mirror of
https://github.com/slsdetectorgroup/aare.git
synced 2025-06-20 02:57:13 +02:00
@ -108,6 +108,79 @@ ClusterVector<int32_t> ClusterFile::read_clusters(size_t n_clusters) {
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return clusters;
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}
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ClusterVector<int32_t> ClusterFile::read_clusters(size_t n_clusters, ROI roi) {
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if (m_mode != "r") {
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throw std::runtime_error("File not opened for reading");
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}
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ClusterVector<int32_t> clusters(3,3);
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clusters.reserve(n_clusters);
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int32_t iframe = 0; // frame number needs to be 4 bytes!
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size_t nph_read = 0;
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uint32_t nn = m_num_left;
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uint32_t nph = m_num_left; // number of clusters in frame needs to be 4
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// auto buf = reinterpret_cast<Cluster3x3 *>(clusters.data());
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// auto buf = clusters.data();
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Cluster3x3 tmp; //this would break if the cluster size changes
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// if there are photons left from previous frame read them first
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if (nph) {
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if (nph > n_clusters) {
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// if we have more photons left in the frame then photons to read we
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// read directly the requested number
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nn = n_clusters;
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} else {
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nn = nph;
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}
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//Read one cluster, in the ROI push back
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// nph_read += fread((buf + nph_read*clusters.item_size()),
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// clusters.item_size(), nn, fp);
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for(size_t i = 0; i < nn; i++){
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fread(&tmp, sizeof(tmp), 1, fp);
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if(tmp.x >= roi.xmin && tmp.x <= roi.xmax && tmp.y >= roi.ymin && tmp.y <= roi.ymax){
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clusters.push_back(tmp.x, tmp.y, reinterpret_cast<std::byte*>(tmp.data));
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nph_read++;
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}
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}
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m_num_left = nph - nn; // write back the number of photons left
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}
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if (nph_read < n_clusters) {
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// keep on reading frames and photons until reaching n_clusters
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while (fread(&iframe, sizeof(iframe), 1, fp)) {
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// read number of clusters in frame
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if (fread(&nph, sizeof(nph), 1, fp)) {
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if (nph > (n_clusters - nph_read))
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nn = n_clusters - nph_read;
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else
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nn = nph;
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// nph_read += fread((buf + nph_read*clusters.item_size()),
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// clusters.item_size(), nn, fp);
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for(size_t i = 0; i < nn; i++){
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fread(&tmp, sizeof(tmp), 1, fp);
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if(tmp.x >= roi.xmin && tmp.x <= roi.xmax && tmp.y >= roi.ymin && tmp.y <= roi.ymax){
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clusters.push_back(tmp.x, tmp.y, reinterpret_cast<std::byte*>(tmp.data));
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nph_read++;
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}
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}
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m_num_left = nph - nn;
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}
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if (nph_read >= n_clusters)
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break;
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}
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}
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// Resize the vector to the number of clusters.
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// No new allocation, only change bounds.
