small documentation progress

data/generate_dataset.py

@@ -1,28 +1,40 @@
-import time
-
-import numpy as np
+import itertools
+import sys
+import os
 import argparse
+
 import torch
 from torch import nn
 import torch.nn.functional as F
 import torchvision.transforms as T
+
+import numpy as np
 import matplotlib.pyplot as plt
 import matplotlib.gridspec as gridspec
-from pathlib import Path
 import pandas as pd
-
+from pathlib import Path
 from tqdm.auto import tqdm
 
-import itertools
-import sys
-import os
-
-from IPython import embed
 
-from matplotlib.patches import Rectangle
-from matplotlib.collections import PatchCollection
-
-def load_spec_data(folder):
+def load_spec_data(folder: str):
+    """
+    Load spectrogram of a given electrode-grid recording generated with the wavetracker package. The spectrograms may
+    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 = [], [], []
 
     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
 
+
 def load_tracking_data(folder):
     base_path = Path(folder)
     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
 
+
 def load_trial_data(folder):
     base_path = Path(folder)
     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)
 
+
 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):
 
@@ -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]
         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)
         right_time_bound += 0.05 * (t1 - t0)
         lower_freq_bound -= 0.01 * (f1 - f0)