fix(pipeline): use sqlmesh plan --auto-apply instead of run

fix(pipeline): also update supervisor.py to use plan --auto-apply
Missed the Python supervisor module — same fix as supervisor.sh and worker.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
2026-03-06 22:34:58 +01:00 · 2026-03-06 22:33:59 +01:00 · 2026-03-06 22:33:17 +01:00 · 2026-03-06 15:51:36 +01:00 · 2026-03-06 15:38:42 +01:00 · 2026-03-06 14:45:45 +01:00
45 changed files with 671 additions and 434 deletions
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,7 +6,20 @@ The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.1.0/).

 ## [Unreleased]

+### Changed
+- **Unified `location_profiles` serving model** — merged `city_market_profile` and `location_opportunity_profile` into a single `serving.location_profiles` table at `(country_code, geoname_id)` grain. Both Marktreife-Score (Market Score) and Marktpotenzial-Score (Opportunity Score) are now computed per location. City data enriched via LEFT JOIN `dim_cities` on `geoname_id`. Downstream models (`planner_defaults`, `pseo_city_costs_de`, `pseo_city_pricing`) updated to query `location_profiles` directly. `city_padel_venue_count` (exact from dim_cities) distinguished from `padel_venue_count` (spatial 5km from dim_locations).
+- **Both scores on all map tooltips** — country map shows avg Market Score + avg Opportunity Score; city map shows Market Score + Opportunity Score per city; opportunity map shows Opportunity Score + Market Score per location. All score labels use the trademarked "Padelnomics Market Score" / "Padelnomics Opportunity Score" names.
+- **API endpoints** — `/api/markets/countries.json` adds `avg_opportunity_score`; `/api/markets/<country>/cities.json` adds `opportunity_score`; `/api/opportunity/<country>.json` adds `market_score`.
+- **Marktpotenzial-Score v3: H3 catchment lens** — addressable market (25pts) and supply gap (30pts) now use a regional H3 catchment (~15-18km radius, res-4 cell + 6 neighbours, ~462km²) instead of local city population and 5km court count. Mid-size cities surrounded by dense Gemeinden (e.g. Oldenburg) now score correctly. New output columns: `catchment_population`, `catchment_padel_courts`, `catchment_venues_per_100k`. Requires one-time `INSTALL h3 FROM community` in DuckDB on each machine.
+
+### Added
+- **Custom 404/500 error pages** — styled error pages extending `base.html` with i18n support (EN/DE). The 404 page is context-aware: when the URL matches `/markets/{country}/{city}`, it shows a city-specific message with a link back to the country overview instead of a generic "page not found".
+- **Map: city article indicators** — country overview map bubbles now differentiate cities with/without published articles. All cities retain score-based colors (green/amber/red); non-article cities are visually receded with lower opacity, dashed borders, desaturated color, and default cursor (no click). Tooltips show scores for all cities — article cities get "Click to explore →", non-article cities get "Coming soon". The `/api/markets/<country>/cities.json` endpoint includes a `has_article` boolean per city.
+
 ### Fixed
+- **Admin template preview maps** — Leaflet maps rendered blank because `article-maps.js` called `L.divIcon()` at the IIFE top level before Leaflet was dynamically loaded, crashing the script. Moved `VENUE_ICON` creation into the `script.onload` callback so it runs after Leaflet is available. Previous commit's `.card` `overflow: visible` fix remains (clips tile layers otherwise).
+- **Admin articles page 500** — `/admin/articles` crashed with `BuildError` when an article generation task was running because `article_stats.html` partial referenced `url_for('admin.article_stats')` but the route didn't exist. Added the missing HTMX partial endpoint.
+- **Silent 500 errors in dev** — `dev_run.sh` used Granian which swallowed Quart's debug error pages, showing generic "Internal Server Error" with no traceback. Switched to `uv run python -m padelnomics.app` for proper debug mode with browser tracebacks. Added `@app.errorhandler(500)` to log exceptions even when running under Granian in production.
 - **Pipeline diagnostic script** (`scripts/check_pipeline.py`) — handle DuckDB catalog naming quirk where `lakehouse.duckdb` uses catalog `lakehouse` instead of `local`, causing SQLMesh logical views to break. Script now auto-detects the catalog via `USE`, and falls back to querying physical tables (`sqlmesh__<schema>.<table>__<hash>`) when views fail.
 - **Eurostat gas prices extractor** — `nrg_pc_203` filter missing `unit` dimension (API returns both KWH and GJ_GCV); now filters to `KWH`.
 - **Eurostat labour costs extractor** — `lc_lci_lev` used non-existent `currency` filter dimension; corrected to `unit: EUR`.
--- a/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py
+++ b/extract/padelnomics_extract/src/padelnomics_extract/eurostat.py
@@ -70,8 +70,8 @@ DATASETS: dict[str, dict] = {
    },
    "lc_lci_lev": {
        # Labour cost levels EUR/hour — NACE N (administrative/support services)
-        # Stored in dim_countries for future staffed-scenario calculations.
-        "filters": {"lcstruct": "D1_D2_A_HW", "nace_r2": "N", "unit": "EUR"},
+        # D1_D4_MD5 = compensation of employees + taxes - subsidies (total labour cost)
+        "filters": {"lcstruct": "D1_D4_MD5", "nace_r2": "N", "unit": "EUR"},
        "geo_dim": "geo",
        "time_dim": "time",
    },
--- a/infra/supervisor/supervisor.sh
+++ b/infra/supervisor/supervisor.sh
@@ -33,7 +33,7 @@ do
        DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
            uv run --package padelnomics_extract extract

-        # Transform — plan detects new/changed models; run only executes existing plans.
+        # Transform — plan detects new/modified/deleted models and applies changes.
        LANDING_DIR="${LANDING_DIR:-/data/padelnomics/landing}" \
        DUCKDB_PATH="${DUCKDB_PATH:-/data/padelnomics/lakehouse.duckdb}" \
            uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply
--- a/infra/supervisor/workflows.toml
+++ b/infra/supervisor/workflows.toml
@@ -70,5 +70,5 @@ description = "UK local authority population estimates from ONS"

 [gisco]
 module = "padelnomics_extract.gisco"
-schedule = "monthly"
+schedule = "0 0 1 1 *"
 description = "EU geographic boundaries (NUTS2 polygons) from Eurostat GISCO"
--- a/src/padelnomics/supervisor.py
+++ b/src/padelnomics/supervisor.py
@@ -247,7 +247,7 @@ def run_shell(cmd: str, timeout_seconds: int = SUBPROCESS_TIMEOUT_SECONDS) -> tu


 def run_transform() -> None:
-    """Run SQLMesh — it evaluates model staleness internally."""
+    """Run SQLMesh — detects new/modified/deleted models and applies changes."""
    logger.info("Running SQLMesh transform")
    ok, err = run_shell(
        "uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply",
@@ -358,6 +358,8 @@ def git_pull_and_sync() -> None:
    run_shell(f"git checkout --detach {latest}")
    run_shell("sops --input-type dotenv --output-type dotenv -d .env.prod.sops > .env")
    run_shell("uv sync --all-packages")
+    # Apply any model changes (FULL→INCREMENTAL, new models, etc.) before re-exec
+    run_shell("uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply")
    # Re-exec so the new code is loaded. os.execv replaces this process in-place;
    # systemd sees it as the same PID and does not restart the unit.
    logger.info("Deploy complete — re-execing to load new code")
--- a/transform/sqlmesh_padelnomics/CLAUDE.md
+++ b/transform/sqlmesh_padelnomics/CLAUDE.md
@@ -56,27 +56,27 @@ Grain must match reality — use `QUALIFY ROW_NUMBER()` to enforce it.
 |-----------|-------|---------|
 | `foundation.dim_countries` | `country_code` | `dim_cities`, `dim_locations`, `pseo_city_costs_de`, `planner_defaults` — single source for country names, income, PLI/cost overrides |
 | `foundation.dim_venues` | `venue_id` | `dim_cities`, `dim_venue_capacity`, `fct_daily_availability` (via capacity join) |
-| `foundation.dim_cities` | `(country_code, city_slug)` | `serving.city_market_profile` → all pSEO serving models |
-| `foundation.dim_locations` | `(country_code, geoname_id)` | `serving.location_opportunity_profile` — all GeoNames locations (pop ≥1K), incl. zero-court locations |
+| `foundation.dim_cities` | `(country_code, city_slug)` | `serving.location_profiles` (city_slug + city_padel_venue_count) → all pSEO serving models |
+| `foundation.dim_locations` | `(country_code, geoname_id)` | `serving.location_profiles` — all GeoNames locations (pop ≥1K), incl. zero-court locations |
 | `foundation.dim_venue_capacity` | `tenant_id` | `foundation.fct_daily_availability` |

 ## Source integration map

 ```
 stg_playtomic_venues  ─┐
-stg_playtomic_resources─┤→ dim_venues ─┬→ dim_cities ──────────────→ city_market_profile
-stg_padel_courts      ─┘               └→ dim_venue_capacity              (Marktreife-Score)
-                                                            ↓
+stg_playtomic_resources─┤→ dim_venues ─┬→ dim_cities ──┐
+stg_padel_courts      ─┘               └→ dim_venue_capacity
+                                                        │
 stg_playtomic_availability ──→ fct_availability_slot ──→ fct_daily_availability
                                                            ↓
                                               venue_pricing_benchmarks
                                                            ↓
 stg_population ──→ dim_cities ─────────────────────────────┘
-stg_income     ──→ dim_cities
-
-stg_population_geonames ─┐
-stg_padel_courts         ─┤→ dim_locations ──→ location_opportunity_profile
-stg_tennis_courts        ─┤                         (Marktpotenzial-Score)
+stg_income     ──→ dim_cities                               │
+                                                            ↓
+stg_population_geonames ─┐                          location_profiles
+stg_padel_courts         ─┤→ dim_locations ────────→  (both scores:
+stg_tennis_courts        ─┤                           Marktreife + Marktpotenzial)
 stg_income               ─┘
 ```

--- a/transform/sqlmesh_padelnomics/config.yaml
+++ b/transform/sqlmesh_padelnomics/config.yaml
@@ -6,6 +6,8 @@ gateways:
        local: "{{ env_var('DUCKDB_PATH', 'data/lakehouse.duckdb') }}"
      extensions:
        - spatial
+        - name: h3
+          repository: community

 default_gateway: duckdb

--- a/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/dim_cities.sql
@@ -2,7 +2,7 @@
 -- Built from venue locations (dim_venues) as the primary source — padelnomics
 -- tracks cities where padel venues actually exist, not an administrative city list.
 --
-- Conformed dimension: used by city_market_profile and all pSEO serving models.
+-- Conformed dimension: used by location_profiles and all pSEO serving models.
 -- Integrates four sources:
 --   dim_venues           → city list, venue count, coordinates (Playtomic + OSM)
 --   foundation.dim_countries → country_name_en, country_slug, median_income_pps
@@ -128,7 +128,7 @@ SELECT
  vc.padel_venue_count,
  c.median_income_pps,
  c.income_year,
-  -- GeoNames ID: FK to dim_locations / location_opportunity_profile.
+  -- GeoNames ID: FK to dim_locations / location_profiles.
  -- String match preferred; spatial fallback used when name doesn't match (Milano→Milan, etc.)
  COALESCE(gn.geoname_id, gs.spatial_geoname_id)            AS geoname_id
 FROM venue_cities vc
--- a/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/dim_locations.sql
@@ -215,6 +215,7 @@ SELECT
  l.location_slug,
  l.lat,
  l.lon,
+  h3_latlng_to_cell(l.lat, l.lon, 5)                                    AS h3_cell_res5,
  l.admin1_code,
  l.admin2_code,
  l.population,
--- a/transform/sqlmesh_padelnomics/models/foundation/fct_availability_slot.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/fct_availability_slot.sql
@@ -14,7 +14,10 @@

 MODEL (
  name foundation.fct_availability_slot,
-  kind FULL,
+  kind INCREMENTAL_BY_TIME_RANGE (
+    time_column snapshot_date
+  ),
+  start '2026-03-01',
  cron '@daily',
  grain (snapshot_date, tenant_id, resource_id, slot_start_time)
 );
@@ -37,7 +40,8 @@ WITH deduped AS (
        captured_at_utc DESC
    ) AS rn
  FROM staging.stg_playtomic_availability
-  WHERE price_amount IS NOT NULL
+  WHERE snapshot_date BETWEEN @start_ds AND @end_ds
+    AND price_amount IS NOT NULL
    AND price_amount > 0
 )
 SELECT
--- a/transform/sqlmesh_padelnomics/models/foundation/fct_daily_availability.sql
+++ b/transform/sqlmesh_padelnomics/models/foundation/fct_daily_availability.sql
@@ -12,7 +12,10 @@

 MODEL (
  name foundation.fct_daily_availability,
-  kind FULL,
+  kind INCREMENTAL_BY_TIME_RANGE (
+    time_column snapshot_date
+  ),
+  start '2026-03-01',
  cron '@daily',
  grain (snapshot_date, tenant_id)
 );
@@ -37,6 +40,7 @@ WITH slot_agg AS (
    MAX(a.price_currency)                                 AS price_currency,
    MAX(a.captured_at_utc)                                AS captured_at_utc
  FROM foundation.fct_availability_slot a
+  WHERE a.snapshot_date BETWEEN @start_ds AND @end_ds
  GROUP BY a.snapshot_date, a.tenant_id
 )
 SELECT
--- a/transform/sqlmesh_padelnomics/models/serving/README.md
+++ b/transform/sqlmesh_padelnomics/models/serving/README.md
@@ -3,4 +3,4 @@
 Analytics-ready views consumed by the web app and programmatic SEO.
 Query these from `analytics.py` via DuckDB read-only connection.

