Cleaned up Pedestal (#370)

- renamed pedestal.hpp to bind_Pedestal.hpp for python bindings
- variance is now always calculated in double and made private
- cost correctness in a few places
- removed push_fast and explicit updates of mean from Pedestal. If
performance is needed use FastPedestal
- Pedestal no longer caches std

python/aare/FastPedestal.py

@@ -22,7 +22,7 @@ def FastPedestal(rows, cols, n_samples=1000, dtype=np.float64):
         cols: Number of image columns.
         n_samples: Initialization frame count and steady-state update-weight
             denominator.
-        dtype: Output dtype for the mean, variance, and standard deviation.
+        dtype: Output dtype for the mean and standard deviation.
             Supported values are ``np.float64``, ``np.float32``, and
             ``np.int16``.
     """
@@ -41,7 +41,7 @@ def from_file(filename, n_samples=1000, skip_first=0, dtype=np.float64):
         filename: Input image file.
         n_samples: Number of frames used for initialization.
         skip_first: Number of leading frames to ignore.
-        dtype: Output dtype for the mean, variance, and standard deviation.
+        dtype: Output dtype for the mean and standard deviation.
 
     Raises:
         RuntimeError: If fewer than ``n_samples`` frames remain after

python/aare/Pedestal.py

@@ -0,0 +1,26 @@
+# SPDX-License-Identifier: MPL-2.0
+
+import numpy as np
+
+from .factory import _get_typed_class
+
+
+def Pedestal(rows, cols, n_samples=1000, dtype=np.float64):
+    """Create an empty per-pixel running pedestal.
+
+    This factory hides the dtype suffix used by the templated C++ bindings.
+    Call ``push()`` to update the statistics and cached mean for each frame.
+    Statistics are available during initialization and are zero for empty pixels.
+    Internal moments and variance always use double precision.
+
+    Args:
+        rows: Number of image rows.
+        cols: Number of image columns.
+        n_samples: Number of samples accumulated before switching to
+            steady-state updates with weight ``1 / n_samples``.
+        dtype: Output dtype for the mean and standard deviation.
+            Supported values are ``np.float64``, ``np.float32``, and
+            ``np.int16``.
+    """
+    cls = _get_typed_class("Pedestal", dtype)
+    return cls(rows, cols, n_samples)

python/aare/__init__.py

@@ -33,6 +33,7 @@ from ._aare import UDPPortPosition
 
 from ._version import __version__
 from .FastPedestal import FastPedestal
+from .Pedestal import Pedestal
 from .ClusterFinder import ClusterFinder, ClusterCollector, ClusterFinderMT, ClusterFileSink, ClusterFile
 from .ClusterVector import ClusterVector
 from .Cluster import Cluster