normalization range) of a PDF is defined by an *observable space*, which is created using the ``zfit.Space`` class:\n\n\n.. code-block:: python\n\n    obs = zfit.Space('x', limits=(-10, 10))\n\n\nTo create a simple Gaussian PDF, we define its parameters and their limits using the ``zfit.Parameter`` class.\n\n.. code-block:: python\n\n  # syntax: zfit.Parameter(\"any_name\", value, lower, upper)\n    mu    = zfit.Parameter(\"mu\"   , 2.4, -1, 5)\n    sigma = zfit.Parameter(\"sigma\", 1.3,  0, 5)\n    gauss = zfit.pdf.Gauss(obs=obs, mu=mu, sigma=sigma)\n\nFor simplicity, we create the dataset to be fitted starting from a numpy array, but zfit allows for the use of other sources such as ROOT files:\n\n.. code-block:: python\n\n    mu_true = 0\n    sigma_true = 1\n    data_np = np.random.normal(mu_true, sigma_true, size=10000)\n    data = zfit.Data.from_numpy(obs=obs, array=data_np)\n\nFits are performed in three steps:\n\n1. Creation of a loss function, in our case a negative log-likelihood.\n2. Instantiation of our minimiser of choice, in the example the ``Minuit``.\n3. Minimisation of the loss function.\n\n.. code-block:: python\n\n    # Stage 1: create an unbinned likelihood with the given PDF and dataset\n    nll = zfit.loss.UnbinnedNLL(model=gauss, data=data)\n\n    # Stage 2: instantiate a minimiser (in this case a basic minuit)\n    minimizer = zfit.minimize.Minuit()\n\n    # Stage 3: minimise the given negative log-likelihood\n    result = minimizer.minimize(nll)\n\nErrors are calculated with a further function call to avoid running potentially expensive operations if not needed:\n\n.. code-block:: python\n\n    param_errors = result.error()\n\nOnce we've performed the fit and obtained the corresponding uncertainties, we can examine the fit results:\n\n.. code-block:: python\n\n    print(\"Function minimum:\", result.fmin)\n    print(\"Converged:\", result.converged)\n    print(\"Full minimizer information:\", result.info)\n\n    # Information on all the parameters in the fit\n    params = result.params\n    print(params)\n\n    # Printing information on specific parameters, e.g. mu\n    print(\"mu={}\".format(params[mu]['value']))\n\nAnd that's it!\nFor more details and information of what you can do with zfit, checkout the documentation_.\n\nPrerequisites\n=============\n\n``zfit`` works with Python versions 3.6 and 3.7.\nThe following packages (amongst others) are required:\n\n- `tensorflow <https://www.tensorflow.org/>`_ >= 1.10.0\n- `tensorflow_probability <https://www.tensorflow.org/probability>`_ >= 0.3.0\n- `scipy <https://www.scipy.org/>`_ >=1.2\n- `uproot <https://github.com/scikit-hep/uproot>`_\n- `iminuit <https://github.com/scikit-hep/iminuit>`_\n\n... and some minor packages. For a full list, check the `requirements <requirements.txt>`_.\n\nInstalling\n==========\n\nTo install zfit, run this command in your terminal:\n\n.. code-block:: console\n\n    $ pip install zfit\n\n\nFor the newest development version, you can install the version from git with\n\n.. code-block:: console\n\n   $ pip install git+https://github.com/zfit/zfit\n\n\nContributing\n============\n\nAny idea of how to improve the library? Or interested to write some code?\nContributions are always welcome, please have a look at the `Contributing guide`_.\n\n.. _Contributing guide: CONTRIBUTING.rst\n\n\nContact\n=======\n\nYou can contact us directly:\n - via e-mail: zfit@physik.uzh.ch\n - join our `Gitter channel <https://gitter.im/zfit/zfit>`_\n\n\nMain Authors\n============\n\n| Jonas Eschle <jonas.eschle@cern.ch>\n| Albert Puig <albert.puig@cern.ch>\n| Rafael Silva Coutinho <rsilvaco@cern.ch>\n\n\n\nAcknowledgements\n================\n\nzfit has been developed with support from the University of Z\u00fcrich and the Swiss National Science Foundation (SNSF) under contracts 168169 and 174182.\n\nThe idea of zfit is inspired by the `TensorFlowAnalysis <https://gitlab.cern.ch/poluekt/TensorFlowAnalysis>`_ framework developed by Anton Poluektov using the TensorFlow open source library.