`fitting`

Modules:

Name	Description
`engine_base`
`fitter`
`minimizers`
`multi_fitter`
`reshaping`	Data reshaping and fit-function wrapping shared by `Fitter` and `Sampler`.
`sampler`	Bayesian MCMC sampling — the `Sampler` class and persistence helpers.
`samplers`

Classes

Modules

`engine_base`

Classes:

Name	Description
`EngineBase`	Base for all evaluation engines: minimizers and samplers.

Classes

`EngineBase(obj, fit_function)`

Base for all evaluation engines: minimizers and samplers.

An engine binds an EasyScience object and a fit function, and repeatedly evaluates the function while writing values back into the object's Parameter instances. EngineBase owns this shared system: the parameter cache, the Parameter-writing wrapped fit function, and value restore on failure. It deliberately declares no abstract methods: the interfaces are defined on its subclasses (MinimizerBase.fit, DreamSampler.run).

Methods:

Name	Description
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `parameters` is not a dictionary.

`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.
`switch_minimizer`	Switch minimizer and initialize.

Attributes:

Name	Type	Description
`available_minimizers`	`List[Minimizers]`	Get a list of the 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 available fitting minimizers.

Returns:

Type	Description
`List[Minimizers]`	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.

For a Fitter this is not one of the supplied fit objects but a read-only, indexable and iterable aggregate.

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.

Methods:

initialize(fit_object, fit_function)

Set the model and callable.

Parameters:

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

switch_minimizer(minimizer_enum)

Switch minimizer and initialize.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Minimizers`	The enum of the minimizer to create and instantiate.	required

Raises:

Type	Description
`TypeError`	If `minimizer_enum` is not a `Minimizers` member.

Functions:

`minimizers`

Modules:

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

Classes

Modules

`bumps_utils`

Modules:

Name	Description
`eval_counter`
`problem`	BUMPS problem construction shared by the `Bumps` minimizer and
`progress_monitor`

Classes

Functions:

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).

problem

BUMPS problem construction shared by the Bumps minimizer and DreamSampler.

These are free functions rather than Bumps methods so that any :class:~easyscience.fitting.engine_base.EngineBase: a minimizer or a sampler, can build a BUMPS Curve/FitProblem without inheriting from the minimizer.

Functions:

Name	Description
`to_bumps_parameter`	Convert an EasyScience `Parameter` to a prefixed `BumpsParameter`.
`build_curve_problem`	Build a BUMPS `FitProblem` around an engine's wrapped fit function.
`parameter_names`	Return the problem's parameter names with the prefix stripped.
`parameter_snapshot`	Snapshot the problem's parameter values as `{name: value}`.

Classes Functions:

to_bumps_parameter(par)

Convert an EasyScience Parameter to a prefixed BumpsParameter.

Parameters:

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

Returns:

Type	Description
`BumpsParameter`	Bumps Parameter compatible object, named `PARAMETER_PREFIX + par.unique_name`.

build_curve_problem(engine, x, y, weights)

Build a BUMPS FitProblem around an engine's wrapped fit function.

Wraps engine._generate_fit_function() in an :class:EvalCounter, converts the engine's cached parameters via :func:to_bumps_parameter, and assembles Curve(fit_func, x, y, dy=1/weights, **bumps_pars) into a FitProblem.

Parameters:

Name	Type	Description	Default
`engine`	`EngineBase`	The engine (minimizer or sampler) supplying the fit function and parameter cache.	required
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array; converted to `dy = 1 / weights`.	required

Returns:

Type	Description
`tuple[FitProblem, EvalCounter, Curve]`	The assembled problem, the evaluation counter wrapping the fit function, and the `Curve` model itself. The `Curve` is returned because `FitProblem.fitness` is deprecated in current BUMPS.

parameter_names(problem)

Return the problem's parameter names with the prefix stripped.

Parameters:

Name	Type	Description	Default
`problem`	`FitProblem`	A BUMPS problem built by :func:`build_curve_problem`.	required

Returns:

Type	Description
`list[str]`	Parameter names in problem order, without `PARAMETER_PREFIX`.

parameter_snapshot(problem, point)

Snapshot the problem's parameter values as {name: value}.

