refactor(phase): merge KS2+KS4 into fact_performance, fix all phase inconsistencies

Root cause: the UNION ALL query in data_loader.py produced two rows per
all-through school per year (one KS2, one KS4), with drop_duplicates()
silently discarding the KS4 row. Fixes:

- New dbt mart `fact_performance`: FULL OUTER JOIN of fact_ks2_performance
  and fact_ks4_performance on (urn, year). One row per school per year.
  All-through schools have both KS2 and KS4 columns populated.
- data_loader.py: replace 175-line UNION ALL with a simple JOIN to
  fact_performance. No more duplicate rows or drop_duplicates needed.
- sync_typesense.py: single LATERAL JOIN to fact_performance instead of
  two separate KS2/KS4 joins.
- app.py: remove drop_duplicates (no longer needed); add PHASE_GROUPS
  constant so all-through/middle schools appear in primary and secondary
  filter results (were previously invisible to both); scope result_filters
  gender/admissions_policies to secondary schools only.
- HomeView.tsx: isSecondaryView is now majority-based (not "any secondary")
  and isMixedView shows both sort option sets for mixed result sets.
- school/[slug]/page.tsx: all-through schools route to SchoolDetailView
  (renders both SATs + GCSE sections) instead of SecondarySchoolDetailView
  (KS4-only). Dedicated SEO metadata for all-through schools.

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

backend/app.py

@@ -35,6 +35,14 @@ from .utils import clean_for_json
 # Values to exclude from filter dropdowns (empty strings, non-applicable labels)
 EXCLUDED_FILTER_VALUES = {"", "Not applicable", "Does not apply"}
 
+# Maps user-facing phase filter values to the GIAS PhaseOfEducation values they include.
+# All-through schools appear in both primary and secondary results.
+PHASE_GROUPS: dict[str, set[str]] = {
+    "primary": {"primary", "middle deemed primary", "all-through"},
+    "secondary": {"secondary", "middle deemed secondary", "all-through", "16 plus"},
+    "all-through": {"all-through"},
+}
+
 BASE_URL = "https://schoolcompare.co.uk"
 MAX_SLUG_LENGTH = 60
 
@@ -343,20 +351,13 @@ async def get_schools(
             )
         df_latest = df_latest.merge(prev_rwm, on="urn", how="left")
 
-    # Phase filter
+    # Phase filter — uses PHASE_GROUPS so all-through/middle schools appear
+    # in the correct phase(s) rather than being invisible to both filters.
     if phase:
-        phase_lower = phase.lower()
-        if phase_lower in ("primary", "secondary", "all-through", "all_through"):
-            # Map param values to GIAS phase strings (partial match)
-            phase_map = {
-                "primary": "primary",
-                "secondary": "secondary",
-                "all-through": "all-through",
-                "all_through": "all-through",
-            }
-            phase_substr = phase_map[phase_lower]
-            schools_df_phase_mask = df_latest["phase"].str.lower().str.contains(phase_substr, na=False)
-            df_latest = df_latest[schools_df_phase_mask]
+        phase_lower = phase.lower().replace("_", "-")
+        allowed = PHASE_GROUPS.get(phase_lower)
+        if allowed:
+            df_latest = df_latest[df_latest["phase"].str.lower().isin(allowed)]
 
     # Secondary-specific filters (after phase filter)
     if gender:
@@ -389,7 +390,8 @@ async def get_schools(
         for c in SCHOOL_COLUMNS + location_cols + result_cols
         if c in df_latest.columns
     ]
-    schools_df = df_latest[available_cols].drop_duplicates(subset=["urn"])
+    # fact_performance guarantees one row per (urn, year); df_latest has one row per urn.
+    schools_df = df_latest[available_cols]
 
     # Location-based search (uses pre-geocoded data from database)
     search_coords = None
@@ -458,13 +460,16 @@ async def get_schools(
             schools_df["school_type"].str.lower() == school_type.lower()
         ]
 
-    # Compute result-scoped filter values (before pagination)
+    # Compute result-scoped filter values (before pagination).
+    # Gender and admissions are secondary-only filters — scope them to schools
+    # with KS4 data so they don't appear for purely primary result sets.
+    _sec_mask = schools_df["attainment_8_score"].notna() if "attainment_8_score" in schools_df.columns else pd.Series(False, index=schools_df.index)
     result_filters = {
         "local_authorities": clean_filter_values(schools_df["local_authority"]) if "local_authority" in schools_df.columns else [],
         "school_types": clean_filter_values(schools_df["school_type"]) if "school_type" in schools_df.columns else [],
         "phases": clean_filter_values(schools_df["phase"]) if "phase" in schools_df.columns else [],
-        "genders": clean_filter_values(schools_df["gender"]) if "gender" in schools_df.columns else [],
-        "admissions_policies": clean_filter_values(schools_df["admissions_policy"]) if "admissions_policy" in schools_df.columns else [],
+        "genders": clean_filter_values(schools_df.loc[_sec_mask, "gender"]) if "gender" in schools_df.columns and _sec_mask.any() else [],
+        "admissions_policies": clean_filter_values(schools_df.loc[_sec_mask, "admissions_policy"]) if "admissions_policy" in schools_df.columns and _sec_mask.any() else [],
     }
 
