mirror of
https://github.com/OPM/ResInsight.git
synced 2026-10-26 07:00:17 -05:00
Kristian Bendiksen
d96159fb7c
Add BinningMode (Linear/Logarithmic) and OutOfRangeHandling (Exclude/Include in Boundary Bins) to RigHistogramCalculator, with defaulted constructor arguments so existing callers are unaffected. All four histogram plot data sources get shared options for binning mode and an optional user-defined bin range through new PDM fields on RimHistogramDataSource. The grid statistics data source computes non-default histograms through a new non-caching RigStatisticsDataCache::computeHistogram() pass-through, leaving the cached statistics used by the 3D overlay and the P10/P90/mean annotation lines untouched. Selecting logarithmic binning enables logarithmic scale on the plot X axis once, via a new signal handled by RimHistogramCurve. Also fix the bin index computation to use uniform bins of width range/nBins, consistent with calculatePercentil() and the bin edges drawn by the plots. The previous divisor (nBins - 1) shifted counts by up to one bin width relative to the drawn bars and slightly biased histogram-based percentile estimates.
227 lines
11 KiB
C++
227 lines
11 KiB
C++
#include "gtest/gtest.h"
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#include "Histogram/RimHistogramDataSource.h"
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, CumulativeLineGraphRelativePercent )
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{
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std::vector<size_t> bins = { 1, 2, 3 };
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std::vector<double> frequencies =
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RimHistogramDataSource::computeHistogramFrequencies( bins,
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RimHistogramPlot::GraphType::LINE_GRAPH,
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RimHistogramPlot::FrequencyType::RELATIVE_FREQUENCY_PERCENT,
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true );
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ASSERT_EQ( 3u, frequencies.size() );
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EXPECT_NEAR( 100.0 / 6.0, frequencies[0], 1e-9 );
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EXPECT_NEAR( 50.0, frequencies[1], 1e-9 );
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EXPECT_NEAR( 100.0, frequencies[2], 1e-9 );
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, CumulativeLineGraphAbsolute )
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{
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std::vector<size_t> bins = { 1, 2, 3 };
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std::vector<double> frequencies = RimHistogramDataSource::computeHistogramFrequencies( bins,
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RimHistogramPlot::GraphType::LINE_GRAPH,
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RimHistogramPlot::FrequencyType::ABSOLUTE_FREQUENCY,
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true );
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ASSERT_EQ( 3u, frequencies.size() );
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EXPECT_NEAR( 1.0, frequencies[0], 1e-9 );
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EXPECT_NEAR( 3.0, frequencies[1], 1e-9 );
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EXPECT_NEAR( 6.0, frequencies[2], 1e-9 );
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}
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//--------------------------------------------------------------------------------------------------
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/// The cumulative sum must be applied per bin before the bar graph expansion: the expanded vector
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/// starts with a 0.0 closer and each cumulative bin value is duplicated. Unlike a regular
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/// histogram, a cumulative curve has no closing 0.0 at the end: it ends at its maximum.
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, CumulativeBarGraphAbsolute )
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{
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std::vector<size_t> bins = { 1, 2, 3 };
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std::vector<double> frequencies = RimHistogramDataSource::computeHistogramFrequencies( bins,
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RimHistogramPlot::GraphType::BAR_GRAPH,
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RimHistogramPlot::FrequencyType::ABSOLUTE_FREQUENCY,
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true );
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std::vector<double> expected = { 0.0, 1.0, 1.0, 3.0, 3.0, 6.0, 6.0 };
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ASSERT_EQ( expected.size(), frequencies.size() );
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for ( size_t i = 0; i < expected.size(); i++ )
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{
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EXPECT_NEAR( expected[i], frequencies[i], 1e-9 );
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}
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std::vector<double> xValues =
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RimHistogramDataSource::computeHistogramBins( 0.0, 3.0, static_cast<int>( bins.size() ), RimHistogramPlot::GraphType::BAR_GRAPH, true );
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EXPECT_EQ( frequencies.size(), xValues.size() );
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, NonCumulativeBarGraphBinsAndFrequenciesMatch )
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{
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std::vector<size_t> bins = { 1, 2, 3 };
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std::vector<double> frequencies =
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RimHistogramDataSource::computeHistogramFrequencies( bins,
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RimHistogramPlot::GraphType::BAR_GRAPH,
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RimHistogramPlot::FrequencyType::ABSOLUTE_FREQUENCY );
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std::vector<double> xValues =
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RimHistogramDataSource::computeHistogramBins( 0.0, 3.0, static_cast<int>( bins.size() ), RimHistogramPlot::GraphType::BAR_GRAPH );
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EXPECT_EQ( frequencies.size(), xValues.size() );
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, NonCumulativeIsUnchanged )
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{
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std::vector<size_t> bins = { 1, 2, 3 };
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std::vector<double> frequencies =
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RimHistogramDataSource::computeHistogramFrequencies( bins,
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RimHistogramPlot::GraphType::LINE_GRAPH,
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RimHistogramPlot::FrequencyType::ABSOLUTE_FREQUENCY );
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ASSERT_EQ( 3u, frequencies.size() );
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EXPECT_NEAR( 1.0, frequencies[0], 1e-9 );
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EXPECT_NEAR( 2.0, frequencies[1], 1e-9 );
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EXPECT_NEAR( 3.0, frequencies[2], 1e-9 );
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, LinearBinsUnchangedWithDefaultBinningMode )
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{
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std::vector<double> xValues = RimHistogramDataSource::computeHistogramBins( 0.0, 10.0, 2, RimHistogramPlot::GraphType::BAR_GRAPH, false );
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std::vector<double> expected = { 0.0, 0.0, 5.0, 5.0, 10.0, 10.0 };
