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only create ficures with freqs

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Till Raab 2023-10-20 09:33:06 +02:00
parent ec6afb0354
commit de72b0bebb
1 changed files with 20 additions and 8 deletions
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28
data/generate_dataset.py
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@ -43,27 +43,39 @@ def load_data(folder):
def main(folder):
min_freq, max_freq, d_freq, d_time, freq_overlap, time_overlap = (
200, 1500, 200, 50, 60*15, 60*5)
freq, times, spec, EODf_v, ident_v, idx_v, times_v = load_data(folder)
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
unique_ids = np.unique(ident_v[~np.isnan(ident_v)])
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
pic_base = tqdm(itertools.product(
np.arange(0, times[-1], d_time),
np.arange(min_freq, max_freq, d_freq)
),
total=(max_freq-min_freq)//d_freq * times[-1] // d_time
)
for t0, f0 in tqdm(list(itertools.product(np.arange(0, times_v[-1], 60*15), np.arange(200, 1500, 200)))):
t1 = t0 + 60*20
f1 = f0 + 250
for t0, f0 in pic_base:
t1 = t0 + d_time + time_overlap
f1 = f0 + d_freq + freq_overlap
f_idx0, f_idx1 = np.argmin(np.abs(freq - f0)), np.argmin(np.abs(freq - f1))
t_idx0, t_idx1 = np.argmin(np.abs(times_v - t0)), np.argmin(np.abs(times_v - t1))
present_freqs = EODf_v[(~np.isnan(ident_v)) &
(t0 <= times_v[idx_v] <= t1)]
if len(present_freqs) == 0:
continue
f_idx0, f_idx1 = np.argmin(np.abs(freq - f0)), np.argmin(np.abs(freq - f1))
t_idx0, t_idx1 = np.argmin(np.abs(times - t0)), np.argmin(np.abs(times - t1))
s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32)
log_s = torch.log10(s)
# s_normed = F.normalize(s.view(-1)).view(s.shape[0], s.shape[1])
transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
s_trans = transformed(log_s.unsqueeze(0))
fig_title = (f'{Path(folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:.0f}-{f1:.0f}Hz').replace(' ', '0')
fig = plt.figure(figsize=(7, 7), num=fig_title)
gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0)# , bottom=0, left=0, right=1, top=1