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31 Commits
7780ee2e4e ... main

Author SHA1 Message Date
Shino
b3d9a31792 mais coisas de estatistica 2025-12-11 15:42:07 -01:00
Shino
14dee58ab2 graficos, estatitsticas e filtros 2025-12-11 15:14:38 -01:00
Shino
490c88085a a 2025-12-11 07:56:11 -01:00
Shino
afef4c4d5c novo json 2025-11-15 16:05:41 -01:00
Shino
047f5e25ac mais coisas 2025-11-15 15:34:07 -01:00
Shino
827e9b5c77 feat: Implementado formato JSON personalizado 2025-11-13 15:18:51 -01:00
Shino
4da96e8e74 Merge 2025-11-11 14:11:20 -01:00
smolsbs
ec17137f4b Merge pull request #5 from aulojor/main 
Json formatado, Criação de rows com baliza, baliza para deleção de rows, tempo adicionado ao TABLE_RET
 2025-11-11 13:55:08 -01:00
aulojor
6826941c55 feat: criação de entrada e balizas para linhas, adicionar tempo à visualização 2025-11-11 13:27:29 -01:00
aulojor
12d23d2c0c feat: identar o output do json 2025-11-11 12:34:14 -01:00
Shino
599e456fcf T1 a T4 implementados, provavelmente 2025-11-09 22:57:16 -01:00
Shino
74dea1c653 a 2025-11-09 22:29:34 -01:00
Shino
1645645017 Alterar como mostro os eventos 2025-11-09 22:29:01 -01:00
smolsbs
47f6ff5ca4 Merge pull request #4 from aulojor/main 
feat: load de json se o ficheiro colocado no [1] for um json
 2025-11-09 21:48:06 -01:00
aulojor
bb10d808f8 feat: adicionar edição de uma linha ao menu 2025-11-09 21:28:19 -01:00
aulojor
987324f7f1 feat: load de json se o ficheiro colocado no [1] for um json 2025-11-09 21:06:17 -01:00
Shino
31563ed0da Merge changes 2025-11-09 20:56:41 -01:00
Shino
8fa3b1ec10 Misc 2025-11-09 20:50:28 -01:00
smolsbs
ef2cdfdc6f Merge pull request #3 from aulojor/main 
feat: adicionar o delete_table_row ao menu
 2025-11-09 20:50:05 -01:00
aulojor
d83a953a12 feat: adicionar o delete_table_row ao menu 2025-11-09 20:44:38 -01:00
Shino
d9ddd5b3ed docs: README atualizado 2025-11-09 18:37:04 -01:00
Shino
36f57ae3c7 fix: atualizada lista de requerimentos 2025-11-09 18:35:04 -01:00
Shino
a490bc7756 feat: implementar estatísticas 2025-11-09 18:32:47 -01:00
Shino
5eed5fbe7a movidos ficheiros, alterado algumas coisas 2025-11-09 15:59:56 -01:00
Shino
b7719295ab feat: R e D do crud feitos 2025-11-08 16:30:24 -01:00
Shino
81cb6f21d6 fix: alterado o parser.py com os valores a procurar 2025-11-08 15:33:21 -01:00
smolsbs
065ecea3b2 Merge pull request #2 from smolsbs/dev 
CRUD & guardar_csv
 2025-11-06 20:11:11 -01:00
aulojor
b30f931e61 feat: CRUD 
Acho que está mais ou menos completo mas os returns ainda estão um pouco inconsistentes entre as funções visto que algumas retornam uma string após completar a ação, outras um novo df para substituir o antigo e outras ambos.
 2025-11-06 09:29:29 -01:00
aulojor
3417c59332 feat: guardar_csv 
adicionei uma função para guardar csv porque estava a precisar de alguma forma de visualizar a tabela enquanto testava o CRUD.
