Interpolate (#137)

- added eta based interpolation
This commit is contained in:
Erik Fröjdh
2025-03-18 17:45:38 +01:00
committed by GitHub
parent 1ad362ccfc
commit 11cd2ec654
17 changed files with 580 additions and 39 deletions

View File

@ -108,6 +108,79 @@ ClusterVector<int32_t> ClusterFile::read_clusters(size_t n_clusters) {
return clusters;
}
ClusterVector<int32_t> ClusterFile::read_clusters(size_t n_clusters, ROI roi) {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
}
ClusterVector<int32_t> clusters(3,3);
clusters.reserve(n_clusters);
int32_t iframe = 0; // frame number needs to be 4 bytes!
size_t nph_read = 0;
uint32_t nn = m_num_left;
uint32_t nph = m_num_left; // number of clusters in frame needs to be 4
// auto buf = reinterpret_cast<Cluster3x3 *>(clusters.data());
// auto buf = clusters.data();
Cluster3x3 tmp; //this would break if the cluster size changes
// if there are photons left from previous frame read them first
if (nph) {
if (nph > n_clusters) {
// if we have more photons left in the frame then photons to read we
// read directly the requested number
nn = n_clusters;
} else {
nn = nph;
}
//Read one cluster, in the ROI push back
// nph_read += fread((buf + nph_read*clusters.item_size()),
// clusters.item_size(), nn, fp);
for(size_t i = 0; i < nn; i++){
fread(&tmp, sizeof(tmp), 1, fp);
if(tmp.x >= roi.xmin && tmp.x <= roi.xmax && tmp.y >= roi.ymin && tmp.y <= roi.ymax){
clusters.push_back(tmp.x, tmp.y, reinterpret_cast<std::byte*>(tmp.data));
nph_read++;
}
}
m_num_left = nph - nn; // write back the number of photons left
}
if (nph_read < n_clusters) {
// keep on reading frames and photons until reaching n_clusters
while (fread(&iframe, sizeof(iframe), 1, fp)) {
// read number of clusters in frame
if (fread(&nph, sizeof(nph), 1, fp)) {
if (nph > (n_clusters - nph_read))
nn = n_clusters - nph_read;
else
nn = nph;
// nph_read += fread((buf + nph_read*clusters.item_size()),
// clusters.item_size(), nn, fp);
for(size_t i = 0; i < nn; i++){
fread(&tmp, sizeof(tmp), 1, fp);
if(tmp.x >= roi.xmin && tmp.x <= roi.xmax && tmp.y >= roi.ymin && tmp.y <= roi.ymax){
clusters.push_back(tmp.x, tmp.y, reinterpret_cast<std::byte*>(tmp.data));
nph_read++;
}
}
m_num_left = nph - nn;
}
if (nph_read >= n_clusters)
break;
}
}
// Resize the vector to the number of clusters.
// No new allocation, only change bounds.
clusters.resize(nph_read);
return clusters;
}
ClusterVector<int32_t> ClusterFile::read_frame() {
if (m_mode != "r") {
throw std::runtime_error("File not opened for reading");
@ -268,11 +341,23 @@ ClusterVector<int32_t> ClusterFile::read_frame() {
NDArray<double, 2> calculate_eta2(ClusterVector<int> &clusters) {
//TOTO! make work with 2x2 clusters
NDArray<double, 2> eta2({static_cast<int64_t>(clusters.size()), 2});
for (size_t i = 0; i < clusters.size(); i++) {
auto e = calculate_eta2(clusters.at<Cluster3x3>(i));
eta2(i, 0) = e.x;
eta2(i, 1) = e.y;
if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
for (size_t i = 0; i < clusters.size(); i++) {
auto e = calculate_eta2(clusters.at<Cluster3x3>(i));
eta2(i, 0) = e.x;
eta2(i, 1) = e.y;
}
}else if(clusters.cluster_size_x() == 2 || clusters.cluster_size_y() == 2){
for (size_t i = 0; i < clusters.size(); i++) {
auto e = calculate_eta2(clusters.at<Cluster2x2>(i));
eta2(i, 0) = e.x;
eta2(i, 1) = e.y;
}
}else{
throw std::runtime_error("Only 3x3 and 2x2 clusters are supported");
}
return eta2;
}
@ -290,7 +375,7 @@ Eta2 calculate_eta2(Cluster3x3 &cl) {
tot2[3] = cl.data[4] + cl.data[5] + cl.data[7] + cl.data[8];
auto c = std::max_element(tot2.begin(), tot2.end()) - tot2.begin();
eta.sum = tot2[c];
switch (c) {
case cBottomLeft:
if ((cl.data[3] + cl.data[4]) != 0)
@ -333,6 +418,20 @@ Eta2 calculate_eta2(Cluster3x3 &cl) {
return eta;
}
Eta2 calculate_eta2(Cluster2x2 &cl) {
Eta2 eta{};
if ((cl.data[0] + cl.data[1]) != 0)
eta.x = static_cast<double>(cl.data[1]) / (cl.data[0] + cl.data[1]);
if ((cl.data[0] + cl.data[2]) != 0)
eta.y = static_cast<double>(cl.data[2]) / (cl.data[0] + cl.data[2]);
eta.sum = cl.data[0] + cl.data[1] + cl.data[2]+ cl.data[3];
eta.c = cBottomLeft; //TODO! This is not correct, but need to put something
return eta;
}
int analyze_cluster(Cluster3x3 &cl, int32_t *t2, int32_t *t3, char *quad,
double *eta2x, double *eta2y, double *eta3x,
double *eta3y) {

