You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: example_TFN_model.m
    path of file: ./repos/HydroSight/algorithms/models/TransferNoise/Example_model
    
    the code of the file until where you have to start completion: % example_TFN_model:
%
% Description
%   This example builds and calibrates a nonlinear transfer-function noise
%   model. The example is taken from Peterson & Western (2014). The model
%   requires the following three data files: 124705_boreData.mat,
%   124676_boreData.mat and 124705_forcingData.mat.
%
%   By default the example models bore 124676. By commenting out line 29
%   and un-commenting line 28, bore 124705 can be modelled.
%
%   Also, logical variables at line 112 and 113 also define which of 
%   two model structures are to be calibrated.
%
% References:
%
%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
%
% Author: 
%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
%   The University of Melbourne.
%
% Date:
%   26 Sept 2014
%

%clear all

% Comment out the one bore ID that you DO NOT want to model.
bore_ID = 'ID124705';

if strcmp(bore_ID,'ID124705')
    load('124705_boreData.mat');
else
    load('124676_boreData.mat');
end

load('124705_forcingData.mat');



% Reformat the matric of forcing data to a sturctire variable containing
% the column names.
forcingDataStruct.data
    
function noise
%   model. The example is taken from Peterson & Western (2014). The model
%   requires the following three data files: 124705_boreData.mat,
%   124676_boreData.mat and 124705_forcingData.mat.
%
%   By default the example models bore 124676. By commenting out line 29
%   and un-commenting line 28, bore 124705 can be modelled.
%
%   Also, logical variables at line 112 and 113 also define which of 
%   two model structures are to be calibrated.
%
% References:
%
%   Peterson and Western (2014), Nonlinear time-series modeling of unconfined
%   groundwater head, Water Resources Research, DOI: 10.1002/2013WR014800
%
% Author: 
%   Dr. Tim Peterson, The Department of Infrastructure Engineering, 
%   The University of Melbourne.
%
% Date:
%   26 Sept 2014
%

%clear all

% Comment out the one bore ID that you DO NOT want to model.
bore_ID = 'ID124705';

if strcmp(bore_ID,'ID124705')
    load('124705_boreData.mat');
else
    load('124676_boreData.mat');
end

load('124705_forcingData.mat');



% Reformat the matric of forcing data to a sturctire variable containing
% the column names.
forcingDataStruct.data = forcingData;
forcingDataStruct.colnames = {'YEAR','MONTH','DAY','PRECIP','APET','RevegFrac'};

% To increase performance, we can reduce the length of the climate record.
% This may cause longer time scales to be less reliably estimated.
yearsOfPriorForcing = 100;
forcingData_thresholddate  = datenum( boreDataWL(1,1)- yearsOfPriorForcing, boreDataWL(1,2), boreDataWL(1,3)); 
filt = datenum(forcingDataStruct.data(:,1), forcingDataStruct.data(:,2), forcingDataStruct.data(:,3)) >= forcingData_thresholddate;
forcingDataStruct.data = forcingDataStruct.data(filt,:);

% Define the bore ID and create sume dummy site coordinates. This must be
% for the bore and each column in the forcing file.
siteCoordinates = {bore_ID, 100, 100; 'PRECIP', 100, 100; 'APET', 100, 100; 'RevegFrac',602, 100};

% Define the way in which the precipitation is transformed. In this case it
% is transformed using the 'climateTransform_soilMoistureModels' soil
% model. 
% Next, the soil ODE needs inputs data 'precip' and 'et' and the forcing
% data input columns are 'PRECIP' and 'ET'.
% Next, the 'outputdata' that is to be taken from the soil model is
% defined. Each model has fixed options and here we're taking
% 'drainage'.
% Lastly, we can set 'options' for the soil model. In this case we are
% defining the initial values for three parameters (SMSC, beta, ksat) and
% fixing alpha to zero.
forcingTransform_Precip = {'transformfunction', 'climateTransform_soilMoistureModels'; ...
               'forcingdata', {'precip','PRECIP';'et','APET'}; ...
               'outputdata', 'drainage'; ...
               'options', {'SMSC',2,[];'beta',0,'';'k_sat',-inf,'fixed';'alpha',0,'fixed'}};
           
% The transformation of the ET is then defined. However because we've already 
% defined the soil model, we only need to specify the output we require.
% Here we're selecting  'evap_gw_potential', which is the potential ET -
% actual soil ET.
forcingTransform_ET = {'transformfunction', 'climateTransform_soilMoistureModels'; ...
               'outputdata', 'evap_gw_potential'};

% Next we create a cell array for all of the model options. The column format is:            
% the forcing name (can be anything), the setting we want to define (ie
% 'weightingfunction' or 'forcingdata'); and the setting we want to apply.
% Note 'responseFunction_Pearsons' is the name of a function.
modelOptions_9params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
                        'precip','forcingdata',forcingTransform_Precip; ...
                        'et','weightingfunction','responseFunction_PearsonsNegative'; ...
                        'et','forcingdata',forcingTransform_ET};

% Alternatively, we can create a cell array for the model options where the
% ET weighting function inherits the shape from the precipitation. The column format is:            
% the forcing name (can be anything), the setting we want to define (ie
% 'weightingfunction' or 'forcingdata'); and the setting we want to apply.
% Note 'responseFunction_Pearsons' is the name of a function.
modelOptions_7params = { 'precip','weightingfunction','responseFunction_Pearsons'; ...
                        'precip','forcingdata',forcingTransform_Precip; ...
                        'et','weightingfunction','derivedweighting_PearsonsNegativeRescaled'; ...
                        'et','inputcomponent','precip'; ...
                        'et','forcingdata',forcingTransform_ET};                    
                    
% Set the maximum frequency of water level obs
maxObsFreq = 7;

% Select which model structures to build and calibrate.
run7paramModel = true;
run9paramModel = false;

% Define a model lable
modelLabel = 'Great Western Catchment - no landuse change';

if run7paramModel
    % Build the 7 parameter model.
    model_7params = HydroSightModel(modelLabel, bore_ID, 'model_TFN', boreDataWL, maxObsFreq, forcingDataStruct, siteCoordinates, modelOptions_7params);

    % Set the number of SP-UCI calibration clusters per parameter
    SchemeSetting.ngs = 2*7;    
    
    % Calibrate the 7 parameter model.
    calibrateModel(model_7params, [], 0, inf, 'SP-UCI', SchemeSetting);
    
    % Plot the calibration results.    
    calibrateModelPlotResults(model_7params,[]);
   
    % Plot the simulation results. 
    time_points = model_7params.model.variables.time_points;
    newForcingData = [];
    simulationLabel = 'default simulation';
    doKrigingOnResiduals = false;    
    solveModel(model_7params, time_points, newForcingData, simulationLabel, doKrigingOnResiduals);    
    solveModelPlotResults(model_7params, simulationLabel, []);    
end