`fitting`

Modules:

Name	Description
`calculators`
`fitter`
`minimizers`
`multi_fitter`

Classes

Modules

`calculators`

Modules:

Name	Description
`interface_factory`

Classes

Modules

`interface_factory`

Classes:

Name	Description
`InterfaceFactoryTemplate`	This class allows for the creation and transference of interfaces.

Classes

InterfaceFactoryTemplate(interface_list, *args, **kwargs)

This class allows for the creation and transference of interfaces.

Methods:

Name	Description
`create`	Create an interface to a calculator from those initialized.
`switch`	Changes the current interface to a new interface.
`generate_bindings`	Automatically bind a `Parameter` to the corresponding
`return_name`	Return an interfaces name.

Attributes:

Name	Type	Description
`available_interfaces`	`List[str]`	Return all available interfaces.
`current_interface`	`ABCMeta`	Returns the constructor for the currently selected interface.
`current_interface_name`	`str`	Returns the constructor name for the currently selected
`fit_func`	`Callable`	Pass through to the underlying interfaces fitting function.

Attributes

available_interfaces property

Return all available interfaces.

Returns:

Type	Description
`List[str]`	List of available interface names.

current_interface property

Returns the constructor for the currently selected interface.

Returns:

Type	Description
`ABCMeta`	Interface constructor.

current_interface_name property

Returns the constructor name for the currently selected interface.

Returns:

Type	Description
`str`	Interface constructor name.

fit_func property

Pass through to the underlying interfaces fitting function.

Returns:

Type	Description
`Callable`	Callable proxy to the underlying interface fit function.

Functions

create(*args, interface_name=None, **kwargs)

Create an interface to a calculator from those initialized.

Interfaces can be selected by interface_name where interface_name is one of obj.available_interfaces. This interface can now be accessed by obj().

Parameters:

Name	Type	Description	Default
`*args`	`Any`	Positional arguments forwarded to the interface constructor.	`()`
`interface_name`	`str \| None`	Name of interface to be created.	`None`
`**kwargs`	`Any`	Keyword arguments forwarded to the interface constructor.	`{}`

Raises:

Type	Description
`NotImplementedError`	If no interfaces are available to instantiate.

switch(new_interface, fitter=None)

Changes the current interface to a new interface.

The current interface is destroyed and all SerializerComponent parameters carried over to the new interface. i.e. pick up where you left off.

Parameters:

Name	Type	Description	Default
`new_interface`	`str`	Name of new interface to be created.	required
`fitter`	`Optional[Type[Fitter]]`	Fitting interface which contains the fitting object which may have bindings which will be updated. By default, None.	`None`

Raises:

Type	Description
`AttributeError`	If `new_interface` is not a valid interface name.

generate_bindings(model, *args, ifun=None, **kwargs)

Automatically bind a Parameter to the corresponding interface.

Parameters:

Name	Type	Description	Default
`model`	`Any`	Model whose linkable attributes should be bound.	required
`*args`	`Any`	Positional arguments reserved for interface-specific binding hooks.	`()`
`ifun`	`Any`	Optional interface hook. By default, None.	`None`
`**kwargs`	`Any`	Keyword arguments reserved for interface-specific binding hooks.	`{}`

return_name(this_interface) staticmethod

Return an interfaces name.

Modules

`fitter`

Classes:

Name	Description
`Fitter`	Fitter is a class which makes it possible to undertake fitting

Classes

`Fitter(fit_object, fit_function)`

Fitter is a class which makes it possible to undertake fitting utilizing one of the supported minimizers.

Methods:

Name	Description
`initialize`	Set the model and callable in the calculator interface.
`create`	Create the required minimizer.
`switch_minimizer`	Switch minimizer and initialize.
`mcmc_sample`	Run Bayesian MCMC sampling using the BUMPS DREAM sampler.

