AbsoluteQuantitation

class pyopenms.AbsoluteQuantitation

Bases: object

Cython implementation of _AbsoluteQuantitation

Original C++ documentation is available here

– Inherits from [‘DefaultParamHandler’]

__init__()

Overload:

__init__(self) → None

Overload:

__init__(self, in_0: AbsoluteQuantitation) → None

Methods

__init__	Overload:
applyCalibration(self, component, ...)
calculateBias(self, actual_concentration, ...)	This function calculates the bias of the calibration
calculateBiasAndR(self, ...)
calculateRatio(self, component_1, ...)
fitCalibration(self, ...)
getDefaults(self)	Returns the default parameters
getName(self)	Returns the name
getParameters(self)	Returns the parameters
getQuantMethods(self)
getSubsections(self)
optimizeCalibrationCurveIterative(self, ...)
optimizeSingleCalibrationCurve(self, ...)
quantifyComponents(self, unknowns)	This function applies the calibration curve, hence quantifying all the components
setName(self, in_0)	Sets the name
setParameters(self, param)	Sets the parameters
setQuantMethods(self, quant_methods)

applyCalibration(self, component: Feature, IS_component: Feature, feature_name: Union[bytes, str, String], transformation_model: Union[bytes, str, String], transformation_model_params: Param) → float

calculateBias(self, actual_concentration: float, calculated_concentration: float) → float

This function calculates the bias of the calibration

calculateBiasAndR(self, component_concentrations: List[AQS_featureConcentration], feature_name: Union[bytes, str, String], transformation_model: Union[bytes, str, String], transformation_model_params: Param, biases: List[float], correlation_coefficient: float) → None

calculateRatio(self, component_1: Feature, component_2: Feature, feature_name: Union[bytes, str, String]) → float

fitCalibration(self, component_concentrations: List[AQS_featureConcentration], feature_name: Union[bytes, str, String], transformation_model: Union[bytes, str, String], transformation_model_params: Param) → Param

getDefaults(self) → Param

Returns the default parameters

getName(self) → Union[bytes, str, String]

Returns the name

getParameters(self) → Param

Returns the parameters

getQuantMethods(self) → List[AbsoluteQuantitationMethod]

getSubsections(self) → List[bytes]

optimizeCalibrationCurveIterative(self, component_concentrations: List[AQS_featureConcentration], feature_name: Union[bytes, str, String], transformation_model: Union[bytes, str, String], transformation_model_params: Param, optimized_params: Param) → bool

optimizeSingleCalibrationCurve(self, component_name: Union[bytes, str, String], component_concentrations: List[AQS_featureConcentration]) → None

quantifyComponents(self, unknowns: FeatureMap) → None

This function applies the calibration curve, hence quantifying all the components

setName(self, in_0: Union[bytes, str, String]) → None

Sets the name

setParameters(self, param: Param) → None

Sets the parameters

setQuantMethods(self, quant_methods: List[AbsoluteQuantitationMethod]) → None