144
src/Interpolator.cpp Normal file
View File

@ -0,0 +1,144 @@
#include "aare/Interpolator.hpp"
#include "aare/algorithm.hpp"
namespace aare {
Interpolator::Interpolator(NDView<double, 3> etacube, NDView<double, 1> xbins,
NDView<double, 1> ybins, NDView<double, 1> ebins)
: m_ietax(etacube), m_ietay(etacube), m_etabinsx(xbins), m_etabinsy(ybins), m_energy_bins(ebins) {
if (etacube.shape(0) != xbins.size() || etacube.shape(1) != ybins.size() ||
etacube.shape(2) != ebins.size()) {
throw std::invalid_argument(
"The shape of the etacube does not match the shape of the bins");
}
// Cumulative sum in the x direction
for (ssize_t i = 1; i < m_ietax.shape(0); i++) {
for (ssize_t j = 0; j < m_ietax.shape(1); j++) {
for (ssize_t k = 0; k < m_ietax.shape(2); k++) {
m_ietax(i, j, k) += m_ietax(i - 1, j, k);
}
}
}
// Normalize by the highest row, if norm less than 1 don't do anything
for (ssize_t i = 0; i < m_ietax.shape(0); i++) {
for (ssize_t j = 0; j < m_ietax.shape(1); j++) {
for (ssize_t k = 0; k < m_ietax.shape(2); k++) {
auto val = m_ietax(m_ietax.shape(0) - 1, j, k);
double norm = val < 1 ? 1 : val;
m_ietax(i, j, k) /= norm;
}
}
}
// Cumulative sum in the y direction
for (ssize_t i = 0; i < m_ietay.shape(0); i++) {
for (ssize_t j = 1; j < m_ietay.shape(1); j++) {
for (ssize_t k = 0; k < m_ietay.shape(2); k++) {
m_ietay(i, j, k) += m_ietay(i, j - 1, k);
}
}
}
// Normalize by the highest column, if norm less than 1 don't do anything
for (ssize_t i = 0; i < m_ietay.shape(0); i++) {
for (ssize_t j = 0; j < m_ietay.shape(1); j++) {
for (ssize_t k = 0; k < m_ietay.shape(2); k++) {
auto val = m_ietay(i, m_ietay.shape(1) - 1, k);
double norm = val < 1 ? 1 : val;
m_ietay(i, j, k) /= norm;
}
}
}
}
std::vector<Photon> Interpolator::interpolate(const ClusterVector<int32_t>& clusters) {
std::vector<Photon> photons;
photons.reserve(clusters.size());
if (clusters.cluster_size_x() == 3 || clusters.cluster_size_y() == 3) {
for (size_t i = 0; i<clusters.size(); i++){
auto cluster = clusters.at<Cluster3x3>(i);
Eta2 eta= calculate_eta2(cluster);
Photon photon;
photon.x = cluster.x;
photon.y = cluster.y;
photon.energy = eta.sum;
// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
// auto ix = nearest_index(m_etabinsx, eta.x)-1;
// auto iy = nearest_index(m_etabinsy, eta.y)-1;
//Finding the index of the last element that is smaller
//should work fine as long as we have many bins
auto ie = last_smaller(m_energy_bins, photon.energy);
auto ix = last_smaller(m_etabinsx, eta.x);
auto iy = last_smaller(m_etabinsy, eta.y);
// fmt::print("ex: {}, ix: {}, iy: {}\n", ie, ix, iy);
double dX, dY;
int ex, ey;
// cBottomLeft = 0,
// cBottomRight = 1,
// cTopLeft = 2,
// cTopRight = 3
switch (eta.c) {
case cTopLeft:
dX = -1.;
dY = 0.;
break;
case cTopRight:;
dX = 0.;
dY = 0.;
break;
case cBottomLeft:
dX = -1.;
dY = -1.;
break;
case cBottomRight:
dX = 0.;
dY = -1.;
break;
}
photon.x += m_ietax(ix, iy, ie)*2 + dX;
photon.y += m_ietay(ix, iy, ie)*2 + dY;
photons.push_back(photon);
}
}else if(clusters.cluster_size_x() == 2 || clusters.cluster_size_y() == 2){
for (size_t i = 0; i<clusters.size(); i++){
auto cluster = clusters.at<Cluster2x2>(i);
Eta2 eta= calculate_eta2(cluster);
Photon photon;
photon.x = cluster.x;
photon.y = cluster.y;
photon.energy = eta.sum;
//Now do some actual interpolation.
//Find which energy bin the cluster is in
// auto ie = nearest_index(m_energy_bins, photon.energy)-1;
// auto ix = nearest_index(m_etabinsx, eta.x)-1;
// auto iy = nearest_index(m_etabinsy, eta.y)-1;
//Finding the index of the last element that is smaller
//should work fine as long as we have many bins
auto ie = last_smaller(m_energy_bins, photon.energy);
auto ix = last_smaller(m_etabinsx, eta.x);
auto iy = last_smaller(m_etabinsy, eta.y);
photon.x += m_ietax(ix, iy, ie)*2; //eta goes between 0 and 1 but we could move the hit anywhere in the 2x2
photon.y += m_ietay(ix, iy, ie)*2;
photons.push_back(photon);
}
}else{
throw std::runtime_error("Only 3x3 and 2x2 clusters are supported for interpolation");
}
return photons;
}
} // namespace aare

