Update app.py

app.py

@@ -725,24 +725,24 @@ st.markdown(f"""
 # """, unsafe_allow_html=True)
 st.markdown("""
 <h3 class="objective-title">OBJECTIVE 3: Alarm Frequency Analysis — When, Where, and Which Tyres Matter Most?</h3>
-<small>*Showing only Red High Pressure Alarms</small>
+<small>*Showing all alarm types: Normal, Amber, Red</small>
 """, unsafe_allow_html=True)
 
-# Filter hanya data alarm
-alarm_data = dff[dff['is_alarm'] == 1].copy()
+# Filter semua data (termasuk alarm normal)
+alarm_data = dff.copy()
 
 col_b, col_a = st.columns(2)
 
-# =============== COL B: Donut Charts (Distribusi Alarm per Position) ===============
+# =============== COL B: Donut Charts (Distribusi Alarm per Position - Semua Jenis) ===============
 with col_b:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Alarm Distribution by Position</h5>', unsafe_allow_html=True)
 
     if alarm_data.empty:
-        st.warning("No alarm data to display.")
+        st.warning("No data to display.")
     else:
         # Group alarm status
         alarm_data['Alarm_Category'] = alarm_data['Alarm Status'].apply(
-            lambda x: 'No Alarm' if 'No Alarm' in x
+            lambda x: 'Normal' if 'No Alarm' in x
             else 'Amber Alarm' if 'Amber' in x
             else 'Red Alarm'
         )
@@ -762,7 +762,7 @@ with col_b:
                 labels = counts.index.tolist()
                 values = counts.values.tolist()
 
-                # Warna: Hijau (No), Kuning (Amber), Merah (Red)
+                # Warna: Hijau (Normal), Kuning (Amber), Merah (Red)
                 colors = ['#2E7D32', '#FFC107', '#D32F2F']
 
                 fig_donut.add_trace(
@@ -791,73 +791,71 @@ with col_b:
         )
         st.plotly_chart(fig_donut, use_container_width=True)
 
-# =============== COL A: Radial Charts (Count Alarm per Jam) ===============
+# =============== COL A: Radial Charts (Count Alarm per Jam - Semua Jenis) ===============
 with col_a:
     st.markdown('<h5 style="text-align:center; margin-top: 0;">Alarm Count by Hour (Radial)</h5>', unsafe_allow_html=True)
 
     if alarm_data.empty:
-        st.warning("No alarm data to display.")
+        st.warning("No data to display.")
     else:
-        # Ambil pressure max untuk warna gradasi
-        max_pressure = alarm_data['Pressure (psi)'].max()
-        min_pressure = alarm_data['Pressure (psi)'].min()
-
         # Buat 4 radial chart
         fig_radial = make_subplots(
             rows=2, cols=2,
             specs=[[{'type': 'polar'}, {'type': 'polar'}],
                    [{'type': 'polar'}, {'type': 'polar'}]],
-            subplot_titles=['Front Position 1 (06:00–18:00)', 'Front Position 2 (18:00–06:00)',
-                            'Rear Position 3 (06:00–18:00)', 'Rear Position 4 (18:00–06:00)']
+            subplot_titles=['Position 1', 'Position 2', 'Position 3', 'Position 4']
         )
 
