Update app.py

app.py

@@ -1053,153 +1053,153 @@ def build_agg_wilayah_total_from_jenis(agg_jenis: pd.DataFrame, faktor_wilayah_j
 
 # ============================================================
 # 9) SUMMARY (PER JENIS) + KESELURUHAN
-#    Ringkasan harus memuat Pop/Target68/Terkumpul/Coverage + Penyesuaian
+#    - Ringkasan selalu 4 baris: sekolah, umum, khusus, keseluruhan
+#    - Pop/Target/Terkumpul/Coverage diisi per jenis (SUM lintas wilayah)
+#    - Keseluruhan: indeks = avg3 (tetap ÷3), Pop/Target/Terkumpul = SUM 3 jenis
 # ============================================================
 
-def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame, faktor_wilayah_jenis: pd.DataFrame):
+def build_summary_per_jenis(agg_jenis: pd.DataFrame, agg_total: pd.DataFrame):
     jenis_list = ["sekolah", "umum", "khusus"]
 
-    def _row_default(j):
+    def _row_default(jenis):
         return {
-            "Jenis": j,
+            "Jenis": jenis,
             "Jumlah_Wilayah": 0,
             "Total_Perpus": 0,
 
+            # === POP/TARGET/TERKUMPUL/COVERAGE (PER JENIS) ===
             "Pop_Total_Jenis": 0,
             "Target68_Total_Jenis": 0,
             "Terkumpul_Jenis": 0,
             "Coverage_Target68_Jenis_%": 0.0,
 
-            "Rata2_sub_koleksi": 0.0,
-            "Rata2_sub_sdm": 0.0,
-            "Rata2_sub_pelayanan": 0.0,
-            "Rata2_sub_pengelolaan": 0.0,
-            "Rata2_dim_kepatuhan": 0.0,
-            "Rata2_dim_kinerja": 0.0,
-
+            # === NILAI INDEKS (PER JENIS) ===
             "Indeks_Dasar_0_100": 0.0,
             "Indeks_Final_Disesuaikan_0_100": 0.0,
             "Penyesuaian_Poin": 0.0,
         }
 
-    rows_by = {j: _row_default(j) for j in jenis_list}
+    rows_by_jenis = {j: _row_default(j) for j in jenis_list}
 
-    # 1) isi indeks & subdimensi dari agg_jenis
     if agg_jenis is not None and not agg_jenis.empty:
         a = agg_jenis.copy()
         a["Jenis"] = a["Jenis"].astype(str).str.lower().str.strip()
 
-        for j in jenis_list:
-            sub = a[a["Jenis"] == j].copy()
+        # pastikan kolom numeric aman
+        for c in [
+            "Jumlah",
+            "Indeks_Dasar_Agregat_0_100",
+            "Indeks_Final_Agregat_0_100",
+            "pop_total_jenis",
+            "target_total_68_jenis",
+        ]:
+            if c in a.columns:
+                a[c] = pd.to_numeric(a[c], errors="coerce").fillna(0)
+
+        for jenis in jenis_list:
+            sub = a[a["Jenis"] == jenis].copy()
             if sub.empty:
                 continue
 
-            dasar = float(pd.to_numeric(sub.get("Indeks_Dasar_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
-            final = float(pd.to_numeric(sub.get("Indeks_Final_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
-
-            rows_by[j].update({
-                "Jumlah_Wilayah": int(sub.shape[0]),
-                "Total_Perpus": int(pd.to_numeric(sub.get("Jumlah", 0), errors="coerce").fillna(0).sum()),
-
-                "Rata2_sub_koleksi": float(pd.to_numeric(sub.get("Rata2_sub_koleksi", 0), errors="coerce").fillna(0).mean()),
-                "Rata2_sub_sdm": float(pd.to_numeric(sub.get("Rata2_sub_sdm", 0), errors="coerce").fillna(0).mean()),
-                "Rata2_sub_pelayanan": float(pd.to_numeric(sub.get("Rata2_sub_pelayanan", 0), errors="coerce").fillna(0).mean()),
-                "Rata2_sub_pengelolaan": float(pd.to_numeric(sub.get("Rata2_sub_pengelolaan", 0), errors="coerce").fillna(0).mean()),
-                "Rata2_dim_kepatuhan": float(pd.to_numeric(sub.get("Rata2_dim_kepatuhan", 0), errors="coerce").fillna(0).mean()),
-                "Rata2_dim_kinerja": float(pd.to_numeric(sub.get("Rata2_dim_kinerja", 0), errors="coerce").fillna(0).mean()),
-
-                "Indeks_Dasar_0_100": dasar,
-                "Indeks_Final_Disesuaikan_0_100": final,
-                "Penyesuaian_Poin": final - dasar,
-            })
-
-    # 2) isi Pop/Target/Terkumpul dari faktor_wilayah_jenis (ini kunci KHUSUS match Excel)
-    if faktor_wilayah_jenis is not None and not faktor_wilayah_jenis.empty:
-        fw = faktor_wilayah_jenis.copy()
-        fw["Jenis"] = fw["Jenis"].astype(str).str.lower().str.strip()
-
-        for j in jenis_list:
-            sub = fw[fw["Jenis"] == j].copy()
-            if sub.empty:
-                continue
-
-            pop = int(pd.to_numeric(sub.get("pop_total_jenis", 0), errors="coerce").fillna(0).sum())
-            tgt = int(pd.to_numeric(sub.get("target_total_68_jenis", 0), errors="coerce").fillna(0).sum())
-            terk = int(pd.to_numeric(sub.get("n_jenis", 0), errors="coerce").fillna(0).sum())
-            cov = (terk / tgt * 100.0) if tgt > 0 else 0.0
-
-            rows_by[j].update({
-                "Pop_Total_Jenis": pop,
-                "Target68_Total_Jenis": tgt,
-                "Terkumpul_Jenis": terk,
-                "Coverage_Target68_Jenis_%": cov,
-            })
+            # jumlah wilayah = banyak baris wilayah untuk jenis tsb
+            jumlah_wilayah = int(sub.shape[0])
 
