WIP

include/aare/CircularFifo.hpp

@@ -0,0 +1,97 @@
+#pragma once
+
+#include <chrono>
+#include <fmt/color.h>
+#include <fmt/format.h>
+#include <memory>
+#include <thread>
+
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+template <class ItemType> class CircularFifo {
+    uint32_t fifo_size;
+    aare::ProducerConsumerQueue<ItemType> free_slots;
+    aare::ProducerConsumerQueue<ItemType> filled_slots;
+
+  public:
+    CircularFifo() : CircularFifo(100){};
+    CircularFifo(uint32_t size) : fifo_size(size), free_slots(size + 1), filled_slots(size + 1) {
+
+        // TODO! how do we deal with alignment for writing? alignas???
+        // Do we give the user a chance to provide memory locations?
+        // Templated allocator?
+        for (size_t i = 0; i < fifo_size; ++i) {
+            free_slots.write(ItemType{});
+        }
+    }
+
+    bool next() {
+        // TODO! avoid default constructing ItemType
+        ItemType it;
+        if (!filled_slots.read(it))
+            return false;
+        if (!free_slots.write(std::move(it)))
+            return false;
+        return true;
+    }
+
+    ~CircularFifo() {}
+
+    using value_type = ItemType;
+
+    auto numFilledSlots() const noexcept { return filled_slots.sizeGuess(); }
+    auto numFreeSlots() const noexcept { return free_slots.sizeGuess(); }
+    auto isFull() const noexcept { return filled_slots.isFull(); }
+
+    ItemType pop_free() {
+        ItemType v;
+        while (!free_slots.read(v))
+            ;
+        return std::move(v);
+        // return v;
+    }
+
+    bool try_pop_free(ItemType &v) { return free_slots.read(v); }
+
+    ItemType pop_value(std::chrono::nanoseconds wait, std::atomic<bool> &stopped) {
+        ItemType v;
+        while (!filled_slots.read(v) && !stopped) {
+            std::this_thread::sleep_for(wait);
+        }
+        return std::move(v);
+    }
+
+    ItemType pop_value() {
+        ItemType v;
+        while (!filled_slots.read(v))
+            ;
+        return std::move(v);
+    }
+
+    ItemType *frontPtr() { return filled_slots.frontPtr(); }
+
+    // TODO! Add function to move item from filled to free to be used
+    // with the frontPtr function
+
+    template <class... Args> void push_value(Args &&...recordArgs) {
+        while (!filled_slots.write(std::forward<Args>(recordArgs)...))
+            ;
+    }
+
+    template <class... Args> bool try_push_value(Args &&...recordArgs) {
+        return filled_slots.write(std::forward<Args>(recordArgs)...);
+    }
+
+    template <class... Args> void push_free(Args &&...recordArgs) {
+        while (!free_slots.write(std::forward<Args>(recordArgs)...))
+            ;
+    }
+
+    template <class... Args> bool try_push_free(Args &&...recordArgs) {
+        return free_slots.write(std::forward<Args>(recordArgs)...);
+    }
+};
+
+} // namespace aare

include/aare/ClusterCollector.hpp

@@ -0,0 +1,52 @@
+#pragma once
+#include <atomic>
+#include <thread>
+
+#include "aare/ProducerConsumerQueue.hpp"
+#include "aare/ClusterVector.hpp"
+#include "aare/ClusterFinderMT.hpp"
+
+namespace aare {
+
+class ClusterCollector{
+        ProducerConsumerQueue<ClusterVector<int>>* m_source;
+        std::atomic<bool> m_stop_requested{false};
+        std::atomic<bool> m_stopped{true};
+        std::chrono::milliseconds m_default_wait{1};
+        std::thread m_thread;
+        std::vector<ClusterVector<int>> m_clusters;
+
+    void process(){
+        m_stopped = false;
+        fmt::print("ClusterCollector started\n");
+        while (!m_stop_requested || !m_source->isEmpty()) { 
+            if (ClusterVector<int> *clusters = m_source->frontPtr();
+                clusters != nullptr) {
+                m_clusters.push_back(std::move(*clusters));
+                m_source->popFront();
+            }else{
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+        fmt::print("ClusterCollector stopped\n");
+        m_stopped = true;
+    }
+
+    public:
+        ClusterCollector(ClusterFinderMT<uint16_t, double, int32_t>* source){
+            m_source = source->sink();
+            m_thread = std::thread(&ClusterCollector::process, this);
+        }
+        void stop(){
+            m_stop_requested = true;
+            m_thread.join();
+        }
+        std::vector<ClusterVector<int>> steal_clusters(){
+            if(!m_stopped){
+                throw std::runtime_error("ClusterCollector is still running");
+            }
+            return std::move(m_clusters);
+        }
+};
+
+} // namespace aare

