import os
import numpy as np
from IPython import embed
import matplotlib.pyplot as plt
from scipy.ndimage import gaussian_filter1d
from thunderfish.dataloader import DataLoader
from thunderfish.powerspectrum import spectrogram, decibel
from modules.filters import bandpass_filter, envelope, highpass_filter, lowpass_filter
def plot_spectogramm(ax, signal: np.ndarray, sampelrate: float) -> None:
spec_power, spec_freqs, spec_times = spectrogram(
signal, ratetime=sampelrate, freq_resolution=50, overlap_frac=0.2
)
ax.pcolormesh(spec_times, spec_freqs, decibel(spec_power), vmin=-100, vmax=-50)
ax.set_ylim(500, 1200)
def double_bandpass(
data: DataLoader, samplerate, freqs: np.ndarray, search_freq: float
):
q25, q75 = np.percentile(freqs, [25, 75])
if q75 - q25 < 5:
baseline = np.median(freqs)
q25, q75 = baseline - 2.5, baseline + 2.5
# filter Baseline
filtered_baseline = bandpass_filter(data, samplerate, lowf=q25, highf=q75)
# filter search area
filtered_searched_freq = bandpass_filter(
data, samplerate, lowf=q25 + search_freq, highf=q75 + search_freq
)
return (filtered_baseline, filtered_searched_freq)
def instantaneos_frequency(signal: np.ndarray, samplerate: int):
time_fdata = np.arange(len(signal)) / samplerate
roll_fdata = np.roll(signal, shift=1)
period_index = np.arange(len(signal))[(roll_fdata < 0) & (signal >= 0)]
upper_bound = np.abs(signal[period_index])
lower_bound = np.abs(signal[period_index - 1])
upper_times = np.abs(time_fdata[period_index])
lower_times = np.abs(time_fdata[period_index - 1])
lower_ratio = lower_bound / (lower_bound + upper_bound)
upper_ratio = upper_bound / (lower_bound + upper_bound)
time_delta = upper_times - lower_times
true_zero = lower_times + time_delta * lower_ratio
inst_freq = 1 / np.diff(true_zero)
filtered_inst_freq = gaussian_filter1d(inst_freq, 5)
# create new time axis
inst_freq_time = true_zero[:-1] + 0.5 * np.diff(true_zero)
return (inst_freq_time, filtered_inst_freq, true_zero)
def main(datapath: str):
# get the data
file = os.path.join(datapath, "traces-grid.raw")
data = DataLoader(datapath, 60.0, 0, channel=-1)
# load wavetracke files
time = np.load(datapath + "times.npy", allow_pickle=True)
freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
# make the right window for snipping
t0 = 3 * 60 * 60 + 6 * 60 + 43.5
dt = 60
start_index = t0 * data.samplerate
stop_index = (t0 + dt) * data.samplerate
# get the window with th data
data_oi = data[start_index:stop_index, :]
# interate over the individuals
# track_id = np.unique(ident)[0]
# index of the electrode
electrode = 10
for track_id in np.unique(ident[~np.isnan(ident)])[:2]:
fig, axs = plt.subplots(
7, 1, figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True, sharex=True
)
plot_spectogramm(axs[0], data_oi[:, electrode], data.samplerate)
# for track_id in np.unique(ident):
# # window_index for time array in time window, fish data for time window
# window_index = np.arange(len(idx))[(ident == track_id) &
# (time[idx] >= t0) &
# (time[idx] <= (t0+dt))]
# freq_temp = freq[window_index]
# time_temp = time[idx[window_index]]
# axs[0].plot(time_temp - t0, freq_temp)
# axs[0].set_ylim(500, 1200)
# # define gap height
# # frequency for searching the chirp above the one fish
search_freq = 50
window_index = np.arange(len(idx))[
(ident == track_id) & (time[idx] >= t0) & (time[idx] <= (t0 + dt))
]
freq_temp = freq[window_index]
time_temp = time[idx[window_index]]
brought_baseline = bandpass_filter(
data_oi[:, electrode],
data.samplerate,
lowf=np.mean(freq_temp) - 5,
highf=np.mean(freq_temp + 200),
)
baseline, search = double_bandpass(
data_oi[:, electrode], data.samplerate, freq_temp, search_freq
)
# calculate and plot the instantaneos freq
time_baseline_freq, basline_freq, ture_zeros = instantaneos_frequency(
baseline, data.samplerate
)
inst_freq_filtered = bandpass_filter(
basline_freq, data.samplerate, lowf=1, highf=100
)
axs[6].plot(time_baseline_freq, np.abs(inst_freq_filtered), marker=".")
axs[1].plot(time_baseline_freq, basline_freq, marker=".")
cutoff = 25
baseline_envelope = envelope(baseline, data.samplerate, cutoff)
axs[2].plot(ture_zeros, np.zeros_like(ture_zeros), marker=".", c="red")
axs[2].plot(np.arange(len(baseline)) / data.samplerate, baseline, c="blue")
axs[2].plot(
np.arange(len(baseline)) / data.samplerate, baseline_envelope, c="orange"
)
search_envelope = envelope(search, data.samplerate, cutoff)
axs[3].plot(np.arange(len(baseline)) / data.samplerate, search)
axs[3].plot(np.arange(len(baseline)) / data.samplerate, search_envelope)
# filter and rectify envelopes
cutoff = 5
filtered_baseline_envelope = highpass_filter(
baseline_envelope, data.samplerate, cutoff=cutoff
)
filtered_searched_envelope = highpass_filter(
search_envelope, data.samplerate, cutoff=cutoff
)
# filter the envelopes bandpass
filtered_baseline_envelope = envelope(
np.abs(filtered_baseline_envelope), data.samplerate, freq=5
)
axs[4].plot(
np.arange(len(baseline)) / data.samplerate, filtered_baseline_envelope
)
axs[5].plot(
np.arange(len(baseline)) / data.samplerate, filtered_searched_envelope
)
axs[0].set_title("Spectogramm")
axs[1].set_title("Instantaneos Frequency")
axs[2].set_title("Filtered Baseline")
axs[3].set_title("Filtered Searched")
axs[4].set_title("Filtered Baseline Envelope")
axs[5].set_title("Filtered Searched Envelope")
plt.show()
if __name__ == "__main__":
datapath = "data/2022-06-02-10_00/"
main(datapath)