switched chirpdetection
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weygoldt
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ed3791bc0d
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ba116aaffe
@ -3,181 +3,220 @@ 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.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 scipy.ndimage import gaussian_filter1d
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from modules.filters import bandpass_filter, envelope, highpass_filter, lowpass_filter
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from modules.filters import bandpass_filter, envelope, highpass_filter
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def plot_spectogramm(ax, signal: np.ndarray, sampelrate: float) -> None:
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spec_power, spec_freqs, spec_times = spectrogram(
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signal, ratetime=sampelrate, freq_resolution=50, overlap_frac=0.2
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)
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ax.pcolormesh(spec_times, spec_freqs, decibel(spec_power), vmin=-100, vmax=-50)
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ax.set_ylim(500, 1200)
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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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# 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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def double_bandpass(
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data: DataLoader, samplerate, freqs: np.ndarray, search_freq: float
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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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q25, q75 = np.percentile(freqs, [25, 75])
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if q75 - q25 < 5:
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baseline = np.median(freqs)
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q25, q75 = baseline - 2.5, baseline + 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_searched_freq = bandpass_filter(
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data, samplerate, lowf=q25 + search_freq, highf=q75 + search_freq
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# create ratios
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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) -> None:
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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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return (filtered_baseline, filtered_searched_freq)
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axis.pcolormesh(
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spec_times,
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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 instantaneos_frequency(signal: np.ndarray, samplerate: int):
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time_fdata = np.arange(len(signal)) / samplerate
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roll_fdata = np.roll(signal, shift=1)
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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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period_index = np.arange(len(signal))[(roll_fdata < 0) & (signal >= 0)]
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# compute boundaries to filter baseline
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q25, q75 = np.percentile(freqs, [25, 75])
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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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# 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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upper_times = np.abs(time_fdata[period_index])
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lower_times = np.abs(time_fdata[period_index - 1])
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# filter baseline
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filtered_baseline = bandpass_filter(data, samplerate, lowf=q25, highf=q75)
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lower_ratio = lower_bound / (lower_bound + upper_bound)
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upper_ratio = upper_bound / (lower_bound + upper_bound)
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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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time_delta = upper_times - lower_times
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true_zero = lower_times + time_delta * lower_ratio
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inst_freq = 1 / np.diff(true_zero)
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filtered_inst_freq = gaussian_filter1d(inst_freq, 5)
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return (filtered_baseline, filtered_search_freq)
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# create new time axis
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inst_freq_time = true_zero[:-1] + 0.5 * np.diff(true_zero)
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return (inst_freq_time, filtered_inst_freq, true_zero)
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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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def main(datapath: str):
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# get the data
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file = os.path.join(datapath, "traces-grid.raw")
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data = DataLoader(datapath, 60.0, 0, channel=-1)
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# load wavetracke files
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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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ident = np.load(datapath + "ident_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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# make the right window for snipping
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# set time window # <------------------------ Iterate through windows here
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t0 = 3 * 60 * 60 + 6 * 60 + 43.5
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dt = 60
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start_index = t0 * data.samplerate
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stop_index = (t0 + dt) * data.samplerate
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# get the window with th data
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# load region of interest of raw data file
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data_oi = data[start_index:stop_index, :]
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# interate over the individuals
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# track_id = np.unique(ident)[0]
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# index of the electrode
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electrode = 10
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# iterate through all fish
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for track_id in np.unique(ident[~np.isnan(ident)])[:2]:
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# <------------------------------------------ Find best electrodes here
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# <------------------------------------------ Iterate through electrodes
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electrode = 10
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# initialize plot
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fig, axs = plt.subplots(
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7, 1, figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True, sharex=True
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)
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plot_spectogramm(axs[0], data_oi[:, electrode], data.samplerate)
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# for track_id in np.unique(ident):
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# # window_index for time array in time window, fish data for time window
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# window_index = np.arange(len(idx))[(ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0+dt))]
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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)
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# axs[0].set_ylim(500, 1200)
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# # define gap height
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# # frequency for searching the chirp above the one fish
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# plot spectrogram
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plot_spectrogram(axs[0], data_oi[:, electrode], data.samplerate)
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# plot wavetracker tracks to spectrogram
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# for track_id in np.unique(ident): # <---------- Find freq gaps later
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# here
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# # get indices for time array in time window
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# window_index = np.arange(len(idx))[
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# (ident == track_id) &
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# (time[idx] >= t0) &
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# (time[idx] <= (t0 + dt))
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# ]
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# freq_temp = freq[window_index]
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# time_temp = time[idx[window_index]]
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# axs[0].plot(time_temp-t0, freq_temp, lw=2)
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# axs[0].set_ylim(500, 1000)
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# track_id = ids
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# frequency where second filter filters
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search_freq = 50
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# get indices for time array in time window
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window_index = np.arange(len(idx))[
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(ident == track_id) & (time[idx] >= t0) & (time[idx] <= (t0 + dt))
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]
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# filter baseline and above
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freq_temp = freq[window_index]
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time_temp = time[idx[window_index]]
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brought_baseline = bandpass_filter(
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data_oi[:, electrode],
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data.samplerate,
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lowf=np.mean(freq_temp) - 5,
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highf=np.mean(freq_temp + 200),
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)
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baseline, search = double_bandpass(
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data_oi[:, electrode], data.samplerate, freq_temp, search_freq
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)
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# calculate and plot the instantaneos freq
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time_baseline_freq, basline_freq, ture_zeros = instantaneos_frequency(
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# plot waveform of filtered signal
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axs[2].plot(np.arange(len(baseline)) / data.samplerate, baseline)
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# plot instatneous frequency
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# broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
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# freq_temp)-5, highf=np.mean(freq_temp)+200)
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baseline_freq_time, baseline_freq = instantaneos_frequency(
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baseline, data.samplerate
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)
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inst_freq_filtered = bandpass_filter(
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basline_freq, data.samplerate, lowf=1, highf=100
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)
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axs[6].plot(time_baseline_freq, np.abs(inst_freq_filtered), marker=".")
