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>
47 lines
2 KiB
Markdown
47 lines
2 KiB
Markdown
# Processed Data
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CSVs derived from the tracking DBs (`/mnt/data/projects/cupido/tracked/`)
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and the merged TSV (`../../all_video_info_merged.tsv`). All files are
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gitignored and regenerable.
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## Files and Regeneration
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| File | Description | Generated By |
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| `distances.csv` | Per-frame inter-fly distances for every (date, machine, ROI, session). Includes metadata columns to filter trained vs naïve, training phase, species, etc. | `scripts/calculate_distances.py` |
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| `*_distances_aligned.csv` | (legacy, 2025-07-15 only) distances aligned to barrier opening | `notebooks/flies_analysis*.ipynb` |
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| `*_tracked.csv` | (legacy) identity-tracked fly positions | `notebooks/flies_analysis_simple.ipynb` |
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| `*_max_velocity.csv` | (legacy) max velocity over 10 s windows | `notebooks/flies_analysis_simple.ipynb` |
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## Loading the data
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```python
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import sys
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sys.path.insert(0, "../scripts")
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from load_roi_data import load_roi_data
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data = load_roi_data() # full batch as one DataFrame
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# Or filter the metadata first:
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import pandas as pd
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tsv = pd.read_csv("../../all_video_info_merged.tsv", sep="\t")
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data = load_roi_data(tsv[tsv.species.str.contains("Melanogaster")])
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```
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The returned DataFrame has columns:
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`id, t, x, y, w, h, phi, is_inferred, has_interacted, session, ROI, date,
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machine_name, species, male, training_date_time, testing_date_time,
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training_length_hr, consolidation_length_hr, memory, age`.
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`session` is `"training"` or `"testing"`; `male` is `"trained"` or
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`"naive"` (canonical — variants like `"naïve"` and `"niave"` are normalized
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at the TSV-export step).
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## Column Reference (`distances.csv`)
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- `date`, `machine_name`, `ROI`, `session`: identifies one fly trajectory
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- `t`: time in ms within that session
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- `distance`: Euclidean distance between the two flies in pixels
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- `n_flies`: number of fly detections at this frame (1 or 2)
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- `area_fly1`, `area_fly2`: bounding-box areas (`w * h`) in pixels²
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- `male`: `trained` or `naive` (carried from the xlsx; normalized)
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- `species`, `memory`, `age`: experimental metadata
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