 2025-11-06 09:28:47 -01:00
aulojor
6f65c237d3 feat: update requirements.txt 
adicionar o pandas aos requirements.txt
 2025-11-05 09:22:36 -01:00
aulojor
0e38283e6f feat: Read 
Read:
dos IDs de evento disponiveis;
do header de um evento;
da tabela de fases um evento
de uma linha da tabela

Output numerado para selação no menu de texto posteriormente mas ainda com um problema de alinhamento quando o indice é maior que 9.
 2025-11-05 09:13:04 -01:00
15 changed files with 1852 additions and 282 deletions
Expand all files Collapse all files

5
.gitignore vendored
View File

@@ -1,3 +1,8 @@
*.zip
*.json
*.csv
# ---> Python # ---> Python
# Byte-compiled / optimized / DLL files # Byte-compiled / optimized / DLL files
__pycache__/ __pycache__/

27
README.md
View File

@@ -1,5 +1,10 @@
# Project I - Earthquakes # Project I - Earthquakes
## Como utilizar
Correr o ficheiro `earthquakes.py` usando `python earthquakes.py`
## Objectivos
First, let's represent the data using Python's Pandas module and implement CRUD operations, including JSON's conversion. Then, let's implement some statistical operations with graphical representations using Python's Matplotlib module over data representation in Pandas data model. First, let's represent the data using Python's Pandas module and implement CRUD operations, including JSON's conversion. Then, let's implement some statistical operations with graphical representations using Python's Matplotlib module over data representation in Pandas data model.
## Tarefas: ## Tarefas:
@@ -8,7 +13,26 @@ First, let's represent the data using Python's Pandas module and implement CRUD
- T2 - Implement CRUD operations through a text menu; - T2 - Implement CRUD operations through a text menu;
- T3 - Implement statistical operations such as: average, variance, standard desviation, max, min, mode; through a text menu; - T3 - Implement statistical operations such as: average, variance, standard desviation, max, min, mode; through a text menu;
- T4 - Convert from Pandas to JSON and save it in a text file; - T4 - Convert from Pandas to JSON and save it in a text file;
- T5 - to be continued ... - T5 - Calcular as seguintes estatísticas:
- Número de eventos por dia e por mês.
- Média e desvio padrão da profundidade e da magnitude por mês.
- Mediana, 1º quartil e 3º quartil da profundidade e da magnitude por mês.
- Máximo e mínimo a profundidade e da magnitude por mês.
- T6 - Para a representação gráfica:
- Um gráfico de barras com o numero de eventos por dia.
- Um gráfico de barras com o numero de eventos por mês.
- Um gráfico linear com a média +/- o desvio padrão das profundidades por mês.
- Um gráfico linear com a média +/- a desvio padrão da magnitude L por mês.
- Um gráfico tipo "boxplot" com as profundidades por mês.
- Um gráfico tipo "boxplot" com as magnitudes L por mês.
- T7 - Implementar os filtros de seleção de eventos para o cálculo / representação gráfica:
- Período temporal (Data inicial, Data final).
- Eventos com GAP menor que um determinado valor.
- Qualidade (EPI ou Todos).
- Zonas SZ.
- Zonas VZ.
- Limitar por Magnitudes L (mínimo, máximo).
- Limitar Profundidades (mínimo, máximo).
