picker changes from trigger_detection branch to main

dug_seis/event_processing/detection/coincidence_trigger.py

@@ -35,6 +35,7 @@ def coincidence_trigger(
     thr_off,
     stream,
     thr_coincidence_sum,
+    active_channels,
     trace_ids=None,
     max_trigger_length=1e6,
     delete_long_trigger=False,
@@ -85,6 +86,7 @@ def coincidence_trigger(
     :param stream: Stream containing waveform data for all stations. These
         data are changed inplace, make a copy to keep the raw waveform data.
     :type thr_coincidence_sum: int or float
+    :type active_channels: list of active channels
     :param thr_coincidence_sum: Threshold for coincidence sum. The network
         coincidence sum has to be at least equal to this value for a trigger to
         be included in the returned trigger list.
@@ -178,9 +180,19 @@ def util_val_of_scalar_or_list(elem, idx):
             new_options = {}
             for key, val in options.items():
                 new_options[key] = util_val_of_scalar_or_list(val, idx)
+        if not any(x in tr.id for x in active_channels):
+            # Linus inserted this on 15.03.2023, no cf performed on trigger channel/s but
+            # filtering/derivative/filtering/scaling and negation performed
             tr.trigger(trigger_type, **new_options)
+        else:
+            kernel_size = 2000
+            kernel = np.ones(kernel_size) / kernel_size
+            data_filtered = np.convolve(tr.data, kernel, mode='same')
+            time = np.arange(0, len(data_filtered)) * 1 / st.traces[0].stats.sampling_rate
+            data_filtered_dif = np.gradient(data_filtered) / np.gradient(time)
+            data_filtered_dif_filtered = np.convolve(data_filtered_dif, kernel, mode='same')
+            tr.data = -1 * data_filtered_dif_filtered/1000
             # end of adjustments
-
         kwargs["max_len"] = int(max_trigger_length * tr.stats.sampling_rate + 0.5)
 
         # original

dug_seis/event_processing/detection/dug_trigger.py

@@ -48,13 +48,13 @@ def dug_trigger(
             the first and the last moment of triggering at different stations
             for which this event is classified as electronic interference.
             Usually set to 0.25e-2.
-        coincidence_trigger_opts: Keyword arguments passed on to the coincidence
+        conincidence_trigger_opts: Keyword arguments passed on to the coincidence
             trigger.
     """
     triggers = coincidence_trigger(stream=st, **conincidence_trigger_opts)
-
     events = []
-    for trig in triggers:
+    event_mask_passive = [False for i in range(len(triggers))]
+    for idx, trig in enumerate(triggers):
         event = {"time": min(trig["time"]), "triggered_channels": trig["trace_ids"]}
         # Too close to previous event.
         if (
@@ -66,7 +66,8 @@ def dug_trigger(
 
         # Classification.
         # Triggered on active triggering channel == active
-        if active_triggering_channel and active_triggering_channel in trig["trace_ids"]:
+        # old: if active_triggering_channel and active_triggering_channel in trig["trace_ids"]:
+        if any(item in trig["trace_ids"] for item in active_triggering_channel):
             classification = "active"
         # Single input trace == passive
         elif len(st) == 1:
@@ -79,8 +80,9 @@ def dug_trigger(
         # Otherwise just treat is as passive.
         else:
             classification = "passive"
+            event_mask_passive[idx] = True
 
         event["classification"] = classification
         events.append(event)
 
-    return events
+    return events, event_mask_passive

dug_seis/event_processing/picking/dug_picker.py

@@ -248,26 +248,35 @@ def dug_picker(
     return picks
 
 
-def sta_lta(stream, st_window, lt_window, threshold_on, threshold_off):
+def sta_lta(stream, st_window, lt_window, thresholds):
     """
     Apply the STA LTA picker
     """
+    stream.detrend("constant")
+    # stream.filter("bandpass", freqmin=1000.0, freqmax=20000.0)
     sampling_rate = stream[0].stats.sampling_rate
 
     picks = []
 
-    for trace in stream:
+    for idx, trace in enumerate(stream):
         # create characteristic function
         cft = recursive_sta_lta(trace, st_window, lt_window)
 
         # do picking
-        trig = trigger_onset(cft, threshold_on, threshold_off)
+        trig = trigger_onset(cft, thresholds[idx], thresholds[idx])
 
         if len(trig):
             t_pick_UTC = (
                 trace.stats.starttime
                 + adjust_pick_time(trig[0][0], cft) / sampling_rate
             )
+            # calculate snr
+            try:
+                snr = max(abs(trace.data[trig[0][0] + 10:trig[0][0] + 100])) / max(
+                    abs(trace.data[trig[0][0] - 100:trig[0][0] - 10]))
+            except ValueError:
+                continue
+
             picks.append(
                 Pick(
                     time=t_pick_UTC,
@@ -279,7 +288,7 @@ def sta_lta(stream, st_window, lt_window, threshold_on, threshold_off):
                     ),
                     method_id="recursive_sta_lta",
                     phase_hint="P",
-                    evaluation_mode="automatic",
+                    evaluation_mode="automatic",  # comments=[str(round(snr, 3))],
                 )
             )