include/aare/ClusterFinder.hpp

@@ -10,26 +10,12 @@
 
 namespace aare {
 
-/** enum to define the event types */
-enum class eventType {
-    PEDESTAL,   /** pedestal */
-    NEIGHBOUR,  /** neighbour i.e. below threshold, but in the cluster of a
-                   photon */
-    PHOTON,     /** photon i.e. above threshold */
-    PHOTON_MAX, /** maximum of a cluster satisfying the photon conditions */
-    NEGATIVE_PEDESTAL, /** negative value, will not be accounted for as pedestal
-                          in order to avoid drift of the pedestal towards
-                          negative values */
-    UNDEFINED_EVENT = -1 /** undefined */
-};
-
 template <typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double,
           typename CT = int32_t>
 class ClusterFinder {
     Shape<2> m_image_size;
     const int m_cluster_sizeX;
     const int m_cluster_sizeY;
-    // const PEDESTAL_TYPE m_threshold;
     const PEDESTAL_TYPE m_nSigma;
     const PEDESTAL_TYPE c2;
     const PEDESTAL_TYPE c3;

include/aare/ClusterFinderMT.hpp

@@ -0,0 +1,189 @@
+#pragma once
+#include <atomic>
+#include <cstdint>
+#include <memory>
+#include <thread>
+#include <vector>
+
+#include "aare/NDArray.hpp"
+#include "aare/ProducerConsumerQueue.hpp"
+
+namespace aare {
+
+enum class FrameType {
+    DATA,
+    PEDESTAL,
+};
+
+struct FrameWrapper {
+    FrameType type;
+    uint64_t frame_number;
+    NDArray<uint16_t, 2> data;
+};
+
+
+template <typename FRAME_TYPE = uint16_t, typename PEDESTAL_TYPE = double,
+          typename CT = int32_t>
+class ClusterFinderMT {
+    size_t m_current_thread{0};
+    size_t m_n_threads{0};
+    using Finder = ClusterFinder<FRAME_TYPE, PEDESTAL_TYPE, CT>;
+    using InputQueue = ProducerConsumerQueue<FrameWrapper>;
+    using OutputQueue = ProducerConsumerQueue<ClusterVector<int>>;
+    std::vector<std::unique_ptr<InputQueue>> m_input_queues;
+    std::vector<std::unique_ptr<OutputQueue>> m_output_queues;
+    
+    
+    OutputQueue m_sink{1000}; //All clusters go into this queue
+
+
+    std::vector<std::unique_ptr<Finder>> m_cluster_finders;
+    std::vector<std::thread> m_threads;
+    std::thread m_collect_thread;
+    std::chrono::milliseconds m_default_wait{1};
+
+    std::atomic<bool> m_stop_requested{false};
+    std::atomic<bool> m_processing_threads_stopped{false};
+
+    void process(int thread_id) {
+        auto cf = m_cluster_finders[thread_id].get();
+        auto q = m_input_queues[thread_id].get();
+        // TODO! Avoid indexing into the vector every time
+        fmt::print("Thread {} started\n", thread_id);
+        //TODO! is this check enough to make sure we process all the frames?
+        while (!m_stop_requested || !q->isEmpty()) { 
+            if (FrameWrapper *frame = q->frontPtr();
+                frame != nullptr) {
+                // fmt::print("Thread {} got frame {}, type: {}\n", thread_id,
+                        //    frame->frame_number, static_cast<int>(frame->type));
+
+                switch (frame->type) {
+                case FrameType::DATA:
+                    cf->find_clusters(
+                        frame->data.view());
+                    m_output_queues[thread_id]->write(cf->steal_clusters());
+                    
+                    break;
+
+                case FrameType::PEDESTAL:
+                    m_cluster_finders[thread_id]->push_pedestal_frame(
+                        frame->data.view());
+                    break;
+
+                default:
+                    break;
+                }
+
+                // frame is processed now discard it
