Merge branch 'master' of https://whale.am28.uni-tuebingen.de/git/raab/GP2023_chirp_detection
This commit is contained in:
commit
1077558868
@ -29,8 +29,8 @@ class LoadData:
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def __init__(self, datapath: str) -> None:
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# load raw data
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file = os.path.join(datapath, "traces-grid1.raw")
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self.data = DataLoader(file, 60.0, 0, channel=-1)
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self.file = os.path.join(datapath, "traces-grid1.raw")
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self.data = DataLoader(self.file, 60.0, 0, channel=-1)
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self.samplerate = self.data.samplerate
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# load wavetracker files
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@ -40,6 +40,12 @@ class LoadData:
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self.ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
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self.ids = np.unique(self.ident[~np.isnan(self.ident)])
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def __repr__(self) -> str:
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return f"LoadData({self.file})"
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def __str__(self) -> str:
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return f"LoadData({self.file})"
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def instantaneos_frequency(
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signal: np.ndarray, samplerate: int
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@ -62,7 +68,8 @@ def instantaneos_frequency(
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# calculate instantaneos frequency with zero crossings
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roll_signal = np.roll(signal, shift=1)
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time_signal = np.arange(len(signal)) / samplerate
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period_index = np.arange(len(signal))[(roll_signal < 0) & (signal >= 0)]
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period_index = np.arange(len(signal))[(
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roll_signal < 0) & (signal >= 0)][1:-1]
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upper_bound = np.abs(signal[period_index])
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lower_bound = np.abs(signal[period_index - 1])
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@ -77,7 +84,7 @@ def instantaneos_frequency(
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true_zero = lower_time + lower_ratio * time_delta
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# create new time array
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inst_freq_time = true_zero[1:] + 0.5 * np.diff(true_zero)
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inst_freq_time = true_zero[:-1] + 0.5 * np.diff(true_zero)
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# compute frequency
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inst_freq = gaussian_filter1d(1 / np.diff(true_zero), 5)
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@ -167,175 +174,204 @@ def main(datapath: str) -> None:
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# load wavetracker files
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time = np.load(datapath + "times.npy", allow_pickle=True)
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freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
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powers = np.load(datapath + "sign_v.npy", allow_pickle=True)
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idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
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ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
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# set time window # <------------------------ Iterate through windows here
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window_duration = 60 * 5 * data.samplerate # 5 minutes window
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window_overlap = 30 * data.samplerate # 30 seconds overlap
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window_duration = 5 * data.samplerate # 5 seconds window
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window_overlap = 0.5 * data.samplerate # 30 seconds overlap
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raw_time = np.arange(data.shape[0])
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window_starts = np.arange(
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raw_time[0], raw_time[-1], window_duration - window_overlap / 2)
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t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.samplerate
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dt = 60 * data.samplerate
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window_starts = np.arange(
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t0, t0 + dt, window_duration - window_overlap, dtype=int)
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t0 = 3 * 60 * 60 + 6 * 60 + 43.5
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dt = 60
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start_index = t0 * data.samplerate
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stop_index = (t0 + dt) * data.samplerate
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# load region of interest of raw data file
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data_oi = data[start_index:stop_index, :]
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for start_index in window_starts:
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# iterate through all fish
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for track_id in np.unique(ident[~np.isnan(ident)])[:2]:
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# make t0 and dt
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t0 = start_index / data.samplerate
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dt = window_duration / data.samplerate
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# <------------------------------------------ Find best electrodes here
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# <------------------------------------------ Iterate through electrodes
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# get indices for time array in time window
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window_index = np.arange(len(idx))[
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(ident == track_id) & (time[idx] >= t0) & (time[idx] <= (t0 + dt))
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]
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# set index window
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stop_index = start_index + window_duration
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# filter baseline and above
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freq_temp = freq[window_index]
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time_temp = time[idx[window_index]]
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# t0 = 3 * 60 * 60 + 6 * 60 + 43.5
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# dt = 60
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# start_index = t0 * data.samplerate
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# stop_index = (t0 + dt) * data.samplerate
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electrode = 10
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# load region of interest of raw data file
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data_oi = data[start_index:stop_index, :]
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# initialize plot
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fig, axs = plt.subplots(
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7,
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1,
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2,
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figsize=(20 / 2.54, 12 / 2.54),
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constrained_layout=True,
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sharex=True,
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sharey='row',
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)
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# plot spectrogram
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plot_spectrogram(axs[0], data_oi[:, electrode], data.samplerate)
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# plot wavetracker tracks to spectrogram
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# for track_id in np.unique(ident): # <---------- Find freq gaps later
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# here