Attributes:

Name	Type	Description
`available_minimizers`	`List[str]`	Get a list of the names of available fitting minimizers.
`minimizer`	`MinimizerBase`	Get the current fitting minimizer object.
`tolerance`	`float`	Get the tolerance for the minimizer.
`max_evaluations`	`int`	Get the maximal number of evaluations for the minimizer.
`fit_function`	`Callable`	Get the raw fit function that the optimizer will call.
`fit_object`	`object`	Get the EasyScience object used as a model.
`fit`	`Callable`	Property which wraps the current `fit` function from the

Attributes

available_minimizers property

Get a list of the names of available fitting minimizers.

Returns:

Type	Description
`List[str]`	List of available fitting minimizers.

minimizer property

Get the current fitting minimizer object.

Returns:

Type	Description
`MinimizerBase`

tolerance property writable

Get the tolerance for the minimizer.

Returns:

Type	Description
`float`	Tolerance for the minimizer.

max_evaluations property writable

Get the maximal number of evaluations for the minimizer.

Returns:

Type	Description
`int`	Maximal number of steps for the minimizer.

fit_function property writable

Get the raw fit function that the optimizer will call.

Returns:

Type	Description
`Callable`	Raw fit function.

fit_object property writable

Get the EasyScience object used as a model.

Returns:

Type	Description
`object`	EasyScience model object.

fit property

Property which wraps the current fit function from the fitting interface.

This property return a wrapped fit function which converts the input data into the correct shape for the optimizer, wraps the fit function to re-constitute the independent variables and once the fit is completed, reshape the inputs to those expected.

Functions

initialize(fit_object, fit_function)

Set the model and callable in the calculator interface.

Parameters:

Name	Type	Description	Default
`fit_object`	`object`	The EasyScience model object.	required
`fit_function`	`Callable`	The function to be optimized against.	required

create(minimizer_enum=DEFAULT_MINIMIZER)

Create the required minimizer.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Union[AvailableMinimizers, str]`	The enum of the minimization engine to create. By default, DEFAULT_MINIMIZER.	`DEFAULT_MINIMIZER`

switch_minimizer(minimizer_enum)

Switch minimizer and initialize.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Union[AvailableMinimizers, str]`	The enum of the minimizer to create and instantiate.	required

mcmc_sample(x, y, weights, samples=10000, burn=2000, thin=10, population=None, vectorized=False, sampler_kwargs=None, progress_callback=None, abort_test=None)

Run Bayesian MCMC sampling using the BUMPS DREAM sampler.

Works with both a plain Fitter (single dataset) and a MultiFitter (multiple datasets) via polymorphic dispatch: _precompute_reshaping and _fit_function_wrapper are resolved on the concrete subclass at call time, so multi-dataset flattening is handled automatically when called on a MultiFitter instance.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array (or list of arrays for `MultiFitter`).	required
`y`	`np.ndarray`	Dependent variable array (or list of arrays for `MultiFitter`).	required
`weights`	`np.ndarray`	Weight array (or list of arrays for `MultiFitter`).	required
`samples`	`int`	Number of retained DREAM samples requested from BUMPS.	`10000`
`burn`	`int`	Burn-in steps to discard before collecting samples.	`2000`
`thin`	`int`	Thinning interval — only every `thin`-th sample is kept, which reduces autocorrelation between consecutive draws.	`10`
`population`	`Optional[int]`	BUMPS DREAM population count (number of parallel chains).	`None`
`vectorized`	`bool`	When `True`, each x array may be multi-dimensional (e.g. an `(N, M, 2)` grid for a 2D model) and is left as-is. When `False` (default), each x array is expected to be 1-D.	`False`
`sampler_kwargs`	`Optional[dict]`	Additional keyword arguments forwarded to the BUMPS DREAM sampler.	`None`
`progress_callback`	`Optional[Callable[[dict], Optional[bool]]]`	Optional callback invoked at each DREAM generation. The payload dict includes `iteration` and `sampling: True`.	`None`
`abort_test`	`Optional[Callable[[], bool]]`	Optional callable that returns `True` to abort sampling early.	`None`

Returns:

Type	Description
`dict`	Dictionary with keys `'draws'`, `'param_names'`, `'internal_bumps_object'`, and `'logp'`.

