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#4683 clang-format on all files in ApplicationCode
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Magne Sjaastad committed
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@@ -1,24 +1,23 @@
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/////////////////////////////////////////////////////////////////////////////////
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//
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// Copyright (C) 2011-2012 Statoil ASA, Ceetron AS
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//
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//
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// ResInsight is free software: you can redistribute it and/or modify
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// it under the terms of the GNU General Public License as published by
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// the Free Software Foundation, either version 3 of the License, or
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// (at your option) any later version.
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//
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//
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// ResInsight is distributed in the hope that it will be useful, but WITHOUT ANY
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// WARRANTY; without even the implied warranty of MERCHANTABILITY or
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// FITNESS FOR A PARTICULAR PURPOSE.
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//
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// See the GNU General Public License at <http://www.gnu.org/licenses/gpl.html>
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//
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// See the GNU General Public License at <http://www.gnu.org/licenses/gpl.html>
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// for more details.
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//
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/////////////////////////////////////////////////////////////////////////////////
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#include "RigStatisticsMath.h"
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#include "cvfMath.h"
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#include <algorithm>
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@@ -26,70 +25,67 @@
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#include <math.h>
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//--------------------------------------------------------------------------------------------------
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/// A function to do basic statistical calculations
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/// A function to do basic statistical calculations
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//--------------------------------------------------------------------------------------------------
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void RigStatisticsMath::calculateBasicStatistics(const std::vector<double>& values, double* min, double* max, double* sum, double* range, double* mean, double* dev)
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void RigStatisticsMath::calculateBasicStatistics(
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const std::vector<double>& values, double* min, double* max, double* sum, double* range, double* mean, double* dev )
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{
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double m_min(HUGE_VAL);
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double m_max(-HUGE_VAL);
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double m_mean(HUGE_VAL);
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double m_dev(HUGE_VAL);
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double m_min( HUGE_VAL );
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double m_max( -HUGE_VAL );
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double m_mean( HUGE_VAL );
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double m_dev( HUGE_VAL );
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double m_sum = 0.0;
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double m_sum = 0.0;
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double sumSquared = 0.0;
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size_t validValueCount = 0;
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for (size_t i = 0; i < values.size(); i++)
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for ( size_t i = 0; i < values.size(); i++ )
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{
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double val = values[i];
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if (RiaStatisticsTools::isInvalidNumber<double>(val)) continue;
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if ( RiaStatisticsTools::isInvalidNumber<double>( val ) ) continue;
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validValueCount++;
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if (val < m_min) m_min = val;
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if (val > m_max) m_max = val;
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if ( val < m_min ) m_min = val;
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if ( val > m_max ) m_max = val;
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m_sum += val;
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sumSquared += (val * val);
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sumSquared += ( val * val );
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}
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if (validValueCount > 0)
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if ( validValueCount > 0 )
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{
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m_mean = m_sum / validValueCount;
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// http://en.wikipedia.org/wiki/Standard_deviation#Rapid_calculation_methods
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// Running standard deviation
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double s0 = static_cast<double>(validValueCount);
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double s0 = static_cast<double>( validValueCount );
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double s1 = m_sum;
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double s2 = sumSquared;
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m_dev = sqrt( (s0 * s2) - (s1 * s1) ) / s0;
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m_dev = sqrt( ( s0 * s2 ) - ( s1 * s1 ) ) / s0;
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}
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if (min) *min = m_min;
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if (max) *max = m_max;
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if (sum) *sum = m_sum;
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if (range) *range = m_max - m_min;
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if ( min ) *min = m_min;
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if ( max ) *max = m_max;
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if ( sum ) *sum = m_sum;
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if ( range ) *range = m_max - m_min;
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if (mean) *mean = m_mean;
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if (dev) *dev = m_dev;
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if ( mean ) *mean = m_mean;
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if ( dev ) *dev = m_dev;
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}
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//--------------------------------------------------------------------------------------------------
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/// Algorithm:
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/// https://en.wikipedia.org/wiki/Percentile#Third_variant,_'%22%60UNIQ--postMath-00000052-QINU%60%22'
