Mostly maintenance and small bug fixes:

• capture and ignore std_dev=0 warnings from uncertainties package
• better ensure that parameter values and sterr are floats, not np.floats
• fix Doniach formula in docstring (#966)
• add bose and fermi lineshapes, and BoseModel and FermiModel (with tests)
• remove deprecated disp argument for ampgo
• fix typos in README.rst
• fix typos in doc
• update to latest SciPy/NumPy version
• update pre-commit and CI hooks
• update zenodo link
• several fixes for deprecations and test failures
• do not test for warnings from upstream code that might change or be suppressed or altered
• update license name for PyPI compatibiilty

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, PR#987, 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

Mostly fixes for bugs introduced in 1.3.0

• allow Model.eval_uncertainty to be performed with single points for x independent variables (PR #952, Issue #951)

• allow Model._parse_param to handle older-style passed-in 'argnames' and 'kwargs' as for variadic function, add test (PR #950)

• better allow (or re-allow) Model function independent variables / keyword argumentss to be given non-default values at model creation time

• add form as independent variable for builtin Step, Rectangle, Thermal Distribution models.

• use a copy of sys.modules when iterating over it. (#949)

• use Model._reprstring(long=True) for model Model.__repr__().

Version 1.3.0 Release Notes (April 4, 2024)

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.

Version 1.2.2 Release Notes (July 14, 2023)

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 datatype 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)

Version 1.2.2 Release Notes (July 14, 2023)

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)

Version 1.2.1 Release Notes (May 02, 2023)

Bug fixes/enhancements:

• fixed bug in Model.make_params() for initial parameter values that were
  not recognized as floats such as np.Int64. (Issue #871; PR #872)

• explicitly set maxfun for l-bfgs-b method when setting
  maxiter. (Issue #864; Discussion #865; PR #866)

New features:

• add create_params function (PR #844)
• add chi2_out and nsigma options to conf_interval2d()
• add ModelResult.summary() to return many resulting fit statistics and attributes into a JSON-able dict.
• add correl_table() function to lmfit.printfuncs and correl_mode option to fit_report() and
  ModelResult.fit_report() to optionally display a RST-formatted table of a correlation matrix.

Bug fixes/enhancements:

• fix bug when setting param.vary=True for a constrained parameter (Issue #859; PR #860)
• fix bug in reported uncertainties for constrained parameters by better propating uncertainties (Issue #855; PR #856)
• Coercing of user input data and independent data for Model to float64 ndarrays is somewhat less aggressive and
  will not increase the precision of numpy ndarrays (see :ref:model_data_coercion_section for details). The resulting
  calculation from a model or objective function is more aggressively coerced to float64. (Issue #850; PR #853)
• the default value of epsfcn is increased to 1.e-10 to allow for handling of data with precision less than float64
  (Issue #850; PR #853)
• fix conf_interval2d to use "increase chi-square by sigma**2*reduced chi-square" to give the sigma-level
  probabilities (Issue #848; PR #852)
• fix reading of older ModelResult (Issue #845; included in PR #844)
• fix deepcopy of Parameters and user data (mguhyo; PR #837)
• improve Model.make_params and create_params to take optional dict of Parameter attributes (PR #844)
• fix reporting of nfev from least_squares to better reflect actual number of function calls (Issue #842; PR #844)
• fix bug in Model.eval when mixing parameters and keyword arguments (PR #844, #839)
• re-adds residual to saved Model result (PR #844, #830)
• ConstantModel and ComplexConstantModel will return an ndarray of the same shape as the independent variable
  x (JeppeKlitgaard, Issue #840; PR #841)
• update tests for latest versions of NumPy and SciPy.
• many fixes of doc typos and updates of dependencies, pre-commit hooks, and CI.

Version 1.2.0rc1 Release Notes (April 03, 2023)

New features:

• add create_params function (PR #844)
• add chi2_out and nsigma options to conf_interval2d()
• add ModelResult.summary() to return many resulting fit statistics and attributes into a JSON-able dict.
• add correl_table() function to lmfit.printfuncs and correl_mode option to fit_report() and
  ModelResult.fit_report() to optionally display a RST-formatted table of a correlation matrix.

Bug fixes/enhancements:

• fix bug in reported uncertainties for constrained parameters by better propating uncertainties (Issue #855; PR #856)
• Coercing of user input data and independent data for Model to float64 ndarrays is somewhat less aggressive and
  will not increase the precision of numpy ndarrays (see :ref:model_data_coercion_section for details). The resulting
  calculation from a model or objective function is more aggressively coerced to float64. (Issue #850; PR #853)
• the default value of epsfcn is increased to 1.e-10 to allow for handling of data with precision less than float64
  (Issue #850; PR #853)
• fix conf_interval2d to use "increase chi-square by sigma**2*reduced chi-square" to give the sigma-level
  probabilities (Issue #848; PR #852)
• fix reading of older ModelResult (Issue #845; included in PR #844)
• fix deepcopy of Parameters and user data (mguhyo; PR #837)
• improve Model.make_params and create_params to take optional dict of Parameter attributes (PR #844)
• fix reporting of nfev from least_squares to better reflect actual number of function calls (Issue #842; PR #844)
• fix bug in Model.eval when mixing parameters and keyword arguments (PR #844, #839)
• re-adds residual to saved Model result (PR #844, #830)
• ConstantModel and ComplexConstantModel will return an ndarray of the same shape as the independent variable
  x (JeppeKlitgaard, Issue #840; PR #841)
• update tests for latest versions of NumPy and SciPy.
• many fixes of doc typos and updates of dependencies, pre-commit hooks, and CI.