aod-diagnostics-and-tooling

Testing & Quality

Use when you need to MEASURE what Area Occupancy Detection is actually doing instead of guessing — downloading and reading the HA diagnostics export, interpreting a health/repair issue, reading hourly-analysis debug logs, or using the simulator to compare a predicted probability against the live sensor. Load this before answering "why is this area stuck occupied", "is the prior learned correctly", "what does this repair mean", or "how do I reproduce this probability by hand".

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AOD Diagnostics and Tooling

What this covers

The four instruments this project has for observing real runtime state instead of eyeballing code: the HA diagnostics export (a JSON snapshot, no debug logging required), the health/repairs system (structured anomaly detection, not just log noise), debug log interpretation (which lines mean what during the hourly analysis pipeline), and the simulator (feed a scenario, compare a predicted probability against reality). It also ships three tested, read-only scripts against the real SQLite schema for answering "what did the DB actually learn" without waiting for a diagnostics export.

When NOT to use this

For the underlying Bayesian math those numbers come from (sigmoid pipeline, logit combination, decay formula), use bayesian-occupancy-reference. For why a number is wrong and how to fix the code, use aod-debugging-playbook. For interpreting DB schema/columns in general (not diagnostics-specific), see aod-architecture-contract. For the open-ended campaign to actually improve prior/likelihood accuracy using these tools, see aod-learning-accuracy-campaign.

1. The diagnostics export

How to download: Home Assistant UI → Settings → Devices & Services → click the Area Occupancy Detection integration card → ⋮ menu → Download diagnostics. Produces a JSON file. Implemented in custom_components/area_occupancy/diagnostics.py (async_get_config_entry_diagnostics); user-facing walkthrough already exists at docs/docs/technical/diagnostics.md — this section is the ground-truth field reference, cross-checked against the source.

Each area subsection is captured in its own try/except (diagnostics.py:_area_snapshot) — if one subsection fails, you get a sibling <section>_error key instead of losing the whole dump. That design choice matters when triaging: a partial diagnostic is still useful, read past the error key for the rest.

Top-level shape

{
  "integration": { ... },
  "areas": [ { ... one per configured area ... } ],
  "database": { ... }
}

`integration`

Field	Meaning
`version`	`DEVICE_SW_VERSION` (matches `manifest.json`)
`config_version` / `config_version_minor`	Config-entry schema version (`CONF_VERSION`/`CONF_VERSION_MINOR`) — a different versioning axis from the release version, used by `migrations.py`
`entry_id` / `entry_title`	HA config entry identifier and label
`setup_complete`	`true` once the coordinator finished its first analysis cycle
`area_count`	Number of configured areas
`sleep_start` / `sleep_end`	Configured sleep window (integration-level)
`people_count`	Number of configured people for sleep-presence tracking

`areas[]`

Each entry has area_name, area_id, purpose, threshold, then these subsections:

`current`

The live snapshot at the moment the diagnostic ran.

Field	Meaning
`probability`	`area.probability()` — computed occupancy probability, 0.0-1.0
`occupied`	`probability >= threshold`
`decay_factor`	Average decay multiplier across all entities
`active_entity_count` / `decaying_entity_count` / `entity_count`	Sensor counts
`adjacency`	Present only if the adjacent-areas feature has fired this tick — see below

adjacency sub-block (shipped in PR #454, merged 2026-07-06 — now on main. Omitted entirely, not a null field, when there's no boost/modifier yet — e.g. before the first update() tick, or the area has no configured neighbours):

