mirror of
https://github.com/Derisis13/SeismStart.git
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46 lines
1.8 KiB
Python
46 lines
1.8 KiB
Python
from scipy.signal import correlate
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from bisect_moonquake import CAT_LUNAR, LUNAR_DATA_DIR, PREPROCESSED_LUNAR_DIR, from_mseed
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from datetime import datetime
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import create_matched_filter
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import matplotlib.pyplot as plt
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from pathlib import Path
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from tqdm import tqdm
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IMGDIR = "./images"
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def do_detection(st, matched_filter):
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st.traces[0].data *= 1 / st.traces[0].data.max()
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st.traces[0].decimate(3)
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likelyhood = correlate(st.traces[0].data, matched_filter, mode='same')
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estimated_arrival = (likelyhood.argmax() - matched_filter.shape[0] / 2) * st.traces[0].stats.delta
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return estimated_arrival, likelyhood
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def main():
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matched_filter = create_matched_filter.create_matched_filter()
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outfolder = Path(IMGDIR + "/lunar/training/")
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outfolder.mkdir(parents=True, exist_ok=True)
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for row in tqdm(CAT_LUNAR.iloc):
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arrival_time = datetime.strptime(row['time_abs(%Y-%m-%dT%H:%M:%S.%f)'],'%Y-%m-%dT%H:%M:%S.%f')
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test_filename = row.filename
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try:
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st, arrival = from_mseed(test_filename, LUNAR_DATA_DIR, arrival_time)
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except FileNotFoundError:
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# Because csv is faulty...
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test_filename = test_filename.replace('HR00', 'HR02')
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st, arrival = from_mseed(test_filename, LUNAR_DATA_DIR, arrival_time)
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estimated_arrival, likelyhood = do_detection(st, matched_filter)
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print(arrival - estimated_arrival)
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# Plot trace
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outfile = outfolder.joinpath(test_filename + "_correlation.svg")
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fig,ax = plt.subplots(1,1,figsize=(10,3))
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ax.plot(st.traces[0].times(), likelyhood)
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ax.axvline(x = arrival, color='red',label='Rel. Arrival')
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ax.axvline(x = estimated_arrival, color='green',label='Est. Arrival')
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# plt.show()
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plt.savefig(outfile)
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plt.close(fig)
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if __name__ == "__main__":
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main()
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