## Prazos ## Prazos
- T1 a T4 -> 10 de novembro - T1 a T4 -> 10 de novembro
@@ -17,7 +41,6 @@ First, let's represent the data using Python's Pandas module and implement CRUD
## Apontamentos ## Apontamentos
Dados parecem estar no formato [Nordic](https://seisan.info/v13/node259.html) Dados parecem estar no formato [Nordic](https://seisan.info/v13/node259.html)
## Bibliografia ## Bibliografia
- [Pandas lib](https://pandas.pydata.org/docs) - [Pandas lib](https://pandas.pydata.org/docs)
- [Matplotlib](https://matplotlib.org/stable/index.html) - [Matplotlib](https://matplotlib.org/stable/index.html)

242
earthquakes.py
View File

@@ -1,31 +1,243 @@
#! /usr/bin/env python #! /usr/bin/env python
import pandas as pd # pyright: basic
import json
from parser import parse import json
import os
import sys
from datetime import datetime
import pandas as pd
from utils import crud, parser, stats, utils
HEADER = """=== Terramotos ==="""
EVENT_COLS = [
"Data",
"Latitude",
"Longitude",
"Profundidade",
"Tipo Evento",
"Gap",
"Magnitudes",
"Regiao",
"Sentido",
]
STATION_COLS = [
"Estacao",
"Hora",
"Min",
"Seg",
"Componente",
"Distancia Epicentro",
"Tipo Onda",
]
MENU = """[1] Criar a base de dados
[3] Apagar um evento
[4] Apagar uma entrada de um evento
[5] Visualizar um evento
[6] Guardar como JSON
[7] Guardar como CSV
[8] Estatísticas
[9] Criar uma entrada
[Q] Sair
"""
def guardar_df(df: pd.DataFrame, fname: str) -> bool:
with open(fname, "w") as fp:
fname = f"{fname}.txt"
try:
fp.write(df.to_string())
except ValueError:
return False
return True
def guardar_json(df: pd.DataFrame, fname: str) -> bool: def guardar_json(df: pd.DataFrame, fname: str) -> bool:
with open(fname , "w") as fp: _retValues = utils.create_dict_struct(df, EVENT_COLS, None)
with open(fname, "w") as fp:
try: try:
json.dump(df.to_json(), fp) json.dump(_retValues, fp)
except: except:
return False return False
return True 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(): def main():
pass isRunning = True
db = None
retInfo = None
while isRunning:
os.system("cls" if sys.platform == "windows" else "clear")
print(HEADER + "\n" + MENU)
usrIn = input("Opção: ").lower()
match usrIn:
case "1":
fname = _get_usr_input("Qual os dados a ler? (dados.txt por defeito): ")
if fname is None:
fname = "dados.txt"
if _file_exists(fname) and fname.endswith(".json"):
db = pd.read_json(fname)
print("Base de dados populada.")
elif _file_exists(fname):
db = parser.parse(fname)
input("Base de dados populada. Enter para voltar ao menu inicial")
else:
input("Base de dados não encontrada. Por favor tenta de novo.")
case "3":
if db is not None:
crud.read_ids(db)
choice = _get_usr_input("Escolhe o ID para apagar: ", int)
if not _event_exists(db, choice):
retInfo = "ID do event não encontrado!"
else:
db = crud.delete_event(db, choice)
input()
else:
retInfo = "Base de dados não encontrada!"
case "4":
if db is not None:
crud.read_ids(db)
eid_choice = _get_usr_input("Escolhe o ID: ", int)
if not _event_exists(db, eid_choice):
retInfo = "ID do event não encontrado!"
else:
os.system("cls" if sys.platform == "windows" else "clear")
table = crud.get_table(db, eid_choice)
_prettify_event(table)
crud.show_table(table)
row_choice = _get_usr_input("Escolhe a linha a apagar:", int)
db, msg = crud.delete_table_row(db, eid_choice, row_choice)
new_table = crud.get_table(db, eid_choice)
crud.show_table(new_table)
print(msg)
input()
else:
retInfo = "Base de dados não encontrada!"
case "5":
if db is not None:
crud.read_ids(db)
choice = _get_usr_input("Escolhe o ID para ver os dados: ", int)
if not _event_exists(db, choice):
retInfo = "ID do event não encontrado!"
else:
os.system("cls" if sys.platform == "windows" else "clear")
table = crud.get_table(db, choice)
_prettify_event(table)
crud.show_table(table)
input()
else:
retInfo = "Base de dados não encontrada!"
