[301] | 1 | // ----------------------------------------------------------------------- |
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| 2 | // Statistics.hh: Definition of the StatsContainer class, that holds |
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| 3 | // statistical parameters such as mean, median, rms, madfm. |
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| 4 | // ----------------------------------------------------------------------- |
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| 5 | // Copyright (C) 2006, Matthew Whiting, ATNF |
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| 6 | // |
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| 7 | // This program is free software; you can redistribute it and/or modify it |
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| 8 | // under the terms of the GNU General Public License as published by the |
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| 9 | // Free Software Foundation; either version 2 of the License, or (at your |
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| 10 | // option) any later version. |
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| 11 | // |
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| 12 | // Duchamp is distributed in the hope that it will be useful, but WITHOUT |
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| 13 | // ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or |
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| 14 | // FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License |
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| 15 | // for more details. |
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| 16 | // |
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| 17 | // You should have received a copy of the GNU General Public License |
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| 18 | // along with Duchamp; if not, write to the Free Software Foundation, |
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| 19 | // Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA |
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| 20 | // |
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| 21 | // Correspondence concerning Duchamp may be directed to: |
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| 22 | // Internet email: Matthew.Whiting [at] atnf.csiro.au |
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| 23 | // Postal address: Dr. Matthew Whiting |
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| 24 | // Australia Telescope National Facility, CSIRO |
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| 25 | // PO Box 76 |
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| 26 | // Epping NSW 1710 |
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| 27 | // AUSTRALIA |
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| 28 | // ----------------------------------------------------------------------- |
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[190] | 29 | #ifndef STATS_H |
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| 30 | #define STATS_H |
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| 31 | |
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[201] | 32 | #include <iostream> |
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[1146] | 33 | #include <fstream> |
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[190] | 34 | #include <math.h> |
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| 35 | |
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[528] | 36 | /// A namespace to control everything to do with statistical |
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| 37 | /// calculations. |
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[221] | 38 | |
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[190] | 39 | namespace Statistics |
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| 40 | { |
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| 41 | |
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[528] | 42 | /// @brief Divide by the following correction factor to convert from MADFM to sigma (rms) estimator. |
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[190] | 43 | const float correctionFactor = 0.6744888; |
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[221] | 44 | |
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[528] | 45 | /// @brief Multiply by the following correction factor to convert from trimmedSigma to sigma estimator. |
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[190] | 46 | const double trimToNormal = 1.17036753077; |
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| 47 | |
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[528] | 48 | /// @brief A templated function to do the MADFM-to-rms conversion. |
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[266] | 49 | template <class T> float madfmToSigma(T madfm); |
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[528] | 50 | /// @brief A non-templated function to do the rms-to-MADFM conversion. |
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[282] | 51 | float sigmaToMADFM(float sigma); |
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[190] | 52 | |
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[266] | 53 | |
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[528] | 54 | /// @brief |
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| 55 | /// Class to hold statistics for a given set of values. |
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| 56 | /// @details |
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| 57 | /// This class is designed to hold all useful statistics for a given |
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| 58 | /// array of numbers. It does *not* hold the data themselves. It |
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| 59 | /// provides the functions to calculate mean, rms, median and MADFM |
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| 60 | /// (median absolute deviation from median), as well as functions to |
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| 61 | /// control detection (ie. defining thresholds) in both standard |
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| 62 | /// (sigma-clipping) cases and False Detection Rate scenarios. |
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[221] | 63 | |
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[190] | 64 | template <class Type> |
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| 65 | class StatsContainer |
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| 66 | { |
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| 67 | public: |
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[1152] | 68 | StatsContainer(); |
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[190] | 69 | virtual ~StatsContainer(){}; |
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[192] | 70 | StatsContainer(const StatsContainer<Type>& s); |
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| 71 | StatsContainer<Type>& operator= (const StatsContainer<Type>& s); |
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[221] | 72 | |
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[528] | 73 | /// @brief A way of printing the statistics. |
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[222] | 74 | template <class T> |
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[282] | 75 | friend std::ostream& operator<< ( std::ostream& theStream, |
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| 76 | StatsContainer<T> &s); |
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[190] | 77 | |
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| 78 | float getMean(){return mean;}; |
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| 79 | void setMean(float f){mean=f;}; |
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| 80 | float getStddev(){return stddev;}; |
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| 81 | void setStddev(float f){stddev=f;}; |
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| 82 | Type getMedian(){return median;}; |