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ASSERT_EQ( expected.size(), xValues.size() );
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for ( size_t i = 0; i < expected.size(); i++ )
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{
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EXPECT_NEAR( expected[i], xValues[i], 1e-9 );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, LogarithmicBinsBarGraph )
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{
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std::vector<double> xValues = RimHistogramDataSource::computeHistogramBins( 1.0,
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1000.0,
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3,
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RimHistogramPlot::GraphType::BAR_GRAPH,
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false,
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RigHistogramCalculator::BinningMode::LOGARITHMIC );
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std::vector<double> expected = { 1.0, 1.0, 10.0, 10.0, 100.0, 100.0, 1000.0, 1000.0 };
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ASSERT_EQ( expected.size(), xValues.size() );
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for ( size_t i = 0; i < expected.size(); i++ )
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{
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EXPECT_NEAR( expected[i], xValues[i], 1e-9 );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, LogarithmicBinsCumulativeBarGraph )
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{
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std::vector<double> xValues = RimHistogramDataSource::computeHistogramBins( 1.0,
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1000.0,
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3,
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RimHistogramPlot::GraphType::BAR_GRAPH,
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true,
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RigHistogramCalculator::BinningMode::LOGARITHMIC );
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// A cumulative curve is not closed on the right side
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std::vector<double> expected = { 1.0, 1.0, 10.0, 10.0, 100.0, 100.0, 1000.0 };
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ASSERT_EQ( expected.size(), xValues.size() );
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for ( size_t i = 0; i < expected.size(); i++ )
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{
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EXPECT_NEAR( expected[i], xValues[i], 1e-9 );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, LogarithmicBinsLineGraph )
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{
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std::vector<double> xValues = RimHistogramDataSource::computeHistogramBins( 1.0,
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1000.0,
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3,
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RimHistogramPlot::GraphType::LINE_GRAPH,
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false,
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RigHistogramCalculator::BinningMode::LOGARITHMIC );
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// Bin centers are the geometric means of the bin edges
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std::vector<double> expected = { std::pow( 10.0, 0.5 ), std::pow( 10.0, 1.5 ), std::pow( 10.0, 2.5 ) };
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ASSERT_EQ( expected.size(), xValues.size() );
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for ( size_t i = 0; i < expected.size(); i++ )
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{
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EXPECT_NEAR( expected[i], xValues[i], 1e-9 );
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}
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}
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//--------------------------------------------------------------------------------------------------
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///
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//--------------------------------------------------------------------------------------------------
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TEST( RimHistogramDataSourceTest, ComputeBinRange )
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{
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using BinRangeMode = RimHistogramDataSource::BinRangeMode;
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using BinningMode = RigHistogramCalculator::BinningMode;
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const double smallestPositive = 0.01;
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{
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auto [min, max] =
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RimHistogramDataSource::computeBinRange( BinRangeMode::AUTOMATIC, 5.0, 6.0, -2.0, 100.0, BinningMode::LINEAR, smallestPositive );
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EXPECT_DOUBLE_EQ( -2.0, min );
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EXPECT_DOUBLE_EQ( 100.0, max );
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}
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{
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auto [min, max] =
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RimHistogramDataSource::computeBinRange( BinRangeMode::USER_DEFINED, 5.0, 6.0, -2.0, 100.0, BinningMode::LINEAR, smallestPositive );
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EXPECT_DOUBLE_EQ( 5.0, min );
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EXPECT_DOUBLE_EQ( 6.0, max );
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}
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{
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// A non-positive minimum is replaced by the smallest positive value for logarithmic binning
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auto [min, max] =
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RimHistogramDataSource::computeBinRange( BinRangeMode::AUTOMATIC, 5.0, 6.0, -2.0, 100.0, BinningMode::LOGARITHMIC, smallestPositive );
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EXPECT_DOUBLE_EQ( smallestPositive, min );
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EXPECT_DOUBLE_EQ( 100.0, max );
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}
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{
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auto [min, max] =
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RimHistogramDataSource::computeBinRange( BinRangeMode::USER_DEFINED, 0.0, 6.0, -2.0, 100.0, BinningMode::LOGARITHMIC, smallestPositive );
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EXPECT_DOUBLE_EQ( smallestPositive, min );
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EXPECT_DOUBLE_EQ( 6.0, max );
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}
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{
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// A positive minimum is used unchanged for logarithmic binning
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auto [min, max] =
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RimHistogramDataSource::computeBinRange( BinRangeMode::USER_DEFINED, 5.0, 6.0, -2.0, 100.0, BinningMode::LOGARITHMIC, smallestPositive );
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EXPECT_DOUBLE_EQ( 5.0, min );
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EXPECT_DOUBLE_EQ( 6.0, max );
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}
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}
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