feat: detection on training sets, works with great accuracy

create_matched_filter.py

@@ -0,0 +1,36 @@
+"""Create the matched filter for correlational detection"""
+from datetime import datetime
+import numpy as np
+import matplotlib.pyplot as plt
+
+from bisect_moonquake import CAT_LUNAR, PREPROCESSED_LUNAR_DIR, from_mseed
+
+FILTER_SHAPE = [46376 // 3 + 1,] # I want it that way
+
+def create_matched_filter():
+    matched_filter = np.zeros(FILTER_SHAPE, 'O')
+    type_collection = CAT_LUNAR
+    for row in type_collection.iloc:
+        arrival_time = datetime.strptime(row['time_abs(%Y-%m-%dT%H:%M:%S.%f)'],'%Y-%m-%dT%H:%M:%S.%f')
+        sample_filename = row.filename + "_trimmed_7000_sec"
+        try:
+            st, _ = from_mseed(sample_filename, PREPROCESSED_LUNAR_DIR, arrival_time)
+        except FileNotFoundError:
+            # Because csv is faulty...
+            sample_filename = sample_filename.replace('HR00', 'HR02')
+            st, _ = from_mseed(sample_filename, PREPROCESSED_LUNAR_DIR, arrival_time)
+        st.traces[0].data *= 1 / st.traces[0].data.max()
+        st.traces[0].decimate(3)
+        try:
+            matched_filter += st.traces[0].data
+        except:
+            # print(st.traces[0].data.shape)
+            pass
+        # # Plot trace
+        # fig,ax = plt.subplots(1,1,figsize=(10,3))
+        # ax.plot(matched_filter)
+        # plt.show()
+    return matched_filter
+
+if __name__ == "__main__":
+    create_matched_filter()