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utils/stats.py

@@ -1,5 +1,3 @@
-# pyright: basic
-
 import os
 import sys
 
@@ -16,6 +14,7 @@ STAT_MENU = """[1] Média
 [4] Máximo
 [5] Mínimo
 [6] Moda
+[T] Estatísticas Temporais (T5)
 
 [Q] Voltar ao menu principal
 """
@@ -43,6 +42,120 @@ def filter_submenu(type: str):
         return None
 
 
+
+# -- t5 funcs
+
+def _get_unique_events(df: pd.DataFrame) -> pd.DataFrame:
+    return df.drop_duplicates(subset="ID", keep='first')
+
+def convert_to_datetime(df: pd.DataFrame) -> pd.DataFrame:
+    # Converte coluna Data para objetos datetime
+    df = df.copy()
+    df['Data'] = pd.to_datetime(df['Data'], format='mixed')
+    return df
+
+def events_per_period(df: pd.DataFrame, period: str):
+    # Calcula o número de eventos por dia ('D') ou mês ('M')
+    df = convert_to_datetime(df)
+    events = _get_unique_events(df)
+
+    if period == 'M':
+        period = 'ME'
+        
+    res = events.set_index('Data').resample(period).size()
+    return res.index, res.values
+
+def stats_depth_month(df: pd.DataFrame):
+     # Calcula estatísticas de Profundidade por Mês
+     df = convert_to_datetime(df)
+     events = _get_unique_events(df)
+     
+     grouped = events.set_index('Data').resample('ME')['Profundidade']
+     
+     stats_df = pd.DataFrame({
+         'Mean': grouped.mean(),
+         'Std': grouped.std(),
+         'Median': grouped.median(),
+         'Q1': grouped.quantile(0.25),
+         'Q3': grouped.quantile(0.75),
+         'Min': grouped.min(),
+         'Max': grouped.max()
+     })
+     return stats_df
+
+def stats_mag_month(df: pd.DataFrame):
+    # Calcula estatísticas de Magnitude por Mês
+    df = convert_to_datetime(df)
+    events = _get_unique_events(df)
+    
+    def get_max_mag(mags):
+        vals = [float(m['Magnitude']) for m in mags if 'Magnitude' in m]
+        return max(vals) if vals else np.nan
+
+    events = events.copy()
+    events['MaxMag'] = events['Magnitudes'].apply(get_max_mag)
+    
+    grouped = events.set_index('Data').resample('ME')['MaxMag']
+    
+    stats_df = pd.DataFrame({
+         'Mean': grouped.mean(),
+         'Std': grouped.std(),
+         'Median': grouped.median(),
+         'Q1': grouped.quantile(0.25),
+         'Q3': grouped.quantile(0.75),
+         'Min': grouped.min(),
+         'Max': grouped.max()
+     })
+    return stats_df
+
+
+# -- t5 menu
+
+T5_MENU = """[1] Número de eventos por dia
+[2] Número de eventos por mês
+[3] Estatísticas Profundidade por mês
+[4] Estatísticas Magnitude por mês
+
+[Q] Voltar
+"""
+
+def t5_menu(df: pd.DataFrame):
+    while True:
+        os.system("cls" if sys.platform == "windows" else "clear")
+        print(STAT_HEADER + "\n" + " == T5: Estatísticas Temporais ==\n" + T5_MENU)
+        usrIn = input("Opção: ").lower()
+        
+        match usrIn:
+            case "1":
+                dates, counts = events_per_period(df, 'D')
+                print("\nEventos por Dia:")
+                print(pd.DataFrame({'Data': dates, 'Contagem': counts}).to_string(index=False))
+            
+            case "2":
+                dates, counts = events_per_period(df, 'M')
+                print("\nEventos por Mês:")
+                print(pd.DataFrame({'Data': dates, 'Contagem': counts}).to_string(index=False))
+
+            case "3":
+                st = stats_depth_month(df)
+                print("\nEstatísticas Profundidade por Mês:")
+                print(st.to_string())
+
+            case "4":
+                st = stats_mag_month(df)
+                print("\nEstatísticas Magnitude por Mês:")
+                print(st.to_string())
+                
+            case "q":
+                return
+            case _:
+                pass
+        
+        input("\n[Enter] para continuar...")
+
+
+# -- stat menu
+
 def stat_menu(df: pd.DataFrame):
     inStats = True
     while inStats:
@@ -51,6 +164,10 @@ def stat_menu(df: pd.DataFrame):
         usrIn = input("Opção: ").lower()
 
         match usrIn:
+            case "t":
+                t5_menu(df)
+                continue
+
             case "1":
                 c = filter_submenu("Média")
 
@@ -106,7 +223,7 @@ def stat_menu(df: pd.DataFrame):
                     continue
 
             case "6":
-                c = filter_submenu("Mínimo")
+                c = filter_submenu("Moda")
 
                 if c is not None:
                     retValue = moda(df, c)
@@ -120,11 +237,12 @@ def stat_menu(df: pd.DataFrame):
 
             case _:
                 pass
+        
         input("Clica `Enter` para continuar")
 
 
 def average(df: pd.DataFrame, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -136,7 +254,7 @@ def average(df: pd.DataFrame, filter_by):
 
 
 def variance(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -149,7 +267,7 @@ def variance(df, filter_by):
 
 
 def std_dev(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -162,7 +280,7 @@ def std_dev(df, filter_by):
 
 
 def max_v(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -172,7 +290,7 @@ def max_v(df, filter_by):
 
 
 def min_v(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
@@ -182,7 +300,7 @@ def min_v(df, filter_by):
 
 
 def moda(df, filter_by):
-    events = df.drop_duplicates(subset="ID", keep='first')
+    events = _get_unique_events(df)
     values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":