     # Pagination

backend/data_loader.py

@@ -109,11 +109,12 @@ def haversine_distance(lat1: float, lon1: float, lat2: float, lon2: float) -> fl
 
 
 # =============================================================================
-# MAIN DATA LOAD — joins dim_school + dim_location + fact_ks2_performance
+# MAIN DATA LOAD — joins dim_school + dim_location + fact_performance
+# fact_performance is a merged KS2+KS4 table (one row per URN per year).
+# All-through schools have both KS2 and KS4 columns populated in the same row.
 # =============================================================================
 
 _MAIN_QUERY = text("""
-    -- Branch 1: Primary schools (KS2 data; KS4 columns NULL)
     SELECT
         s.urn,
         s.school_name,
@@ -139,155 +140,67 @@ _MAIN_QUERY = text("""
         l.postcode,
         l.latitude,
         l.longitude,
-        k.year,
-        k.source_urn,
-        k.total_pupils,
-        k.eligible_pupils,
-        -- KS2 columns
-        k.rwm_expected_pct,
-        k.rwm_high_pct,
-        k.reading_expected_pct,
-        k.reading_high_pct,
-        k.reading_avg_score,
-        k.reading_progress,
-        k.writing_expected_pct,
-        k.writing_high_pct,
-        k.writing_progress,
-        k.maths_expected_pct,
-        k.maths_high_pct,
-        k.maths_avg_score,
-        k.maths_progress,
-        k.gps_expected_pct,
-        k.gps_high_pct,
-        k.gps_avg_score,
-        k.science_expected_pct,
-        k.reading_absence_pct,
-        k.writing_absence_pct,
-        k.maths_absence_pct,
-        k.gps_absence_pct,
-        k.science_absence_pct,
-        k.rwm_expected_boys_pct,
-        k.rwm_high_boys_pct,
-        k.rwm_expected_girls_pct,
-        k.rwm_high_girls_pct,
-        k.rwm_expected_disadvantaged_pct,
-        k.rwm_expected_non_disadvantaged_pct,
-        k.disadvantaged_gap,
-        k.disadvantaged_pct,
-        k.eal_pct,
-        k.sen_support_pct,
-        k.sen_ehcp_pct,
-        k.stability_pct,
-        -- KS4 columns (NULL for primary)
-        NULL::numeric AS attainment_8_score,
-        NULL::numeric AS progress_8_score,
-        NULL::numeric AS progress_8_lower_ci,
-        NULL::numeric AS progress_8_upper_ci,
-        NULL::numeric AS progress_8_english,
-        NULL::numeric AS progress_8_maths,
-        NULL::numeric AS progress_8_ebacc,
-        NULL::numeric AS progress_8_open,
-        NULL::numeric AS english_maths_strong_pass_pct,
-        NULL::numeric AS english_maths_standard_pass_pct,
-        NULL::numeric AS ebacc_entry_pct,
-        NULL::numeric AS ebacc_strong_pass_pct,
-        NULL::numeric AS ebacc_standard_pass_pct,
-        NULL::numeric AS ebacc_avg_score,
-        NULL::numeric AS gcse_grade_91_pct,
-        NULL::numeric AS prior_attainment_avg
+        p.year,
+        p.source_urn,
+        p.total_pupils,
+        p.eligible_pupils,
+        -- KS2 columns (NULL for pure secondary schools)
+        p.rwm_expected_pct,
+        p.rwm_high_pct,
+        p.reading_expected_pct,
+        p.reading_high_pct,
+        p.reading_avg_score,
+        p.reading_progress,
+        p.writing_expected_pct,
+        p.writing_high_pct,
+        p.writing_progress,
+        p.maths_expected_pct,
+        p.maths_high_pct,
+        p.maths_avg_score,
+        p.maths_progress,
+        p.gps_expected_pct,
+        p.gps_high_pct,
+        p.gps_avg_score,
+        p.science_expected_pct,
+        p.reading_absence_pct,
+        p.writing_absence_pct,
+        p.maths_absence_pct,
+        p.gps_absence_pct,
+        p.science_absence_pct,
+        p.rwm_expected_boys_pct,
+        p.rwm_high_boys_pct,
+        p.rwm_expected_girls_pct,
+        p.rwm_high_girls_pct,
+        p.rwm_expected_disadvantaged_pct,
+        p.rwm_expected_non_disadvantaged_pct,
+        p.disadvantaged_gap,
+        p.disadvantaged_pct,
+        p.eal_pct,
+        p.stability_pct,
+        -- KS4 columns (NULL for pure primary schools)
+        p.attainment_8_score,
