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spec_peak_detect.py
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spec_peak_detect.py
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import os
import sys
import argparse
import multiprocessing
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from functools import partial
from IPython import embed
from tqdm import tqdm
from thunderfish.dataloader import DataLoader as open_data
from thunderfish.powerspectrum import spectrogram, next_power_of_two, decibel
def load_data(folder):
filename = os.path.join(folder, 'traces-grid1.raw')
data = open_data(filename, -1, 60.0, 10.0)
samplerate = data.samplerate
channels = data.channels
return filename, data, samplerate, channels
def compute_spectrogram(data, samplerate, freq_res, overlap_frac, channels, start_idx, end_idx):
core_count = multiprocessing.cpu_count()
pool = multiprocessing.Pool(core_count - 1)
func = partial(spectrogram, ratetime=samplerate, freq_resolution=freq_res, overlap_frac=overlap_frac)
a = pool.map(func, [data[start_idx: end_idx+1, channel] for channel in np.arange(channels)])
spectra = [a[channel][0] for channel in range(len(a))]
spec_freqs = a[0][1]
spec_times = a[0][2]
pool.terminate()
# ToDo: return full spectrum
comb_spectra = np.sum(spectra, axis=0)
db_comb_spectra = decibel(comb_spectra)
tmp_times = spec_times + (start_idx / samplerate)
return comb_spectra, db_comb_spectra, tmp_times, spec_freqs
def clean_peaks(peaks, trough, spec_freq, power, peak_trough_max_df = 10, peak_power_th=5):
peaks = peaks[np.argsort(power[peaks])]
power_diff = np.repeat(power[peaks][:, np.newaxis], len(trough), axis=1) - np.repeat(power[trough][np.newaxis, :], len(peaks), axis=0)
freq_diff = np.abs(np.repeat(spec_freq[peaks][:, np.newaxis], len(trough), axis=1) - np.repeat(spec_freq[trough][np.newaxis, :], len(peaks), axis=0))
mask = (freq_diff < peak_trough_max_df) & (power_diff < peak_power_th)
sorter = np.argsort(np.sum(mask, axis = 0))
trough = trough[sorter]
mask = mask[:, sorter]
i0s, i1s = mask.nonzero()
del_peaks = []
for i0, i1 in zip(i0s, i1s):
if mask[i0, i1] == True:
del_peaks.append(i0)
mask[:, i1] = False
mask[i0, :] = False
real_peaks_idx = np.array(list(set(np.arange(len(peaks))) - set(del_peaks)), dtype=int)
return peaks[real_peaks_idx], trough
def harmonic_groups(peaks, spec_freq, power, min_freq, max_freq, plot=False):
freq_res = spec_freq[1]
peak_freq = spec_freq[peaks]
peak_power = power[peaks]
mask = np.ones(len(peaks), dtype=bool)
groups = []
for i in np.argsort(peak_power)[::-1]:
if not min_freq < peak_freq[i] < max_freq:
continue
if not mask[i]:
continue
df_harmonics = np.abs(np.repeat(peak_freq[np.newaxis, :], 3, axis=0) -
np.array([peak_freq[i] * 0.5, peak_freq[i] * 2, peak_freq[i] * 3])[:, None])
matches = (df_harmonics < np.array([freq_res, freq_res*3, freq_res * 6])[:, None]) * mask
# ToDo: if match in first row --> recalculate matrix
harmonic_indices = np.array(matches.nonzero())
if np.sum(matches) == 0:
continue
if len(harmonic_indices[0]) != len(np.unique(harmonic_indices[0])):
del_indices = []
