/*
    * Licensed to the Apache Software Foundation (ASF) under one or more
    * contributor license agreements.  See the NOTICE file distributed with
    * this work for additional information regarding copyright ownership.
    * The ASF licenses this file to You under the Apache License, Version 2.0
    * (the "License"); you may not use this file except in compliance with
    * the License.  You may obtain a copy of the License at
    *
    *      https://www.apache.org/licenses/LICENSE-2.0
   *
   * Unless required by applicable law or agreed to in writing, software
   * distributed under the License is distributed on an "AS IS" BASIS,
   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   * See the License for the specific language governing permissions and
   * limitations under the License.
   */
  
  /*
   * This is not the original file distributed by the Apache Software Foundation
   * It has been modified by the Hipparchus project
   */
  
  package org.hipparchus.ode;
  
  import java.io.Serializable;
  import java.util.ArrayList;
  import java.util.List;
  
  import org.hipparchus.exception.LocalizedCoreFormats;
  import org.hipparchus.exception.MathIllegalArgumentException;
  import org.hipparchus.exception.MathIllegalStateException;
  import org.hipparchus.ode.sampling.ODEStateInterpolator;
  import org.hipparchus.ode.sampling.ODEStepHandler;
  import org.hipparchus.util.FastMath;
  
  /**
   * This class stores all information provided by an ODE integrator
   * during the integration process and build a continuous model of the
   * solution from this.
   *
   * <p>This class act as a step handler from the integrator point of
   * view. It is called iteratively during the integration process and
   * stores a copy of all steps information in a sorted collection for
   * later use. Once the integration process is over, the user can use
   * the {@link #getInterpolatedState(double) getInterpolatedState}
   * method to retrieve this information at any time. It is important
   * to wait for the integration to be over before attempting to call
   * {@link #getInterpolatedState(double) getInterpolatedState} because
   * some internal variables are set only once the last step has been
   * handled.</p>
   *
   * <p>This is useful for example if the main loop of the user
   * application should remain independent from the integration process
   * or if one needs to mimic the behaviour of an analytical model
   * despite a numerical model is used (i.e. one needs the ability to
   * get the model value at any time or to navigate through the
   * data).</p>
   *
   * <p>If problem modeling is done with several separate
   * integration phases for contiguous intervals, the same
   * DenseOutputModel can be used as step handler for all
   * integration phases as long as they are performed in order and in
   * the same direction. As an example, one can extrapolate the
   * trajectory of a satellite with one model (i.e. one set of
   * differential equations) up to the beginning of a maneuver, use
   * another more complex model including thrusters modeling and
   * accurate attitude control during the maneuver, and revert to the
   * first model after the end of the maneuver. If the same continuous
   * output model handles the steps of all integration phases, the user
   * do not need to bother when the maneuver begins or ends, he has all
   * the data available in a transparent manner.</p>
   *
   * <p>An important feature of this class is that it implements the
   * <code>Serializable</code> interface. This means that the result of
   * an integration can be serialized and reused later (if stored into a
   * persistent medium like a filesystem or a database) or elsewhere (if
   * sent to another application). Only the result of the integration is
   * stored, there is no reference to the integrated problem by
   * itself.</p>
   *
   * <p>One should be aware that the amount of data stored in a
   * DenseOutputModel instance can be important if the state vector
   * is large, if the integration interval is long or if the steps are
   * small (which can result from small tolerance settings in {@link
   * org.hipparchus.ode.nonstiff.AdaptiveStepsizeIntegrator adaptive
   * step size integrators}).</p>
   *
   * @see ODEStepHandler
   * @see ODEStateInterpolator
   */
  
  public class DenseOutputModel implements ODEStepHandler, Serializable {
  
      /** Serializable version identifier */
      private static final long serialVersionUID = 20160328L;
  
      /** Initial integration time. */
      private double initialTime;
  
     /** Final integration time. */
     private double finalTime;
 
     /** Integration direction indicator. */
     private boolean forward;
 
     /** Current interpolator index. */
     private int index;
 
     /** Steps table. */
     private List<ODEStateInterpolator> steps;
 
