FastPedestal + optimizations in the cluster finder (#341)

Optimized pedestal tracking with FastPedestal and performance improvements to the cluster finder. 
- ~2x faster hitting 14k FPS on benchmark dataset on 16 core threadripper pro


**Changes**
- No bounds check on interior of frame. (only within half of a cluster)
(~15% improvement)
- Cache threshold (std*n_rms) Biggest improvement comes from not
computing std for each access
- Cache pedestal subtracted frame
- Switched to FastPedestal which assumes we reached steady state

**Things tried but rejected:**
- Always store cluster values, commit on val==max (~15% drop in frame
rate)

**Options**
- using 16 bit clusters and 16 bit pedestal. (slows down the single
threaded case but allow us to reach 12k with multi threading)

**Other notes on performance**
- Passing in memory frames from python and doing nothing: 0.8us/frame
- Passing + pedestal subtraction: 28us/frame
- Passing + pedestal + cluster finding (no store): 652us/frame
- Passing + pedestal + cluster finding: 886 us/frame

python/tests/test_Cluster.py

@@ -103,15 +103,18 @@ def test_max_sum():
 
 def test_cluster_finder(): 
     """Test ClusterFinder""" 
+    shape = [100,100]
+    cf = _aare.ClusterFinder_Cluster3x3i(shape)
 
-    clusterfinder = _aare.ClusterFinder_Cluster3x3i([100,100])
+    #Push 1000 frames to the pedestal
+    for i in range(1000):
+        frame = np.random.normal(loc = 100, scale = 5, size = shape).astype(np.uint16)
+        cf.push_pedestal_frame(frame)
+    cf.update_threshold()
+    frame = np.zeros(shape=shape, dtype=np.uint16)
+    cf.find_clusters(frame)
 
-    #frame = np.random.rand(100,100)
-    frame = np.zeros(shape=[100,100])
-
-    clusterfinder.find_clusters(frame)
-
-    clusters = clusterfinder.steal_clusters(False) #conversion does not work
+    clusters = cf.steal_clusters(False) #conversion does not work
 
     assert clusters.size == 0