When --jwt-secret / BINCIO_AUTH_JWT_SECRET is set, auth is validated
locally by decoding the bincio-auth-issued JWT — no DB session lookup.
Falls back to existing DB-based session lookup when the flag is absent,
so standalone deployments keep working without any config change.
Changes:
- deps.py: add jwt_secret global, _decode_jwt helper, wire into
_current_user and _require_auth
- cli.py: add --jwt-secret option; log active auth mode on startup
- pyproject.toml: add PyJWT>=2.8 to serve and dev extras
API (gear.py):
- POST /api/gear/{id}/parts
- PATCH /api/gear/{id}/parts/{pid}
- DELETE /api/gear/{id}/parts/{pid}
- POST /api/gear/{id}/parts/{pid}/replacements
- DELETE /api/gear/{id}/parts/{pid}/replacements/{rid}
UI (AthleteView.svelte):
- Gear rows are now accordion-expandable
- Collapsed row shows colored status dots (green/yellow/red) per part
- Expanded section: parts list with km-since-replacement colored by threshold,
Replaced button with date+note form, recent log entries, add-part form
- Contextual suggestion for first part (chain for bikes, shoes for running)
- Edit/delete gear moved into expanded section
Move gear backfill logic from the route handler into
import_garmin_gear(data_dir, user_dir) in garmin_sync.py so it can be
called both from the API and from the CLI script.
scripts/backfill_garmin_gear.py finds all users with Garmin credentials
and runs the backfill for each, printing a per-user summary.
Compute total distance from allActivities where gear name matches and
display it inline next to each gear item. Also add gear field to
ActivitySummary type so index shard gear data is accessible in the UI.
- garmin_sync_iter: sync gear registry from Garmin on every sync run and
resolve gear for each newly imported activity via get_activity_gear()
- POST /api/garmin/import-gear: one-time backfill that matches Garmin gear
activities to existing local activities by UTC timestamp (±60 s)
- New /api/gear CRUD endpoints (gear.json per user)
- Gear tab in AthleteView (owner-only): add, edit, retire items
- EditDrawer gear field becomes a dropdown when registry has items
- Strava API sync now resolves gear_id → name, adds to registry automatically
- Strava ZIP import reads Gear column from activities.csv
- POST /api/strava/import-gear for one-time backfill from stored originals
Previous thresholds (10 m GPS, 5 m barometric, 30 s MA) were calibrated for
raw noisy GPS. Strava-exported FIT files carry elevation already pre-processed
by Strava (smooth 1 m quantisation, no steps > 5 m), so the aggressive
filtering suppressed real climbing — avg −17 % error across 37 reference
activities.
New strategy, keyed on source + altitude_source:
strava_export → MA 5 s, threshold 1.0 m
fit_file / barometric → no MA, threshold 1.5 m
fit_file / gps → MA 5 s, threshold 2.0 m
unknown non-strava → MA 5 s, threshold 1.5 m
Result on 37 cross-referenced activities: avg −2.8 %, std 4.6 %,
37/37 within ±15 % (was 0/37).
Both paths — initial import (metrics._elevation) and bulk recalculate
(dem.recalculate_elevation_hysteresis) — now use the same elevation_params()
function from metrics.py.
Adds a List/Map toggle to the feed and @user profile pages. The map view
plots all filtered activities as sport-coloured tracks on a MapLibre map
with no extra requests (uses preview_coords already in memory). Clicking
a track or list row selects it: pans the map to fit, expands the list
item with key stats, and scrolls it into view.
Matches the Strava sync table layout. Accumulates total_imported in
garmin_sync.json state on each sync run; admin API exposes last_sync_at
and total_imported from that file.
Extract _haversine_m from the inline block in _gps_speed_kmh, add
_spatial_downsample (keep one sample per 10 m traveled, GPS haversine
primary / speed×Δt fallback, indoor activities unchanged), and wire it
into build_timeseries() after the 1 s dedup loop.
Add --downsample-timeseries migration flag to bincio render that applies
the same downsampling to existing stored timeseries files without
re-extracting from original FIT/GPX files.
Extract the synchronous segment-file scan into a plain function and
dispatch it via asyncio.to_thread so it runs in a thread pool instead
of blocking the event loop during concurrent fetches.
Write the activity summary to sessionStorage on click in ActivityFeed,
then read it synchronously at module init in ActivityDetailLoader so the
page renders immediately without the "Loading activity…" blank screen
or the 2 round-trip index fetch.
