feat: R e D do crud feitos

CRUD & guardar_csv

crud.py

@@ -0,0 +1,136 @@
+# pyright: basic
+
+import pandas as pd
+import parser
+import earthquakes as eq
+
+pd.set_option('display.max_rows', 500)
+pd.set_option('display.max_columns', 500)
+pd.set_option('display.width', 150)
+
+def read_ids(df):
+    # Lista de IDs únicos no DataFrame
+    return sorted(set(df["ID"]))
+
+def read_header(df, event_id):
+    # Informações do header do evento
+    row = df[df["ID"] == event_id].iloc[0]
+    cols = list(df.columns)
+    headerCols = ["DateTime", "Distance Indicator", "Event ID", "Lat", "Long", "Depth", "Agency", "Magnitudes"]
+    # end = cols.index("ID") - 1
+    # header_cols = cols[:end]
+    # Para selecionar todas as colunas em vez de só algumas
+    info = []
+    for (i, col) in enumerate(headerCols):
+        info.append(f"{i+1} {col}: {row[col]}")
+    infoString = f"Header do evento {event_id}:\n" + "\n".join(info) 
+    return infoString
+
+
+def get_table(df, event_id):
+    # retorna a tabela de dados do evento
+    rows = df[df["ID"] == event_id]
+    cols = list(df.columns)
+    start = cols.index("ID") + 1
+    table = rows[cols[start:]].iloc[1:]
+    return table
+
+def read_table_row(df, event_id, row_number_1):
+    # retorna uma linha específica da tabela
+    row_number_0 = row_number_1 - 1
+    table = get_table(df, event_id)
+    if row_number_0 < 0 or row_number_0 >= len(table):
+        return f"Linha {row_number_1} não pertence ao evento {event_id}."
+    row = table.iloc[row_number_0]
+    cols = list(df.columns)
+    start = cols.index("STAT")
+    tableCols = cols[start:]
+    info = []
+    for (i, col) in enumerate(tableCols):
+        info.append(f"{i+1} {col}: {row[col]}")
+    return f"Linha {row_number_1:02d} do evento {event_id}:\n" + "\n".join(info) 
+
+def update_table_row(df, event_id, row_number_1, new_data):
+    # atualiza uma linha específica da tabela do evento
+    row_number_0 = row_number_1 - 1
+    table = get_table(df, event_id)
+    if row_number_0 < 0 or row_number_0 >= len(table):
+        return f"Linha {row_number_1} não pertence ao evento {event_id}."
+    for key, value in new_data.items():
+        if key in table.columns:
+            df.loc[(df["ID"] == event_id) & (df.index == table.index[row_number_0]), key] = value
+    return f"Linha {row_number_1} do evento {event_id} atualizada com sucesso."
+
+def update_header(df, event_id, new_data):
+    # atualiza o header de um evento
+    for key, value in new_data.items():
+        if key in df.columns:
+            df.loc[(df["ID"] == event_id) | df.iloc[0], key] = value
+    return f"Header do evento {event_id} atualizado com sucesso."
+
+def delete_event(df, event_id):
+    # Apaga um evento inteiro (header + tabela)
+    new_df = df.drop(df[df["ID"] == event_id].index)
+    new_df.loc[df["ID"] > event_id, "ID"] -= 1
+    return new_df
+
+def delete_table_row(df, event_id, row_number_1):
+    # Apaga uma linha específica da tabela do evento
+    row_number_0 = row_number_1 - 1
+    table = get_table(df, event_id)
+    if row_number_0 < 0 or row_number_0 >= len(table):
+        return f"Linha {row_number_1} não pertence ao evento {event_id}."
+    new_df = df.drop(table.index[row_number_0])
+    return new_df
+
+def create_blank_event(df, event_id):
+    # Criar um evento vazio com linha de header e 1 linha de coluna
+    df.loc[df["ID"] >= event_id, "ID"] += 1
+
+    blank_row_df = pd.DataFrame(columns=df.columns, index=[0, 1])
+    blank_row_df["ID"] = event_id
+    blank_row_df = blank_row_df.astype(df.dtypes)
+
+    new_df = pd.concat([df, blank_row_df], ignore_index=True)
+    new_df = new_df.sort_values(by="ID", kind="mergesort").reset_index(drop=True)
+
+    return new_df
+
+    
+def create_table_row(df, event_id, row_number_1):
+    event_rows = df[df["ID"] == event_id]
+    if event_rows.empty:
+        return df, f"Erro: Evento com ID {event_id} não encontrado."
+
+    header_idx = event_rows.index[0]
+    table_size = len(event_rows.index) - 1
+
+    # Validar posição da nova linha
+    if not (1 <= row_number_1 <= table_size + 1):
+        return df, f"Erro: Posição {row_number_1} inválida. Evento {event_id} tem {table_size} linha(s) na tabela."
+    insertion_point = header_idx + row_number_1
+
+    new_row_df = pd.DataFrame(columns=df.columns, index=[0])
+    new_row_df['ID'] = event_id
+    new_row_df = new_row_df.astype(df.dtypes)
+    df_before = df.iloc[:insertion_point]
+    df_after = df.iloc[insertion_point:]
+
+    new_df = pd.concat([df_before, new_row_df, df_after], ignore_index=True)
+
+    return new_df, f"Linha inserida com sucesso na posição {row_number_1} do evento {event_id}."
+
+''' teste temporário enquanto não temnos menu
+if __name__ == "__main__":
+    df = parser.parse()
+    first_id = read_ids(df)[0]
+    for i in range(5):
+        df = delete_event(df, i)
+    for i in range(5):
+        df = create_blank_event(df, i+5)
+    update_table_row(df, 5, 1, {"Velo": 5.1})
+    df, msg = insert_table_row(df, 5, 1)
+    df, msg = insert_table_row(df, 5, 3)
+    eq.guardar_csv(df, "dados.csv")
+    eq.guardar_df(df, "data.txt")
+'''

