816 lines
27 KiB
Python
816 lines
27 KiB
Python
from itertools import compress
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from dataclasses import dataclass
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from IPython import embed
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import numpy as np
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import matplotlib.pyplot as plt
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from scipy.signal import find_peaks
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from scipy.ndimage import gaussian_filter1d
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from thunderfish.dataloader import DataLoader
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from thunderfish.powerspectrum import spectrogram, decibel
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from sklearn.preprocessing import normalize
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from modules.filters import bandpass_filter, envelope, highpass_filter
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from modules.filehandling import ConfLoader, LoadData, make_outputdir
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from modules.datahandling import flatten, purge_duplicates, group_timestamps
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from modules.plotstyle import PlotStyle
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from modules.logger import makeLogger
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logger = makeLogger(__name__)
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ps = PlotStyle()
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@dataclass
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class PlotBuffer:
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"""
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Buffer to save data that is created in the main detection loop
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and plot it outside the detecion loop.
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"""
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config: ConfLoader
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t0: float
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dt: float
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track_id: float
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electrode: int
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data: LoadData
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time: np.ndarray
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baseline: np.ndarray
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baseline_envelope: np.ndarray
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baseline_peaks: np.ndarray
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search: np.ndarray
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search_envelope: np.ndarray
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search_peaks: np.ndarray
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frequency_time: np.ndarray
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frequency: np.ndarray
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frequency_filtered: np.ndarray
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frequency_peaks: np.ndarray
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def plot_buffer(self, chirps: np.ndarray, plot: str) -> None:
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logger.debug("Starting plotting")
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# make data for plotting
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# # get index of track data in this time window
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# window_idx = np.arange(len(self.data.idx))[
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# (self.data.ident == self.track_id) & (self.data.time[self.data.idx] >= self.t0) & (
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# self.data.time[self.data.idx] <= (self.t0 + self.dt))
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# ]
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# get tracked frequencies and their times
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# freq_temp = self.data.freq[window_idx]
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# time_temp = self.data.times[window_idx]
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# get indices on raw data
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start_idx = self.t0 * self.data.raw_rate
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window_duration = self.dt * self.data.raw_rate
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stop_idx = start_idx + window_duration
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# get raw data
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data_oi = self.data.raw[start_idx:stop_idx, self.electrode]
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fig, axs = plt.subplots(
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7,
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1,
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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, self.data.raw_rate, self.t0)
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for chirp in chirps:
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axs[0].scatter(chirp, np.median(self.frequency),
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c=ps.black, marker="x")
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# plot waveform of filtered signal
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axs[1].plot(self.time, self.baseline, c=ps.green)
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# plot waveform of filtered search signal
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axs[2].plot(self.time, self.search)
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# plot baseline instantaneos frequency
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axs[3].plot(self.frequency_time, self.frequency)
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# plot filtered and rectified envelope
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axs[4].plot(self.time, self.baseline_envelope)
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axs[4].scatter(
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(self.time)[self.baseline_peaks],
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self.baseline_envelope[self.baseline_peaks],
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c=ps.red,
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)
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# plot envelope of search signal
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axs[5].plot(self.time, self.search_envelope)
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axs[5].scatter(
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(self.time)[self.search_peaks],
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self.search_envelope[self.search_peaks],
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c=ps.red,
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)
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# plot filtered instantaneous frequency
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axs[6].plot(self.frequency_time, self.frequency_filtered)
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axs[6].scatter(
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self.frequency_time[self.frequency_peaks],
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self.frequency_filtered[self.frequency_peaks],
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c=ps.red,
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)
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axs[0].set_ylim(
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np.max(self.frequency) - 200, top=np.max(self.frequency) + 200
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)
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axs[6].set_xlabel("Time [s]")
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axs[0].set_title("Spectrogram")
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axs[1].set_title("Fitered baseline")
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axs[2].set_title("Fitered above")
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axs[3].set_title("Fitered baseline instanenous frequency")
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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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if plot == "show":
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plt.show()
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elif plot == "save":
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make_outputdir(self.config.outputdir)
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out = make_outputdir(
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self.config.outputdir + self.data.datapath.split("/")[-2] + "/"
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)
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plt.savefig(f"{out}{self.track_id}_{self.t0}.pdf")
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plt.close()
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def plot_spectrogram(
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axis, signal: np.ndarray, samplerate: float, t0: float
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) -> None:
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"""
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Plot a spectrogram of a signal.