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//--------------------------------------------------------------------------------------------------
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void RigStatisticsMath::calculateStatisticsCurves(const std::vector<double>& values,
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double* p10,
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double* p50,
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double* p90,
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double* mean)
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void RigStatisticsMath::calculateStatisticsCurves(
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const std::vector<double>& values, double* p10, double* p50, double* p90, double* mean )
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{
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CVF_ASSERT(p10 && p50 && p90 && mean);
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CVF_ASSERT( p10 && p50 && p90 && mean );
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enum PValue
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{
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@@ -102,12 +98,12 @@ void RigStatisticsMath::calculateStatisticsCurves(const std::vector<double>& val
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double valueSum = 0;
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{
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std::multiset<double> vSet(values.begin(), values.end());
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for (double v : vSet)
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std::multiset<double> vSet( values.begin(), values.end() );
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for ( double v : vSet )
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{
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if (RiaStatisticsTools::isValidNumber(v))
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if ( RiaStatisticsTools::isValidNumber( v ) )
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{
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sortedValues.push_back(v);
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sortedValues.push_back( v );
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valueSum += v;
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}
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}
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@@ -117,21 +113,21 @@ void RigStatisticsMath::calculateStatisticsCurves(const std::vector<double>& val
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double percentiles[] = {0.1, 0.5, 0.9};
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double pValues[] = {HUGE_VAL, HUGE_VAL, HUGE_VAL};
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for (int i = P10; i <= P90; i++)
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for ( int i = P10; i <= P90; i++ )
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{
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// Check valid params
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if ((percentiles[i] < 1.0 / ((double)valueCount + 1)) || (percentiles[i] > (double)valueCount / ((double)valueCount + 1)))
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if ( ( percentiles[i] < 1.0 / ( (double)valueCount + 1 ) ) ||
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( percentiles[i] > (double)valueCount / ( (double)valueCount + 1 ) ) )
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continue;
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double rank = percentiles[i] * (valueCount + 1) - 1;
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double rank = percentiles[i] * ( valueCount + 1 ) - 1;
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double rankRem;
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double rankFrac = std::modf(rank, &rankRem);
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int rankInt = static_cast<int>(rankRem);
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double rankFrac = std::modf( rank, &rankRem );
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int rankInt = static_cast<int>( rankRem );
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if (rankInt < valueCount - 1)
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if ( rankInt < valueCount - 1 )
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{
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pValues[i] = sortedValues[rankInt] + rankFrac * (sortedValues[rankInt + 1] - sortedValues[rankInt]);
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pValues[i] = sortedValues[rankInt] + rankFrac * ( sortedValues[rankInt + 1] - sortedValues[rankInt] );
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}
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else
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{
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@@ -151,33 +147,34 @@ void RigStatisticsMath::calculateStatisticsCurves(const std::vector<double>& val
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/// the inputValues does not contain any valid values
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//--------------------------------------------------------------------------------------------------
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std::vector<double> RigStatisticsMath::calculateNearestRankPercentiles(const std::vector<double> & inputValues, const std::vector<double>& pValPositions)
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std::vector<double> RigStatisticsMath::calculateNearestRankPercentiles( const std::vector<double>& inputValues,
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const std::vector<double>& pValPositions )
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{
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std::vector<double> sortedValues;
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sortedValues.reserve(inputValues.size());
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sortedValues.reserve( inputValues.size() );
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for (size_t i = 0; i < inputValues.size(); ++i)
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for ( size_t i = 0; i < inputValues.size(); ++i )
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{
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if (RiaStatisticsTools::isValidNumber<double>(inputValues[i]))
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if ( RiaStatisticsTools::isValidNumber<double>( inputValues[i] ) )
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{
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sortedValues.push_back(inputValues[i]);
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sortedValues.push_back( inputValues[i] );
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}
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}
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std::sort(sortedValues.begin(), sortedValues.end());
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std::sort( sortedValues.begin(), sortedValues.end() );
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std::vector<double> percentiles(pValPositions.size(), HUGE_VAL);
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if (sortedValues.size())
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std::vector<double> percentiles( pValPositions.size(), HUGE_VAL );
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if ( sortedValues.size() )
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{
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for (size_t i = 0; i < pValPositions.size(); ++i)
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for ( size_t i = 0; i < pValPositions.size(); ++i )
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{
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double pVal = HUGE_VAL;
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size_t pValIndex = static_cast<size_t>(sortedValues.size() * cvf::Math::abs(pValPositions[i]) / 100);
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size_t pValIndex = static_cast<size_t>( sortedValues.size() * cvf::Math::abs( pValPositions[i] ) / 100 );