+                m_input_queues[thread_id]->popFront();
+            } else {
+                std::this_thread::sleep_for(m_default_wait);
+            }
+            
+        }
+        fmt::print("Thread {} stopped\n", thread_id);
+    }
+
+    /**
+     * @brief Collect all the clusters from the output queues and write them to the sink
+     */
+    void collect(){
+        bool empty = true;
+        while(!m_stop_requested || !empty || !m_processing_threads_stopped){
+            empty = true;
+            for (auto &queue : m_output_queues) {
+                if (!queue->isEmpty()) {
+
+                    while(!m_sink.write(std::move(*queue->frontPtr()))){
+                        std::this_thread::sleep_for(m_default_wait);
+                    }
+                    queue->popFront();
+                    empty = false;
+                }
+            }
+        }
+    }
+
+  public:
+    ClusterFinderMT(Shape<2> image_size, Shape<2> cluster_size,
+                    PEDESTAL_TYPE nSigma = 5.0, size_t capacity = 2000,
+                    size_t n_threads = 3)
+        : m_n_threads(n_threads) {
+        fmt::print("ClusterFinderMT: using {} threads\n", n_threads);
+        for (size_t i = 0; i < n_threads; i++) {
+            m_cluster_finders.push_back(
+                std::make_unique<ClusterFinder<FRAME_TYPE, PEDESTAL_TYPE, CT>>(
+                    image_size, cluster_size, nSigma, capacity));
+        }
+        for (size_t i = 0; i < n_threads; i++) {
+            m_input_queues.emplace_back(std::make_unique<InputQueue>(200));
+            m_output_queues.emplace_back(std::make_unique<OutputQueue>(200));
+
+        }
+
+        start();
+    }
+
+    ProducerConsumerQueue<ClusterVector<int>> *sink() { return &m_sink; }
+
+    void start(){
+        for (size_t i = 0; i < m_n_threads; i++) {
+            m_threads.push_back(
+                std::thread(&ClusterFinderMT::process, this, i));
+        }
+        m_collect_thread = std::thread(&ClusterFinderMT::collect, this);
+    }
+
+    void stop() {
+        m_stop_requested = true;
+        for (auto &thread : m_threads) {
+            thread.join();
+        }
+        m_processing_threads_stopped = true;
+        m_collect_thread.join();
+    }
+
+    void sync(){
+        for (auto &q : m_input_queues) {
+            while(!q->isEmpty()){
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+    }
+
+    void push_pedestal_frame(NDView<FRAME_TYPE, 2> frame) {
+        FrameWrapper fw{FrameType::PEDESTAL, 0, NDArray(frame)};
+
+        for (auto &queue : m_input_queues) {
+            while (!queue->write(fw)) {
+                // fmt::print("push_pedestal_frame: queue full\n");
+                std::this_thread::sleep_for(m_default_wait);
+            }
+        }
+    }
+
+    void find_clusters(NDView<FRAME_TYPE, 2> frame) {
+        FrameWrapper fw{FrameType::DATA, 0, NDArray(frame)};
+        while (!m_input_queues[m_current_thread%m_n_threads]->write(fw)) {
+            std::this_thread::sleep_for(m_default_wait);
+        }
+        m_current_thread++;
+    }
+
+    ClusterVector<int> steal_clusters(bool realloc_same_capacity = false) {
+        ClusterVector<int> clusters(3,3);
+        for (auto &finder : m_cluster_finders) {
+            clusters += finder->steal_clusters();
+        }
+        return clusters;
+    }
+
+    
+    // void push(FrameWrapper&& frame) {
+    //     //TODO! need to loop until we are successful
+    //     auto rc = m_input_queue.write(std::move(frame));
+    //     fmt::print("pushed frame {}\n", rc);
+    // }
+};
+
+} // namespace aare