Raises:

Type	Description
`ValueError`	If `samples`, `burn`, or `thin` are invalid.
`RuntimeError`	If the active minimizer is not a BUMPS instance.

Modules

`minimizers`

Modules:

Name	Description
`bumps_utils`
`minimizer_base`
`minimizer_bumps`
`minimizer_dfo`
`minimizer_lmfit`
`utils`

Classes

Modules

`bumps_utils`

Modules:

Name	Description
`eval_counter`
`progress_monitor`

Classes:

Name	Description
`EvalCounter`	Wrap a callable so the number of invocations is recorded on
`BumpsProgressMonitor`	BUMPS :class:`Monitor` that forwards per-step progress information

Classes

EvalCounter(fn)

Wrap a callable so the number of invocations is recorded on count.

Used by the BUMPS minimizer to count objective-function evaluations for cross-backend consistency with LMFit (nfev) and DFO-LS (nf).

BumpsProgressMonitor(problem, callback, payload_builder)

BUMPS :class:Monitor that forwards per-step progress information to a user-supplied callback.

The monitor delegates payload construction to payload_builder so the BUMPS minimizer can keep all backend-specific payload semantics in one place.

Modules

eval_counter

Classes:

Name	Description
`EvalCounter`	Wrap a callable so the number of invocations is recorded on

Classes

EvalCounter(fn)

Wrap a callable so the number of invocations is recorded on count.

Used by the BUMPS minimizer to count objective-function evaluations for cross-backend consistency with LMFit (nfev) and DFO-LS (nf).

progress_monitor

Classes:

Name	Description
`BumpsProgressMonitor`	BUMPS :class:`Monitor` that forwards per-step progress information

Classes

BumpsProgressMonitor(problem, callback, payload_builder)

BUMPS :class:Monitor that forwards per-step progress information to a user-supplied callback.

The monitor delegates payload construction to payload_builder so the BUMPS minimizer can keep all backend-specific payload semantics in one place.

`minimizer_base`

Classes:

Name	Description
`MinimizerBase`	This template class is the basis for all minimizer engines in

Classes

MinimizerBase(obj, fit_function, minimizer_enum)

This template class is the basis for all minimizer engines in EasyScience.

Methods:

Name	Description
`fit`	Perform a fit using the engine.
`evaluate`	Evaluate the fit function for values of x.
`convert_to_pars_obj`	Create an engine compatible container with the `Parameters`
`supported_methods`	Return a list of supported methods for the minimizer.
`all_methods`	Return a list of all available methods for the minimizer.
`convert_to_par_object`	Convert an `EasyScience.variable.Parameter` object to an

Functions

fit(x, y, weights, model=None, parameters=None, method=None, tolerance=None, max_evaluations=None, progress_callback=None, **kwargs)

abstractmethod

Perform a fit using the engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Points to be calculated at.	required
`y`	`np.ndarray`	Measured points.	required
`weights`	`np.ndarray`	Weights for supplied measured points.	required
`model`	`Callable \| None`	Optional Model which is being fitted to. By default, None.	`None`
`parameters`	`List[Parameter] \| None`	Optional parameters for the fit. By default, None.	`None`
`method`	`str \| None`	Method for the minimizer to use. By default, None.	`None`
`tolerance`	`float \| None`	Requested convergence tolerance. By default, None.	`None`
`max_evaluations`	`int \| None`	Maximum number of objective evaluations. By default, None.	`None`
`progress_callback`	`Callable[[dict], bool \| None] \| None`	Optional progress callback. By default, None.	`None`
`**kwargs`		Additional arguments for the fitting function.	`{}`

Returns:

Type	Description
`FitResults`	Fit results.

evaluate(x, minimizer_parameters=None, **kwargs)

Evaluate the fit function for values of x.