+        p.progress_8_score,
+        p.progress_8_lower_ci,
+        p.progress_8_upper_ci,
+        p.progress_8_english,
+        p.progress_8_maths,
+        p.progress_8_ebacc,
+        p.progress_8_open,
+        p.english_maths_strong_pass_pct,
+        p.english_maths_standard_pass_pct,
+        p.ebacc_entry_pct,
+        p.ebacc_strong_pass_pct,
+        p.ebacc_standard_pass_pct,
+        p.ebacc_avg_score,
+        p.gcse_grade_91_pct,
+        p.prior_attainment_avg,
+        -- SEN (coalesced KS2+KS4 in fact_performance)
+        p.sen_support_pct,
+        p.sen_ehcp_pct
     FROM marts.dim_school s
     JOIN marts.dim_location l ON s.urn = l.urn
-    JOIN marts.fact_ks2_performance k ON s.urn = k.urn
-
-    UNION ALL
-
-    -- Branch 2: Secondary schools (KS4 data; KS2 columns NULL)
-    SELECT
-        s.urn,
-        s.school_name,
-        s.phase,
-        s.school_type,
-        s.academy_trust_name   AS trust_name,
-        s.academy_trust_uid    AS trust_uid,
-        s.religious_character  AS religious_denomination,
-        s.gender,
-        s.age_range,
-        s.admissions_policy,
-        s.capacity,
-        s.headteacher_name,
-        s.website,
-        s.ofsted_grade,
-        s.ofsted_date,
-        s.ofsted_framework,
-        l.local_authority_name AS local_authority,
-        l.local_authority_code,
-        l.address_line1        AS address1,
-        l.address_line2        AS address2,
-        l.town,
-        l.postcode,
-        l.latitude,
-        l.longitude,
-        k4.year,
-        k4.source_urn,
-        k4.total_pupils,
-        k4.eligible_pupils,
-        -- KS2 columns (NULL for secondary)
-        NULL::numeric AS rwm_expected_pct,
-        NULL::numeric AS rwm_high_pct,
-        NULL::numeric AS reading_expected_pct,
-        NULL::numeric AS reading_high_pct,
-        NULL::numeric AS reading_avg_score,
-        NULL::numeric AS reading_progress,
-        NULL::numeric AS writing_expected_pct,
-        NULL::numeric AS writing_high_pct,
-        NULL::numeric AS writing_progress,
-        NULL::numeric AS maths_expected_pct,
-        NULL::numeric AS maths_high_pct,
-        NULL::numeric AS maths_avg_score,
-        NULL::numeric AS maths_progress,
-        NULL::numeric AS gps_expected_pct,
-        NULL::numeric AS gps_high_pct,
-        NULL::numeric AS gps_avg_score,
-        NULL::numeric AS science_expected_pct,
-        NULL::numeric AS reading_absence_pct,
-        NULL::numeric AS writing_absence_pct,
-        NULL::numeric AS maths_absence_pct,
-        NULL::numeric AS gps_absence_pct,
-        NULL::numeric AS science_absence_pct,
-        NULL::numeric AS rwm_expected_boys_pct,
-        NULL::numeric AS rwm_high_boys_pct,
-        NULL::numeric AS rwm_expected_girls_pct,
-        NULL::numeric AS rwm_high_girls_pct,
-        NULL::numeric AS rwm_expected_disadvantaged_pct,
-        NULL::numeric AS rwm_expected_non_disadvantaged_pct,
-        NULL::numeric AS disadvantaged_gap,
-        NULL::numeric AS disadvantaged_pct,
-        NULL::numeric AS eal_pct,
-        k4.sen_support_pct,
-        k4.sen_ehcp_pct,
-        NULL::numeric AS stability_pct,
-        -- KS4 columns
-        k4.attainment_8_score,
-        k4.progress_8_score,
-        k4.progress_8_lower_ci,
-        k4.progress_8_upper_ci,
-        k4.progress_8_english,
-        k4.progress_8_maths,
-        k4.progress_8_ebacc,
-        k4.progress_8_open,
-        k4.english_maths_strong_pass_pct,
-        k4.english_maths_standard_pass_pct,
-        k4.ebacc_entry_pct,
-        k4.ebacc_strong_pass_pct,
-        k4.ebacc_standard_pass_pct,
-        k4.ebacc_avg_score,
-        k4.gcse_grade_91_pct,
-        k4.prior_attainment_avg
-    FROM marts.dim_school s
-    JOIN marts.dim_location l ON s.urn = l.urn
-    JOIN marts.fact_ks4_performance k4 ON s.urn = k4.urn
-
-    ORDER BY school_name, year
+    JOIN marts.fact_performance p ON s.urn = p.urn
+    ORDER BY s.school_name, p.year
 """)