View File

@ -2,6 +2,7 @@
#include <array>
#include <catch2/benchmark/catch_benchmark.hpp>
#include <catch2/catch_test_macros.hpp>
#include <numeric>
using aare::NDArray;
using aare::NDView;
@ -34,6 +35,24 @@ TEST_CASE("Construct from an NDView") {
}
}
TEST_CASE("3D NDArray from NDView"){
std::vector<int> data(27);
std::iota(data.begin(), data.end(), 0);
NDView<int, 3> view(data.data(), Shape<3>{3, 3, 3});
NDArray<int, 3> image(view);
REQUIRE(image.shape() == view.shape());
REQUIRE(image.size() == view.size());
REQUIRE(image.data() != view.data());
for(int64_t i=0; i<image.shape(0); i++){
for(int64_t j=0; j<image.shape(1); j++){
for(int64_t k=0; k<image.shape(2); k++){
REQUIRE(image(i, j, k) == view(i, j, k));
}
}
}
}
TEST_CASE("1D image") {
std::array<int64_t, 1> shape{{20}};
NDArray<short, 1> img(shape, 3);

73
src/algorithm.test.cpp Normal file
View File

@ -0,0 +1,73 @@
#include <catch2/catch_test_macros.hpp>
#include <aare/algorithm.hpp>
TEST_CASE("Find the closed index in a 1D array", "[algorithm]") {
aare::NDArray<double, 1> arr({5});
for (size_t i = 0; i < arr.size(); i++) {
arr[i] = i;
}
// arr 0, 1, 2, 3, 4
REQUIRE(aare::nearest_index(arr, 2.3) == 2);
REQUIRE(aare::nearest_index(arr, 2.6) == 3);
REQUIRE(aare::nearest_index(arr, 45.0) == 4);
REQUIRE(aare::nearest_index(arr, 0.0) == 0);
REQUIRE(aare::nearest_index(arr, -1.0) == 0);
}
TEST_CASE("Passing integers to nearest_index works", "[algorithm]"){
aare::NDArray<int, 1> arr({5});
for (size_t i = 0; i < arr.size(); i++) {
arr[i] = i;
}
// arr 0, 1, 2, 3, 4
REQUIRE(aare::nearest_index(arr, 2) == 2);
REQUIRE(aare::nearest_index(arr, 3) == 3);
REQUIRE(aare::nearest_index(arr, 45) == 4);
REQUIRE(aare::nearest_index(arr, 0) == 0);
REQUIRE(aare::nearest_index(arr, -1) == 0);
}
TEST_CASE("nearest_index works with std::vector", "[algorithm]"){
std::vector<double> vec = {0, 1, 2, 3, 4};
REQUIRE(aare::nearest_index(vec, 2.123) == 2);
REQUIRE(aare::nearest_index(vec, 2.66) == 3);
REQUIRE(aare::nearest_index(vec, 4555555.0) == 4);
REQUIRE(aare::nearest_index(vec, 0.0) == 0);
REQUIRE(aare::nearest_index(vec, -10.0) == 0);
}
TEST_CASE("nearest index works with std::array", "[algorithm]"){
std::array<double, 5> arr = {0, 1, 2, 3, 4};
REQUIRE(aare::nearest_index(arr, 2.123) == 2);
REQUIRE(aare::nearest_index(arr, 2.501) == 3);
REQUIRE(aare::nearest_index(arr, 4555555.0) == 4);
REQUIRE(aare::nearest_index(arr, 0.0) == 0);
REQUIRE(aare::nearest_index(arr, -10.0) == 0);
}
TEST_CASE("last smaller", "[algorithm]"){
aare::NDArray<double, 1> arr({5});
for (size_t i = 0; i < arr.size(); i++) {
arr[i] = i;
}
// arr 0, 1, 2, 3, 4
REQUIRE(aare::last_smaller(arr, -10.0) == 0);
REQUIRE(aare::last_smaller(arr, 0.0) == 0);
REQUIRE(aare::last_smaller(arr, 2.3) == 2);
REQUIRE(aare::last_smaller(arr, 253.) == 4);
}
TEST_CASE("returns last bin strictly smaller", "[algorithm]"){
aare::NDArray<double, 1> arr({5});
for (size_t i = 0; i < arr.size(); i++) {
arr[i] = i;
}
// arr 0, 1, 2, 3, 4
REQUIRE(aare::last_smaller(arr, 2.0) == 2);
}