earthquakes.py

@@ -1,7 +1,23 @@
 #! /usr/bin/env python
-import pandas as pd
+# pyright: basic
+
 import json
-from parser import parse
+import os
+
+import pandas as pd
+
+import parser
+import crud
+
+HEADER = """=== Terramotos ==="""
+
+MENU ="""[1] Criar a base de dados
+[2] Atualizar uma entrada
+[3] Apagar uma entrada
+[4] Visualizar uma entrada
+
+[Q] Sair
+"""
 
 def guardar_df(df: pd.DataFrame, fname: str) -> bool:
     with open(fname, "w") as fp:
@@ -20,10 +36,99 @@ def guardar_json(df: pd.DataFrame, fname: str) -> bool:
             return False
     return True
 
+def guardar_csv(df: pd.DataFrame, fname: str):
+    with open(fname, "w") as fp:
+        try:
+            df.to_csv(fp, index=False)
+        except ValueError:
+            return False
+    return True
 
 def main():
-    pass
+    isRunning = True
+    db = parser.parse("dados.txt")
+    retInfo = None
 
+    while isRunning:
+        os.system("cls")
+        print(HEADER + "\n" + MENU)
+        usrIn = input("Opção: ").lower()
+
+        match usrIn:
+            case "1":
+                os.system("cls")
+                print(HEADER + "\nCRIAR")
+                fname = input("Nome do ficheiro com os dados. (Branco para dados.txt)")
+                if fname == "":
+                    fname = "dados.txt"
+                if _file_exists(fname):
+                    db = parser.parse(fname)
+                else:
+                    retInfo = "Nenhum ficheiro encontrado!"
+
+            case "2":
+                if db is not None:
+                    continue
+                else:
+                    retInfo = "Base de dados não encontrada!"
+
+            case "3":
+                if db is not None:
+                    a = _get_uniques(db)
+                    ev_ids = _show_events(a)
+
+                    _select = input("Qual a entrada a apagar: ")
+
+                    db = db.drop(db[db["ID"] == ev_ids[_select]].index)
+
+                else:
+                    retInfo = "Base de dados não encontrada!"
+
+            case "4":
+                if db is not None:
+                    a = _get_uniques(db)
+                    ev_ids = _show_events(a)
+
+                    _select = input("Qual a entrada a visualizar: ")
+                    _view_event(db, ev_ids[_select])
+
+                    input()
+
+                else:
+                    retInfo = "Base de dados não encontrada!"
+            case "q":
+                isRunning = False
+                continue
+            case _:
+                pass
+
+        if retInfo:
+            print(retInfo)
+            retInfo = None
+            input("Clique Enter para continuar")
+
+
+def _file_exists(name: str) -> bool:
+    currFiles = os.listdir(os.getcwd())
+    if name in currFiles:
+        return True
+    return False
+
+def _get_uniques(df) -> pd.DataFrame:
+    return df.get(["ID", "Data", "Regiao"]).drop_duplicates(subset="ID", keep="first")
+
+def _show_events(df):
+    events = {}
+    idx = 1
+    for (_, row) in df.iterrows():
+        print(f"{idx:2d}| {row["Regiao"]}")
+        events[str(idx)] = row["ID"]
+        idx += 1
+    return events
+
+def _view_event(df, id):
+    for idx, row in df.loc[df["ID"] == id ].iterrows():
+        print(row)
 
