pcl2::Neighborhood
pcl2::computeFixedRadiusNeighborhood (Cloud & cloud, const MatF & query, float r)
{
// Convert point cloud
MatF xyz = cloud["xyz"];
assert (xyz.rows () >= 1);
assert (xyz.cols () == 3);
pcl::PointCloud<pcl::PointXYZ>::Ptr input (new pcl::PointCloud<pcl::PointXYZ>);
input->width = cloud.size ();
input->height = 1;
input->is_dense = false;
input->points.resize (cloud.size ());
for (size_t i = 0; i < xyz.rows (); ++i)
{
input->points[i].x = xyz (i, 0);
input->points[i].y = xyz (i, 1);
input->points[i].z = xyz (i, 2);
}
// Convert query point
assert (query.rows () == 1);
assert (query.cols () == 3);
pcl::PointXYZ q;
q.x = query (0, 0);
q.y = query (0, 1);
q.z = query (0, 2);
// Perform neighbor search
pcl::KdTreeFLANN<pcl::PointXYZ> tree;
tree.setInputCloud (input);
std::vector<int> idx_vec;
std::vector<float> dist_vec;
size_t k = (size_t) tree.radiusSearch (q, r, idx_vec, dist_vec);
assert (k == idx_vec.size ());
// Convert output
EigenMat<int> neighbor_indices (k, 1);
EigenMat<float> squared_distances (k, 1);
for (size_t i = 0; i < k; ++i)
{
neighbor_indices (i, 0) = idx_vec[i];
squared_distances (i, 0) = dist_vec[i];
}
//Cloud neighborhood = cloud (neighbor_indices);
Neighborhood neighborhood (cloud, neighbor_indices);
neighborhood.insert ("dist", squared_distances);
return (neighborhood);
}
// 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);
}
}
