/*
    * 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
    *
    *      http://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.
   */
  package org.apache.commons.math4.legacy.analysis.interpolation;
  
  import org.apache.commons.math4.legacy.analysis.BivariateFunction;
  import org.apache.commons.statistics.distribution.ContinuousDistribution;
  import org.apache.commons.statistics.distribution.UniformContinuousDistribution;
  import org.apache.commons.math4.legacy.exception.DimensionMismatchException;
  import org.apache.commons.math4.legacy.exception.InsufficientDataException;
  import org.apache.commons.math4.legacy.exception.NonMonotonicSequenceException;
  import org.apache.commons.math4.legacy.exception.NullArgumentException;
  import org.apache.commons.rng.UniformRandomProvider;
  import org.apache.commons.rng.simple.RandomSource;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test case for the piecewise bicubic interpolator.
   */
  public final class PiecewiseBicubicSplineInterpolatorTest {
      /**
       * Test preconditions.
       */
      @Test
      public void testPreconditions() {
          double[] xval = new double[] { 3, 4, 5, 6.5, 7.5 };
          double[] yval = new double[] { -4, -3, -1, 2.5, 3.5 };
          double[][] zval = new double[xval.length][yval.length];
  
          BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator();
  
          try {
              interpolator.interpolate( null, yval, zval );
              Assert.fail( "Failed to detect x null pointer" );
          } catch ( NullArgumentException iae ) {
              // Expected.
          }
  
          try {
              interpolator.interpolate( xval, null, zval );
              Assert.fail( "Failed to detect y null pointer" );
          } catch ( NullArgumentException iae ) {
              // Expected.
          }
  
          try {
              interpolator.interpolate( xval, yval, null );
              Assert.fail( "Failed to detect z null pointer" );
          } catch ( NullArgumentException iae ) {
              // Expected.
          }
  
          try {
              double xval1[] = { 0.0, 1.0, 2.0, 3.0 };
              interpolator.interpolate( xval1, yval, zval );
              Assert.fail( "Failed to detect insufficient x data" );
          } catch ( InsufficientDataException iae ) {
              // Expected.
          }
  
          try  {
              double yval1[] = { 0.0, 1.0, 2.0, 3.0 };
              interpolator.interpolate( xval, yval1, zval );
              Assert.fail( "Failed to detect insufficient y data" );
          } catch ( InsufficientDataException iae ) {
              // Expected.
          }
  
          try {
              double zval1[][] = new double[4][4];
              interpolator.interpolate( xval, yval, zval1 );
              Assert.fail( "Failed to detect insufficient z data" );
          } catch ( InsufficientDataException iae ) {
              // Expected.
          }
  
          try {
              double xval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
              interpolator.interpolate( xval1, yval, zval );
              Assert.fail( "Failed to detect data set array with different sizes." );
          } catch ( DimensionMismatchException iae ) {
              // Expected.
          }
  
          try {
             double yval1[] = { 0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0 };
             interpolator.interpolate( xval, yval1, zval );
             Assert.fail( "Failed to detect data set array with different sizes." );
         } catch ( DimensionMismatchException iae ) {
             // Expected.
         }
 
         // X values not sorted.
         try {
             double xval1[] = { 0.0, 1.0, 0.5, 7.0, 3.5 };
             interpolator.interpolate( xval1, yval, zval );
             Assert.fail( "Failed to detect unsorted x arguments." );
         } catch ( NonMonotonicSequenceException iae ) {
             // Expected.
         }
 
         // Y values not sorted.
         try {
             double yval1[] = { 0.0, 1.0, 1.5, 0.0, 3.0 };
             interpolator.interpolate( xval, yval1, zval );
             Assert.fail( "Failed to detect unsorted y arguments." );
         } catch ( NonMonotonicSequenceException iae ) {
             // Expected.
         }
     }
 
     /**
      * Interpolating a plane.
      * <p>
      * z = 2 x - 3 y + 5
      */
     @Test
     public void testInterpolation1() {
         final int sz = 21;
         double[] xval = new double[sz];
         double[] yval = new double[sz];
         // Coordinate values
         final double delta = 1d / (sz - 1);
         for ( int i = 0; i < sz; i++ ){
             xval[i] = -1 + 15 * i * delta;
             yval[i] = -20 + 30 * i * delta;
         }
 
         // Function values
         BivariateFunction f = new BivariateFunction() {
                 @Override
                 public double value( double x, double y ) {
                     return 2 * x - 3 * y + 5;
                 }
             };
         double[][] zval = new double[xval.length][yval.length];
         for ( int i = 0; i < xval.length; i++ ) {
             for ( int j = 0; j < yval.length; j++ ) {
                 zval[i][j] = f.value(xval[i], yval[j]);
             }
         }
 
         BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator();
         BivariateFunction p = interpolator.interpolate(xval, yval, zval);
         double x;
         double y;
 
         final UniformRandomProvider rng = RandomSource.WELL_19937_C.create(1234567L);
         final ContinuousDistribution.Sampler distX = UniformContinuousDistribution.of(xval[0], xval[xval.length - 1]).createSampler(rng);
         final ContinuousDistribution.Sampler distY = UniformContinuousDistribution.of(yval[0], yval[yval.length - 1]).createSampler(rng);
 
         final int numSamples = 50;
         final double tol = 2e-14;
         for ( int i = 0; i < numSamples; i++ ) {
             x = distX.sample();
             for ( int j = 0; j < numSamples; j++ ) {
                 y = distY.sample();
 //                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                 Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
             }
 //             System.out.println();
         }
     }
 
     /**
      * Interpolating a paraboloid.
      * <p>
      * z = 2 x<sup>2</sup> - 3 y<sup>2</sup> + 4 x y - 5
      */
     @Test
     public void testInterpolation2() {
         final int sz = 21;
         double[] xval = new double[sz];
         double[] yval = new double[sz];
         // Coordinate values
         final double delta = 1d / (sz - 1);
         for ( int i = 0; i < sz; i++ ) {
             xval[i] = -1 + 15 * i * delta;
             yval[i] = -20 + 30 * i * delta;
         }
 
         // Function values
         BivariateFunction f = new BivariateFunction() {
                 @Override
                 public double value( double x, double y ) {
                     return 2 * x * x - 3 * y * y + 4 * x * y - 5;
                 }
             };
         double[][] zval = new double[xval.length][yval.length];
         for ( int i = 0; i < xval.length; i++ ) {
             for ( int j = 0; j < yval.length; j++ ) {
                 zval[i][j] = f.value(xval[i], yval[j]);
             }
         }
 
         BivariateGridInterpolator interpolator = new PiecewiseBicubicSplineInterpolator();
         BivariateFunction p = interpolator.interpolate(xval, yval, zval);
         double x;
         double y;
 
         final UniformRandomProvider rng = RandomSource.WELL_19937_C.create(1234567L);
         final ContinuousDistribution.Sampler distX = UniformContinuousDistribution.of(xval[0], xval[xval.length - 1]).createSampler(rng);
         final ContinuousDistribution.Sampler distY = UniformContinuousDistribution.of(yval[0], yval[yval.length - 1]).createSampler(rng);
 
         final int numSamples = 50;
         final double tol = 5e-13;
         for ( int i = 0; i < numSamples; i++ ) {
             x = distX.sample();
             for ( int j = 0; j < numSamples; j++ ) {
                 y = distY.sample();
 //                 System.out.println(x + " " + y + " " + f.value(x, y) + " " + p.value(x, y));
                 Assert.assertEquals(f.value(x, y),  p.value(x, y), tol);
             }
 //             System.out.println();
         }
     }
 }