Non-Linear Least Squares Minimization, with flexible Parameter settings, based on scipy.optimize, and with many additional classes and methods for curve fitting.
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__().
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 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)
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)
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:
create_params function (PR #844)chi2_out and nsigma options to conf_interval2d()
ModelResult.summary() to return many resulting fit statistics and attributes into a JSON-able dict.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:
param.vary=True for a constrained parameter (Issue #859; PR #860)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)epsfcn is increased to 1.e-10 to allow for handling of data with precision less than float64
(Issue #850; PR #853)conf_interval2d to use "increase chi-square by sigma**2*reduced chi-square" to give the sigma-level
probabilities (Issue #848; PR #852)ModelResult (Issue #845; included in PR #844)Parameters and user data (mguhyo; PR #837)Model.make_params and create_params to take optional dict of Parameter attributes (PR #844)nfev from least_squares to better reflect actual number of function calls (Issue #842; PR #844)Model.eval when mixing parameters and keyword arguments (PR #844, #839)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)New features:
create_params function (PR #844)chi2_out and nsigma options to conf_interval2d()
ModelResult.summary() to return many resulting fit statistics and attributes into a JSON-able dict.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:
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)epsfcn is increased to 1.e-10 to allow for handling of data with precision less than float64
(Issue #850; PR #853)conf_interval2d to use "increase chi-square by sigma**2*reduced chi-square" to give the sigma-level
probabilities (Issue #848; PR #852)ModelResult (Issue #845; included in PR #844)Parameters and user data (mguhyo; PR #837)Model.make_params and create_params to take optional dict of Parameter attributes (PR #844)nfev from least_squares to better reflect actual number of function calls (Issue #842; PR #844)Model.eval when mixing parameters and keyword arguments (PR #844, #839)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)Supported Python Versions: 3.7, 3.8, 3.9, 3.10, 3.11 Minimal requirements: numpy>=1.19, scipy>=1.6, uncertainties>=3.1.4, asteval>=0.9.28
New features:
Pearson4Model (@lellid; PR #800)SplineModel (PR #804)rsquared statistic to fit outputs and reports for Model fits (Issue #803; PR #810)dely for model components of composite models (Issue #761; PR #826)Bug fixes/enhancements:
spercent is always defined in params_html_table functions (reported by @MySlientWind; Issue #768, PR #770)success and covar the MinimizerResult (reported by Marc W. Pound; PR #771)pyproject.toml and setup.cfg; leave setup.py for now (PR #777)CompositeModel can now have different independent variables (@Julian-Hochhaus; Discussion #787; PR #788)StepModel(form='linear'), was not consistent with the other ones (@matpompili; PR #794)Gaussian2dModel, was not correct (@matpompili; PR #795)None (PR #813)RectangleModel (@arunpersaud; Issue #815; PR #816)Deprecations:
New features:
dely for model components of composite models (Issue #761; PR #826)rsquared statistic to fit outputs and reports for Model fits (Issue #803; PR #810)SplineModel (PR #804)Pearson4Model (@lellid; PR #800)Bug fixes/enhancements:
spercent is always defined in params_html_table functions (reported by @MySlientWind; Issue #768, PR #770)success and covar the MinimizerResult (reported by Marc W. Pound; PR #771)pyproject.toml and setup.cfg; leave setup.py for now (PR #777)CompositeModel can now have different independent variables (@Julian-Hochhaus; Discussion #787; PR #788))StepModel(form='linear'), was not consistent with the other ones. (@matpompili; PR #794)Gaussian2dModel, was not correct. (@matpompili; PR #795)None (PR #813)RectangleModel (@arunpersaud; Issue #815; PR #816)Deprecations:
Potentially breaking change:
x is now required for the guess method of Models (Issue #747; PR #748)To get reasonable estimates for starting values one should always supply both x and y values; in some cases it would work
when only providing data (i.e., y-values). With the change above, x is now required in the guess method call, so scripts might
need to be updated to explicitly supply x.
Bug fixes/enhancements:
title keyword argument (PR #711)data and indepdent_vars to NumPy array with dtype=float64 or dtype=complex128 where applicable (Issues #723 and #728)max_nfev to maxiter when using differential_evolution (PR #749, reported by Olivier B.)eval method more precisely and allow for plotting of (Complex)ConstantModel by coercing their
float, int, or complex return value to a numpy.ndarray (Issue #684 and PR #754)dho (Damped Harmonic Oscillator) lineshape (PR #755; @rayosborn)Minimizer._abort to False before starting a new fit (Issue #756 and PR #757; @azelcer)guess_from_peak2d (@ivan-usovl; PR #758)Various:
DonaichModel and donaich lineshape, deprecated in version 1.0.1 (PR #707)name argument in Parameters.add (@kristianmeyerr; PR #725)setuptools_scm for version info instead of versioneer (PR #729)f-strings (PR #730)test_manypeaks_speed.py as flaky to avoid intermittent test failures (repeat up to 5 times; PR #745)lmfit.printfuncs.report_errors and asteval argument in Parameters class (PR #759)