Field	Meaning
`boost.fired`	Whether a Bayesian logit-space boost was applied this tick
`boost.trajectory_prev` / `trajectory_prev_prev`	The 2-hop trajectory (last two recently-occupied neighbour areas) used to look up the transition probability
`boost.hour_of_week`	0-167 bucket (day-of-week × 24 + hour) used for the lookup
`boost.raw_probability`	The learned/fallback transition probability before gain is applied
`boost.fallback_level`	Which of the 6 smoothing levels answered the lookup — `2hop_hour_of_week`, `2hop_hour_of_day`, `2hop_unbucketed`, `1hop_hour_of_week`, `1hop_unbucketed`, or `static_default` (falls back to the hand-configured `influence_weight`, observed/total forced to 0 to signal "no learned data")
`boost.observed_count` / `total_count`	How many observations backed the chosen level
`boost.logit_contribution`	`ADJACENCY_BOOST_GAIN (0.5) * (logit(raw_probability) - logit(0.5))` — the actual amount added to the area's logit-space probability this tick
`decay_modifier.fired`	Whether a decay half-life stretch was applied
`decay_modifier.silence_score`	Σ over silent neighbours of `(1 - P_neighbour_lagged) * P(target→neighbour \| trajectory)`, clamped to [0,1]
`decay_modifier.decay_modifier`	`min(1 + 0.75*silence_score, 1.75)` — the multiplier stretching this area's decay half-life (max +75%)
`decay_modifier.silent_neighbours[]`	Per-neighbour `(neighbour, lagged_probability, transition_probability)` breakdown — read this to see which neighbour is holding this area's decay open

A boost.fallback_level of static_default with observed_count/total_count both 0 means the adjacency feature has no learned data for that pair yet — it's using the flat config-time influence_weight, not learned behavior.

`prior`

From Prior.diagnostic_snapshot() (data/prior.py) — surfaces which term is driving occupancy, most useful when an area looks "stuck" with no active evidence:

Field	Meaning
`prior_value`	Effective prior used in the current calculation (post-combination, post-floor)
`global_prior`	Long-run learned probability (`null` if not learned yet)
`time_prior`	Day-of-week + time-slot specific learned prior
`min_prior_floor_applied`	`none`, `purpose`, or `override` — which floor (if any) raised the value. A non-`none` floor here plus a probability sitting suspiciously close to `threshold` is a strong signal the floor (not learned evidence) is what's keeping the area "occupied"
`threshold`	Configured occupancy threshold for the area

A floor can never push the prior to or above threshold by itself (capped at threshold - 0.01, see data/prior.py, fix for issue #435) — so if the area actually reads occupied purely from prior with no evidence, that came from a genuinely learned value, not the floor.

`config`

Sensor counts (not entity IDs) per type, decay settings, wasp-in-box settings, per-type weights, min_prior_override, motion_timeout. Shape only — cross-reference aod-config-and-flags for what each value means.

`entities[]`

One row per configured sensor:

Field	Meaning
`entity_id`	The HA entity
`input_type`	`motion`, `media`, `door`, `temperature`, etc.
`weight`	Configured per-type weight (not `effective_weight` — the diagnostic exposes raw `weight`; `effective_weight = weight * information_gain` is computed at calculation time, not persisted here)
`prob_given_true` / `prob_given_false`	Likelihoods used in the Bayesian update — learned via correlation analysis if available, else the `EntityType` default
`evidence`	Current state contribution: `true` / `false` / `null` (unavailable/unknown)
`previous_evidence`	What it was before the last transition — used by `has_new_evidence()` to decide whether to start/stop decay
`last_updated`	ISO-8601 UTC timestamp of the last evidence transition
`analysis_error`	Why correlation analysis was skipped or failed. Verified exact values (`data/entity_type.py::AnalysisStatus`): `not_analyzed` (hasn't run yet), `motion_sensor_excluded` (by design — motion always uses configured priors, never learned correlation), `analyzed` (ran; check `correlation_type`/`correlation_strength` for the result). Correlation-failure strings (`too_few_samples`, `no_occupied_intervals`, etc.) live in `db/correlation.py::CORRELATION_FAILURE_ERRORS`, a different, larger set than the 3-value `AnalysisStatus` enum — don't conflate the two when reading this field
`correlation_strength`	Cached learned Pearson correlation or binary-likelihood strength (`null` if not learned)
`correlation_type`	`strong_positive`, `positive`, `strong_negative`, `negative`, `none`, or `binary_likelihood`
`learned_active_range`	Learned numeric active-range override for environmental sensors (`null` if not learned)
`learned_gaussian_params`	`mean_occupied`/`std_occupied`/`mean_unoccupied`/`std_unoccupied` for continuous-likelihood sensors (`null` if not learned)
`decay.is_decaying` / `half_life` / `decay_start` / `decay_factor`	Current decay state for this entity

`health`

The cached output of the last health check (no re-check triggered) — see §2 for the full issue-type/threshold reference. Same array shape as the HA Repairs UI shows; issue_type distinguishes sensor-scope (stuck_active, etc., with entity_id/input_type populated) from pipeline-scope (insufficient_priors, etc., both null).

