sorted plots
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weygoldt
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02b204c7d4
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ee6bf94139
@ -51,7 +51,7 @@ def instantaneos_frequency(
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signal: np.ndarray, samplerate: int
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Compute the instantaneous frequency of a signal.
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Compute the instantaneous frequency of a signal.
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Parameters
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----------
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@ -92,7 +92,7 @@ def instantaneos_frequency(
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return inst_freq_time, inst_freq
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def plot_spectrogram(axis, signal: np.ndarray, samplerate: float) -> None:
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def plot_spectrogram(axis, signal: np.ndarray, samplerate: float, t0: float) -> None:
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"""
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Plot a spectrogram of a signal.
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@ -104,7 +104,8 @@ def plot_spectrogram(axis, signal: np.ndarray, samplerate: float) -> None:
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Signal to plot the spectrogram from.
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samplerate : float
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Samplerate of the signal.
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t0 : float
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Start time of the signal.
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"""
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# compute spectrogram
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spec_power, spec_freqs, spec_times = spectrogram(
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@ -115,7 +116,7 @@ def plot_spectrogram(axis, signal: np.ndarray, samplerate: float) -> None:
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)
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axis.pcolormesh(
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spec_times,
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spec_times + t0,
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spec_freqs,
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decibel(spec_power),
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)
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@ -320,6 +321,8 @@ def main(datapath: str) -> None:
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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# CUT OFF OVERLAP -------------------------------------------------
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# cut off first and last 0.5 * overlap at start and end
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valid = np.arange(
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int(0.5 * window_overlap), len(baseline_envelope) -
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@ -329,48 +332,46 @@ def main(datapath: str) -> None:
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search_envelope = search_envelope[valid]
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# get inst freq valid snippet
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valid_t0 = int(0.5 * window_overlap)
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valid_t1 = len(baseline_envelope) - int(0.5 * window_overlap)
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valid_t0 = int(0.5 * window_overlap) / data.samplerate
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valid_t1 = baseline_freq_time[-1] - \
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(int(0.5 * window_overlap) / data.samplerate)
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inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
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baseline_freq = baseline_freq[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)]
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baseline_freq_time = baseline_freq_time[(baseline_freq_time >= valid_t0) & (
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baseline_freq_time <= valid_t1)] + t0
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# overwrite raw time to valid region
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time_oi = time_oi[valid]
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baseline = baseline[valid]
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broad_baseline = broad_baseline[valid]
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search = search[valid]
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# PEAK DETECTION --------------------------------------------------
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# detect peaks baseline_enelope
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prominence = np.percentile(baseline_envelope, 90)
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baseline_peaks, _ = find_peaks(
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np.abs(baseline_envelope), prominence=prominence)
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axs[4, i].scatter(
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(time_oi)[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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prominence = np.percentile(search_envelope, 75)
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search_peaks, _ = find_peaks(
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search_envelope, prominence=prominence)
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axs[5, i].scatter(
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(time_oi)[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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prominence = 2
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inst_freq_peaks, _ = find_peaks(
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np.abs(inst_freq_filtered), prominence=prominence)
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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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# PLOT ------------------------------------------------------------
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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_spectrogram(axs[0, i], data_oi[:, electrode], data.samplerate, t0)
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# plot baseline instantaneos frequency
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axs[1, i].plot(baseline_freq_time, baseline_freq -
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@ -379,9 +380,6 @@ def main(datapath: str) -> None:
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# plot waveform of filtered signal
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axs[2, i].plot(time_oi, baseline, c="k")
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# plot waveform of filtered search signal
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axs[3, i].plot(time_oi, search)
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# plot narrow filtered baseline
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axs[2, i].plot(
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time_oi,
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@ -396,6 +394,9 @@ def main(datapath: str) -> None:
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c="green",
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)
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# plot waveform of filtered search signal
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axs[3, i].plot(time_oi, search)
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# plot envelope of search signal
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axs[3, i].plot(
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time_oi,
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@ -404,15 +405,29 @@ def main(datapath: str) -> None:
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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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time_oi, baseline_envelope
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axs[4, i].plot(time_oi, baseline_envelope)
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axs[4, i].scatter(
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(time_oi)[baseline_peaks],
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baseline_envelope[baseline_peaks],
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c="red",
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)
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# plot envelope of search signal
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axs[5, i].plot(time_oi, search_envelope)
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axs[5, i].scatter(
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(time_oi)[search_peaks],
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search_envelope[search_peaks],
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c="red",
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)
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# plot filtered instantaneous frequency
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axs[6, i].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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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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