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Parameters
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----------
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axis : matplotlib axis
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Axis to plot the spectrogram on.
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signal : np.ndarray
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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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logger.debug("Plotting spectrogram")
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# compute spectrogram
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spec_power, spec_freqs, spec_times = spectrogram(
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signal,
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ratetime=samplerate,
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freq_resolution=20,
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overlap_frac=0.5,
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)
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# axis.pcolormesh(
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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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axis.imshow(
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decibel(spec_power),
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extent=[spec_times[0] + t0, spec_times[-1] +
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t0, spec_freqs[0], spec_freqs[-1]],
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aspect="auto",
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origin="lower",
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interpolation="gaussian",
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)
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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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Parameters
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----------
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signal : np.ndarray
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Signal to compute the instantaneous frequency from.
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samplerate : int
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Samplerate of the signal.
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Returns
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-------
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tuple[np.ndarray, np.ndarray]
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"""
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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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1:-1
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]
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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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upper_time = np.abs(time_signal[period_index])
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lower_time = np.abs(time_signal[period_index - 1])
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# create ratio
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lower_ratio = lower_bound / (lower_bound + upper_bound)
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# appy to time delta
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time_delta = upper_time - lower_time
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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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# compute frequency
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inst_freq = gaussian_filter1d(1 / np.diff(true_zero), 5)
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return inst_freq_time, inst_freq
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def double_bandpass(
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data: DataLoader,
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samplerate: int,
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freqs: np.ndarray,
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search_freq: float,
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config: ConfLoader
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) -> tuple[np.ndarray, np.ndarray]:
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"""
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Apply a bandpass filter to the baseline of a signal and a second bandpass
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filter above or below the baseline, as specified by the search frequency.
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Parameters
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----------
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data : DataLoader
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Data to apply the filter to.
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samplerate : int
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Samplerate of the signal.
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freqs : np.ndarray
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Tracked fundamental frequencies of the signal.
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search_freq : float
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Frequency to search for above or below the baseline.
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Returns
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-------
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tuple[np.ndarray, np.ndarray]
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"""
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# compute boundaries to filter baseline
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q25, q50, q75 = np.percentile(freqs, [25, 50, 75])
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# check if percentile delta is too small
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if q75 - q25 < 5:
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median = np.median(freqs)
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q25, q75 = median - 2.5, median + 2.5
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# filter baseline
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filtered_baseline = bandpass_filter(data, samplerate, lowf=q25, highf=q75)
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# filter search area
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filtered_search_freq = bandpass_filter(
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data, samplerate,
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lowf=search_freq + q50 - config.search_bandwidth / 2,
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highf=search_freq + q50 + config.search_bandwidth / 2
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)
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return filtered_baseline, filtered_search_freq
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def freqmedian_allfish(
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data: LoadData, t0: float, dt: float
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) -> tuple[float, list[int]]:
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"""
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Calculate the median frequency of all fish in a given time window.
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Parameters
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----------
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data : LoadData
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Data to calculate the median frequency from.
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t0 : float
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Start time of the window.
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dt : float
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Duration of the window.
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Returns
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-------
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tuple[float, list[int]]
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"""
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median_freq = []
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track_ids = []
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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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(data.ident == track_id)
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& (data.time[data.idx] >= t0)
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& (data.time[data.idx] <= (t0 + dt))
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]
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if len(data.freq[window_idx]) > 0:
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median_freq.append(np.median(data.freq[window_idx]))
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track_ids.append(track_id)
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# convert to numpy array
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median_freq = np.asarray(median_freq)
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track_ids = np.asarray(track_ids)
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return median_freq, track_ids
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def find_search_freq(
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freq_temp: np.ndarray,
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median_ids: np.ndarray,
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median_freq: np.ndarray,
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config: ConfLoader,
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data: LoadData,
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) -> float:
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"""
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Find the search frequency for each fish by checking which fish EODs are
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above the current EOD and finding a gap in them.