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# # get indices for time array in time window
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# window_index = np.arange(len(idx))[
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# (ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0 + dt))
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# ]
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# freq_temp = freq[window_index]
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# time_temp = time[idx[window_index]]
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# axs[0].plot(time_temp-t0, freq_temp, lw=2)
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# axs[0].set_ylim(500, 1000)
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# track_id = ids
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# frequency where second filter filters
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search_freq = -50
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# filter baseline and above
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.samplerate, freq_temp, search_freq
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)
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# plot waveform of filtered signal
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axs[2].plot(np.arange(len(baseline)) / data.samplerate, baseline)
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# plot instatneous frequency
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# broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
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# freq_temp)-5, highf=np.mean(freq_temp)+200)
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.samplerate
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)
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axs[1].plot(baseline_freq_time, baseline_freq)
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# plot waveform of filtered search signal
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axs[3].plot(np.arange(len(baseline)) / data.samplerate, search)
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# compute envelopes
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cutoff = 25
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baseline_envelope = envelope(baseline, data.samplerate, cutoff)
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axs[2].plot(
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np.arange(len(baseline)) / data.samplerate,
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baseline_envelope,
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c="orange",
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)
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search_envelope = envelope(search, data.samplerate, cutoff)
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axs[3].plot(
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np.arange(len(baseline)) / data.samplerate,
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search_envelope,
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c="orange",
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)
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# highpass filter envelopes
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cutoff = 5
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baseline_envelope = highpass_filter(
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baseline_envelope, data.samplerate, cutoff=cutoff
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)
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# search_envelope = highpass_filter(
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# search_envelope, data.samplerate, cutoff=cutoff)
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# envelopes of filtered envelope of filtered baseline
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baseline_envelope = envelope(
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np.abs(baseline_envelope), data.samplerate, cutoff
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)
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# search_envelope = bandpass_filter(
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# search_envelope, data.samplerate, lowf=lowf, highf=highf)
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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# plot filtered and rectified envelope
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axs[4].plot(
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np.arange(len(baseline)) / data.samplerate, baseline_envelope
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)
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axs[5].plot(np.arange(len(baseline)) /
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data.samplerate, search_envelope)
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axs[6].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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# detect peaks baseline_enelope
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embed()
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prominence = iqr(baseline_envelope)
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baseline_peaks, _ = find_peaks(
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baseline_envelope, prominence=prominence)
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axs[4].scatter(
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(np.arange(len(baseline)) / data.samplerate)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c="red",
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)
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# detect peaks search_envelope
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search_peaks, _ = find_peaks(search_envelope, height=0.0001)
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axs[5].scatter(
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(np.arange(len(baseline)) / data.samplerate)[search_peaks],
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search_envelope[search_peaks],
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c="red",
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)
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# detect peaks inst_freq_filtered
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inst_freq_peaks, _ = find_peaks(np.abs(inst_freq_filtered), height=2)
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axs[6].scatter(
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baseline_freq_time[inst_freq_peaks],
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np.abs(inst_freq_filtered)[inst_freq_peaks],
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c="red",
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)
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axs[0].set_title("Spectrogram")
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axs[1].set_title("Fitered baseline instanenous frequency")
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axs[2].set_title("Fitered baseline")
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axs[3].set_title("Fitered above")
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axs[4].set_title("Filtered envelope of baseline envelope")
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axs[5].set_title("Search envelope")
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axs[6].set_title("Filtered absolute instantaneous frequency")
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# iterate through all fish
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for i, track_id in enumerate(np.unique(ident[~np.isnan(ident)])[:2]):
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# <------------------------------------------ Find best electrodes here
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# <------------------------------------------ Iterate through electrodes
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# get indices for time array in time window
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window_index = np.arange(len(idx))[
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(ident == track_id) & (time[idx] >= t0) & (
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time[idx] <= (t0 + dt))
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]
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# get tracked frequencies and their times
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freq_temp = freq[window_index]
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powers_temp = powers[window_index, :]
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# time_temp = time[idx[window_index]]
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track_samplerate = np.mean(1 / np.diff(time))
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expected_duration = ((t0 + dt) - t0) * track_samplerate
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# check if tracked data available in this window
