raab/GP2023_chirp_detection
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changed to class

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weygoldt 2023-01-13 17:32:52 +01:00 committed by sprause
parent 98d98818cb
commit 5c48efb2e9
2 changed files with 43 additions and 40 deletions
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78
code/chirpdetection.py
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@ -10,7 +10,7 @@ from thunderfish.dataloader import DataLoader
from thunderfish.powerspectrum import spectrogram, decibel
from modules.filters import bandpass_filter, envelope, highpass_filter
from modules.filehandling import ConfLoader
from modules.filehandling import ConfLoader, LoadData
def instantaneos_frequency(
@ -136,22 +136,24 @@ def main(datapath: str) -> None:
# load raw file
file = os.path.join(datapath, "traces-grid1.raw")
data = DataLoader(file, 60.0, 0, channel=-1)
# data = DataLoader(file, 60.0, 0, channel=-1)
data = LoadData(datapath)
# load wavetracker files
time = np.load(datapath + "times.npy", allow_pickle=True)
freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
powers = np.load(datapath + "sign_v.npy", allow_pickle=True)
idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
# time = np.load(datapath + "times.npy", allow_pickle=True)
# freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
# powers = np.load(datapath + "sign_v.npy", allow_pickle=True)
# idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
# ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
# load config file
config = ConfLoader("chirpdetector_conf.yml")
# set time window # <------------------------ Iterate through windows here
window_duration = config.window * data.samplerate
window_overlap = config.overlap * data.samplerate
window_edge = config.edge * data.samplerate
window_duration = config.window * data.raw_rate
window_overlap = config.overlap * data.raw_rate
window_edge = config.edge * data.raw_rate
# check if window duration is even
if window_duration % 2 == 0:
@ -165,11 +167,11 @@ def main(datapath: str) -> None:
else:
raise ValueError("Window overlap must be even.")
raw_time = np.arange(data.shape[0]) / data.samplerate
raw_time = np.arange(data.raw.shape[0]) / data.raw_rate
# good chirp times for data: 2022-06-02-10_00
t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.samplerate
dt = 60 * data.samplerate
t0 = (3 * 60 * 60 + 6 * 60 + 43.5) * data.raw_rate
dt = 60 * data.raw_rate
window_starts = np.arange(
t0,
@ -182,31 +184,31 @@ def main(datapath: str) -> None:
for start_index in window_starts[:nwindows]:
# make t0 and dt
t0 = start_index / data.samplerate
dt = window_duration / data.samplerate
t0 = start_index / data.raw_rate
dt = window_duration / data.raw_rate
# set index window
stop_index = start_index + window_duration
# t0 = 3 * 60 * 60 + 6 * 60 + 43.5
# dt = 60
# start_index = t0 * data.samplerate
# stop_index = (t0 + dt) * data.samplerate
# start_index = t0 * data.raw_rate
# stop_index = (t0 + dt) * data.raw_rate
# iterate through all fish
for i, track_id in enumerate(np.unique(ident[~np.isnan(ident)])[:2]):
for i, track_id in enumerate(np.unique(data.ident[~np.isnan(data.ident)])[:2]):
# get indices for time array in time window
window_index = np.arange(len(idx))[
(ident == track_id) & (time[idx] >= t0) & (
time[idx] <= (t0 + dt))
window_index = np.arange(len(data.idx))[
(data.ident == track_id) & (data.time[data.idx] >= t0) & (
data.time[data.idx] <= (t0 + dt))
]
# get tracked frequencies and their times
freq_temp = freq[window_index]
powers_temp = powers[window_index, :]
freq_temp = data.freq[window_index]
powers_temp = data.powers[window_index, :]