Parameters:

Name	Type	Description	Default
`problem`	`FitProblem`	A BUMPS problem built by :func:`build_curve_problem`.	required
`point`	`np.ndarray \| None`	Parameter values to report; when `None` the problem's current values (`problem.getp()`) are used.	required

Returns:

Type	Description
`dict`	Mapping of prefix-stripped parameter names to `float` values.

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.
`supported_methods`	Return a list of supported methods for the minimizer.
`all_methods`	Return a list of all available methods for the minimizer.
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

fit(x, y, weights, 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
`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], None] \| None`	Optional progress callback. Its return value is ignored by every backend; use `abort_test` to stop a running fit. By default, None.	`None`
`**kwargs`		Additional arguments for the fitting function.	`{}`

Returns:

Type	Description
`FitResults`	Fit results.

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.

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `parameters` is not a dictionary.

`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.ModelBase.

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`	`Minimizers \| None`	Selected BUMPS minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the BUMPS engine.
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

fit(x, y, weights, 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
`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], None] \| None`	Optional callback for progress updates. The field `iteration` carries the BUMPS optimizer step index. The return value is ignored: use `abort_test` to stop a running fit. 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, and once more after the optimizer returns in order to distinguish an aborted fit from a converged one, so it must be side-effect free. An aborted fit returns `FitResults(success=False)` rather than raising.	`None`
`minimizer_kwargs`	`dict \| None`	Additional keyword arguments passed to the BUMPS minimizer. The mapping is copied before use, so it is never mutated. 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. FitResults.iterations is the number of BUMPS optimizer steps consumed (the last reported step index plus one), which is what max_evaluations tests against; it is not comparable to LMFit's nfev or DFO-LS' nf. The objective-call count is reported separately as FitResults.n_evaluations, which is the cross-backend consistent figure. Note that BUMPS derives the step index from whatever the selected fitter reports to its monitors, so the granularity of a "step" varies between fitters.

Raises:

Type	Description
`FitError`	If the BUMPS fit raises. A fit that merely fails to converge is reported as `FitResults(success=False)` instead.
`ValueError`	If the input shapes or weights are invalid, or if `progress_callback` is not callable.

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `parameters` is not a dictionary.

Functions:

`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`	`Minimizers \| None`	Selected DFO minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the DFO-ls engine.
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

fit(x, y, weights, 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
`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], None] \| None`	Optional callback receiving normalized progress payloads. Its return value is ignored.	`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.

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `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.ModelBase.

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`	`Minimizers \| None`	Selected LMFit minimizer configuration. By default, None.	`None`

Methods:

Name	Description
`fit`	Perform a fit using the lmfit engine.
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

fit(x, y, weights=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`
`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], None] \| None`	Optional callback receiving normalized progress payloads. Its return value is ignored.	`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.

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `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.
`switch_minimizer`	Switch minimizer and initialize.

Attributes:

Name	Type	Description
`fit_functions`	`list[Callable]`	Get the per-dataset fit functions, in dataset order.
`available_minimizers`	`List[Minimizers]`	Get a list of the 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

fit_functions property

Get the per-dataset fit functions, in dataset order.

Returns:

Type	Description
`list[Callable]`	One fit function per dataset.

available_minimizers property

Get a list of the available fitting minimizers.

Returns:

Type	Description
`List[Minimizers]`	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.

For a Fitter this is not one of the supplied fit objects but a read-only, indexable and iterable aggregate.

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.

Methods:

initialize(fit_object, fit_function)

Set the model and callable.

Parameters:

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

switch_minimizer(minimizer_enum)

Switch minimizer and initialize.

Parameters:

Name	Type	Description	Default
`minimizer_enum`	`Minimizers`	The enum of the minimizer to create and instantiate.	required

Raises:

Type	Description
`TypeError`	If `minimizer_enum` is not a `Minimizers` member.

Functions:

`reshaping`

Data reshaping and fit-function wrapping shared by Fitter and Sampler.

Both fitting and sampling provide the engine witha flat 1-D y array and a dummy 1-D x index array, while the user's fit function is called with the real x. The helpers here do the translation in one place: reshape_dataset/inject_x do one dataset, reshape_datasets/inject_x_multi do a list of datasets.