-Naming convention: `serving.<purpose>` (e.g. `serving.city_market_profile`)
+Naming convention: `serving.<purpose>` (e.g. `serving.location_profiles`)
--- a/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql
@@ -1,117 +0,0 @@
-- One Big Table: per-city padel market intelligence.
-- Consumed by: SEO article generation, planner city-select pre-fill, API endpoints.
--
-- Padelnomics Marktreife-Score v3 (0–100):
-- Answers "How mature/established is this padel market?"
-- Only computed for cities with ≥1 padel venue (padel_venue_count > 0).
-- For white-space opportunity scoring, see serving.location_opportunity_profile.
--
--   40 pts  supply development — log-scaled density (LN ceiling 20/100k) × count gate
--                                (min(1, count/5) kills small-town inflation)
--   25 pts  demand evidence   — occupancy when available; 40% density proxy otherwise
--   15 pts  addressable market — log-scaled population, ceiling 1M (context only)
--   10 pts  economic context  — income PPS normalised to 200 ceiling
--   10 pts  data quality      — completeness discount
--   No saturation discount: high density = maturity, not a penalty
-
-MODEL (
-  name serving.city_market_profile,
-  kind FULL,
-  cron '@daily',
-  grain (country_code, city_slug)
-);
-
-WITH base AS (
-  SELECT
-    c.country_code,
-    c.country_name_en,
-    c.country_slug,
-    c.city_name,
-    c.city_slug,
-    c.lat,
-    c.lon,
-    c.population,
-    c.population_year,
-    c.padel_venue_count,
-    c.median_income_pps,
-    c.income_year,
-    c.geoname_id,
-    -- Venue density: padel venues per 100K residents
-    CASE WHEN c.population > 0
-      THEN ROUND(c.padel_venue_count::DOUBLE / c.population * 100000, 2)
-      ELSE NULL
-    END                          AS venues_per_100k,
-    -- Data confidence: 1.0 if both population and venues are present
-    CASE
-      WHEN c.population > 0 AND c.padel_venue_count > 0 THEN 1.0
-      WHEN c.population > 0 OR  c.padel_venue_count > 0 THEN 0.5
-      ELSE 0.0
-    END                          AS data_confidence,
-    -- Pricing / occupancy from Playtomic (NULL when no availability data)
-    vpb.median_hourly_rate,
-    vpb.median_peak_rate,
-    vpb.median_offpeak_rate,
-    vpb.median_occupancy_rate,
-    vpb.median_daily_revenue_per_venue,
-    vpb.price_currency
-  FROM foundation.dim_cities c
-  LEFT JOIN serving.venue_pricing_benchmarks vpb
-    ON c.country_code = vpb.country_code
-    AND c.city_slug = vpb.city_slug
-  WHERE c.padel_venue_count > 0
-),
-scored AS (
-  SELECT *,
-    ROUND(
-      -- Supply development (40 pts): THE maturity signal.
-      -- Log-scaled density: LN(density+1)/LN(21) → 20/100k ≈ full marks.
-      -- Count gate: min(1, count/5) — 1 venue=20%, 5+ venues=100%.
-      -- Kills small-town inflation (1 court / 5k pop = 20/100k) without hard cutoffs.
-      40.0 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
-           * LEAST(1.0, padel_venue_count / 5.0)
-      -- Demand evidence (25 pts): occupancy when Playtomic data available.
-      -- Fallback: 40% of density score (avoids double-counting with supply component).
-      + 25.0 * CASE
-          WHEN median_occupancy_rate IS NOT NULL
-            THEN LEAST(1.0, median_occupancy_rate / 0.65)
-          ELSE 0.4 * LEAST(1.0, LN(COALESCE(venues_per_100k, 0) + 1) / LN(21))
-                   * LEAST(1.0, padel_venue_count / 5.0)
-        END
-      -- Addressable market (15 pts): population as context, not maturity signal.
-      -- LN(1) = 0 so zero-pop cities score 0 here.
-      + 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
-      -- Economic context (10 pts): country-level income PPS.
-      -- Flat per country — kept as context modifier, not primary signal.
-      + 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
-      -- Data quality (10 pts): completeness discount.
-      + 10.0 * data_confidence
-    , 1)
-                                 AS market_score
-  FROM base
-)
-SELECT
-  s.country_code,
-  s.country_name_en,
-  s.country_slug,
-  s.city_name,
-  s.city_slug,
-  s.lat,
-  s.lon,
-  s.population,
-  s.population_year,
-  s.padel_venue_count,
-  s.venues_per_100k,
-  s.data_confidence,
-  s.market_score,
-  s.median_income_pps,
-  s.income_year,
-  s.median_hourly_rate,
-  s.median_peak_rate,
-  s.median_offpeak_rate,
-  s.median_occupancy_rate,
-  s.median_daily_revenue_per_venue,
-  s.price_currency,
-  s.geoname_id,
-  CURRENT_DATE                   AS refreshed_date
-FROM scored s
-ORDER BY s.market_score DESC
--- a/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql
@@ -1,86 +0,0 @@
-- Per-location padel investment opportunity intelligence.
-- Consumed by: Gemeinde-level pSEO pages, opportunity map, "top markets" lists.
--
-- Padelnomics Marktpotenzial-Score v2 (0–100):
-- Answers "Where should I build a padel court?"
-- Covers ALL GeoNames locations (pop ≥ 1K) — NOT filtered to existing padel markets.
-- Zero-court locations score highest on supply gap component (white space = opportunity).
--
--   25 pts  addressable market — log-scaled population, ceiling 500K
--           (opportunity peaks in mid-size cities; megacities already served)
--   20 pts  economic power     — country income PPS, normalised to 35,000
--                               EU PPS values range 18k-37k; /35k gives real spread.
--                               DE ≈ 13.2pts, ES ≈ 10.7pts, SE ≈ 14.3pts.
--                               Previously /200 caused all countries to saturate at 20/20.
--   30 pts  supply gap         — INVERTED venue density; 0 courts/100K = full marks.
--                               Ceiling raised to 8/100K (was 4) for a gentler gradient
--                               and to account for ~87% data undercount vs FIP totals.
--                               Linear: GREATEST(0, 1 - density/8)
--   15 pts  catchment gap      — distance to nearest padel court.
--                               DuckDB LEAST ignores NULLs: LEAST(1.0, NULL/30) = 1.0,
--                               so NULL nearest_km = full marks (no court in bounding box
--                               = high opportunity). COALESCE fallback is dead code.
--   10 pts  sports culture     — tennis courts within 25km (≥10 = full marks).
--                               NOTE: dim_locations tennis data is empty (all 0 rows).
--                               Component contributes 0 pts everywhere until data lands.
-
-MODEL (
-  name serving.location_opportunity_profile,
-  kind FULL,
-  cron '@daily',
-  grain (country_code, geoname_id)
-);
-
-SELECT
-  l.geoname_id,
-  l.country_code,
-  l.country_name_en,
-  l.country_slug,
-  l.location_name,
-  l.location_slug,
-  l.lat,
-  l.lon,
-  l.admin1_code,
-  l.admin2_code,
-  l.population,
-  l.population_year,
-  l.median_income_pps,
-  l.income_year,
-  l.padel_venue_count,
-  l.padel_venues_per_100k,
-  l.nearest_padel_court_km,
-  l.tennis_courts_within_25km,
-  ROUND(
-    -- Addressable market (25 pts): log-scaled to 500K ceiling.
-    -- Lower ceiling than Marktreife (1M) — opportunity peaks in mid-size cities
-    -- that can support a court but aren't already saturated by large-city operators.
-    25.0 * LEAST(1.0, LN(GREATEST(l.population, 1)) / LN(500000))
-
-    -- Economic power (20 pts): country-level income PPS normalised to 35,000.
-    -- Drives willingness-to-pay for court fees (€20-35/hr target range).
-    -- EU PPS values range 18k-37k; ceiling 35k gives meaningful spread.
-    -- v1 used /200 which caused LEAST(1.0, 115) = 1.0 for ALL countries (flat, no differentiation).
-    -- v2: /35000 → DE 0.66×20=13.2pts, ES 0.53×20=10.7pts, SE 0.71×20=14.3pts.
-    -- Default 15000 for missing data = reasonable developing-market assumption (~0.43).
-    + 20.0 * LEAST(1.0, COALESCE(l.median_income_pps, 15000) / 35000.0)
-
-    -- Supply gap (30 pts): INVERTED venue density.
-    -- 0 courts/100K = full 30 pts (white space); ≥8/100K = 0 pts (served market).
-    -- Ceiling raised from 4→8/100K for a gentler gradient and to account for data
-    -- undercount (~87% of real courts not in our data).
-    -- This is the key signal that separates Marktpotenzial from Marktreife.
-    + 30.0 * GREATEST(0.0, 1.0 - COALESCE(l.padel_venues_per_100k, 0) / 8.0)
-
-    -- Catchment gap (15 pts): distance to nearest existing padel court.
-    -- >30km = full 15 pts (underserved catchment area).
-    -- NULL = no courts found anywhere (rare edge case) → neutral 0.5.
-    + 15.0 * COALESCE(LEAST(1.0, l.nearest_padel_court_km / 30.0), 0.5)
-
-    -- Sports culture proxy (10 pts): tennis courts within 25km.
-    -- ≥10 courts = full 10 pts (proven racket sport market = faster padel adoption).
-    -- 0 courts = 0 pts. Many new padel courts open inside existing tennis clubs.
-    + 10.0 * LEAST(1.0, l.tennis_courts_within_25km / 10.0)
-  , 1)                                                                   AS opportunity_score,
-  CURRENT_DATE                                                           AS refreshed_date
-FROM foundation.dim_locations l
-ORDER BY opportunity_score DESC
--- a/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/location_profiles.sql
@@ -0,0 +1,243 @@
+-- Unified location profile: both scores at (country_code, geoname_id) grain.
+-- Base: dim_locations (ALL GeoNames locations, pop ≥ 1K, ~140K rows).
+-- Enriched with dim_cities (city_slug, city_name, exact venue count) and
+-- venue_pricing_benchmarks (Playtomic pricing/occupancy).
+--
+-- Two scores per location:
+--
+-- Padelnomics Market Score (Marktreife-Score v3, 0–100):
+--   "How mature/established is this padel market?"
+--   Only meaningful for locations matched to a dim_cities row (city_slug IS NOT NULL)
+--   with padel venues. 0 for all other locations.
+--
+--   40 pts  supply development — log-scaled density (LN ceiling 20/100k) × count gate
+--   25 pts  demand evidence   — occupancy when available; 40% density proxy otherwise
+--   15 pts  addressable market — log-scaled population, ceiling 1M
+--   10 pts  economic context  — income PPS normalised to 200 ceiling
+--   10 pts  data quality      — completeness discount
+--
+-- Padelnomics Opportunity Score (Marktpotenzial-Score v3, 0–100):
+--   "Where should I build a padel court?"
+--   Computed for ALL locations — zero-court locations score highest on supply gap.
+--   H3 catchment methodology: addressable market and supply gap use a regional
+--   H3 catchment (res-5 cell + 6 neighbours, ~24km radius).
+--
+--   25 pts  addressable market — log-scaled catchment population, ceiling 500K
+--   20 pts  economic power     — income PPS, normalised to 35,000
+--   30 pts  supply gap         — inverted catchment venue density; 0 courts = full marks
+--   15 pts  catchment gap      — distance to nearest padel court
+--   10 pts  sports culture     — tennis courts within 25km
+--
+-- Consumers query directly with WHERE filters:
+--   cities API:       WHERE country_slug = ? AND city_slug IS NOT NULL
+--   opportunity API:  WHERE country_slug = ? AND opportunity_score > 0
+--   planner_defaults: WHERE city_slug IS NOT NULL
+--   pseo_*:           WHERE city_slug IS NOT NULL AND city_padel_venue_count > 0
+
+MODEL (
+  name serving.location_profiles,
+  kind FULL,
+  cron '@daily',
+  grain (country_code, geoname_id)
+);
+
+WITH
+-- All locations from dim_locations (superset)
+base AS (
+  SELECT
+    l.geoname_id,
+    l.country_code,
