Imaging

This section introduces the way SAV calculates the Imaging tab. This introduces a limited set of functions. The key classes used below are:

• run_metrics: model holding the binary InterOp data
• imaging_column_vector: Vector of columns for the imaging table
• map_id_offset: map row ID to the offset in the float array
• valid_to_load: byte array (std::vector<unsigned char> or uchar_vector) indicating which InterOp files to load

The primary functions used below are:

• run_metrics::read: read the InterOp files from disk
• create_imaging_table_columns: Generate the available column groups from the metric set
• count_table_rows: Count the number of necessary rows from the metric set
• count_table_columns: Count the number of subcolumns for the table
• populate_imaging_table_data: Populate the array of data for the table
• list_imaging_table_metrics_to_load: list which imaging metrics will be loaded

C#

using System;
using Illumina.InterOp.Run;
using Illumina.InterOp.Metrics;
using Illumina.InterOp.RunMetrics;
using Illumina.InterOp.Comm;
using Illumina.InterOp.Table;

class ImagingTableExample
{
    static int Main(string[] args)
    {
        if (args.Length != 1)
        {
            if (args.Length < 1)
                Console.WriteLine ("No run folder");
            else
                Console.WriteLine ("Too many arguments");
            return 1;
        }

// @ [Reading only metrics required by the Imaging Tab]
        run_metrics metrics = new run_metrics();
        uchar_vector interopsToLoad = new uchar_vector();

        // Load only the metrics required by imaging table
        c_csharp_table.list_imaging_table_metrics_to_load(interopsToLoad);
        metrics.read(args[0], interopsToLoad);
// @ [Reading only metrics required by the Imaging Tab]


        imaging_column_vector columnVector = new imaging_column_vector();
        map_id_offset rowOffsets = new map_id_offset();

        c_csharp_table.create_imaging_table_columns(metrics, columnVector);
        c_csharp_table.count_table_rows(metrics, rowOffsets);
        int rowCount = rowOffsets.Count;
        int columnCount = (int)c_csharp_table.count_table_columns(columnVector);
        float[] data = new float[rowCount * columnCount];

        c_csharp_table.populate_imaging_table_data(metrics, columnVector, rowOffsets, data, (uint)data.Length);

        for(int rowIndex=0;rowIndex<rowCount;++rowIndex)
        {
            for(int groupIndex=0;groupIndex<columnVector.Count;++groupIndex)
            {
                if(columnVector[groupIndex].has_children())
                {
                    for(int subColumnIndex=0;subColumnIndex<columnVector[groupIndex].subcolumns().Count;++subColumnIndex)
                    {
                        int columnIndex = (int)(columnVector[groupIndex].offset()+subColumnIndex);
                        Console.Write("{0},", data[rowIndex*columnCount + columnIndex]);
                    }
                }
                else
                {
                    int columnIndex = (int)(columnVector[groupIndex].offset());
                    Console.Write("{0},", data[rowIndex*columnCount + columnIndex]);
                }
            }
            Console.WriteLine();
        }
        return 0;
    }
}

C++

The following shows how to calculate the Imaging metrics:

    std::vector<unsigned char> valid_to_load;

    logic::table::list_imaging_table_metrics_to_load(valid_to_load);
    for (int i = 1; i < argc; i++)
    {
        run_metrics run;
        std::cout << "# Run Folder: " << io::basename(argv[i]) << std::endl;
        int ret = read_run_metrics(argv[i], run, valid_to_load, thread_count);
        if (ret != SUCCESS) return ret;

#ifdef INTEROP_TEST_CSHARP_BINDING
        std::vector<model::table::imaging_column> columns;
        logic::table::row_offset_map_t row_offsets;
        logic::table::create_imaging_table_columns(run, columns);
        const size_t column_count = logic::table::count_table_columns(columns);
        logic::table::count_table_rows(run, row_offsets);
        std::vector<float> data(row_offsets.size()*column_count);
        logic::table::populate_imaging_table_data(run, columns, row_offsets, &data[0], data.size());
#endif

        model::table::imaging_table table;

        try
        {
            logic::table::create_imaging_table(run, table);
        }
        catch(const std::exception& ex)
        {
            std::cerr << ex.what() << std::endl;
            return UNEXPECTED_EXCEPTION;
        }
        std::cout << table << std::endl;
    }

The following shows the implementation of read_run_metrics above and how to read the InterOp data from disk:

    using namespace illumina::interop;
    using namespace illumina::interop::model;
    try
    {
        metrics.clear();
        metrics.read(filename, valid_to_load, thread_count);
    }
    catch(const xml::xml_file_not_found_exception& ex)
    {
        std::cerr << ex.what() << std::endl;
        return MISSING_RUNINFO_XML;
    }
    catch(const xml::xml_parse_exception& ex)
    {
        std::cerr << ex.what() << std::endl;
        return MALFORMED_XML;
    }
    catch(const io::bad_format_exception& ex)
    {
        std::cerr << ex.what() << std::endl;
        return BAD_FORMAT;
    }
    catch(const model::invalid_run_info_cycle_exception& ex)
    {
        std::cerr << ex.what() << std::endl;
    }
    catch(const std::exception& ex)
    {
        std::cerr << ex.what() << std::endl;
        return UNEXPECTED_EXCEPTION;
    }
    if(check_empty && metrics.empty())
    {
        std::cerr << "No InterOp files found" << std::endl;
        return EMPTY_INTEROP;
    }