-    # 3) susun 3 jenis + keseluruhan (avg3 untuk indeks & subdim, TOTAL untuk pop/target/terkumpul)
-    rows = [rows_by[j] for j in jenis_list]
+            # terkumpul = total entitas yang masuk (Jumlah per wilayah dijumlahkan)
+            terkumpul = int(pd.to_numeric(sub.get("Jumlah", 0), errors="coerce").fillna(0).sum())
 
-    def _avg3(field):
-        return (float(rows_by["sekolah"][field]) + float(rows_by["umum"][field]) + float(rows_by["khusus"][field])) / 3.0
+            # POP & TARGET per jenis: SUM lintas wilayah
+            pop_total = int(pd.to_numeric(sub.get("pop_total_jenis", 0), errors="coerce").fillna(0).sum())
+            target68  = int(pd.to_numeric(sub.get("target_total_68_jenis", 0), errors="coerce").fillna(0).sum())
 
-    pop_all = int(rows_by["sekolah"]["Pop_Total_Jenis"] + rows_by["umum"]["Pop_Total_Jenis"] + rows_by["khusus"]["Pop_Total_Jenis"])
-    tgt_all = int(rows_by["sekolah"]["Target68_Total_Jenis"] + rows_by["umum"]["Target68_Total_Jenis"] + rows_by["khusus"]["Target68_Total_Jenis"])
-    terk_all = int(rows_by["sekolah"]["Terkumpul_Jenis"] + rows_by["umum"]["Terkumpul_Jenis"] + rows_by["khusus"]["Terkumpul_Jenis"])
-    cov_all = (terk_all / tgt_all * 100.0) if tgt_all > 0 else 0.0
+            coverage = (terkumpul / target68 * 100.0) if target68 > 0 else 0.0
 
-    total_perpus_all = int(rows_by["sekolah"]["Total_Perpus"] + rows_by["umum"]["Total_Perpus"] + rows_by["khusus"]["Total_Perpus"])
-    jumlah_wilayah_all = int(agg_total.shape[0]) if (agg_total is not None and not agg_total.empty) else int(max(rows_by["sekolah"]["Jumlah_Wilayah"], rows_by["umum"]["Jumlah_Wilayah"], rows_by["khusus"]["Jumlah_Wilayah"]))
+            dasar = float(pd.to_numeric(sub.get("Indeks_Dasar_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
+            final = float(pd.to_numeric(sub.get("Indeks_Final_Agregat_0_100", 0), errors="coerce").fillna(0).mean())
 