Parameters used are either the latest or user supplied. If the parameters are user supplied, it must be in a dictionary of {'parameter_name': parameter_value,...}.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	X values for which the fit function will be evaluated.	required
`minimizer_parameters`	`dict[str, float] \| None`	Dictionary of parameters which will be used in the fit function. They must be in a dictionary of {'parameter_name': parameter_value,...}. By default, None.	`None`
`**kwargs`		Additional arguments.	`{}`

Returns:

Type	Description
`np.ndarray`	Y values calculated at points x for a set of parameters.

Raises:

Type	Description
`TypeError`	If `minimizer_parameters` is not a dictionary.

convert_to_pars_obj(par_list=None) abstractmethod

Create an engine compatible container with the Parameters converted from the base object.

Parameters:

Name	Type	Description	Default
`par_list`	`List[Parameter] \| None`	If only a single/selection of parameter is required. Specify as a list. By default, None.	`None`

Returns:

Type	Description
`Any`	Engine Parameters compatible object.

supported_methods() abstractmethod staticmethod

Return a list of supported methods for the minimizer.

Returns:

Type	Description
`List[str]`	List of supported methods.

all_methods() abstractmethod staticmethod

Return a list of all available methods for the minimizer.

Returns:

Type	Description
`List[str]`	List of all available methods.

convert_to_par_object(obj) abstractmethod staticmethod

Convert an EasyScience.variable.Parameter object to an engine Parameter object.

`minimizer_bumps`

Classes:

Name	Description
`Bumps`	This is a wrapper to Bumps: https://bumps.readthedocs.io/ It allows

Classes

Bumps(obj, fit_function, minimizer_enum=None)

This is a wrapper to Bumps: https://bumps.readthedocs.io/ It allows for the Bumps fitting engine to use parameters declared in an EasyScience.base_classes.ObjBase.

Parameters:

Name	Type	Description	Default
`obj`	`object`	Object containing the `Parameter` instances to fit.	required
`fit_function`	`Callable`	Callable returning model y values for the supplied x values.	required
`minimizer_enum`	`AvailableMinimizers \| None`	Selected BUMPS minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the BUMPS engine.
`convert_to_pars_obj`	Create a container with the `Parameters` converted from the
`convert_to_par_object`	Convert an `EasyScience.variable.Parameter` object to a bumps
`mcmc_sample`	Run Bayesian MCMC sampling using the BUMPS DREAM sampler.
`evaluate`	Evaluate the fit function for values of x.

Functions

fit(x, y, weights, model=None, parameters=None, method=None, tolerance=None, max_evaluations=None, progress_callback=None, abort_test=None, minimizer_kwargs=None, engine_kwargs=None, **kwargs)

Perform a fit using the BUMPS engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Points to be calculated at.	required
`y`	`np.ndarray`	Measured points.	required
`weights`	`np.ndarray`	Weights for supplied measured points.	required
`model`	`Callable \| None`	Optional Model which is being fitted to. By default, None.	`None`
`parameters`	`list[Parameter] \| None`	Optional parameters for the fit. By default, None.	`None`
`method`	`str \| None`	Method for minimization. By default, None.	`None`
`tolerance`	`float \| None`	Requested optimizer tolerance. By default, None.	`None`
`max_evaluations`	`int \| None`	Maximum number of optimizer steps. Forwarded to BUMPS as its `steps` parameter. If `None`, the default value defined by the selected BUMPS fitter (`fitclass.settings`) is used. By default, None.	`None`
`progress_callback`	`Callable[[dict], bool \| None] \| None`	Optional callback for progress updates. The payload field `iteration` carries the BUMPS optimizer step index. By default, None.	`None`
`abort_test`	`Callable[[], bool] \| None`	Optional callback that returns `True` to signal that the fit should be aborted. Called periodically during the BUMPS optimizer iteration loop.	`None`
`minimizer_kwargs`	`dict \| None`	Additional keyword arguments passed to the BUMPS minimizer. By default, None.	`None`
`engine_kwargs`	`dict \| None`	Additional engine keyword arguments. By default, None.	`None`
`**kwargs`	`Any`	Additional keyword arguments passed to `FitDriver`.	`{}`

Returns:

Type	Description
`FitResults`	Fit results.

