Zfit

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

Update to fix iminuit version

Bug fixes and small changes
---------------------------
- Fix issue when using a ``ComposedParameter`` as the ``rate`` argument of a ``Poisson`` PDF

Requirement changes
-------------------
- require iminuit < 2 to avoid breaking changes

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

Upgrade to TensorFlow 2.3 and support for weighted hessian error estimation.

Added a one dimensional Convolution PDF

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

- upgrad to TensorFlow 2.3

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

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

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

- print parameter inside function context works now correctly

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

- Computation of the covariance matrix and hessian errors with weighted data
- Convolution PDF (FFT in 1Dim) added (experimental, feedback welcome!)

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

- TensorFlow==2.3 (before 2.2)
- ``tensorflow_probability==0.11``
- tensorflow-addons spline interpolation in convolution


Thanks
------

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


Major Features and Improvements
-------------------------------
- completely new doc design

Breaking changes
------------------
- Minuit uses its own, internal gradient by default. To change this back, use ``use_minuit_grad=False``
- ``minimize(params=...)`` now filters correctly non-floating parameters.
- ``z.log`` has been moved to ``z.math.log`` (following TF)


Bug fixes and small changes
---------------------------
- ncalls is not correctly using the internal heuristc or the ncalls explicitly
- ``minimize(params=...)`` automatically extracts independent parameters.
- fix copy issue of KDEV1 and change name to 'adaptive' (instead of 'adaptiveV1')
- change exp name of ``lambda_`` to lam (in init)
- add ``set_yield`` to BasePDF to allow setting the yield in place
- Fix possible bug in SumPDF with extended pdfs (automatically)

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

Requirement changes
-------------------
- upgrade to iminuit>=1.4
- remove cloudpickle hack fix

Thanks
------
Johannes for the docs re-design

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

Kernel density estimation for 1 dimension.

Major Features and Improvements
-------------------------------
- add correlation method to FitResult
- Gaussian (Truncated) Kernel Density Estimation in one dimension ``zfit.pdf.GaussianKDE1DimV1`` implementation with fixed and
adaptive bandwidth added as V1. This
is a feature that needs to be improved and feedback is welcome
- Non-relativistic Breit-Wigner PDF, called Cauchy, implementation added.

Breaking changes
------------------
- change human-readable name of ``Gauss``, ``Uniform`` and ``TruncatedGauss`` to remove the ``'_tfp'`` at the end of the name



Bug fixes and small changes
---------------------------
- fix color wrong in printout of results, params
- packaging: moved to pyproject.toml and a setup.cfg mainly, development requirements can
be installed with the ``dev`` extra as (e.g.) ``pip install zfit[dev]``
- Fix shape issue in TFP distributions for partial integration
- change zfit internal algorithm (``zfit_error``) to compute error/intervals from the profile likelihood,
which is 2-3 times faster than previous algorithm.
- add ``from_minuit`` constructor to ``FitResult`` allowing to create it when
using directly iminuit
- fix possible bias with sampling using accept-reject

Requirement changes
-------------------
- pin down cloudpickle version (upstream bug with pip install) and TF, TFP versions

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


Major Features and Improvements
-------------------------------
- Python 3.8 and TF 2.2 support
- easier debugigng with ``set_graph_mode`` that can also be used temporarily
with a context manager. False will make everything execute Numpy-like.

Bug fixes and small changes
---------------------------
- added ``get_params`` to loss
- fix a bug with the ``fixed_params`` when creating a sampler
- improve exponential PDF stability and shift when normalized
- improve accept reject sampling to account for low statistics


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

- TensorFlow >= 2.2

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