 
 

parser.py

@@ -1,3 +1,4 @@
+# pyright: basic
 import io
 import warnings
 
@@ -6,6 +7,12 @@ from datetime import datetime
 
 import pandas as pd
 
+# --- globals ---
+DIST_IND = {"L": "Local", "R": "Regional", "D": "Distante"}
+TYPE = {"Q": "Quake", "V": "Volcanic", "U": "Unknown", "E": "Explosion"}
+
+
+# --- helper funcs --- 
 def is_blank(l: str) -> bool:
     return len(l.strip(" ")) == 0
 
@@ -32,22 +39,24 @@ def into_dataframe(data) -> pd.DataFrame:
 
     return pd.DataFrame(data=aux)
 
-# ------------ principal
+def _concat(preamble, df: pd.DataFrame):
+    for (k,v) in preamble.items():
+        df.insert(len(df.columns)-1, k, [v for _ in range(len(df))])
 
-def parse(fname="dados.txt"):
+    return df
+
+
+# --- principal ---
+def parse(fname):
     fp = open(fname)
     data = [l for l in fp.read().split("\n")]
     chunks = boundaries(data)
     df = pd.DataFrame()
     for (idx,c) in enumerate(chunks):
-        a = parse_chunk(data[c[0]:c[1]], idx)
+        a = parse_chunk(data[c[0]:c[1]])
         aux = pd.concat([df, a], axis=0, ignore_index=True)
         df = aux
-    print(df)
-    aux = df.loc[df["ID"] == 14]
-    print(aux)
     fp.close()
-
     return df
 
 def boundaries(data: list[str]):
@@ -63,23 +72,18 @@ def boundaries(data: list[str]):
                 start = None
     return boundaries
 
-
+def parse_chunk(chunk_lines: list[str]):
-def parse_chunk(chunk_lines: list[str], iD):
     hIdx = None
     for (idx, l) in enumerate(chunk_lines):
         if l[-1] == "7":
             hIdx = idx
             break
-    headersRet = parse_header(chunk_lines[:hIdx])
+    preambleRet = _parse_preamble(chunk_lines[:hIdx])
-    phaseRet = parse_type_7(chunk_lines[hIdx:])
+    phaseRet = _parse_type_7(chunk_lines[hIdx:])
 
-    hDF = into_dataframe(headersRet)
+    return _concat(preambleRet, phaseRet)
-    hDF["ID"] = iD
-    phaseRet["ID"] = iD
-    return pd.concat([hDF, phaseRet])
 
-
+def _parse_preamble(hLines: list[str]):
-def parse_header(hLines: list[str]):
     aux = defaultdict(list)
 
     for line in hLines:
@@ -91,13 +95,15 @@ def parse_header(hLines: list[str]):
             case "6":
                 aux[6].append(line)
             case "E":
-                aux["E"].append(line)
+                pass
+                # aux["E"].append(line)
             case "I":
                 aux["I"].append(line)
             case "F":
-                aux["F"].append(line)
+                pass
-            case unknown:
+                # aux["F"].append(line)
-                warnings.warn(f"header type not implemented: {unknown}")
+            case _:
+                pass
 
     headerDict = dict()
     for (k,v) in aux.items():
@@ -106,19 +112,7 @@ def parse_header(hLines: list[str]):
     return headerDict
 
 
-def parse_mag(line: str):
+def _parse_type_1(data: list[str]):
-    magnitudes = []
-    base = 55
-    while base < 79:
-        m = line[base:base+4]
-        mt = line[base+4]
-        if not is_blank(m):
-            magnitudes.append({"M": m, "T": mt})
-        base += 8
-    return magnitudes
-
-
-def parse_type_1(data: list[str]):
     aux = data[0]
     y = int(aux[1:5])
     mo = int(aux[6:8])
@@ -129,55 +123,65 @@ def parse_type_1(data: list[str]):
     mil = int(aux[19]) * 10**5
     dt = datetime(y,mo,d,h,m,s,mil)
 
