finishing the peakdetection algorythm
This commit is contained in:
parent
8d11e5ff7a
commit
c42c1f3ce5
@ -183,19 +183,6 @@ def main(datapath: str) -> None:
|
||||
chirps = []
|
||||
fish_ids = []
|
||||
|
||||
baseline_ts = [[[
|
||||
[] for el in range(config.number_electrodes)]
|
||||
for tr in range(len(data.ids))]
|
||||
for wi in range(nwindows)]
|
||||
search_ts = [[[
|
||||
[] for el in range(config.number_electrodes)]
|
||||
for tr in range(len(data.ids))]
|
||||
for wi in range(nwindows)]
|
||||
freq_ts = [[[
|
||||
[] for el in range(config.number_electrodes)]
|
||||
for tr in range(len(data.ids))]
|
||||
for wi in range(nwindows)]
|
||||
|
||||
for st, start_index in enumerate(window_starts[: nwindows]):
|
||||
|
||||
# make t0 and dt
|
||||
@ -208,7 +195,7 @@ def main(datapath: str) -> None:
|
||||
# calucate median of fish frequencies in window
|
||||
median_freq = []
|
||||
track_ids = []
|
||||
for el, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
|
||||
for _, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
|
||||
window_idx = np.arange(len(data.idx))[
|
||||
(data.ident == track_id) & (data.time[data.idx] >= t0) & (
|
||||
data.time[data.idx] <= (t0 + dt))
|
||||
@ -333,7 +320,7 @@ def main(datapath: str) -> None:
|
||||
|
||||
# iterate through electrodes
|
||||
for el, electrode in enumerate(best_electrodes):
|
||||
|
||||
print(el)
|
||||
# load region of interest of raw data file
|
||||
data_oi = data.raw[start_index:stop_index, :]
|
||||
time_oi = raw_time[start_index:stop_index]
|
||||
@ -454,47 +441,6 @@ def main(datapath: str) -> None:
|
||||
prominence=prominence
|
||||
)
|
||||
|
||||
# DETECT CHIRPS IN SEARCH WINDOW -------------------------------
|
||||
|
||||
baseline_ts = time_oi[baseline_peaks]
|
||||
search_ts = time_oi[search_peaks]
|
||||
freq_ts = baseline_freq_time[inst_freq_peaks]
|
||||
|
||||
# check if one list is empty
|
||||
if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
|
||||
continue
|
||||
|
||||
# get index for each feature
|
||||
baseline_idx = np.zeros_like(baseline_ts)
|
||||
search_idx = np.ones_like(search_ts)
|
||||
freq_idx = np.ones_like(freq_ts) * 2
|
||||
|
||||
timestamps_features = np.hstack(
|
||||
[baseline_idx, search_idx, freq_idx])
|
||||
timestamps = np.hstack([baseline_ts, search_ts, freq_ts])
|
||||
|
||||
# sort timestamps
|
||||
timestamps_idx = np.arange(len(timestamps))
|
||||
timestamps_features = timestamps_features[np.argsort(timestamps)]
|
||||
timestamps = timestamps[np.argsort(timestamps)]
|
||||
|
||||
# # get chirps
|
||||
# diff = np.empty(timestamps.shape)
|
||||
# diff[0] = np.inf # always retain the 1st element
|
||||
# diff[1:] = np.diff(timestamps)
|
||||
# mask = diff < config.chirp_window_threshold
|
||||
# shared_peak_indices = timestamp_idx[mask]
|
||||
|
||||
current_chirps = []
|
||||
for tt in timestamps:
|
||||
cm = timestamps_idx[(timestamps >= tt) & (
|
||||
timestamps <= tt + config.chirp_window_threshold)]
|
||||
if all([0, 1, 2]) in timestamps_features[cm]:
|
||||
