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/**
 * Copyright (c) 2016-2019 JEP AUTHORS.
 *
 * This file is licensed under the the zlib/libpng License.
 *
 * This software is provided 'as-is', without any express or implied
 * warranty. In no event will the authors be held liable for any
 * damages arising from the use of this software.
 * 
 * Permission is granted to anyone to use this software for any
 * purpose, including commercial applications, and to alter it and
 * redistribute it freely, subject to the following restrictions:
 * 
 *     1. The origin of this software must not be misrepresented; you
 *     must not claim that you wrote the original software. If you use
 *     this software in a product, an acknowledgment in the product
 *     documentation would be appreciated but is not required.
 * 
 *     2. Altered source versions must be plainly marked as such, and
 *     must not be misrepresented as being the original software.
 * 
 *     3. This notice may not be removed or altered from any source
 *     distribution.
 */
package jep;

import java.nio.Buffer;
import java.nio.CharBuffer;
import java.util.Arrays;

/**
 * 

* Represents a numpy.ndarray in Java. If Jep was compiled with numpy support, this * object will not be wrapped as a PyJobject in the Python * sub-interpreter(s), it will instead be wrapped as a numpy.ndarray * automatically. The numpy.ndarray will reference the exact same memory as the * buffer so changes in either language will be immediately visible in both. * *

* DirectNDArrays only support direct buffers as the underlying type of data. * The data can conceptually be multi-dimensional, but it must be represented as * a one-dimensional direct buffer in Java to ensure the memory is contiguous. *

* * * @author Ben Steffensmeier * @since 3.7 */ public class DirectNDArray extends AbstractNDArray { /** * Constructor for a Java DirectNDArray. Presumes the data is one * dimensional. * * @param data * a direct Buffer */ public DirectNDArray(T data) { super(data); } /** * Constructor for a Java DirectNDArray. Presumes the data is one * dimensional. * * @param data * a direct Buffer * @param unsigned * whether the data is to be interpreted as unsigned */ public DirectNDArray(T data, boolean unsigned) { super(data, unsigned); } /** * Constructor for a Java DirectNDArray. * * @param data * a direct Buffer * @param dimensions * the conceptual dimensions of the data (corresponds to the * numpy.ndarray dimensions in C-contiguous order) */ public DirectNDArray(T data, int... dimensions) { super(data, dimensions); } /** * Constructor for a Java DirectNDArray. * * @param data * a direct Buffer * @param unsigned * whether the data is to be interpreted as unsigned * @param dimensions * the conceptual dimensions of the data (corresponds to the * numpy.ndarray dimensions in C-contiguous order) */ public DirectNDArray(T data, boolean unsigned, int... dimensions) { super(data, unsigned, dimensions); } @Override protected void validate(T data) { if (!data.isDirect()) { throw new IllegalArgumentException( "DirectNDArray only supports direct buffers."); } else if (data instanceof CharBuffer) { throw new IllegalArgumentException( "DirectNDArray only supports numeric primitives, not CharBuffer"); } } @Override public int getLength(T data) { return data.capacity(); } @Override public boolean equals(Object obj) { if (this == obj) { return true; } if (obj == null) { return false; } if (getClass() != obj.getClass()) { return false; } DirectNDArray other = (DirectNDArray) obj; // unsigned should be same if (this.unsigned != other.unsigned) { return false; } /* * compare dimensions first cause that's most likely a shorter array to * compare and will be faster */ if (!Arrays.equals(dimensions, other.dimensions)) { return false; } // compare the data if (other.data == null) { return false; } // neither has null, let's compare values return data.equals(other.data); } @Override public int hashCode() { final int prime = 31; int result = 1; if (data == null) { result = prime * result + 0; } else { result = prime * result + data.hashCode(); } result = prime * result + Arrays.hashCode(dimensions) + (unsigned ? 1 : 0); return result; } // TODO override toString() to make it look like ndarray.__str__() }




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