525 lines
18 KiB
Python
525 lines
18 KiB
Python
import os
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import numpy as np
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from IPython import embed
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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
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from modules.plotstyle import PlotStyle
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ps = PlotStyle()
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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))[(
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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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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 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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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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# 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=50,
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overlap_frac=0.2,
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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.set_ylim(200, 1200)
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def double_bandpass(
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data: DataLoader, samplerate: int, freqs: np.ndarray, search_freq: float
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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, q75 = np.percentile(freqs, [25, 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, lowf=q25 + search_freq, highf=q75 + search_freq
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)
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return (filtered_baseline, filtered_search_freq)
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def main(datapath: str) -> None:
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# load raw file
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file = os.path.join(datapath, "traces-grid1.raw")
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# data = DataLoader(file, 60.0, 0, channel=-1)
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data = LoadData(datapath)
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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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# load config file
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config = ConfLoader("chirpdetector_conf.yml")
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# set time window # <------------------------ Iterate through windows here
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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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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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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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# ask how many windows should be calulated
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nwindows = int(
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input("How many windows should be calculated (integer number)? "))
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for start_index in window_starts[:nwindows]:
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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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# t0 = 3 * 60 * 60 + 6 * 60 + 43.5
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# dt = 60
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# start_index = t0 * data.raw_rate
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# stop_index = (t0 + dt) * data.raw_rate
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# calucate frequencies in wndow
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median_freq = []
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track_ids = []
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for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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window_index = np.arange(len(data.idx))[
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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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]
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median_freq.append(np.median(data.freq[window_index]))
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track_ids.append(track_id)
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median_freq = np.asarray(median_freq)
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track_ids = np.asarray(track_ids)
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# iterate through all fish
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for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])):
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print(f"Track ID: {track_id}")
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window_index = np.arange(len(data.idx))[
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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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]
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# get tracked frequencies and their times
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freq_temp = data.freq[window_index]
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powers_temp = data.powers[window_index, :]
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# time_temp = time[idx[window_index]]
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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 * 0.9:
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continue
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fig, axs = plt.subplots(
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7,
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config.electrodes,
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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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# get best electrode
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best_electrodes = np.argsort(np.nanmean(
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powers_temp, axis=0))[-config.electrodes:]
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# frequency where second filter filters
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search_window = np.arange(np.median(freq_temp)+config.search_df_lower, np.median(
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freq_temp)+config.search_df_upper, config.search_res)
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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 = track_ids[(median_freq > search_window[0]) & (
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median_freq < search_window[-1])]
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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], config.search_freq_percentiles)
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search_window_bool[(search_window > q1) & (
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search_window < q2)] = 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(
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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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# 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], len(search_window))
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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], len(search_window))
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embed()
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search_freq = 50
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# <------------------------------------------ Iterate through electrodes
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for i, electrode in enumerate(best_electrodes):
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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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time_oi = raw_time[start_index:stop_index]
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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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# filter baseline and above
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.raw_rate, freq_temp, search_freq
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)
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# compute instantaneous frequency on broad signal
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broad_baseline = bandpass_filter(
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data_oi[:, electrode],
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data.raw_rate,
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lowf=np.mean(freq_temp)-5,
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highf=np.mean(freq_temp)+100
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)
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# compute instantaneous frequency on narrow signal
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.raw_rate
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)
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# compute envelopes
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baseline_envelope_unfiltered = envelope(
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baseline, data.raw_rate, config.envelope_cutoff)
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search_envelope = envelope(
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search, data.raw_rate, config.envelope_cutoff)
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# highpass filter envelopes
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baseline_envelope = highpass_filter(
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baseline_envelope_unfiltered,
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data.raw_rate,
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config.envelope_highpass_cutoff
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)
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# baseline_envelope = np.abs(baseline_envelope)
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# search_envelope = highpass_filter(
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# search_envelope,
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# data.raw_rate,
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# config.envelope_highpass_cutoff
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# )
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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),
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data.raw_rate,
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config.envelope_envelope_cutoff
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)
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# search_envelope = bandpass_filter(
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# search_envelope, data.raw_rate, 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,
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data.raw_rate,
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lowf=config.instantaneous_lowf,
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highf=config.instantaneous_highf
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)
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# test taking the log of the envelopes
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# baseline_envelope = np.log(baseline_envelope)
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# search_envelope = np.log(search_envelope)
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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(window_edge), len(baseline_envelope) -
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int(window_edge)
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)
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baseline_envelope_unfiltered = baseline_envelope_unfiltered[valid]
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baseline_envelope = baseline_envelope[valid]
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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(window_edge) / data.raw_rate
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valid_t1 = baseline_freq_time[-1] - \
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(int(window_edge) / data.raw_rate)
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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 = 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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# NORMALIZE ----------------------------------------------------
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baseline_envelope = normalize([baseline_envelope])[0]
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search_envelope = normalize([search_envelope])[0]
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inst_freq_filtered = normalize([inst_freq_filtered])[0]
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# PEAK DETECTION -----------------------------------------------
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# detect peaks baseline_enelope
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prominence = np.percentile(
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baseline_envelope, config.baseline_prominence_percentile)
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baseline_peaks, _ = find_peaks(
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np.abs(baseline_envelope), prominence=prominence)
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# detect peaks search_envelope
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prominence = np.percentile(
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search_envelope, config.search_prominence_percentile)
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search_peaks, _ = find_peaks(
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search_envelope, prominence=prominence)
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# detect peaks inst_freq_filtered
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prominence = np.percentile(
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inst_freq_filtered, config.instantaneous_prominence_percentile)
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inst_freq_peaks, _ = find_peaks(
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np.abs(inst_freq_filtered), prominence=prominence)
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# PLOT ------------------------------------------------------------
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# plot spectrogram
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plot_spectrogram(
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axs[0, i], data_oi[:, electrode], data.raw_rate, 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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np.median(baseline_freq))
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# plot waveform of filtered signal
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axs[2, i].plot(time_oi, baseline, c=ps.green)
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# plot broad filtered baseline
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axs[2, i].plot(
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time_oi,
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broad_baseline,
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)
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# plot narrow filtered baseline envelope
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axs[2, i].plot(
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time_oi,
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baseline_envelope_unfiltered,
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c=ps.red
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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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search_envelope,
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c=ps.red
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)
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# plot filtered and rectified 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=ps.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=ps.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=ps.red,
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)
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axs[6, i].set_xlabel("Time [s]")
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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(
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"Filtered absolute instantaneous frequency")
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|
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plt.show()
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|
|
|
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if __name__ == "__main__":
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datapath = "../data/2022-06-02-10_00/"
|
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main(datapath)
|