127 lines
5.3 KiB
Python
127 lines
5.3 KiB
Python
import numpy as np
|
|
import argparse
|
|
import torch
|
|
from torch import nn
|
|
import torch.nn.functional as F
|
|
import torchvision.transforms as T
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.gridspec as gridspec
|
|
from pathlib import Path
|
|
|
|
from tqdm.auto import tqdm
|
|
|
|
import itertools
|
|
import sys
|
|
import os
|
|
|
|
from IPython import embed
|
|
|
|
|
|
def load_data(folder):
|
|
fill_freqs, fill_times, fill_spec = [], [], []
|
|
|
|
if os.path.exists(os.path.join(folder, 'fill_spec.npy')):
|
|
fill_freqs = np.load(os.path.join(folder, 'fill_freqs.npy'))
|
|
fill_times = np.load(os.path.join(folder, 'fill_times.npy'))
|
|
fill_spec_shape = np.load(os.path.join(folder, 'fill_spec_shape.npy'))
|
|
fill_spec = np.memmap(os.path.join(folder, 'fill_spec.npy'), dtype='float', mode='r',
|
|
shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
|
|
|
|
elif os.path.exists(os.path.join(folder, 'fine_spec.npy')):
|
|
fill_freqs = np.load(os.path.join(folder, 'fine_freqs.npy'))
|
|
fill_times = np.load(os.path.join(folder, 'fine_times.npy'))
|
|
fill_spec_shape = np.load(os.path.join(folder, 'fine_spec_shape.npy'))
|
|
fill_spec = np.memmap(os.path.join(folder, 'fine_spec.npy'), dtype='float', mode='r',
|
|
shape=(fill_spec_shape[0], fill_spec_shape[1]), order='F')
|
|
|
|
base_path = Path(folder)
|
|
EODf_v = np.load(base_path / 'fund_v.npy')
|
|
ident_v = np.load(base_path / 'ident_v.npy')
|
|
idx_v = np.load(base_path / 'idx_v.npy')
|
|
times_v = np.load(base_path / 'times.npy')
|
|
|
|
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 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):
|
|
|
|
|
|
min_freq = 200
|
|
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, 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)])
|
|
|
|
pic_base = tqdm(itertools.product(
|
|
np.arange(0, times[-1], d_time),
|
|
np.arange(min_freq, max_freq, d_freq)
|
|
),
|
|
total=int(((max_freq-min_freq)//d_freq) * (times[-1] // d_time))
|
|
)
|
|
|
|
for t0, f0 in pic_base:
|
|
t1 = t0 + d_time + time_overlap
|
|
f1 = f0 + d_freq + freq_overlap
|
|
|
|
present_freqs = EODf_v[(~np.isnan(ident_v)) &
|
|
(t0 <= times_v[idx_v]) &
|
|
(times_v[idx_v] <= t1) &
|
|
(EODf_v >= f0) &
|
|
(EODf_v <= f1)]
|
|
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)
|
|
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.colorbar(im, cax=cax, orientation='vertical')
|
|
plt.show()
|
|
|
|
# # ax.imshow(spec[f0:f1, t0:t1], cmap='gray')
|
|
|
|
if __name__ == '__main__':
|
|
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) |