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if (pValIndex >= sortedValues.size() ) pValIndex = sortedValues.size() - 1;
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if ( pValIndex >= sortedValues.size() ) pValIndex = sortedValues.size() - 1;
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pVal = sortedValues[pValIndex];
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pVal = sortedValues[pValIndex];
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percentiles[i] = pVal;
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}
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}
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@@ -190,39 +187,41 @@ std::vector<double> RigStatisticsMath::calculateNearestRankPercentiles(const std
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/// This method treats HUGE_VAL as "undefined" values, and ignores these. Will return HUGE_VAL if
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/// the inputValues does not contain any valid values
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//--------------------------------------------------------------------------------------------------
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std::vector<double> RigStatisticsMath::calculateInterpolatedPercentiles(const std::vector<double> & inputValues, const std::vector<double>& pValPositions)
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std::vector<double> RigStatisticsMath::calculateInterpolatedPercentiles( const std::vector<double>& inputValues,
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const std::vector<double>& pValPositions )
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{
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std::vector<double> sortedValues;
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sortedValues.reserve(inputValues.size());
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sortedValues.reserve( inputValues.size() );
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for (size_t i = 0; i < inputValues.size(); ++i)
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for ( size_t i = 0; i < inputValues.size(); ++i )
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{
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if (RiaStatisticsTools::isValidNumber<double>(inputValues[i]))
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if ( RiaStatisticsTools::isValidNumber<double>( inputValues[i] ) )
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{
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sortedValues.push_back(inputValues[i]);
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sortedValues.push_back( inputValues[i] );
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}
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}
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std::sort(sortedValues.begin(), sortedValues.end());
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std::sort( sortedValues.begin(), sortedValues.end() );
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std::vector<double> percentiles(pValPositions.size(), HUGE_VAL);
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if (sortedValues.size())
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std::vector<double> percentiles( pValPositions.size(), HUGE_VAL );
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if ( sortedValues.size() )
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{
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for (size_t i = 0; i < pValPositions.size(); ++i)
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for ( size_t i = 0; i < pValPositions.size(); ++i )
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{
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double pVal = HUGE_VAL;
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double doubleIndex = (sortedValues.size() - 1) * cvf::Math::abs(pValPositions[i]) / 100.0;
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double doubleIndex = ( sortedValues.size() - 1 ) * cvf::Math::abs( pValPositions[i] ) / 100.0;
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size_t lowerValueIndex = static_cast<size_t>(floor(doubleIndex));
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size_t lowerValueIndex = static_cast<size_t>( floor( doubleIndex ) );
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size_t upperValueIndex = lowerValueIndex + 1;
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double upperValueWeight = doubleIndex - lowerValueIndex;
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assert(upperValueWeight < 1.0);
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assert( upperValueWeight < 1.0 );
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if (upperValueIndex < sortedValues.size())
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if ( upperValueIndex < sortedValues.size() )
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{
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pVal = (1.0 - upperValueWeight) * sortedValues[lowerValueIndex] + upperValueWeight * sortedValues[upperValueIndex];
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pVal = ( 1.0 - upperValueWeight ) * sortedValues[lowerValueIndex] +
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upperValueWeight * sortedValues[upperValueIndex];
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}
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else
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{
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@@ -236,101 +235,105 @@ std::vector<double> RigStatisticsMath::calculateInterpolatedPercentiles(const st
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}
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//--------------------------------------------------------------------------------------------------
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///
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///
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//--------------------------------------------------------------------------------------------------
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RigHistogramCalculator::RigHistogramCalculator(double min, double max, size_t nBins, std::vector<size_t>* histogram)
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RigHistogramCalculator::RigHistogramCalculator( double min, double max, size_t nBins, std::vector<size_t>* histogram )
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{
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assert(histogram);
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assert(nBins > 0);
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assert( histogram );
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assert( nBins > 0 );
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if (max == min) { nBins = 1; } // Avoid dividing on 0 range
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if ( max == min )
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{
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nBins = 1;
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} // Avoid dividing on 0 range
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m_histogram = histogram;
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m_min = min;
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m_histogram = histogram;
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m_min = min;
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m_observationCount = 0;
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// Initialize bins
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m_histogram->resize(nBins);
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for (size_t i = 0; i < m_histogram->size(); ++i) (*m_histogram)[i] = 0;
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m_histogram->resize( nBins );
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for ( size_t i = 0; i < m_histogram->size(); ++i )
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( *m_histogram )[i] = 0;
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m_range = max - min;
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m_maxIndex = nBins-1;
