raab/GP2023_chirp_detection
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gapfinder works

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This commit is contained in:
weygoldt 2023-01-16 16:00:18 +01:00
parent 9896926a3e
commit e1fdb13787
2 changed files with 80 additions and 17 deletions
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85
code/chirpdetection.py
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@ -20,7 +20,7 @@ def instantaneos_frequency(
signal: np.ndarray, samplerate: int
) -> tuple[np.ndarray, np.ndarray]:
"""
Compute the instantaneous frequency of a signal.
Compute the instantaneous frequency of a signal.
Parameters
----------
@ -185,6 +185,7 @@ def main(datapath: str) -> None:
# ask how many windows should be calulated
nwindows = int(
input("How many windows should be calculated (integer number)? "))
for start_index in window_starts[:nwindows]:
# make t0 and dt
@ -209,14 +210,23 @@ def main(datapath: str) -> None:
]
median_freq.append(np.median(data.freq[window_index]))
track_ids.append(track_id)
median_freq= np.asarray(median_freq)
track_ids= np.asarray(track_ids)
median_freq = np.asarray(median_freq)
track_ids = np.asarray(track_ids)
# iterate through all fish
for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])[:2]):
for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
print(f"Track ID: {track_id}")
window_index = np.arange(len(data.idx))[
(data.ident == track_id) & (data.time[data.idx] >= t0) & (
data.time[data.idx] <= (t0 + dt))
]
# get tracked frequencies and their times
freq_temp = data.freq[window_index]
powers_temp = data.powers[window_index, :]
# time_temp = time[idx[window_index]]
track_samplerate = np.mean(1 / np.diff(data.time))
expected_duration = ((t0 + dt) - t0) * track_samplerate
@ -238,22 +248,75 @@ def main(datapath: str) -> None:
powers_temp, axis=0))[-config.electrodes:]
# frequency where second filter filters
search_window = np.arange(np.median(freq_temp)+config.search_df_lower, np.median(freq_temp)+config.search_df_upper, config.search_res)
check_track_ids = track_ids[(median_freq>search_window[0]) & (median_freq<search_window[-1])]
search_window = np.arange(np.median(freq_temp)+config.search_df_lower, np.median(
freq_temp)+config.search_df_upper, config.search_res)
# search window in boolean
search_window_bool = np.ones(len(search_window), dtype=bool)
# get tracks that fall into search window
check_track_ids = track_ids[(median_freq > search_window[0]) & (
median_freq < search_window[-1])]
# iterate through theses tracks
if check_track_ids.size != 0:
for j, check_track_id in enumerate(check_track_ids):
q1, q2 = np.percentile(data.freq[data.ident==check_track_id], config.search_freq_percentiles)
q1, q2 = np.percentile(
data.freq[data.ident == check_track_id], config.search_freq_percentiles)
search_window_bool[(search_window > q1) & (
search_window < q2)] = False
# find gaps in search window
search_window_indices = np.arange(len(search_window))
# get search window gaps
search_window_gaps = np.diff(search_window_bool, append=np.nan)
nonzeros = search_window_gaps[np.nonzero(
search_window_gaps)[0]]
nonzeros = nonzeros[~np.isnan(nonzeros)]
# if the first value is -1, the array starst with true, so a gap
if nonzeros[0] == -1:
stops = search_window_indices[search_window_gaps == -1]
starts = np.append(
0, search_window_indices[search_window_gaps == 1])
# if the last value is -1, the array ends with true, so a gap
if nonzeros[-1] == 1:
stops = np.append(
search_window_indices[search_window_gaps == -1], len(search_window))
# else it starts with false, so no gap
if nonzeros[0] == 1:
stops = search_window_indices[search_window_gaps == -1]
starts = search_window_indices[search_window_gaps == 1]
# if the last value is -1, the array ends with true, so a gap
if nonzeros[-1] == 1:
stops = np.append(
search_window_indices[search_window_gaps == -1], len(search_window))
embed()
# search_window_gaps = search_window_indices[np.diff(
# search_window_indices, append=np.nan) != 1]
# startstop = [[x, y] for x, y in zip(
# search_window_gaps, search_window_gaps[1:])]
# search_windows = [search_window[startstop[i][0]:startstop[i][1]] for i in range(len(startstop))]
embed()
search_freq = 50
# <------------------------------------------ Iterate through electrodes
for i, electrode in enumerate(best_electrodes):
# load region of interest of raw data file
data_oi=data.raw[start_index:stop_index, :]
time_oi=raw_time[start_index:stop_index]
data_oi = data.raw[start_index:stop_index, :]
time_oi = raw_time[start_index:stop_index]
# plot wavetracker tracks to spectrogram
# for track_id in np.unique(ident): # <---------- Find freq gaps later
@ -275,7 +338,7 @@ def main(datapath: str) -> None:
# track_id = ids
# filter baseline and above
baseline, search=double_bandpass(
baseline, search = double_bandpass(
data_oi[:, electrode], data.raw_rate, freq_temp, search_freq
)
@ -345,7 +408,7 @@ def main(datapath: str) -> None:
baseline_envelope = baseline_envelope[valid]
search_envelope = search_envelope[valid]
# get inst freq valid snippet
# get inst freq valid snippet
valid_t0 = int(window_edge) / data.raw_rate
valid_t1 = baseline_freq_time[-1] - \
(int(window_edge) / data.raw_rate)

12
code/chirpdetector_conf.yml
View File

@ -31,10 +31,10 @@ search_prominence_percentile: 75
# Instantaneous frequency peak detection parameters
instantaneous_prominence_percentile: 90
# search freq parameter
# search freq parameter
search_df_lower: 25
search_df_upper: 100
search_res: 0.1
search_freq_percentiles:
- 5
- 95
search_df_upper: 300
search_res: 1
search_freq_percentiles:
- 5
- 95