Port the frozen and CUDA finders to FastPedestal

- main moved ClusterFinder to FastPedestal and dropped Pedestal's
  push_fast/get_sum/get_sum2, which ClusterFinderFrozen and both CUDA
  drivers still called.

- Move all three to FastPedestal. Its uniform sample count is what the
  CUDA kernel already assumes, and matching ClusterFinder keeps CPU and
  GPU warm-up identical, which ClusterFinderFrozen exists to verify.
  FastPedestal gains get_sum/get_sum2 to de-interleave its internal
  Entry{sum,sum2} for the CUDA upload.

- Also expose min_pedestal_samples on these constructors and factories,
  make the factories keyword-only after image_size. Same API in C++ and Python.

python/src/bind_ClusterFinderCUDA.hpp

@@ -2,8 +2,8 @@
 #pragma once
 #include "aare/ClusterFinderCUDA.hpp"
 #include "aare/ClusterVector.hpp"
+#include "aare/FastPedestal.hpp"
 #include "aare/NDView.hpp"
-#include "aare/Pedestal.hpp"
 #include "np_helper.hpp"
 
 #include <cstdint>
@@ -112,9 +112,10 @@ void define_ClusterFinderCUDA(py::module &m, const std::string &typestr) {
             coordinates as (x, y).)");
 
     py::class_<CF>(m, class_name.c_str())
-        .def(py::init<Shape<2>, float, size_t, int, bool>(),
+        .def(py::init<Shape<2>, float, size_t, int, size_t, bool>(),
              py::arg("image_size"), py::arg("n_sigma") = 5.0f,
              py::arg("max_clusters_per_frame") = 2048, py::arg("n_streams") = 4,
+             py::arg("min_pedestal_samples") = 1000,
              py::arg("time_kernels") = false)
 
         .def_property(