case "6":
if db is not None:
fname = _get_usr_input("Nome do ficheiro a guardar? ")
if fname is None:
fname = "valores.json"
guardar_json(db, fname)
else:
retInfo = "Base de dados não encontrada!"
case "7":
if db is not None:
fname = _get_usr_input("Nome do ficheiro a guardar? ")
if fname is None:
fname = "valores.csv"
guardar_csv(db, fname)
else:
retInfo = "Base de dados não encontrada!"
case "8":
if db is not None:
stats.stats(db)
else:
retInfo = "Base de dados não encontrada!"
case "9":
if db is not None:
crud.read_ids(db)
eid_choice = _get_usr_input("Escolhe o ID: ", int)
if not _event_exists(db, eid_choice):
retInfo = "ID do event não encontrado!"
else:
os.system("cls" if sys.platform == "windows" else "clear")
table = crud.get_table(db, eid_choice)
_prettify_event(table)
crud.show_table(table)
insertion_point = _get_usr_input("Posição da nova linha: ", int)
# TODO: balizar a escolha para apenas as linhas do evento em questao
db, msg = crud.create_table_row(db, eid_choice, insertion_point)
new_table = crud.get_table(db, eid_choice)
crud.show_table(new_table)
print(msg)
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
if __name__ == '__main__': def _event_exists(df, eid) -> bool:
allEvents = set(df["ID"])
return eid in allEvents
def _get_usr_input(msg: str, asType=str):
usrIn = input(msg)
if usrIn == "":
return None
return asType(usrIn)
def _prettify_event(df):
preambleInfo = df.drop_duplicates(subset="ID", keep="first")
stations = df[["Estacao", "Componente", "Tipo Onda", "Amplitude"]]
info = df.drop_duplicates(subset="Data", keep="first")
data = datetime.fromisoformat(info.Data.values[0]).strftime("%c")
print(
f"Região: {info['Regiao'].values[0]}\nData: {data}\nLatitude: {info.Lat.values[0]}\nLongitude: {info.Long.values[0]}"
+ f"\nProfundidade: {info.Prof.values[0]}\nTipo de evento: {info['Tipo Ev'].values[0]}\n"
)
if __name__ == "__main__":
main() main()

839
info/dados.txt Normal file
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@@ -0,0 +1,839 @@
2014 1 5 0332 36.3 LQ 38.433 -28.403 0.0 *AZO 11 0.5 2.8LAZO 1
GAP=208 1.45 9.4 4.7 8.3 -0.1953E+02 0.2063E+02 -0.5068E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140105033236 I
OP: CVUA-RM/RC 3
STATUS: OK SENTIDO 3
SENTIDO: II/III -Pico: S. Caetano 3
PUB: NAO 3
WEB: SIM 3
OBS: Por ordem do CT nao foi emitido novo comunicado 3
OBS: Sismo sobreposto 3
REGIAO: Pico,VZ14,SZ06,FE95 405 3
S. Caetano F 2 +MM AZO 2
2014-01-05-0331-37S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PBOI EZ EP 332 36.87 102 0.1210 2.48 82
PBOI EZ E 332 37.72 2.48 82
