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