Functions:

Name	Description
`reshape_dataset`	Check the dimensions of the inputs and reshape if necessary.
`reshape_datasets`	Convert a list of X's and Y's to an acceptable shape for fitting.
`inject_x`	Wrap a fit function so it ignores the engine's dummy x and evaluates
`inject_x_multi`	Wrap one fit function per dataset into a single function whose output

Functions:

`reshape_dataset(x, y, weights, vectorized)`

Check the dimensions of the inputs and reshape if necessary.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent points; 1-D, or ND with the coordinate components on the last axis.	required
`y`	`np.ndarray`	Dependent points, one per observation.	required
`weights`	`np.ndarray \| None`	Optional weights for the fit.	required
`vectorized`	`bool`	Whether `x` already stores vectorized coordinates.	required

Returns:

Type	Description
`tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray \| None, tuple[int, ...]]`	Dummy x index array for the engine, reshaped x values, flattened y values, flattened weights, and the shape of `y` (the dependent dimensions; its product is the number of observations, which the multi-dataset helpers use to slice the combined output).

Raises:

Type	Description
`ValueError`	If the shapes of `x` and `y` are incompatible.

`reshape_datasets(x, y, weights, vectorized)`

Convert a list of X's and Y's to an acceptable shape for fitting.

Parameters:

Name	Type	Description	Default
`x`	`list[np.ndarray]`	List of independent variables.	required
`y`	`list[np.ndarray]`	List of dependent variables.	required
`weights`	`list[np.ndarray] \| None`	Optional weights for each dataset.	required
`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.	required

Returns:

Type	Description
`tuple[np.ndarray, list[np.ndarray], np.ndarray, np.ndarray \| None, list[tuple[int, ...]]]`	Dummy x index array for the engine, per-dataset reshaped x values, concatenated y values, concatenated weights, and the per-dataset dependent dimensions.

`inject_x(fit_function, real_x=None, flatten=True)`

Wrap a fit function so it ignores the engine's dummy x and evaluates on real_x instead, flattening the result if needed.

Parameters:

Name	Type	Description	Default
`fit_function`	`Callable`	The user's fit function.	required
`real_x`	`np.ndarray \| None`	Independent x values to be injected. By default, None.	`None`
`flatten`	`bool`	Should the result be a flat 1D array? By default, True.	`True`

Returns:

Type	Description
`Callable`	Wrapped optimizer function.

`inject_x_multi(fit_functions, real_x, dims, flatten=True)`

Wrap one fit function per dataset into a single function whose output is the concatenation of the per-dataset outputs.

Parameters:

Name	Type	Description	Default
`fit_functions`	`list[Callable]`	One fit function per dataset.	required
`real_x`	`list[np.ndarray]`	One independent x array per dataset, injected into the matching fit function.	required
`dims`	`list[tuple[int, ...]]`	Per-dataset dependent (`y`) shapes used to slice the combined output, as returned by `reshape_datasets`.	required
`flatten`	`bool`	Should each per-dataset result be flattened? By default, True.	`True`

Returns:

Type	Description
`Callable`	Wrapped optimizer function.

`sampler`

Bayesian MCMC sampling — the Sampler class and persistence helpers.

Classes:

Name	Description
`SamplingResults`	Structured result of an MCMC sampling run (analogous to `FitResults`).
`Sampler`	Bayesian MCMC sampler for one dataset, backed by the BUMPS DREAM engine.

Functions:

Name	Description
`load_chain`	Reload a DREAM chain state saved by `Sampler.save`.

Classes

`SamplingResults(draws, param_names, logp, state)` `dataclass`

Structured result of an MCMC sampling run (analogous to FitResults).

Attributes:

Name	Type	Description
`draws`	`np.ndarray`	Posterior samples, shape `(n_samples, n_params)`. For results from `Sampler.sample()`/`Sampler.extend()` this is a trimmed, outlier-filtered view of the chain, not the raw buffer, so it holds fewer rows than `samples / thin` — see the notes on `Sampler`.
`param_names`	`list[str]`	Parameter names (one per column of `draws`).
`logp`	`np.ndarray`	Log-posterior values, shape `(n_samples,)`.
`state`	`MCMCDraw`	The raw BUMPS chain state, holding the full untrimmed chain.

