fixe muti-point showing in gsm app in starup issues

apps/multi_points_distance_calculator.py

@@ -1,46 +1,7 @@
 import pandas as pd
 import streamlit as st
-from geopy.distance import geodesic
-
-
-# Function to calculate distances while preserving all original columns
-def calculate_distances(
-    df1: pd.DataFrame,
-    df2: pd.DataFrame,
-    code_col1,
-    lat_col1,
-    long_col1,
-    code_col2,
-    lat_col2,
-    long_col2,
-):
-    distances = []
-
-    for _, row1 in df1.iterrows():
-        for _, row2 in df2.iterrows():
-            coord1 = (row1[lat_col1], row1[long_col1])
-            coord2 = (row2[lat_col2], row2[long_col2])
-            distance_km = geodesic(coord1, coord2).kilometers  # Compute distance
-
-            # Combine all original columns + distance
-            combined_row = {
-                **row1.to_dict(),  # Keep all columns from Dataset1
-                **{
-                    f"{col}_Dataset2": row2[col] for col in df2.columns
-                },  # Keep all columns from Dataset2
-                "Distance_km": distance_km,
-            }
-            distances.append(combined_row)
-
-    df_distances = pd.DataFrame(distances)
-
-    # Find the closest point for each Point1
-    df_closest: pd.DataFrame = df_distances.loc[
-        df_distances.groupby(code_col1)["Distance_km"].idxmin()
-    ]
-
-    return df_distances, df_closest
 
+from utils.utils_functions import calculate_distances
 
 # Streamlit UI
 st.title("Multi Points Distance Calculator")

process_kpi/process_gsm_capacity.py

@@ -1,7 +1,6 @@
 import numpy as np
 import pandas as pd
 
-from apps.multi_points_distance_calculator import calculate_distances
 from queries.process_gsm import combined_gsm_database
 from utils.check_sheet_exist import execute_checks_sheets_exist
 from utils.convert_to_excel import convert_dfs, save_dataframe
@@ -18,6 +17,7 @@ from utils.kpi_analysis_utils import (
     create_hourly_date,
     kpi_naming_cleaning,
 )
+from utils.utils_functions import calculate_distances
 
 GSM_ANALYSIS_COLUMNS = [
     "ID_BTS",

utils/utils_functions.py

@@ -0,0 +1,41 @@
+import pandas as pd
+from geopy.distance import geodesic
+
+
+# Function to calculate distances while preserving all original columns
+def calculate_distances(
+    df1: pd.DataFrame,
+    df2: pd.DataFrame,
+    code_col1,
+    lat_col1,
+    long_col1,
+    code_col2,
+    lat_col2,
+    long_col2,
+):
+    distances = []
+
+    for _, row1 in df1.iterrows():
+        for _, row2 in df2.iterrows():
+            coord1 = (row1[lat_col1], row1[long_col1])
+            coord2 = (row2[lat_col2], row2[long_col2])
+            distance_km = geodesic(coord1, coord2).kilometers  # Compute distance
+
+            # Combine all original columns + distance
+            combined_row = {
+                **row1.to_dict(),  # Keep all columns from Dataset1
+                **{
+                    f"{col}_Dataset2": row2[col] for col in df2.columns
+                },  # Keep all columns from Dataset2
+                "Distance_km": distance_km,
+            }
+            distances.append(combined_row)
+
+    df_distances = pd.DataFrame(distances)
+
+    # Find the closest point for each Point1
+    df_closest: pd.DataFrame = df_distances.loc[
+        df_distances.groupby(code_col1)["Distance_km"].idxmin()
+    ]
+
+    return df_distances, df_closest