v0.2.5
Key updates:
- Network scoring system and modification before interferogram generation. Based on S1 global coherence dataset and weather data
- add quick_overview in mintpy for quality control
- re-oragnize fastaoi into seperate sub modules
Pair Quality Database:
- DB no longer rebuilds when pair selection parameters change (dt_max, pb_max, min_degree, etc.) — only rebuilds when the scene set actually changes
- Removing scenes from the network no longer triggers a rebuild; the existing DB already covers all subsets
- DB now stores _scene_names for exact scene-set comparison instead of relying on count alone
- Backward compatible: old DBs without _scene_names fall back to count comparison and migrate on the next rebuild
Performance - S1 global coherence prefetch is now parallelized in two phases: season tile S3 downloads run concurrently (up to 4 threads), followed by per-pair numpy coherence evaluation in parallel (8 threads). Expected speedup for 32,000+ pairs: 4–6× on first run, with warm cache unchanged
select_pairs: Avoid Bad Acquisition Days (avoid_low_quality_days)
- Default changed to True — bad-weather scenes are excluded from the network by default
- Default precipitation threshold tightened to 25 mm (3-day accumulation)
- Weather and snow data fetched during bad-scene filtering is now seeded directly into the pair quality cache — the scorer reuses it instead of making a second round of API calls, eliminating duplicate fetches entirely
- select_pairs returns a prefetch_cache containing the pre-fetched weather/snow keyed by stack, which _run_folder_select_pairs writes into each subfolder's CacheManager before scoring begins
- Status message "Building weather/snow cache — P{path}/F{frame}…" shown in the GUI while seeding so the user can see progress during long cache writes
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v0.2.5
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Full Changelog: v0.2.4...v0.2.5