raab/efishSignalDetector
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trying to import and implement the rises

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Till Raab 2023-10-20 10:30:50 +02:00
parent 59f71c48ab
commit f5b2fef550
1 changed files with 15 additions and 10 deletions
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25
data/generate_dataset.py
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@ -39,12 +39,18 @@ def load_data(folder):
idx_v = np.load(base_path / 'idx_v.npy') idx_v = np.load(base_path / 'idx_v.npy')
times_v = np.load(base_path / 'times.npy') times_v = np.load(base_path / 'times.npy')
return fill_freqs, fill_times, fill_spec, EODf_v, ident_v, idx_v, times_v rise_idx = np.load(base_path / 'analysis' / 'rise_idx.npy')
return fill_freqs, fill_times, fill_spec, EODf_v, ident_v, idx_v, times_v, rise_idx
def main(folder): def main(folder):
min_freq, max_freq, d_freq, freq_overlap, d_time, time_overlap = ( min_freq = 200
200, 1500, 200, 50, 60*15, 60*5) max_freq = 1500
d_freq = 200
freq_overlap = 50
d_time = 60*15
time_overlap = 60*5
freq, times, spec, EODf_v, ident_v, idx_v, times_v = load_data(folder) 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] f_res, t_res = freq[1] - freq[0], times[1] - times[0]
@ -55,7 +61,7 @@ def main(folder):
np.arange(0, times[-1], d_time), np.arange(0, times[-1], d_time),
np.arange(min_freq, max_freq, d_freq) np.arange(min_freq, max_freq, d_freq)
), ),
total=((max_freq-min_freq)//d_freq) * (times[-1] // d_time) total=int(((max_freq-min_freq)//d_freq) * (times[-1] // d_time))
) )
for t0, f0 in pic_base: for t0, f0 in pic_base:
@ -64,7 +70,7 @@ def main(folder):
present_freqs = EODf_v[(~np.isnan(ident_v)) & present_freqs = EODf_v[(~np.isnan(ident_v)) &
(t0 <= times_v[idx_v]) & (t0 <= times_v[idx_v]) &
(times_v[idx_v]<= t1) & (times_v[idx_v] <= t1) &
(EODf_v >= f0) & (EODf_v >= f0) &
(EODf_v <= f1)] (EODf_v <= f1)]
if len(present_freqs) == 0: if len(present_freqs) == 0:
@ -73,6 +79,9 @@ def main(folder):
f_idx0, f_idx1 = np.argmin(np.abs(freq - f0)), np.argmin(np.abs(freq - f1)) 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)) t_idx0, t_idx1 = np.argmin(np.abs(times - t0)), np.argmin(np.abs(times - t1))
embed()
quit()
s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32) s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32)
log_s = torch.log10(s) log_s = torch.log10(s)
transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s)) transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
@ -84,11 +93,7 @@ def main(folder):
gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0)# , bottom=0, left=0, right=1, top=1 gs = gridspec.GridSpec(1, 2, width_ratios=(8, 1), wspace=0)# , bottom=0, left=0, right=1, top=1
gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1)# gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1)#
ax = fig.add_subplot(gs2[0, 0]) ax = fig.add_subplot(gs2[0, 0])
# cax = fig.add_subplot(gs[0, 1]) im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower', extent=(times[t_idx0]/3600, times[t_idx1]/3600 + t_res, freq[f_idx0], freq[f_idx1] + f_res))
im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower', extent=(times_v[t_idx0]/3600, times_v[t_idx1]/3600 + t_res, freq[f_idx0], freq[f_idx1] + f_res))
# im = ax.imshow(log_s, cmap='gray', aspect='auto')
# ax.invert_yaxis()
# fig.colorbar(im, cax=cax)
ax.axis(False) ax.axis(False)
plt.savefig(os.path.join('train', fig_title + '.png'), dpi=256) plt.savefig(os.path.join('train', fig_title + '.png'), dpi=256)