improve functional coding

process_kpi/process_wbts_capacity.py

@@ -1,5 +1,11 @@
 import pandas as pd
 
+from utils.kpi_analysis_utils import (
+    create_daily_date,
+    create_dfs_per_kpi,
+    kpi_naming_cleaning,
+)
+
 
 class WbtsCapacity:
     final_results: pd.DataFrame = None
@@ -31,45 +37,6 @@ def check_deviation(row: pd.Series, max_diff: float = 3.0, type: str = "") -> st
         return ""
 
 
-def create_daily_date(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    Create a daily date column from PERIOD_START_TIME and drop unnecessary columns.
-
-    Args:
-        df: DataFrame containing PERIOD_START_TIME column
-
-    Returns:
-        DataFrame with new date column and unnecessary columns removed
-    """
-    date_df = df.copy()
-    date_df[["mois", "jour", "annee"]] = date_df["PERIOD_START_TIME"].str.split(
-        ".", expand=True
-    )
-    date_df["date"] = date_df["annee"] + "-" + date_df["mois"] + "-" + date_df["jour"]
-    # Remove unnecessary columns
-    date_df = date_df.drop(["annee", "mois", "jour", "PERIOD_START_TIME"], axis=1)
-    return date_df
-
-
-def kpi_naming_cleaning(df: pd.DataFrame) -> pd.DataFrame:
-    """
-    Clean KPI column names by replacing special characters and standardizing format.
-
-    Args:
-        df: DataFrame with KPI column names to clean
-
-    Returns:
-        DataFrame with cleaned column names
-    """
-    name_df = df.copy()
-    name_df.columns = name_df.columns.str.replace("[ /(),-.']", "_", regex=True)
-    name_df.columns = name_df.columns.str.replace("___", "_")
-    name_df.columns = name_df.columns.str.replace("__", "_")
-    name_df.columns = name_df.columns.str.replace("%", "perc")
-    name_df.columns = name_df.columns.str.rstrip("_")
-    return name_df
-
-
 def max_used_bb_subunits_analysis(
     df: pd.DataFrame,
     days: int = 7,
@@ -288,7 +255,7 @@ def ce_comments_analysis(df: pd.DataFrame) -> pd.DataFrame:
     )
 
 
-def create_dfs_per_kpi(
+def wbts_kpi_analysis(
     df: pd.DataFrame,
     num_days: int = 7,
     threshold: int = 80,
@@ -306,20 +273,12 @@ def create_dfs_per_kpi(
     Returns:
         DataFrame with combined analysis results
     """
-    kpi_columns = df.columns[5:]
+    # kpi_columns = df.columns[5:]
     pivoted_kpi_dfs = {}
 
-    # Loop through each KPI and create pivoted DataFrames
-    for kpi in kpi_columns:
-        temp_df = df[["date", "DN", kpi]].copy()
-
-        # Pivot the dataframe
-        pivot_df = temp_df.pivot(index="DN", columns="date", values=kpi)
-        pivot_df.columns = pd.MultiIndex.from_product([[kpi], pivot_df.columns])
-        pivot_df.columns.names = ["KPI", "Date"]
-
-        # Store in dictionary with KPI name as key
-        pivoted_kpi_dfs[kpi] = pivot_df
+    pivoted_kpi_dfs = create_dfs_per_kpi(
+        df=df, pivot_date_column="date", pivot_name_column="DN", kpi_columns_from=5
+    )
 
     # Extract individual KPI DataFrames
     wbts_name_df = pivoted_kpi_dfs["WBTS_name"].iloc[:, 0]
@@ -403,5 +362,9 @@ def load_data(
     df = df[[col for col in df.columns if col != "WBTS_name"] + ["WBTS_name"]]
 
     # Perform KPI analysis
-    df = create_dfs_per_kpi(df, num_days, threshold, number_of_threshold_days)
+    df = wbts_kpi_analysis(df, num_days, threshold, number_of_threshold_days)
+
+    # for col, col_index in zip(df.columns, df.columns.get_indexer(df.columns)):
+    #     print(f"Column: {col}, Index: {col_index}")
+
     return df