chirps.append(np.mean(timestamps[cm]))
|
||||
current_chirps.append(np.mean(timestamps[cm]))
|
||||
fish_ids.append(track_id)
|
||||
|
||||
|
||||
# # SAVE DATA ---------------------------------------------------
|
||||
|
||||
# PLOT --------------------------------------------------------
|
||||
@ -503,9 +449,6 @@ def main(datapath: str) -> None:
|
||||
plot_spectrogram(
|
||||
axs[0, el], data_oi[:, electrode], data.raw_rate, t0)
|
||||
|
||||
for ct in current_chirps:
|
||||
axs[0, el].axvline(ct, color='r', lw=1)
|
||||
|
||||
# plot baseline instantaneos frequency
|
||||
axs[1, el].plot(baseline_freq_time, baseline_freq -
|
||||
np.median(baseline_freq))
|
||||
@ -569,6 +512,68 @@ def main(datapath: str) -> None:
|
||||
axs[5, el].set_title("Search envelope")
|
||||
axs[6, el].set_title(
|
||||
"Filtered absolute instantaneous frequency")
|
||||
print(el)
|
||||
# DETECT CHIRPS IN SEARCH WINDOW -------------------------------
|
||||
|
||||
baseline_ts = time_oi[baseline_peaks]
|
||||
search_ts = time_oi[search_peaks]
|
||||
freq_ts = baseline_freq_time[inst_freq_peaks]
|
||||
|
||||
# check if one list is empty
|
||||
if len(baseline_ts) == 0 or len(search_ts) == 0 or len(freq_ts) == 0:
|
||||
continue
|
||||
|
||||
# get index for each feature
|
||||
baseline_idx = np.zeros_like(baseline_ts)
|
||||
search_idx = np.ones_like(search_ts)
|
||||
freq_idx = np.ones_like(freq_ts) * 2
|
||||
|
||||
timestamps_features = np.hstack(
|
||||
[baseline_idx, search_idx, freq_idx])
|
||||
timestamps = np.hstack([baseline_ts, search_ts, freq_ts])
|
||||
|
||||
# sort timestamps
|
||||
timestamps_idx = np.arange(len(timestamps))
|
||||
timestamps_features = timestamps_features[np.argsort(
|
||||
timestamps)]
|
||||
timestamps = timestamps[np.argsort(timestamps)]
|
||||
|
||||
# # get chirps
|
||||
# diff = np.empty(timestamps.shape)
|
||||
# diff[0] = np.inf # always retain the 1st element
|
||||
# diff[1:] = np.diff(timestamps)
|
||||
# mask = diff < config.chirp_window_threshold
|
||||
# shared_peak_indices = timestamp_idx[mask]
|
||||
|
||||
current_chirps = []
|
||||
for tt in timestamps:
|
||||
cm = timestamps_idx[(timestamps >= tt) & (
|
||||
timestamps <= tt + config.chirp_window_threshold)]
|
||||
if set([0, 1, 2]).issubset(timestamps_features[cm]):
|
||||
chirps.append(np.mean(timestamps[cm]))
|
||||
current_chirps.append(np.mean(timestamps[cm]))
|
||||
fish_ids.append(track_id)
|
||||
|
||||
for ct in current_chirps:
|
||||
axs[0, el].axvline(ct, color='r', lw=1)
|
||||
|
||||
axs[0, el].scatter(
|
||||
baseline_freq_time[inst_freq_peaks],
|
||||
np.ones_like(baseline_freq_time[inst_freq_peaks]) * 600,
|
||||
c=ps.red,
|
||||
)
|
||||
axs[0, el].scatter(
|
||||
(time_oi)[search_peaks],
|
||||
np.ones_like((time_oi)[search_peaks]) * 600,
|
||||
c=ps.red,
|
||||
)
|
||||
|
||||
axs[0, el].scatter(
|
||||
(time_oi)[baseline_peaks],
|
||||
np.ones_like((time_oi)[baseline_peaks]) * 600,
|
||||
c=ps.red,
|
||||
)
|
||||
|
||||
|
||||
plt.show()
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user