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clusters.resize(nph_read);
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return clusters;
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}
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ClusterVector<int32_t> ClusterFile::read_frame() {
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if (m_mode != "r") {
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throw std::runtime_error("File not opened for reading");
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@ -268,11 +341,23 @@ ClusterVector<int32_t> ClusterFile::read_frame() {
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NDArray<double, 2> calculate_eta2(ClusterVector<int> &clusters) {
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//TOTO! make work with 2x2 clusters
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NDArray<double, 2> eta2({static_cast<int64_t>(clusters.size()), 2});
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for (size_t i = 0; i < clusters.size(); i++) {
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auto e = calculate_eta2(clusters.at<Cluster3x3>(i));
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eta2(i, 0) = e.x;
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eta2(i, 1) = e.y;
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if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
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for (size_t i = 0; i < clusters.size(); i++) {
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auto e = calculate_eta2(clusters.at<Cluster3x3>(i));
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eta2(i, 0) = e.x;
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eta2(i, 1) = e.y;
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}
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}else if(clusters.cluster_size_x() == 2 || clusters.cluster_size_y() == 2){
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for (size_t i = 0; i < clusters.size(); i++) {
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auto e = calculate_eta2(clusters.at<Cluster2x2>(i));
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eta2(i, 0) = e.x;
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eta2(i, 1) = e.y;
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}
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}else{
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throw std::runtime_error("Only 3x3 and 2x2 clusters are supported");
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}
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return eta2;
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}
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@ -290,7 +375,7 @@ Eta2 calculate_eta2(Cluster3x3 &cl) {
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tot2[3] = cl.data[4] + cl.data[5] + cl.data[7] + cl.data[8];
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auto c = std::max_element(tot2.begin(), tot2.end()) - tot2.begin();
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eta.sum = tot2[c];
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switch (c) {
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case cBottomLeft:
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if ((cl.data[3] + cl.data[4]) != 0)
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@ -333,6 +418,20 @@ Eta2 calculate_eta2(Cluster3x3 &cl) {
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return eta;
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}
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Eta2 calculate_eta2(Cluster2x2 &cl) {
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Eta2 eta{};
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if ((cl.data[0] + cl.data[1]) != 0)
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eta.x = static_cast<double>(cl.data[1]) / (cl.data[0] + cl.data[1]);
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if ((cl.data[0] + cl.data[2]) != 0)
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eta.y = static_cast<double>(cl.data[2]) / (cl.data[0] + cl.data[2]);
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eta.sum = cl.data[0] + cl.data[1] + cl.data[2]+ cl.data[3];
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eta.c = cBottomLeft; //TODO! This is not correct, but need to put something
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return eta;
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}
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int analyze_cluster(Cluster3x3 &cl, int32_t *t2, int32_t *t3, char *quad,
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double *eta2x, double *eta2y, double *eta3x,
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double *eta3y) {
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144
src/Interpolator.cpp
Normal file
144
src/Interpolator.cpp
Normal file
@ -0,0 +1,144 @@
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#include "aare/Interpolator.hpp"
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#include "aare/algorithm.hpp"
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namespace aare {
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Interpolator::Interpolator(NDView<double, 3> etacube, NDView<double, 1> xbins,
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NDView<double, 1> ybins, NDView<double, 1> ebins)
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: m_ietax(etacube), m_ietay(etacube), m_etabinsx(xbins), m_etabinsy(ybins), m_energy_bins(ebins) {
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if (etacube.shape(0) != xbins.size() || etacube.shape(1) != ybins.size() ||
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etacube.shape(2) != ebins.size()) {
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throw std::invalid_argument(
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"The shape of the etacube does not match the shape of the bins");
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}
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// Cumulative sum in the x direction
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for (ssize_t i = 1; i < m_ietax.shape(0); i++) {
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for (ssize_t j = 0; j < m_ietax.shape(1); j++) {
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for (ssize_t k = 0; k < m_ietax.shape(2); k++) {
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m_ietax(i, j, k) += m_ietax(i - 1, j, k);
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}
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}
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}
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// Normalize by the highest row, if norm less than 1 don't do anything
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for (ssize_t i = 0; i < m_ietax.shape(0); i++) {
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for (ssize_t j = 0; j < m_ietax.shape(1); j++) {
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for (ssize_t k = 0; k < m_ietax.shape(2); k++) {
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auto val = m_ietax(m_ietax.shape(0) - 1, j, k);
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double norm = val < 1 ? 1 : val;
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m_ietax(i, j, k) /= norm;
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}
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}
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}
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// Cumulative sum in the y direction
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for (ssize_t i = 0; i < m_ietay.shape(0); i++) {