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Parameters
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----------
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freq_temp : np.ndarray
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Current EOD frequency array / the current fish of interest.
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median_ids : np.ndarray
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Array of track IDs of the medians of all other fish in the current window.
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median_freq : np.ndarray
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Array of median frequencies of all other fish in the current window.
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config : ConfLoader
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Configuration file.
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data : LoadData
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Data to find the search frequency from.
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Returns
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-------
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float
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"""
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# frequency where second filter filters
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search_window = np.arange(
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np.median(freq_temp) + config.search_df_lower,
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np.median(freq_temp) + config.search_df_upper,
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config.search_res,
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)
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# search window in boolean
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search_window_bool = np.ones(len(search_window), dtype=bool)
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# get tracks that fall into search window
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check_track_ids = median_ids[
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(median_freq > search_window[0]) & (median_freq < search_window[-1])
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]
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# iterate through theses tracks
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if check_track_ids.size != 0:
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for j, check_track_id in enumerate(check_track_ids):
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q1, q2 = np.percentile(
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data.freq[data.ident == check_track_id],
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config.search_freq_percentiles,
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)
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search_window_bool[
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(search_window > q1) & (search_window < q2)
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] = False
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# find gaps in search window
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search_window_indices = np.arange(len(search_window))
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# get search window gaps
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search_window_gaps = np.diff(search_window_bool, append=np.nan)
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nonzeros = search_window_gaps[np.nonzero(search_window_gaps)[0]]
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nonzeros = nonzeros[~np.isnan(nonzeros)]
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# if the first value is -1, the array starst with true, so a gap
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if nonzeros[0] == -1:
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stops = search_window_indices[search_window_gaps == -1]
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starts = np.append(
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0, search_window_indices[search_window_gaps == 1]
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)
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# if the last value is -1, the array ends with true, so a gap
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if nonzeros[-1] == 1:
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stops = np.append(
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search_window_indices[search_window_gaps == -1],
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len(search_window) - 1,
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)
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# else it starts with false, so no gap
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if nonzeros[0] == 1:
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stops = search_window_indices[search_window_gaps == -1]
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starts = search_window_indices[search_window_gaps == 1]
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# if the last value is -1, the array ends with true, so a gap
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if nonzeros[-1] == 1:
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stops = np.append(
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search_window_indices[search_window_gaps == -1],
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len(search_window),
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)
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# get the frequency ranges of the gaps
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search_windows = [search_window[x:y] for x, y in zip(starts, stops)]
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search_windows_lens = [len(x) for x in search_windows]
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longest_search_window = search_windows[np.argmax(search_windows_lens)]
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search_freq = (
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longest_search_window[-1] - longest_search_window[0]) / 2
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else:
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search_freq = config.default_search_freq
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return search_freq
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def main(datapath: str, plot: str) -> None:
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assert plot in ["save", "show", "false"]
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# load raw file
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data = LoadData(datapath)
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# load config file
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config = ConfLoader("chirpdetector_conf.yml")
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# set time window
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window_duration = config.window * data.raw_rate
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window_overlap = config.overlap * data.raw_rate
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window_edge = config.edge * data.raw_rate
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# check if window duration is even
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if window_duration % 2 == 0:
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window_duration = int(window_duration)
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else:
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raise ValueError("Window duration must be even.")
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# check if window ovelap is even
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if window_overlap % 2 == 0:
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window_overlap = int(window_overlap)
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else:
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raise ValueError("Window overlap must be even.")
