raab/efishSignalDetector
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Till Raab 2023-10-20 09:10:01 +02:00
parent 7a8790d9af
commit ec6afb0354
1 changed files with 4 additions and 1 deletions
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data/generate_dataset.py
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@ -47,6 +47,8 @@ def main(folder):
unique_ids = np.unique(ident_v[~np.isnan(ident_v)]) unique_ids = np.unique(ident_v[~np.isnan(ident_v)])
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
for t0, f0 in tqdm(list(itertools.product(np.arange(0, times_v[-1], 60*15), np.arange(200, 1500, 200)))): 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 t1 = t0 + 60*20
f1 = f0 + 250 f1 = f0 + 250
@ -54,6 +56,7 @@ 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_v - t0)), np.argmin(np.abs(times_v - t1)) t_idx0, t_idx1 = np.argmin(np.abs(times_v - t0)), np.argmin(np.abs(times_v - t1))
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)
# s_normed = F.normalize(s.view(-1)).view(s.shape[0], s.shape[1]) # s_normed = F.normalize(s.view(-1)).view(s.shape[0], s.shape[1])
@ -67,7 +70,7 @@ def main(folder):
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]) # cax = fig.add_subplot(gs[0, 1])
im = ax.imshow(s_trans.squeeze(), cmap='gray', aspect='auto', origin='lower', extent=(times_v[t_idx0]/3600, times_v[t_idx1+1]/3600, freq[f_idx0], freq[f_idx1+1])) 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') # im = ax.imshow(log_s, cmap='gray', aspect='auto')
# ax.invert_yaxis() # ax.invert_yaxis()
# fig.colorbar(im, cax=cax) # fig.colorbar(im, cax=cax)