Zfit

========

Major Features and Improvements
-------------------------------
- Save results by pickling, unpickling a frozen (``FitResult.freeze()``) result and using
``zfit.param.set_values(params, result)`` to set the values of ``params``.



Deprecations
-------------
- the default name of the uncertainty methods ``hesse`` and ``errors`` depended on
the method used (such as ``"minuit_hesse"``, ``"zfit_errors"`` etc.) and would be the exact method name.
New names are now 'hesse' and 'errors', independent of the method used. This reflects better that the
methods, while internally different, produce the same result.
To update, use 'hesse' instead of 'minuit_hesse' or 'hesse_np' and 'errors' instead of 'zfit_errors'
or ``"minuit_minos"`` in order to access the uncertainties in the fitresult.
Currently, the old names are still available for backwards compatibility.
If a name was explicitly chosen in the error method, nothing changed.

Bug fixes and small changes
---------------------------
- KDE datasets are now correctly mirrored around observable space limits
- multinomial sampling would return wrong results when invoked multiple times in graph mode due to
a non-dynamic shape. This is fixed and the sampling is now working as expected.
- increase precision in FitResult string representation and add that the value is rounded


Thanks
------
- schmitse for finding and fixing a mirroring bug in the KDEs
- Sebastian Bysiak for finding a bug in the multinomial sampling

========

Major Features and Improvements
-------------------------------

- Binned fits support, although limited in content, is here! This includes BinnedData, binned PDFs, and
binned losses. TODO: extend to include changes/point to binned introduction.
- new Poisson PDF
- added Poisson constraint, LogNormal Constraint
- Save results by pickling, unpickling a frozen (``FitResult.freeze()``) result and using
``zfit.param.set_values(params, result)`` to set the values of ``params``.

Breaking changes
------------------

- params given in ComposedParameters are not sorted anymore. Rely on their name instead.
- ``norm_range`` is now called ``norm`` and should be replaced everywhere if possible. This will break in
the future.

Deprecation
-------------

Bug fixes and small changes
---------------------------
- remove warning when using ``rect_limits`` or similar.
- gauss integral accepts now also tensor inputs in limits
- parameters at limits is now shown correctly

Experimental
------------

Requirement changes
-------------------
- add TensorFlow 2.7 support

Thanks
------

===================
- fixate nlopt to < 2.7.1

====================

Bug fixes and small changes
---------------------------
- fixed a longstanding bug in the DoubleCB implementation of the integral.
- remove outdated deprecations

======================

Major Features and Improvements
-------------------------------

- allow ``FitResult`` to ``freeze()``, making it pickleable. The parameters
are replaced by their name, the objects such as loss and minimizer as well.
- improve the numerical integration by adding a one dimensional efficient integrator, testing for the accuracy of
multidimensional integrals. If there is a sharp peak, this maybe fails to integrate and the number of points
has to be manually raised
- add highly performant kernel density estimation (mainly contributed by Marc Steiner)
in 1 dimension which allow
for the choice of arbitrary kernels, support
boundary mirroring of the data and allow for large (millions) of data samples:
- :class:`~zfit.pdf.KDE1DimExact` for the normal density estimation
- :class:`~zfit.pdf.KDE1DimGrid` using a binning
- :class:`~zfit.pdf.KDE1DimFFT` using a binning and FFT
- :class:`~zfit.pdf.KDE1DimISJ` using a binning and an algorithm (ISJ) to solve the optimal bandwidth

For an introduction, see either :ref:`sec-kernel-density-estimation` or the tutorial :ref:`sec-components-model`

- add windows in CI

Breaking changes
------------------
- the numerical integration improved with more sensible values for tolerance. This means however that some fits will
greatly increase the runtime. To restore the old behavior globally, do
for each instance ``pdf.update_integration_options(draws_per_dim=40_000, max_draws=40_000, tol=1)``
This will integrate regardless of the chosen precision and it may be non-optimal.
However, the precision estimate in the integrator is also not perfect and maybe overestimates the error, so that
the integration by default takes longer than necessary. Feel free to play around with the parameters and report back.


Bug fixes and small changes
---------------------------
- Double crystallball: move a minus sign down, vectorize the integral, fix wrong output shape of pdf
- add a minimal value in the loss to avoid NaNs when taking the log of 0
- improve feedback when taking the derivative with respect to a parameter that
a function does not depend on or if the function is purely Python.
- make parameters deletable, especially it works now to create parameters in a function only
and no NameAlreadyTakenError will be thrown.


Requirement changes
-------------------

- add TensorFlow 2.6 support (now 2.5 and 2.6 are supported)

Thanks
------
- Marc Steiner for contributing many new KDE methods!

======================

Bug fixes and small changes
---------------------------
- fix wrong arguments to ``minimize``
- make BaseMinimizer arguments optional