HG_REPOSITORIES/LPP/INSTRUMENTATION/USERS/JEANDET/SciQLOPCore Files · include/Data/TimeSeriesUtils.h

Some refac for new PySide2 bindings...

Some refac for new PySide2 bindings - made DataSourceItem iterable - added some tree print function for debug - fixed minor bug on DataSourceContorller which prevented from adding products in more than one call per provider - improved IDataProvider for future refac Signed-off-by: Alexis Jeandet <alexis.jeandet@member.fsf.org>

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             TimeSeriesUtils.h
        
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             / include / Data / TimeSeriesUtils.h
          
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        jeandet
    
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      #ifndef TIMESERIESUTILS_H

      #define TIMESERIESUTILS_H

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      #include "MultiComponentTimeSerie.h"

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      #include "ScalarTimeSerie.h"

      #include "SpectrogramTimeSerie.h"

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      #include "VectorTimeSerie.h"

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      #include <TimeSeries.h>

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      #include <cmath>

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      #include <memory>

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      namespace TimeSeriesUtils

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      {

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        template<typename T> TimeSeries::ITimeSerie* copy(T input_ts)

        {

          if constexpr(std::is_base_of_v<TimeSeries::ITimeSerie, T>)

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          {

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            if(auto ts = dynamic_cast<VectorTimeSerie*>(input_ts))

            { return new VectorTimeSerie(*ts); }

            if(auto ts = dynamic_cast<ScalarTimeSerie*>(input_ts))

            { return new ScalarTimeSerie(*ts); }

            if(auto ts = dynamic_cast<MultiComponentTimeSerie*>(input_ts))

            { return new MultiComponentTimeSerie(*ts); }

            if(auto ts = dynamic_cast<SpectrogramTimeSerie*>(input_ts))

            { return new SpectrogramTimeSerie(*ts); }

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          }

          else

          {

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            if(auto ts = std::dynamic_pointer_cast<VectorTimeSerie>(input_ts))

            { return new VectorTimeSerie(*ts); }

            if(auto ts = std::dynamic_pointer_cast<ScalarTimeSerie>(input_ts))

            { return new ScalarTimeSerie(*ts); }

            if(auto ts = std::dynamic_pointer_cast<SpectrogramTimeSerie>(input_ts))

            { return new SpectrogramTimeSerie(*ts); }

            if(auto ts = std::dynamic_pointer_cast<MultiComponentTimeSerie>(input_ts))

            { return new MultiComponentTimeSerie(*ts); }

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          }

          return nullptr;

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        }

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        struct axis_properties

        {

          double range;

          double max_resolution;

          bool is_log;

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          double min;

          double max;

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        };

        constexpr auto IsLog    = true;

        constexpr auto IsLinear = false;

        constexpr auto CheckMedian     = true;

        constexpr auto DontCheckMedian = false;

        template<bool is_log, bool check_median>

        axis_properties axis_analysis(

            typename std::conditional<is_log, std::vector<double>&,

        jeandet
    
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                                      const std::vector<double>&>::type axis,

            double given_max_resolution = std::nan(""))

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        {

          std::vector<double> axis_diff(axis.size());

          if constexpr(is_log)

          {

            std::transform(std::cbegin(axis), std::cend(axis), std::begin(axis),

                           [](const auto v) { return std::log10(v); });

          }

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          auto min      = *std::begin(axis);

          auto max      = *(std::cend(axis) - 1);

          auto range    = max - min;

          auto min_diff = given_max_resolution;

          if(std::isnan(min_diff))

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          {

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            std::adjacent_difference(std::cbegin(axis), std::cend(axis),

                                     std::begin(axis_diff));

            min_diff =

                (*std::min_element(std::cbegin(axis_diff) + 1, std::cend(axis_diff)));

            if constexpr(check_median)

            {

              std::nth_element(std::begin(axis_diff) + 1,

                               std::begin(axis_diff) + axis_diff.size() / 2,

                               std::end(axis_diff));

              auto median_diff = *(std::begin(axis_diff) + axis_diff.size() / 2);

              if(median_diff > (4 * min_diff)) min_diff = median_diff;

            }

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          }

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          return {range, min_diff, is_log, min, max};

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        }

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      } // namespace TimeSeriesUtils

