add avail and Abis checking BH

apps/kpi_analysis/gsm_capacity.py

@@ -135,7 +135,7 @@ if (
             gsm_analysis_df.groupby("Final comment").size().reset_index(name="count")
         )
         fig = px.bar(final_comments_df, x="Final comment", y="count")
-        fig.update_layout(height=1500)
+        fig.update_layout(height=1000)
         fig.update_traces(texttemplate="%{value}", textposition="outside")
         st.plotly_chart(fig, use_container_width=True)
         st.write(final_comments_df)
@@ -147,7 +147,7 @@ if (
             .reset_index(name="count")
         )
         fig = px.bar(bh_congestion_status_df, x="BH Congestion status", y="count")
-        fig.update_layout(height=1500)
+        fig.update_layout(height=800)
         fig.update_traces(texttemplate="%{value}", textposition="outside")
         st.plotly_chart(fig, use_container_width=True)
         st.write(bh_congestion_status_df)
@@ -158,7 +158,7 @@ if (
             .reset_index(name="count")
         )
         fig = px.bar(operational_comments_df, x="operational_comment", y="count")
-        fig.update_layout(height=1500)
+        fig.update_layout(height=600)
         fig.update_traces(texttemplate="%{value}", textposition="outside")
         st.plotly_chart(fig, use_container_width=True)
         st.write(operational_comments_df)

process_kpi/process_gsm_capacity.py

@@ -74,19 +74,26 @@ BH_COLUMNS_FOR_CAPACITY = [
     "max_tch_call_blocking_bh",
     "avg_tch_call_blocking_bh",
     "number_of_days_with_tch_blocking_exceeded_bh",
+    "tch_call_blocking_bh_comment",
     "max_sdcch_real_blocking_bh",
     "avg_sdcch_real_blocking_bh",
     "number_of_days_with_sdcch_blocking_exceeded_bh",
-    "tch_call_blocking_bh_comment",
     "sdcch_real_blocking_bh_comment",
+    "Average_cell_availability_bh",
+    "number_of_days_exceeding_availability_threshold_bh",
+    "availability_comment_bh",
+    "max_tch_abis_fail_bh",
+    "avg_tch_abis_fail_bh",
+    "number_of_days_with_tch_abis_fail_exceeded_bh",
+    "tch_abis_fail_bh_comment",
 ]
 
 DAILY_COLUMNS_FOR_CAPACITY = [
-    "Average_cell_availability",
-    "number_of_days_exceeding_threshold",
-    "availability_comment",
-    "avg_tch_abis_fail_daily",
+    "Average_cell_availability_daily",
+    "number_of_days_exceeding_availability_threshold_daily",
+    "availability_comment_daily",
     "max_tch_abis_fail_daily",
+    "avg_tch_abis_fail_daily",
     "number_of_days_with_tch_abis_fail_exceeded_daily",
     "tch_abis_fail_daily_comment",
 ]
@@ -112,6 +119,8 @@ def bh_dfs_per_kpi(
     tch_blocking_threshold: int = 0.50,
     sdcch_blocking_threshold: int = 0.50,
     number_of_threshold_days: int = 3,
+    tch_abis_fails_threshold: int = 10,
+    availability_threshold: int = 95,
 ) -> pd.DataFrame:
     """
     Create pivoted DataFrames for each KPI and perform analysis.
@@ -138,6 +147,7 @@ def bh_dfs_per_kpi(
     sdcch_real_blocking_df: pd.DataFrame = pivoted_kpi_dfs["SDCCH_real_blocking"]
     Carried_Traffic_df: pd.DataFrame = pivoted_kpi_dfs["2G_Carried_Traffic"]
     tch_availability_ratio_df: pd.DataFrame = pivoted_kpi_dfs["TCH_availability_ratio"]
+    tch_abis_fails_df: pd.DataFrame = pivoted_kpi_dfs["TCH_ABIS_FAIL_CALL_c001084"]
 
     # ANALISYS
 
@@ -162,12 +172,27 @@ def bh_dfs_per_kpi(
         number_of_kpi_days=number_of_kpi_days,
     )
 
