04.2025

configuration/user_scripts/SATOP31-ATT01_Debug_proc.py

@@ -20,22 +20,37 @@ def initialize(params):
     initialized = True
 
 
-def find_edge(data, step_length=50, edge_type="falling"):
-    # prepare a step function and refine it
+def find_edge(data, step_length=50, edge_type="falling", roi=None):
+    """
+    Find an edge in the given data using cross-correlation with a step waveform.
+    If a region-of-interest (roi) is provided as [start, end], the search is limited
+    to that slice of the data and the resulting edge position is offset appropriately.
+    """
+    # If ROI is provided, slice the data accordingly.
+    if roi is not None:
+        data_roi = data[roi[0]:roi[1]]
+    else:
+        data_roi = data
+
+    # Prepare the step function.
     step_waveform = np.ones(shape=(step_length,))
     if edge_type == "rising":
         step_waveform[: int(step_length / 2)] = -1
     elif edge_type == "falling":
-        step_waveform[int(step_length / 2) :] = -1
+        step_waveform[int(step_length / 2):] = -1
 
-    # find edges
-    xcorr = signal.correlate(data, step_waveform, mode="valid")
+    # Perform cross-correlation on the (possibly sliced) data.
+    xcorr = signal.correlate(data_roi, step_waveform, mode="valid")
     edge_position = np.argmax(xcorr)
     xcorr_amplitude = np.amax(xcorr)
 
-    # correct edge_position for step_length
+    # Correct edge_position for step_length.
     edge_position += np.floor(step_length / 2)
 
+    # If ROI is provided, add the offset to get the position in the full signal.
+    if roi is not None:
+        edge_position += roi[0]
+
     return {
         "edge_pos": edge_position,
         "xcorr": xcorr,
@@ -71,7 +86,8 @@ def process(data, pulse_id, timestamp, params):
         prof_sig_norm = prof_sig_savgol
 
     if events[fel_on_event] and not events[dark_event]:
-        edge_results = find_edge(prof_sig_norm, step_length, edge_type)
+        # Limit the edge search to the region-of-interest provided in params.
+        edge_results = find_edge(prof_sig_norm, step_length, edge_type, roi=params.get("roi"))
         edge_results["arrival_time"] = np.polyval(calib, edge_results["edge_pos"])
     else:
         edge_results = {
@@ -82,7 +98,7 @@ def process(data, pulse_id, timestamp, params):
         }
         edge_results["arrival_time"] = None
 
-    # Set bs outputs
+    # Set beam-synchronization outputs.
     output = {}
     for key, value in edge_results.items():
         output[f"{device}:{key}"] = value
@@ -90,24 +106,9 @@ def process(data, pulse_id, timestamp, params):
     output[f"{device}:raw_wf"] = prof_sig
     output[f"{device}:raw_wf_savgol"] = prof_sig_savgol
 
-    # if events[dark_event]:
-    #     output[f"{device}:dark_wf"] = prof_sig
-    #     output[f"{device}:dark_wf_savgol"] = prof_sig_savgol
-    # else:
-    #     output[f"{device}:dark_wf"] = None
-    #     output[f"{device}:dark_wf_savgol"] = None
-
     if buffer_dark:
         output[f"{device}:avg_dark_wf"] = np.mean(buffer_dark, axis=0)
     else:
-        #output[f"{device}:avg_dark_wf"] = np.zeros_like(prof_sig)
-        #Changed By Gobbo to avoid type errors
-        output[f"{device}:avg_dark_wf"] = None # np.zeros_like(prof_sig)
+        output[f"{device}:avg_dark_wf"] = None
 
-
-    # if buffer_savgol:
-    #     output[f"{device}:avg_dark_wf_savgol"] = np.mean(buffer_savgol, axis=0)
-    # else:
-    #     output[f"{device}:avg_dark_wf_savgol"] = None
-
-    return output
+    return output