diff --git a/extract_from_bbox.py b/extract_from_bbox.py
new file mode 100644
index 0000000..eae2c2b
--- /dev/null
+++ b/extract_from_bbox.py
@@ -0,0 +1,103 @@
+import pathlib
+import argparse
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib.patches import Rectangle
+from PIL import Image
+from tqdm.auto import tqdm
+
+from IPython import embed
+
+def extract_time_freq_range_from_filename(img_path):
+    file_name_str, time_span_str, freq_span_str = str(img_path.with_suffix('').name).split('__')
+    time_span_str = time_span_str.replace('s', '')
+    freq_span_str = freq_span_str.replace('Hz', '')
+
+    t0, t1 = np.array(time_span_str.split('-'), dtype=float)
+    f0, f1 = np.array(freq_span_str.split('-'), dtype=float)
+    return file_name_str, t0, t1, f0, f1
+
+def bbox_to_data(img_path, t_min, t_max, f_min, f_max):
+    label_path = img_path.parent.parent / 'labels' / img_path.with_suffix('.txt').name
+
+    annotations = np.loadtxt(label_path, delimiter=' ')
+    if len(annotations.shape) == 1:
+        annotations = np.array([annotations])
+    if annotations.shape[1] == 0:
+        print('no rises detected in this window')
+        return []
+
+
+    boxes = np.array([[x[1] - x[3] / 2, 1 - (x[2] + x[4] / 2), x[1] + x[3] / 2, 1 - (x[2] - x[4] / 2)] for x in annotations]) # x0, y0, x1, y1
+    boxes[:, 0] = boxes[:, 0] * (t_max - t_min) + t_min
+    boxes[:, 2] = boxes[:, 2] * (t_max - t_min) + t_min
+
+    boxes[:, 1] = boxes[:, 1] * (f_max - f_min) + f_min
+    boxes[:, 3] = boxes[:, 3] * (f_max - f_min) + f_min
+
+    return boxes
+
+def load_wavetracker_data(raw_path):
+    fund_v = np.load(raw_path.parent / 'fund_v.npy')
+    ident_v = np.load(raw_path.parent / 'ident_v.npy')
+    idx_v = np.load(raw_path.parent / 'idx_v.npy')
+    times = np.load(raw_path.parent / 'times.npy')
+
+    return fund_v, ident_v, idx_v, times
+
+
+def assign_rises_to_ids(raw_path, time_frequency_bboxes):
+    fund_v, ident_v, idx_v, times = load_wavetracker_data(raw_path)
+
+    fig, ax = plt.subplots()
+    ax.plot(times[idx_v[~np.isnan(ident_v)]], fund_v[~np.isnan(ident_v)], '.')
+
+    mask = time_frequency_bboxes['file_name'] == raw_path.parent.name
+    for index, bbox in time_frequency_bboxes[mask].iterrows():
+        name, t0, f0, t1, f1 = (bbox[0], *bbox[1:].astype(float))
+        ax.add_patch(
+            Rectangle((t0, f0),
+                      (t1 - t0),
+                      (f1 - f0),
+                      fill=False, color="tab:green", linestyle='--', linewidth=2, zorder=10)
+        )
+    plt.show()
+
+    # ToDo: eliminate double rises -- overlap
+    # ToDo: double detections -- non overlap --> the one with higher probability ?!
+    # ToDo: assign rises to traces --> who is at lower right corner
+
+def main(args):
+    img_paths = sorted(list(pathlib.Path(args.annotations).absolute().rglob('*.png')))
+    df_collect = []
+
+    for img_path in img_paths:
+        # convert to time_frequency
+        file_name_str, t_min, t_max, f_min, f_max = extract_time_freq_range_from_filename(img_path)
+
+        boxes = bbox_to_data(img_path, t_min, t_max, f_min, f_max ) # t0, t1, f0, f1
+
+        # store values in df
+        if not len(boxes) == 0:
+            for x0, y0, x1, y1 in boxes:
+                df_collect.append([file_name_str, x0, y0, x1, y1])
+
+    time_frequency_bboxes = pd.DataFrame(data= np.array(df_collect), columns=['file_name', 't0', 'f0', 't1', 'f1'])
+
+    if args.tracking_data_path:
+        file_paths = sorted(list(pathlib.Path(args.tracking_data_path).absolute().rglob('*.raw')))
+        for raw_path in file_paths:
+            if not raw_path.parent.name in time_frequency_bboxes['file_name'].to_list():
+                continue
+
+            assign_rises_to_ids(raw_path, time_frequency_bboxes)
+
+    pass
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Extract time, frequency and identity association of bboxes')
+    parser.add_argument('annotations', nargs='?', type=str, help='path to annotations')
+    parser.add_argument('-t', '--tracking_data_path', type=str, help='path to tracking dataa')
+    args = parser.parse_args()
+    main(args)
\ No newline at end of file