Mzfit

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**Special release for conda deployment and version fix (TF 2.0 is out)**

**This is the last release before breaking changes occur**


Major Features and Improvements
-------------------------------
- added ConstantParameter and ``zfit.param`` namespace
- Available on conda-forge

Behavioral changes
------------------
- an implicitly created parameter with a Python numerical (e.g. when instantiating a model)
will be converted to a ConstantParameter instead of a fixed Parameter and therefore
cannot be set to floating later on.

Bug fixes and small changes
---------------------------
- added native support TFP distributions for analytic sampling
- fix Gaussian (TFP Distribution) Constraint with mixed up order of parameters

- ``from_numpy`` automatically converts to default float regardless the original numpy dtype,
``dtype`` has to be used as an explicit argument


Requirement changes
-------------------
- TensorFlow >= 1.14 is required


Thanks
------
- Chris Burr for the conda-forge deployment

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**This is the last release before breaking changes occur**

Major Features and Improvements
-------------------------------
- create ``Constraint`` class which allows for more fine grained control and information on the applied constraints.
- Added Polynomial models
- Improved and fixed sampling (can still be slightly biased)

Behavioral changes
------------------
None

Bug fixes and small changes
---------------------------

- fixed various small bugs

Thanks
------
for the contribution of the Constraints to Matthieu Marinangeli <matthieu.marinangelicern.ch>

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Fixed Partial numeric integration

Bugfixes mostly, a few major fixes. Partial numeric integration works now.

Bugfixes
- data_range cuts are now applied correctly, also in several dimensions when a subset is selected
(which happens internally of some Functors, e.g. ProductPDF). Before, only the selected obs was respected for cuts.
- parital integration had a wrong take on checking limits (now uses supports).

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With 0.3.2, bugfixes and three changes in the API/behavior

Breaking changes
----------------
- tfp distributions wrapping is now different with dist_kwargs allowing for non-Parameter arguments (like other dists)
- sampling allows now for importance sampling (sampler in Model specified differently)
- ``model.sample`` now also returns a tensor, being consistent with ``pdf`` and ``integrate``

Bugfixes
--------
- 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,)

Improvements
------------
- refactor the sampling for more flexibility and performance (less graph constructed)
- allow to use more sophisticated importance sampling (e.g. phasespace)
- on-the-fly normalization (experimentally) implemented with correct gradient

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Minor improvements and bugfixes including:

- improved importance sampling allowing to preinstantiate objects before it's called inside the while loop
- fixing a problem with ``ztf.sqrt``

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Beta stage and first pip release