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for (ssize_t j = 1; j < m_ietay.shape(1); j++) {
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for (ssize_t k = 0; k < m_ietay.shape(2); k++) {
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m_ietay(i, j, k) += m_ietay(i, j - 1, k);
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}
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}
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}
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// Normalize by the highest column, if norm less than 1 don't do anything
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for (ssize_t i = 0; i < m_ietay.shape(0); i++) {
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for (ssize_t j = 0; j < m_ietay.shape(1); j++) {
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for (ssize_t k = 0; k < m_ietay.shape(2); k++) {
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auto val = m_ietay(i, m_ietay.shape(1) - 1, k);
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double norm = val < 1 ? 1 : val;
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m_ietay(i, j, k) /= norm;
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}
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}
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}
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}
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std::vector<Photon> Interpolator::interpolate(const ClusterVector<int32_t>& clusters) {
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std::vector<Photon> photons;
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photons.reserve(clusters.size());
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if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
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for (size_t i = 0; i<clusters.size(); i++){
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auto cluster = clusters.at<Cluster3x3>(i);
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Eta2 eta= calculate_eta2(cluster);
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Photon photon;
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photon.x = cluster.x;
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photon.y = cluster.y;
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photon.energy = eta.sum;
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// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
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// auto ix = nearest_index(m_etabinsx, eta.x)-1;
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// auto iy = nearest_index(m_etabinsy, eta.y)-1;
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//Finding the index of the last element that is smaller
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//should work fine as long as we have many bins
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auto ie = last_smaller(m_energy_bins, photon.energy);
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auto ix = last_smaller(m_etabinsx, eta.x);
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auto iy = last_smaller(m_etabinsy, eta.y);
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// fmt::print("ex: {}, ix: {}, iy: {}\n", ie, ix, iy);
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double dX, dY;
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int ex, ey;
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// cBottomLeft = 0,
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// cBottomRight = 1,
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// cTopLeft = 2,
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// cTopRight = 3
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switch (eta.c) {
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case cTopLeft:
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dX = -1.;
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dY = 0.;
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break;
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case cTopRight:;
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dX = 0.;
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dY = 0.;
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break;
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case cBottomLeft:
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dX = -1.;
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dY = -1.;
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break;
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case cBottomRight:
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dX = 0.;
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dY = -1.;
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break;
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}
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photon.x += m_ietax(ix, iy, ie)*2 + dX;
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photon.y += m_ietay(ix, iy, ie)*2 + dY;
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photons.push_back(photon);
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}
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}else if(clusters.cluster_size_x() == 2 || clusters.cluster_size_y() == 2){
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for (size_t i = 0; i<clusters.size(); i++){
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auto cluster = clusters.at<Cluster2x2>(i);
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Eta2 eta= calculate_eta2(cluster);
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Photon photon;
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photon.x = cluster.x;
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photon.y = cluster.y;
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photon.energy = eta.sum;
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//Now do some actual interpolation.
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//Find which energy bin the cluster is in
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// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
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// auto ix = nearest_index(m_etabinsx, eta.x)-1;
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// auto iy = nearest_index(m_etabinsy, eta.y)-1;
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//Finding the index of the last element that is smaller
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//should work fine as long as we have many bins
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auto ie = last_smaller(m_energy_bins, photon.energy);
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auto ix = last_smaller(m_etabinsx, eta.x);
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auto iy = last_smaller(m_etabinsy, eta.y);
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photon.x += m_ietax(ix, iy, ie)*2; //eta goes between 0 and 1 but we could move the hit anywhere in the 2x2
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photon.y += m_ietay(ix, iy, ie)*2;
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photons.push_back(photon);
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}
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}else{
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throw std::runtime_error("Only 3x3 and 2x2 clusters are supported for interpolation");
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}
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return photons;
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}
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} // namespace aare
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@ -2,6 +2,7 @@
