Update app.py

app.py

@@ -607,7 +607,7 @@ def load_default_files(force=False):
 # 6) FAKTOR WILAYAH (TOTAL) — hanya untuk faktor/target/pop/coverage
 # ============================================================
 
-def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_prov: pd.DataFrame, kew_value: str):
+def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_prov: pd.DataFrame, pop_khusus: pd.DataFrame, kew_value: str):
     if df_filtered is None or df_filtered.empty:
         return pd.DataFrame()
 
@@ -622,6 +622,7 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
+        khusus_group_key = "kab_key"
     elif "PROV" in kew_norm:
         key_col = "prov_key"
         label_col = "PROV_DISP"
@@ -630,6 +631,7 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total_Prov"
         pop_field = "Pop_Total_Prov"
         name_field = "Provinsi_Label"
+        khusus_group_key = "prov_key"
     else:
         key_col = "kab_key"
         label_col = "KAB_DISP"
@@ -638,11 +640,13 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
         target_field = "Target68_Total"
         pop_field = "Pop_Total"
         name_field = "Kab_Kota_Label"
+        khusus_group_key = "kab_key"
 
     base = df.groupby([key_col, label_col], dropna=False).agg(
         n_total=("Indeks_Dasar_0_100", "size"),
     ).reset_index().rename(columns={key_col: "group_key", label_col: label_name})
 
+    # --- ambil target & pop dari POP_KAB / POP_PROV (existing) ---
     target_vals, pop_vals, label_fix = [], [], []
     for _, r in base.iterrows():
         gk = r["group_key"]
@@ -660,33 +664,59 @@ def build_faktor_wilayah(df_filtered: pd.DataFrame, pop_kab: pd.DataFrame, pop_p
     base["target_total_68"] = pd.to_numeric(pd.Series(target_vals), errors="coerce")
     base["pop_total"] = pd.to_numeric(pd.Series(pop_vals), errors="coerce")
 
+    # fallback pop dari target kalau pop kosong
     m = base["pop_total"].isna() & base["target_total_68"].notna() & (base["target_total_68"] > 0)
     base.loc[m, "pop_total"] = base.loc[m, "target_total_68"] / float(FALLBACK_TARGET_RATIO)
 
+    # =========================================================
+    # ✅ UPDATE LO: Tambahkan komponen KHUSUS dari POP_KHUSUS
+    # target_total_68 += target_khusus
+    # pop_total       += pop_khusus
+    # =========================================================
+    if (pop_khusus is not None) and (not pop_khusus.empty):
+        pk = pop_khusus.copy()
+
+        # pastikan numeric
+        pk["Target68_Total_Jenis"] = pd.to_numeric(pk.get("Target68_Total_Jenis", 0), errors="coerce").fillna(0.0)
+        pk["Pop_Total_Jenis"]      = pd.to_numeric(pk.get("Pop_Total_Jenis", 0), errors="coerce").fillna(0.0)
+
+        if khusus_group_key in pk.columns:
+            add_df = pk.groupby(khusus_group_key, as_index=False).agg(
+                add_target=("Target68_Total_Jenis", "sum"),
+                add_pop=("Pop_Total_Jenis", "sum"),
+            ).rename(columns={khusus_group_key: "group_key"})
+
+            base = base.merge(add_df, on="group_key", how="left")
+            base["add_target"] = pd.to_numeric(base.get("add_target", 0), errors="coerce").fillna(0.0)
+            base["add_pop"]    = pd.to_numeric(base.get("add_pop", 0), errors="coerce").fillna(0.0)
+
+            base["target_total_68"] = pd.to_numeric(base.get("target_total_68", 0), errors="coerce").fillna(0.0) + base["add_target"]
+            base["pop_total"]       = pd.to_numeric(base.get("pop_total", 0), errors="coerce").fillna(0.0) + base["add_pop"]
+
+            base = base.drop(columns=[c for c in ["add_target", "add_pop"] if c in base.columns])
+
+    # faktor penyesuaian pakai TOTAL baru
     base["faktor_penyesuaian"] = [
         faktor_penyesuaian_total(n, t)
         for n, t in zip(
             pd.to_numeric(base["n_total"], errors="coerce").fillna(0).astype(float).tolist(),
-            pd.to_numeric(base["target_total_68"], errors="coerce").tolist()
+            pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0).astype(float).tolist()
         )
     ]
 
+    # coverage pakai TOTAL baru
     base["coverage_total_%"] = [
         (safe_div(n, p) * 100) if (p is not None and not pd.isna(p) and float(p) > 0) else np.nan
         for n, p in zip(
             pd.to_numeric(base["n_total"], errors="coerce").fillna(0).astype(float).tolist(),
-            base["pop_total"].tolist()
+            pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).astype(float).tolist()
         )
     ]
 
-    # ====== UPDATE SESUAI PERMINTAAN (DISPLAY) ======
-    # 1) target_total_68 bulat
-    # 2) pop_total bulat
-    # 3) coverage_total_% 2 desimal
+    # ===== display fix (sesuai permintaan lo sebelumnya) =====
     base["target_total_68"] = pd.to_numeric(base["target_total_68"], errors="coerce").fillna(0).round(0).astype(int)
-    base["pop_total"] = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).round(0).astype(int)
-    base["coverage_total_%"] = pd.to_numeric(base["coverage_total_%"], errors="coerce").fillna(0.0).round(2)
-    # tetap seperti sebelumnya:
+    base["pop_total"]       = pd.to_numeric(base["pop_total"], errors="coerce").fillna(0).round(0).astype(int)
+    base["coverage_total_%"]= pd.to_numeric(base["coverage_total_%"], errors="coerce").fillna(0.0).round(2)
     base["faktor_penyesuaian"] = pd.to_numeric(base["faktor_penyesuaian"], errors="coerce").fillna(1.0).round(3)
 
     return base
@@ -1495,7 +1525,7 @@ def run_calc(prov_value, kab_value, kew_value, df_all, df_raw, pop_kab, pop_prov
             return _empty_outputs("Tidak ada data untuk filter ini.")
 
         # ==== PIPELINE BARU (KUNCI KONSISTENSI) ====
-        faktor_wilayah = build_faktor_wilayah(df, pop_kab, pop_prov, kew_value or "(Semua)")
+        faktor_wilayah = build_faktor_wilayah(df, pop_kab, pop_prov, pop_khusus, kew_value or "(Semua)")
         agg_jenis_full = build_agg_wilayah_jenis(df, faktor_wilayah, pop_khusus, kew_value or "(Semua)")
         agg_total      = build_agg_wilayah_total_from_jenis(agg_jenis_full, faktor_wilayah, pop_khusus, kew_value or "(Semua)")
         summary_jenis  = build_summary_per_jenis(agg_jenis_full, agg_total)