add site_band_config

README.md

@@ -39,6 +39,7 @@ You can access the hosted version of the app at [https://davmelchi-db-query.hf.s
 - [x] Add option to download Neighbors database
 - [x] Add page to update physical db
 - [x] Add Core dump checking App
+- [x] Add site config band in database
 - [ ] Add dashboards for each database (Count of NE)
 - [ ] Add the ability to select columns
 - [ ] Error handling

apps/import_physical_db.py

@@ -39,7 +39,9 @@ if uploaded_file is not None:
     elif len(df) <= 500:
         st.error("Error: The file must contain more than 500 rows.")
     else:
-        st.success("File successfully validated.")
+        st.success(
+            "File successfully validated. Click on the Save button to save the new physical database file."
+        )
 
         # Display the DataFrame
         st.write(df)

queries/process_gsm.py

@@ -1,6 +1,7 @@
 import pandas as pd
 
 from queries.process_trx import process_trx_data
+from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.utils_vars import UtilsVars
 
@@ -43,6 +44,7 @@ BTS_COLUMNS = [
     "band_frequence",
     "type_cellule",
     "configuration_schema",
+    "band",
 ]
 
 BCF_COLUMNS = [
@@ -80,11 +82,16 @@ def process_gsm_data(file_path: str):
         df_bts["sectorId"].map(UtilsVars.sector_mapping).fillna(df_bts["sectorId"])
     )
     df_bts["band_frequence"] = (
-        df_bts["frequencyBandInUse"].map(UtilsVars.band_frequence).fillna("not found")
+        df_bts["frequencyBandInUse"]
+        .map(UtilsVars.oml_band_frequence)
+        .fillna("not found")
     )
     df_bts["type_cellule"] = (
         df_bts["frequencyBandInUse"].map(UtilsVars.type_cellule).fillna("not found")
     )
+    df_bts["band"] = (
+        df_bts["frequencyBandInUse"].map(UtilsVars.gsm_band).fillna("not found")
+    )
     df_bts["configuration_schema"] = (
         df_bts["frequencyBandInUse"]
         .map(UtilsVars.configuration_schema)
@@ -107,15 +114,19 @@ def process_gsm_data(file_path: str):
 
     df_trx = process_trx_data(file_path)
 
+    # create band dataframe
+    df_band = config_band(df_bts)
+
     # Merge dataframes
     df_bts_bcf = pd.merge(df_bts, df_bcf, on="ID_BCF", how="left")
     df_2g = pd.merge(df_bts_bcf, df_trx, on="ID_BTS", how="left")
+    df_2g = pd.merge(df_2g, df_band, on="code", how="left")
 
     df_physical_db = UtilsVars.physisal_db
     df_2g = pd.merge(df_2g, df_physical_db, on="Code_Sector", how="left")
 
     # Save dataframes
-    # save_dataframe(df_bts, "bts")
+    # save_dataframe(df_band, "BAND")
     # save_dataframe(df_bcf, "bcf")
     # save_dataframe(df_trx, "trx")
     # df_2g2 = save_dataframe(df_2g, "2g")
@@ -127,5 +138,11 @@ def process_gsm_data(file_path: str):
 
 
 def process_gsm_data_to_excel(file_path: str):
+    """
+    Process data from the specified file path and save it to a excel file.
+
+    Args:
+        file_path (str): The path to the file.
+    """
     gsm_dfs = process_gsm_data(file_path)
     UtilsVars.final_gsm_database = convert_dfs([gsm_dfs], ["GSM"])

queries/process_lte.py

@@ -1,6 +1,7 @@
 import numpy as np
 import pandas as pd
 
+from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.utils_vars import UtilsVars, get_band
 
@@ -94,6 +95,9 @@ def process_lte_data(file_path: str):
     df_lncel["band_type"] = np.where(df_lncel["band"] == "L2300", "TDD", "FDD")
     df_lncel = df_lncel[LNCEL_COLUMNS]
 
+    # create band dataframe
+    df_band = config_band(df_lncel)
+
     # Process LNBTS data
     df_lnbts = dfs["LNBTS"]
     df_lnbts.columns = df_lnbts.columns.str.replace(r"[ ]", "", regex=True)
@@ -105,6 +109,7 @@ def process_lte_data(file_path: str):
 