-    dist_ind = aux[21]
+    dist_ind = DIST_IND[aux[21]]
-    eId = aux[22]
+    ev_type = TYPE[aux[22]]
     lat = float(aux[23:30])
     long = float(aux[30:38])
     depth = float(aux[38:43])
-    rep_ag = aux[45:48]
+    no_stat = int(aux[48:51])
 
-    hypo = {"DateTime": dt.isoformat(), "Distance Indicator": dist_ind, "Event ID": eId, "Lat": lat, "Long": long, "Depth": depth, "Agency": rep_ag, "Magnitudes": list()}
+    hypo = {"Data": dt.isoformat(), "Distancia": dist_ind, "Event Type": ev_type, "Lat": lat, "Long": long, "Depth": depth, "No. Stations": no_stat, "Magnitudes": list()}
     for l in data:
-        hypo["Magnitudes"] = hypo["Magnitudes"] + parse_mag(l)
+        hypo["Magnitudes"] = hypo["Magnitudes"] + _parse_mag(l)
 
     return hypo
 
-def parse_type_3(data: list[str]):
+def _parse_mag(line: str):
-    comments = []
+    magnitudes = []
+    base = 55
+    while base < 79:
+        m = line[base:base+4]
+        mt = line[base+4]
+        if not is_blank(m):
+            magnitudes.append({"Magnitude": m, "Tipo": mt})
+        base += 8
+    return magnitudes
+
+
+def _parse_type_3(data: list[str]):
+    comments = {}
     for line in data:
-        comments.append(line[:-2].strip())
+        if line.startswith(" SENTIDO") or line.startswith(" REGIAO"):
-    return {"Comments": comments}
+            c, v = line[:-2].strip().split(": ", maxsplit=1)
+            comments[c.capitalize()] = v
+
+    return comments
 
 
-def parse_type_6(data: list[str]):
+def _parse_type_6(data: list[str]):
     waves = []
     for l in data:
         waves.append(l.strip().split(" ")[0])
     return {"Wave": waves}
 
-def parse_type_7(data: list[str]):
+
+def _parse_type_7(data: list[str]):
     aux = io.StringIO("\n".join(data))
-    dados = pd.read_fwf(aux, colspecs=[(1,5), (6,8), (9,10), (10,15), (16,17), (18,22), (23,28), (29,33), (34,40), (41,45), (46,50), (51,56), (57,60), (61,63), (64,68), (69,70), (72,75), (76,79)])
+    dados = pd.read_fwf(aux, colspecs=[(1,5), (6,8),(10,15), (18,20), (20,22), (23,28), (34,38)])
     return dados
 
 
-
+def _parse_type_e(data: list[str]):
-def parse_type_e(data: list[str]):
     aux = data[0]
     error = {"Gap": int(aux[5:8]), "Origin": float(aux[14:20]), "Error_lat": float(aux[24:30]), "Error_long": float(aux[32:38]), "Error_depth": float(aux[38:43]), "Cov_xy": float(aux[43:55]), "Cov_xz": float(aux[55:67]), "Cov_yz": float(aux[67:79])}
     return error
 
 
-def parse_type_f(data: list[str]):
+def _parse_type_i(data: list[str]):
-    return {}
-
-
-def parse_type_i(data: list[str]):
     aux = data[0]
-    dt = datetime.strptime(aux[12:26], "%y-%m-%d %H:%M")
+    return {"ID":int(aux[60:74])}
-    return {"Action": aux[8:11], "Action Extra": {"Date": dt.isoformat(), "OP": aux[30:35].strip(), "Status": aux[42:57].strip(), "ID":int(aux[60:74])}}
 
 
-FUNCS = {1: parse_type_1, 3: parse_type_3, 6: parse_type_6, "E": parse_type_e, "F": parse_type_f, "I": parse_type_i}
+FUNCS = {1: _parse_type_1, 3: _parse_type_3, 6: _parse_type_6, "E": _parse_type_e, "I": _parse_type_i}
 
-parse()
+parse("dados.txt")

requirements.txt

@@ -1 +1,2 @@
 pytest==8.4.2
+pandas==2.3.3