`Sampler(fit_object, fit_function, x, y, weights, vectorized=False, sampler_kwargs=None)`

Bayesian MCMC sampler for one dataset, backed by the BUMPS DREAM engine.

One Sampler instance represents one chain over one (x, y, weights) dataset. The data is bound at construction; sample() and extend() take no data arguments, so a chain can never be extended against different data (undefined behaviour in BUMPS). The bound data is a defensive, read-only copy of the caller's arrays, exposed via the x, y and weights properties — mutating the originals after construction has no effect on the sampler, and there are deliberately no setters: to sample different data, create a new Sampler.

Sampler is a parallel entry point to Fitter: it takes the same (fit_object, fit_function) pair plus the data.

Parameters:

Name	Type	Description	Default
`fit_object`	`object`	The EasyScience model object holding the `Parameter` instances to sample. For multiple datasets this is one object exposing all the parameters (for example an `EasyList` of models, or the `fit_object` of a `MultiFitter`).	required
`fit_function`	`Callable \| list[Callable]`	The model function, or one per dataset when `x`, `y` and `weights` are lists of arrays.	required
`x`	`np.ndarray \| list[np.ndarray]`	Independent variable array (or list of arrays, one per dataset).	required
`y`	`np.ndarray \| list[np.ndarray]`	Dependent variable array (or list of arrays, one per dataset).	required
`weights`	`np.ndarray \| list[np.ndarray]`	Weight array (or list of arrays, one per dataset). Required: sampling has no default weighting, so a missing weight array is rejected here rather than deep inside the sampling engine.	required
`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.	`False`
`sampler_kwargs`	`dict \| None`	Per-instance default keyword arguments forwarded to the BUMPS DREAM sampler on every run, merged with (and overridden by) per-call `sampler_kwargs`.	`None`

Raises:

Type	Description
`TypeError`	If `fit_object` has no `get_free_parameters`, `fit_function` is not callable, any dataset in `x`/`y`/`weights` is not a numeric array (e.g. a string), or `vectorized`/`sampler_kwargs` have the wrong type.
`ValueError`	If `fit_function`, `x`, `y` and `weights` do not hold matching structures (all single, or lists of the same length), or any dataset is a scalar or empty array.

Notes

The retained draws are a trimmed view, not the whole chain. BUMPS' DREAM sampler defaults to trim=True: once sampling finishes it runs a convergence-based burn-point detector over the chain and returns only the portion after that point. state.draw() additionally drops chains flagged as outliers. So results.draws is usually smaller than the chain, and its length is not deterministic — the detector re-runs from scratch on every sample() and extend() call and may place the burn point differently each time. Read the count off the array; do not predict it.

Nor is the chain itself exactly samples / thin rows: samples is a guaranteed minimum, not an exact count. DREAM advances in blocks of 10 generations (one generation = one draw per chain) and only checks its stopping condition between blocks, so the raw chain length is samples rounded up to a multiple of 10 * n_chains.

The trimming is only a view — nothing is dropped from the buffer. To read the full untrimmed chain::

results = sampler.sample(samples=10000, burn=500, thin=2)
full = results.state.draw(portion=1.0, outliers=True)
full.points  # (n_chain_rows, n_params)
full.logp

To switch the automatic trimming off entirely, pass BUMPS' own trim option straight through sampler_kwargs; results.draws then holds the whole chain::

sampler = Sampler(
    parameter_container,
    model_function,
    x,
    y,
    weights=w,
    sampler_kwargs={'trim': False},
)

Note also that trimming does not survive a save()/load_state() round-trip: BUMPS does not persist the trim point, so a reloaded chain comes back untrimmed and load_state() reports more draws than the sample() call that created it. The chain itself is identical.

Methods:

Name	Description
`from_fitter`	Build a `Sampler` from the model bound to an existing `Fitter`.
`sample`	Run fresh Bayesian MCMC sampling on the bound data.
`extend`	Continue the existing chain with additional samples.
`save`	Persist the chain state and metadata to disk.
`load_state`	Load a previously saved chain into this sampler.