PBOI EN E 332 37.73 101 2.48 82
PCND EZ EP 332 37.04 96 -0.4310 6.51 308
PCND EN E 332 38.28 97 6.51 308
PCND EZ IAML 332 38.42 15553.4 0.30 6.51 308
PTEI EZ EP 332 37.21 96 -0.5410 7.99 31
PTEI EN E 332 38.82 96 7.99 31
PTEI EZ IAML 332 40.06 16089.8 0.48 7.99 31
PLGR EZ EP 332 40.80 59 -0.1310 26.4 311
PLGR EN ES 332 43.64 59 -0.7710 26.4 311
PCTB EZ EP 332 41.70 59 0.0410 31.3 290
PCTB EN ES 332 46.87 59 1.1810 31.3 290
PCTB EZ IAML 332 48.56 793.4 0.48 31.3 290
PAMA EZ EP 332 42.17 59 0.1410 33.6 35
PAMA EN ES 332 46.74 59 0.3910 33.6 35
PAMA EZ IAML 332 48.13 1860.1 0.42 33.6 35
PCUT EE E 332 48.90 34.3 309
ROSA BZ EP 9 332 41.30 59 -0.91 0 34.8 24
ROSA BN ES 332 46.33 59 0.5810 34.8 24
PTCA EN E 332 48.36 38.2 296
PLUZ EN E 332 55.18 74.6 30
STGR EZ E 332 48.34 79.8 26
2014 1 5 0333 4.5 LQ 38.431 -28.405 0.0 *AZO 10 0.4 2.6LAZO 2.6CAZO 1
GAP=214 1.01 5.2 3.4 5.0 -0.8103E+01 0.1158E+02 -0.9278E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140105033304 I
OP: CVUA-RM/RC 3
STATUS: OK SENTIDO 3
SENTIDO: II/III -Pico: S. Caetano 3
PUB: NAO 3
WEB: SIM 3
OBS: Por ordem do CT nao foi emitido novo comunicado 3
REGIAO: Pico,VZ14,SZ06,FE95 405 3
S. Caetano F 2 +MM AZO 2
2014-01-05-0331-37S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PBOI EZ EP 333 5.10 48 101 0.1110 2.70 76
PBOI EN E 333 5.94 2.70 76
PCND EZ EP 333 5.32 51 96 -0.3710 6.56 311
PCND EN E 333 6.53 6.56 311
PTEI EZ EP 333 5.42 52 96 -0.5910 8.32 31
PTEI EN ES 333 6.96 96 -0.1910 8.32 31
PTEI EZ IAML 333 8.23 13778.4 0.34 8.32 31
PLGR EZ EP 333 8.99 59 -0.1510 26.5 312
PLGR EN ES 333 12.45 59 -0.1810 26.5 312
PLGR EZ IAML 333 13.67 617.9 0.30 26.5 312
PCTB EZ EP 333 9.95 59 0.1010 31.3 291
PCTB EN ES 333 13.85 59 -0.0310 31.3 291
PAMA EN ES 333 15.36 59 0.7210 33.9 35
PCUT EN ES 333 14.68 59 -0.0710 34.4 309
PCUT EZ IAML 333 16.71 530.5 0.47 34.4 309
ROSA BN ES 4 333 16.65 59 1.70 0 35.1 24
PTCA EN ES 333 16.43 59 0.6610 38.1 297
PTCA EZ IAML 333 16.98 652.6 0.37 38.1 297
PLUZ EN E 333 25.77 74.9 30
STGR EN E 333 24.54 80.2 26
2014 1 6 0839 22.5 LQ 38.082 -26.498 0.0 *AZO 19 0.3 2.8LAZO 2.7CAZO 1
GAP=199 0.92 16.1 14.1 5.6 0.2263E+03 -0.6104E+02 -0.7019E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140106083922 I
OP: CVUA-AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa Hirondelle,SZ18,FE95 405 3
2014-01-06-0839-02S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
SET4 EZ EP 839 32.92 44 50 -0.1310 65.6 110
SET4 EZ ES 839 40.72 50 -0.2310 65.6 110
SET2 EZ EP 839 33.33 41 50 0.1710 66.2 113
SET2 EZ ES 839 41.05 50 -0.0910 66.2 113