include/aare/ClusterVector.hpp

@@ -22,6 +22,7 @@ template <typename T, typename CoordType=int16_t> class ClusterVector {
     std::byte *m_data{};
     size_t m_size{0};
     size_t m_capacity;
+    uint64_t m_frame_number{0}; //TODO! Check frame number size and type
     /*
     Format string used in the python bindings to create a numpy
     array from the buffer
@@ -39,7 +40,7 @@ template <typename T, typename CoordType=int16_t> class ClusterVector {
      * @param cluster_size_y size of the cluster in y direction
      * @param capacity initial capacity of the buffer in number of clusters
      */
-    ClusterVector(size_t cluster_size_x, size_t cluster_size_y,
+    ClusterVector(size_t cluster_size_x = 3, size_t cluster_size_y = 3,
                   size_t capacity = 1024)
         : m_cluster_size_x(cluster_size_x), m_cluster_size_y(cluster_size_y),
           m_capacity(capacity) {
@@ -108,7 +109,14 @@ template <typename T, typename CoordType=int16_t> class ClusterVector {
                   ptr);
         m_size++;
     }
-
+    ClusterVector& operator+=(const ClusterVector& other){
+        if (m_size + other.m_size > m_capacity) {
+            allocate_buffer(m_capacity + other.m_size);
+        }
+        std::copy(other.m_data, other.m_data + other.m_size * element_offset(), m_data + m_size * element_offset());
+        m_size += other.m_size;
+        return *this;
+    }
 
     /**
      * @brief Sum the pixels in each cluster
@@ -166,6 +174,9 @@ template <typename T, typename CoordType=int16_t> class ClusterVector {
         return m_fmt_base;
     }
 
+    uint64_t frame_number() const { return m_frame_number; }
+    void set_frame_number(uint64_t frame_number) { m_frame_number = frame_number; }
+
   private:
     void allocate_buffer(size_t new_capacity) {
         size_t num_bytes = element_offset() * new_capacity;

