C++ BamAlignment::IsMapped方法代码示例

// Function to fill in predicted signal values
void BaseHypothesisEvaluator(BamTools::BamAlignment    &alignment,
                             const string              &flow_order_str,
                             const string              &alt_base_hyp,
                             float                     &delta_score,
                             float                     &fit_score,
                             int                       heavy_verbose) {

    // --- Step 1: Initialize Objects and retrieve relevant tags

	delta_score = 1e5;
	fit_score   = 1e5;
	vector<string>   Hypotheses(2);
    vector<float>    measurements, phase_params;
    int              start_flow, num_flows, prefix_flow=0;

    if (not GetBamTags(alignment, flow_order_str.length(), measurements, phase_params, start_flow))
      return;
	num_flows = measurements.size();
	ion::FlowOrder flow_order(flow_order_str, num_flows);
	BasecallerRead master_read;
	master_read.SetData(measurements, flow_order.num_flows());
	TreephaserLite   treephaser(flow_order);
    treephaser.SetModelParameters(phase_params[0], phase_params[1]);

    // --- Step 2: Solve beginning of the read
    // Look at mapped vs. unmapped reads in BAM
    Hypotheses[0] = alignment.QueryBases;
    Hypotheses[1] = alt_base_hyp;
    // Safety: reverse complement reverse strand reads in mapped bam
    if (alignment.IsMapped() and alignment.IsReverseStrand()) {
      RevComplementInPlace(Hypotheses[0]);
      RevComplementInPlace(Hypotheses[1]);
    }

    prefix_flow = GetMasterReadPrefix(treephaser, flow_order, start_flow, Hypotheses[0], master_read);
    unsigned int prefix_size = master_read.sequence.size();

    // --- Step 3: creating predictions for the individual hypotheses

    vector<BasecallerRead> hypothesesReads(Hypotheses.size());
    vector<float> squared_distances(Hypotheses.size(), 0.0);
    int max_last_flow = 0;

    for (unsigned int i_hyp=0; i_hyp<hypothesesReads.size(); ++i_hyp) {

      hypothesesReads[i_hyp] = master_read;
      // --- add hypothesis sequence to clipped prefix
      unsigned int i_base = 0;
      int i_flow = prefix_flow;

      while (i_base<Hypotheses[i_hyp].length() and i_base<(2*(unsigned int)flow_order.num_flows()-prefix_size)) {
        while (i_flow < flow_order.num_flows() and flow_order.nuc_at(i_flow) != Hypotheses[i_hyp][i_base])
          i_flow++;
        if (i_flow < flow_order.num_flows() and i_flow > max_last_flow)
          max_last_flow = i_flow;
        if (i_flow >= flow_order.num_flows())
          break;
        // Add base to sequence only if it fits into flow order
        hypothesesReads[i_hyp].sequence.push_back(Hypotheses[i_hyp][i_base]);
        i_base++;
      }
      i_flow = min(i_flow, flow_order.num_flows()-1);

      // Solver simulates beginning of the read and then fills in the remaining clipped bases for which we have flow information
      treephaser.Solve(hypothesesReads[i_hyp], num_flows, i_flow);
    }
    // Compute L2-distance of measurements and predictions
    for (unsigned int i_hyp=0; i_hyp<hypothesesReads.size(); ++i_hyp) {
      for (int iFlow=0; iFlow<=max_last_flow; iFlow++)
        squared_distances[i_hyp] += (measurements.at(iFlow) - hypothesesReads[i_hyp].prediction.at(iFlow)) *
                                    (measurements.at(iFlow) - hypothesesReads[i_hyp].prediction.at(iFlow));
    }

    // Delta: L2-distance of alternative base Hypothesis - L2-distance of bases as called
    delta_score = squared_distances.at(1) - squared_distances.at(0);
    fit_score   = min(squared_distances.at(1), squared_distances.at(0));


    // --- verbose ---
    if (heavy_verbose > 1 or (delta_score < 0 and heavy_verbose > 0)) {
      cout << "Processed read " << alignment.Name << endl;
      cout << "Delta Fit: " << delta_score << " Overall Fit: " << fit_score << endl;
      PredictionGenerationVerbose(Hypotheses, hypothesesReads, phase_params, flow_order, start_flow, prefix_size);
    }

}