     /** Simple constructor.
      * Build an empty continuous output model.
      */
     public DenseOutputModel() {
         steps       = new ArrayList<>();
         initialTime = Double.NaN;
         finalTime   = Double.NaN;
         forward     = true;
         index       = 0;
     }
 
     /** Append another model at the end of the instance.
      * @param model model to add at the end of the instance
      * @exception MathIllegalArgumentException if the model to append is not
      * compatible with the instance (dimension of the state vector,
      * propagation direction, hole between the dates)
      * @exception MathIllegalStateException if the number of functions evaluations is exceeded
      * during step finalization
      */
     public void append(final DenseOutputModel model)
         throws MathIllegalArgumentException, MathIllegalStateException {
 
         if (model.steps.isEmpty()) {
             return;
         }
 
         if (steps.isEmpty()) {
             initialTime = model.initialTime;
             forward     = model.forward;
         } else {
 
             final ODEStateAndDerivative s1 = steps.get(0).getPreviousState();
             final ODEStateAndDerivative s2 = model.steps.get(0).getPreviousState();
             checkDimensionsEquality(s1.getPrimaryStateDimension(), s2.getPrimaryStateDimension());
             checkDimensionsEquality(s1.getNumberOfSecondaryStates(), s2.getNumberOfSecondaryStates());
             for (int i = 0; i < s1.getNumberOfSecondaryStates(); ++i) {
                 checkDimensionsEquality(s1.getSecondaryStateDimension(i), s2.getSecondaryStateDimension(i));
             }
 
             if (forward ^ model.forward) {
                 throw new MathIllegalArgumentException(LocalizedODEFormats.PROPAGATION_DIRECTION_MISMATCH);
             }
 
             final ODEStateInterpolator lastInterpolator = steps.get(index);
             final double current  = lastInterpolator.getCurrentState().getTime();
             final double previous = lastInterpolator.getPreviousState().getTime();
             final double step = current - previous;
             final double gap = model.getInitialTime() - current;
             if (FastMath.abs(gap) > 1.0e-3 * FastMath.abs(step)) {
                 throw new MathIllegalArgumentException(LocalizedODEFormats.HOLE_BETWEEN_MODELS_TIME_RANGES,
                                                        FastMath.abs(gap));
             }
 
         }
 
         for (ODEStateInterpolator interpolator : model.steps) {
             steps.add(interpolator);
         }
 
         index = steps.size() - 1;
         finalTime = (steps.get(index)).getCurrentState().getTime();
 
     }
 
     /** Check dimensions equality.
      * @param d1 first dimension
      * @param d2 second dimansion
      * @exception MathIllegalArgumentException if dimensions do not match
      */
     private void checkDimensionsEquality(final int d1, final int d2)
         throws MathIllegalArgumentException {
         if (d1 != d2) {
             throw new MathIllegalArgumentException(LocalizedCoreFormats.DIMENSIONS_MISMATCH,
                                                    d2, d1);
         }
     }
 
     /** {@inheritDoc} */
     @Override
     public void init(final ODEStateAndDerivative initialState, final double targetTime) {
         initialTime    = initialState.getTime();
         this.finalTime = targetTime;
         forward        = true;
         index          = 0;
         steps.clear();
     }
 
     /** {@inheritDoc} */
     @Override
     public void handleStep(final ODEStateInterpolator interpolator) {
         if (steps.isEmpty()) {
             initialTime = interpolator.getPreviousState().getTime();
             forward     = interpolator.isForward();
         }
         steps.add(interpolator);
     }
 
     /** {@inheritDoc} */
     @Override
     public void finish(final ODEStateAndDerivative finalState) {
         finalTime = finalState.getTime();
         index     = steps.size() - 1;
     }
 
     /**
      * Get the initial integration time.
      * @return initial integration time
      */
     public double getInitialTime() {
         return initialTime;
     }
 
     /**
      * Get the final integration time.
      * @return final integration time
      */
     public double getFinalTime() {
         return finalTime;
     }
 
     /**
      * Get the state at interpolated time.
      * @param time time of the interpolated point
      * @return state at interpolated time
      */
     public ODEStateAndDerivative getInterpolatedState(final double time) {
 