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m_range = max - min;
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m_maxIndex = nBins - 1;
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}
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//--------------------------------------------------------------------------------------------------
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///
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///
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//--------------------------------------------------------------------------------------------------
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void RigHistogramCalculator::addValue(double value)
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void RigHistogramCalculator::addValue( double value )
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{
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if (RiaStatisticsTools::isInvalidNumber<double>(value)) return;
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if ( RiaStatisticsTools::isInvalidNumber<double>( value ) ) return;
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size_t index = 0;
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if (m_maxIndex > 0) index = (size_t)(m_maxIndex*(value - m_min)/m_range);
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if ( m_maxIndex > 0 ) index = ( size_t )( m_maxIndex * ( value - m_min ) / m_range );
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if (index < m_histogram->size()) // Just clip to the max min range (-index will overflow to positive )
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if ( index < m_histogram->size() ) // Just clip to the max min range (-index will overflow to positive )
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{
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(*m_histogram)[index]++;
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( *m_histogram )[index]++;
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m_observationCount++;
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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///
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//--------------------------------------------------------------------------------------------------
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void RigHistogramCalculator::addData(const std::vector<double>& data)
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void RigHistogramCalculator::addData( const std::vector<double>& data )
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{
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assert(m_histogram);
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for (size_t i = 0; i < data.size(); ++i)
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assert( m_histogram );
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for ( size_t i = 0; i < data.size(); ++i )
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{
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addValue(data[i]);
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addValue( data[i] );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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///
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//--------------------------------------------------------------------------------------------------
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void RigHistogramCalculator::addData(const std::vector<float>& data)
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void RigHistogramCalculator::addData( const std::vector<float>& data )
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{
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assert(m_histogram);
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for (size_t i = 0; i < data.size(); ++i)
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assert( m_histogram );
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for ( size_t i = 0; i < data.size(); ++i )
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{
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addValue(data[i]);
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addValue( data[i] );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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///
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//--------------------------------------------------------------------------------------------------
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double RigHistogramCalculator::calculatePercentil(double pVal)
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double RigHistogramCalculator::calculatePercentil( double pVal )
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{
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assert(m_histogram);
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assert(m_histogram->size());
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assert( 0.0 <= pVal && pVal <= 1.0);
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assert( m_histogram );
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assert( m_histogram->size() );
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assert( 0.0 <= pVal && pVal <= 1.0 );
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double pValObservationCount = pVal*m_observationCount;
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if (pValObservationCount == 0.0) return m_min;
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double pValObservationCount = pVal * m_observationCount;
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if ( pValObservationCount == 0.0 ) return m_min;
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size_t accObsCount = 0;
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double binWidth = m_range/m_histogram->size();
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for (size_t binIdx = 0; binIdx < m_histogram->size(); ++binIdx)
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double binWidth = m_range / m_histogram->size();
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for ( size_t binIdx = 0; binIdx < m_histogram->size(); ++binIdx )
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{
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size_t binObsCount = (*m_histogram)[binIdx];
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size_t binObsCount = ( *m_histogram )[binIdx];
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accObsCount += binObsCount;
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if (accObsCount >= pValObservationCount)
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if ( accObsCount >= pValObservationCount )
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{
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double domainValueAtEndOfBin = m_min + (binIdx+1) * binWidth;
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double unusedFractionOfLastBin = (double)(accObsCount - pValObservationCount)/binObsCount;
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double domainValueAtEndOfBin = m_min + ( binIdx + 1 ) * binWidth;
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double unusedFractionOfLastBin = (double)( accObsCount - pValObservationCount ) / binObsCount;
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double histogramBasedEstimate = domainValueAtEndOfBin - unusedFractionOfLastBin*binWidth;
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double histogramBasedEstimate = domainValueAtEndOfBin - unusedFractionOfLastBin * binWidth;
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// See https://resinsight.org/docs/casegroupsandstatistics/#percentile-methods for details
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return histogramBasedEstimate;
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}
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}
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assert(false);
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assert( false );
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return HUGE_VAL;
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}
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