package org.opentrafficsim.road.gtu.generator.headway;
   
   import org.djunits.value.vdouble.scalar.Duration;
   import org.djunits.value.vdouble.scalar.Time;
   import org.opentrafficsim.base.parameters.ParameterException;
   import org.opentrafficsim.core.distributions.Generator;
   import org.opentrafficsim.core.distributions.ProbabilityException;
   import org.opentrafficsim.core.dsol.OtsSimulatorInterface;
   
  import nl.tudelft.simulation.jstats.distributions.DistNormal;
  import nl.tudelft.simulation.jstats.streams.StreamInterface;
  
  /**
   * Headway generation based on {@code Arrivals}.
   * <p>
   * Copyright (c) 2013-2024 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved. <br>
   * BSD-style license. See <a href="https://opentrafficsim.org/docs/license.html">OpenTrafficSim License</a>.
   * </p>
   * @author <a href="https://github.com/averbraeck">Alexander Verbraeck</a>
   * @author <a href="https://tudelft.nl/staff/p.knoppers-1">Peter Knoppers</a>
   * @author <a href="https://github.com/wjschakel">Wouter Schakel</a>
   */
  public class ArrivalsHeadwayGenerator implements Generator<Duration>
  {
  
      /** Arrivals. */
      private final Arrivals arrivals;
  
      /** Simulator. */
      private final OtsSimulatorInterface simulator;
  
      /** Random stream to draw headway. */
      private final StreamInterface stream;
  
      /** Random headway generator. */
      private final HeadwayDistribution distribution;
  
      /** First GTU. */
      private boolean first = true;
  
      /**
       * @param arrivals Arrivals; arrivals
       * @param simulator OtsSimulatorInterface; simulator
       * @param stream StreamInterface; random stream to draw headway
       * @param distribution HeadwayDistribution; random headway distribution
       */
      public ArrivalsHeadwayGenerator(final Arrivals arrivals, final OtsSimulatorInterface simulator,
              final StreamInterface stream, final HeadwayDistribution distribution)
      {
          this.arrivals = arrivals;
          this.simulator = simulator;
          this.stream = stream;
          this.distribution = distribution;
      }
  
      /**
       * Returns a new headway {@code h} assuming that the previous vehicle arrived at the current time {@code t0}. The vehicle
       * thus arrives at {@code t1 = t0 + h}. This method guarantees that no vehicle arrives during periods where demand is zero,
       * while maintaining random headways based on average demand over a certain time period.<br>
       * <br>
       * The general method is to find {@code h} such that the integral of the demand pattern {@code D} from {@code t0} until
       * {@code t1} equals {@code r}: &#931;{@code D(t0 > t1) = r}. One can think of {@code r} as being 1 and representing an
       * additional vehicle to arrive. The headway {@code h} that results correlates directly to the mean demand between
       * {@code t0} and {@code t1}.<br>
       * <br>
       * The value of {@code r} always has a mean of 1, but may vary between specific vehicle arrivals depending on the headway
       * distribution. When assuming constant headways for any given demand level, {@code r} always equals 1. For exponentially
       * distributed headways {@code r} may range anywhere between 0 and infinity.<br>
       * <br>
       * This usage of {@code r} guarantees that no vehicles arrive during periods with 0 demand. For example:
       * <ul>
       * <li>Suppose we have 0 demand between 300s and 400s.</li>
       * <li>The previous vehicle was generated at 299s.</li>
       * <li>The demand at 299s equals 1800veh/h (1 veh per 2s).</li>
       * <li>For both constant and exponentially distributed headways, the expected next vehicle arrival based on this demand
       * value alone would be 299 + 2 = 301s. This is within the 0-demand period and should not happen. It's also not
       * theoretically sound, as the demand from 299s until 301s is not 1800veh/h on average.</li>
       * <li>Using integration we find that the surface of demand from 299s until 300s equals 0.5 veh for stepwise demand, and
       * 0.25 veh for linear demand. Consequently, the vehicle will not arrive until later slices integrate to an additional 0.5
       * veh or 0.75 veh respectively. This additional surface under the demand curve is only found after 400s.</li>
       * <li>In case the exponential headway distribution would have resulted in {@code r} &lt; 0.5 (stepwise demand) or 0.25
       * (linear demand), a vehicle will simply arrive between 299s and 300s.</li>
       * </ul>
       * <br>
       * @return Duration; new headway
       * @throws ProbabilityException if the stored collection is empty
       * @throws ParameterException in case of a parameter exception
       */
      @Override
      public Duration draw() throws ProbabilityException, ParameterException
      {
          Time now = this.simulator.getSimulatorAbsTime();
          // initial slice times and frequencies
          Time t1 = now;
          double f1 = this.arrivals.getFrequency(t1, true).si;
          Time t2 = this.arrivals.nextTimeSlice(t1);
          if (t2 == null)
          {
              return null; // no new vehicle
         }
         double f2 = this.arrivals.getFrequency(t2, false).si;
         // next vehicle's random factor
         double rem = this.distribution.draw(this.stream);
         if (this.first)
         {
             // first headway may be partially in the past, take a random factor
             rem *= this.stream.nextDouble();
             this.first = false;
         }
         // integrate until rem (by reducing it to 0.0, possibly in steps per slice)
         while (rem > 0.0)
         {
             // extrapolate to find 'integration = rem' in this slice giving demand slope, this may be beyond the slice length
             double dt = t2.si - t1.si;
             double t;
             double slope = (f2 - f1) / dt;
             if (Math.abs(slope) < 1e-12) // no slope
             {
                 if (f1 > 0.0)
                 {
                     t = rem / f1; // rem = t * f1, t = rem / f1
                 }
                 else
                 {
                     t = Double.POSITIVE_INFINITY; // no demand in this slice
                 }
             }
             else
             {
                 // reverse of trapezoidal rule: rem = t * (f1 + (f1 + t * slope)) / 2
                 double sqrt = 2 * slope * rem + f1 * f1;
                 if (sqrt >= 0.0)
                 {
                     t = (-f1 + Math.sqrt(sqrt)) / slope;
                 }
                 else
                 {
                     t = Double.POSITIVE_INFINITY; // not sufficient demand in this slice, with negative slope
                 }
             }
             if (t > dt)
             {
                 // next slice
                 rem -= dt * (f1 + f2) / 2; // subtract integral of this slice using trapezoidal rule
                 t1 = t2;
                 t2 = this.arrivals.nextTimeSlice(t1);
                 if (t2 == null)
                 {
                     return null; // no new vehicle
                 }
                 f1 = this.arrivals.getFrequency(t1, true).si; // we can't use f1 = f2 due to possible steps in demand
                 f2 = this.arrivals.getFrequency(t2, false).si;
             }
             else
             {
                 // return resulting integration times
                 return Duration.instantiateSI(t1.si + t - now.si);
             }
         }
         throw new RuntimeException("Exception while determining headway from Arrivals.");
     }
 
