raab/GP2023_chirp_detection
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adding peak detection

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This commit is contained in:
wendtalexander 2023-01-11 15:49:42 +01:00
parent 98dee7a6ae
commit 9c2b4d0634
1 changed files with 29 additions and 5 deletions
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code/chirpdetection.py
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@ -3,9 +3,12 @@ import os
import numpy as np
from IPython import embed
import matplotlib.pyplot as plt
from scipy.stats import iqr
from scipy.signal import find_peaks
from scipy.ndimage import gaussian_filter1d
from thunderfish.dataloader import DataLoader
from thunderfish.powerspectrum import spectrogram, decibel
from scipy.ndimage import gaussian_filter1d
from modules.filters import bandpass_filter, envelope, highpass_filter
@ -32,7 +35,7 @@ def instantaneos_frequency(
true_zero = lower_time + lower_ratio * time_delta
# create new time array
inst_freq_time = true_zero[:-1] + 0.5 * np.diff(true_zero)
inst_freq_time = true_zero[1:] + 0.5 * np.diff(true_zero)
# compute frequency
inst_freq = gaussian_filter1d(1 / np.diff(true_zero), 5)
@ -139,7 +142,7 @@ def main(datapath: str) -> None:
# track_id = ids
# frequency where second filter filters
search_freq = 50
search_freq = -50
# get indices for time array in time window
window_index = np.arange(len(idx))[
@ -157,7 +160,7 @@ def main(datapath: str) -> None:
axs[2].plot(np.arange(len(baseline)) / data.samplerate, baseline)
# plot instatneous frequency
# broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
#broad_baseline = bandpass_filter(data_oi[:, electrode], data.samplerate, lowf=np.mean(
# freq_temp)-5, highf=np.mean(freq_temp)+200)
baseline_freq_time, baseline_freq = instantaneos_frequency(
@ -198,14 +201,35 @@ def main(datapath: str) -> None:
inst_freq_filtered = bandpass_filter(
baseline_freq, data.samplerate, lowf=15, highf=8000
)
axs[6].plot(baseline_freq_time, np.abs(inst_freq_filtered))
# plot filtered and rectified envelope
axs[4].plot(np.arange(len(baseline)) /
data.samplerate, baseline_envelope)
axs[5].plot(np.arange(len(baseline)) /
data.samplerate, search_envelope)
axs[6].plot(baseline_freq_time, np.abs(inst_freq_filtered))
# detect peaks baseline_enelope
prominence = iqr(baseline_envelope)
baseline_peaks, _ = find_peaks(baseline_envelope, prominence=prominence)
axs[4].scatter((np.arange(len(baseline)) /
data.samplerate)[baseline_peaks], baseline_envelope[baseline_peaks], c="red")
# detect peaks search_envelope
search_peaks, _ = find_peaks(search_envelope, height=0.0001)
axs[5].scatter((np.arange(len(baseline)) /
data.samplerate)[search_peaks], search_envelope[search_peaks], c="red")
# detect peaks inst_freq_filtered
inst_freq_peaks, _ = find_peaks(np.abs(inst_freq_filtered), height=2)
axs[6].scatter(baseline_freq_time[inst_freq_peaks], np.abs(inst_freq_filtered)[inst_freq_peaks], c="red")
#
axs[0].set_title("Spectrogram")
axs[1].set_title("Fitered baseline instanenous frequency")
axs[2].set_title("Fitered baseline")