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#include <array>
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#include <catch2/benchmark/catch_benchmark.hpp>
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#include <catch2/catch_test_macros.hpp>
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#include <numeric>
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using aare::NDArray;
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using aare::NDView;
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@ -34,6 +35,24 @@ TEST_CASE("Construct from an NDView") {
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}
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}
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TEST_CASE("3D NDArray from NDView"){
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std::vector<int> data(27);
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std::iota(data.begin(), data.end(), 0);
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NDView<int, 3> view(data.data(), Shape<3>{3, 3, 3});
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NDArray<int, 3> image(view);
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REQUIRE(image.shape() == view.shape());
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REQUIRE(image.size() == view.size());
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REQUIRE(image.data() != view.data());
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for(int64_t i=0; i<image.shape(0); i++){
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for(int64_t j=0; j<image.shape(1); j++){
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for(int64_t k=0; k<image.shape(2); k++){
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REQUIRE(image(i, j, k) == view(i, j, k));
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}
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}
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}
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}
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TEST_CASE("1D image") {
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std::array<int64_t, 1> shape{{20}};
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NDArray<short, 1> img(shape, 3);
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|
73
src/algorithm.test.cpp
Normal file
73
src/algorithm.test.cpp
Normal file
@ -0,0 +1,73 @@
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#include <catch2/catch_test_macros.hpp>
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#include <aare/algorithm.hpp>
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TEST_CASE("Find the closed index in a 1D array", "[algorithm]") {
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aare::NDArray<double, 1> arr({5});
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for (size_t i = 0; i < arr.size(); i++) {
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arr[i] = i;
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}
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// arr 0, 1, 2, 3, 4
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REQUIRE(aare::nearest_index(arr, 2.3) == 2);
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REQUIRE(aare::nearest_index(arr, 2.6) == 3);
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REQUIRE(aare::nearest_index(arr, 45.0) == 4);
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REQUIRE(aare::nearest_index(arr, 0.0) == 0);
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REQUIRE(aare::nearest_index(arr, -1.0) == 0);
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}
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TEST_CASE("Passing integers to nearest_index works", "[algorithm]"){
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aare::NDArray<int, 1> arr({5});
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for (size_t i = 0; i < arr.size(); i++) {
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arr[i] = i;
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}
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// arr 0, 1, 2, 3, 4
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REQUIRE(aare::nearest_index(arr, 2) == 2);
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REQUIRE(aare::nearest_index(arr, 3) == 3);
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REQUIRE(aare::nearest_index(arr, 45) == 4);
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REQUIRE(aare::nearest_index(arr, 0) == 0);
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REQUIRE(aare::nearest_index(arr, -1) == 0);
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}
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TEST_CASE("nearest_index works with std::vector", "[algorithm]"){
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std::vector<double> vec = {0, 1, 2, 3, 4};
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REQUIRE(aare::nearest_index(vec, 2.123) == 2);
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REQUIRE(aare::nearest_index(vec, 2.66) == 3);
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REQUIRE(aare::nearest_index(vec, 4555555.0) == 4);
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REQUIRE(aare::nearest_index(vec, 0.0) == 0);
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REQUIRE(aare::nearest_index(vec, -10.0) == 0);
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}
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TEST_CASE("nearest index works with std::array", "[algorithm]"){
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std::array<double, 5> arr = {0, 1, 2, 3, 4};
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REQUIRE(aare::nearest_index(arr, 2.123) == 2);
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REQUIRE(aare::nearest_index(arr, 2.501) == 3);
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REQUIRE(aare::nearest_index(arr, 4555555.0) == 4);
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REQUIRE(aare::nearest_index(arr, 0.0) == 0);
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REQUIRE(aare::nearest_index(arr, -10.0) == 0);
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}
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TEST_CASE("last smaller", "[algorithm]"){
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aare::NDArray<double, 1> arr({5});
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for (size_t i = 0; i < arr.size(); i++) {
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arr[i] = i;
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}
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// arr 0, 1, 2, 3, 4
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REQUIRE(aare::last_smaller(arr, -10.0) == 0);
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REQUIRE(aare::last_smaller(arr, 0.0) == 0);
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REQUIRE(aare::last_smaller(arr, 2.3) == 2);
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REQUIRE(aare::last_smaller(arr, 253.) == 4);
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}
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TEST_CASE("returns last bin strictly smaller", "[algorithm]"){
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aare::NDArray<double, 1> arr({5});
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for (size_t i = 0; i < arr.size(); i++) {
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arr[i] = i;
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}
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// arr 0, 1, 2, 3, 4
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REQUIRE(aare::last_smaller(arr, 2.0) == 2);
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}
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