Attributes:

Name	Type	Description
`fit_object`	`object`	The EasyScience model object holding the sampled parameters (read-only).
`fit_function`	`Callable \| list[Callable]`	The model function, or list of them for multiple datasets (read-only).
`x`	`np.ndarray \| list[np.ndarray]`	The bound independent variable data (read-only copy).
`y`	`np.ndarray \| list[np.ndarray]`	The bound dependent variable data (read-only copy).
`weights`	`np.ndarray \| list[np.ndarray]`	The bound weight data (read-only copy).
`state`	`MCMCDraw \| None`	Raw BUMPS MCMCDraw state (None before first sample/load_state).
`results`	`SamplingResults \| None`	Results of the most recent sample/extend/load_state call.
`draws`	`np.ndarray \| None`	Posterior draws from the most recent run (or None).
`param_names`	`list[str] \| None`	Parameter names from the most recent run (or None).
`logp`	`np.ndarray \| None`	Log-posterior values from the most recent run (or None).

Attributes

fit_object property

The EasyScience model object holding the sampled parameters (read-only).

fit_function property

The model function, or list of them for multiple datasets (read-only).

x property

The bound independent variable data (read-only copy).

y property

The bound dependent variable data (read-only copy).

weights property

The bound weight data (read-only copy).

state property

Raw BUMPS MCMCDraw state (None before first sample/load_state).

results property

Results of the most recent sample/extend/load_state call.

draws property

Posterior draws from the most recent run (or None).

param_names property

Parameter names from the most recent run (or None).

logp property

Log-posterior values from the most recent run (or None).

Methods:

from_fitter(fitter, x, y, weights, vectorized=False, sampler_kwargs=None) classmethod

Build a Sampler from the model bound to an existing Fitter.

A convenience for the common fit-then-sample workflow: the sampler takes the fitter's fit_object and fit function(s) and is otherwise identical to one constructed directly. The fitter is not retained or modified. A MultiFitter yields a multi-dataset sampler, so x, y and weights must then be lists of arrays.

Parameters:

Name	Type	Description	Default
`fitter`	`Fitter`	A configured `Fitter` or `MultiFitter`. It does not need to have been fitted.	required
`x`	`np.ndarray \| list[np.ndarray]`	Independent variable array (or list of arrays, one per dataset).	required
`y`	`np.ndarray \| list[np.ndarray]`	Dependent variable array (or list of arrays, one per dataset).	required
`weights`	`np.ndarray \| list[np.ndarray]`	Weight array (or list of arrays, one per dataset).	required
`vectorized`	`bool`	See `Sampler`.	`False`
`sampler_kwargs`	`dict \| None`	See `Sampler`.	`None`

Returns:

Type	Description
`Sampler`	A sampler bound to the fitter's model and the given data.

sample(samples=10000, burn=2000, thin=10, population=None, sampler_kwargs=None, progress_callback=None, abort_test=None)

Run fresh Bayesian MCMC sampling on the bound data.

Calling sample() on a sampler that already holds a chain starts a fresh chain — the previous state and results are replaced. Use extend() to continue an existing chain.

Parameters:

Name	Type	Description	Default
`samples`	`int`	Number of raw samples to draw across all chains, before thinning. This is a guaranteed minimum, not an exact count: DREAM advances in blocks of 10 generations (one generation = one draw per chain) and stops at the first block boundary at or past `samples`.	`10000`
`burn`	`int`	Burn-in generations to discard before collecting samples. Note BUMPS counts `burn` in generations while `samples` counts raw draws, so `burn=500` discards `500 * n_chains` raw samples.	`2000`
`thin`	`int`	Thinning interval — only every `thin`-th generation is kept, which reduces autocorrelation between consecutive draws.	`10`
`population`	`int \| None`	DREAM population scale factor (not an absolute chain count): BUMPS creates `ceil(population * n_parameters)` parallel chains.	`None`
`sampler_kwargs`	`dict \| None`	Additional keyword arguments forwarded to the BUMPS DREAM sampler (merged over the instance defaults).	`None`
`progress_callback`	`Callable[[dict], None] \| None`	Optional callback invoked at each DREAM generation. The payload dict includes `iteration` and `sampling: True`. Any return value is ignored.	`None`
`abort_test`	`Callable[[], bool] \| None`	Optional callable that returns `True` to abort sampling early.	`None`

Returns:

Type	Description
`SamplingResults`	Structured sampling results (also stored on `results`).