Raises:

Type	Description
`FitError`	If the BUMPS fit fails.
`ValueError`	If the input shapes or weights are invalid.

convert_to_pars_obj(par_list=None)

Create a container with the Parameters converted from the base object.

Parameters:

Name	Type	Description	Default
`par_list`	`list[Parameter] \| None`	If only a single/selection of parameter is required. Specify as a list. By default, None.	`None`

Returns:

Type	Description
`list[BumpsParameter]`	Bumps Parameters list.

convert_to_par_object(obj) staticmethod

Convert an EasyScience.variable.Parameter object to a bumps Parameter object.

Parameters:

Name	Type	Description	Default
`obj`	`Parameter`	EasyScience parameter to convert.	required

Returns:

Type	Description
`BumpsParameter`	Bumps Parameter compatible object.

mcmc_sample(x, y, weights, samples=10000, burn=2000, thin=10, population=None, sampler_kwargs=None, progress_callback=None, abort_test=None)

Run Bayesian MCMC sampling using the BUMPS DREAM sampler.

Builds a BUMPS FitProblem from the current model and runs the DREAM sampler. This is the public minimizer-level entry point for Bayesian sampling; the higher-level MultiFitter.mcmc_sample delegates to this method after flattening multi-dataset arrays.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Flattened independent variable array.	required
`y`	`np.ndarray`	Flattened dependent variable array.	required
`weights`	`np.ndarray`	Flattened weight array.	required
`samples`	`int`	Number of retained DREAM samples requested from BUMPS.	`10000`
`burn`	`int`	Burn-in steps.	`2000`
`thin`	`int`	Thinning interval.	`10`
`population`	`int \| None`	BUMPS DREAM population count (number of parallel chains).	`None`
`sampler_kwargs`	`dict \| None`	Additional keyword arguments forwarded to `bumps.fitters.fit`.	`None`
`progress_callback`	`Callable[[dict], bool \| None] \| None`	Optional callback for progress updates during sampling. The payload dict includes `iteration` (DREAM generation number) and `sampling: True`.	`None`
`abort_test`	`Callable[[], bool] \| None`	Optional callback that returns `True` to signal that sampling should be aborted. Called periodically during the DREAM sampling loop.	`None`

Returns:

Type	Description
`dict`	Dictionary with keys `'draws'`, `'param_names'`, `'internal_bumps_object'`, and `'logp'`.

Raises:

Type	Description
`ValueError`	If the input shapes or weights are invalid, or if `progress_callback` is not callable.
`FitError`	If DREAM sampling was aborted by the user (via `abort_test`).
`Exception`	Re-raised from DREAM fitting if any unexpected error occurs (parameter values are restored beforehand).

evaluate(x, minimizer_parameters=None, **kwargs)

Evaluate the fit function for values of x.

Parameters used are either the latest or user supplied. If the parameters are user supplied, it must be in a dictionary of {'parameter_name': parameter_value,...}.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	X values for which the fit function will be evaluated.	required
`minimizer_parameters`	`dict[str, float] \| None`	Dictionary of parameters which will be used in the fit function. They must be in a dictionary of {'parameter_name': parameter_value,...}. By default, None.	`None`
`**kwargs`		Additional arguments.	`{}`

Returns:

Type	Description
`np.ndarray`	Y values calculated at points x for a set of parameters.

Raises:

Type	Description
`TypeError`	If `minimizer_parameters` is not a dictionary.

`minimizer_dfo`

Classes:

Name	Description
`DFOCallbackState`	Snapshot of a DFO objective evaluation.
`DFO`	This is a wrapper to Derivative Free Optimisation for Least Square:

Classes

DFOCallbackState(evaluation, xk, residuals, objective, parameters, best_xk, best_objective, best_parameters, improved) dataclass

Snapshot of a DFO objective evaluation.

DFO(obj, fit_function, minimizer_enum=None)

This is a wrapper to Derivative Free Optimisation for Least Square: https://numericalalgorithmsgroup.github.io/dfols/.

