Giorgio Gilestro
f60a9d0530
- Tracked DBs now live at /mnt/data/projects/cupido/tracked/ (out of
ownCloud to avoid sync conflicts and bandwidth churn). config.py
TRACKING_OUTPUT_DIR points there; the docker-compose for ethoscope-lab
mounts it world-readable for JupyterHub users.
- New scripts/export_video_db_index.py joins all_video_info_merged.xlsx
with the video inventory and the on-disk DBs, producing a TSV that has
one row per fly/ROI plus training/testing video and DB paths. Handles
approximate xlsx times, cross-day training/testing, the 12 AM/PM
ambiguity, and date typos.
- scripts/load_roi_data.py rewritten as a TSV-driven loader returning a
single DataFrame with session and metadata columns. calculate_distances
and the two flies_analysis notebooks migrated to use it; downstream
trained/naive splits remain available via simple equality filters.
- Metadata vocabulary canonicalized: {naïve, niave, untrained, test} all
resolve to {trained, naive}. Normalization happens at the TSV-export
boundary (idempotent); the xlsx and the 2025-07-15 legacy CSV were
edited in place to remove the worst variants.
- scripts/monitor_tracking.py rate calculation fixed: with N parallel
workers, completions arrive in bursts; the old formula divided by burst
width and reported nonsense rates. Now uses a 6 h window denominator.
- scripts/track_videos.py: BGRMovieCamera retries cv2.read on transient
NFS hiccups and a post-tracking completeness gate (≥ 90 % of expected
duration via MAX(t) across all 6 ROIs) deletes silent partial DBs.
Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
100 lines
3.8 KiB
Python
100 lines
3.8 KiB
Python
"""Compute per-frame inter-fly distances for every (date, machine, ROI, session).
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Reads tracking data via :func:`load_roi_data.load_roi_data` (which is driven
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by ``all_video_info_merged.tsv``) and produces one distances DataFrame
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spanning every fly/session in the batch. Group membership (``trained`` /
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``untrained``) is preserved from the ``male`` column.
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"""
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import numpy as np
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import pandas as pd
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from scipy.spatial.distance import euclidean
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from config import DATA_PROCESSED
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from load_roi_data import load_roi_data
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def calculate_fly_distances(data: pd.DataFrame | None = None) -> pd.DataFrame:
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"""Compute inter-fly distances over time for every fly/session.
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For each time point inside one (date, machine, ROI, session) trajectory:
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- 2+ flies detected: Euclidean distance between the first two by id
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- 1 fly detected: distance = 0 if its bbox area exceeds the global
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mean (likely a single blob containing both flies), else NaN
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Args:
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data: optional pre-loaded DataFrame from :func:`load_roi_data`. If
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None, the full batch is loaded.
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Returns:
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DataFrame with one row per (track, time) pair, including ``distance``,
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``n_flies``, ``area_fly1``, ``area_fly2``, plus the metadata columns
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propagated from the source row (``date``, ``machine_name``, ``ROI``,
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``session``, ``male``, ``species``, ``memory``, ``age``).
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"""
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if data is None:
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data = load_roi_data()
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if data.empty:
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return pd.DataFrame()
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data = data.copy()
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data["area"] = data["w"] * data["h"]
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avg_area = data["area"].mean()
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print(f"Average area across all data: {avg_area:.2f}")
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# Carry these onto every output row (constant within a track).
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keep_meta = ["date", "machine_name", "ROI", "session", "male",
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"species", "memory", "age"]
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rows: list[dict] = []
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track_keys = ["date", "machine_name", "ROI", "session"]
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for track, track_df in data.groupby(track_keys, sort=False):
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meta_row = {k: v for k, v in zip(track_keys, track)}
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# Carry the rest of the metadata from any sample (constant per track).
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sample = track_df.iloc[0]
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for col in keep_meta:
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if col not in meta_row:
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meta_row[col] = sample[col]
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for t, time_group in track_df.groupby("t", sort=False):
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time_group = time_group.sort_values("id").reset_index(drop=True)
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row = dict(meta_row)
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row["t"] = t
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if len(time_group) >= 2:
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f1, f2 = time_group.iloc[0], time_group.iloc[1]
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row["distance"] = euclidean([f1["x"], f1["y"]], [f2["x"], f2["y"]])
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row["n_flies"] = len(time_group)
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row["area_fly1"] = f1["area"]
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row["area_fly2"] = f2["area"]
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else:
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f = time_group.iloc[0]
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row["distance"] = 0.0 if f["area"] > avg_area else np.nan
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row["n_flies"] = 1
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row["area_fly1"] = f["area"]
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row["area_fly2"] = np.nan
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rows.append(row)
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return pd.DataFrame(rows)
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def main() -> None:
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distances = calculate_fly_distances()
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print("\nDistance summary:")
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print(f" Shape: {distances.shape}")
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if not distances.empty:
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print(f" Distance count: {distances['distance'].count()}")
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print(f" Distance mean: {distances['distance'].mean():.2f}")
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print(f" Distance std: {distances['distance'].std():.2f}")
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male = distances["male"]
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print(f" Trained tracks: {(male == 'trained').sum()}")
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print(f" Naive tracks: {(male == 'naive').sum()}")
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DATA_PROCESSED.mkdir(parents=True, exist_ok=True)
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out = DATA_PROCESSED / "distances.csv"
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distances.to_csv(out, index=False)
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print(f"\nSaved {out}")
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if __name__ == "__main__":
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main()
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