# time_temp = time[idx[window_index]]
track_samplerate = np.mean(1 / np.diff(time))
track_samplerate = np.mean(1 / np.diff(data.time))
expected_duration = ((t0 + dt) - t0) * track_samplerate
# check if tracked data available in this window
@ -229,7 +231,7 @@ def main(datapath: str) -> None:
for i, electrode in enumerate(best_electrodes):
# load region of interest of raw data file
data_oi = data[start_index:stop_index, :]
data_oi = data.raw[start_index:stop_index, :]
time_oi = raw_time[start_index:stop_index]
# plot wavetracker tracks to spectrogram
@ -256,56 +258,56 @@ def main(datapath: str) -> None:
# filter baseline and above
baseline, search = double_bandpass(
data_oi[:, electrode], data.samplerate, freq_temp, search_freq
data_oi[:, electrode], data.raw_rate, freq_temp, search_freq
)
# compute instantaneous frequency on broad signal
broad_baseline = bandpass_filter(
data_oi[:, electrode],
data.samplerate,
data.raw_rate,
lowf=np.mean(freq_temp)-5,
highf=np.mean(freq_temp)+100
)
# compute instantaneous frequency on narrow signal
baseline_freq_time, baseline_freq = instantaneos_frequency(
baseline, data.samplerate
baseline, data.raw_rate
)
# compute envelopes
baseline_envelope = envelope(
baseline, data.samplerate, config.envelope_cutoff)
baseline, data.raw_rate, config.envelope_cutoff)
search_envelope = envelope(
search, data.samplerate, config.envelope_cutoff)
search, data.raw_rate, config.envelope_cutoff)
# highpass filter envelopes
baseline_envelope = highpass_filter(
baseline_envelope,
data.samplerate,
data.raw_rate,
config.envelope_highpass_cutoff
)
baseline_envelope = np.abs(baseline_envelope)
# search_envelope = highpass_filter(
# search_envelope,
# data.samplerate,
# data.raw_rate,
# config.envelope_highpass_cutoff
# )
# envelopes of filtered envelope of filtered baseline
baseline_envelope = envelope(
np.abs(baseline_envelope),
data.samplerate,
data.raw_rate,
config.envelope_envelope_cutoff
)
# search_envelope = bandpass_filter(
# search_envelope, data.samplerate, lowf=lowf, highf=highf)
# search_envelope, data.raw_rate, lowf=lowf, highf=highf)
# bandpass filter the instantaneous
inst_freq_filtered = bandpass_filter(
baseline_freq,
data.samplerate,
data.raw_rate,
lowf=config.instantaneous_lowf,
highf=config.instantaneous_highf
)
@ -325,9 +327,9 @@ def main(datapath: str) -> None:
search_envelope = search_envelope[valid]
# get inst freq valid snippet
valid_t0 = int(window_edge) / data.samplerate
valid_t0 = int(window_edge) / data.raw_rate
valid_t1 = baseline_freq_time[-1] - \
(int(window_edge) / data.samplerate)
(int(window_edge) / data.raw_rate)
inst_freq_filtered = inst_freq_filtered[(baseline_freq_time >= valid_t0) & (
baseline_freq_time <= valid_t1)]
@ -368,7 +370,7 @@ def main(datapath: str) -> None:
# plot spectrogram
plot_spectrogram(
axs[0, i], data_oi[:, electrode], data.samplerate, t0)
axs[0, i], data_oi[:, electrode], data.raw_rate, t0)
# plot baseline instantaneos frequency
axs[1, i].plot(baseline_freq_time, baseline_freq -

5
code/modules/filehandling.py
View File

@ -37,12 +37,13 @@ class LoadData:
# load raw data
self.file = os.path.join(datapath, "traces-grid1.raw")
self.data = DataLoader(self.file, 60.0, 0, channel=-1)
self.samplerate = self.data.samplerate
self.raw = DataLoader(self.file, 60.0, 0, channel=-1)
self.raw_rate = self.raw.samplerate
# load wavetracker files
self.time = np.load(datapath + "times.npy", allow_pickle=True)
self.freq = np.load(datapath + "fund_v.npy", allow_pickle=True)
self.powers = np.load(datapath + "sign_v.npy", allow_pickle=True)
self.idx = np.load(datapath + "idx_v.npy", allow_pickle=True)
self.ident = np.load(datapath + "ident_v.npy", allow_pickle=True)
self.ids = np.unique(self.ident[~np.isnan(self.ident)])