Parameters:

Name	Type	Description	Default
`obj`	`object`	Object containing the `Parameter` instances to fit.	required
`fit_function`	`Callable`	Callable returning model y values for the supplied x values.	required
`minimizer_enum`	`AvailableMinimizers \| None`	Selected DFO minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the DFO-ls engine.
`convert_to_pars_obj`	Required by interface but not needed for DFO-LS.
`convert_to_par_object`	Required by interface but not needed for DFO-LS.
`evaluate`	Evaluate the fit function for values of x.

Functions

fit(x, y, weights, model=None, parameters=None, method=None, tolerance=None, max_evaluations=None, progress_callback=None, callback=None, **kwargs)

Perform a fit using the DFO-ls engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Points to be calculated at.	required
`y`	`np.ndarray`	Measured points.	required
`weights`	`np.ndarray`	Weights for supplied measured points.	required
`model`	`Callable \| None`	Optional Model which is being fitted to. By default, None.	`None`
`parameters`	`List[Parameter] \| None`	Optional parameters for the fit. By default, None.	`None`
`method`	`str \| None`	Method for minimization. By default, None.	`None`
`tolerance`	`float \| None`	Requested optimizer tolerance. By default, None.	`None`
`max_evaluations`	`int \| None`	Maximum number of evaluations. By default, None.	`None`
`progress_callback`	`Callable[[dict], bool \| None] \| None`	Optional callback receiving normalized progress payloads.	`None`
`callback`	`Callable[[DFOCallbackState], None] \| None`	Optional native DFO callback.	`None`
`**kwargs`		Additional arguments for the fitting function.	`{}`

Returns:

Type	Description
`FitResults`	Fit results should be 1/sigma, where sigma is the standard deviation of the measurement. For unweighted least squares, these should be 1.

Raises:

Type	Description
`FitError`	If the DFO fit fails.
`ValueError`	If the input shapes, weights, or tolerance are invalid.

convert_to_pars_obj(par_list=None)

Required by interface but not needed for DFO-LS.

convert_to_par_object(obj) staticmethod

Required by interface but not needed for DFO-LS.

evaluate(x, minimizer_parameters=None, **kwargs)

Evaluate the fit function for values of x.

Parameters used are either the latest or user supplied. If the parameters are user supplied, it must be in a dictionary of {'parameter_name': parameter_value,...}.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	X values for which the fit function will be evaluated.	required
`minimizer_parameters`	`dict[str, float] \| None`	Dictionary of parameters which will be used in the fit function. They must be in a dictionary of {'parameter_name': parameter_value,...}. By default, None.	`None`
`**kwargs`		Additional arguments.	`{}`

Returns:

Type	Description
`np.ndarray`	Y values calculated at points x for a set of parameters.

Raises:

Type	Description
`TypeError`	If `minimizer_parameters` is not a dictionary.

`minimizer_lmfit`

Classes:

Name	Description
`LMFit`	This is a wrapper to the extended Levenberg-Marquardt Fit:

Classes

LMFit(obj, fit_function, minimizer_enum=None)

This is a wrapper to the extended Levenberg-Marquardt Fit: https://lmfit.github.io/lmfit-py/ It allows for the lmfit fitting engine to use parameters declared in an EasyScience.base_classes.ObjBase.

Parameters:

Name	Type	Description	Default
`obj`	`object`	Object containing the `Parameter` instances to fit.	required
`fit_function`	`Callable`	Callable returning model y values for the supplied x values.	required
`minimizer_enum`	`AvailableMinimizers \| None`	Selected LMFit minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the lmfit engine.
`convert_to_pars_obj`	Create an lmfit compatible container with the `Parameters`
`convert_to_par_object`	Convert an EasyScience Parameter object to a lmfit Parameter
`evaluate`	Evaluate the fit function for values of x.

