refactor: flatten padelnomics/padelnomics/ → repo root

git mv all tracked files from the nested padelnomics/ workspace
directory to the git repo root. Merged .gitignore files.
No code changes — pure path rename.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

transform/sqlmesh_padelnomics/models/serving/city_market_profile.sql

@@ -0,0 +1,68 @@
+-- One Big Table: per-city padel market intelligence.
+-- Consumed by: SEO article generation, planner city-select pre-fill, API endpoints.
+--
+-- Market score (0–100) is a simple composite:
+--   40% population (log-scaled, city > 500K = max)
+--   40% venue density (courts per 100K residents)
+--   20% data confidence (completeness of both population + venue data)
+
+MODEL (
+  name padelnomics.city_market_profile,
+  kind FULL,
+  cron '@daily',
+  grain city_slug
+);
+
+WITH base AS (
+  SELECT
+    c.city_code,
+    c.country_code,
+    c.city_name,
+    c.city_slug,
+    c.lat,
+    c.lon,
+    c.population,
+    c.population_year,
+    c.padel_venue_count,
+    -- 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
+  FROM padelnomics.dim_cities c
+  WHERE c.padel_venue_count > 0
+),
+scored AS (
+  SELECT *,
+    ROUND(
+      -- Population component (log scale, 500K+ city → 40 pts)
+      40.0 * LEAST(1.0, LN(GREATEST(population, 1)) / LN(500000))
+      -- Density component (5 courts/100K → 40 pts)
+    + 40.0 * LEAST(1.0, COALESCE(venues_per_100k, 0) / 5.0)
+      -- Confidence component
+    + 20.0 * data_confidence
+    , 1)                         AS market_score
+  FROM base
+)
+SELECT
+  city_code,
+  country_code,
+  city_name,
+  city_slug,
+  lat,
+  lon,
+  population,
+  population_year,
+  padel_venue_count,
+  venues_per_100k,
+  data_confidence,
+  market_score,
+  CURRENT_DATE                   AS refreshed_date
+FROM scored
+ORDER BY market_score DESC

transform/sqlmesh_padelnomics/models/serving/planner_defaults.sql

@@ -0,0 +1,72 @@
+-- Per-city planner defaults for the financial calculator.
+-- When a user selects a city in the planner, these values pre-fill the inputs.
+-- Consumed by: padelnomics.planner.routes — city_defaults(city_slug) lookup.
+--
+-- Values are derived from market data where available, otherwise fall back to
+-- country-level medians, then to global fallbacks from market research report.
+--
+-- Units are explicit in column names (EUR, %, h). All monetary values in EUR.
+
+MODEL (
+  name padelnomics.planner_defaults,
+  kind FULL,
+  cron '@daily',
+  grain city_slug
+);
+
+WITH country_medians AS (
+  -- Country-level fallback values from market research (hardcoded until we
+  -- have richer pricing data from Playtomic or direct scraping).
+  SELECT * FROM (VALUES
+    -- (country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
+    --  avg_utilisation_pct, courts_typical)
+    ('DE', 22.0,  14.0, 42000.0, 0.55, 4),
+    ('ES', 16.0,   9.0, 32000.0, 0.62, 6),
+    ('GB', 24.0,  18.0, 48000.0, 0.52, 4),
+    ('FR', 18.0,  12.0, 36000.0, 0.58, 5),
+    ('IT', 15.0,  10.0, 30000.0, 0.60, 6),
+    ('PT', 12.0,   8.0, 28000.0, 0.65, 6),
+    ('AT', 20.0,  13.0, 40000.0, 0.54, 4),
+    ('CH', 28.0,  22.0, 55000.0, 0.50, 4),
+    ('NL', 20.0,  15.0, 40000.0, 0.56, 4),
+    ('BE', 18.0,  13.0, 36000.0, 0.57, 4),
+    ('SE', 22.0,  14.0, 42000.0, 0.50, 4),
+    ('US', 20.0,  12.0, 38000.0, 0.58, 6)
+  ) AS t(country_code, hourly_rate_peak_eur, monthly_rent_eur_sqm, capex_court_eur,
+         avg_utilisation_pct, courts_typical)
+),
+city_venue_density AS (
+  SELECT
+    city_slug,
+    country_code,
+    padel_venue_count,
+    population,
+    venues_per_100k,
+    market_score
+  FROM padelnomics.city_market_profile
+)
+SELECT
+  cvd.city_slug,
+  cvd.country_code,
+  cvd.padel_venue_count,
+  cvd.population,
+  cvd.market_score,
+  -- Hourly rate: adjust country median by market maturity
+  -- (high-density markets → slightly lower rates from competition)
+  ROUND(
+    cm.hourly_rate_peak_eur
+    * CASE
+        WHEN cvd.venues_per_100k > 4  THEN 0.90  -- very competitive
+        WHEN cvd.venues_per_100k > 2  THEN 0.95  -- competitive
+        WHEN cvd.venues_per_100k < 0.5 THEN 1.10 -- underserved premium
+        ELSE 1.0
+      END
+  , 2)                                         AS hourly_rate_peak_eur,
+  ROUND(cm.hourly_rate_peak_eur * 0.75, 2)     AS hourly_rate_offpeak_eur,
+  cm.monthly_rent_eur_sqm,
+  cm.capex_court_eur,
+  cm.avg_utilisation_pct,
+  cm.courts_typical,
+  CURRENT_DATE                                 AS refreshed_date
+FROM city_venue_density cvd
+LEFT JOIN country_medians cm ON cvd.country_code = cm.country_code