Notes

results.draws is a trimmed, outlier-filtered view of the chain, so it is usually smaller than the chain and its length is not predictable from samples/thin. See the Sampler class notes for how to read the full chain or switch trimming off.

Exceptions propagate from the sampling engine: ValueError if samples, burn, or thin are invalid, and RuntimeError if the bumps package is not installed.

extend(additional_samples=5000, thin=10, total_samples=None, sampler_kwargs=None, progress_callback=None, abort_test=None)

Continue the existing chain with additional samples.

DREAM stores draws in a fixed-size ring buffer sized to its samples parameter; this method does the ring-buffer arithmetic for you (samples = stored_generations * population + additional_samples) so no existing draws are dropped from the buffer, regardless of the thinning interval. Runs with burn=0 — re-burning a converged chain is usually a mistake, and BUMPS forces it to 0 on resume in any case. The DREAM population is recovered from the saved state and cannot be changed on extend.

Parameters:

Name	Type	Description	Default
`additional_samples`	`int`	Number of additional DREAM samples to draw, in the same units as `samples` in `sample()`. This grows the ring buffer by exactly `additional_samples`; how many of the new draws become visible in `results.draws` depends on the thinning interval and on BUMPS' automatic trimming (see Notes).	`5000`
`thin`	`int`	Thinning interval for the retained draws.	`10`
`total_samples`	`int \| None`	Advanced: total retained samples requested from the ring buffer, overriding the `additional_samples` arithmetic. With `total_samples=N`, only the last N draws are retained.	`None`
`sampler_kwargs`	`dict \| None`	Additional keyword arguments forwarded to the BUMPS DREAM sampler (merged over the instance defaults).	`None`
`progress_callback`	`Callable[[dict], None] \| None`	Optional callback invoked at each DREAM generation. Any return value is ignored.	`None`
`abort_test`	`Callable[[], bool] \| None`	Optional callable that returns `True` to abort sampling early.	`None`

Returns:

Type	Description
`SamplingResults`	Structured sampling results for the full (extended) chain.

Raises:

Type	Description
`RuntimeError`	If there is no chain to extend (call `sample()` or `load_state()` first), or the `bumps` package is not installed.

Notes

results.draws will not grow by exactly additional_samples / thin. Two things get in the way, neither of them a re-applied burn-in: BUMPS re-runs its burn-point detector over the whole extended chain and re-trims the returned view (so the visible count can even shrink), and DREAM advances in blocks of 10 generations, so the raw growth is additional_samples rounded up to a multiple of 10 * n_chains (i.e. at least additional_samples / thin retained rows). To see the chain itself, compare results.state.draw(portion=1.0, outliers=True) before and after, or run with sampler_kwargs={'trim': False}. See the Sampler class notes.

save(path)

Persist the chain state and metadata to disk.

Writes the BUMPS native files (<path>-chain.mc.gz, <path>-point.mc.gz and <path>-stats.mc.gz) plus a <path>.params.json sidecar with the parameter names, the easyscience version, and a fingerprint of the bound data (verified with a warning on load_state()). Use load_chain to read the files back without the model.

Parameters:

Name	Type	Description	Default
`path`	`str \| os.PathLike`	File path prefix. BUMPS appends its own suffixes.	required

Raises:

Type	Description
`TypeError`	If `path` is not a `str` or `os.PathLike`.
`RuntimeError`	If no chain state exists yet (call `sample()` first).

load_state(path, skip=0)

Load a previously saved chain into this sampler.

The sampler must be constructed with the same model and data used to create the chain — extend() then continues the saved chain. If the sidecar carries a data fingerprint and it does not match this sampler's bound data, a warning is logged (extending a chain against different data is undefined behaviour).

Populates state and results (draws, log-posterior and parameter names) from the saved chain, so summaries and PosteriorResults.from_sampler() work without resampling.

Parameters:

Name	Type	Description	Default
`path`	`str \| os.PathLike`	File path prefix used in `save()`.	required
`skip`	`int`	Discard the first `skip` saved generations on load. Useful for trimming additional burn-in without re-sampling.	`0`

Returns:

Type	Description
`SamplingResults`	The reloaded chain results (also stored on `results`).