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| 83 | void setMedian(Type f){median=f;}; |
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| 84 | Type getMadfm(){return madfm;}; |
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| 85 | void setMadfm(Type f){madfm=f;}; |
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| 86 | float getThreshold(){return threshold;}; |
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| 87 | void setThreshold(float f){threshold=f;}; |
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| 88 | float getPThreshold(){return pThreshold;}; |
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| 89 | void setPThreshold(float f){pThreshold=f;}; |
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| 90 | bool getRobust(){return useRobust;}; |
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| 91 | void setRobust(bool b){useRobust=b;}; |
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| 92 | bool setUseFDR(){return useFDR;}; |
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| 93 | void setUseFDR(bool b){useFDR=b;}; |
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| 94 | |
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[528] | 95 | /// @brief Return the threshold as a signal-to-noise ratio. |
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[271] | 96 | float getThresholdSNR(); |
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[222] | 97 | |
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[528] | 98 | /// @brief Set the threshold in units of a signal-to-noise ratio. |
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[271] | 99 | void setThresholdSNR(float snr); |
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| 100 | |
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[528] | 101 | /// @brief Convert a value to a signal-to-noise ratio. |
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[481] | 102 | float valueToSNR(float value); |
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| 103 | |
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[528] | 104 | /// @brief Convert a signal-to-noise ratio to a flux value |
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[481] | 105 | float snrToValue(float snr); |
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[258] | 106 | |
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[528] | 107 | /// @brief Return the estimator of the middle value of the data. |
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[271] | 108 | float getMiddle(); |
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[475] | 109 | void setMiddle(float middle); |
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[271] | 110 | |
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[528] | 111 | /// @brief Return the estimator of the amount of spread of the data. |
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[271] | 112 | float getSpread(); |
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[475] | 113 | void setSpread(float spread); |
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[222] | 114 | |
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[528] | 115 | /// @brief Scale the noise by a given factor. |
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[275] | 116 | void scaleNoise(float scale); |
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| 117 | |
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[528] | 118 | /// @brief Return the Gaussian probability of a value given the stats. |
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[271] | 119 | float getPValue(float value); |
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[190] | 120 | |
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[528] | 121 | /// @brief Is a value above the threshold? |
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[271] | 122 | bool isDetection(float value); |
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[190] | 123 | |
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[1137] | 124 | /// @brief Set the comment characters |
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[1163] | 125 | void setCommentString(std::string comment){commentString = comment;}; |
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[1137] | 126 | |
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[190] | 127 | // Functions to calculate the stats for a given array. |
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| 128 | // The idea here is that there are two options to do the calculations: |
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| 129 | // *The first just uses all the points in the array. If you need to |
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| 130 | // remove BLANK points (or something similar), do this beforehand. |
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[282] | 131 | // *Alternatively, construct a mask array of the same size, |
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| 132 | // showing which points are good, and use the second option. |
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[222] | 133 | |
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[528] | 134 | /// @brief Calculate statistics for all elements of a data array |
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[190] | 135 | void calculate(Type *array, long size); |
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[222] | 136 | |
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[528] | 137 | /// @brief Calculate statistics for a subset of a data array |
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[282] | 138 | void calculate(Type *array, long size, bool *mask); |
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[190] | 139 | |
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[1146] | 140 | void writeToBinaryFile(std::string filename); |
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| 141 | std::streampos readFromBinaryFile(std::string filename, std::streampos loc=0); |
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| 142 | |
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[190] | 143 | private: |
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| 144 | bool defined; // a flag indicating whether the stats are defined. |
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| 145 | |
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[258] | 146 | float mean; ///< The mean, or average, of the values. |
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[221] | 147 | float stddev; ///< The standard deviation, or R.M.S., or sigma... |
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[258] | 148 | Type median; ///< The median of the values. |
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[221] | 149 | Type madfm; ///< The median absolute deviation from the median |
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[190] | 150 | |
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[221] | 151 | float threshold; ///< a threshold for simple sigma-clipping |
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[528] | 152 | float pThreshold; ///< a threshold for the FDR case -- the upper limit of P values that detected pixels can have. |
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[221] | 153 | bool useRobust; ///< whether we use the two robust stats or not |
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[528] | 154 | bool useFDR; ///< whether the FDR method is used for determining a detection |
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[190] | 155 | |
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[1137] | 156 | std::string commentString; ///< Any comment characters etc that need to be prepended to any output via the << operator. |
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| 157 | |
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[190] | 158 | }; |
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| 159 | |
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| 160 | } |
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| 161 | |
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[531] | 162 | #endif // STATS_H |
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