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 * ImageJ software for multidimensional image processing and analysis.
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package net.imagej.ops.filter.derivativeGauss;

import static org.junit.Assert.assertEquals;

import net.imagej.ops.AbstractOpTest;
import net.imglib2.Cursor;
import net.imglib2.RandomAccess;
import net.imglib2.img.Img;
import net.imglib2.type.numeric.real.DoubleType;

import org.junit.Test;

/**
 * Contains tests for {@link DefaultDerivativeGauss}.
 *
 * @author Gabe Selzer
 */
public class DefaultDerivativeGaussTest extends AbstractOpTest {

	@Test(expected = IllegalArgumentException.class)
	public void testTooFewDimensions() {
		final Img<DoubleType> input = ops.convert().float64(
			generateFloatArrayTestImg(false, 30));

		final Img<DoubleType> output = ops.create().img(input);

		final int[] derivatives = new int[] { 1, 0 };
		final double[] sigmas = new double[] { 1, 1 };
		ops.filter().derivativeGauss(output, input, derivatives, sigmas);
	}

	@Test(expected = IllegalArgumentException.class)
	public void testTooManyDimensions() {
		final Img<DoubleType> input = ops.convert().float64(
			generateFloatArrayTestImg(false, 30, 30, 30));

		final Img<DoubleType> output = ops.create().img(input);

		final int[] derivatives = new int[] { 1, 0 };
		final double[] sigmas = new double[] { 1, 1 };
		ops.filter().derivativeGauss(output, input, derivatives, sigmas);
	}

	@Test
	public void regressionTest() {
		final int width = 10;
		final Img<DoubleType> input = ops.convert().float64(
			generateFloatArrayTestImg(false, width, width));

		final Img<DoubleType> output = ops.create().img(input);

		// Draw a line on the image
		final RandomAccess<DoubleType> inputRA = input.randomAccess();
		inputRA.setPosition(5, 0);
		for (int i = 0; i < 10; i++) {
			inputRA.setPosition(i, 1);
			inputRA.get().set(255);
		}

		// filter the image
		final int[] derivatives = new int[] { 1, 0 };
		final double[] sigmas = new double[] { 0.5, 0.5 };
		ops.filter().derivativeGauss(output, input, derivatives, sigmas);

		final Cursor<DoubleType> cursor = output.localizingCursor();
		int currentPixel = 0;
		while (cursor.hasNext()) {
			cursor.fwd();
			assertEquals(cursor.get().getRealDouble(),
				regressionRowValues[currentPixel % width], 0);
			currentPixel++;
		}
	}

	double[] regressionRowValues = { 0.0, 0.0, 0.0, 2.1876502452391353,
		117.25400606437196, 0.0, -117.25400606437196, -2.1876502452391353, 0.0,
		0.0 };

}

@@ -1969,439 +1969,34 @@
 test
-TooFewDimensions() %7B%0A%09%09final Img%3CDoubleType%3E input = ops.convert().float64(%0A%09%09%09generateFloatArrayTestImg(false, 30));%0A%0A%09%09final Img%3CDoubleType%3E output = ops.create().img(input);%0A%0A%09%09final int%5B%5D derivatives = new int%5B%5D %7B 1, 0 %7D;%0A%09%09final double%5B%5D sigmas = new double%5B%5D %7B 1, 1 %7D;%0A%09%09ops.filter().derivativeGauss(output, input, derivatives, sigmas);%0A%09%7D%0A%0A%09@Test(expected = IllegalArgumentException.class)%0A%09public void testTooManyDimensions
+ImgParamDimensionsMismatch
 () %7B

Update derivativeGauss tests