Raises:

Type	Description
`TypeError`	If `path` is not a `str` or `os.PathLike`.
`ValueError`	If `skip` is not a non-negative integer.

Notes

BUMPS does not persist its automatic trim point, so a reloaded chain comes back untrimmed: this method reports more draws than the sample() call that wrote the file, even though the chain is identical. Use skip to discard leading generations explicitly. See the Sampler class notes.

Functions:

`load_chain(path, skip=0)`

Reload a DREAM chain state saved by Sampler.save.

This is the standalone reader: unlike Sampler.load_state it needs no model or data, so a saved chain can be inspected or post-processed on a machine that does not have the model. Parameter names are restored from the sidecar when available (schema versions 1 and 2), falling back to the state's labels with the minimizer prefix stripped.

Parameters:

Name	Type	Description	Default
`path`	`str \| os.PathLike`	File path prefix used when saving.	required
`skip`	`int`	Discard the first `skip` saved generations on load, forwarded to `bumps.dream.state.load_state`.	`0`

Returns:

Type	Description
`tuple[MCMCDraw, list[str] \| None, dict]`	The reloaded BUMPS chain state, the parameter names (or `None` if neither sidecar nor labels yielded them), and the raw sidecar dict (empty if absent/unreadable).

Raises:

Type	Description
`TypeError`	If `path` is not a `str` or `os.PathLike`.
`ValueError`	If `skip` is not a non-negative integer.

Modules

`samplers`

Modules:

Name	Description
`sampler_bumps`	The BUMPS DREAM MCMC engine — `DreamSampler`.
`validation`	Run-settings validation for the DREAM sampler.

Classes

Functions:

Modules

`sampler_bumps`

The BUMPS DREAM MCMC engine — DreamSampler.

Classes:

Name	Description
`DreamSampler`	BUMPS DREAM MCMC engine. Runs and resumes chains for one

Classes

DreamSampler(obj, fit_function)

BUMPS DREAM MCMC engine. Runs and resumes chains for one (obj, fit_function) binding.

This is the minimizer-independent location of Bayesian sampling: it builds its own BUMPS FitProblem via the shared bumps_utils helpers, so sampling no longer requires the Fitter's active minimizer to be BUMPS, only an installed bumps package.

DreamSampler is internal. The public entry point is :class:easyscience.fitting.Sampler.

Parameters:

Name	Type	Description	Default
`obj`	`object`	Object containing the `Parameter` instances to sample.	required
`fit_function`	`Callable`	Callable returning model y values for the supplied x values.	required

Methods:

Name	Description
`run`	Run Bayesian MCMC sampling using the BUMPS DREAM sampler.
`validate_arrays`	Validate the (x, y, weights) arrays handed to an engine.
`evaluate`	Evaluate the fit function for values of x.

Methods:

run(x, y, weights, samples=10000, burn=2000, thin=10, population=None, resume_state=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 bound object and fit function and runs the DREAM sampler. This is the engine-level entry point for Bayesian sampling; the public :class:~easyscience.fitting.Sampler delegates to this method after flattening its bound data.

