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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29 | #ifndef STATS_H |
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30 | #define STATS_H |
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31 | |
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32 | #include <iostream> |
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33 | #include <math.h> |
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34 | |
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35 | /// A namespace to control everything to do with statistical |
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36 | /// calculations. |
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37 | |
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38 | namespace Statistics |
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39 | { |
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40 | |
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41 | /// @brief Divide by the following correction factor to convert from MADFM to sigma (rms) estimator. |
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42 | const float correctionFactor = 0.6744888; |
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43 | |
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44 | /// @brief Multiply by the following correction factor to convert from trimmedSigma to sigma estimator. |
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45 | const double trimToNormal = 1.17036753077; |
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46 | |
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47 | /// @brief A templated function to do the MADFM-to-rms conversion. |
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48 | template <class T> float madfmToSigma(T madfm); |
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49 | /// @brief A non-templated function to do the rms-to-MADFM conversion. |
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50 | float sigmaToMADFM(float sigma); |
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51 | |
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52 | |
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53 | /// @brief |
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54 | /// Class to hold statistics for a given set of values. |
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55 | /// @details |
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56 | /// This class is designed to hold all useful statistics for a given |
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57 | /// array of numbers. It does *not* hold the data themselves. It |
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58 | /// provides the functions to calculate mean, rms, median and MADFM |
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59 | /// (median absolute deviation from median), as well as functions to |
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60 | /// control detection (ie. defining thresholds) in both standard |
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61 | /// (sigma-clipping) cases and False Detection Rate scenarios. |
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62 | |
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63 | template <class Type> |
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64 | class StatsContainer |
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65 | { |
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66 | public: |
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67 | StatsContainer(){useRobust=true; defined=false; useFDR=false;}; |
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68 | virtual ~StatsContainer(){}; |
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69 | StatsContainer(const StatsContainer<Type>& s); |
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70 | StatsContainer<Type>& operator= (const StatsContainer<Type>& s); |
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71 | |
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72 | /// @brief A way of printing the statistics. |
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73 | template <class T> |
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74 | friend std::ostream& operator<< ( std::ostream& theStream, |
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75 | StatsContainer<T> &s); |
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76 | |
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77 | float getMean(){return mean;}; |
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78 | void setMean(float f){mean=f;}; |
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79 | float getStddev(){return stddev;}; |
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80 | void setStddev(float f){stddev=f;}; |
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81 | Type getMedian(){return median;}; |
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82 | void setMedian(Type f){median=f;}; |
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83 | Type getMadfm(){return madfm;}; |
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84 | void setMadfm(Type f){madfm=f;}; |
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85 | float getThreshold(){return threshold;}; |
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86 | void setThreshold(float f){threshold=f;}; |
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87 | float getPThreshold(){return pThreshold;}; |
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88 | void setPThreshold(float f){pThreshold=f;}; |
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89 | bool getRobust(){return useRobust;}; |
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90 | void setRobust(bool b){useRobust=b;}; |
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91 | bool setUseFDR(){return useFDR;}; |
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92 | void setUseFDR(bool b){useFDR=b;}; |
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93 | |
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94 | /// @brief Return the threshold as a signal-to-noise ratio. |
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95 | float getThresholdSNR(); |
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96 | |
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97 | /// @brief Set the threshold in units of a signal-to-noise ratio. |
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98 | void setThresholdSNR(float snr); |
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99 | |
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100 | /// @brief Convert a value to a signal-to-noise ratio. |
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101 | float valueToSNR(float value); |
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102 | |
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103 | /// @brief Convert a signal-to-noise ratio to a flux value |
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104 | float snrToValue(float snr); |
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105 | |
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106 | /// @brief Return the estimator of the middle value of the data. |
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107 | float getMiddle(); |
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108 | void setMiddle(float middle); |
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109 | |
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110 | /// @brief Return the estimator of the amount of spread of the data. |
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111 | float getSpread(); |
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112 | void setSpread(float spread); |
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113 | |
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114 | /// @brief Scale the noise by a given factor. |
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115 | void scaleNoise(float scale); |
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116 | |
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117 | /// @brief Return the Gaussian probability of a value given the stats. |
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118 | float getPValue(float value); |
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119 | |
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120 | /// @brief Is a value above the threshold? |
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121 | bool isDetection(float value); |
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122 | |
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123 | // Functions to calculate the stats for a given array. |
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124 | // The idea here is that there are two options to do the calculations: |
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125 | // *The first just uses all the points in the array. If you need to |
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126 | // remove BLANK points (or something similar), do this beforehand. |
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127 | // *Alternatively, construct a mask array of the same size, |
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128 | // showing which points are good, and use the second option. |
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129 | |
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130 | /// @brief Calculate statistics for all elements of a data array |
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131 | void calculate(Type *array, long size); |
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132 | |
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133 | /// @brief Calculate statistics for a subset of a data array |
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134 | void calculate(Type *array, long size, bool *mask); |
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135 | |
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136 | private: |
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137 | bool defined; // a flag indicating whether the stats are defined. |
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138 | |
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139 | float mean; ///< The mean, or average, of the values. |
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140 | float stddev; ///< The standard deviation, or R.M.S., or sigma... |
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141 | Type median; ///< The median of the values. |
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142 | Type madfm; ///< The median absolute deviation from the median |
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143 | |
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144 | float threshold; ///< a threshold for simple sigma-clipping |
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145 | float pThreshold; ///< a threshold for the FDR case -- the upper limit of P values that detected pixels can have. |
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146 | bool useRobust; ///< whether we use the two robust stats or not |
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147 | bool useFDR; ///< whether the FDR method is used for determining a detection |
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148 | |
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149 | }; |
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150 | |
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151 | } |
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152 | |
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153 | #endif // STATS_H |
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