     /** {@inheritDoc} */
     @Override
     public String toString()
     {
         return "ArrivalsHeadwayGenerator [arrivals=" + this.arrivals + ", simulator=" + this.simulator + ", stream="
                 + this.stream + ", distribution=" + this.distribution + ", first=" + this.first + "]";
     }
 
     /**
      * Headway distribution.
      * <p>
      * Copyright (c) 2013-2024 Delft University of Technology, PO Box 5, 2600 AA, Delft, the Netherlands. All rights reserved.
      * <br>
      * BSD-style license. See <a href="https://opentrafficsim.org/docs/license.html">OpenTrafficSim License</a>.
      * </p>
      * @author <a href="https://github.com/averbraeck">Alexander Verbraeck</a>
      * @author <a href="https://tudelft.nl/staff/p.knoppers-1">Peter Knoppers</a>
      * @author <a href="https://github.com/wjschakel">Wouter Schakel</a>
      */
     public interface HeadwayDistribution
     {
 
         /** Constant headway. */
         HeadwayDistribution CONSTANT = new HeadwayDistribution()
         {
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 return 1.0;
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "CONSTANT";
             }
         };
 
         /** Exponential headway distribution. */
         HeadwayDistribution EXPONENTIAL = new HeadwayDistribution()
         {
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 return -Math.log(randomStream.nextDouble());
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "EXPONENTIAL";
             }
         };
 
         /** Uniform headway distribution. */
         HeadwayDistribution UNIFORM = new HeadwayDistribution()
         {
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 return 2.0 * randomStream.nextDouble();
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "UNIFORM";
             }
         };
 
         /** Triangular headway distribution. */
         HeadwayDistribution TRIANGULAR = new HeadwayDistribution()
         {
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 double r = randomStream.nextDouble();
                 if (r < .5)
                 {
                     return Math.sqrt(r * 2.0);
                 }
                 return 2.0 - Math.sqrt((1.0 - r) * 2.0);
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "TRIANGULAR";
             }
         };
 
         /** Triangular (left side, mean 2/3) and exponential (right side, mean 4/3) headway distribution. */
         HeadwayDistribution TRI_EXP = new HeadwayDistribution()
         {
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 double r = randomStream.nextDouble();
                 if (r < .5)
                 {
                     return Math.sqrt(r * 2.0); // left-hand side of triangular distribution, with mean 2/3
                 }
                 return 1.0 - Math.log((1.0 - r) * 2.0) / 3.0; // 1 + 1/3, where 1/3 is mean of exponential right-hand side
                 // note: 50% with mean 2/3 and 50% with mean 1 + 1/3 gives a mean of 1
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "TRI_EXP";
             }
         };
 
         /** Log-normal headway distribution (variance = 1.0). */
         HeadwayDistribution LOGNORMAL = new HeadwayDistribution()
         {
             /** Mu. */
             private final double mu = Math.log(1.0 / Math.sqrt(2.0));
 
             /** Sigma. */
             private final double sigma = Math.sqrt(Math.log(2.0));
 
             /** {@inheritDoc} */
             @Override
             public double draw(final StreamInterface randomStream)
             {
                 return Math.exp(new DistNormal(randomStream, this.mu, this.sigma).draw());
             }
 
             /** {@inheritDoc} */
             @Override
             public String getName()
             {
                 return "LOGNORMAL";
             }
         };
 
         /**
          * Draws a randomized headway factor. The average value returned is always 1.0. The returned value is applied on the
          * demand pattern by (reversed) integration to derive actual headways.
          * @param randomStream StreamInterface; random number stream
          * @return randomized headway factor
          */
         double draw(StreamInterface randomStream);
 
         /**
          * Returns the distribution name.
          * @return distribution name
          */
         String getName();
 
     }
 
 }