for j in np.unique(harmonic_indices[0]):
if len(harmonic_indices[0][harmonic_indices[0] == j]) >= 2:
ioi = np.arange(len(harmonic_indices[0]))[harmonic_indices[0] == j]
del_idx = np.argsort(df_harmonics[harmonic_indices[0][ioi], harmonic_indices[1][ioi]])[1:]
del_indices.extend(ioi[del_idx])
non_del_idx = np.array(list(set(np.arange(np.shape(harmonic_indices)[1])) - set(del_indices)), dtype=int)
harmonic_indices = np.array(harmonic_indices)[:, non_del_idx]
# ToDo: clean this up for already occupied peaks & double matches
g = np.full((np.max(harmonic_indices[0]+1), 2), np.nan)
g[0] = peak_freq[i], peak_power[i]
g[harmonic_indices[0]] = np.array([ peak_freq[harmonic_indices[1]], peak_power[harmonic_indices[1]] ]).T
mask[i] = False
mask[harmonic_indices[1]] = False
groups.append(g)
if plot:
fig, ax = plt.subplots(figsize=(30 / 2.54, 20 / 2.54))
ax.plot(spec_freq, power)
ax.plot(spec_freq[peaks], power[peaks], 'o', color='forestgreen')
plt.show()
return groups
def eod_detection(db_spectra, spec_freq, spec_time, db_th0, db_th1, min_freq = 250, max_freq = 1200, min_group_size=2, plot=False):
all_groups = []
all_fundamentals = []
f_mask = np.arange(len(spec_freq))[(spec_freq <= 3000)]
db_spectra = db_spectra[f_mask[0]:f_mask[-1]+1, np.arange(np.shape(db_spectra)[1])]
spec_freq = spec_freq[f_mask]
for i in tqdm(np.arange(np.shape(db_spectra)[1]), desc='EOD extract'):
psd_mask = np.array(db_spectra[:, i] >= db_th0, dtype=bool)
# include 1 entry before and after valid (to valid bool-mask)
help_array = np.diff(np.array(psd_mask, dtype=int))
psd_mask[np.arange(len(help_array))[help_array == 1]] = 1
psd_mask[np.arange(len(help_array))[help_array == -1] + 1] = 1
# 1st-derivation of valid psd (zeros crossings are peaks in PSDs)
psd_derivation = np.diff(db_spectra[:, i])
psd_derivation[~psd_mask[1:]] = np.nan
peaks = np.arange(len(psd_derivation)-1)[(psd_derivation[:-1] > 0) & (psd_derivation[1:] < 0)] + 1
trough = np.arange(len(psd_derivation)-1)[(psd_derivation[:-1] <= 0) & (psd_derivation[1:] > 0)] + 1
all_peaks = peaks
peaks, trough = clean_peaks(peaks, trough, spec_freq, db_spectra[:, i], peak_trough_max_df = 10, peak_power_th=5)
groups = harmonic_groups(peaks, spec_freq, db_spectra[:, i], min_freq, max_freq)
fundamentals = list(map(lambda x: x[0, 0], groups))
all_groups.append(groups)
all_fundamentals.append(fundamentals)
#############################
if plot:
f_peaks = peaks[(spec_freq[peaks] >= min_freq) & (spec_freq[peaks] <= max_freq)]
f_trough = trough[(spec_freq[trough] >= min_freq) & (spec_freq[trough] <= max_freq)]
h1_peaks = np.array(list(set(peaks) - set(f_peaks)), dtype=int)
h1_trough = np.array(list(set(trough) - set(f_trough)), dtype=int)
fig, ax = plt.subplots(2, 1, sharex=True, figsize=(30/2.54, 20/2.54))
ax[0].plot(spec_freq, db_spectra[:, i])
ax[0].plot([spec_freq[0], spec_freq[-1]], [db_th0, db_th0], 'k-')
# ax[1].plot(spec_freq[psd_mask][1:], psd_derivation)
ax[1].plot(spec_freq[1:], np.diff(db_spectra[:, i]), color='grey')
ax[1].plot(spec_freq[1:], psd_derivation)
ax[1].plot([spec_freq[0], spec_freq[-1]], [0, 0], 'k--', lw=1, alpha=0.8)