+    l.country_name_en,
+    l.country_slug,
+    l.location_name,
+    l.location_slug,
+    l.lat,
+    l.lon,
+    l.admin1_code,
+    l.admin2_code,
+    l.population,
+    l.population_year,
+    l.median_income_pps,
+    l.income_year,
+    l.padel_venue_count,
+    l.padel_venues_per_100k,
+    l.nearest_padel_court_km,
+    l.tennis_courts_within_25km,
+    l.h3_cell_res5
+  FROM foundation.dim_locations l
+),
+-- Aggregate population and court counts per H3 cell (res 5, ~8.5km edge).
+-- Grouping by cell first (~50-80K distinct cells vs 140K locations) keeps the
+-- subsequent lateral join small.
+hex_stats AS (
+  SELECT
+    h3_cell_res5,
+    SUM(population)        AS hex_population,
+    SUM(padel_venue_count) AS hex_padel_courts
+  FROM foundation.dim_locations
+  GROUP BY h3_cell_res5
+),
+-- For each location, sum hex_stats across the cell + 6 neighbours (k_ring=1).
+-- Effective catchment: ~24km radius — realistic driving distance.
+catchment AS (
+  SELECT
+    l.geoname_id,
+    SUM(hs.hex_population)   AS catchment_population,
+    SUM(hs.hex_padel_courts) AS catchment_padel_courts
+  FROM base l,
+  LATERAL (SELECT UNNEST(h3_grid_disk(l.h3_cell_res5, 1)) AS cell) ring
+  JOIN hex_stats hs ON hs.h3_cell_res5 = ring.cell
+  GROUP BY l.geoname_id
+),
+-- Match dim_cities via (country_code, geoname_id) to get city_slug + exact venue count.
+-- QUALIFY handles rare multi-city-per-geoname collisions (keep highest venue count).
+city_match AS (
+  SELECT
+    c.country_code,
+    c.geoname_id,
+    c.city_slug,
+    c.city_name,
+    c.padel_venue_count AS city_padel_venue_count
+  FROM foundation.dim_cities c
+  WHERE c.geoname_id IS NOT NULL
+  QUALIFY ROW_NUMBER() OVER (
+    PARTITION BY c.country_code, c.geoname_id
+    ORDER BY c.padel_venue_count DESC
+  ) = 1
+),
+-- Pricing / occupancy from Playtomic (via city_slug) + H3 catchment
+with_pricing AS (
+  SELECT
+    b.*,
+    cm.city_slug,
+    cm.city_name,
+    cm.city_padel_venue_count,
+    vpb.median_hourly_rate,
+    vpb.median_peak_rate,
+    vpb.median_offpeak_rate,
+    vpb.median_occupancy_rate,
+    vpb.median_daily_revenue_per_venue,
+    vpb.price_currency,
+    COALESCE(ct.catchment_population, b.population)::BIGINT           AS catchment_population,
+    COALESCE(ct.catchment_padel_courts, b.padel_venue_count)::INTEGER AS catchment_padel_courts
+  FROM base b
+  LEFT JOIN city_match cm
+    ON b.country_code = cm.country_code
+    AND b.geoname_id = cm.geoname_id
+  LEFT JOIN serving.venue_pricing_benchmarks vpb
+    ON cm.country_code = vpb.country_code
+    AND cm.city_slug = vpb.city_slug
+  LEFT JOIN catchment ct
+    ON b.geoname_id = ct.geoname_id
+),
+-- Both scores computed from the enriched base
+scored AS (
+  SELECT *,
+    -- City-level venue density (from dim_cities exact count, not dim_locations spatial 5km)
+    CASE WHEN population > 0
+      THEN ROUND(COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000, 2)
+      ELSE NULL
+    END AS city_venues_per_100k,
+    -- Data confidence (for market_score)
+    CASE
+      WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
+      WHEN population > 0 OR  COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
+      ELSE 0.0
+    END AS data_confidence,
+    -- ── Market Score (Marktreife-Score v3) ──────────────────────────────────
+    -- 0 when no city match or no venues (city_padel_venue_count NULL or 0)
+    CASE WHEN COALESCE(city_padel_venue_count, 0) > 0 THEN
+      ROUND(
+        -- Supply development (40 pts)
+        40.0 * LEAST(1.0, LN(
+            COALESCE(
+              CASE WHEN population > 0
+                THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
+                ELSE 0 END
+            , 0) + 1) / LN(21))
+             * LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
+        -- Demand evidence (25 pts)
+        + 25.0 * CASE
+            WHEN median_occupancy_rate IS NOT NULL
+              THEN LEAST(1.0, median_occupancy_rate / 0.65)
+            ELSE 0.4 * LEAST(1.0, LN(
+                COALESCE(
+                  CASE WHEN population > 0
+                    THEN COALESCE(city_padel_venue_count, 0)::DOUBLE / population * 100000
+                    ELSE 0 END
+                , 0) + 1) / LN(21))
+                     * LEAST(1.0, COALESCE(city_padel_venue_count, 0) / 5.0)
+          END
+        -- Addressable market (15 pts)
+        + 15.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(1000000))
+        -- Economic context (10 pts)
+        + 10.0 * LEAST(1.0, COALESCE(median_income_pps, 100) / 200.0)
+        -- Data quality (10 pts)
+        + 10.0 * CASE
+            WHEN population > 0 AND COALESCE(city_padel_venue_count, 0) > 0 THEN 1.0
+            WHEN population > 0 OR  COALESCE(city_padel_venue_count, 0) > 0 THEN 0.5
+            ELSE 0.0
+          END
+      , 1)
+    ELSE 0
+    END AS market_score,
+    -- ── Opportunity Score (Marktpotenzial-Score v3, H3 catchment) ──────────
+    ROUND(
+      -- Addressable market (25 pts): log-scaled catchment population, ceiling 500K
+      25.0 * LEAST(1.0, LN(GREATEST(catchment_population, 1)) / LN(500000))
+      -- Economic power (20 pts): income PPS normalised to 35,000
+      + 20.0 * LEAST(1.0, COALESCE(median_income_pps, 15000) / 35000.0)
+      -- Supply gap (30 pts): inverted catchment venue density
+      + 30.0 * GREATEST(0.0, 1.0 - COALESCE(
+          CASE WHEN catchment_population > 0
+            THEN catchment_padel_courts::DOUBLE / catchment_population * 100000
+            ELSE 0.0
+          END, 0.0) / 8.0)
+      -- Catchment gap (15 pts): distance to nearest court
+      + 15.0 * COALESCE(LEAST(1.0, nearest_padel_court_km / 30.0), 0.5)
+      -- Sports culture (10 pts): tennis courts within 25km
+      + 10.0 * LEAST(1.0, tennis_courts_within_25km / 10.0)
+    , 1) AS opportunity_score
+  FROM with_pricing
+)
+SELECT
+  s.geoname_id,
+  s.country_code,
+  s.country_name_en,
+  s.country_slug,
+  s.location_name,
+  s.location_slug,
+  s.city_slug,
+  s.city_name,
+  s.lat,
+  s.lon,
+  s.admin1_code,
+  s.admin2_code,
+  s.population,
+  s.population_year,
+  s.median_income_pps,
+  s.income_year,
+  s.padel_venue_count,
+  s.padel_venues_per_100k,
+  s.nearest_padel_court_km,
+  s.tennis_courts_within_25km,
+  s.city_padel_venue_count,
+  s.city_venues_per_100k,
+  s.data_confidence,
+  s.catchment_population,
+  s.catchment_padel_courts,
+  CASE WHEN s.catchment_population > 0
+    THEN ROUND(s.catchment_padel_courts::DOUBLE / s.catchment_population * 100000, 2)
+    ELSE NULL
+  END AS catchment_venues_per_100k,
+  s.market_score,
+  s.opportunity_score,
+  s.median_hourly_rate,
+  s.median_peak_rate,
+  s.median_offpeak_rate,
+  s.median_occupancy_rate,
+  s.median_daily_revenue_per_venue,
+  s.price_currency,
+  CURRENT_DATE AS refreshed_date
+FROM scored s
+ORDER BY s.market_score DESC, s.opportunity_score DESC
--- a/transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql
@@ -76,11 +76,12 @@ city_profiles AS (
    city_slug,
    country_code,
    city_name,
-    padel_venue_count,
+    city_padel_venue_count AS padel_venue_count,
    population,
    market_score,
-    venues_per_100k
-  FROM serving.city_market_profile
+    city_venues_per_100k AS venues_per_100k
+  FROM serving.location_profiles
+  WHERE city_slug IS NOT NULL
 )
 SELECT
  cp.city_slug,
--- a/transform/sqlmesh_padelnomics/models/serving/pseo_city_costs_de.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/pseo_city_costs_de.sql
@@ -31,10 +31,10 @@ SELECT
  c.lon,
  -- Market metrics
  c.population,
-  c.padel_venue_count,
-  c.venues_per_100k,
+  c.city_padel_venue_count AS padel_venue_count,
+  c.city_venues_per_100k AS venues_per_100k,
  c.market_score,
-  lop.opportunity_score,
+  c.opportunity_score,
  c.data_confidence,
  -- Pricing (from Playtomic, NULL when no coverage)
  c.median_hourly_rate,
@@ -85,15 +85,13 @@ SELECT
  cc.working_capital                AS "workingCapital",
  cc.permits_compliance             AS "permitsCompliance",
  CURRENT_DATE                      AS refreshed_date
-FROM serving.city_market_profile c
+FROM serving.location_profiles c
 LEFT JOIN serving.planner_defaults p
  ON c.country_code = p.country_code
  AND c.city_slug = p.city_slug
-LEFT JOIN serving.location_opportunity_profile lop
-  ON c.country_code = lop.country_code
-  AND c.geoname_id = lop.geoname_id
 LEFT JOIN foundation.dim_countries cc
  ON c.country_code = cc.country_code
 -- Only cities with actual padel presence and at least some rate data
-WHERE c.padel_venue_count > 0
+WHERE c.city_slug IS NOT NULL
+  AND c.city_padel_venue_count > 0
  AND (p.rate_peak IS NOT NULL OR c.median_peak_rate IS NOT NULL)
--- a/transform/sqlmesh_padelnomics/models/serving/pseo_city_pricing.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/pseo_city_pricing.sql
@@ -1,6 +1,6 @@
 -- pSEO article data: per-city padel court pricing.
 -- One row per city — consumed by the city-pricing.md.jinja template.
-- Joins venue_pricing_benchmarks (real Playtomic data) with city_market_profile
+-- Joins venue_pricing_benchmarks (real Playtomic data) with location_profiles
 -- (population, venue count, country metadata).
 --
 -- Stricter filter than pseo_city_costs_de: requires >= 2 venues with real
@@ -16,7 +16,7 @@ MODEL (
 SELECT
  -- Composite natural key: country_slug + city_slug ensures uniqueness across countries
  c.country_slug || '-' || c.city_slug  AS city_key,
-  -- City identity (from city_market_profile, which has the canonical city_slug)
+  -- City identity (from location_profiles, which has the canonical city_slug)
  c.city_slug,
  c.city_name,
  c.country_code,
@@ -24,8 +24,8 @@ SELECT
  c.country_slug,
  -- Market context
  c.population,
-  c.padel_venue_count,
-  c.venues_per_100k,
+  c.city_padel_venue_count AS padel_venue_count,
+  c.city_venues_per_100k AS venues_per_100k,
  c.market_score,
  -- Pricing benchmarks (from Playtomic availability data)
  vpb.median_hourly_rate,
@@ -38,9 +38,10 @@ SELECT
  vpb.price_currency,
  CURRENT_DATE AS refreshed_date
 FROM serving.venue_pricing_benchmarks vpb
-- Join city_market_profile to get the canonical city_slug and country metadata
-INNER JOIN serving.city_market_profile c
+-- Join location_profiles to get canonical city metadata
+INNER JOIN serving.location_profiles c
  ON  vpb.country_code = c.country_code
  AND vpb.city_slug = c.city_slug
+  AND c.city_slug IS NOT NULL
 -- Only cities with enough venues for meaningful pricing statistics
 WHERE vpb.venue_count >= 2
--- a/transform/sqlmesh_padelnomics/models/serving/venue_pricing_benchmarks.sql
+++ b/transform/sqlmesh_padelnomics/models/serving/venue_pricing_benchmarks.sql
@@ -27,7 +27,7 @@ WITH venue_stats AS (
    MAX(da.active_court_count)                          AS court_count,
    COUNT(DISTINCT da.snapshot_date)                    AS days_observed
  FROM foundation.fct_daily_availability da
-  WHERE TRY_CAST(da.snapshot_date AS DATE) >= CURRENT_DATE - INTERVAL '30 days'
+  WHERE da.snapshot_date >= CURRENT_DATE - INTERVAL '30 days'
    AND da.occupancy_rate IS NOT NULL
    AND da.occupancy_rate BETWEEN 0 AND 1.5
  GROUP BY da.tenant_id, da.country_code, da.city, da.city_slug, da.price_currency
--- a/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_availability.sql
+++ b/transform/sqlmesh_padelnomics/models/staging/stg_playtomic_availability.sql
@@ -13,44 +13,28 @@

