create_pipeline_output()

template<ComputeData InputType = std::vector<Kakshya::DataVariant>, ComputeData OutputType = Eigen::VectorXd>

output_type MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::create_pipeline_output ( const input_type &  input, const StatisticalAnalysis &  analysis_result, DataStructureInfo &  info  )

inlineprivate

Create pipeline output for operation chaining.

Definition at line 630 of file StatisticalAnalyzer.hpp.

    {
        std::vector<std::vector<double>> channel_stats;
        channel_stats.reserve(analysis_result.channel_statistics.size());
        for (const auto& ch : analysis_result.channel_statistics) {
            channel_stats.push_back(ch.statistical_values);
        }
 
        output_type output = this->convert_result(channel_stats, info);
 
        output.metadata = input.metadata;
 
        output.metadata["source_analyzer"] = "StatisticalAnalyzer";
        output.metadata["statistical_method"] = method_to_string(analysis_result.method_used);
        output.metadata["window_size"] = analysis_result.window_size;
        output.metadata["hop_size"] = analysis_result.hop_size;
        output.metadata["num_channels"] = analysis_result.channel_statistics.size();
 
        if (!analysis_result.channel_statistics.empty()) {
            std::vector<double> channel_means, channel_maxs, channel_mins, channel_variances, channel_stddevs, channel_skewness, channel_kurtosis, channel_medians;
            std::vector<size_t> channel_window_counts;
 
            for (const auto& ch : analysis_result.channel_statistics) {
                channel_means.push_back(ch.mean_stat);
                channel_maxs.push_back(ch.max_stat);
                channel_mins.push_back(ch.min_stat);
                channel_variances.push_back(ch.stat_variance);
                channel_stddevs.push_back(ch.stat_std_dev);
                channel_skewness.push_back(ch.skewness);
                channel_kurtosis.push_back(ch.kurtosis);
                channel_medians.push_back(ch.median);
                channel_window_counts.push_back(ch.statistical_values.size());
            }
 
            output.metadata["mean_per_channel"] = channel_means;
            output.metadata["max_per_channel"] = channel_maxs;
            output.metadata["min_per_channel"] = channel_mins;
            output.metadata["variance_per_channel"] = channel_variances;
            output.metadata["stddev_per_channel"] = channel_stddevs;
            output.metadata["skewness_per_channel"] = channel_skewness;
            output.metadata["kurtosis_per_channel"] = channel_kurtosis;
            output.metadata["median_per_channel"] = channel_medians;
            output.metadata["window_count_per_channel"] = channel_window_counts;
        }
 
        return output;
    }

References MayaFlux::Yantra::StatisticalAnalysis::channel_statistics, MayaFlux::Yantra::ComputeOperation< InputType, OutputType >::convert_result(), MayaFlux::Yantra::StatisticalAnalysis::hop_size, input, MayaFlux::Yantra::Datum< T >::metadata, MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::method_to_string(), MayaFlux::Yantra::StatisticalAnalysis::method_used, and MayaFlux::Yantra::StatisticalAnalysis::window_size.

Referenced by MayaFlux::Yantra::StatisticalAnalyzer< InputType, OutputType >::analyze_implementation().

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