`database`

Field	Meaning
`interval_count`	Total occupancy-state interval rows
`prior_count`	Time-prior rows across all areas (compare against `area_count * 168` to see time-prior learning completeness)
`correlation_count`	Stored correlation rows
`entity_count` / `area_count`	Persisted entity / area row totals
`occupied_intervals_cache`	Per-area `{ "valid": true/false }` — `false` means the next hourly analysis cycle rebuilds it

Privacy

Contains: HA entity IDs, current sensor evidence, learned priors/weights, config shape, area names. Does not contain: HA access tokens, user account info, raw recorder history. Entity IDs/area names are still user-identifying strings — review before attaching to a public issue.

2. Health / repairs as a diagnostic

Implemented in custom_components/area_occupancy/data/health.py. Two independent check families, both surfaced as HA repair issues (Settings → Repairs) and both cached into the diagnostics export's health section without re-running:

Sensor-scope (per-entity): stuck_active, stuck_inactive, unavailable, never_triggered
Pipeline-scope (per-area, entity_id/input_type both null): insufficient_priors, stale_intervals_cache, slow_analysis, correlation_failures

InputType.SLEEP is excluded from all health checks (_EXCLUDED_TYPES). media_player.* entities are exempt from the unavailable check specifically (TVs powering off is normal, not a fault — this was the #466 complaint).

Sensor-scope thresholds

Check	Base threshold	Purpose multiplier	Notes
`stuck_active` — motion	8h	×6 sleeping / ×4 relaxing / ×3 working	Was 2h; raised because mmWave/presence sensors legitimately stay on for hours in bedrooms/offices (#465, #468)
`stuck_active` — media	12h	same multiplier table
`stuck_active` — appliance	24h	same
`stuck_active` — door	48h	same
`stuck_active` — window	72h	same
`stuck_active` — cover	24h	same
`stuck_inactive` — motion	7 days	none
`stuck_inactive` — media	14 days	none
`stuck_inactive` — appliance	28 days	none
`stuck_inactive` — door/window/cover/power	14 days each	none
`unavailable`	1h	none	Clock starts from first-seen-unavailable this HA session, not persisted `last_updated` — avoids false trips when a source integration is just slow to load at startup
`never_triggered`	7 days	none	Uses persisted `last_updated`; only for `_STUCK_CHECK_TYPES` (binary + power + motion + cover)

Purpose multiplier table (_PURPOSE_STUCK_ACTIVE_MULTIPLIER, applies only to stuck_active): SLEEPING ×6 (8h→48h), RELAXING ×4 (8h→32h), WORKING ×3 (8h→24h). Purposes not listed use the base threshold (multiplier 1.0).

Pipeline-scope thresholds

Check	Threshold	Grace period
`insufficient_priors`	No `global_prior` after grace	7 days (`PRIORS_TRAINING_GRACE_PERIOD`)
`stale_intervals_cache`	Cache older than 25h, or never populated past grace	Same 7-day grace for the "never populated" case
`slow_analysis`	Last full cycle > 180,000ms (3 min)	n/a — deliberately conservative; large installs can legitimately exceed 30s on a first warm cycle
`correlation_failures`	≥50% of correlatable entities have a real (not soft/expected) `analysis_error`	Same 7-day grace

Exemptions / suppression the user controls

Ignored repairs survive condition flaps. If a user clicks "Ignore" in HA's Repairs UI, the monitor won't delete-then-recreate the issue when the underlying condition briefly clears and recurs (e.g. TV unavailable again the next night) — HealthMonitor._is_ignored() checks IssueEntry.dismissed_version is not None and preserves the ignore across flap cycles (fix for the pattern behind #472/#473).
Integration-level off-switch. health_enabled (coordinator-level toggle, PR #472) — when False, clear_all_issues() deletes every active repair without resetting the in-memory unavailable-since clock, so re-enabling later doesn't cause every currently-unavailable sensor to trip instantly.
There is no per-sensor suppression yet (requested in open issue #466) and no vacation-aware suppression yet (requested in open issue #485) — don't describe either as shipped; re-check with gh issue view 466 / gh issue view 485.