 MODEL (
  name staging.stg_playtomic_availability,
-  kind FULL,
+  kind INCREMENTAL_BY_TIME_RANGE (
+    time_column snapshot_date
+  ),
+  start '2026-03-01',
  cron '@daily',
  grain (snapshot_date, tenant_id, resource_id, slot_start_time, snapshot_type, captured_at_utc)
 );

 WITH
-morning_jsonl AS (
+all_jsonl AS (
  SELECT
-    date                             AS snapshot_date,
+    CAST(date AS DATE)               AS snapshot_date,
    captured_at_utc,
-    'morning'                        AS snapshot_type,
-    NULL::INTEGER                    AS recheck_hour,
-    tenant_id,
-    slots                            AS slots_json
-  FROM read_json(
-    @LANDING_DIR || '/playtomic/*/*/availability_*.jsonl.gz',
-    format = 'newline_delimited',
-    columns = {
-      date: 'VARCHAR',
-      captured_at_utc: 'VARCHAR',
-      tenant_id: 'VARCHAR',
-      slots: 'JSON'
-    },
-    filename = true
-  )
-  WHERE filename NOT LIKE '%_recheck_%'
-    AND tenant_id IS NOT NULL
-),
-recheck_jsonl AS (
-  SELECT
-    date                             AS snapshot_date,
-    captured_at_utc,
-    'recheck'                        AS snapshot_type,
+    CASE
+      WHEN filename LIKE '%_recheck_%' THEN 'recheck'
+      ELSE 'morning'
+    END                              AS snapshot_type,
    TRY_CAST(recheck_hour AS INTEGER) AS recheck_hour,
    tenant_id,
    slots                            AS slots_json
  FROM read_json(
-    @LANDING_DIR || '/playtomic/*/*/availability_*_recheck_*.jsonl.gz',
+    @LANDING_DIR || '/playtomic/*/*/availability_' || @start_ds || '*.jsonl.gz',
    format = 'newline_delimited',
    columns = {
      date: 'VARCHAR',
@@ -63,11 +47,6 @@ recheck_jsonl AS (
  )
  WHERE tenant_id IS NOT NULL
 ),
-all_venues AS (
-  SELECT * FROM morning_jsonl
-  UNION ALL
-  SELECT * FROM recheck_jsonl
-),
 raw_resources AS (
  SELECT
    av.snapshot_date,
@@ -76,7 +55,7 @@ raw_resources AS (
    av.recheck_hour,
    av.tenant_id,
    resource_json
-  FROM all_venues av,
+  FROM all_jsonl av,
  LATERAL UNNEST(
    from_json(av.slots_json, '["JSON"]')
  ) AS t(resource_json)
--- a/uv.lock
+++ b/uv.lock
@@ -150,6 +150,11 @@ dependencies = [
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/84/85/57c314a6b35336efbbdc13e5fc9ae13f6b60a0647cfa7c1221178ac6d8ae/brotlicffi-1.2.0.0.tar.gz", hash = "sha256:34345d8d1f9d534fcac2249e57a4c3c8801a33c9942ff9f8574f67a175e17adb", size = 476682, upload-time = "2025-11-21T18:17:57.334Z" }
 wheels = [
+    { url = "https://files.pythonhosted.org/packages/7c/87/ba6298c3d7f8d66ce80d7a487f2a487ebae74a79c6049c7c2990178ce529/brotlicffi-1.2.0.0-cp314-cp314t-macosx_11_0_arm64.whl", hash = "sha256:b13fb476a96f02e477a506423cb5e7bc21e0e3ac4c060c20ba31c44056e38c68", size = 433038, upload-time = "2026-03-05T17:57:37.96Z" },
+    { url = "https://files.pythonhosted.org/packages/00/49/16c7a77d1cae0519953ef0389a11a9c2e2e62e87d04f8e7afbae40124255/brotlicffi-1.2.0.0-cp314-cp314t-manylinux2014_aarch64.manylinux_2_17_aarch64.manylinux_2_28_aarch64.whl", hash = "sha256:17db36fb581f7b951635cd6849553a95c6f2f53c1a707817d06eae5aeff5f6af", size = 1541124, upload-time = "2026-03-05T17:57:39.488Z" },
+    { url = "https://files.pythonhosted.org/packages/e8/17/fab2c36ea820e2288f8c1bf562de1b6cd9f30e28d66f1ce2929a4baff6de/brotlicffi-1.2.0.0-cp314-cp314t-manylinux2014_x86_64.manylinux_2_17_x86_64.manylinux_2_28_x86_64.whl", hash = "sha256:40190192790489a7b054312163d0ce82b07d1b6e706251036898ce1684ef12e9", size = 1541983, upload-time = "2026-03-05T17:57:41.061Z" },
+    { url = "https://files.pythonhosted.org/packages/78/c9/849a669b3b3bb8ac96005cdef04df4db658c33443a7fc704a6d4a2f07a56/brotlicffi-1.2.0.0-cp314-cp314t-win32.whl", hash = "sha256:a8079e8ecc32ecef728036a1d9b7105991ce6a5385cf51ee8c02297c90fb08c2", size = 349046, upload-time = "2026-03-05T17:57:42.76Z" },
+    { url = "https://files.pythonhosted.org/packages/a4/25/09c0fd21cfc451fa38ad538f4d18d8be566746531f7f27143f63f8c45a9f/brotlicffi-1.2.0.0-cp314-cp314t-win_amd64.whl", hash = "sha256:ca90c4266704ca0a94de8f101b4ec029624273380574e4cf19301acfa46c61a0", size = 385653, upload-time = "2026-03-05T17:57:44.224Z" },
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--- a/web/scripts/dev_run.sh
+++ b/web/scripts/dev_run.sh
@@ -165,7 +165,7 @@ echo ""
 echo "Press Ctrl-C to stop all processes."
 echo ""

-run_with_label "$COLOR_APP"    "app   " uv run granian --interface asgi --host 127.0.0.1 --port 5000 --reload --reload-paths web/src padelnomics.app:app
+run_with_label "$COLOR_APP"    "app   " uv run python -m padelnomics.app
 run_with_label "$COLOR_WORKER" "worker" uv run python -u -m padelnomics.worker
 run_with_label "$COLOR_CSS"    "css   " make css-watch

--- a/web/src/padelnomics/admin/pipeline_routes.py
+++ b/web/src/padelnomics/admin/pipeline_routes.py
@@ -111,13 +111,12 @@ _DAG: dict[str, list[str]] = {
    "fct_daily_availability": ["fct_availability_slot", "dim_venue_capacity"],
    # Serving
    "venue_pricing_benchmarks": ["fct_daily_availability"],
-    "city_market_profile": ["dim_cities", "venue_pricing_benchmarks"],
-    "planner_defaults": ["venue_pricing_benchmarks", "city_market_profile"],
-    "location_opportunity_profile": ["dim_locations"],
+    "location_profiles": ["dim_locations", "dim_cities", "venue_pricing_benchmarks"],
+    "planner_defaults": ["venue_pricing_benchmarks", "location_profiles"],
    "pseo_city_costs_de": [
-        "city_market_profile", "planner_defaults", "location_opportunity_profile",
+        "location_profiles", "planner_defaults",
    ],
-    "pseo_city_pricing": ["venue_pricing_benchmarks", "city_market_profile"],
+    "pseo_city_pricing": ["venue_pricing_benchmarks", "location_profiles"],
    "pseo_country_overview": ["pseo_city_costs_de"],
 }

--- a/web/src/padelnomics/admin/routes.py
+++ b/web/src/padelnomics/admin/routes.py
@@ -2465,6 +2465,18 @@ async def articles():
    )


+@bp.route("/articles/stats")
+@role_required("admin")
+async def article_stats():
+    """HTMX partial: article stats bar (polled while generating)."""
+    stats = await _get_article_stats()
+    return await render_template(
+        "admin/partials/article_stats.html",
+        stats=stats,
+        is_generating=await _is_generating(),
+    )
+
+
@bp.route("/articles/results")
@role_required("admin")
 async def article_results():
@@ -2736,13 +2748,13 @@ async def article_edit(article_id: int):
    body = raw[m.end():].lstrip("\n") if m else raw

    body_html = mistune.html(body) if body else ""
-    css_url = url_for("static", filename="css/output.css")
    preview_doc = (
-        f"<!doctype html><html><head>"
-        f"<link rel='stylesheet' href='{css_url}'>"
-        f"<style>html,body{{margin:0;padding:0}}body{{padding:2rem 2.5rem}}</style>"
-        f"</head><body><div class='article-body'>{body_html}</div></body></html>"
-    ) if body_html else ""
+        await render_template(
+            "admin/partials/article_preview_doc.html", body_html=body_html
+        )
+        if body_html
+        else ""
+    )

    data = {**dict(article), "body": body}
    return await render_template(
@@ -2764,13 +2776,13 @@ async def article_preview():
    m = _FRONTMATTER_RE.match(body)
    body = body[m.end():].lstrip("\n") if m else body
    body_html = mistune.html(body) if body else ""
-    css_url = url_for("static", filename="css/output.css")
    preview_doc = (
-        f"<!doctype html><html><head>"
-        f"<link rel='stylesheet' href='{css_url}'>"
-        f"<style>html,body{{margin:0;padding:0}}body{{padding:2rem 2.5rem}}</style>"
-        f"</head><body><div class='article-body'>{body_html}</div></body></html>"
-    ) if body_html else ""
+        await render_template(
+            "admin/partials/article_preview_doc.html", body_html=body_html
+        )
+        if body_html
+        else ""
+    )
    return await render_template("admin/partials/article_preview.html", preview_doc=preview_doc)


