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