raab/GP2023_chirp_detection
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weygoldt 2023-05-22 17:22:01 +02:00
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3 changed files with 27 additions and 429 deletions
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.python-version
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@ -1 +0,0 @@
chirpdetection

405
chirp_instantaneous_freq/chirp_exploration.ipynb
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50
code/chirpdetection.py
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@ -749,8 +749,9 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
multiwindow_chirps = [] multiwindow_chirps = []
multiwindow_ids = [] multiwindow_ids = []
for st, window_start_index in enumerate(window_start_indices): for st, window_start_index in enumerate(window_start_indices[1853:]):
logger.info(f"Processing window {st+1} of {len(window_start_indices)}")
logger.info(f"Processing window {st} of {len(window_start_indices)}")
window_start_seconds = window_start_index / data.raw_rate window_start_seconds = window_start_index / data.raw_rate
window_duration_seconds = window_duration / data.raw_rate window_duration_seconds = window_duration / data.raw_rate
@ -914,17 +915,19 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
baseline_frequency - np.median(baseline_frequency) baseline_frequency - np.median(baseline_frequency)
) )
# check if there is at least one superthreshold peak on the # # check if there is at least one superthreshold peak on the
# instantaneous and exit the loop if not. This is used to # # instantaneous and exit the loop if not. This is used to
# prevent windows that do definetely not include a chirp # # prevent windows that do definetely not include a chirp
# to enter normalization, where small changes due to noise # # to enter normalization, where small changes due to noise
# would be amplified # # would be amplified
if not has_chirp( # if not has_chirp(
baseline_frequency_filtered[amplitude_mask], # baseline_frequency_filtered[amplitude_mask],
config.baseline_frequency_peakheight, # config.baseline_frequency_peakheight,
): # ):
continue # logger.warning(
# f"Amplitude to small for the chirp detection of track {track_id} window {st},")
# continue
# CUT OFF OVERLAP --------------------------------------------- # CUT OFF OVERLAP ---------------------------------------------
@ -974,11 +977,11 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# normalize all three feature arrays to the same range to make # normalize all three feature arrays to the same range to make
# peak detection simpler # peak detection simpler
baseline_envelope = minmaxnorm([baseline_envelope])[0] # baseline_envelope = minmaxnorm([baseline_envelope])[0]
search_envelope = minmaxnorm([search_envelope])[0] # search_envelope = minmaxnorm([search_envelope])[0]
baseline_frequency_filtered = minmaxnorm( # baseline_frequency_filtered = minmaxnorm(
[baseline_frequency_filtered] # [baseline_frequency_filtered]
)[0] # )[0]
# PEAK DETECTION ---------------------------------------------- # PEAK DETECTION ----------------------------------------------
@ -996,6 +999,7 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
prominence=config.frequency_prominence, prominence=config.frequency_prominence,
) )
# DETECT CHIRPS IN SEARCH WINDOW ------------------------------ # DETECT CHIRPS IN SEARCH WINDOW ------------------------------
# get the peak timestamps from the peak indices # get the peak timestamps from the peak indices
@ -1010,11 +1014,10 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
# check if one list is empty and if so, skip to the next # check if one list is empty and if so, skip to the next
# electrode because a chirp cannot be detected if one is empty # electrode because a chirp cannot be detected if one is empty
one_feature_empty = ( one_feature_empty = (
len(baseline_peak_timestamps) == 0 len(baseline_peak_timestamps) == 0
or len(search_peak_timestamps) == 0 or len(search_peak_timestamps) == 0
or len(frequency_peak_timestamps) == 0 #or len(frequency_peak_timestamps) == 0
) )
if one_feature_empty and (debug == "false"): if one_feature_empty and (debug == "false"):
@ -1026,15 +1029,16 @@ def chirpdetection(datapath: str, plot: str, debug: str = "false") -> None:
sublists = [ sublists = [
list(baseline_peak_timestamps), list(baseline_peak_timestamps),
list(search_peak_timestamps), list(search_peak_timestamps),
list(frequency_peak_timestamps), #list(frequency_peak_timestamps),
] ]
singleelectrode_chirps = group_timestamps( singleelectrode_chirps = group_timestamps(
sublists=sublists, sublists=sublists,
at_least_in=3, at_least_in=2,
difference_threshold=config.chirp_window_threshold, difference_threshold=config.chirp_window_threshold,
) )
# check it there are chirps detected after grouping, continue # check it there are chirps detected after grouping, continue
# with the loop if not # with the loop if not
@ -1184,5 +1188,5 @@ if __name__ == "__main__":
# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_00/" # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_00/"
# datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/" # datapath = "/home/weygoldt/Data/uni/efishdata/2016-colombia/fishgrid/2016-04-09-22_25/"
# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/" # datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-03-13-10_00/"
datapath = "../../chirpdetector-cnn/testing_data/" datapath = "../data/2022-06-02-10_00/"
chirpdetection(datapath, plot="save", debug="false") chirpdetection(datapath, plot="show", debug="false")