--- a/web/src/padelnomics/admin/templates/admin/article_form.html
+++ b/web/src/padelnomics/admin/templates/admin/article_form.html
@@ -384,7 +384,7 @@
          <iframe
            srcdoc="{{ preview_doc | e }}"
            style="flex:1;width:100%;border:none;display:block;"
-            sandbox="allow-same-origin"
+            sandbox="allow-same-origin allow-scripts"
            title="Article preview"
          ></iframe>
          {% else %}
--- a/web/src/padelnomics/admin/templates/admin/partials/article_preview.html
+++ b/web/src/padelnomics/admin/templates/admin/partials/article_preview.html
@@ -4,7 +4,7 @@
 <iframe
  srcdoc="{{ preview_doc | e }}"
  style="flex:1;width:100%;border:none;display:block;"
-  sandbox="allow-same-origin"
+  sandbox="allow-same-origin allow-scripts"
  title="Article preview"
 ></iframe>
 {% else %}
--- a/web/src/padelnomics/admin/templates/admin/partials/article_preview_doc.html
+++ b/web/src/padelnomics/admin/templates/admin/partials/article_preview_doc.html
@@ -0,0 +1,15 @@
+{# Standalone HTML document used as iframe srcdoc for the article editor preview.
+   Includes Leaflet so map shortcodes render correctly. #}
+<!doctype html>
+<html>
+<head>
+  <link rel="stylesheet" href="{{ url_for('static', filename='css/output.css') }}">
+  <link rel="stylesheet" href="{{ url_for('static', filename='vendor/leaflet/leaflet.min.css') }}">
+  <style>html,body{margin:0;padding:0}body{padding:2rem 2.5rem}</style>
+</head>
+<body>
+  <div class="article-body">{{ body_html | safe }}</div>
+  <script>window.LEAFLET_JS_URL = '{{ url_for("static", filename="vendor/leaflet/leaflet.min.js") }}';</script>
+  <script src="{{ url_for('static', filename='js/article-maps.js') }}"></script>
+</body>
+</html>
--- a/web/src/padelnomics/admin/templates/admin/partials/pipeline_query.html
+++ b/web/src/padelnomics/admin/templates/admin/partials/pipeline_query.html
@@ -171,7 +171,7 @@
        autocomplete="off"
        autocorrect="off"
        autocapitalize="off"
-        placeholder="-- SELECT * FROM serving.city_market_profile&#10;-- WHERE country_code = 'DE'&#10;-- ORDER BY marktreife_score DESC&#10;-- LIMIT 20"
+        placeholder="-- SELECT * FROM serving.location_profiles&#10;-- WHERE country_code = 'DE' AND city_slug IS NOT NULL&#10;-- ORDER BY market_score DESC&#10;-- LIMIT 20"
      ></textarea>

      <div class="query-controls">
--- a/web/src/padelnomics/admin/templates/admin/template_preview.html
+++ b/web/src/padelnomics/admin/templates/admin/template_preview.html
@@ -3,6 +3,10 @@

 {% block title %}Preview - {{ preview.title }} - Admin{% endblock %}

+{% block head %}{{ super() }}
+<link rel="stylesheet" href="{{ url_for('static', filename='vendor/leaflet/leaflet.min.css') }}">
+{% endblock %}
+
 {% block admin_content %}
  <a href="{{ url_for('admin.template_detail', slug=config.slug) }}" class="text-sm text-slate">&larr; Back to template</a>

@@ -21,11 +25,14 @@
    </div>
  </div>

-  {# Rendered article #}
-  <div class="card">
+  {# Rendered article — overflow:visible needed so Leaflet tile layers render #}
+  <div class="card" style="overflow: visible;">
    <h2 class="text-lg mb-4">Rendered HTML</h2>
-    <div class="prose" style="max-width: none;">
+    <div class="article-body" style="max-width: none;">
      {{ preview.html | safe }}
    </div>
  </div>
+
+  <script>window.LEAFLET_JS_URL = '{{ url_for("static", filename="vendor/leaflet/leaflet.min.js") }}';</script>
+  <script src="{{ url_for('static', filename='js/article-maps.js') }}"></script>
 {% endblock %}
--- a/web/src/padelnomics/analytics.py
+++ b/web/src/padelnomics/analytics.py
@@ -13,7 +13,7 @@ Usage:

    rows = await fetch_analytics("SELECT * FROM serving.planner_defaults WHERE city_slug = ?", ["berlin"])

-    cols, rows, error, elapsed_ms = await execute_user_query("SELECT city_slug FROM serving.city_market_profile LIMIT 5")
+    cols, rows, error, elapsed_ms = await execute_user_query("SELECT city_slug FROM serving.location_profiles LIMIT 5")
 """
 import asyncio
 import logging
--- a/web/src/padelnomics/api.py
+++ b/web/src/padelnomics/api.py
@@ -8,7 +8,7 @@ daily when the pipeline runs).
 from quart import Blueprint, abort, jsonify

 from .analytics import fetch_analytics
-from .core import is_flag_enabled
+from .core import fetch_all, is_flag_enabled

 bp = Blueprint("api", __name__)

@@ -32,12 +32,14 @@ async def countries():
    rows = await fetch_analytics("""
        SELECT country_code, country_name_en, country_slug,
               COUNT(*) AS city_count,
-               SUM(padel_venue_count) AS total_venues,
+               SUM(city_padel_venue_count) AS total_venues,
               ROUND(AVG(market_score), 1) AS avg_market_score,
+               ROUND(AVG(opportunity_score), 1) AS avg_opportunity_score,
               AVG(lat) AS lat, AVG(lon) AS lon
-        FROM serving.city_market_profile
+        FROM serving.location_profiles
+        WHERE city_slug IS NOT NULL
        GROUP BY country_code, country_name_en, country_slug
-        HAVING SUM(padel_venue_count) > 0
+        HAVING SUM(city_padel_venue_count) > 0
        ORDER BY total_venues DESC
    """)
    return jsonify(rows), 200, _CACHE_HEADERS
@@ -51,14 +53,29 @@ async def country_cities(country_slug: str):
    rows = await fetch_analytics(
        """
        SELECT city_name, city_slug, lat, lon,
-               padel_venue_count, market_score, population
-        FROM serving.city_market_profile
-        WHERE country_slug = ?
-        ORDER BY padel_venue_count DESC
+               city_padel_venue_count AS padel_venue_count,
+               market_score, opportunity_score, population
+        FROM serving.location_profiles
+        WHERE country_slug = ? AND city_slug IS NOT NULL
+        ORDER BY city_padel_venue_count DESC
        LIMIT 200
        """,
        [country_slug],
    )
+    # Check which cities have published articles (any language).
+    article_rows = await fetch_all(
+        """SELECT url_path FROM articles
+           WHERE url_path LIKE ? AND status = 'published'
+           AND published_at <= datetime('now')""",
+        (f"/markets/{country_slug}/%",),
+    )
+    article_slugs = set()
+    for a in article_rows:
+        parts = a["url_path"].rstrip("/").split("/")
+        if len(parts) >= 4:
+            article_slugs.add(parts[3])
+    for row in rows:
+        row["has_article"] = row["city_slug"] in article_slugs
    return jsonify(rows), 200, _CACHE_HEADERS


@@ -88,9 +105,10 @@ async def opportunity(country_slug: str):
    rows = await fetch_analytics(
        """
        SELECT location_name, location_slug, lat, lon,
-               opportunity_score, nearest_padel_court_km,
+               opportunity_score, market_score,
+               nearest_padel_court_km,
               padel_venue_count, population
-        FROM serving.location_opportunity_profile
+        FROM serving.location_profiles
        WHERE country_slug = ? AND opportunity_score > 0
        ORDER BY opportunity_score DESC
        LIMIT 500
--- a/web/src/padelnomics/app.py
+++ b/web/src/padelnomics/app.py
@@ -5,7 +5,7 @@ import json
 import time
 from pathlib import Path

-from quart import Quart, Response, abort, g, redirect, request, session, url_for
+from quart import Quart, Response, abort, g, redirect, render_template, request, session, url_for

 from .analytics import close_analytics_db, open_analytics_db
 from .core import (
@@ -270,6 +270,40 @@ def create_app() -> Quart:
        from .sitemap import sitemap_response
        return await sitemap_response(config.BASE_URL)

+    # -------------------------------------------------------------------------
+    # Error pages
+    # -------------------------------------------------------------------------
+
+    def _error_lang() -> str:
+        """Best-effort language from URL path prefix (no g.lang in error handlers)."""
+        path = request.path
+        if path.startswith("/de/"):
+            return "de"
+        return "en"
+
+    @app.errorhandler(404)
+    async def handle_404(error):
+        import re
+        lang = _error_lang()
+        t = get_translations(lang)
+        country_slug = None
+        country_name = None
+        m = re.match(r"^/(?:en|de)/markets/([^/]+)/[^/]+/?$", request.path)
+        if m:
+            country_slug = m.group(1)
+            country_name = country_slug.replace("-", " ").title()
+        return await render_template(
+            "404.html", lang=lang, t=t, country_slug=country_slug,
+            country_name=country_name or "",
+        ), 404
+
+    @app.errorhandler(500)
+    async def handle_500(error):
+        app.logger.exception("Unhandled 500 error: %s", error)
+        lang = _error_lang()
+        t = get_translations(lang)
+        return await render_template("500.html", lang=lang, t=t), 500
+
    # Health check
    @app.route("/health")
    async def health():
--- a/web/src/padelnomics/content/templates/article_detail.html
+++ b/web/src/padelnomics/content/templates/article_detail.html
@@ -60,106 +60,6 @@
 {% endblock %}

