.. _PhysiCell_java_CancerImmune_ImmunityEvent_java:

ImmunityEvent.java
==================

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      white-space: pre-wrap !important;       /* Сохраняет пробелы, но разрешает перенос */
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.. code-block:: console

    import java.util.Set;

    import ru.biosoft.physicell.biofvm.Microenvironment;
    import ru.biosoft.physicell.biofvm.VectorUtil;
    import ru.biosoft.physicell.core.Cell;
    import ru.biosoft.physicell.core.CellDefinition;
    import ru.biosoft.physicell.core.Model;
    import ru.biosoft.physicell.core.Model.Event;
    import ru.biosoft.physicell.core.RandomGenerator;

    public class ImmunityEvent extends Event
    {
    
        public ImmunityEvent(Model model)
        {
            super( model );
            executionTime = model.getParameterDouble( "immune_activation_time" );
        }

        @Override
        public void execute() throws Exception
        {
            //        System.out.println( "Therapy started!" );
            model.setSaveFullInterval( model.getParameterDouble( "save_interval_after_therapy_start" ) ); // 3.0; 
            introduceImmuneCells( model );
        }
        
        public void introduceImmuneCells(Model model) throws Exception
        {
            double[] box = model.getMicroenvironment().mesh.boundingBox;
            double[] center = new double[] {(box[0] +box[3])/2, (box[1] + box[4])/2, (box[2] + box[5])/2};
            
            RandomGenerator rng = model.getRNG();
            CellDefinition cd = model.getCellDefinition( "immune cell" );
            Microenvironment m = model.getMicroenvironment();
            Set<Cell> cells = m.getAgents( Cell.class );
            double tumor_radius = -9e9; // 250.0; 
            double temp_radius = 0.0;

            // for the loop, deal with the (faster) norm squared 
            for( Cell cell : cells )//int i=0; i < (all_cells).size() ; i++ )
            {
                temp_radius = VectorUtil.dist( center, cell.position );////VectorUtil.norm_squared( cell.position );
                if( temp_radius > tumor_radius )
                {
                    tumor_radius = temp_radius;
                }
            }
            // now square root to get to radius 
    //        tumor_radius = Math.sqrt( tumor_radius );

            // if this goes wackadoodle, choose 250 
    //        if( tumor_radius < 100.0 )
    //        {
    //            tumor_radius = 100.0;
    //        }
    //        tumor_radius = 100;
            System.out.println( "current tumor radius: " + tumor_radius );

            // now seed immune cells 

            int number_of_immune_cells = model.getParameterInt( "number_of_immune_cells" ); // 7500; // 100; // 40; 
            double radius_inner = tumor_radius + model.getParameterDouble( "initial_min_immune_distance_from_tumor" );// 30.0; // 75 // 50; 
            double radius_outer = radius_inner + model.getParameterDouble( "thickness_of_immune_seeding_region" ); // 75.0; // 100; // 1000 - 50.0; 

            double mean_radius = 0.5 * ( radius_inner + radius_outer );
            double std_radius = 0.33 * ( radius_outer - radius_inner ) / 2.0;


            for( int i = 0; i < number_of_immune_cells; i++ )
            {
            double theta = rng.UniformRandom() * 6.283185307179586476925286766559;
            double phi = Math.acos( 2.0 * rng.UniformRandom() - 1.0 );
            double radius = rng.NormalRandom( mean_radius, std_radius );
            double[] position = new double[] {radius * Math.cos( theta ) * Math.sin( phi ) +center[0],
                            radius * Math.sin( theta ) * Math.sin( phi )+center[1], radius * Math.cos( phi )+center[2]};
                Cell.createCell( cd, model, position );
            }
        }
    }