Direct URL / bookmark / shared link falls through to the existing slow
path unchanged.
explore lives at /u/{handle}/athlete/explore/ — was classified as
profile. Add path-contains check so it's detected correctly.
Add community (/community/) which was falling into the feed catchall.
Extend feature map tuples to (host, startswith, contains, label).
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
resample("D") produces midnight-aligned bins; reindexing against a
range built from a raw timestamp (not midnight) caused all values to
be 0. Also switched the logins panel from daily to weekly to match the
feature usage panel — less noisy for a small app.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
scripts/usage_stats.py: standalone script (PEP 723, runs via uv run)
that parses all nginx access.log files, filters bots, maps Referer
headers to feature labels, and produces a 3-panel matplotlib figure:
daily logins + 7-day rolling mean, hour×weekday API heatmap, and
weekly feature usage stacked area. Output saved to
/var/bincio/stats/latest.png. Intended for a weekly cron job.
bincio/serve/routers/admin.py: GET /api/admin/stats serves the PNG
via the existing _require_admin() check — no new auth logic or nginx
changes needed.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Replace time-based rolling mean with a 400 m distance-weighted sliding-
window average (O(n), two advancing pointers) matching the spec in
SLOPE_COLORING.md. Slope values are now spatially consistent regardless
of riding speed. Smoothing buttons are hidden when the slope tab is
active.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
New "Slope" tab colours the filled area and stroke line with an SVG
linearGradient driven by per-point slope data (green→yellow→orange→red→
purple scale). Slope is computed from smoothed elevation + cumulative
distance, reusing the existing raw/10s/20s smoothing controls. The
hover tooltip shows slope % (in slope colour) and elevation. Tab is
enabled only when both elevation and distance data are present; the
X-mode and histogram toggles are hidden for this tab.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Reads each activity's timeseries, re-runs the VAM algorithm (which now returns
both climbing_vam_mh and climbing_time_s), and patches activities/*.json and
index.json in-place. Run once after upgrading to the new schema so NerdCorner
can filter and opacity-encode existing data.
- Exclude per-activity VAM contributions where climbing_time_s < 10 min; short
punchy efforts don't represent aerobic fitness and were skewing monthly averages
- Store climbing_time_s alongside climbing_vam_mh in metrics, detail JSON, and
summary JSON so the frontend has the data to reason about confidence
- Accumulate total climbing time per period; opacity scales from 0.25 (10 min,
minimum threshold) to 1.0 (≥ 1 h) so thin-evidence months read as faint dots
- Render VAM as dots only (no lines) since each period is an independent average,
not a cumulative — lines implied continuity that isn't there
- Tooltip now shows "1060 m/h · 38 min climbing"
Layout: map + charts stacked left, stats panel (2-col) on the right.
Cadence moved to last stat. Charts sit directly below the map.
Speed coloring: most FIT files don't record per-second speed, leaving
timeseries speed_kmh all-null and the hover link dead. Fix: derive speed
from consecutive GPS coordinates (haversine + 5-pt moving average) when
the device didn't record it. Add --backfill-speed render flag to retrofit
existing timeseries files.
statColorMode() hid hasSpeedTrack etc. from Svelte's compiler so the
{#each} block never re-rendered when timeseries loaded. Inline the
ternary directly so all reactive variables are visible to the tracker.
Refactor the metric gradient code into shared _computeProgress +
_buildGradient helpers. Hovering any of speed/HR/power/elevation/cadence
stats switches the track to a blue→green→yellow→red gradient for that
metric. A small legend (min ·gradient bar· max + label) appears in the
bottom-left corner of the map while active. Absolute elevation used
(not slope), so blue=valleys, red=peaks.
Hovering avg/max speed in the stats panel switches the track from the
default gradient to a blue→green→yellow→red speed gradient. Progress
along the track is computed from cumulative distance (speed × time) so
fast sections appear proportionally long rather than time-weighted.
Reverts to default on mouse-leave. No-op when timeseries has no speed
data or the activity has no GPS track.
Find the activity that holds each MMP record by scanning per-activity
mmp arrays. Activity title appears in the chart hover tooltip. A table
below the chart lists every duration with the record watts, activity
title (linked), and date. The table has its own all-time/365d/90d toggle
independent of the chart overlays.
Davide Scaini