+    tch_abis_fails_df = analyze_tch_abis_fails(
+        df=tch_abis_fails_df,
+        number_of_kpi_days=number_of_kpi_days,
+        tch_abis_fails_threshold=tch_abis_fails_threshold,
+        number_of_threshold_days=number_of_threshold_days,
+        analysis_type="BH",
+    )
+    tch_availability_ratio_df = cell_availability_analysis(
+        df=tch_availability_ratio_df,
+        days=number_of_kpi_days,
+        availability_threshold=availability_threshold,
+        analysis_type="BH",
+    )
+
     bh_kpi_df = pd.concat(
         [
-            tch_availability_ratio_df,
             Carried_Traffic_df,
             tch_call_blocking_df,
             sdcch_real_blocking_df,
+            tch_availability_ratio_df,
+            tch_abis_fails_df,
         ],
         axis=1,
     )
@@ -180,6 +205,8 @@ def analyse_bh_data(
     tch_blocking_threshold: int,
     sdcch_blocking_threshold: int,
     number_of_threshold_days: int,
+    tch_abis_fails_threshold: int,
+    availability_threshold: int,
 ) -> pd.DataFrame:
     df = pd.read_csv(bh_report_path, delimiter=";")
     df = kpi_naming_cleaning(df)
@@ -191,6 +218,8 @@ def analyse_bh_data(
         tch_blocking_threshold=tch_blocking_threshold,
         sdcch_blocking_threshold=sdcch_blocking_threshold,
         number_of_threshold_days=number_of_threshold_days,
+        tch_abis_fails_threshold=tch_abis_fails_threshold,
+        availability_threshold=availability_threshold,
     )
 
     bh_df_for_capacity = df.copy()
@@ -287,7 +316,7 @@ def daily_dfs_per_kpi(
 
     daily_kpi_df = combine_comments(
         daily_kpi_df,
-        "availability_comment",
+        "availability_comment_daily",
         "tch_abis_fail_daily_comment",
         "sdcch_real_blocking_daily_comment",
         new_column="sdcch_comments",
@@ -295,7 +324,7 @@ def daily_dfs_per_kpi(
 
     daily_kpi_df = combine_comments(
         daily_kpi_df,
-        "availability_comment",
+        "availability_comment_daily",
         "tch_abis_fail_daily_comment",
         "tch_call_blocking_daily_comment",
         new_column="tch_comments",
@@ -410,6 +439,8 @@ def analyze_gsm_data(
         tch_blocking_threshold=tch_blocking_threshold,
         sdcch_blocking_threshold=sdcch_blocking_threshold,
         number_of_threshold_days=number_of_threshold_days,
+        tch_abis_fails_threshold=tch_abis_fails_threshold,
+        availability_threshold=availability_threshold,
     )
 
     bh_kpi_df = bh_kpi_dfs[0]
@@ -471,22 +502,22 @@ def analyze_gsm_data(
         gsm_analysis_df["Target TRXs"] - gsm_analysis_df["number_trx_per_cell"]
     )
 
