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load_roi_data: filter on barrier_opening.csv and stamp opening_s

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For every session (training and testing alike), the loader now looks
up the corresponding row in barrier_opening.csv and:
  - drops the read if the ROI is in bad_rois (barrier never opened
    for that fly so its tracking has no biological meaning)
  - drops the read if the session is flagged unusable
  - stamps the session's opening_s onto every sample so downstream
    code can compute t_from_opening = t - opening_s

Tested against ETHOSCOPE_082 2024-09-17: training (bad_rois=1,3,5)
correctly drops ROIs 1/3/5; testing keeps all six; opening_s differs
between sessions as expected (646.8 vs 154.7).

Opt out with apply_barrier_filter=False if you need raw data.
This commit is contained in:
Giorgio Gilestro 2026-05-12 09:45:59 +01:00
parent b8f23a4884
commit 28b7a227c0
2 changed files with 93 additions and 4 deletions
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5
scripts/config.py
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@ -26,6 +26,11 @@ VIDEO_INFO_TSV = DATA_VOLUME / "all_video_info_merged.tsv"
# A small CSV listing every video file we know about (built locally).
INVENTORY_CSV = DATA_METADATA / "video_inventory.csv"
# Hand-annotated barrier-opening times (output of the picker app). One
# row per testing session; columns: machine_name, session_date,
# session_time, opening_s, trim_first_s, bad_rois, analyst, notes.
BARRIER_OPENING_CSV = DATA_METADATA / "barrier_opening.csv"
# Where the ethoscope source tree is checked out (used by track_videos.py
# and auto_detect_targets.py — host-side scripts that import ethoscope
# from a local clone rather than from pip). Default assumes the standard