         for i, pos in enumerate([1, 2, 3, 4], 1):
             pos_data = alarm_data[alarm_data['Position'] == pos].copy()
             if not pos_data.empty:
-                # Kelompokkan jam berdasarkan periode
-                if i in [1, 3]:  # Pagi–sore
-                    pos_data = pos_data[pos_data['hour'].between(6, 17, inclusive='both')]
-                else:  # Sore–pagi
-                    pos_data = pos_data[~pos_data['hour'].between(6, 17, inclusive='both')]
-
-                if not pos_data.empty:
-                    hourly_counts = pos_data.groupby('hour').size().reindex(range(24), fill_value=0)
-                    if i in [1, 3]:  # 06:00–18:00
-                        hourly_counts = hourly_counts[6:18]
-                    else:  # 18:00–06:00
-                        hourly_counts = pd.concat([hourly_counts[18:], hourly_counts[:6]]).reindex(range(12), fill_value=0)
-
-                    # Warna berdasarkan rata-rata pressure
-                    avg_pressure_per_hour = pos_data.groupby('hour')['Pressure (psi)'].mean().reindex(hourly_counts.index, fill_value=0)
-                    colorscale = avg_pressure_per_hour
-
-                    fig_radial.add_trace(
-                        go.Barpolar(
-                            r=hourly_counts.values,
-                            theta=hourly_counts.index * 30,  # 12 jam * 30° = 360°
-                            name=f'Position {pos}',
-                            marker=dict(
-                                color=colorscale,
-                                colorscale='Reds',
-                                cmin=min_pressure,
-                                cmax=max_pressure
-                            ),
-                            opacity=0.8
-                        ),
-                        row=(i - 1) // 2 + 1,
-                        col=(i - 1) % 2 + 1
-                    )
+                # Kelompokkan jam dan alarm
+                hourly_counts = pos_data.groupby(['hour', 'Alarm_Category']).size().unstack(fill_value=0)
+                
+                # Ambil total alarm per jam
+                total_per_hour = hourly_counts.sum(axis=1).reindex(range(24), fill_value=0)
+
+                # Warna berdasarkan jenis alarm dominan per jam
+                dominant_alarm_per_hour = pos_data.groupby('hour')['Alarm_Category'].apply(
+                    lambda x: x.mode().iloc[0] if not x.empty else 'Normal'
+                ).reindex(range(24), fill_value='Normal')
+
+                # Map warna
+                color_map = {'Normal': '#2E7D32', 'Amber Alarm': '#FFC107', 'Red Alarm': '#D32F2F'}
+                colorscale = [color_map.get(cat, '#2E7D32') for cat in dominant_alarm_per_hour]
+
+                # Sudut: jam 0 → 0° (atas), jam 6 → 90° (kanan), jam 12 → 180° (bawah), jam 18 → 270° (kiri)
+                theta = [h * 15 for h in range(24)]  # 24 jam * 15° = 360°
+
+                fig_radial.add_trace(
+                    go.Barpolar(
+                        r=total_per_hour.values,
+                        theta=theta,
+                        name=f'Position {pos}',
+                        marker_color=colorscale,
+                        opacity=0.8
+                    ),
+                    row=(i - 1) // 2 + 1,
+                    col=(i - 1) % 2 + 1
+                )
 
         fig_radial.update_layout(
             height=600,
             showlegend=False,
-            margin=dict(t=60, b=20, l=20, r=20)
+            margin=dict(t=60, b=20, l=20, r=20),
+            polar=dict(
+                angularaxis=dict(
+                    direction="clockwise",
+                    period=24,
+                    rotation=90  # Jam 0 di atas
+                )
+            )
         )
         st.plotly_chart(fig_radial, use_container_width=True)
 
 # =============== INSIGHT 3 ===============
 if alarm_data.empty:
-    insight_text = "• No alarm data available for analysis."
+    insight_text = "• No data available for analysis."
 else:
     # Insight tetap sama
     alarm_hours = alarm_data['hour']
@@ -879,23 +877,15 @@ else:
     dominant_pct = (top_bands.iloc[0] / band_counts.sum() * 100) if len(top_bands) > 0 else 0
     second_pct = (top_bands.iloc[1] / band_counts.sum() * 100) if len(top_bands) > 1 else 0
 
-    front_alarms = alarm_data[alarm_data['Position'].isin([1, 2])].shape[0]
-    rear_alarms = alarm_data[alarm_data['Position'].isin([3, 4])].shape[0]
-    total_alarms = front_alarms + rear_alarms
-    front_pct = front_alarms / total_alarms * 100 if total_alarms > 0 else 0
-
-    top_zone = alarm_data['Zone'].value_counts().index[0] if not alarm_data.empty else "N/A"
+    normal_alarms = alarm_data[alarm_data['Alarm_Status'] == 'No Alarm'].shape[0] if 'Alarm_Status' in alarm_data.columns else 0
+    amber_alarms = alarm_data[alarm_data['Alarm_Status'].str.contains('Amber', na=False)].shape[0] if 'Alarm_Status' in alarm_data.columns else 0
+    red_alarms = alarm_data[alarm_data['Alarm_Status'].str.contains('Red', na=False)].shape[0] if 'Alarm_Status' in alarm_data.columns else 0
 
     insight_lines = [
-        f"• {dominant_band} is the dominant alarm period ({dominant_pct:.1f}% of all alarms).",
-        f"• {second_dominant_band} is the second-highest period ({second_pct:.1f}% of alarms)."
+        f"• {dominant_band} is the dominant period ({dominant_pct:.1f}% of all data).",
+        f"• {second_dominant_band} is the second-highest period ({second_pct:.1f}% of data).",
+        f"• Total: Normal={normal_alarms}, Amber={amber_alarms}, Red={red_alarms}"
     ]
-    if front_alarms > 0:
-        insight_lines.append(f"• Front tyres (Pos 1 & 2) account for {front_pct:.1f}% of all alarms, indicating higher stress or usage intensity upfront.")
-    if top_zone != "N/A":
-        insight_lines.append(f"• Zone {top_zone} records the highest alarm frequency across all positions.")
-    insight_lines.append("• Alarm clustering in specific hours and front positions suggests opportunity for targeted inspection scheduling.")
-
     insight_text = "\n".join(insight_lines)
 
 # =============== DISPLAY INSIGHT ===============