         // initialize the search with the complete steps table
         int iMin = 0;
         final ODEStateInterpolator sMin = steps.get(iMin);
         double tMin = 0.5 * (sMin.getPreviousState().getTime() + sMin.getCurrentState().getTime());
 
         int iMax = steps.size() - 1;
         final ODEStateInterpolator sMax = steps.get(iMax);
         double tMax = 0.5 * (sMax.getPreviousState().getTime() + sMax.getCurrentState().getTime());
 
         // handle points outside of the integration interval
         // or in the first and last step
         if (locatePoint(time, sMin) <= 0) {
             index = iMin;
             return sMin.getInterpolatedState(time);
         }
         if (locatePoint(time, sMax) >= 0) {
             index = iMax;
             return sMax.getInterpolatedState(time);
         }
 
         // reduction of the table slice size
         while (iMax - iMin > 5) {
 
             // use the last estimated index as the splitting index
             final ODEStateInterpolator si = steps.get(index);
             final int location = locatePoint(time, si);
             if (location < 0) {
                 iMax = index;
                 tMax = 0.5 * (si.getPreviousState().getTime() + si.getCurrentState().getTime());
             } else if (location > 0) {
                 iMin = index;
                 tMin = 0.5 * (si.getPreviousState().getTime() + si.getCurrentState().getTime());
             } else {
                 // we have found the target step, no need to continue searching
                 return si.getInterpolatedState(time);
             }
 
             // compute a new estimate of the index in the reduced table slice
             final int iMed = (iMin + iMax) / 2;
             final ODEStateInterpolator sMed = steps.get(iMed);
             final double tMed = 0.5 * (sMed.getPreviousState().getTime() + sMed.getCurrentState().getTime());
 
             if ((FastMath.abs(tMed - tMin) < 1e-6) || (FastMath.abs(tMax - tMed) < 1e-6)) {
                 // too close to the bounds, we estimate using a simple dichotomy
                 index = iMed;
             } else {
                 // estimate the index using a reverse quadratic polynom
                 // (reverse means we have i = P(t), thus allowing to simply
                 // compute index = P(time) rather than solving a quadratic equation)
                 final double d12 = tMax - tMed;
                 final double d23 = tMed - tMin;
                 final double d13 = tMax - tMin;
                 final double dt1 = time - tMax;
                 final double dt2 = time - tMed;
                 final double dt3 = time - tMin;
                 final double iLagrange = ((dt2 * dt3 * d23) * iMax -
                                 (dt1 * dt3 * d13) * iMed +
                                 (dt1 * dt2 * d12) * iMin) /
                                 (d12 * d23 * d13);
                 index = (int) FastMath.rint(iLagrange);
             }
 
             // force the next size reduction to be at least one tenth
             final int low  = FastMath.max(iMin + 1, (9 * iMin + iMax) / 10);
             final int high = FastMath.min(iMax - 1, (iMin + 9 * iMax) / 10);
             if (index < low) {
                 index = low;
             } else if (index > high) {
                 index = high;
             }
 
         }
 
         // now the table slice is very small, we perform an iterative search
         index = iMin;
         while ((index <= iMax) && (locatePoint(time, steps.get(index)) > 0)) {
             ++index;
         }
 
         return steps.get(index).getInterpolatedState(time);
 
     }
 
     /** Compare a step interval and a double.
      * @param time point to locate
      * @param interval step interval
      * @return -1 if the double is before the interval, 0 if it is in
      * the interval, and +1 if it is after the interval, according to
      * the interval direction
      */
     private int locatePoint(final double time, final ODEStateInterpolator interval) {
         if (forward) {
             if (time < interval.getPreviousState().getTime()) {
                 return -1;
             } else if (time > interval.getCurrentState().getTime()) {
                 return +1;
             } else {
                 return 0;
             }
         }
         if (time > interval.getPreviousState().getTime()) {
             return -1;
         } else if (time < interval.getCurrentState().getTime()) {
             return +1;
         } else {
             return 0;
         }
     }
 
 }