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      #endif

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jeandet Added TS utils module...	r74	#ifndef TIMESERIESUTILS_H
		#define TIMESERIESUTILS_H
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	#include "MultiComponentTimeSerie.h"
jeandet Added TS utils module...	r74	#include "ScalarTimeSerie.h"
		#include "SpectrogramTimeSerie.h"
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	#include "VectorTimeSerie.h"
jeandet Added TS utils module...	r74
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	#include <TimeSeries.h>
jeandet Mostly working Spectrograms...	r84	#include <cmath>
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	#include <memory>
jeandet Added TS utils module...	r74
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	namespace TimeSeriesUtils
jeandet Added TS utils module...	r74	{
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	template<typename T> TimeSeries::ITimeSerie* copy(T input_ts)
		{
		if constexpr(std::is_base_of_v<TimeSeries::ITimeSerie, T>)
jeandet Added TS utils module...	r74	{
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	if(auto ts = dynamic_cast<VectorTimeSerie*>(input_ts))
		{ return new VectorTimeSerie(*ts); }
		if(auto ts = dynamic_cast<ScalarTimeSerie*>(input_ts))
		{ return new ScalarTimeSerie(*ts); }
		if(auto ts = dynamic_cast<MultiComponentTimeSerie*>(input_ts))
		{ return new MultiComponentTimeSerie(*ts); }
		if(auto ts = dynamic_cast<SpectrogramTimeSerie*>(input_ts))
		{ return new SpectrogramTimeSerie(*ts); }
jeandet Added TS utils module...	r74	}
		else
		{
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	if(auto ts = std::dynamic_pointer_cast<VectorTimeSerie>(input_ts))
		{ return new VectorTimeSerie(*ts); }
		if(auto ts = std::dynamic_pointer_cast<ScalarTimeSerie>(input_ts))
		{ return new ScalarTimeSerie(*ts); }
		if(auto ts = std::dynamic_pointer_cast<SpectrogramTimeSerie>(input_ts))
		{ return new SpectrogramTimeSerie(*ts); }
		if(auto ts = std::dynamic_pointer_cast<MultiComponentTimeSerie>(input_ts))
		{ return new MultiComponentTimeSerie(*ts); }
jeandet Added TS utils module...	r74	}
		return nullptr;
jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	}
jeandet Mostly working Spectrograms...	r84
		struct axis_properties
		{
		double range;
		double max_resolution;
		bool is_log;
jeandet Updated to fixed TS lib, added spectrogram min/max sampling hints when available...	r85	double min;
		double max;
jeandet Mostly working Spectrograms...	r84	};

		constexpr auto IsLog = true;
		constexpr auto IsLinear = false;

		constexpr auto CheckMedian = true;
		constexpr auto DontCheckMedian = false;

		template<bool is_log, bool check_median>
		axis_properties axis_analysis(
		typename std::conditional<is_log, std::vector<double>&,
jeandet Updated to fixed TS lib, added spectrogram min/max sampling hints when available...	r85	const std::vector<double>&>::type axis,
		double given_max_resolution = std::nan(""))
jeandet Mostly working Spectrograms...	r84	{
		std::vector<double> axis_diff(axis.size());
		if constexpr(is_log)
		{
		std::transform(std::cbegin(axis), std::cend(axis), std::begin(axis),
		[](const auto v) { return std::log10(v); });
		}
jeandet Updated to fixed TS lib, added spectrogram min/max sampling hints when available...	r85	auto min = *std::begin(axis);
		auto max = *(std::cend(axis) - 1);
		auto range = max - min;
		auto min_diff = given_max_resolution;
		if(std::isnan(min_diff))
jeandet Mostly working Spectrograms...	r84	{
jeandet Updated to fixed TS lib, added spectrogram min/max sampling hints when available...	r85	std::adjacent_difference(std::cbegin(axis), std::cend(axis),
		std::begin(axis_diff));
		min_diff =
		(*std::min_element(std::cbegin(axis_diff) + 1, std::cend(axis_diff)));
		if constexpr(check_median)
		{
		std::nth_element(std::begin(axis_diff) + 1,
		std::begin(axis_diff) + axis_diff.size() / 2,
		std::end(axis_diff));
		auto median_diff = *(std::begin(axis_diff) + axis_diff.size() / 2);
		if(median_diff > (4 * min_diff)) min_diff = median_diff;
		}
jeandet Mostly working Spectrograms...	r84	}
jeandet Updated to fixed TS lib, added spectrogram min/max sampling hints when available...	r85
		return {range, min_diff, is_log, min, max};
jeandet Mostly working Spectrograms...	r84	}

jeandet MultiComponent TS almost done, still blows up when built from python with empty data...	r76	} // namespace TimeSeriesUtils
jeandet Added TS utils module...	r74
		#endif