Add city-level traffic and availability drill-down with daily aggregation and SLA comparison across all RATs

apps/kpi_analysis/trafic_analysis.py

@@ -1229,6 +1229,105 @@ if "full_df" in st.session_state:
                     degraded_site_df = pd.DataFrame(degraded_rows_site)
                     st.dataframe(degraded_site_df.round(2))
 
+        if "City" in analysis_df.columns and analysis_df["City"].notna().any():
+            st.subheader("City drill-down: traffic and availability over time")
+            cities_df = (
+                analysis_df[["City"]].dropna().drop_duplicates().sort_values(by="City")
+            )
+            selected_city = st.selectbox(
+                "Select a City for aggregated view",
+                options=cities_df["City"].tolist(),
+            )
+            city_detail_df = analysis_df[analysis_df["City"] == selected_city].copy()
+            if not city_detail_df.empty:
+                city_detail_df = city_detail_df.sort_values("date")
+                traffic_cols_city = [
+                    col
+                    for col in ["total_voice_trafic", "total_data_trafic"]
+                    if col in city_detail_df.columns
+                ]
+                if traffic_cols_city:
+                    city_traffic = (
+                        city_detail_df.groupby("date")[traffic_cols_city]
+                        .sum()
+                        .reset_index()
+                    )
+                    traffic_long_city = city_traffic.melt(
+                        id_vars="date",
+                        value_vars=traffic_cols_city,
+                        var_name="metric",
+                        value_name="value",
+                    )
+                    fig_traffic_city = px.line(
+                        traffic_long_city,
+                        x="date",
+                        y="value",
+                        color="metric",
+                    )
+                    st.plotly_chart(
+                        fig_traffic_city,
+                        key=f"traffic_city_{selected_city}",
+                    )
+
+                avail_cols_city = []
+                rename_map_city = {}
+                if "2g_tch_avail" in city_detail_df.columns:
+                    avail_cols_city.append("2g_tch_avail")
+                    rename_map_city["2g_tch_avail"] = "2G"
+                if "3g_cell_avail" in city_detail_df.columns:
+                    avail_cols_city.append("3g_cell_avail")
+                    rename_map_city["3g_cell_avail"] = "3G"
+                if "lte_cell_avail" in city_detail_df.columns:
+                    avail_cols_city.append("lte_cell_avail")
+                    rename_map_city["lte_cell_avail"] = "LTE"
+                if avail_cols_city:
+                    avail_city_df = city_detail_df[["date"] + avail_cols_city].copy()
+                    avail_city_df = avail_city_df.rename(columns=rename_map_city)
+                    value_cols_city = [c for c in avail_city_df.columns if c != "date"]
+                    avail_long_city = avail_city_df.melt(
+                        id_vars="date",
+                        value_vars=value_cols_city,
+                        var_name="RAT",
+                        value_name="availability",
+                    )
+                    fig_avail_city = px.line(
+                        avail_long_city,
+                        x="date",
+                        y="availability",
+                        color="RAT",
+                    )
+                    st.plotly_chart(
+                        fig_avail_city,
+                        key=f"avail_city_{selected_city}",
+                    )
+
+                    city_detail_df["date_only"] = city_detail_df["date"].dt.date
+                    degraded_rows_city = []
+                    for rat_col, rat_name, sla_value in [
+                        ("2g_tch_avail", "2G", sla_2g),
+                        ("3g_cell_avail", "3G", sla_3g),
+                        ("lte_cell_avail", "LTE", sla_lte),
+                    ]:
+                        if rat_col in city_detail_df.columns:
+                            daily_city = (
+                                city_detail_df.groupby("date_only")[rat_col]
+                                .mean()
+                                .dropna()
+                            )
+                            mask_city = daily_city < sla_value
+                            for d, val in daily_city[mask_city].items():
+                                degraded_rows_city.append(
+                                    {
+                                        "RAT": rat_name,
+                                        "date": d,
+                                        "avg_availability": val,
+                                        "SLA": sla_value,
+                                    }
+                                )
+                    if degraded_rows_city:
+                        degraded_city_df = pd.DataFrame(degraded_rows_city)
+                        st.dataframe(degraded_city_df.round(2))
+
     # Temporal availability analysis - daily averages per RAT
     if any(
         col in analysis_df.columns