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axs[1].plot(time_baseline_freq, basline_freq, marker=".")
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axs[1].plot(baseline_freq_time, baseline_freq)
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# plot waveform of filtered search signal
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axs[3].plot(np.arange(len(baseline)) / data.samplerate, search)
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# compute envelopes
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cutoff = 25
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baseline_envelope = envelope(baseline, data.samplerate, cutoff)
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axs[2].plot(ture_zeros, np.zeros_like(ture_zeros), marker=".", c="red")
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axs[2].plot(np.arange(len(baseline)) / data.samplerate, baseline, c="blue")
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axs[2].plot(
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np.arange(len(baseline)) / data.samplerate, baseline_envelope, c="orange"
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)
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search_envelope = envelope(search, data.samplerate, cutoff)
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axs[3].plot(np.arange(len(baseline)) / data.samplerate, search)
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axs[3].plot(np.arange(len(baseline)) / data.samplerate, search_envelope)
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axs[3].plot(
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np.arange(len(baseline)) / data.samplerate, search_envelope, c="orange"
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)
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# filter and rectify envelopes
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# highpass filter envelopes
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cutoff = 5
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filtered_baseline_envelope = highpass_filter(
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baseline_envelope = highpass_filter(
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baseline_envelope, data.samplerate, cutoff=cutoff
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)
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filtered_searched_envelope = highpass_filter(
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search_envelope, data.samplerate, cutoff=cutoff
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)
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# search_envelope = highpass_filter(
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# search_envelope, data.samplerate, cutoff=cutoff)
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# filter the envelopes bandpass
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# envelopes of filtered envelope of filtered baseline
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baseline_envelope = envelope(
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np.abs(baseline_envelope), data.samplerate, cutoff)
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filtered_baseline_envelope = envelope(
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np.abs(filtered_baseline_envelope), data.samplerate, freq=5
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)
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# search_envelope = bandpass_filter(
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# search_envelope, data.samplerate, lowf=lowf, highf=highf)
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axs[4].plot(
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np.arange(len(baseline)) / data.samplerate, filtered_baseline_envelope
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)
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axs[5].plot(
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np.arange(len(baseline)) / data.samplerate, filtered_searched_envelope
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# bandpass filter the instantaneous
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inst_freq_filtered = bandpass_filter(
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baseline_freq, data.samplerate, lowf=15, highf=8000
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)
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axs[0].set_title("Spectogramm")
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axs[1].set_title("Instantaneos Frequency")
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axs[2].set_title("Filtered Baseline")
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axs[3].set_title("Filtered Searched")
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axs[4].set_title("Filtered Baseline Envelope")
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axs[5].set_title("Filtered Searched Envelope")
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axs[6].plot(baseline_freq_time, np.abs(inst_freq_filtered))
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# plot filtered and rectified envelope
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axs[4].plot(np.arange(len(baseline)) /
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data.samplerate, baseline_envelope)
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axs[5].plot(np.arange(len(baseline)) /
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data.samplerate, search_envelope)
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axs[0].set_title("Spectrogram")
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axs[1].set_title("Fitered baseline instanenous frequency")
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axs[2].set_title("Fitered baseline")
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axs[3].set_title("Fitered above")
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axs[4].set_title("Filtered envelope of baseline envelope")
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axs[5].set_title("Search envelope")
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axs[6].set_title("Filtered absolute instantaneous frequency")
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plt.show()
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if __name__ == "__main__":
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datapath = "data/2022-06-02-10_00/"
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datapath = "../data/2022-06-02-10_00/"
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main(datapath)
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