code: /*******************************************************************************
   * Copyright (c) 2013 Attila Kovacs <attila[AT]sigmyne.com>.
   * All rights reserved. 
   * 
   * This file is part of crush.
   * 
   *     crush is free software: you can redistribute it and/or modify
   *     it under the terms of the GNU General Public License as published by
   *     the Free Software Foundation, either version 3 of the License, or
   *     (at your option) any later version.
   * 
   *     crush is distributed in the hope that it will be useful,
   *     but WITHOUT ANY WARRANTY; without even the implied warranty of
   *     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
   *     GNU General Public License for more details.
   * 
   *     You should have received a copy of the GNU General Public License
   *     along with crush.  If not, see <http://www.gnu.org/licenses/>.
   * 
   * Contributors:
   *     Attila Kovacs <attila[AT]sigmyne.com> - initial API and implementation
   ******************************************************************************/
  package crush.filters;
  
  import java.util.Arrays;
  import java.util.stream.IntStream;
  
  import crush.Channel;
  import crush.Integration;
  import jnum.data.Statistics;
  
  public abstract class AdaptiveFilter extends VariedFilter {
  	/**
  	 * 
  	 */
  	private static final long serialVersionUID = -2499384682820252338L;
  	
  	// TODO noiseFiltering to be replaced by dependents accounting...
  	protected float[][] channelProfiles;
  	protected double dF;	
  	
  	private float[] profile;
  		
  	public AdaptiveFilter(Integration<?> integration) {
  		super(integration);
  	}
  
  	@Override
  	public AdaptiveFilter clone() {
  		AdaptiveFilter clone = (AdaptiveFilter) super.clone();
  		if(profile != null) clone.profile = new float[profile.length];
  		return clone;
  	}
  	
  	public AdaptiveFilter(Integration<?> integration, float[] data) {
  		super(integration, data);
  	}
  	
  	public float[] getProfile() { return profile; }
  	
  	@Override
  	protected void setIntegration(Integration<?> integration) {
  		super.setIntegration(integration);
  		channelProfiles = new float[getInstrument().size()][];		
  	}
  
  	protected void setSize(int nF) {
  		if(profile == null) profile = new float[nF];
  		else if(profile.length != nF) profile = new float[nF];
  		
  		dF = 0.5 / (nF * getInstrument().samplingInterval);	
  
  		for(int i=channelProfiles.length; --i >= 0; ) {
  			final float[] oldProfile = channelProfiles[i];
  			if(oldProfile == null) continue;
  			channelProfiles[i] = new float[nF];
  			resample(oldProfile, channelProfiles[i]);
  		}
  	}
  	
  	protected void resample(final float[] from, final float[] to) {
  		final double n = (double) from.length / to.length; 
  		
  		for(int i=to.length; --i >= 0; ) {
  			final int fromj = (int) Math.round(i * n);
  			final int toj = (int) Math.round((i+1) * n);
  			if(toj == fromj) to[i] = from[fromj];
  			else to[i] = Statistics.mean(from, fromj, toj);
  		}	
  	}
  	
  	@Override
  	protected void preFilter(Channel channel) {
  		super.preFilter(channel);
  	}
  	
  	@Override
  	protected void postFilter(Channel channel) {
  		accumulateProfile(channel);
  		super.postFilter(channel);
  	}
  
  	protected void accumulateProfile(Channel channel) {	
  	    final int c = channel.getIndex();
  	    
  	    IntStream.range(0, profile.length).parallel().forEach(f -> {
  	        channelProfiles[c][f] *= profile[f];
              profile[f] = channelProfiles[c][f];
  	    });
  	}
  	
  	@Override
  	protected double responseAt(int fch) {
  		if(profile == null) return 1.0;	
  		return profile[(fch * profile.length) / (nf+1)];
  	}
  	
  	public float[] getValidProfile(Channel channel) {
  		float[] response = channelProfiles[channel.getIndex()];
  		
  		if(response == null) if(profile != null) {
  			response = new float[profile.length];
  			Arrays.fill(response, 1.0F);
  		}
  		
  		return response;
  	}
  
  	@Override
  	protected double countParms() {
  		double hipassf = 0.5 / (integration.filterTimeScale);
  		final int minF = (int) Math.ceil(hipassf / dF);
  		
  		if(profile == null) return 0.0;
  		
  		return IntStream.range(minF, profile.length).parallel().mapToDouble(F ->  1.0 - profile[F] * profile[F]).sum();
  	}
  	
  }
  