Misc

earthquakes.py

@@ -146,7 +146,7 @@ def main():
 
             case "8":
                 if db is not None:
-                    pass
+                    stats.stat_menu(db)
                 else:
                     retInfo = "Base de dados não encontrada!"
 
@@ -180,6 +180,9 @@ def _get_usr_input(msg:str, asType=str):
         return None
     return asType(usrIn)
 
+def _prettify_event(df):
+    preambleInfo = df.drop_duplicates(subset="ID", keep="first")
+    stations = df[["Estacao", "Componente", "Tipo Onda", "Amplitude"]]
 
 if __name__ == '__main__':
     main()

utils/parser.py

@@ -191,4 +191,3 @@ def _parse_type_i(data: list[str]):
 
 FUNCS = {1: _parse_type_1, 3: _parse_type_3, 6: _parse_type_6, "E": _parse_type_e, "I": _parse_type_i}
 
-parse("dados.txt")

utils/stats.py

@@ -5,45 +5,119 @@ import os
 import pandas as pd
 import numpy as np
 
-STAT_MENU = """=== Earthquakes ===
+STAT_HEADER ="""=== Earthquakes ===
  == Estatísticas == 
-[1] Média
+"""
+
+STAT_MENU = """[1] Média
 [2] Variância
 [3] Desvio padrão
 [4] Máximo
 [5] Mínimo
+[6] Moda
 
 [Q] Voltar ao menu principal
 """
 
+FILTER_CHOICES = """[1] Magnitudes
+[2] Distância
+[3] Profundidade
+
+"""
+
+CHOICE = {"1": "Magnitudes", "2": "Distancia","3": "Prof"}
+
+
+def filter_submenu(type: str):
+    os.system("cls")
+    print(f"{STAT_HEADER}\n  = {type} =  ")
+    print(FILTER_CHOICES)
+
+    choice = input("Qual dos valores: ")
+
+    try:
+        usrChoice = CHOICE[choice]
+        return usrChoice
+    except KeyError:
+        return None
+
 
 def stat_menu(df: pd.DataFrame):
     inStats = True
     while inStats:
         os.system("cls")
-        print(STAT_MENU)
+        print(STAT_HEADER + "\n" + STAT_MENU)
         usrIn = input("Opção: ").lower()
 
         match usrIn:
             case "1":
-                pass
+                # TODO: verificar se estamos a tratar de numeros ou strings
+                c = filter_submenu("Média")
+
+                if c is not None:
+                    retValue = average(df, c)
+                    print(f"A média de {c} é {retValue}")
+                else:
+                    continue
+
             case "2":
-                pass
+                c = filter_submenu("Variância")
+
+                if c is not None:
+                    retValue = variance(df, c)
+                    print(f"A variância dos dados de {c} é {retValue}")
+                else:
+                    continue
+
             case "3":
-                pass
+                # TODO: verificar se estamos a tratar de numeros ou strings
+                c = filter_submenu("Desvio Padrão")
+
+                if c is not None:
+                    retValue = std_dev(df, c)
+                    print(f"O desvio padrão de {c} é {retValue}")
+                else:
+                    continue
+
             case "4":
-                pass
+                c = filter_submenu("Máximo")
+
+                if c is not None:
+                    retValue = max_v(df, c)
+                    print(f"O valor máximo em {c} é {retValue}")
+                else:
+                    continue
+
             case "5":
-                pass
+                c = filter_submenu("Mínimo")
+
+                if c is not None:
+                    retValue = min_v(df, c)
+                    print(f"O valor mínimo em {c} é {retValue}")
+                else:
+                    continue
+
+            case "6":
+                c = filter_submenu("Mínimo")
+
+                if c is not None:
+                    retValue = moda(df, c)
+                    print(f"O valor moda em {c} é {retValue}")
+                else:
+                    continue
+
             case "q":
                 inStats = False
-                pass
+                continue
+
             case _:
                 pass
+        input("Clica Enter para continuar")
 
 
 def average(df: pd.DataFrame, filter_by):
-    values = df[filter_by].to_numpy()
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
@@ -52,7 +126,8 @@ def average(df: pd.DataFrame, filter_by):
 
 
 def variance(df, filter_by):
-    values = df[filter_by].to_numpy()
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
@@ -61,7 +136,8 @@ def variance(df, filter_by):
 
 
 def std_dev(df, filter_by):
-    values = df[filter_by].to_numpy()
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
@@ -69,8 +145,9 @@ def std_dev(df, filter_by):
     return np.std(values)
 
 
-def max(df, filter_by):
+def max_v(df, filter_by):
-    values = df[filter_by].to_numpy()
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
@@ -78,19 +155,32 @@ def max(df, filter_by):
     return np.max(values)
 
 
-def min(df, filter_by):
+def min_v(df, filter_by):
-    values = df[filter_by].to_numpy()
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
 
     if filter_by == "Magnitudes":
         values = _unpack_mags(values)
     
     return np.min(values)
 
+def moda(df, filter_by):
+    events = df.drop_duplicates(subset="ID", keep='first')
+    values = events[filter_by].to_numpy()
+
+    if filter_by == "Magnitudes":
+        values = _unpack_mags(values)
+
+    uniques, count = np.unique(values, return_counts=True)
+    uniques_list = list(zip(uniques, count))
+
+    return sorted(uniques_list, reverse=True ,key=lambda x: x[1])[0][0]
+
 
 def _unpack_mags(arr: np.ndarray):
     newVals = np.empty(0)
     for v in arr:
         for m in v:
-            newVals = np.append(newVals, np.float32(m["Magnitude"]))
+            newVals = np.append(newVals, float(m["Magnitude"]))
     return newVals