Functions

fit(x, y, weights=None, model=None, parameters=None, method=None, tolerance=None, max_evaluations=None, progress_callback=None, minimizer_kwargs=None, engine_kwargs=None, **kwargs)

Perform a fit using the lmfit engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Points to be calculated at.	required
`y`	`np.ndarray`	Measured points.	required
`weights`	`np.ndarray`	Weights for supplied measured points. By default, None.	`None`
`model`	`LMModel \| None`	Optional Model which is being fitted to. By default, None.	`None`
`parameters`	`LMParameters \| None`	Optional parameters for the fit. By default, None.	`None`
`method`	`str \| None`	Minimizer method. By default, None.	`None`
`tolerance`	`float \| None`	Requested optimizer tolerance. By default, None.	`None`
`max_evaluations`	`int \| None`	Maximum number of function evaluations. By default, None.	`None`
`progress_callback`	`Callable[[dict], bool \| None] \| None`	Optional callback receiving normalized progress payloads.	`None`
`minimizer_kwargs`	`dict \| None`	Additional keyword arguments passed to LMFit's minimizer. By default, None.	`None`
`engine_kwargs`	`dict \| None`	Additional engine keyword arguments. By default, None.	`None`
`**kwargs`		Additional arguments for the fitting function.	`{}`

Returns:

Type	Description
`FitResults`	Fit results should be 1/sigma, where sigma is the standard deviation of the measurement. For unweighted least squares, these should be 1.

Raises:

Type	Description
`FitError`	If the LMFit optimization fails.
`ValueError`	If the input shapes or weights are invalid.

convert_to_pars_obj(parameters=None)

Create an lmfit compatible container with the Parameters converted from the base object.

Parameters:

Name	Type	Description	Default
`parameters`	`List[Parameter] \| None`	If only a single/selection of parameter is required. Specify as a list. By default, None.	`None`

Returns:

Type	Description
`LMParameters`	Lmfit Parameters compatible object.

convert_to_par_object(parameter) staticmethod

Convert an EasyScience Parameter object to a lmfit Parameter object.

Parameters:

Name	Type	Description	Default
`parameter`	`Parameter`	EasyScience parameter to convert.	required

Returns:

Type	Description
`LMParameter`	Lmfit Parameter compatible object.

evaluate(x, minimizer_parameters=None, **kwargs)

Evaluate the fit function for values of x.

Parameters used are either the latest or user supplied. If the parameters are user supplied, it must be in a dictionary of {'parameter_name': parameter_value,...}.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	X values for which the fit function will be evaluated.	required
`minimizer_parameters`	`dict[str, float] \| None`	Dictionary of parameters which will be used in the fit function. They must be in a dictionary of {'parameter_name': parameter_value,...}. By default, None.	`None`
`**kwargs`		Additional arguments.	`{}`

Returns:

Type	Description
`np.ndarray`	Y values calculated at points x for a set of parameters.

Raises:

Type	Description
`TypeError`	If `minimizer_parameters` is not a dictionary.

`utils`

Classes:

Name	Description
`FitResults`	At the moment this is just a dummy way of unifying the returned fit

Classes

FitResults()

At the moment this is just a dummy way of unifying the returned fit parameters.

`multi_fitter`

Classes:

Name	Description
`MultiFitter`	Extension of Fitter to enable multiple dataset/fit function fitting.

Classes

`MultiFitter(fit_objects=None, fit_functions=None)`

Extension of Fitter to enable multiple dataset/fit function fitting.

We can fit these types of data simultaneously: - Multiple models on multiple datasets.

The inherited fit wrapper from Fitter is used unchanged, including support for forwarding progress callbacks to the active minimizer.

Methods:

Name	Description
`initialize`	Set the model and callable in the calculator interface.
`create`	Create the required minimizer.
`switch_minimizer`	Switch minimizer and initialize.
`mcmc_sample`	Run Bayesian MCMC sampling using the BUMPS DREAM sampler.

Attributes:

Name	Type	Description
`available_minimizers`	`List[str]`	Get a list of the names of available fitting minimizers.
`minimizer`	`MinimizerBase`	Get the current fitting minimizer object.
`tolerance`	`float`	Get the tolerance for the minimizer.
`max_evaluations`	`int`	Get the maximal number of evaluations for the minimizer.
`fit_function`	`Callable`	Get the raw fit function that the optimizer will call.
`fit_object`	`object`	Get the EasyScience object used as a model.
`fit`	`Callable`	Property which wraps the current `fit` function from the

Attributes

available_minimizers property

Get a list of the names of available fitting minimizers.

