raab/efishSignalDetector
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small documentation progress

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Till Raab 2023-10-27 13:04:53 +02:00
parent e2b540c329
commit 3dbc78685d
1 changed files with 29 additions and 17 deletions
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46
data/generate_dataset.py
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@ -1,28 +1,40 @@
import time import itertools
import sys
import numpy as np import os
import argparse import argparse
import torch import torch
from torch import nn from torch import nn
import torch.nn.functional as F import torch.nn.functional as F
import torchvision.transforms as T import torchvision.transforms as T
import numpy as np
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec import matplotlib.gridspec as gridspec
from pathlib import Path
import pandas as pd import pandas as pd
from pathlib import Path
from tqdm.auto import tqdm from tqdm.auto import tqdm
import itertools
import sys
import os
from IPython import embed
from matplotlib.patches import Rectangle def load_spec_data(folder: str):
from matplotlib.collections import PatchCollection """
Load spectrogram of a given electrode-grid recording generated with the wavetracker package. The spectrograms may
def load_spec_data(folder): be to large to load in total, thats why memmory mapping is used (numpy.memmap).
Parameters
----------
folder: str
Folder where fine spec numpy files generated for grid recordings with the wavetracker package can be found.
Returns
-------
fill_freqs: ndarray
Freuqencies corresponding to 1st dimension of the spectrogram.
fill_times: ndarray
Times corresponding to the 2nd dimenstion if the spectrigram.
fill_spec: ndarray
Spectrigram of the recording refered to in the input folder.
"""
fill_freqs, fill_times, fill_spec = [], [], [] fill_freqs, fill_times, fill_spec = [], [], []
if os.path.exists(os.path.join(folder, 'fill_spec.npy')): if os.path.exists(os.path.join(folder, 'fill_spec.npy')):
@ -41,6 +53,7 @@ def load_spec_data(folder):
return fill_freqs, fill_times, fill_spec return fill_freqs, fill_times, fill_spec
def load_tracking_data(folder): def load_tracking_data(folder):
base_path = Path(folder) base_path = Path(folder)
EODf_v = np.load(base_path / 'fund_v.npy') EODf_v = np.load(base_path / 'fund_v.npy')
@ -50,6 +63,7 @@ def load_tracking_data(folder):
return EODf_v, ident_v, idx_v, times_v return EODf_v, ident_v, idx_v, times_v
def load_trial_data(folder): def load_trial_data(folder):
base_path = Path(folder) base_path = Path(folder)
fish_freq = np.load(base_path / 'analysis' / 'fish_freq.npy') fish_freq = np.load(base_path / 'analysis' / 'fish_freq.npy')
@ -79,6 +93,7 @@ def save_spec_pic(folder, s_trans, times, freq, t_idx0, t_idx1, f_idx0, f_idx1,
return fig_title, (size[0]*dpi, size[1]*dpi) return fig_title, (size[0]*dpi, size[1]*dpi)
def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq_time, fish_baseline_freq, pic_save_str, def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq_time, fish_baseline_freq, pic_save_str,
bbox_df, cols, width, height, t0, t1, f0, f1): bbox_df, cols, width, height, t0, t1, f0, f1):
@ -119,9 +134,6 @@ def bboxes_from_file(times_v, fish_freq, rise_idx, rise_size, fish_baseline_freq
lower_freq_bound = lower_freq_bound[mask] lower_freq_bound = lower_freq_bound[mask]
upper_freq_bound = upper_freq_bound[mask] upper_freq_bound = upper_freq_bound[mask]
# dt_bbox = right_time_bound - left_time_bound
# df_bbox = upper_freq_bound - lower_freq_bound
left_time_bound -= 0.01 * (t1 - t0) left_time_bound -= 0.01 * (t1 - t0)
right_time_bound += 0.05 * (t1 - t0) right_time_bound += 0.05 * (t1 - t0)
lower_freq_bound -= 0.01 * (f1 - f0) lower_freq_bound -= 0.01 * (f1 - f0)