locan.analysis.drift.DriftComponent#

class locan.analysis.drift.DriftComponent(type=None, **kwargs)[source]#

Bases: object

Class carrying model functions to describe drift over time (in unit of frames). DriftComponent provides a transformation to apply a drift correction.

Standard models for constant, linear or polynomial drift correction are taken from lmfit.models. For fitting splines we use the scipy function scipy.interpolate.splrep().

Parameters:

type (Literal["none", "zero", "one", "constant", "linear", "polynomial", "spline"] | lmfit.models.Model | None) – Model class or indicator for setting up the corresponding model class.

Variables:

  • type – String indicator for model.

  • model (lmfit.models.Model | None) – The model definition (return value of scipy.interpolate.splrep())

  • model_result (lmfit.model.ModelResult, collection of model results.) – The results collected from fitting the model to specified data.

Methods

__init__([type])

eval(x)

Compute a transformation for time x from the drift model.

fit(x, y[, verbose])

Fit model to the given data and create self.model_results.
eval(x)[source]#

Compute a transformation for time x from the drift model.

Parameters:

x (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – frame values

Return type:

npt.NDArray[np.float64].

fit(x, y, verbose=False, **kwargs)[source]#

Fit model to the given data and create self.model_results.

Parameters:

  • x (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – x data

  • y (Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], bool, int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]) – y values

  • verbose (bool) – show plot

  • kwargs (Any) – Other parameters passed to lmfit.model.fit() or to scipy.interpolate.splrep() Use the parameter s to set the amount of smoothing.

Return type:

Self