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. Sampling has no default weighting, so weights are required; `Sampler` enforces this at construction.	required
`samples`	`int`	Number of raw samples to draw across all chains, before thinning. A guaranteed minimum, not an exact count: DREAM advances in blocks of 10 generations (one generation = one draw per chain) and stops at the first block boundary at or past `samples`.	`10000`
`burn`	`int`	Burn-in generations to discard. BUMPS counts `burn` in generations while `samples` counts raw draws, so `burn=500` discards `500 * n_chains` raw samples.	`2000`
`thin`	`int`	Thinning interval: only every `thin`-th generation is stored.	`10`
`population`	`int \| None`	BUMPS DREAM population count per parameter (number of parallel chains): BUMPS creates `ceil(population * n_parameters)` chains.	`None`
`resume_state`	`MCMCDraw \| None`	A BUMPS `MCMCDraw` state object from a previous `run()` call. When provided, DREAM continues the saved chain instead of starting cold. The population, parameter count, and parameter names must match the current model. Otherwise, a `ValueError` is raised. `samples` must be the total number of raw samples, not an increment: to extend an existing chain of `N` raw samples by `M`, pass `samples=N + M` (DREAM keeps only the last `samples` draws in its buffer). The `Sampler.extend` helper computes this for you. `burn` is forced to 0 on resume: a previously-converged chain is never re-burned. The `population` and `initializer` parameters have no effect when `resume_state` is provided. They are determined by the saved state. Resuming against different data is undefined behaviour (the chain's likelihood changes underneath it).	`None`
`sampler_kwargs`	`dict \| None`	Additional keyword arguments forwarded to `bumps.fitters.fit`.	`None`
`progress_callback`	`Callable[[dict], None] \| None`	Optional callback for progress updates during sampling. The payload dict includes `iteration` (DREAM generation number) and `sampling: True`. Any return value is ignored.	`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, if `progress_callback` is not callable, or if `resume_state` is incompatible with the current model (parameter count, names/order, or population mismatch).
`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).

validate_arrays(x, y, weights) staticmethod

Validate the (x, y, weights) arrays handed to an engine.

Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Independent variable array.	required
`y`	`np.ndarray`	Dependent variable array.	required
`weights`	`np.ndarray`	Weight array.	required

Raises:

Type	Description
`ValueError`	If any array is non-numeric, the shapes disagree, x or y contain NaN or infinite values, or the weights are non-finite or non-positive.

evaluate(x, 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
`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 `parameters` is not a dictionary.

Functions:

`validation`

Run-settings validation for the DREAM sampler.

Functions:

Name	Description
`validate_run_settings`	Validate the DREAM run settings.

Functions:

validate_run_settings(samples, burn, thin)

Validate the DREAM run settings.

Parameters:

Name	Type	Description	Default
`samples`	`int`	Number of raw samples to draw; must be a positive integer.	required
`burn`	`int`	Burn-in generations to discard; must be a non-negative integer.	required
`thin`	`int`	Thinning interval; must be a positive integer.	required

Raises:

Type	Description
`ValueError`	If any value is out of range or not an integer.

fitting

Classes

Modules

engine_base

Classes

EngineBase(obj, fit_function)

Methods:

fitter

Classes

Fitter(fit_object, fit_function)

Attributes

Methods:

Functions:

minimizers

Classes

Modules

bumps_utils

Classes

Functions:

Modules

minimizer_base

Classes

minimizer_bumps

Classes

Functions:

minimizer_dfo

Classes

minimizer_lmfit

Classes

utils

Classes

multi_fitter

Classes

MultiFitter(fit_objects=None, fit_functions=None)

Attributes

Methods:

Functions:

reshaping

Functions:

reshape_dataset(x, y, weights, vectorized)

reshape_datasets(x, y, weights, vectorized)

inject_x(fit_function, real_x=None, flatten=True)

inject_x_multi(fit_functions, real_x, dims, flatten=True)

sampler

Classes

SamplingResults(draws, param_names, logp, state) dataclass

Sampler(fit_object, fit_function, x, y, weights, vectorized=False, sampler_kwargs=None)

Attributes

Methods:

Functions:

load_chain(path, skip=0)

Modules

samplers

Classes

Functions:

Modules

sampler_bumps

Classes

Functions:

validation

Functions:

`fitting`

`engine_base`

`EngineBase(obj, fit_function)`

`fitter`

`Fitter(fit_object, fit_function)`

`minimizers`

`bumps_utils`

`minimizer_base`

`minimizer_bumps`

`minimizer_dfo`

`minimizer_lmfit`

`utils`

`multi_fitter`

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

`reshaping`

`reshape_dataset(x, y, weights, vectorized)`

`reshape_datasets(x, y, weights, vectorized)`

`inject_x(fit_function, real_x=None, flatten=True)`

`inject_x_multi(fit_functions, real_x, dims, flatten=True)`

`sampler`

`SamplingResults(draws, param_names, logp, state)` `dataclass`

`Sampler(fit_object, fit_function, x, y, weights, vectorized=False, sampler_kwargs=None)`

`load_chain(path, skip=0)`

`samplers`

`sampler_bumps`

`validation`