\n\n.. _documentation: https://zfit.readthedocs.io/en/latest/\n.. _API: https://zfit.readthedocs.io/en/latest/API.html\n\n\n*********\nChangelog\n*********\n\nDevelop\n=======\n\nMajor Features and Improvements\n-------------------------------\n - full TF 2.0 compatibility, tests against 1.x and 2.x\n\nBehavioral changes\n------------------\n\nBug fixes and small changes\n---------------------------\n\n\nRequirement changes\n-------------------\n\n\nThanks\n------\n\n0.3.6 (12.10.19)\n================\n\n**Special relese for conda deployment and version fix (TF 2.0 is out)**\n\n**This is the last release before breaking changes occur**\n\n\nMajor Features and Improvements\n-------------------------------\n - added ConstantParameter and `zfit.param` namespace\n - Available on conda-forge\n\nBehavioral changes\n------------------\n - an implicitly created parameter with a Python numerical (e.g. when instantiating a model)\n   will be converted to a ConstantParameter instead of a fixed Parameter and therefore\n   cannot be set to floating later on.\n\nBug fixes and small changes\n---------------------------\n - added native support TFP distributions for analytic sampling\n - fix Gaussian (TFP Distribution) Constraint with mixed up order of parameters\n\n - `from_numpy` automatically converts to default float regardless the original numpy dtype,\n   `dtype` has to be used as an explicit argument\n\n\nRequirement changes\n-------------------\n - TensorFlow >= 1.14 is required\n\n\nThanks\n------\n - Chris Burr for the conda-forge deployment\n\n\n0.3.4 (30-07-19)\n================\n\n**This is the last release before breaking changes occur**\n\nMajor Features and Improvements\n-------------------------------\n- create `Constraint` class which allows for more fine grained control and information on the applied constraints.\n- Added Polynomial models\n- Improved and fixed sampling (can still be slightly biased)\n\nBehavioral changes\n------------------\nNone\n\nBug fixes and small changes\n---------------------------\n\n- fixed various small bugs\n\nThanks\n------\nfor the contribution of the Constraints to Matthieu Marinangeli <matthieu.marinangeli@cern.ch>\n\n\n\n0.3.3 (15-05-19)\n================\n\nFixed Partial numeric integration\n\nBugfixes mostly, a few major fixes. Partial numeric integration works now.\n\nBugfixes\n - data_range cuts are now applied correctly, also in several dimensions when a subset is selected\n   (which happens internally of some Functors, e.g. ProductPDF). Before, only the selected obs was respected for cuts.\n - parital integration had a wrong take on checking limits (now uses supports).\n\n\n0.3.2 (01-05-19)\n================\n\nWith 0.3.2, bugfixes and three changes in the API/behavior\n\nBreaking changes\n----------------\n - tfp distributions wrapping is now different with dist_kwargs allowing for non-Parameter arguments (like other dists)\n - sampling allows now for importance sampling (sampler in Model specified differently)\n - `model.sample` now also returns a tensor, being consistent with `pdf` and `integrate`\n\nBugfixes\n--------\n - shape handling of tfp dists was \"wrong\" (though not producing wrong results!), fixed. TFP distributions now get a tensor with shape (nevents, nobs) instead of a list of tensors with (nevents,)\n\nImprovements\n------------\n - refactor the sampling for more flexibility and performance (less graph constructed)\n - allow to use more sophisticated importance sampling (e.g. phasespace)\n - on-the-fly normalization (experimentally) implemented with correct gradient\n\n\n\n0.3.1 (30-04-19)\n================\n\n\nMinor improvements and bugfixes including:\n\n- improved importance sampling allowing to preinstantiate objects before it's called inside the while loop\n- fixing a problem with `ztf.sqrt`\n\n\n\n0.3.0 (2019-03-20)\n==================\n\n\nBeta stage and first pip release\n\n\n0.0.1 (2018-03-22)\n==================\n\n\n* First creation of the package.\n\n\n",
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