     # Merge dataframes
     df_lncel_lnbts = pd.merge(df_lncel, df_lnbts, on="ID_LNBTS", how="left")
+    df_lncel_lnbts = pd.merge(df_lncel_lnbts, df_band, on="code", how="left")
 
     df_physical_db = UtilsVars.physisal_db
     df_physical_db = df_physical_db[

queries/process_wcdma.py

@@ -1,5 +1,6 @@
 import pandas as pd
 
+from utils.config_band import config_band
 from utils.convert_to_excel import convert_dfs, save_dataframe
 from utils.extract_code import extract_code_from_mrbts
 from utils.utils_vars import UtilsVars
@@ -39,6 +40,7 @@ WCEL_COLUMNS = [
     "Code_Sector",
     "code_wcel",
     "porteuse",
+    "band",
 ]
 
 WBTS_COLUMNS = [
@@ -96,6 +98,11 @@ def process_wcdma_data(file_path: str):
     df_wcel["porteuse"] = (
         df_wcel["UARFCN"].map(UtilsVars.porteuse_mapping).fillna("not found")
     )
+    df_wcel["band"] = df_wcel["UARFCN"].map(UtilsVars.wcdma_band).fillna("not found")
+
+    # create config_band dataframe
+    df_band = config_band(df_wcel)
+
     # Process WBTS data
     df_wbts = dfs["WBTS"]
     df_wbts.columns = df_wbts.columns.str.replace(r"[ ]", "", regex=True)
@@ -120,6 +127,7 @@ def process_wcdma_data(file_path: str):
     df_3g = df_3g[WCEL_COLUMNS]
 
     df_physical_db = UtilsVars.physisal_db
+    df_3g = pd.merge(df_3g, df_band, on="code", how="left")
     df_3g = pd.merge(df_3g, df_physical_db, on="Code_Sector", how="left")
     # Save dataframes
     # save_dataframe(df_wcel, "wcel")
@@ -136,5 +144,11 @@ def process_wcdma_data(file_path: str):
 
 
 def process_wcdma_data_to_excel(file_path: str):
+    """
+    Process WCDMA data from the specified file path and convert it to Excel format
+
+    Args:
+        file_path (str): The path to the file.
+    """
     wcdma_dfs = process_wcdma_data(file_path)
     UtilsVars.final_wcdma_database = convert_dfs([wcdma_dfs], ["WCDMA"])

utils/config_band.py

@@ -0,0 +1,31 @@
+import pandas as pd
+
+
+def config_band(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create a dataframe that contains the site configuration band for each site code.
+
+    Parameters
+    ----------
+    df : pd.DataFrame
+        The dataframe containing the site information, with columns "code" and "band"
+
+    Returns
+    -------
+    pd.DataFrame
+        The dataframe containing the site configuration band for each site code, with columns "code" and "site_config_band"
+    """
+    df_band = df[["code", "band"]].copy()
+    df_band["ID"] = df_band[["code", "band"]].astype(str).apply("_".join, axis=1)
+    # remove duplicates ID
+    df_band = df_band.drop_duplicates(subset=["ID"])
+    df_band = df_band[["code", "band"]]
+    df_band = (
+        df_band.groupby("code")["band"]
+        .apply(lambda x: "/".join(sorted(x)))
+        .reset_index()
+    )
+    # rename band to config
+    df_band.rename(columns={"band": "site_config_band"}, inplace=True)
+
+    return df_band

utils/utils_vars.py

@@ -19,7 +19,8 @@ def get_physical_db():
 class UtilsVars:
     sector_mapping = {4: 1, 5: 2, 6: 3, 11: 1, 12: 2, 13: 3}
     type_cellule = {1: "Macro Cell 1800", 0: "Macro Cell 900"}
-    band_frequence = {1: "OML BAND GSM 1800", 0: "OML BAND GSM 900"}
+    oml_band_frequence = {1: "OML BAND GSM 1800", 0: "OML BAND GSM 900"}
+    gsm_band = {1: "G1800", 0: "G900"}
     configuration_schema = {1: "EGPRS 1800", 0: "EGPRS 900"}
     channeltype_mapping = {4: "BCCH", 3: "TCH"}
     porteuse_mapping = {
@@ -29,6 +30,13 @@ class UtilsVars:
         10787: "OML UTRA Band I",
         10837: "OML UTRA Band I",
     }
+    wcdma_band = {
+        3004: "U900",
+        3006: "U900",
+        10787: "U2100",
+        10837: "U2100",
+        10812: "U2100",
+    }
     final_lte_database = ""
     final_gsm_database = ""
     final_wcdma_database = ""