 {% block scripts %}
-<script>
-(function() {
-  var countryMapEl = document.getElementById('country-map');
-  var cityMapEl = document.getElementById('city-map');
-  if (!countryMapEl && !cityMapEl) return;
-
-  var TILES = 'https://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}{r}.png';
-  var TILES_ATTR = '&copy; <a href="https://www.openstreetmap.org/copyright">OSM</a> &copy; <a href="https://carto.com/">CARTO</a>';
-
-  function scoreColor(score) {
-    if (score >= 60) return '#16A34A';
-    if (score >= 30) return '#D97706';
-    return '#DC2626';
-  }
-
-  function makeIcon(size, color) {
-    var s = Math.round(size);
-    return L.divIcon({
-      className: '',
-      html: '<div class="pn-marker" style="width:' + s + 'px;height:' + s + 'px;background:' + color + ';opacity:0.82;"></div>',
-      iconSize: [s, s],
-      iconAnchor: [s / 2, s / 2],
-    });
-  }
-
-  function initCountryMap(el) {
-    var slug = el.dataset.countrySlug;
-    var map = L.map(el, {scrollWheelZoom: false});
-    L.tileLayer(TILES, { attribution: TILES_ATTR, maxZoom: 18 }).addTo(map);
-    var lang = document.documentElement.lang || 'en';
-    fetch('/api/markets/' + slug + '/cities.json')
-      .then(function(r) { return r.json(); })
-      .then(function(data) {
-        if (!data.length) return;
-        var maxV = Math.max.apply(null, data.map(function(d) { return d.padel_venue_count || 1; }));
-        var bounds = [];
-        data.forEach(function(c) {
-          if (!c.lat || !c.lon) return;
-          var size = 10 + 36 * Math.sqrt((c.padel_venue_count || 1) / maxV);
-          var color = scoreColor(c.market_score);
-          var pop = c.population >= 1000000
-            ? (c.population / 1000000).toFixed(1) + 'M'
-            : (c.population >= 1000 ? Math.round(c.population / 1000) + 'K' : (c.population || ''));
-          var tip = '<strong>' + c.city_name + '</strong><br>'
-            + (c.padel_venue_count || 0) + ' venues'
-            + (pop ? ' · ' + pop : '') + '<br>'
-            + '<span style="color:' + color + ';font-weight:600;">Score ' + Math.round(c.market_score) + '/100</span>';
-          L.marker([c.lat, c.lon], { icon: makeIcon(size, color) })
-            .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -Math.round(size / 2)] })
-            .on('click', function() { window.location = '/' + lang + '/markets/' + slug + '/' + c.city_slug; })
-            .addTo(map);
-          bounds.push([c.lat, c.lon]);
-        });
-        if (bounds.length) map.fitBounds(bounds, { padding: [24, 24] });
-      });
-  }
-
-  var VENUE_ICON = L.divIcon({
-    className: '',
-    html: '<div class="pn-venue"></div>',
-    iconSize: [10, 10],
-    iconAnchor: [5, 5],
-  });
-
-  function initCityMap(el) {
-    var countrySlug = el.dataset.countrySlug;
-    var citySlug = el.dataset.citySlug;
-    var lat = parseFloat(el.dataset.lat);
-    var lon = parseFloat(el.dataset.lon);
-    var map = L.map(el, {scrollWheelZoom: false}).setView([lat, lon], 13);
-    L.tileLayer(TILES, { attribution: TILES_ATTR, maxZoom: 18 }).addTo(map);
-    fetch('/api/markets/' + countrySlug + '/' + citySlug + '/venues.json')
-      .then(function(r) { return r.json(); })
-      .then(function(data) {
-        data.forEach(function(v) {
-          if (!v.lat || !v.lon) return;
-          var indoor = v.indoor_court_count || 0;
-          var outdoor = v.outdoor_court_count || 0;
-          var total = v.court_count || (indoor + outdoor);
-          var courtLine = total
-            ? total + ' court' + (total > 1 ? 's' : '')
-              + (indoor || outdoor
-                ? ' (' + [indoor ? indoor + ' indoor' : '', outdoor ? outdoor + ' outdoor' : ''].filter(Boolean).join(', ') + ')'
-                : '')
-            : '';
-          var tip = '<strong>' + v.name + '</strong>' + (courtLine ? '<br>' + courtLine : '');
-          L.marker([v.lat, v.lon], { icon: VENUE_ICON })
-            .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -7] })
-            .addTo(map);
-        });
-      });
-  }
-
-  var script = document.createElement('script');
-  script.src = '{{ url_for("static", filename="vendor/leaflet/leaflet.min.js") }}';
-  script.onload = function() {
-    if (countryMapEl) initCountryMap(countryMapEl);
-    if (cityMapEl) initCityMap(cityMapEl);
-  };
-  document.head.appendChild(script);
-})();
-</script>
+<script>window.LEAFLET_JS_URL = '{{ url_for("static", filename="vendor/leaflet/leaflet.min.js") }}';</script>
+<script src="{{ url_for('static', filename='js/article-maps.js') }}"></script>
 {% endblock %}
--- a/web/src/padelnomics/content/templates/markets.html
+++ b/web/src/padelnomics/content/templates/markets.html
@@ -102,9 +102,11 @@
        if (!c.lat || !c.lon) return;
        var size = 12 + 44 * Math.sqrt(c.total_venues / maxV);
        var color = scoreColor(c.avg_market_score);
+        var oppColor = c.avg_opportunity_score >= 60 ? '#16A34A' : (c.avg_opportunity_score >= 30 ? '#D97706' : '#3B82F6');
        var tip = '<strong>' + c.country_name_en + '</strong><br>'
          + c.total_venues + ' venues · ' + c.city_count + ' cities<br>'
-          + '<span style="color:' + color + ';font-weight:600;">Score ' + c.avg_market_score + '/100</span>';
+          + '<span style="color:' + color + ';font-weight:600;">Padelnomics Market Score: ' + c.avg_market_score + '/100</span><br>'
+          + '<span style="color:' + oppColor + ';font-weight:600;">Padelnomics Opportunity Score: ' + (c.avg_opportunity_score || 0) + '/100</span>';
        L.marker([c.lat, c.lon], { icon: makeIcon(size, color) })
          .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -Math.round(size / 2)] })
          .on('click', function() { window.location = '/' + lang + '/markets/' + c.country_slug; })
--- a/web/src/padelnomics/locales/de.json
+++ b/web/src/padelnomics/locales/de.json
@@ -1825,5 +1825,16 @@
  "affiliate_pros_label": "Vorteile",
  "affiliate_cons_label": "Nachteile",
  "affiliate_at_retailer": "bei {retailer}",
-  "affiliate_our_picks": "Unsere Empfehlungen"
+  "affiliate_our_picks": "Unsere Empfehlungen",
+
+  "error_404_title": "Seite nicht gefunden",
+  "error_404_heading": "Diese Seite gibt es nicht",
+  "error_404_message": "Die gesuchte Seite wurde verschoben oder existiert noch nicht.",
+  "error_404_city_message": "Die Marktanalyse für diese Stadt ist noch nicht verfügbar.",
+  "error_404_back_home": "Zur Startseite",
+  "error_404_back_country": "Zurück zur {country}-Übersicht",
+  "error_500_title": "Etwas ist schiefgelaufen",
+  "error_500_heading": "Etwas ist schiefgelaufen",
+  "error_500_message": "Wir arbeiten an einer Lösung. Bitte versuche es gleich noch einmal.",
+  "error_500_back_home": "Zur Startseite"
 }
--- a/web/src/padelnomics/locales/en.json
+++ b/web/src/padelnomics/locales/en.json
@@ -1828,5 +1828,16 @@
  "affiliate_pros_label": "Pros",
  "affiliate_cons_label": "Cons",
  "affiliate_at_retailer": "at {retailer}",
-  "affiliate_our_picks": "Our picks"
+  "affiliate_our_picks": "Our picks",
+
+  "error_404_title": "Page Not Found",
+  "error_404_heading": "This page doesn't exist",
+  "error_404_message": "The page you're looking for may have been moved or doesn't exist yet.",
+  "error_404_city_message": "The market analysis for this city isn't available yet.",
+  "error_404_back_home": "Back to Home",
+  "error_404_back_country": "Back to {country} overview",
+  "error_500_title": "Something Went Wrong",
+  "error_500_heading": "Something went wrong",
+  "error_500_message": "We're working on fixing this. Please try again in a moment.",
+  "error_500_back_home": "Back to Home"
 }
--- a/web/src/padelnomics/public/routes.py
+++ b/web/src/padelnomics/public/routes.py
@@ -80,7 +80,8 @@ async def opportunity_map():
        abort(404)
    countries = await fetch_analytics("""
        SELECT DISTINCT country_slug, country_name_en
-        FROM serving.city_market_profile
+        FROM serving.location_profiles
+        WHERE city_slug IS NOT NULL
        ORDER BY country_name_en
    """)
    return await render_template("opportunity_map.html", countries=countries)
--- a/web/src/padelnomics/public/templates/opportunity_map.html
+++ b/web/src/padelnomics/public/templates/opportunity_map.html
@@ -104,8 +104,10 @@
          var dist = loc.nearest_padel_court_km != null
            ? loc.nearest_padel_court_km.toFixed(1) + ' km to nearest court'
            : 'No nearby courts';
+          var mktColor = loc.market_score >= 60 ? '#16A34A' : (loc.market_score >= 30 ? '#D97706' : '#DC2626');
          var tip = '<strong>' + loc.location_name + '</strong><br>'
-            + '<span style="color:' + color + ';font-weight:600;">Score ' + loc.opportunity_score + '/100</span><br>'
+            + '<span style="color:' + color + ';font-weight:600;">Padelnomics Opportunity Score: ' + loc.opportunity_score + '/100</span><br>'
+            + '<span style="color:' + mktColor + ';font-weight:600;">Padelnomics Market Score: ' + (loc.market_score || 0) + '/100</span><br>'
            + dist + ' · Pop. ' + fmtPop(loc.population);
          L.marker([loc.lat, loc.lon], { icon: makeIcon(size, color) })
            .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -Math.round(size / 2)] })
--- a/web/src/padelnomics/static/css/input.css
+++ b/web/src/padelnomics/static/css/input.css
@@ -892,6 +892,18 @@
  transform: scale(1.1);
 }