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# make time array for raw data
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raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
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# good chirp times for data: 2022-06-02-10_00
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t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
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dt = 60 * data.raw_rate
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# t0 = 0
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# dt = data.raw.shape[0]
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# generate starting points of rolling window
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window_starts = np.arange(
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t0,
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t0 + dt,
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window_duration - (window_overlap + 2 * window_edge),
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dtype=int,
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)
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# ititialize lists to store data
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multiwindow_chirps = []
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multiwindow_ids = []
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for st, start_index in enumerate(window_starts):
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logger.info(f"Processing window {st} of {len(window_starts)}")
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# make t0 and dt
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t0 = start_index / data.raw_rate
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dt = window_duration / data.raw_rate
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# set index window
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stop_index = start_index + window_duration
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# calucate median of fish frequencies in window
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median_freq, median_ids = freqmedian_allfish(data, t0, dt)
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# iterate through all fish
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for tr, track_id in enumerate(
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np.unique(data.ident[~np.isnan(data.ident)])
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):
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logger.debug(f"Processing track {tr} of {len(data.ids)}")
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# get index of track data in this time window
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window_idx = np.arange(len(data.idx))[
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(data.ident == track_id)
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& (data.time[data.idx] >= t0)
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& (data.time[data.idx] <= (t0 + dt))
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]
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# get tracked frequencies and their times
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freq_temp = data.freq[window_idx]
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powers_temp = data.powers[window_idx, :]
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# approximate sampling rate to compute expected durations if there
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# is data available for this time window for this fish id
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track_samplerate = np.mean(1 / np.diff(data.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 * 0.5:
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logger.warning(
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f"Track {track_id} has no data in window {st}, skipping."
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)
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continue
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# check if there are powers available in this window
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nanchecker = np.unique(np.isnan(powers_temp))
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if (len(nanchecker) == 1) and nanchecker[0]:
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logger.warning(
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f"No powers available for track {track_id} window {st}, \
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skipping."
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)
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continue
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# find the strongest electrodes for the current fish in the current
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# window
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best_electrodes = np.argsort(np.nanmean(powers_temp, axis=0))[