-    # if "availability_comment" equal to "Down Site" then "Down Site"
-    # if "availability_comment" is not "Availability OK" and  "tch_abis_fail_daily_comment" equal to "tch abis fail exceeded threshold" then "Availability and TX issues"
-    # if "availability_comment" is not "Availability OK" and  "tch_abis_fail_daily_comment" is empty then "Availability issues"
-    # if "availability_comment" is "Availability OK" and  "tch_abis_fail_daily_comment" equal to "tch abis fail exceeded threshold" then "TX issues"
+    # if "availability_comment_daily" equal to "Down Site" then "Down Site"
+    # if "availability_comment_daily" is not "Availability OK" and  "tch_abis_fail_daily_comment" equal to "tch abis fail exceeded threshold" then "Availability and TX issues"
+    # if "availability_comment_daily" is not "Availability OK" and  "tch_abis_fail_daily_comment" is empty then "Availability issues"
+    # if "availability_comment_daily" is "Availability OK" and  "tch_abis_fail_daily_comment" equal to "tch abis fail exceeded threshold" then "TX issues"
     # Else "Operational is OK"
     gsm_analysis_df["operational_comment"] = np.select(
         [
-            gsm_analysis_df["availability_comment"] == "Down Site",  # 1
-            (gsm_analysis_df["availability_comment"] != "Availability OK")
+            gsm_analysis_df["availability_comment_daily"] == "Down Site",  # 1
+            (gsm_analysis_df["availability_comment_daily"] != "Availability OK")
             & (
                 gsm_analysis_df["tch_abis_fail_daily_comment"]
                 == "tch abis fail exceeded threshold"
             ),  # 2
-            (gsm_analysis_df["availability_comment"] != "Availability OK")
+            (gsm_analysis_df["availability_comment_daily"] != "Availability OK")
             & pd.isna(gsm_analysis_df["tch_abis_fail_daily_comment"]),  # 3
-            (gsm_analysis_df["availability_comment"] == "Availability OK")
+            (gsm_analysis_df["availability_comment_daily"] == "Availability OK")
             & (
                 gsm_analysis_df["tch_abis_fail_daily_comment"]
                 == "tch abis fail exceeded threshold"

process_kpi/process_wbts_capacity.py

@@ -173,7 +173,7 @@ def bb_comments_analysis(df: pd.DataFrame) -> pd.DataFrame:
         df,
         "num_bb_subunits_comment",
         "Average_used_bb_ratio_comment",
-        "availability_comment",
+        "availability_comment_daily",
         new_column="bb_comments",
     )
 
@@ -192,7 +192,7 @@ def ce_comments_analysis(df: pd.DataFrame) -> pd.DataFrame:
         df,
         "avail_ce_comment",
         "Average_used_ce_ratio_comment",
-        "availability_comment",
+        "availability_comment_daily",
         new_column="ce_comments",
     )
 

utils/kpi_analysis_utils.py

@@ -348,7 +348,10 @@ def create_dfs_per_kpi(
 
 
 def cell_availability_analysis(
-    df: pd.DataFrame, days: int = 7, availability_threshold: int = 95
+    df: pd.DataFrame,
+    days: int = 7,
+    availability_threshold: int = 95,
+    analysis_type: str = "daily",
 ) -> pd.DataFrame:
     """
     Analyze cell availability and categorize sites based on availability metrics.
@@ -360,12 +363,16 @@ def cell_availability_analysis(
     Returns:
         DataFrame with availability analysis and site status comments
     """
-    result_df = df.copy().fillna(0)
-    last_days_df = result_df.iloc[:, -days:]
-    result_df["Average_cell_availability"] = last_days_df.mean(axis=1).round(2)
+    result_df: pd.DataFrame = df.copy().fillna(0)
+    last_days_df: pd.DataFrame = result_df.iloc[:, -days:]
+    result_df[f"Average_cell_availability_{analysis_type.lower()}"] = last_days_df.mean(
+        axis=1
+    ).round(2)
 
     # Count the number of days above threshold
-    result_df["number_of_days_exceeding_threshold"] = last_days_df.apply(
+    result_df[
+        f"number_of_days_exceeding_availability_threshold_{analysis_type.lower()}"
+    ] = last_days_df.apply(
         lambda row: sum(1 for x in row if x <= availability_threshold), axis=1
     )
 
@@ -380,9 +387,9 @@ def cell_availability_analysis(
         else:
             return "Availability OK"
 
-    result_df["availability_comment"] = result_df["Average_cell_availability"].apply(
-        categorize_availability
-    )
+    result_df[f"availability_comment_{analysis_type.lower()}"] = result_df[
+        f"Average_cell_availability_{analysis_type.lower()}"
+    ].apply(categorize_availability)
 
     return result_df
 
@@ -395,7 +402,7 @@ def analyze_tch_abis_fails(
     tch_abis_fails_threshold: int,
 ) -> pd.DataFrame:
 
-    result_df = df.copy()
+    result_df: pd.DataFrame = df.copy()
     last_days_df: pd.DataFrame = result_df.iloc[:, -number_of_kpi_days:]
     # last_days_df = last_days_df.fillna(0)