fitting package¶
base module¶
Exposes base class for subclassing in specific data models.
- class cytoskeleton_analyser.fitting.base.Base(x: Sequence, p0: Sequence[float], x_units: Optional[str] = None)¶
Bases:
objectBase class for subclassing in specific data models.
- class Summary(name, p, chi2, mean, saturation)¶
Bases:
tuple- property chi2¶
Alias for field number 2
- property mean¶
Alias for field number 3
- property name¶
Alias for field number 0
- property p¶
Alias for field number 1
- property saturation¶
Alias for field number 4
- aik¶
AIK indicator.
- bounds¶
Bounds on the model parameters.
- cc¶
Components of the model prediction.
- chi2¶
Chi square.
- cov¶
Covariamce matrix of the fitted parameters.
- equilibrium_ind¶
Data index at equilibration.
- fano¶
Fano factor
- logger: logging.Logger = None¶
- mean¶
Model mean.
- name: Optional[str]¶
Model name.
- p: numpy.ndarray¶
Optilized values of model parameters.
- p0: numpy.ndarray¶
Initial values of model parameters.
- par¶
value}
- Type
Model parameters as a dictionary {‘name’
- predict(f: Callable, sl: slice) → numpy.ndarray¶
Model prediction.
- Parameters
f – Model function.
sl – Slice selecting model-specific parameters.
- prediction¶
Model prediction.
- report()¶
Dump a report summarizing the model to the logger.
- residnorm¶
Norm of residuals.
- saturation¶
Predicted saturation value.
- set_equilibration(y: numpy.ndarray) → None¶
Determines predicted time to equilibration if such exists.
Is only applicable if the model achieves saturation. Assumes that equilibration point is reached whenever monotonously decreasing difference between saturation value and the model prediction becomes less than standard deviation. Sets
equilibrium_ind: data array index at which equilibration is assumed to be achieved.- Parameters
y – Fitted data array.
- set_quality_indicators(y: numpy.ndarray) → None¶
Calculate quality indicators of the model after minimization.
- Parameters
y – Fitted data array.
- summary() → dict¶
Create a dictionary sumarizing the model.
- var¶
Model variance.
- x: numpy.ndarray¶
Data x-values.
- x_units: Optional[str]¶
Units for data x-values.
- cytoskeleton_analyser.fitting.base.set_logger(logger)¶