cluster finder (#72)

implement fixed size cluster finder algorithm 
clusterFile functions are commented and put on hold.

---------

Co-authored-by: Bechir <bechir.brahem420@gmail.com>
Co-authored-by: Erik Fröjdh <erik.frojdh@gmail.com>

examples/CMakeLists.txt

@@ -2,7 +2,7 @@
 set(EXAMPLE_LIST "json_example;logger_example;numpy_read_example;multiport_example;raw_example;zmq_restream_example")
 set(EXAMPLE_LIST "${EXAMPLE_LIST};mythen_example;numpy_write_example;zmq_receiver_example;zmq_sender_example;")    
 set(EXAMPLE_LIST "${EXAMPLE_LIST};cluster_example;zmq_multi_receiver;zmq_task_ventilator;zmq_worker;zmq_sink")
-set(EXAMPLE_LIST "${EXAMPLE_LIST};testing_pedestal;")
+set(EXAMPLE_LIST "${EXAMPLE_LIST};testing_pedestal;cluster_finder_example;")
 
 foreach(example ${EXAMPLE_LIST})
     add_executable(${example} ${example}.cpp)

examples/cluster_example.cpp

@@ -7,48 +7,33 @@
 using namespace aare;
 int main() {
     auto PROJECT_ROOT_DIR = std::filesystem::path(getenv("AARE_ROOT_DIR"));
-    std::filesystem::path const fpath(PROJECT_ROOT_DIR / "data" / "clusters" / "single_frame_97_clustrers.clust");
+    std::filesystem::path const fpath("/mnt/sls_det_storage/moench_data/testNewFW20230714/cu_half_speed_master_4.json");
 
-    // reading a file
-    aare::ClusterFile cf(fpath, "r");
-    std::cout << "file opened " << '\n';
-    std::cout << "n_clusters " << cf.count() << '\n';
-    std::cout << "frame_number " << cf.frame() << '\n';
-    std::cout << "file size: " << cf.count() << '\n';
-    cf.seek(0); // seek to the beginning of the file (this is the default behavior of the constructor)
+    NDArray<double, 2> frame({10, 10});
+    frame = 0;
 
-    auto cluster = cf.read(97);
-
-    std::cout << "read 10 clusters" << '\n';
-    int offset = 0;
-    int data_offset = 0;
-    for (auto c : cluster) {
-        assert(c.y == offset + 200);
-        for (int i = 0; i < 9; i++) {
-            assert(c.data[i] == data_offset + i);
+    for (int i = 5; i < 8; i++) {
+        for (int j = 5; j < 8; j++) {
+            frame(i, j) = (i + j) % 10;
         }
-
-        offset++;
-        data_offset += 9;
     }
 
-    // writing a file
-    std::filesystem::path const fpath_out("/tmp/cluster_example_file.clust");
-    aare::ClusterFile cf_out(fpath_out, "w", ClusterFileConfig(1, 44));
-    std::cout << "file opened for writing" << '\n';
-    std::vector<Cluster> clusters;
-    for (int i = 0; i < 1084; i++) {
-        Cluster c;
-        c.x = i;
-        c.y = i + 200;
-        for (int j = 0; j < 9; j++) {
-            c.data[j] = j;
+    for (int i = 0; i < frame.shape(0); i++) {
+        for (int j = 0; j < frame.shape(1); j++) {
+            std::cout << frame(i, j) << " ";
         }
-        clusters.push_back(c);
+        std::cout << std::endl;
     }
-    cf_out.write(clusters);
-    std::cout << "wrote 10 clusters" << '\n';
-    cf_out.update_header();
 
-    return 0;
+    ClusterFinder clusterFinder(3, 3, 1, 1); // 3x3 cluster, 1 nSigma, 1 threshold
+
+    Pedestal p(10, 10);
+
+    auto clusters = clusterFinder.find_clusters(frame.span(), p);
+
+    aare::logger::info("nclusters:", clusters.size());
+
+    for (auto &cluster : clusters) {
+        aare::logger::info("cluster center:", cluster.to_string<double>());
+    }
 }

examples/cluster_finder_example.cpp

@@ -0,0 +1,63 @@
+
+#include "aare.hpp"
+#include "aare/examples/defs.hpp"
+
+#include <cassert>
+#include <iostream>
+
+using namespace aare;
+int main() {
+    auto PROJECT_ROOT_DIR = std::filesystem::path(getenv("AARE_ROOT_DIR"));
+    std::filesystem::path const fpath_frame("/home/l_bechir/tmp/testNewFW20230714/cu_half_speed_master_4.json");
+    std::filesystem::path const fpath_cluster("/home/l_bechir/tmp/testNewFW20230714/clust/cu_half_speed_d0_f0_4.clust");
+    auto file = RawFile(fpath_frame, "r");
+
+    logger::info("RAW file");
+    logger::info("file rows:", file.rows(), "cols:", file.cols());
+    logger::info(file.total_frames());
+    Frame frame(0, 0, 0);
+    for (auto i = 0; i < 10000; i++) {
+        if (file.frame_number(i) == 23389) {
+            logger::info("frame_number:", file.frame_number(i));
+            frame = file.read();
+        }
+    }
+    logger::info("frame", frame.rows(), frame.cols(), frame.bitdepth());
+
+    ClusterFileV2 cf(fpath_cluster, "r");
+    auto anna_clusters = cf.read();
+    logger::info("Cluster file");
+    logger::info("nclusters:", anna_clusters.size());
+    logger::info("frame_number", anna_clusters[0].frame_number);
+
+    ClusterFinder clusterFinder(3, 3, 5, 0);
+    Pedestal p(file.rows(), file.cols());
+    file.seek(0);
+    logger::info("Starting Pedestal calculation");
+    for (auto i = 0; i < 1000; i++) {
+        p.push(file.read().view<uint16_t>());
+    }
+    logger::info("Pedestal calculation done");
+    logger::info("Pedestal mean:", p.mean(0, 0), "std:", p.standard_deviation(0, 0));
+    logger::info("Pedestal mean:", p.mean(200, 200), "std:", p.standard_deviation(200, 200));
+    FileConfig cfg;
+    cfg.dtype = DType(typeid(double));
+    cfg.rows = p.rows();
+    cfg.cols = p.cols();
+
+    NumpyFile np_pedestal("/home/l_bechir/tmp/testNewFW20230714/pedestal.npy", "w", cfg);
+    cfg.dtype = DType(typeid(uint16_t));
+    NumpyFile np_frame("/home/l_bechir/tmp/testNewFW20230714/frame.npy", "w", cfg);
+
+    np_pedestal.write(p.mean());
+    np_frame.write(frame.view<uint16_t>());
+
+    auto clusters = clusterFinder.find_clusters(frame.view<uint16_t>(), p);
+    logger::info("nclusters:", clusters.size());
+
+    // aare::logger::info("nclusters:", clusters.size());
+
+    // for (auto &cluster : clusters) {
+    //     aare::logger::info("cluster center:", cluster.to_string<double>());
+    // }
+}