-    dasar_all = _avg3("Indeks_Dasar_0_100")
-    final_all = _avg3("Indeks_Final_Disesuaikan_0_100")
+            rows_by_jenis[jenis] = {
+                "Jenis": jenis,
+                "Jumlah_Wilayah": jumlah_wilayah,
+                "Total_Perpus": terkumpul,
+
+                "Pop_Total_Jenis": pop_total,
+                "Target68_Total_Jenis": target68,
+                "Terkumpul_Jenis": terkumpul,
+                "Coverage_Target68_Jenis_%": float(coverage),
+
+                "Indeks_Dasar_0_100": float(dasar),
+                "Indeks_Final_Disesuaikan_0_100": float(final),
+                "Penyesuaian_Poin": float(final - dasar),
+            }
+
+    # ===== susun 3 baris jenis =====
+    rows = [rows_by_jenis[j] for j in jenis_list]
+
+    # ===== baris keseluruhan (FIX) =====
+    # indeks keseluruhan = avg3 (tetap ÷3, missing=0)
+    dasar_all = (rows_by_jenis["sekolah"]["Indeks_Dasar_0_100"]
+                 + rows_by_jenis["umum"]["Indeks_Dasar_0_100"]
+                 + rows_by_jenis["khusus"]["Indeks_Dasar_0_100"]) / 3.0
+
+    final_all = (rows_by_jenis["sekolah"]["Indeks_Final_Disesuaikan_0_100"]
+                 + rows_by_jenis["umum"]["Indeks_Final_Disesuaikan_0_100"]
+                 + rows_by_jenis["khusus"]["Indeks_Final_Disesuaikan_0_100"]) / 3.0
+
+    # Pop/Target/Terkumpul keseluruhan = SUM 3 jenis
+    pop_all = int(rows_by_jenis["sekolah"]["Pop_Total_Jenis"]
+                  + rows_by_jenis["umum"]["Pop_Total_Jenis"]
+                  + rows_by_jenis["khusus"]["Pop_Total_Jenis"])
+
+    target_all = int(rows_by_jenis["sekolah"]["Target68_Total_Jenis"]
+                     + rows_by_jenis["umum"]["Target68_Total_Jenis"]
+                     + rows_by_jenis["khusus"]["Target68_Total_Jenis"])
+
+    terkumpul_all = int(rows_by_jenis["sekolah"]["Terkumpul_Jenis"]
+                        + rows_by_jenis["umum"]["Terkumpul_Jenis"]
+                        + rows_by_jenis["khusus"]["Terkumpul_Jenis"])
+
+    coverage_all = (terkumpul_all / target_all * 100.0) if target_all > 0 else 0.0
+
+    jumlah_wilayah_all = int(agg_total.shape[0]) if (agg_total is not None and not agg_total.empty) else int(
+        max(rows_by_jenis["sekolah"]["Jumlah_Wilayah"],
+            rows_by_jenis["umum"]["Jumlah_Wilayah"],
+            rows_by_jenis["khusus"]["Jumlah_Wilayah"])
+    )
 
     rows.append({
         "Jenis": "keseluruhan",
         "Jumlah_Wilayah": jumlah_wilayah_all,
-        "Total_Perpus": total_perpus_all,
+        "Total_Perpus": terkumpul_all,
 
         "Pop_Total_Jenis": pop_all,
-        "Target68_Total_Jenis": tgt_all,
-        "Terkumpul_Jenis": terk_all,
-        "Coverage_Target68_Jenis_%": cov_all,
-
-        "Rata2_sub_koleksi": _avg3("Rata2_sub_koleksi"),
-        "Rata2_sub_sdm": _avg3("Rata2_sub_sdm"),
-        "Rata2_sub_pelayanan": _avg3("Rata2_sub_pelayanan"),
-        "Rata2_sub_pengelolaan": _avg3("Rata2_sub_pengelolaan"),
-        "Rata2_dim_kepatuhan": _avg3("Rata2_dim_kepatuhan"),
-        "Rata2_dim_kinerja": _avg3("Rata2_dim_kinerja"),
-
-        "Indeks_Dasar_0_100": dasar_all,
-        "Indeks_Final_Disesuaikan_0_100": final_all,
-        "Penyesuaian_Poin": final_all - dasar_all,
+        "Target68_Total_Jenis": target_all,
+        "Terkumpul_Jenis": terkumpul_all,
+        "Coverage_Target68_Jenis_%": float(coverage_all),
+
+        "Indeks_Dasar_0_100": float(dasar_all),
+        "Indeks_Final_Disesuaikan_0_100": float(final_all),
+        "Penyesuaian_Poin": float(final_all - dasar_all),
     })
 
     out = pd.DataFrame(rows)
 
-    # display rounding
-    int_cols = ["Jumlah_Wilayah", "Total_Perpus", "Pop_Total_Jenis", "Target68_Total_Jenis", "Terkumpul_Jenis"]
-    for c in int_cols:
+    # ===== formatting tampilan =====
+    # integer columns
+    for c in ["Jumlah_Wilayah", "Total_Perpus", "Pop_Total_Jenis", "Target68_Total_Jenis", "Terkumpul_Jenis"]:
         if c in out.columns:
             out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0).round(0).astype(int)
 
-    for c in ["Coverage_Target68_Jenis_%"]:
-        if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
-
-    for c in [
-        "Rata2_sub_koleksi","Rata2_sub_sdm","Rata2_sub_pelayanan","Rata2_sub_pengelolaan",
-        "Rata2_dim_kepatuhan","Rata2_dim_kinerja"
-    ]:
-        if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(3)
-
-    for c in ["Indeks_Dasar_0_100","Indeks_Final_Disesuaikan_0_100","Penyesuaian_Poin"]:
+    # float columns
+    for c in ["Coverage_Target68_Jenis_%", "Indeks_Dasar_0_100", "Indeks_Final_Disesuaikan_0_100", "Penyesuaian_Poin"]:
         if c in out.columns:
-            out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+            if c == "Coverage_Target68_Jenis_%":
+                out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
+            else:
+                out[c] = pd.to_numeric(out[c], errors="coerce").fillna(0.0).round(2)
 
     return out
 
 
+
 # ============================================================
 # 10) DETAIL ENTITAS: Final menempel dari agg_total (wilayah)
 # ============================================================