Lmfit

Fixes:

- fix loading spline models with more than five knots (Issue 985)
- improved SplineModel to explicitly allow more knots, make it easier to evaluate and save/reload (Issue 985 PR 989, Paul Müller)
- improvements to adding Parameters.
- support Model functions with "barestar" syntax (PR 982)
- fix several related problems with providing a Jacobian function, especially for consistency across solvers (including least_squares), and for pickling (PR 973 Ville Yrjänä)
- fix Step and Rectangle Models to allow a negative value for sigma, indicating a downward step (PR 970)

Build, Maintenance:

- update issue templates
- add test for consistent init_fit and best_fit for saved/looded SplineModel
- fix NumPy v2 DeprecationWarning
- uppdate SciPy/NumPy dependencies.
- remove numexpr dependency (again)
- drop support for Python 3.8, add Python 3.13
- asteval no longer raises NameError to Python, so we catch exceptions from asteval when creating parameters.
- avoid setting ``stderr`` to ``None`` for uncertainties calculations.

Documentation and Examples:

- add example with uncertainties in both x and y (992)
- make sign of residual calculations in model.py consistent with documentation (Discussion 986, PR987, Timothy-J-Warner)
- add example fitting multiple datasets using Model interface (Discussion 904, PR 967, mstekiel)
- tweaks to 'sphinx-gallery' settings.
- update names of the documentation examples in Gallery

Fixes:
* fix typo in restoring a `_buildmodel` dict (PR 957, Issue 956)
* fixes for Numpy2 support (PR 959, Issue 958)
* ensure that correct initial params are used when re-fitting a ModeRresult (PR 961, Issue 960)
* make sure that CompositeModels cannot have a prefix (PR 961, Issue 954)

Build, Maintenance:
* update pre-commit hooks, adding codespell exceptions
* update to latest SciPy/NumPy versions, including dependency versions for NumPy 2.
* now require asteval>=1.0 and uncertainties>=3.2.2
*

----------------------------------------------

New features:

- add ``'min_rel_change'`` as optional variable in calculation of confidence intervals with
``Model.conf_interval()``. (PR 937).

- ``Model.eval_uncertainty`` now takes an optional ``dscale`` parameter (default value of 0.01) to
set the step size for calculating derivatives (PR 933).

- add calculation of ``predicted_interval`` to ``Model.eval_uncertainty`` (PR 933).


Bug fixes/enhancements:

- restore best-fit parameter values for high accuracy values of constrained values (PR 907)

- improvement to Model for the difference between Parameter, "independent variable", and
"option". With this change, keyword arguments to model functions with non-numerice
default values such as ``do_thing=True``, or ``form='linear'`` has those arguments
become clearly identified as independent variables,and use the provided values as
default values. (PR 941)

- better saving/loading saved states of Model now use dill, have several cleanups, and
are now versioned for future-proofing. Also, propagate funcdets for Parameters when
loading a Model. (PR 932, PR 934)

- in the TNC method, ``maxfun`` is used instead of ``maxiter``.

- fix bug calculating r-squared for fits with weights (PR 921, PR 923)

- fix bug in ``modelresult.eval_uncertainty()`` after ``load_modelresult()`` (PR 909)

- use StringIO for ``pandas.read_json``.

- add test for MinimizerResult.uvars after successful fit (PR 913)

- adding an example using basinhopping, can take other methods as command-line argument

Maintenance/Deprecations:

- drop support for Python 3.7 that reached EOL on 2023-06-27 (PR 927)

- fix tests for Python 3.12 and Python 3.13-dev

- increase minimum numpy verstio to 1.23 and scipy to 1.8.

- updates for compatibility with numpy 2.0

- the ``dill`` package is now required. (940)

- build switchded to use pyproject.toml (928)

- fix broken links in Examples gallery

- fix intersphinx mapping to scipy docs.

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

New features:

- add ``ModelResult.uvars`` output to a ``ModelResult`` after a successful fit
that contains ``ufloats`` from the ``uncertainties`` package which can be
used for downstream calculations that propagate the uncertainties (and
correlations) of the variable Parameters. (PR 888)

- Outputs of residual functions, including ``Model._residual``, are more
explicitly coerced to 1d-arrays of dataype Float64. This decreases the
expectation for the user-supplied code to return ndarrays, and increases the
tolerance for more "array-like" objects or ndarrays that are not Float64 or
1-dimensional. (PR 899)

- ``Model.fit`` now takes a ``coerce_farray`` option, defaulting to ``True`` to
control whether to input data and independent variables that are "array-like"
are coerced to ndarrays of datatype Float64 or Complex128. If set to
``False`` then independent data that "array-like" (``pandas.Series``, int32
arrays, etc) will be sent to the model function unaltered. The user may then
use other features of these objects, but may also need to explicitly coerce
the datatype of the result the change described above about coercing the
result causes problems. (Discussion 873; PR 899)

Bug fixes/enhancements:

- fixed bug in ``Model.make_params()`` for non-composite models that use a
prefix (Discussion 892; Issue 893; PR 895)

- fixed bug with aborted fits for several methods having incorrect or invalid
fit statistics. (Discussion 894; Issue 896; PR 897)

- ``Model.eval_uncertainty`` now correctly calculates complex (real/imaginary pairs)
uncertainties for Models that generate complex results. (Issue 900; PR 901)

- ``Model.eval`` now returns and array-like value. This adds to the coercion
features above and fixes a bug for composite models that return lists (Issue 875; PR 901)

- the HTML representation for a ``ModelResult`` or ``MinimizerResult`` are
improved, and create fewer entries in the Table of Contents for Jupyter lab.
(Issue 884; PR 883; PR 902)