include/aare/ProducerConsumerQueue.hpp

@@ -0,0 +1,203 @@
+/*
+ * Copyright (c) Meta Platforms, Inc. and affiliates.
+ *
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ *     http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+// @author Bo Hu (bhu@fb.com)
+// @author Jordan DeLong (delong.j@fb.com)
+
+// Changes made by PSD Detector Group:
+// Copied: Line 34 constexpr std::size_t hardware_destructive_interference_size = 128; from folly/lang/Align.h
+// Changed extension to .hpp
+// Changed namespace to aare
+
+#pragma once
+
+#include <atomic>
+#include <cassert>
+#include <cstdlib>
+#include <memory>
+#include <stdexcept>
+#include <type_traits>
+#include <utility>
+
+constexpr std::size_t hardware_destructive_interference_size = 128;
+namespace aare {
+
+/*
+ * ProducerConsumerQueue is a one producer and one consumer queue
+ * without locks.
+ */
+template <class T> struct ProducerConsumerQueue {
+    typedef T value_type;
+
+    ProducerConsumerQueue(const ProducerConsumerQueue &) = delete;
+    ProducerConsumerQueue &operator=(const ProducerConsumerQueue &) = delete;
+
+    
+    ProducerConsumerQueue(ProducerConsumerQueue &&other){
+        size_ = other.size_;
+        records_ = other.records_;
+        other.records_ = nullptr;
+        readIndex_ = other.readIndex_.load(std::memory_order_acquire);
+        writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
+    }
+    ProducerConsumerQueue &operator=(ProducerConsumerQueue &&other){
+        size_ = other.size_;
+        records_ = other.records_;
+        other.records_ = nullptr;
+        readIndex_ = other.readIndex_.load(std::memory_order_acquire);
+        writeIndex_ = other.writeIndex_.load(std::memory_order_acquire);
+        return *this;
+    }
+    
+    
+    ProducerConsumerQueue():ProducerConsumerQueue(2){};
+    // size must be >= 2.
+    //
+    // Also, note that the number of usable slots in the queue at any
+    // given time is actually (size-1), so if you start with an empty queue,
+    // isFull() will return true after size-1 insertions.
+    explicit ProducerConsumerQueue(uint32_t size)
+        : size_(size), records_(static_cast<T *>(std::malloc(sizeof(T) * size))), readIndex_(0), writeIndex_(0) {
+        assert(size >= 2);
+        if (!records_) {
+            throw std::bad_alloc();
+        }
+    }
+
+    ~ProducerConsumerQueue() {
+        // We need to destruct anything that may still exist in our queue.
+        // (No real synchronization needed at destructor time: only one
+        // thread can be doing this.)
+        if (!std::is_trivially_destructible<T>::value) {
+            size_t readIndex = readIndex_;
+            size_t endIndex = writeIndex_;
+            while (readIndex != endIndex) {
+                records_[readIndex].~T();
+                if (++readIndex == size_) {
+                    readIndex = 0;
+                }
+            }
+        }
+
+        std::free(records_);
+    }
+
+    template <class... Args> bool write(Args &&...recordArgs) {
+        auto const currentWrite = writeIndex_.load(std::memory_order_relaxed);
+        auto nextRecord = currentWrite + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
+            new (&records_[currentWrite]) T(std::forward<Args>(recordArgs)...);
+            writeIndex_.store(nextRecord, std::memory_order_release);
+            return true;
+        }
+
+        // queue is full
+        return false;
+    }
+
+    // move (or copy) the value at the front of the queue to given variable
+    bool read(T &record) {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
+            // queue is empty
+            return false;
+        }
+
+        auto nextRecord = currentRead + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        record = std::move(records_[currentRead]);
+        records_[currentRead].~T();
+        readIndex_.store(nextRecord, std::memory_order_release);
+        return true;
+    }
+
+    // pointer to the value at the front of the queue (for use in-place) or
+    // nullptr if empty.
+    T *frontPtr() {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        if (currentRead == writeIndex_.load(std::memory_order_acquire)) {
+            // queue is empty
+            return nullptr;
+        }
+        return &records_[currentRead];
+    }
+
+    // queue must not be empty
+    void popFront() {
+        auto const currentRead = readIndex_.load(std::memory_order_relaxed);
+        assert(currentRead != writeIndex_.load(std::memory_order_acquire));
+
+        auto nextRecord = currentRead + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        records_[currentRead].~T();
+        readIndex_.store(nextRecord, std::memory_order_release);
+    }
+
+    bool isEmpty() const {
+        return readIndex_.load(std::memory_order_acquire) == writeIndex_.load(std::memory_order_acquire);
+    }
+
+    bool isFull() const {
+        auto nextRecord = writeIndex_.load(std::memory_order_acquire) + 1;
+        if (nextRecord == size_) {
+            nextRecord = 0;
+        }
+        if (nextRecord != readIndex_.load(std::memory_order_acquire)) {
+            return false;
+        }
+        // queue is full
+        return true;
+    }
+
+    // * If called by consumer, then true size may be more (because producer may
+    //   be adding items concurrently).
+    // * If called by producer, then true size may be less (because consumer may
+    //   be removing items concurrently).
+    // * It is undefined to call this from any other thread.
+    size_t sizeGuess() const {
+        int ret = writeIndex_.load(std::memory_order_acquire) - readIndex_.load(std::memory_order_acquire);
+        if (ret < 0) {
+            ret += size_;
+        }
+        return ret;
+    }
+
+    // maximum number of items in the queue.
+    size_t capacity() const { return size_ - 1; }
+
+  private:
+    using AtomicIndex = std::atomic<unsigned int>;
+
+    char pad0_[hardware_destructive_interference_size];
+    // const uint32_t size_;
+    uint32_t size_;
+    // T *const records_;
+    T* records_;
+
+    alignas(hardware_destructive_interference_size) AtomicIndex readIndex_;
+    alignas(hardware_destructive_interference_size) AtomicIndex writeIndex_;
+
+    char pad1_[hardware_destructive_interference_size - sizeof(AtomicIndex)];
+};
+
+} // namespace aare