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-config.number_electrodes:
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]
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# find a frequency above the baseline of the current fish in which
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# no other fish is active to search for chirps there
|
|
search_freq = find_search_freq(
|
|
config=config,
|
|
freq_temp=freq_temp,
|
|
median_ids=median_ids,
|
|
data=data,
|
|
median_freq=median_freq,
|
|
)
|
|
|
|
# add all chirps that are detected on mulitple electrodes for one
|
|
# fish fish in one window to this list
|
|
multielectrode_chirps = []
|
|
|
|
# iterate through electrodes
|
|
for el, electrode in enumerate(best_electrodes):
|
|
|
|
logger.debug(
|
|
f"Processing electrode {el} of {len(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]
|
|
|
|
# filter baseline and above
|
|
baseline, search = double_bandpass(
|
|
data_oi[:, electrode],
|
|
data.raw_rate,
|
|
freq_temp,
|
|
search_freq,
|
|
config=config,
|
|
)
|
|
|
|
# compute instantaneous frequency on narrow signal
|
|
baseline_freq_time, baseline_freq = instantaneos_frequency(
|
|
baseline, data.raw_rate
|
|
)
|
|
|
|
# compute envelopes
|
|
baseline_envelope_unfiltered = envelope(
|
|
baseline, data.raw_rate, config.envelope_cutoff
|
|
)
|
|
search_envelope = envelope(
|
|
search, data.raw_rate, config.envelope_cutoff
|
|
)
|
|
|
|
# highpass filter envelopes
|
|
baseline_envelope = highpass_filter(
|
|
baseline_envelope_unfiltered,
|
|
data.raw_rate,
|
|
config.envelope_highpass_cutoff,
|
|
)
|
|
|
|
# envelopes of filtered envelope of filtered baseline
|
|
baseline_envelope = envelope(
|
|
np.abs(baseline_envelope),
|
|
data.raw_rate,
|
|
config.envelope_envelope_cutoff,
|
|
)
|
|
|
|
# bandpass filter the instantaneous frequency to put it to 0
|
|
inst_freq_filtered = bandpass_filter(
|
|
baseline_freq,
|
|
data.raw_rate,
|
|
lowf=config.instantaneous_lowf,
|
|
highf=config.instantaneous_highf,
|
|
)
|
|
|
|
# CUT OFF OVERLAP ---------------------------------------------
|
|
|
|
# overwrite raw time to valid region, i.e. cut off snippet at
|
|
# start and end of each window to remove filter effects
|
|
valid = np.arange(
|
|
int(window_edge), len(baseline_envelope) - int(window_edge)
|
|
)
|
|
baseline_envelope_unfiltered = baseline_envelope_unfiltered[
|
|
valid
|
|
]
|
|
baseline_envelope = baseline_envelope[valid]
|
|
search_envelope = search_envelope[valid]
|
|
|
|
# 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
|
|
)
|
|
|
|
inst_freq_filtered = inst_freq_filtered[
|
|
(baseline_freq_time >= valid_t0)
|
|
& (baseline_freq_time <= valid_t1)
|
|
]
|
|
|
|
baseline_freq = baseline_freq[
|
|
(baseline_freq_time >= valid_t0)
|
|
& (baseline_freq_time <= valid_t1)
|
|
]
|
|
|
|
baseline_freq_time = (
|
|
baseline_freq_time[
|
|
(baseline_freq_time >= valid_t0)
|
|
& (baseline_freq_time <= valid_t1)
|
|
]
|
|
+ t0
|
|
)
|
|
|
|
time_oi = time_oi[valid]
|
|
baseline = baseline[valid]
|
|
search = search[valid]
|
|
|
|
# NORMALIZE ---------------------------------------------------
|
|
|
|
baseline_envelope = normalize([baseline_envelope])[0]
|
|
search_envelope = normalize([search_envelope])[0]
|
|
inst_freq_filtered = normalize([np.abs(inst_freq_filtered)])[0]
|
|
|
|
# PEAK DETECTION ----------------------------------------------
|
|
|
|
prominence = config.prominence
|
|
|
|
# detect peaks baseline_enelope
|
|
baseline_peaks, _ = find_peaks(
|
|
baseline_envelope, prominence=prominence
|
|
)
|
|
# detect peaks search_envelope
|
|
search_peaks, _ = find_peaks(
|
|
search_envelope, prominence=prominence
|
|
)
|
|
# detect peaks inst_freq_filtered
|
|
inst_freq_peaks, _ = find_peaks(
|
|
inst_freq_filtered, prominence=prominence
|
|
)
|
|
|
|
# DETECT CHIRPS IN SEARCH WINDOW ------------------------------
|
|
|
|
# get the peak timestamps from the peak indices
|
|
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 and if so, skip to the next
|
|
# electrode because a chirp cannot be detected if one is empty
|
|
if (
|
|
len(baseline_ts) == 0
|
|
or len(search_ts) == 0
|
|
or len(freq_ts) == 0
|
|
):
|
|
continue
|
|
|
|
# group peak across feature arrays but only if they
|
|
# occur in all 3 feature arrays
|
|
singleelectrode_chirps = group_timestamps(
|
|
[list(baseline_ts), list(search_ts), list(freq_ts)],
|
|
3,
|
|
config.chirp_window_threshold,
|
|
)
|
|
|
|
# check it there are chirps detected after grouping, continue
|
|
# with the loop if not
|
|
if len(singleelectrode_chirps) == 0:
|
|
continue
|
|
|
|
# append chirps from this electrode to the multilectrode list
|
|
multielectrode_chirps.append(singleelectrode_chirps)
|
|
|
|
# only initialize the plotting buffer if chirps are detected
|
|
if (
|
|
(el == config.number_electrodes - 1)
|
|
& (len(singleelectrode_chirps) > 0)
|
|
& (plot in ["show", "save"])
|
|
):
|
|
|
|
logger.debug("Detected chirp, ititialize buffer ...")