+/* Non-article city markers: faded + dashed border, no click affordance */
+.pn-marker--muted {
+  opacity: 0.45;
+  border: 2px dashed rgba(255,255,255,0.6);
+  cursor: default;
+  filter: saturate(0.7);
+}
+.pn-marker--muted:hover {
+  transform: none;
+  box-shadow: 0 2px 8px rgba(0,0,0,0.28);
+}
+
 /* Small fixed venue dot */
 .pn-venue {
  width: 10px;
--- a/web/src/padelnomics/static/css/planner.css
+++ b/web/src/padelnomics/static/css/planner.css
@@ -455,6 +455,8 @@
  border-radius: 18px;
  padding: 1rem;
  box-shadow: 0 1px 4px rgba(0,0,0,0.04);
+  min-width: 0;
+  overflow: hidden;
 }
 .chart-container__label {
  font-size: 11px;
--- a/web/src/padelnomics/static/js/article-maps.js
+++ b/web/src/padelnomics/static/js/article-maps.js
@@ -0,0 +1,122 @@
+/**
+ * Leaflet map initialisation for article pages (country + city maps).
+ *
+ * Looks for #country-map and #city-map elements. If neither exists, does nothing.
+ * Expects data-* attributes on the map elements and a global LEAFLET_JS_URL
+ * variable pointing to the Leaflet JS bundle.
+ */
+(function() {
+  var countryMapEl = document.getElementById('country-map');
+  var cityMapEl = document.getElementById('city-map');
+  if (!countryMapEl && !cityMapEl) return;
+
+  var TILES = 'https://{s}.basemaps.cartocdn.com/light_all/{z}/{x}/{y}{r}.png';
+  var TILES_ATTR = '&copy; <a href="https://www.openstreetmap.org/copyright">OSM</a> &copy; <a href="https://carto.com/">CARTO</a>';
+
+  function scoreColor(score) {
+    if (score >= 60) return '#16A34A';
+    if (score >= 30) return '#D97706';
+    return '#DC2626';
+  }
+
+  function makeIcon(size, color, muted) {
+    var s = Math.round(size);
+    var cls = 'pn-marker' + (muted ? ' pn-marker--muted' : '');
+    return L.divIcon({
+      className: '',
+      html: '<div class="' + cls + '" style="width:' + s + 'px;height:' + s + 'px;background:' + color + ';"></div>',
+      iconSize: [s, s],
+      iconAnchor: [s / 2, s / 2],
+    });
+  }
+
+  function initCountryMap(el) {
+    var slug = el.dataset.countrySlug;
+    var map = L.map(el, {scrollWheelZoom: false});
+    L.tileLayer(TILES, { attribution: TILES_ATTR, maxZoom: 18 }).addTo(map);
+    var lang = document.documentElement.lang || 'en';
+    fetch('/api/markets/' + slug + '/cities.json')
+      .then(function(r) { return r.json(); })
+      .then(function(data) {
+        if (!data.length) return;
+        var maxV = Math.max.apply(null, data.map(function(d) { return d.padel_venue_count || 1; }));
+        var bounds = [];
+        data.forEach(function(c) {
+          if (!c.lat || !c.lon) return;
+          var size = 10 + 36 * Math.sqrt((c.padel_venue_count || 1) / maxV);
+          var hasArticle = c.has_article !== false;
+          var color = scoreColor(c.market_score);
+          var pop = c.population >= 1000000
+            ? (c.population / 1000000).toFixed(1) + 'M'
+            : (c.population >= 1000 ? Math.round(c.population / 1000) + 'K' : (c.population || ''));
+          var oppColor = c.opportunity_score >= 60 ? '#16A34A' : (c.opportunity_score >= 30 ? '#D97706' : '#3B82F6');
+          var tip = '<strong>' + c.city_name + '</strong><br>'
+            + (c.padel_venue_count || 0) + ' venues'
+            + (pop ? ' · ' + pop : '')
+            + '<br><span style="color:' + color + ';font-weight:600;">Padelnomics Market Score: ' + Math.round(c.market_score) + '/100</span>'
+            + '<br><span style="color:' + oppColor + ';font-weight:600;">Padelnomics Opportunity Score: ' + Math.round(c.opportunity_score || 0) + '/100</span>';
+          if (hasArticle) {
+            tip += '<br><span style="color:#94A3B8;font-size:0.75rem;">Click to explore →</span>';
+          } else {
+            tip += '<br><span style="color:#94A3B8;font-size:0.75rem;">Coming soon</span>';
+          }
+          var marker = L.marker([c.lat, c.lon], { icon: makeIcon(size, color, !hasArticle) })
+            .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -Math.round(size / 2)] })
+            .addTo(map);
+          if (hasArticle) {
+            marker.on('click', function() { window.location = '/' + lang + '/markets/' + slug + '/' + c.city_slug; });
+          }
+          bounds.push([c.lat, c.lon]);
+        });
+        if (bounds.length) map.fitBounds(bounds, { padding: [24, 24] });
+      })
+      .catch(function(err) { console.error('Country map fetch failed:', err); });
+  }
+
+  function initCityMap(el, venueIcon) {
+    var countrySlug = el.dataset.countrySlug;
+    var citySlug = el.dataset.citySlug;
+    var lat = parseFloat(el.dataset.lat);
+    var lon = parseFloat(el.dataset.lon);
+    var map = L.map(el, {scrollWheelZoom: false}).setView([lat, lon], 13);
+    L.tileLayer(TILES, { attribution: TILES_ATTR, maxZoom: 18 }).addTo(map);
+    fetch('/api/markets/' + countrySlug + '/' + citySlug + '/venues.json')
+      .then(function(r) { return r.json(); })
+      .then(function(data) {
+        data.forEach(function(v) {
+          if (!v.lat || !v.lon) return;
+          var indoor = v.indoor_court_count || 0;
+          var outdoor = v.outdoor_court_count || 0;
+          var total = v.court_count || (indoor + outdoor);
+          var courtLine = total
+            ? total + ' court' + (total > 1 ? 's' : '')
+              + (indoor || outdoor
+                ? ' (' + [indoor ? indoor + ' indoor' : '', outdoor ? outdoor + ' outdoor' : ''].filter(Boolean).join(', ') + ')'
+                : '')
+            : '';
+          var tip = '<strong>' + v.name + '</strong>' + (courtLine ? '<br>' + courtLine : '');
+          L.marker([v.lat, v.lon], { icon: venueIcon })
+            .bindTooltip(tip, { className: 'map-tooltip', direction: 'top', offset: [0, -7] })
+            .addTo(map);
+        });
+      })
+      .catch(function(err) { console.error('City map fetch failed:', err); });
+  }
+
+  /* Dynamically load Leaflet JS then init maps */
+  var script = document.createElement('script');
+  script.src = window.LEAFLET_JS_URL || '/static/vendor/leaflet/leaflet.min.js';
+  script.onload = function() {
+    if (countryMapEl) initCountryMap(countryMapEl);
+    if (cityMapEl) {
+      var venueIcon = L.divIcon({
+        className: '',
+        html: '<div class="pn-venue"></div>',
+        iconSize: [10, 10],
+        iconAnchor: [5, 5],
+      });
+      initCityMap(cityMapEl, venueIcon);
+    }
+  };
+  document.head.appendChild(script);
+})();
--- a/web/src/padelnomics/templates/404.html
+++ b/web/src/padelnomics/templates/404.html
@@ -0,0 +1,23 @@
+{% extends "base.html" %}
+
+{% block title %}{{ t.error_404_title }} — {{ config.APP_NAME }}{% endblock %}
+
+{% block content %}
+<div class="container-page py-12">
+  <div style="max-width:28rem;margin:0 auto;text-align:center;">
+    <p style="font-size:6rem;font-weight:800;line-height:1;color:var(--slate);opacity:0.3;margin:0;">404</p>
+    <h1 class="text-navy" style="font-size:1.5rem;font-weight:700;margin:1rem 0 0.5rem;">{{ t.error_404_heading }}</h1>
+    {% if country_slug %}
+      <p class="text-slate" style="font-size:1rem;line-height:1.6;">{{ t.error_404_city_message }}</p>
+      <a href="/{{ lang }}/markets/{{ country_slug }}" class="btn" style="margin-top:1.5rem;display:inline-block;">
+        {{ t.error_404_back_country.replace('{country}', country_name) }}
+      </a>
+    {% else %}
+      <p class="text-slate" style="font-size:1rem;line-height:1.6;">{{ t.error_404_message }}</p>
+      <a href="/{{ lang }}" class="btn" style="margin-top:1.5rem;display:inline-block;">
+        {{ t.error_404_back_home }}
+      </a>
+    {% endif %}
+  </div>
+</div>
+{% endblock %}
--- a/web/src/padelnomics/templates/500.html
+++ b/web/src/padelnomics/templates/500.html
@@ -0,0 +1,16 @@
+{% extends "base.html" %}
+
+{% block title %}{{ t.error_500_title }} — {{ config.APP_NAME }}{% endblock %}
+
+{% block content %}
+<div class="container-page py-12">
+  <div style="max-width:28rem;margin:0 auto;text-align:center;">
+    <p style="font-size:6rem;font-weight:800;line-height:1;color:var(--slate);opacity:0.3;margin:0;">500</p>
+    <h1 class="text-navy" style="font-size:1.5rem;font-weight:700;margin:1rem 0 0.5rem;">{{ t.error_500_heading }}</h1>
+    <p class="text-slate" style="font-size:1rem;line-height:1.6;">{{ t.error_500_message }}</p>
+    <a href="/{{ lang }}" class="btn" style="margin-top:1.5rem;display:inline-block;">
+      {{ t.error_500_back_home }}
+    </a>
+  </div>
+</div>
+{% endblock %}
--- a/web/src/padelnomics/worker.py
+++ b/web/src/padelnomics/worker.py
@@ -737,7 +737,7 @@ async def handle_run_extraction(payload: dict) -> None:

@task("run_transform")
 async def handle_run_transform(payload: dict) -> None:
-    """Run SQLMesh transform (prod plan --auto-apply) in the background.
+    """Run SQLMesh transform (prod plan + apply) in the background.

    Shells out to `uv run sqlmesh -p transform/sqlmesh_padelnomics plan prod --auto-apply`.
    2-hour absolute timeout — same as extraction.
--- a/web/tests/test_pipeline.py
+++ b/web/tests/test_pipeline.py
@@ -64,7 +64,7 @@ def serving_meta_dir():
        meta = {
            "exported_at_utc": "2026-02-25T08:30:00+00:00",
            "tables": {
-                "city_market_profile": {"row_count": 612},
+                "location_profiles": {"row_count": 612},
                "planner_defaults": {"row_count": 612},
                "pseo_city_costs_de": {"row_count": 487},
            },
@@ -78,16 +78,16 @@ def serving_meta_dir():
 # ── Schema + query mocks ──────────────────────────────────────────────────────

 _MOCK_SCHEMA_ROWS = [
-    {"table_name": "city_market_profile", "column_name": "city_slug", "data_type": "VARCHAR", "ordinal_position": 1},
-    {"table_name": "city_market_profile", "column_name": "country_code", "data_type": "VARCHAR", "ordinal_position": 2},
-    {"table_name": "city_market_profile", "column_name": "marktreife_score", "data_type": "DOUBLE", "ordinal_position": 3},
+    {"table_name": "location_profiles", "column_name": "city_slug", "data_type": "VARCHAR", "ordinal_position": 1},
+    {"table_name": "location_profiles", "column_name": "country_code", "data_type": "VARCHAR", "ordinal_position": 2},
+    {"table_name": "location_profiles", "column_name": "market_score", "data_type": "DOUBLE", "ordinal_position": 3},
    {"table_name": "planner_defaults", "column_name": "city_slug", "data_type": "VARCHAR", "ordinal_position": 1},
 ]

 _MOCK_TABLE_EXISTS = [{"1": 1}]
 _MOCK_SAMPLE_ROWS = [
-    {"city_slug": "berlin", "country_code": "DE", "marktreife_score": 82.5},
-    {"city_slug": "munich", "country_code": "DE", "marktreife_score": 77.0},
+    {"city_slug": "berlin", "country_code": "DE", "market_score": 82.5},
+    {"city_slug": "munich", "country_code": "DE", "market_score": 77.0},
 ]


@@ -100,7 +100,7 @@ def _make_fetch_analytics_mock(schema=True):
            return [r for r in _MOCK_SCHEMA_ROWS if r["table_name"] == params[0]]
        if "information_schema.columns" in sql:
            return _MOCK_SCHEMA_ROWS
-        if "city_market_profile" in sql:
+        if "location_profiles" in sql:
            return _MOCK_SAMPLE_ROWS
        return []
    return _mock
@@ -162,7 +162,7 @@ async def test_pipeline_overview(admin_client, state_db_dir, serving_meta_dir):
        resp = await admin_client.get("/admin/pipeline/overview")
    assert resp.status_code == 200
    data = await resp.get_data(as_text=True)
-    assert "city_market_profile" in data
+    assert "location_profiles" in data
    assert "612" in data  # row count from serving meta


@@ -314,7 +314,7 @@ async def test_pipeline_catalog(admin_client, serving_meta_dir):
        resp = await admin_client.get("/admin/pipeline/catalog")
    assert resp.status_code == 200
    data = await resp.get_data(as_text=True)
-    assert "city_market_profile" in data
+    assert "location_profiles" in data
    assert "612" in data  # row count from serving meta


@@ -322,7 +322,7 @@ async def test_pipeline_catalog(admin_client, serving_meta_dir):
 async def test_pipeline_table_detail(admin_client):
    """Table detail returns columns and sample rows."""
    with patch("padelnomics.analytics.fetch_analytics", side_effect=_make_fetch_analytics_mock()):
-        resp = await admin_client.get("/admin/pipeline/catalog/city_market_profile")
+        resp = await admin_client.get("/admin/pipeline/catalog/location_profiles")
    assert resp.status_code == 200
    data = await resp.get_data(as_text=True)
    assert "city_slug" in data
@@ -362,7 +362,7 @@ async def test_pipeline_query_editor_loads(admin_client):
    data = await resp.get_data(as_text=True)
    assert "query-editor" in data
    assert "schema-panel" in data
-    assert "city_market_profile" in data
+    assert "location_profiles" in data


@pytest.mark.asyncio
@@ -380,7 +380,7 @@ async def test_pipeline_query_execute_valid(admin_client):
    with patch("padelnomics.analytics.execute_user_query", new_callable=AsyncMock, return_value=mock_result):
        resp = await admin_client.post(
            "/admin/pipeline/query/execute",
-            form={"csrf_token": "test", "sql": "SELECT city_slug, country_code FROM serving.city_market_profile"},
+            form={"csrf_token": "test", "sql": "SELECT city_slug, country_code FROM serving.location_profiles"},
        )
    assert resp.status_code == 200
    data = await resp.get_data(as_text=True)
@@ -397,7 +397,7 @@ async def test_pipeline_query_execute_blocked_keyword(admin_client):
    with patch("padelnomics.analytics.execute_user_query", new_callable=AsyncMock) as mock_q:
        resp = await admin_client.post(
            "/admin/pipeline/query/execute",
-            form={"csrf_token": "test", "sql": "DROP TABLE serving.city_market_profile"},
+            form={"csrf_token": "test", "sql": "DROP TABLE serving.location_profiles"},
        )
    assert resp.status_code == 200
    data = await resp.get_data(as_text=True)
@@ -532,8 +532,8 @@ def test_load_serving_meta(serving_meta_dir):
    with patch.object(pipeline_mod, "_SERVING_DUCKDB_PATH", str(Path(serving_meta_dir) / "analytics.duckdb")):
        meta = pipeline_mod._load_serving_meta()
    assert meta is not None
-    assert "city_market_profile" in meta["tables"]
-    assert meta["tables"]["city_market_profile"]["row_count"] == 612
+    assert "location_profiles" in meta["tables"]
+    assert meta["tables"]["location_profiles"]["row_count"] == 612


