1 /* 2 * Licensed to the Apache Software Foundation (ASF) under one or more 3 * contributor license agreements. See the NOTICE file distributed with 4 * this work for additional information regarding copyright ownership. 5 * The ASF licenses this file to You under the Apache License, Version 2.0 6 * (the "License"); you may not use this file except in compliance with 7 * the License. You may obtain a copy of the License at 8 * 9 * https://www.apache.org/licenses/LICENSE-2.0 10 * 11 * Unless required by applicable law or agreed to in writing, software 12 * distributed under the License is distributed on an "AS IS" BASIS, 13 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 14 * See the License for the specific language governing permissions and 15 * limitations under the License. 16 */ 17 18 /* 19 * This is not the original file distributed by the Apache Software Foundation 20 * It has been modified by the Hipparchus project 21 */ 22 package org.hipparchus.transform; 23 24 /** 25 * This enumeration defines the various types of normalizations that can be 26 * applied to discrete Fourier transforms (DFT). The exact definition of these 27 * normalizations is detailed below. 28 * 29 * @see FastFourierTransformer 30 */ 31 public enum DftNormalization { 32 /** 33 * Should be passed to the constructor of {@link FastFourierTransformer} 34 * to use the <em>standard</em> normalization convention. This normalization 35 * convention is defined as follows 36 * <ul> 37 * <li>forward transform: y<sub>n</sub> = ∑<sub>k=0</sub><sup>N-1</sup> 38 * x<sub>k</sub> exp(-2πi n k / N),</li> 39 * <li>inverse transform: x<sub>k</sub> = N<sup>-1</sup> 40 * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li> 41 * </ul> 42 * where N is the size of the data sample. 43 */ 44 STANDARD, 45 46 /** 47 * Should be passed to the constructor of {@link FastFourierTransformer} 48 * to use the <em>unitary</em> normalization convention. This normalization 49 * convention is defined as follows 50 * <ul> 51 * <li>forward transform: y<sub>n</sub> = (1 / √N) 52 * ∑<sub>k=0</sub><sup>N-1</sup> x<sub>k</sub> 53 * exp(-2πi n k / N),</li> 54 * <li>inverse transform: x<sub>k</sub> = (1 / √N) 55 * ∑<sub>n=0</sub><sup>N-1</sup> y<sub>n</sub> exp(2πi n k / N),</li> 56 * </ul> 57 * which makes the transform unitary. N is the size of the data sample. 58 */ 59 UNITARY; 60 }