An object representing a calibration curve for TOC, IC or NPOC Measurements.
+
Description
+
This object instanciates a class that relates the area $A$ measured in the TOC Analyser to the concentration $c$ used in the calibration solutions. The calibration curve is affine, $c=aA+b$ where $a$ and $b$ denote slope and intercept, respectively, of the affine map. This object can be used to represent linear calibration curves for TC (total carbon)-, IC (inorganic carbon)- and NPOC (non-purgeable organic carbon) measurements.
+
Attributes
+
+exists : Bool- This value is set to
True if a calibration curve is already available, else it is set to False.
+slope : float- Value of the slope for the calibration of concentration (dependent variable) with measured area (independent variable).
+intercep : float- Value of the intercep for the calibration of concentration (dependent variable) with measured area (independent variable).
+R2_value : float- The coefficient of determination of the calibration curve. For a good calibration curve, this should be above 0.995.\
+excluded : list- A list of integers denoting the samples in the calibrant data that are excluded.
+lower_bound : float- The lower bound value for which this calibration curve can be used.
+upper_bound : float- The upper bound value for which this calibration curve can be used.
+computed_on : pd.Timestamp- A timestamp indicating the time at which the calibration curve was computed.
+
+
+Expand source code
+
+class CalibrationCurve():
+
+ r"""An object representing a calibration curve for TOC, IC or NPOC Measurements.
+
+ Description
+ -----------
+ This object instanciates a class that relates the area $A$ measured in the TOC Analyser to the concentration $c$ used in the calibration solutions. The calibration curve is affine, $c=aA+b$ where $a$ and $b$ denote slope and intercept, respectively, of the affine map. This object can be used to represent linear calibration curves for TC (total carbon)-, IC (inorganic carbon)- and NPOC (non-purgeable organic carbon) measurements.
+
+ Attributes
+ ----------
+ exists: Bool
+ This value is set to `True` if a calibration curve is already available, else it is set to `False`.
+ slope: float
+ Value of the slope for the calibration of concentration (dependent variable) with measured area (independent variable).
+ intercep: float
+ Value of the intercep for the calibration of concentration (dependent variable) with measured area (independent variable).
+ R2_value: float
+ The coefficient of determination of the calibration curve. For a good calibration curve, this should be above 0.995.\
+ excluded: list
+ A list of integers denoting the samples in the calibrant data that are excluded.
+ lower_bound: float
+ The lower bound value for which this calibration curve can be used.
+ upper_bound: float
+ The upper bound value for which this calibration curve can be used.
+ computed_on: pd.Timestamp
+ A timestamp indicating the time at which the calibration curve was computed.
+ """
+
+ def __init__(self):
+
+ # initialises all member variables
+
+ self.exists = False
+ self.slope = None
+ self.intercept = None
+ self.R2_value = None
+ self.excluded = None
+ self.lower_bound = None
+ self.upper_bound = None
+ self.computed_on = None
+
+ def __linear_function(self, x_values, a, b):
+
+ r"""Computes the value of the calibration curve for the passed parameters.
+
+ Description
+ -----------
+ Computes the affine map, $c=aA+b$, given data $A$ and parameters $a$ (slope) and $b$ (intercept).
+
+ Parameters
+ ----------
+ x_values: array-like
+ An array-like (array or list) object of length $n$ containing the data for the independent variable that are to be mapped.
+ a: float
+ The slope of the calibration curve.
+ b: float
+ The intercept of the calibration curve.
+
+ Returns
+ -------
+ y_values: np.ndarray
+ An array (of size $n$) containing the image under the affine map of `x_values`.
+ """
+
+ if not isinstance(x_values, np.ndarray):
+ x_values = np.array(x_values)
+
+ return a * x_values + b
+
+ def __optimise(self, x_data, y_data, p0):
+ return curve_fit(self.__linear_function, x_data, y_data, p0)
+
+ def __check_exist(self):
+
+ if self.exists == True:
+ return True
+ else:
+ return False
+
+ def __check(self):
+
+ if self.__check_exist():
+ warnings.warn("Calibration alreadyt exists, will be overwritten...")
+ else:
+ return
+
+ def calibrate(self, x_data, y_data):
+
+ """The docstring for this method
+
+ Description
+ -----------
+ Bla Bla Bla
+
+ Parameters
+ ----------
+ x_data: np.ndarray
+ Bla bla bla
+ y_data: np.ndarray
+ Bla Bla Bla
+ """
+
+ self.__check()
+
+ parameters, _ = self.__optimise(x_data=x_data, y_data=y_data, p0=[10, 0.1])
+
+ self.exists = True
+ self.slope = parameters[0]
+ self.intercept = parameters[1]
+ self.R2_value = compute_R2(y_true = y_data, y_est = self.__linear_function(x_values=x_data, a = parameters[0], b=parameters[1]))
+ self.lower_bound = np.min(y_data)
+ self.upper_bound = np.max(y_data)
+ self.computed_on = pd.to_datetime(datetime.datetime.now())
+
+ def compute(self, sample_areas):
+ return self.__linear_function(x_values=sample_areas, a = self.slope, b = self.intercept)
+
+