 def test_load_serving_meta_missing():
Author	SHA1	Message	Date
Deeman	fee0d6913b	fix(pipeline): use sqlmesh plan --auto-apply instead of run All checks were successful CI / test (push) Successful in 56s Details CI / tag (push) Successful in 3s Details	2026-03-06 22:34:58 +01:00
Deeman	71e08a5fa6	fix(pipeline): also update supervisor.py to use plan --auto-apply Missed the Python supervisor module — same fix as supervisor.sh and worker.py. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 22:33:59 +01:00
Deeman	27e86db6a1	fix(pipeline): use `sqlmesh plan --auto-apply` instead of `sqlmesh run` `sqlmesh run` only re-evaluates intervals for already-planned models — it does not detect new, modified, or deleted models. Switching to `plan prod --auto-apply` ensures schema changes (like the new location_profiles model) are picked up automatically. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 22:33:17 +01:00
Deeman	90754b8d9f	chore: move ci.py to ~/.claude/scripts (uv inline script, no project dep) All checks were successful CI / test (push) Successful in 53s Details CI / tag (push) Successful in 2s Details Script now lives globally as a uv inline-dependency script. Removes per-project scripts/ci.py and the msgspec dev dependency. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 15:51:36 +01:00
Deeman	277c92e507	chore: add scripts/ci.py for Gitea CI pipeline status Copies ci.py from beanflows (same script, shared across projects). Adds msgspec dev dependency required by the script. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 15:38:42 +01:00
Deeman	77ec3a289f	feat(transform): H3 catchment index, res 5 k_ring(1) ~24km radius All checks were successful CI / test (push) Successful in 54s Details CI / tag (push) Successful in 3s Details Merges worktree-h3-catchment-index. dim_locations now computes h3_cell_res5 (res 5, ~8.5km edge). location_profiles and dim_locations updated; old location_opportunity_profile.sql already removed on master. Conflict: location_opportunity_profile.sql deleted on master, kept deletion and applied h3_cell_res4→res5 rename to location_profiles instead. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 14:45:45 +01:00
Deeman	f81d5f19da	fix(transform): tighten H3 catchment to res 5 (~24km radius) Res 4 + k_ring(1) gave ~50-60km effective radius, causing Oldenburg to absorb Bremen (40km away) and destroying score differentiation. Res 5 + k_ring(1) gives ~24km — captures adjacent Gemeinden (Delmenhorst at 15km) without bleeding into unrelated cities at 40km+. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 14:34:56 +01:00
Deeman	4d29ecf1d6	merge: unified location_profiles serving model + both scores on map tooltips All checks were successful CI / test (push) Successful in 55s Details CI / tag (push) Successful in 3s Details # Conflicts: # CHANGELOG.md # transform/sqlmesh_padelnomics/models/serving/location_opportunity_profile.sql	2026-03-06 14:03:55 +01:00
Deeman	a3b4e1fab6	docs: update CHANGELOG, CLAUDE.md, and comments for location_profiles Update transform CLAUDE.md source integration map and conformed dimensions table. Update CHANGELOG with unified model + tooltip changes. Fix stale comments in dim_cities.sql and serving README. Subtask 5/5: documentation. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 11:45:08 +01:00
Deeman	8b794d24a6	feat(maps): show both scores in all map tooltips Country map: avg Market Score + avg Opportunity Score. City map: Market Score + Opportunity Score per city. Opportunity map: Opportunity Score + Market Score per location. Subtask 4/5: tooltip updates. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 11:42:36 +01:00
Deeman	688f2dd1ee	refactor(web): update all references to location_profiles Update api.py (3 endpoints), public/routes.py, analytics.py docstring, pipeline_routes.py DAG, pipeline_query.html placeholder, and test_pipeline.py fixtures to use the new unified model. Subtask 3/5: web app references. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 11:41:42 +01:00
Deeman	81b556b205	refactor(serving): replace old models with location_profiles Delete city_market_profile.sql and location_opportunity_profile.sql. Update downstream models (planner_defaults, pseo_city_costs_de, pseo_city_pricing) to read from location_profiles instead. Subtask 2/5: delete old models + update downstream SQL. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 11:39:52 +01:00
Deeman	cda94c9ee4	feat(serving): add unified location_profiles model Combines city_market_profile and location_opportunity_profile into a single serving model at (country_code, geoname_id) grain. Both Market Score and Opportunity Score computed per location. City data enriched via LEFT JOIN dim_cities on geoname_id. Subtask 1/5: create new model (old models not yet removed). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 11:36:36 +01:00
Deeman	4fbd91b59b	merge: automate h3 community extension install via sqlmesh config	2026-03-06 10:27:03 +01:00
Deeman	159d1b5b9a	fix(transform): use community repository for h3 extension install SQLMesh's extensions config supports dict form with 'repository' key, which runs INSTALL h3 FROM community + LOAD h3 automatically at connect time. No manual one-time install needed per machine. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 10:26:56 +01:00
Deeman	fcd0c9b007	docs: update CHANGELOG with H3 catchment score v3	2026-03-06 10:20:15 +01:00
Deeman	f841ae105a	merge: use full trademarked score names in map tooltips	2026-03-06 10:19:56 +01:00
Deeman	dec4f07fbb	merge: H3 catchment index for Marktpotenzial-Score v3	2026-03-06 10:19:51 +01:00
Deeman	4e4ff61699	feat(transform): H3 catchment index for Marktpotenzial-Score v3 Add H3 res-4 regional catchment metrics (~15-18km radius, cell + 6 neighbours) to both the addressable market (25pts) and supply gap (30pts) components of location_opportunity_profile. Changes: - config.yaml: add h3 to DuckDB extensions (requires one-time INSTALL h3 FROM community on each machine) - dim_locations: add h3_cell_res4 column via h3_latlng_to_cell() - location_opportunity_profile: add hex_stats + catchment CTEs; update score formula to use catchment_population and catchment_padel_courts; expose catchment_population, catchment_padel_courts, catchment_venues_per_100k as output cols Motivation: local population underestimates functional market for mid-size cities (e.g. Oldenburg ~170K misses surrounding Gemeinden). H3 k_ring(1) captures the realistic driving-distance catchment (~462km²) consistently across both score components. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 10:19:43 +01:00
Deeman	f907f2cd60	fix(maps): use full trademarked score names in all map tooltips "Score X/100" → "Padelnomics Market Score: X/100" on country map (markets hub), city map (country overview). Opportunity map uses "Padelnomics Opportunity Score: X/100". Consistent branding across all three map views. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 10:19:08 +01:00
Deeman	3ad2885c84	merge: fix map bubble styling + improve hover UX	2026-03-06 10:11:26 +01:00
Deeman	e2f54552b0	fix(maps): restore score colors for non-article cities, improve hover UX Non-article cities were fully gray (#9CA3AF), stripping informational value. Now all cities show score-based colors (green/amber/red). Non-article cities are differentiated via lower opacity, dashed border, desaturation, and default cursor (no click handler). Tooltips show scores for all cities — article cities get "Click to explore →", non-article cities get "Coming soon". Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 10:09:58 +01:00
Deeman	07ca1ce15b	merge: custom 404/500 error pages + smarter map city clicks	2026-03-06 10:01:50 +01:00
Deeman	be9b10c13f	docs: update CHANGELOG with error pages and map improvements Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 09:59:29 +01:00
Deeman	82d6333517	feat: differentiate cities with/without articles on country map Cities without published articles appear in muted gray and are not clickable. The cities.json API endpoint now queries SQLite for published articles and adds a has_article boolean to each city row. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 09:57:01 +01:00
Deeman	ed48936dad	feat: add styled 404/500 error pages with i18n support Custom error templates extending base.html with centered layout. 404 is context-aware: detects /markets/{country}/{city} paths and shows city-specific message with link back to country overview. Both pages support EN/DE translations. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 09:55:13 +01:00
Deeman	e3bda5b816	merge: fix admin template preview UX issues (maps, article_stats route, dev debug mode)	2026-03-06 09:35:45 +01:00
Deeman	831233cb29	fix(admin): add missing article_stats route, 500 handler, dev debug mode - Add /admin/articles/stats HTMX partial endpoint that was referenced by article_stats.html but never created (caused 500 during generation) - Add @app.errorhandler(500) to log exceptions with traceback - Switch dev_run.sh from Granian to Quart debug mode for browser tracebacks and auto-reload Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 09:34:22 +01:00
Deeman	c5327c4012	fix(maps): move VENUE_ICON creation after Leaflet loads L.divIcon() was called at IIFE top level before the dynamic Leaflet script loaded, throwing ReferenceError and preventing all maps from rendering. Move icon creation into script.onload callback. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-06 09:01:54 +01:00
Deeman	4426ab2cb6	fix(admin): render Leaflet maps in template preview	2026-03-05 22:58:34 +01:00
Deeman	93c9408f6b	fix(admin): render Leaflet maps in template preview The .card wrapper has overflow:hidden which clips Leaflet's absolutely-positioned tile layers. Override to overflow:visible on the rendered-article card. Add .catch() to map fetch calls. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 22:58:27 +01:00
Deeman	84128a3a64	merge: fix map scripts in template preview	2026-03-05 22:33:16 +01:00
Deeman	e9b4faa05c	fix(admin): move map scripts inline in template preview Put Leaflet init scripts inside admin_content block instead of relying on the scripts block inheritance chain through base_admin → base. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 22:33:16 +01:00
Deeman	a834bb481d	merge: load Leaflet maps in admin template preview	2026-03-05 22:29:13 +01:00
Deeman	9515ec8ae9	fix(admin): load Leaflet maps in template preview page The /admin/templates/<slug>/preview/<key> page renders article HTML directly but never loaded Leaflet CSS/JS, so country-map and city-map divs appeared empty. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 22:29:00 +01:00
Deeman	fb99d6e0db	merge: fix sqlmesh worker command + article preview maps	2026-03-05 22:14:37 +01:00
Deeman	4ee80603ef	fix(articles): load Leaflet maps in article editor preview The admin article preview iframe was missing Leaflet CSS/JS and had scripts blocked by the sandbox policy, so map shortcodes rendered as empty divs. - Extract inline map script to static/js/article-maps.js (shared between article_detail.html and admin preview) - Replace f-string preview doc with a proper Jinja template that includes Leaflet assets - Add allow-scripts to iframe sandbox on both initial load and HTMX preview updates Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 22:03:50 +01:00
Deeman	2e42245ad5	fix(worker): use sqlmesh run prod instead of plan prod --auto-apply `plan --auto-apply` only detects SQL model changes and won't re-run for new data. `run prod` evaluates missing cron intervals and picks up newly extracted data — matching the fix already applied to the supervisor. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 21:49:51 +01:00
Deeman	2f47d1e589	fix(pipeline): make availability chain incremental + fix supervisor Convert the availability chain (stg_playtomic_availability → fct_availability_slot → fct_daily_availability) from FULL to INCREMENTAL_BY_TIME_RANGE so sqlmesh run processes only new daily intervals instead of re-reading all files. Supervisor changes: - run_transform(): plan prod --auto-apply → run prod (evaluates missing cron intervals, picks up new data) - git_pull_and_sync(): add plan prod --auto-apply before re-exec so model code changes are applied on deploy - supervisor.sh: same plan → run change Staging model uses a date-scoped glob (@start_ds) to read only the current interval's files. snapshot_date cast to DATE (was VARCHAR) as required by time_column. Clean up redundant TRY_CAST(snapshot_date AS DATE) in venue_pricing_benchmarks since it's already DATE from foundation. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 21:34:02 +01:00
Deeman	ead12c4552	fix(planner): prevent chart containers from overflowing on small screens All checks were successful CI / test (push) Successful in 54s Details CI / tag (push) Successful in 2s Details	2026-03-05 18:27:33 +01:00
Deeman	c54eb50004	fix(planner): prevent chart containers from overflowing on small screens Grid children default to min-width:auto, letting the Chart.js canvas push the container wider than its grid track. Adding min-width:0 and overflow:hidden constrains charts to their column width. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 18:27:27 +01:00
Deeman	5d7fcec17a	chore: change GISCO extraction schedule from monthly to yearly All checks were successful CI / test (push) Successful in 54s Details CI / tag (push) Successful in 3s Details	2026-03-05 17:50:19 +01:00
Deeman	f7faf7ab57	chore: change GISCO extraction schedule from monthly to yearly NUTS2 boundaries rarely change; yearly (Jan 1) is sufficient. Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 17:50:14 +01:00
Deeman	add5f8ddfa	fix(extract): correct lc_lci_lev lcstruct filter value All checks were successful CI / test (push) Successful in 53s Details CI / tag (push) Successful in 3s Details	2026-03-05 17:39:37 +01:00
Deeman	15ca316682	fix(extract): correct lc_lci_lev lcstruct filter value D1_D2_A_HW doesn't exist in the API; use D1_D4_MD5 (total labour cost = compensation + taxes - subsidies). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>	2026-03-05 17:32:49 +01:00