trainlog/docs/android.md

Android application

1. Purpose

The Android application is Trainlog's low-friction capture client.

It is a native Kotlin/Jetpack Compose application with local SQLite persistence.

The desktop remains the canonical long-term history and analytics store.

2. Implemented navigation

Accueil
├── Reprendre la séance en cours (si un brouillon existe)
├── Enregistrer une séance
├── Enregistrer un exercice
├── Enregistrer des mensurations
├── Historique des séances
└── Synchroniser avec le PC

3. Local persistence

Android local database version:

4

Domain tables cover:

exercises
sessions
session_exercises
performed_sets
continuous_activity
body_observations

This database is Android-local. It is not copied to the PC.

Schema v4 adds active_session_draft, draft_session_exercises, draft_performed_sets and draft_continuous_activity. The additive v3 -> v4 migration preserves catalog, completed sessions/actuals and body observations. Exactly one active draft is supported; it is separate from completed history.

4. Exercise catalog

Exercise creation records:

name
recording_mode
tracking_mode
data_fields

Stable identity:

ex_<uuid-v4>

The UI rejects invalid profile combinations and local normalized-name collisions.

An exercise may be created standalone or inline while building a session.

Editing an exercise

Every existing catalog item exposes Modifier. Editing a name trims its input, recomputes normalized_name, and rejects a normalized-name collision. The row retains its existing exercise_id; naming is presentation metadata, not identity. Completed session rows and the active draft retain their catalog row relationship and immediately resolve the renamed display text after reopen.

Profile fields (recording_mode, tracking_mode, data_fields) are editable only while an exercise has no completed-session or active-draft reference. Once referenced, Android displays the lock and returns an explicit incompatible profile result rather than silently reinterpreting work or creating another exercise. Renaming remains available independently.

The shared Compose TrainlogScreen header is used by Accueil, Séance, Exercice, Mensurations, Historique, Détail séance and Sync. Its compact ◆ TRAINLOG ◆ accent plaque and muted subtitle intentionally mirror the Notcurses TUI identity in a flat mobile layout.

5. Session recording

Stable session identity:

se_<uuid-v4>

Session entry is profile-aware.

Sets + repetitions

Actual set values may be heterogeneous.

Compact entry supports:

5x10
4,5,6,7,8,9,10,9,8,7,6,5,4
4..10..4

Sets + duration

Each performed set stores its own duration.

Continuous + duration

The form asks for duration and only the configured supplemental fields such as speed or distance.

Continuous work does not create fake sets.

6. Session draft editing

The repository durably saves every meaningful mutation, including session type, exercise selection/addition/removal, actual values and raw form edits. Partial text such as 4,5,6, is retained without normalization. A failed write displays a specific error and does not claim the latest change was saved.

Home shows Reprendre la séance en cours and an exercise-count/type summary. The ordinary new-session action opens an existing draft without overwriting it. Back returns Home and preserves the draft. Backgrounding, switching apps, Activity/configuration recreation, background process death and force-stop with relaunch preserve the draft; these paths were validated on the Samsung SM_G990B.

Retirer removes only that draft exercise and its actual values. It does not change the catalog or completed history. Removal survives restart. Supprimer la séance en cours requires deliberate confirmation; cancellation preserves the draft. Confirmed deletion leaves no completed session or stale resume action after relaunch.

Final save validates the durable draft, inserts the completed session and actual values, and removes the draft in one SQLite transaction. Failure rolls back and retains the draft for retry; repeated completion does not create duplicates. The existing completed-session save-time timestamp behavior is unchanged.

PC catalog reconciliation preserves draft references through catalog row ownership. If an editing selection no longer resolves, only the selection is cleared; added exercises and raw text remain, with a specific diagnostic. When a received or exported catalog entry has the same exercise_id, a changed display name is reconciled in that same row. A different-ID normalized-name collision is rejected, so a rename cannot become a duplicate exercise.

7. Session history

Android exposes persisted local session history and profile-aware detail.

Set-based history renders ordered performed sets.

Continuous history renders its one activity record with configured supplemental values.

8. Body measurements

Supported metrics:

weight
neck
shoulders
chest
waist
hips
left/right arm
left/right forearm
left/right thigh
left/right calf

Rules:

empty field = not measured
at least one positive metric required
comma or dot accepted for decimal entry

Stable identity:

bo_<uuid-v4>

9. Automatic mobile snapshot

Android maintains:

Download/Trainlog/trainlog-mobile-export-v1.json

The snapshot is refreshed after relevant local changes, including exercise, session, body-observation, and PC-catalog updates.

The user does not need a separate manual export step before synchronization.

An active draft is never included in completed history, session detail or this snapshot. Synchronization continues to exchange completed data while the draft stays local; no draft fields were added to the frozen mobile artifact.

10. PC catalog access

PC-created files are accessed through a persistent Storage Access Framework grant.

The selected folder must be:

Download/Trainlog

The Sync screen always permits changing the stored folder selection.

No application-data reset is required to fix a wrong folder choice.

11. Android-triggered synchronization

The Sync screen exposes:

Synchroniser maintenant

Android writes:

trainlog-sync-request-v1.json

and waits for a matching:

trainlog-sync-receipt-v1.json

The receipt is matched by request_id.

On success Android then applies the latest PC catalog and displays the final result.

A receipt belonging to another request is ignored as pending rather than misreported as the current result.

12. Synchronization ownership

Android does not initiate raw MTP operations itself.

MTP is host-initiated:

Android request
    -> PC trainlog-syncd
    -> shared desktop sync engine
    -> receipt

13. Build

Example local configuration:

cd android

printf 'sdk.dir=%s\n' "$HOME/Android/Sdk" > local.properties

JAVA_HOME=/usr/lib/jvm/java-17-openjdk \
./gradlew assembleDebug

Install to a connected test device:

adb install -r app/build/outputs/apk/debug/app-debug.apk

local.properties is local machine configuration and must not be committed.

Host regression suite: 8 tests. Device instrumentation: 5 tests (2 repository, 3 production-screen UI tests using an isolated database and no shared export). The real device matrix additionally exercised production MainActivity, including verified process exit with am kill, force-stop, configuration relaunch, raw-form recovery, removal, discard and unchanged user data. Final-save UI checks use isolated data so fictitious workouts do not enter user history. See tests for commands and the precise validation boundary.

14. Non-goals

Android is not intended to own:

• canonical long-term analytics;
• complex body/performance graphs;
• cloud accounts;
• direct SQLite-file synchronization;
• exercise-name heuristics;
• a mounted-filesystem dependency.

15. Test-max sessions

Android session entry exposes:

Entraînement
Test max

The selection is persisted in the existing Android sessions.session_type column and exported in the mobile snapshot as:

training
max_test

History and detail visibly identify max-test sessions.

Selecting Test max is explicit metadata; Trainlog does not infer max tests from large repetition or duration values.

Android's current session form still records the exercise data fields it supports. Measured-max classification on the desktop uses only actual values that were truly captured and synchronized.