Analysis - Calibration ====================== Calibrating a generic model for given data, ie identifying model parameters so that the result of the model was in some sense the closest measured data. Configuration ------------- WrappedAction Workflow of calibrated model Parameters List of parameters name Name of parameter in calibration and inside of model group Group of parameter bounds Upper and lower bound of parameter init_value Init value of parameter offset, scale Body param. = scale * tuned param. + offset; default offset = 0.0, scale = 1.0 fixed If True then parameter is fixed in init value log_transform If True with parameter is internally operated as log10(parameter value) tied_params Parameters used in tied_expression tied_expression Python expression, may use other parameters (defined in tied_params), this parameter is not calibrated Observations List of observations name Name of observation group Group of observation weight Observation weight in target function upper_bound: If computed value is greater than this parameter, special penalization is applied lower_bound: If computed value is smaller than this parameter, special penalization is applied AlgorithmParameters Define approximation of derivatives, for each group of parameters. group: Parameter group diff_inc_rel Step for derivation eval relative diff_inc_abs Step for derivation eval absolute TerminationCriteria Define criteria for termination of calibration process. n_lowest, tol_lowest Stop if difference of min and max from n_lowest min values of objective function n_from_lowest Stop if n iterations without improvement n_param_change, tol_rel_param_change Stop if max relative change of parameter form last n_param_change is lower than tol_rel_param_change (must be satisfied for all parameters). If parameter is log transformed relative change is measured on log10 its value (this will be changed in future versions). n_max_steps Maximum number of iterations to perform MinimizationMethod Sets minimalization method. Must be one of: - L-BFGS-B - Limited-memory Broyden–Fletcher–Goldfarb–Shanno with bounds - SLSQP - Sequential Least SQuares Programming BoundsType Sets type of bounds of parameters. Must be one of: - hard - use bounds from underlying SciPy minimize - soft - use penalization if parameter go out of bounds Calibration input ----------------- observations Struct of individual observations Calibration output ------------------ optimisation Sequence of individual iterations: converge_reason Reason of convergence cumulative_n_evaluation Number of evaluation of criterial functon residual Criterial function in this iteration observations measured_value Desired value model_value Observation value corresponding to input parameters residual Difference between measured_value and model_value sensitivity Sensitivity of observation j is the magnitude of the j’th row of the Jacobian multiplied by the weight associated with that observation; this magnitude is then divided by the number of adjustable parameters. It is thus a measure of the sensitivity of that observation to all parameters involved in the parameter estimation process. parameters parameter_type Type of parameter, is one of Free, Tied, Fixed, Frozen value Parameter value in this iteration interval_estimate Not implemented yet sensitivity Sensitivity of the i’th parameter is the normalised (with respect to the number of observations) magnitude of the column of the Jacobian matrix pertaining to that parameter, with each element of that column multiplied by the weight pertaining to the respective observation. relative_sensitivity The relative sensitivity of a parameter is obtained by multiplying its sensitivity by the magnitude of the value of the parameter. It is thus a measure of the changes in model outputs that are incurred by a fractional change in the value of the parameter. result n_iter Number of iterations converge_reason Reason of convergence residual Criterial function after calibration Objective function ------------------ As objective function is used sum over individual observation of square of difference between measured value and modeled value multiplied by square of observation weight.