3. Debug log interpretation

Enable per CLAUDE.md's standard snippet:

logger:
  default: info
  logs:
    custom_components.area_occupancy: debug
    custom_components.area_occupancy.db: debug

Step timings (every hourly analysis cycle)

Each pipeline step logs its own timing (data/analysis.py::_run_step):

Step 7: recalculate_priors completed in 842.13 ms

On failure: Step %d: %s FAILED in %.2f ms (via _LOGGER.exception, so a full traceback follows). At the end of a full cycle:

Full analysis completed: 12/12 steps succeeded in 4213.87 ms

or, with failures: Analysis completed: 11/12 steps succeeded (FAILED: correlation_analysis) in ... ms. A cycle cancelled by EVENT_HOMEASSISTANT_STOP mid-run logs Analysis cancelled mid-run after ... ms — shutdown in progress instead — deliberately not counted toward the slow-analysis health threshold.

On main (post PR #454, merged 2026-07-06) the pipeline is 13 steps, in order: sync_states, health_check_and_prune (pruning + integrity), sensor_health_check, populate_occupied_intervals_cache, interval_aggregation, numeric_aggregation, recalculate_priors, correlation_analysis, transition_learning, pipeline_health_check, save_data_before_refresh, refresh_coordinator, save_data_after_refresh. Totals log as 13/13. Re-verify: grep -n "total_steps" custom_components/area_occupancy/data/analysis.py.

Prior calculation — the "Period calculation" line

The single most useful debug line for prior-accuracy debugging (data/analysis.py, inside calculate_and_update_prior):

Period calculation for area %s: first_interval_start=%s (tz=%s), last_interval_end=%s (tz=%s), now=%s (tz=%s)

This is exactly the line that would have exposed issue #483 (global prior inflating during quiet periods) before it was diagnosed from code — if last_interval_end is far in the past relative to now and the stored prior still looks too high, check which formula produced it (see script #2 below). SETTLED: PR #491, merged 2026-07-06, changed the period-end logic so actual_period_end is now always now — it no longer silently truncates to last_interval_end and the quiet-tail inflation bug (#483) is closed. Verified on main: actual_period_end = now unconditionally (data/analysis.py); the old actual_period_end = last_interval_end behavior is gone. Kept here as dated history because the "Period calculation" line is still the right first place to look if a similar quiet-period discrepancy ever resurfaces.

Companion debug lines in the same function: Prior calculation for area %s: %d merged intervals, %.1f hours total duration (logged early, before period math) and Prior analysis completed for area %s: global_prior=%.3f (occupied: %.1f hours over %.1f days, %d intervals) (logged at the end, the final answer). Reading these three lines together tells you the full story of one area's prior calculation without touching the DB.

Interval query performance

db/queries.py::get_occupied_intervals logs Interval query executed in %.3fs for %s (total=%d, motion=%d) and Unified occupancy calculation for %s: %d raw -> %d merged intervals (processing: %.3fs) — useful for spotting a slow correlation/prior recalculation caused by index gaps or an oversized intervals table before assuming it's a math bug.

4. The simulator as a measurement instrument

Located at simulator/ (Flask backend, app.py) + a browser UI served from the MkDocs site (docs/docs/simulator/simulator.html). Workflow:

In HA, call the area_occupancy.run_analysis service (Developer Tools → Actions, or automations) — it forces an immediate analysis cycle and returns YAML/dict output per area: current_probability, current_occupied, current_threshold, current_prior, global_prior, time_prior, prior_entity_ids, entity_states, likelihoods (see custom_components/area_occupancy/service.py::_build_analysis_data).
Run the simulator locally: python main.py from repo root (listens on 0.0.0.0:5000 by default; PORT env var overrides), then either open the simulator UI directly or cd docs && mkdocs serve and browse to /simulator/, pointing its API Connection field at http://127.0.0.1:5000.
Paste the run_analysis output, click Load Simulation — this reconstructs the area's Entity objects and priors inside the simulator process.
Toggle binary sensors / adjust numeric values and read the recomputed probability plus the per-entity contribution breakdown (calculate_probability_breakdown in simulator/app.py) to see which sensor is driving the number.