python/examples/play.py

@@ -13,36 +13,66 @@ from aare import File, ClusterFinder, VarClusterFinder
 base = Path('/mnt/sls_det_storage/matterhorn_data/aare_test_data/')
 
 f = File(base/'Moench03new/cu_half_speed_master_4.json')
-cf = ClusterFinder((400,400), (3,3))
+
+
+from aare._aare import ClusterFinderMT, ClusterCollector
+
+
+cf = ClusterFinderMT((400,400), (3,3), n_threads = 3)
+collector = ClusterCollector(cf)
+
 for i in range(1000):
-    cf.push_pedestal_frame(f.read_frame())
+    img = f.read_frame()
+    cf.push_pedestal_frame(img)
+print('Pedestal done')
+cf.sync()
 
-fig, ax = plt.subplots()
-im = ax.imshow(cf.pedestal())
-cf.pedestal()
-cf.noise()
+for i in range(100):
+    img = f.read_frame()
+    cf.find_clusters(img)
+
+
+# time.sleep(1)
+cf.stop()  
+collector.stop()
+cv = collector.steal_clusters()
+print(f'Processed {len(cv)} frames')
+
+print('Done')
 
 
 
-N = 500
-t0 = time.perf_counter()
-hist1 = bh.Histogram(bh.axis.Regular(40, -2, 4000))
-f.seek(0)
 
-t0 = time.perf_counter()
-data = f.read_n(N)
-t_elapsed = time.perf_counter()-t0
+# cf = ClusterFinder((400,400), (3,3))
+# for i in range(1000):
+#     cf.push_pedestal_frame(f.read_frame())
+
+# fig, ax = plt.subplots()
+# im = ax.imshow(cf.pedestal())
+# cf.pedestal()
+# cf.noise()
 
 
-n_bytes = data.itemsize*data.size
 
-print(f'Reading {N} frames took {t_elapsed:.3f}s {N/t_elapsed:.0f} FPS, {n_bytes/1024**2:.4f} GB/s')
+# N = 500
+# t0 = time.perf_counter()
+# hist1 = bh.Histogram(bh.axis.Regular(40, -2, 4000))
+# f.seek(0)
+
+# t0 = time.perf_counter()
+# data = f.read_n(N)
+# t_elapsed = time.perf_counter()-t0
 
 
-for frame in data:
-    a = cf.find_clusters(frame)
+# n_bytes = data.itemsize*data.size
 
-clusters = cf.steal_clusters()
+# print(f'Reading {N} frames took {t_elapsed:.3f}s {N/t_elapsed:.0f} FPS, {n_bytes/1024**2:.4f} GB/s')
+
+
+# for frame in data:
+#     a = cf.find_clusters(frame)
+
+# clusters = cf.steal_clusters()
 
 # t_elapsed = time.perf_counter()-t0
 # print(f'Clustering {N} frames took {t_elapsed:.2f}s  {N/t_elapsed:.0f} FPS')

python/src/cluster.hpp

@@ -1,5 +1,7 @@
 #include "aare/ClusterFinder.hpp"
+#include "aare/ClusterFinderMT.hpp"
 #include "aare/ClusterVector.hpp"
+#include "aare/ClusterCollector.hpp"
 #include "aare/NDView.hpp"
 #include "aare/Pedestal.hpp"
 #include "np_helper.hpp"
@@ -7,11 +9,13 @@
 #include <cstdint>
 #include <filesystem>
 #include <pybind11/pybind11.h>
+#include <pybind11/stl_bind.h>
 #include <pybind11/stl.h>
 
 namespace py = pybind11;
 using pd_type = double;
 