|
|
|
|
# save data to Buffer
|
|
buffer = PlotBuffer(
|
|
config=config,
|
|
t0=t0,
|
|
dt=dt,
|
|
electrode=electrode,
|
|
track_id=track_id,
|
|
data=data,
|
|
time=time_oi,
|
|
baseline=baseline,
|
|
baseline_envelope=baseline_envelope,
|
|
baseline_peaks=baseline_peaks,
|
|
search=search,
|
|
search_envelope=search_envelope,
|
|
search_peaks=search_peaks,
|
|
frequency_time=baseline_freq_time,
|
|
frequency=baseline_freq,
|
|
frequency_filtered=inst_freq_filtered,
|
|
frequency_peaks=inst_freq_peaks,
|
|
)
|
|
|
|
logger.debug("Buffer initialized!")
|
|
|
|
logger.debug(
|
|
f"Processed all electrodes for fish {track_id} for this \
|
|
window, sorting chirps ..."
|
|
)
|
|
|
|
# check if there are chirps detected in multiple electrodes and
|
|
# continue the loop if not
|
|
if len(multielectrode_chirps) == 0:
|
|
continue
|
|
|
|
# validate multielectrode chirps, i.e. check if they are
|
|
# detected in at least 'config.min_electrodes' electrodes
|
|
multielectrode_chirps_validated = group_timestamps(
|
|
multielectrode_chirps,
|
|
config.minimum_electrodes,
|
|
config.chirp_window_threshold
|
|
)
|
|
|
|
# add validated chirps to the list that tracks chirps across there
|
|
# rolling time windows
|
|
multiwindow_chirps.append(multielectrode_chirps_validated)
|
|
multiwindow_ids.append(track_id)
|
|
|
|
logger.debug(
|
|
"Found %d chirps, starting plotting ... "
|
|
% len(multielectrode_chirps_validated)
|
|
)
|
|
# if chirps are detected and the plot flag is set, plot the
|
|
# chirps, otheswise try to delete the buffer if it exists
|
|
if len(multielectrode_chirps_validated) > 0:
|
|
try:
|
|
buffer.plot_buffer(multielectrode_chirps_validated, plot)
|
|
except NameError:
|
|
pass
|
|
else:
|
|
try:
|
|
del buffer
|
|
except NameError:
|
|
pass
|
|
|
|
# flatten list of lists containing chirps and create
|
|
# an array of fish ids that correspond to the chirps
|
|
multiwindow_chirps_flat = []
|
|
multiwindow_ids_flat = []
|
|
for tr in np.unique(multiwindow_ids):
|
|
tr_index = np.asarray(multiwindow_ids) == tr
|
|
ts = flatten(list(compress(multiwindow_chirps, tr_index)))
|
|
multiwindow_chirps_flat.extend(ts)
|
|
multiwindow_ids_flat.extend(list(np.ones_like(ts) * tr))
|
|
|
|
# purge duplicates, i.e. chirps that are very close to each other
|
|
# duplites arise due to overlapping windows
|
|
purged_chirps = []
|
|
purged_ids = []
|
|
for tr in np.unique(multiwindow_ids_flat):
|
|
tr_chirps = np.asarray(multiwindow_chirps_flat)[
|
|
np.asarray(multiwindow_ids_flat) == tr]
|
|
if len(tr_chirps) > 0:
|
|
tr_chirps_purged = purge_duplicates(
|
|
tr_chirps, config.chirp_window_threshold
|
|
)
|
|
purged_chirps.extend(list(tr_chirps_purged))
|
|
purged_ids.extend(list(np.ones_like(tr_chirps_purged) * tr))
|
|
|
|
# sort chirps by time
|
|
purged_chirps = np.asarray(purged_chirps)
|
|
purged_ids = np.asarray(purged_ids)
|
|
purged_ids = purged_ids[np.argsort(purged_chirps)]
|
|
purged_chirps = purged_chirps[np.argsort(purged_chirps)]
|
|
|
|
# save them into the data directory
|
|
np.save(datapath + "chirps.npy", purged_chirps)
|
|
np.save(datapath + "chirp_ids.npy", purged_ids)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# datapath = "/home/weygoldt/Data/uni/chirpdetection/GP2023_chirp_detection/data/mount_data/2020-05-13-10_00/"
|
|
datapath = "../data/2022-06-02-10_00/"
|
|
main(datapath, plot="show")
|