raab/efishSignalDetector
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dev mode implemented

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This commit is contained in:
Till Raab 2023-10-20 11:10:35 +02:00
parent 76d182243a
commit 4b9bdcda29
1 changed files with 38 additions and 22 deletions
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60
data/generate_dataset.py
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@ -1,4 +1,5 @@
import numpy as np
import argparse
import torch
from torch import nn
import torch.nn.functional as F
@ -40,14 +41,26 @@ def load_data(folder):
times_v = np.load(base_path / 'times.npy')
rise_idx = np.load(base_path / 'analysis' / 'rise_idx.npy')
ff = np.load(base_path / 'analysis' / 'fish_freq.npy')
embed()
quit()
return fill_freqs, fill_times, fill_spec, EODf_v, ident_v, idx_v, times_v, rise_idx
def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res):
fig_title = (f'{Path(folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}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
gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1) #
ax = fig.add_subplot(gs2[0, 0])
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))
ax.axis(False)
plt.savefig(os.path.join('train', fig_title + '.png'), dpi=256)
plt.close()
def main(args):
def main(folder):
min_freq = 200
max_freq = 1500
d_freq = 200
@ -55,7 +68,7 @@ def main(folder):
d_time = 60*15
time_overlap = 60*5
freq, times, spec, EODf_v, ident_v, idx_v, times_v, rise_idx = load_data(folder)
freq, times, spec, EODf_v, ident_v, idx_v, times_v, rise_idx = load_data(args.folder)
f_res, t_res = freq[1] - freq[0], times[1] - times[0]
unique_ids = np.unique(ident_v[~np.isnan(ident_v)])
@ -82,30 +95,33 @@ def main(folder):
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))
embed()
quit()
s = torch.from_numpy(spec[f_idx0:f_idx1, t_idx0:t_idx1].copy()).type(torch.float32)
log_s = torch.log10(s)
transformed = T.Normalize(mean=torch.mean(log_s), std=torch.std(log_s))
s_trans = transformed(log_s.unsqueeze(0))
if not args.dev:
save_spec_pic(args.folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1, t_res, f_res)
exit()
else:
fig_title = (f'{Path(folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}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
ax = fig.add_subplot(gs[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))
fig_title = (f'{Path(folder).name}__{t0:.0f}s-{t1:.0f}s__{f0:4.0f}-{f1:4.0f}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
gs2 = gridspec.GridSpec(1, 1, bottom=0, left=0, right=1, top=1)#
ax = fig.add_subplot(gs2[0, 0])
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))
ax.axis(False)
fig.colorbar(im, cax=cax, orientation='vertical')
plt.show()
plt.savefig(os.path.join('train', fig_title + '.png'), dpi=256)
plt.close()
# # ax.imshow(spec[f0:f1, t0:t1], cmap='gray')
if __name__ == '__main__':
main(sys.argv[1])
parser = argparse.ArgumentParser(description='Evaluated electrode array recordings with multiple fish.')
parser.add_argument('file', type=str, help='single recording analysis', default='')
parser.add_argument('-d', "--dev", action="store_true", help="developer mode; no data saved")
# parser.add_argument('-x', type=int, nargs=2, default=[1272, 1282], help='x-borders of LED detect area (in pixels)')
# parser.add_argument('-y', type=int, nargs=2, default=[1500, 1516], help='y-borders of LED area (in pixels)')
args = parser.parse_args()
main(args)