+
 template <typename T>
 void define_cluster_vector(py::module &m, const std::string &typestr) {
     auto class_name = fmt::format("ClusterVector_{}", typestr);
@@ -31,6 +35,9 @@ void define_cluster_vector(py::module &m, const std::string &typestr) {
                  auto *vec = new std::vector<T>(self.sum());
                  return return_vector(vec);
              })
+        .def_property_readonly("capacity", &ClusterVector<T>::capacity)
+        .def_property("frame_number", &ClusterVector<T>::frame_number,
+                      &ClusterVector<T>::set_frame_number)
         .def_buffer([typestr](ClusterVector<T> &self) -> py::buffer_info {
             return py::buffer_info(
                 self.data(),           /* Pointer to buffer */
@@ -45,6 +52,55 @@ void define_cluster_vector(py::module &m, const std::string &typestr) {
         });
 }
 
+void define_cluster_finder_mt_bindings(py::module &m) {
+    py::class_<ClusterFinderMT<uint16_t, pd_type>>(m, "ClusterFinderMT")
+        .def(py::init<Shape<2>, Shape<2>, pd_type, size_t, size_t>(),
+             py::arg("image_size"), py::arg("cluster_size"),
+             py::arg("n_sigma") = 5.0, py::arg("capacity") = 1000,
+             py::arg("n_threads") = 3)
+        .def("push_pedestal_frame",
+             [](ClusterFinderMT<uint16_t, pd_type> &self,
+                py::array_t<uint16_t> frame) {
+                 auto view = make_view_2d(frame);
+                 self.push_pedestal_frame(view);
+             })
+        .def("find_clusters",
+             [](ClusterFinderMT<uint16_t, pd_type> &self,
+                py::array_t<uint16_t> frame) {
+                 auto view = make_view_2d(frame);
+                 self.find_clusters(view);
+                 return;
+             })
+        .def("sync", &ClusterFinderMT<uint16_t, pd_type>::sync)
+        .def(
+            "steal_clusters",
+            [](ClusterFinderMT<uint16_t, pd_type> &self,
+               bool realloc_same_capacity) {
+                auto v = new ClusterVector<int>(
+                    self.steal_clusters(realloc_same_capacity));
+                return v;
+            },
+            py::arg("realloc_same_capacity") = false)
+        .def("stop", &ClusterFinderMT<uint16_t, pd_type>::stop);
+}
+
+
+void define_cluster_collector_bindings(py::module &m) {
+    py::class_<ClusterCollector>(m, "ClusterCollector")
+        .def(py::init<ClusterFinderMT<uint16_t, double, int32_t>*>())
+        .def("stop", &ClusterCollector::stop)
+        .def("steal_clusters",
+             [](ClusterCollector &self) {
+                 auto v = new std::vector<ClusterVector<int>>(
+                     self.steal_clusters());
+                 return v;
+             }, py::return_value_policy::take_ownership);
+
+
+
+}
+
+
 void define_cluster_finder_bindings(py::module &m) {
     py::class_<ClusterFinder<uint16_t, pd_type>>(m, "ClusterFinder")
         .def(py::init<Shape<2>, Shape<2>, pd_type, size_t>(),

python/src/module.cpp

@@ -25,5 +25,8 @@ PYBIND11_MODULE(_aare, m) {
     define_pedestal_bindings<double>(m, "Pedestal_d");
     define_pedestal_bindings<float>(m, "Pedestal_f");
     define_cluster_finder_bindings(m);
+    define_cluster_finder_mt_bindings(m);
     define_cluster_file_io_bindings(m);
+    define_cluster_collector_bindings(m);
+
 }