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
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: bytes | str | String, transformation_model: 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: bytes | str | String, transformation_model: bytes | str | String, transformation_model_params: Param, biases: List[float], correlation_coefficient: float) None #
- calculateRatio(self, component_1: Feature, component_2: Feature, feature_name: bytes | str | String) float #
- fitCalibration(self, component_concentrations: List[AQS_featureConcentration], feature_name: bytes | str | String, transformation_model: bytes | str | String, transformation_model_params: Param) Param #
- getQuantMethods(self) List[AbsoluteQuantitationMethod] #
- getSubsections(self) List[bytes] #
- optimizeCalibrationCurveIterative(self, component_concentrations: List[AQS_featureConcentration], feature_name: bytes | str | String, transformation_model: bytes | str | String, transformation_model_params: Param, optimized_params: Param) bool #
- optimizeSingleCalibrationCurve(self, component_name: 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
- setQuantMethods(self, quant_methods: List[AbsoluteQuantitationMethod]) None #