SparseMatrixSerializeTest.java
/**
* Copyright (c) 2023, RTE (http://www.rte-france.com)
* This Source Code Form is subject to the terms of the Mozilla Public
* License, v. 2.0. If a copy of the MPL was not distributed with this
* file, You can obtain one at http://mozilla.org/MPL/2.0/.
* SPDX-License-Identifier: MPL-2.0
*/
package com.powsybl.math.matrix.serializer;
import com.google.common.jimfs.Configuration;
import com.google.common.jimfs.Jimfs;
import com.powsybl.math.matrix.MatrixFactory;
import com.powsybl.math.matrix.SparseMatrix;
import com.powsybl.math.matrix.SparseMatrixFactory;
import org.junit.jupiter.api.BeforeEach;
import org.junit.jupiter.api.Test;
import org.mockito.Mockito;
import java.io.ByteArrayInputStream;
import java.io.IOException;
import java.io.InputStream;
import java.io.OutputStream;
import java.io.UncheckedIOException;
import java.nio.file.FileSystem;
import java.nio.file.Path;
import java.util.Random;
import static org.junit.jupiter.api.Assertions.assertEquals;
import static org.junit.jupiter.api.Assertions.assertThrows;
/**
* @author Christian Biasuzzi {@literal <christian.biasuzzi at soft.it>}
*/
class SparseMatrixSerializeTest {
private final MatrixFactory matrixFactory = new SparseMatrixFactory();
private FileSystem fileSystem;
@BeforeEach
void setUp() {
fileSystem = Jimfs.newFileSystem(Configuration.unix());
}
private SparseMatrix createRandomSparseMatrix(int numRows, int numCols, double density) {
Random random = new Random();
SparseMatrix matrix = (SparseMatrix) matrixFactory.create(numRows, numCols, 0);
for (int col = 0; col < numCols; col++) {
for (int row = 0; row < numRows; row++) {
//Add a random value to the matrix with probability based on density
if (random.nextDouble() < density) {
matrix.set(row, col, random.nextDouble());
}
}
}
return matrix;
}
private SparseMatrix createSimpleSparseMatrix() {
SparseMatrix matrix = (SparseMatrix) matrixFactory.create(2, 5, 4);
matrix.set(0, 0, 1d);
matrix.set(1, 0, 2d);
matrix.set(0, 2, 3d);
matrix.set(1, 4, 4d);
return matrix;
}
@Test
void testSerializeToFile() {
SparseMatrix matrix = createSimpleSparseMatrix();
Path file = fileSystem.getPath("/work/sparse-matrix-test.bin");
matrix.write(file);
SparseMatrix m1 = SparseMatrix.read(file);
assertEquals(matrix, m1);
}
@Test
void testSerializeToFileExceptions() throws IOException {
SparseMatrix matrix = createSimpleSparseMatrix();
Path file = fileSystem.getPath("");
assertThrows(UncheckedIOException.class, () -> matrix.write(file));
assertThrows(UncheckedIOException.class, () -> SparseMatrix.read(file));
InputStream testInputStream = new ByteArrayInputStream(new byte[0]);
assertThrows(UncheckedIOException.class, () -> SparseMatrix.read(testInputStream));
OutputStream mockOutputStream = Mockito.mock(OutputStream.class);
Mockito.doThrow(new IOException()).when(mockOutputStream).close();
assertThrows(UncheckedIOException.class, () -> matrix.write(mockOutputStream));
}
@Test
void testSerializeLargerSparseMatrixToFile() {
//create a 100x100 sparse matrix with 20% (circa) non zero values
SparseMatrix matrix = createRandomSparseMatrix(100, 100, 0.2);
Path file = fileSystem.getPath("/work/sparse-large-matrix-test.bin");
matrix.write(file);
SparseMatrix m1 = SparseMatrix.read(file);
assertEquals(matrix, m1);
}
@Test
void testSerializeSparseMatrixToMatlabFormat() {
SparseMatrix matrix = createSimpleSparseMatrix();
Path file = fileSystem.getPath("/work/sparse-matrix-test.mat");
SparseMatrixMatSerializer.exportMat(matrix, file);
SparseMatrix m1 = SparseMatrixMatSerializer.importMat(file);
assertEquals(matrix, m1);
}
@Test
void testSerializeToMatlabFormatExceptions() throws IOException {
SparseMatrix matrix = createSimpleSparseMatrix();
Path file = fileSystem.getPath("");
assertThrows(UncheckedIOException.class, () -> SparseMatrixMatSerializer.exportMat(matrix, file));
assertThrows(UncheckedIOException.class, () -> SparseMatrixMatSerializer.importMat(file));
}
@Test
void testSerializeLargerSparseMatrixToMatlabFormat() {
//create a 100x100 sparse matrix with 30% (circa) non zero values
SparseMatrix matrix = createRandomSparseMatrix(100, 100, 0.3);
Path file = fileSystem.getPath("/work/sparse-large-matrix-test.mat");
SparseMatrixMatSerializer.exportMat(matrix, file);
SparseMatrix m1 = SparseMatrixMatSerializer.importMat(file);
assertEquals(matrix, m1);
}
}