Returns:

Type	Description
`List[str]`	List of available fitting minimizers.

minimizer property

Get the current fitting minimizer object.

Returns:

Type	Description
`MinimizerBase`

tolerance property writable

Get the tolerance for the minimizer.

Returns:

Type	Description
`float`	Tolerance for the minimizer.

max_evaluations property writable

Get the maximal number of evaluations for the minimizer.

Returns:

Type	Description
`int`	Maximal number of steps for the minimizer.

fit_function property writable

Get the raw fit function that the optimizer will call.

Returns:

Type	Description
`Callable`	Raw fit function.

fit_object property writable

Get the EasyScience object used as a model.

Returns:

Type	Description
`object`	EasyScience model object.

fit property

Property which wraps the current fit function from the fitting interface.

This property return a wrapped fit function which converts the input data into the correct shape for the optimizer, wraps the fit function to re-constitute the independent variables and once the fit is completed, reshape the inputs to those expected.

Functions

initialize(fit_object, fit_function)

Set the model and callable in the calculator interface.

Parameters:

Name	Type	Description	Default
`fit_object`	`object`	The EasyScience model object.	required
`fit_function`	`Callable`	The function to be optimized against.	required

create(minimizer_enum=DEFAULT_MINIMIZER)

Create the required minimizer.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Union[AvailableMinimizers, str]`	The enum of the minimization engine to create. By default, DEFAULT_MINIMIZER.	`DEFAULT_MINIMIZER`

switch_minimizer(minimizer_enum)

Switch minimizer and initialize.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Union[AvailableMinimizers, str]`	The enum of the minimizer to create and instantiate.	required

mcmc_sample(x, y, weights, samples=10000, burn=2000, thin=10, population=None, vectorized=False, sampler_kwargs=None, progress_callback=None, abort_test=None)

Run Bayesian MCMC sampling using the BUMPS DREAM sampler.

Works with both a plain Fitter (single dataset) and a MultiFitter (multiple datasets) via polymorphic dispatch: _precompute_reshaping and _fit_function_wrapper are resolved on the concrete subclass at call time, so multi-dataset flattening is handled automatically when called on a MultiFitter instance.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array (or list of arrays for `MultiFitter`).	required
`y`	`np.ndarray`	Dependent variable array (or list of arrays for `MultiFitter`).	required
`weights`	`np.ndarray`	Weight array (or list of arrays for `MultiFitter`).	required
`samples`	`int`	Number of retained DREAM samples requested from BUMPS.	`10000`
`burn`	`int`	Burn-in steps to discard before collecting samples.	`2000`
`thin`	`int`	Thinning interval — only every `thin`-th sample is kept, which reduces autocorrelation between consecutive draws.	`10`
`population`	`Optional[int]`	BUMPS DREAM population count (number of parallel chains).	`None`
`vectorized`	`bool`	When `True`, each x array may be multi-dimensional (e.g. an `(N, M, 2)` grid for a 2D model) and is left as-is. When `False` (default), each x array is expected to be 1-D.	`False`
`sampler_kwargs`	`Optional[dict]`	Additional keyword arguments forwarded to the BUMPS DREAM sampler.	`None`
`progress_callback`	`Optional[Callable[[dict], Optional[bool]]]`	Optional callback invoked at each DREAM generation. The payload dict includes `iteration` and `sampling: True`.	`None`
`abort_test`	`Optional[Callable[[], bool]]`	Optional callable that returns `True` to abort sampling early.	`None`

Returns:

Type	Description
`dict`	Dictionary with keys `'draws'`, `'param_names'`, `'internal_bumps_object'`, and `'logp'`.

Raises:

Type	Description
`ValueError`	If `samples`, `burn`, or `thin` are invalid.
`RuntimeError`	If the active minimizer is not a BUMPS instance.