raab/GP2023_chirp_detection
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reformatted

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This commit is contained in:
weygoldt 2023-01-11 17:15:21 +01:00
parent 9c2b4d0634
commit e174979e9c
1 changed files with 35 additions and 18 deletions
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53
code/chirpdetection.py
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@ -116,7 +116,11 @@ def main(datapath: str) -> None:
# initialize plot
fig, axs = plt.subplots(
7, 1, figsize=(20 / 2.54, 12 / 2.54), constrained_layout=True, sharex=True
7,
1,
figsize=(20 / 2.54, 12 / 2.54),
constrained_layout=True,
sharex=True,
)
# plot spectrogram
@ -160,7 +164,7 @@ 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(
@ -175,11 +179,15 @@ def main(datapath: str) -> None:
cutoff = 25
baseline_envelope = envelope(baseline, data.samplerate, cutoff)
axs[2].plot(
np.arange(len(baseline)) / data.samplerate, baseline_envelope, c="orange"
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_envelope, c="orange"
np.arange(len(baseline)) / data.samplerate,
search_envelope,
c="orange",
)
# highpass filter envelopes
@ -192,7 +200,8 @@ def main(datapath: str) -> None:
# envelopes of filtered envelope of filtered baseline
baseline_envelope = envelope(
np.abs(baseline_envelope), data.samplerate, cutoff)
np.abs(baseline_envelope), data.samplerate, cutoff
)
# search_envelope = bandpass_filter(
# search_envelope, data.samplerate, lowf=lowf, highf=highf)
@ -202,33 +211,41 @@ def main(datapath: str) -> None:
baseline_freq, data.samplerate, lowf=15, highf=8000
)
# plot filtered and rectified envelope
axs[4].plot(np.arange(len(baseline)) /
data.samplerate, baseline_envelope)
axs[4].plot(
np.arange(len(baseline)) / data.samplerate, baseline_envelope
)
axs[5].plot(np.arange(len(baseline)) /
data.samplerate, search_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)
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")
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")
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[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")