ax[0].plot(spec_freq[all_peaks], db_spectra[all_peaks, i], 'o', color='grey', alpha=0.5)
ax[0].plot(spec_freq[f_peaks], db_spectra[f_peaks, i], 'o', color='forestgreen')
ax[0].plot(spec_freq[f_trough], db_spectra[f_trough, i], 'o', markeredgecolor='forestgreen', color='none')
ax[0].plot(spec_freq[h1_peaks], db_spectra[h1_peaks, i], 'o', color='orange')
ax[0].plot(spec_freq[h1_trough], db_spectra[h1_trough, i], 'o', markeredgecolor='orange', color='none')
ax[1].plot(spec_freq[peaks], np.zeros(len(peaks)), 'ko')
ax[1].plot(spec_freq[trough], np.zeros(len(trough)), 'o', markeredgecolor='k', color='none')
plt.show()
#############################
return all_fundamentals
def main(folder):
freq_res = 1
overlap_frac = 0.8
# load data
filename, data, samplerate, channels = load_data(folder)
start_idx = int(120 * 60 * samplerate)
analysis_window_sec = 5 * 60
end_idx = int(start_idx + analysis_window_sec * samplerate)
# spectrogram
comb_spectra, db_comb_spectra, tmp_times, spec_freqs = compute_spectrogram(data, samplerate, freq_res, overlap_frac,
channels, start_idx, end_idx)
# detection
min_freq, max_freq = 250, 1200
db_th0 = -90
detection_mask = np.full(db_comb_spectra.shape, np.nan)
detection_mask[db_comb_spectra >= db_th0] = 1
db_th2 = -90
detection_mask2 = np.full(db_comb_spectra.shape, np.nan)
detection_mask2[db_comb_spectra >= db_th2] = 1
fundamentals = eod_detection(db_comb_spectra, spec_freqs, tmp_times, db_th0, db_th2, min_freq = min_freq, max_freq = max_freq, plot=True)
###############################
f_mask = np.arange(len(spec_freqs))[(spec_freqs >= min_freq) & (spec_freqs <= max_freq)]
f_mask2 = np.arange(len(spec_freqs))[(spec_freqs >= min_freq*2) & (spec_freqs <= max_freq*2)]
t_mask = np.arange(len(tmp_times))
fig = plt.figure(figsize=(20/2.54, 14/2.54))
gs = gridspec.GridSpec(2, 1, left=0.1, bottom = 0.1, right=0.95, top=0.95)
ax = fig.add_subplot(gs[1, 0])
ax2 = fig.add_subplot(gs[0, 0])
h = ax.imshow(db_comb_spectra[f_mask[0]:f_mask[-1]+1, t_mask[0]:t_mask[-1]+1][::-1],
extent=[tmp_times[0], tmp_times[-1], spec_freqs[f_mask[0]], spec_freqs[f_mask[-1]+1]],
aspect='auto', vmin=-100, vmax=-50, alpha=0.7, cmap='jet', interpolation='gaussian')
h1 = ax2.imshow(db_comb_spectra[f_mask2[0]:f_mask2[-1]+1, t_mask[0]:t_mask[-1]+1][::-1],
extent=[tmp_times[0], tmp_times[-1], spec_freqs[f_mask2[0]], spec_freqs[f_mask2[-1]+1]],
aspect='auto', vmin=-100, vmax=-50, alpha=0.7, cmap='jet', interpolation='gaussian')
m = ax.imshow(detection_mask[f_mask[0]:f_mask[-1]+1, t_mask[0]:t_mask[-1]+1][::-1],
extent=[tmp_times[0], tmp_times[-1], spec_freqs[f_mask[0]], spec_freqs[f_mask[-1]+1]],
aspect='auto', vmin=0, vmax=1, alpha=1, cmap='binary')
m1 = ax2.imshow(detection_mask2[f_mask[0]:f_mask[-1]+1, t_mask[0]:t_mask[-1]+1][::-1],
extent=[tmp_times[0], tmp_times[-1], spec_freqs[f_mask2[0]], spec_freqs[f_mask2[-1]+1]],
aspect='auto', vmin=0, vmax=1, alpha=1, cmap='binary')
for i in range(len(fundamentals)):
ax.plot(np.ones(len(fundamentals[i]))*tmp_times[i], fundamentals[i], '.', color='firebrick', markersize=8)
ax.set_xlabel('time', fontsize=12)
plt.show()
pass
if __name__ == '__main__':
main(sys.argv[1])