/*
    * 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.apache.commons.math4.core.jdkmath.JdkMath;
  import org.apache.commons.numbers.core.Precision;
  import org.junit.Assert;
  import org.junit.Test;
  
  /**
   * Test case for the piecewise bicubic function.
   */
  public final class PiecewiseBicubicSplineInterpolatingFunctionTest {
      /**
       * 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];
  
          @SuppressWarnings("unused")
          PiecewiseBicubicSplineInterpolatingFunction bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval, zval);
  
          try {
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(null, yval, zval);
              Assert.fail("Failed to detect x null pointer");
          } catch (NullArgumentException iae) {
              // Expected.
          }
  
          try {
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, null, zval);
              Assert.fail("Failed to detect y null pointer");
          } catch (NullArgumentException iae) {
              // Expected.
          }
  
          try {
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(xval, yval1, zval);
              Assert.fail("Failed to detect insufficient y data");
          } catch (InsufficientDataException iae) {
              // Expected.
          }
  
          try {
              double zval1[][] = new double[4][4];
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
              bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
             bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
             bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 };
             bcf = new PiecewiseBicubicSplineInterpolatingFunction(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 testPlane() {
         final int numberOfElements = 10;
         final double minimumX = -10;
         final double maximumX = 10;
         final double minimumY = -10;
         final double maximumY = 10;
         final int numberOfSamples = 100;
 
         final double interpolationTolerance = 7e-15;
         final double maxTolerance = 6e-14;
 
         // Function values
         BivariateFunction f = new BivariateFunction() {
                 @Override
                 public double value(double x, double y) {
                     return 2 * x - 3 * y + 5;
                 }
             };
 
         testInterpolation(minimumX,
                           maximumX,
                           minimumY,
                           maximumY,
                           numberOfElements,
                           numberOfSamples,
                           f,
                           interpolationTolerance,
                           maxTolerance);
     }
 
     /**
      * Interpolating a paraboloid.
      * <p>
      * z = 2 x<sup>2</sup> - 3 y<sup>2</sup> + 4 x y - 5
      */
     @Test
     public void testParabaloid() {
         final int numberOfElements = 10;
         final double minimumX = -10;
         final double maximumX = 10;
         final double minimumY = -10;
         final double maximumY = 10;
         final int numberOfSamples = 100;
 
         final double interpolationTolerance = 1e-13;
         final double maxTolerance = 6e-14;
 
         // 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;
                 }
             };
 
         testInterpolation(minimumX,
                           maximumX,
                           minimumY,
                           maximumY,
                           numberOfElements,
                           numberOfSamples,
                           f,
                           interpolationTolerance,
                           maxTolerance);
     }
 
     /**
      * @param minimumX Lower bound of interpolation range along the x-coordinate.
      * @param maximumX Higher bound of interpolation range along the x-coordinate.
      * @param minimumY Lower bound of interpolation range along the y-coordinate.
      * @param maximumY Higher bound of interpolation range along the y-coordinate.
      * @param numberOfElements Number of data points (along each dimension).
      * @param numberOfSamples Number of test points.
      * @param f Function to test.
      * @param meanTolerance Allowed average error (mean error on all interpolated values).
      * @param maxTolerance Allowed error on each interpolated value.
      */
     private void testInterpolation(double minimumX,
                                    double maximumX,
                                    double minimumY,
                                    double maximumY,
                                    int numberOfElements,
                                    int numberOfSamples,
                                    BivariateFunction f,
                                    double meanTolerance,
                                    double maxTolerance) {
         double expected;
         double actual;
         double currentX;
         double currentY;
         final double deltaX = (maximumX - minimumX) / ((double) numberOfElements);
         final double deltaY = (maximumY - minimumY) / ((double) numberOfElements);
         final double[] xValues = new double[numberOfElements];
         final double[] yValues = new double[numberOfElements];
         final double[][] zValues = new double[numberOfElements][numberOfElements];
 
         for (int i = 0; i < numberOfElements; i++) {
             xValues[i] = minimumX + deltaX * (double) i;
             for (int j = 0; j < numberOfElements; j++) {
                 yValues[j] = minimumY + deltaY * (double) j;
                 zValues[i][j] = f.value(xValues[i], yValues[j]);
             }
         }
 
         final BivariateFunction interpolation
             = new PiecewiseBicubicSplineInterpolatingFunction(xValues,
                                                               yValues,
                                                               zValues);
 
         for (int i = 0; i < numberOfElements; i++) {
             currentX = xValues[i];
             for (int j = 0; j < numberOfElements; j++) {
                 currentY = yValues[j];
                 expected = f.value(currentX, currentY);
                 actual = interpolation.value(currentX, currentY);
                 Assert.assertTrue(Precision.equals(expected, actual));
             }
         }
 
         final UniformRandomProvider rng = RandomSource.WELL_19937_C.create(1234567L);
         final ContinuousDistribution.Sampler distX = UniformContinuousDistribution.of(xValues[0], xValues[xValues.length - 1]).createSampler(rng);
         final ContinuousDistribution.Sampler distY = UniformContinuousDistribution.of(yValues[0], yValues[yValues.length - 1]).createSampler(rng);
 
         double sumError = 0;
         for (int i = 0; i < numberOfSamples; i++) {
             currentX = distX.sample();
             currentY = distY.sample();
             expected = f.value(currentX, currentY);
             actual = interpolation.value(currentX, currentY);
             sumError += JdkMath.abs(actual - expected);
             Assert.assertEquals(expected, actual, maxTolerance);
         }
 
         final double meanError = sumError / numberOfSamples;
         Assert.assertEquals(0, meanError, meanTolerance);
     }
 }