utils/kpi_analysis_utils.py

@@ -0,0 +1,320 @@
+import pandas as pd
+
+
+class GsmAnalysis:
+    hf_rate_coef = {
+        10: 1.1,
+        20: 1.2,
+        40: 1.4,
+        60: 1.6,
+        70: 1.7,
+        80: 1.8,
+        99: 2.0,
+        100: 1.4,
+    }
+    erlangB_table = {
+        1: 0.0204,
+        2: 0.2234,
+        3: 0.6022,
+        4: 1.092,
+        5: 1.657,
+        6: 2.276,
+        7: 2.935,
+        8: 3.627,
+        9: 4.345,
+        10: 5.084,
+        11: 5.841,
+        12: 6.614,
+        13: 7.401,
+        14: 8.2,
+        15: 9.009,
+        16: 9.828,
+        17: 10.66,
+        18: 11.49,
+        19: 12.33,
+        20: 13.18,
+        21: 14.04,
+        22: 14.9,
+        23: 15.76,
+        24: 16.63,
+        25: 17.5,
+        26: 18.38,
+        27: 19.26,
+        28: 20.15,
+        29: 21.04,
+        30: 21.93,
+        31: 22.83,
+        32: 23.72,
+        33: 24.63,
+        34: 25.53,
+        35: 26.43,
+        36: 27.34,
+        37: 28.25,
+        38: 29.17,
+        39: 30.08,
+        40: 31,
+        41: 31.91,
+        42: 32.84,
+        43: 33.76,
+        44: 34.68,
+        45: 35.61,
+        46: 36.53,
+        47: 37.46,
+        48: 38.39,
+        49: 39.32,
+        50: 40.25,
+        51: 41.19,
+        52: 42.12,
+        53: 43.06,
+        54: 44,
+        55: 44.93,
+        56: 45.88,
+        57: 46.81,
+        58: 47.75,
+        59: 48.7,
+        60: 49.64,
+        61: 50.59,
+        62: 51.53,
+        63: 52.48,
+        64: 53.43,
+        65: 54.38,
+        66: 55.32,
+        67: 56.27,
+        68: 57.22,
+        69: 58.18,
+        70: 59.13,
+        71: 60.08,
+        72: 61.04,
+        73: 61.99,
+        74: 62.94,
+        75: 63.9,
+        76: 64.86,
+        77: 65.81,
+        78: 66.77,
+        79: 67.73,
+        80: 68.69,
+        81: 69.64,
+        82: 70.61,
+        83: 71.57,
+        84: 72.53,
+        85: 73.49,
+        86: 74.45,
+        87: 75.41,
+        88: 76.38,
+        89: 77.34,
+        90: 78.3,
+        91: 79.27,
+        92: 80.23,
+        93: 81.2,
+        94: 82.16,
+        95: 83.13,
+        96: 84.09,
+        97: 85.06,
+        98: 86.03,
+        99: 87,
+        100: 87.97,
+        101: 88.94,
+        102: 89.91,
+        103: 90.88,
+        104: 91.85,
+        105: 92.82,
+        106: 93.79,
+        107: 94.76,
+        108: 95.73,
+        109: 96.71,
+        110: 97.68,
+        111: 98.65,
+        112: 99.63,
+        113: 100.6,
+        114: 101.57,
+        115: 102.54,
+        116: 103.52,
+        117: 104.49,
+        118: 105.47,
+        119: 106.44,
+        120: 107.42,
+        121: 108.4,
+        122: 109.37,
+        123: 110.35,
+        124: 111.32,
+        125: 112.3,
+        126: 113.28,
+        127: 114.25,
+        128: 115.23,
+        129: 116.21,
+        130: 117.19,
+        131: 118.17,
+        132: 119.15,
+        133: 120.12,
+        134: 121.1,
+        135: 122.08,
+        136: 123.07,
+        137: 124.04,
+        138: 125.02,
+        139: 126.01341,
+        140: 127.00918,
+        141: 127.96752,
+        142: 128.98152,
+        143: 129.92152,
+        144: 130.88534,
+        145: 131.96461,
+        146: 132.89897,
+        147: 133.86373,
+        148: 134.82569,
+        149: 135.76295,
+        150: 136.82988,
+        151: 137.79,
+        152: 138.77,
+        153: 139.75,
+        154: 140.74,
+        155: 141.72,
+        156: 142.7,
+        157: 143.69,
+        158: 144.67,
+        159: 145.66,
+        160: 146.64,
+        161: 147.63,
+        162: 148.61,
+        163: 149.6,
+        164: 150.58,
+        165: 151.57,
+        166: 152.55,
+        167: 153.54,
+        168: 154.53,
+        169: 155.51,
+        170: 156.5,
+        171: 157.48,
+        172: 158.47,
+        173: 159.46,
+        174: 160.44,
+        175: 161.43,
