def interpolate(self, interpValues):
        """
        interpValues should be a dictionnary where the keys are the parameter names
        for the independant variable for which interpolation should be run and
        the values are the value to which the parameter should be interpolated.
        """        
        self.__operations.append(["interpolate", interpValues])          
        
        df = self.sampleDF
        self.interpValues = interpValues
        for interParamName, value  in interpValues.items():
            self.__report += "Interpolation of the parameters for independent variables '" \
                             + interParamName + "' at value " + str(value) + ".\n"   
            for ind, (paramTrace, resType) in enumerate(zip(df["obj_parameter"], df["Result type"])):
                if resType == "numericalTrace" and interParamName in paramTrace.indepNames:
                    val = paramTrace.getInterp1dValues(value, interParamName, statsToReturn=["mean"])
                    if isinstance(val, list):
                        if len(val) == 1:
                            val = val[0]
                        else:
                            raise ValueError("This case has not been implemented yet.")
                    df.loc[ind, "Values"] = float(val)
interpValues should be a dictionnary where the keys are the parameter names
        for the independant variable for which interpolation should be run and
        the values are the value to which the parameter should be interpolated.