Important caveat: the simulator does not run production's math

The simulator's sensor-fusion step calls the legacy dead-code bayesian_probability() path, not the live sigmoid/logit pipeline — see aod-run-and-operate §6 for the full verified explanation (PR #353 history, what is and isn't shared). Measurement consequence here: treat simulator probabilities as a relative sensitivity tool (which sensor moves the number, which direction, roughly how much), never as an exact stand-in for the live probability sensor. For exact production numbers, read the live sensor or the diagnostics export's current.probability.

5. Scripts (in `scripts/`)

Three read-only scripts, tested against a scratch SQLite DB built from the real schema (db/schema.py) during authoring — no test databases are committed. Each takes a path to a real area_occupancy.db (default location: config/.storage/area_occupancy.db inside the HA config dir) and uses only the Python stdlib sqlite3 module — no Home Assistant import required, so they run in any Python 3.10+ environment.

Script	Answers	Requires
`dump_priors.py <db> <area>`	What did the area learn — global prior + all 168 time-of-week buckets?	`global_priors`, `priors` tables (present on `main` today)
`prior_quiet_tail_diff.py <db> <area> [--now ISO]`	Is this area's stored prior exhibiting the #483 quiet-tail-inflation pattern? Recomputes both the pre-#491 and post-#491 formulas by hand and diffs against what's stored	`intervals`, `entities`, `global_priors` tables (present on `main` today)
`transitions_summary.py <db> [--top N]`	How much has adjacency learning actually observed — which chains, how recent, do they clear the smoothing-fallback thresholds?	`area_transitions` table — shipped with PR #454, merged 2026-07-06, present on `main` today; the script still detects a missing table and says so rather than crashing (useful against older DBs created pre-migration)

Each script's own docstring has a full usage line, example output, and an interpretation guide — read the script header before running it. Run directly, e.g.:

python .claude/skills/aod-diagnostics-and-tooling/scripts/dump_priors.py \
  config/.storage/area_occupancy.db "Living Room"

Do not point these at a live HA instance's DB file while HA is running without copying it first — SQLite allows concurrent readers, but copy the file if you want a stable snapshot to diff against a second run.

Provenance and maintenance

Re-verified 2026-07-06 against main at 17b71d2 (post merge-wave; integration release version is still 2026.5.17 in pyproject.toml line 7 / manifest.json line 20 / const.py DEVICE_SW_VERSION — none of this wave's merges are in a tagged release yet). feat/adjacent-areas (PR #454) merged into main 2026-07-06 — the AreaTransitions table, adjacency diagnostics sub-block, and the six-level transition-lookup smoothing described in §1/§5 are now on main and generally available; the feature remains unvalidated on real homes (still worth treating as a candidate signal, not ground truth, until it's watched running for a while).

Re-verification commands, by volatile fact category:

Diagnostics export shape: sed -n '1,410p' custom_components/area_occupancy/diagnostics.py (re-read _area_snapshot, _entity_snapshot, _adjacency_snapshot)
Health thresholds: grep -n "THRESHOLD\|MULTIPLIER" custom_components/area_occupancy/data/health.py
Adjacent-areas / PR #454 merge state: gh pr view 454 --json state,mergeStateStatus,mergeable (confirmed MERGED 2026-07-06)
Prior quiet-tail fix / PR #491 merge state: gh pr view 491 --json state,mergeable (confirmed MERGED 2026-07-06; actual_period_end = now unconditionally on main)
AnalysisStatus exact values: grep -n "NOT_ANALYZED\|MOTION_EXCLUDED\|ANALYZED =" custom_components/area_occupancy/data/entity_type.py
Correlation failure-error set: grep -n "CORRELATION_FAILURE_ERRORS" -A 20 custom_components/area_occupancy/db/correlation.py
Adjacency constants: grep -n "ADJACENCY_" custom_components/area_occupancy/const.py
Simulator legacy-math caveat still true: grep -n "bayesian_probability" simulator/app.py (confirms it's still imported/called) and git log --oneline -- simulator/app.py (confirms no post-#353 migration commit)
Open per-sensor-suppression / vacation-suppression issues: gh issue view 466, gh issue view 485
Scripts still run clean against current schema: re-run each script's usage example against a fresh scratch DB built from custom_components/area_occupancy/db/schema.py::Base.metadata.create_all()