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if len(freq_temp) < expected_duration * 0.9:
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continue
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# get best electrode
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electrode = np.argsort(np.nanmean(powers_temp, axis=0))[-1]
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# electrode = best_electrodes[0]
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# plot spectrogram
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plot_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate)
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# plot wavetracker tracks to spectrogram
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# for track_id in np.unique(ident): # <---------- Find freq gaps later
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# here
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# # get indices for time array in time window
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# window_index = np.arange(len(idx))[
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# (ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0 + dt))
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# ]
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# freq_temp = freq[window_index]
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# time_temp = time[idx[window_index]]
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# axs[0].plot(time_temp-t0, freq_temp, lw=2)
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# axs[0].set_ylim(500, 1000)
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# track_id = ids
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# frequency where second filter filters
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search_freq = -50
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# filter baseline and above
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.samplerate, freq_temp, search_freq
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)
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# plot waveform of filtered signal
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axs[2, i].plot(np.arange(len(baseline)) /
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data.samplerate, baseline, c="k")
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# plot instatneous frequency
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broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
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freq_temp)-5, highf=np.mean(freq_temp)+100)
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.samplerate
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)
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axs[1, i].plot(baseline_freq_time, baseline_freq -
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np.median(baseline_freq), marker=".")
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# plot waveform of filtered search signal
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axs[3, i].plot(np.arange(len(baseline)) / data.samplerate, search)
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# compute envelopes
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cutoff = 25
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baseline_envelope = envelope(baseline, data.samplerate, cutoff)
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axs[2, i].plot(
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np.arange(len(baseline)) / data.samplerate,
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baseline_envelope,
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c="orange",
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)
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axs[2, i].plot(
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np.arange(len(baseline)) / data.samplerate,
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broad_baseline,
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c="green",
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)
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search_envelope = envelope(search, data.samplerate, cutoff)
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axs[3, i].plot(
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np.arange(len(baseline)) / data.samplerate,
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search_envelope,
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c="orange",
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)
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# highpass filter envelopes
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cutoff = 5
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baseline_envelope = highpass_filter(
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baseline_envelope, data.samplerate, cutoff=cutoff
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)
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# search_envelope = highpass_filter(
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# search_envelope, data.samplerate, cutoff=cutoff)
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# envelopes of filtered envelope of filtered baseline
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baseline_envelope = envelope(
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np.abs(baseline_envelope), data.samplerate, cutoff
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)
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# search_envelope = bandpass_filter(
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# search_envelope, data.samplerate, lowf=lowf, highf=highf)
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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# plot filtered and rectified envelope
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axs[4, i].plot(
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np.arange(len(baseline)) / data.samplerate, baseline_envelope
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)
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axs[5, i].plot(np.arange(len(baseline)) /
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data.samplerate, search_envelope)
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axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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# detect peaks baseline_enelope
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prominence = iqr(baseline_envelope)
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baseline_peaks, _ = find_peaks(
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baseline_envelope, prominence=prominence)
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axs[4, i].scatter(
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(np.arange(len(baseline)) / data.samplerate)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c="red",
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)
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# detect peaks search_envelope
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search_peaks, _ = find_peaks(search_envelope, height=0.0001)
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axs[5, i].scatter(
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(np.arange(len(baseline)) / data.samplerate)[search_peaks],
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search_envelope[search_peaks],
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c="red",
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)
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# detect peaks inst_freq_filtered
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inst_freq_peaks, _ = find_peaks(
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np.abs(inst_freq_filtered), height=2)
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axs[6, i].scatter(
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baseline_freq_time[inst_freq_peaks],
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np.abs(inst_freq_filtered)[inst_freq_peaks],
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c="red",
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)
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axs[0, i].set_title("Spectrogram")
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axs[1, i].set_title("Fitered baseline instanenous frequency")
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axs[2, i].set_title("Fitered baseline")
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axs[3, i].set_title("Fitered above")
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axs[4, i].set_title("Filtered envelope of baseline envelope")
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axs[5, i].set_title("Search envelope")
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axs[6, i].set_title("Filtered absolute instantaneous frequency")
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plt.show()
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