+        176: 162.42,
+        177: 163.41,
+        178: 164.39,
+        179: 165.38,
+        180: 166.37,
+        181: 167.36,
+        182: 168.35,
+        183: 169.33,
+        184: 170.32,
+        185: 171.31,
+        186: 172.3,
+        187: 173.29,
+        188: 174.28,
+        189: 175.27,
+        190: 176.26,
+        191: 177.25,
+        192: 178.24,
+        193: 179.23,
+        194: 180.22,
+        195: 181.21,
+        196: 182.2,
+        197: 183.19,
+        198: 184.18,
+        199: 185.17,
+        200: 186.16,
+    }
+
+
+def kpi_naming_cleaning(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Clean KPI column names by replacing special characters and standardizing format.
+
+    Args:
+        df: DataFrame with KPI column names to clean
+
+    Returns:
+        DataFrame with cleaned column names
+    """
+    name_df = df.copy()
+    name_df.columns = name_df.columns.str.replace("[ /(),-.']", "_", regex=True)
+    name_df.columns = name_df.columns.str.replace("___", "_")
+    name_df.columns = name_df.columns.str.replace("__", "_")
+    name_df.columns = name_df.columns.str.replace("%", "perc")
+    name_df.columns = name_df.columns.str.rstrip("_")
+    return name_df
+
+
+def create_daily_date(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create a daily date column from PERIOD_START_TIME and drop unnecessary columns.
+
+    Args:
+        df: DataFrame containing PERIOD_START_TIME column
+
+    Returns:
+        DataFrame with new date column and unnecessary columns removed
+    """
+    date_df = df.copy()
+    date_df[["mois", "jour", "annee"]] = date_df["PERIOD_START_TIME"].str.split(
+        ".", expand=True
+    )
+    date_df["date"] = date_df["annee"] + "-" + date_df["mois"] + "-" + date_df["jour"]
+    # Remove unnecessary columns
+    date_df = date_df.drop(["annee", "mois", "jour", "PERIOD_START_TIME"], axis=1)
+    return date_df
+
+
+def create_hourly_date(df: pd.DataFrame):
+    date_df = df
+    date_df[["date_t", "hour"]] = date_df["PERIOD_START_TIME"].str.split(
+        " ", expand=True
+    )
+    date_df[["mois", "jour", "annee"]] = date_df["date_t"].str.split(".", expand=True)
+    date_df["datetime"] = (
+        date_df["annee"]
+        + "-"
+        + date_df["mois"]
+        + "-"
+        + date_df["jour"]
+        + " "
+        + date_df["hour"]
+    )
+
+    date_df["date"] = date_df["annee"] + "-" + date_df["mois"] + "-" + date_df["jour"]
+
+    # Remove columns 'année' and 'mois'
+    date_df = date_df.drop(
+        ["annee", "mois", "jour", "date_t", "PERIOD_START_TIME"], axis=1
+    )
+    return date_df
+
+
+def create_dfs_per_kpi(
+    df: pd.DataFrame = None,
+    pivot_date_column: str = "date",
+    pivot_name_column: str = "BTS_name",
+    kpi_columns_from: int = None,
+) -> pd.DataFrame:
+    """
+    Create pivoted DataFrames for each KPI and perform analysis.
+
+    Args:
+        df: DataFrame containing KPI data
+    Returns:
+        DataFrame with combined analysis results
+    """
+    kpi_columns = df.columns[kpi_columns_from:]
+    # print(kpi_columns)
+    pivoted_kpi_dfs = {}
+
+    # Loop through each KPI and create pivoted DataFrames
+    for kpi in kpi_columns:
+        temp_df = df[[pivot_date_column, pivot_name_column, kpi]].copy()
+        # remove duplicates
+        temp_df = temp_df.drop_duplicates(
+            subset=[pivot_name_column, pivot_date_column], keep="first"
+        )
+        temp_df = temp_df.reset_index()
+        # Pivot the dataframe
+        pivot_df = temp_df.pivot(
+            index=pivot_name_column, columns=pivot_date_column, values=kpi
+        )
+        # print(pivot_df)
+        pivot_df.columns = pd.MultiIndex.from_product([[kpi], pivot_df.columns])
+        pivot_df.columns.names = ["KPI", "Date"]
+
+        # Store in dictionary with KPI name as key
+        pivoted_kpi_dfs[kpi] = pivot_df
+
+    return pivoted_kpi_dfs