PFET EZ EP 839 33.07 45 50 -0.4310 68.9 115
PSAN EZ EP 839 34.49 38 50 0.4810 72.5 110
PSAN EZ ES 839 42.69 50 0.0610 72.5 110
FAC EZ ES 839 44.88 43 0.1510 82.0 114
PFAV EZ EP 839 35.60 47 43 -0.1810 86.6 324
PFAV EN IS 839 45.57 43 -0.1610 86.6 324
PFAV EZ IAML 839 45.61 78.0 0.28 86.6 324
PRIB EZ EP 839 35.52 42 43 -0.4810 88.1 318
PRIB EN ES 839 46.30 43 0.1910 88.1 318
PRIB EZ IAML 839 46.91 173.0 0.28 88.1 318
CML EZ EP 839 36.79 52 43 0.5010 90.5 112
CMLA B2 ES 4 839 50.79 43 3.70 0 92.6 112
PVNV EZ EP 839 36.34 46 43 -0.4710 94.8 323
PVNV EN ES 839 47.55 43 0.0210 94.8 323
PVNV EZ IAML 839 47.85 148.4 0.29 94.8 323
PMAT EN ES 839 47.10 43 -0.4810 94.9 108
PMAT EZ IAML 839 47.62 420.5 0.58 94.9 108
LFA EZ EP 839 36.53 52 43 -0.4510 95.6 111
PPAD EZ EP 839 37.15 49 43 0.0110 97.3 315
PPAD EN IS 839 48.59 43 0.4910 97.3 315
PPAD EZ IAML 839 49.04 149.7 0.25 97.3 315
PRCH EZ EP 839 37.21 41 43 0.0210 97.8 113
PRCH EN ES 839 48.28 43 0.0910 97.8 113
MESC EZ EP 839 37.08 46 43 -0.2010 97.9 109
MESC EN ES 839 48.14 43 -0.2110 97.9 109
MESC EZ IAML 839 48.28 160.4 0.36 97.9 109
VIF EN ES 839 48.93 43 0.0910 100 112
PBIS EN E 839 50.26 100 319
ASBA EN ES 839 49.44 43 0.3210 102 314
PCNG EZ EP 839 37.94 39 43 0.0510 103 109
PCNG EN ES 839 50.30 43 0.8810 103 109
MIRA EZ E 839 51.05 112 107
2014 1 8 2246 5.4 LQ 38.460 -29.065 8.7 *AZO 7 0.1 2.1LAZO 2.0CAZO 1
GAP=327 0.40 3.5 3.3 4.2 -0.2535E+01 -0.1768E+01 0.9548E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140108224605 I
OP: CVUA-SA/AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: WSW Faial,SZ31,FE95 405 3
2014-01-08-2245-41S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PTCA EZ EP 2246 10.07 19 96 0.1710 27.2 59
PTCA EN ES 2246 13.49 96 0.2210 27.2 59
PTCA EZ IAML 2246 14.04 389.6 0.16 27.2 59
PCTB EZ EP 2246 10.14 20 95 -0.0510 29.3 74
PCTB EN ES 2246 13.80 95 0.0310 29.3 74
PCTB EZ IAML 2246 14.63 353.7 0.28 29.3 74
PCUT EZ EP 2246 11.05 21 94 -0.0910 36.0 59
PCUT EN ES 2246 15.34 94 -0.0910 36.0 59
PCUT EZ IAML 2246 15.76 459.8 0.09 36.0 59
PLGR EN ES 2246 16.34 93 -0.2310 40.5 69
PLGR EZ IAML 2246 16.51 114.5 0.30 40.5 69
PCND EZ EP 2246 13.44 29 92 -0.0410 52.6 89
PCND EN ES 2246 19.47 92 -0.0610 52.6 89
PCND EZ IAML 2246 19.88 36.3 0.19 52.6 89
PBOI EZ EP 2246 14.51 29 64 -0.0210 60.2 92
PBOI EN ES 2246 21.54 64 0.1710 60.2 92
PBOI EZ IAML 2246 21.57 43.4 0.16 60.2 92
PTEI EN E 2246 22.41 61.9 86
2014 1 9 0607 7.2 LQ 37.714 -26.259 0.0 *AZO 17 0.3 2.1LAZO 2.4CAZO 1
GAP=235 0.87 4.9 2.7 3.6 0.9360E+01 -0.1490E+01 -0.5738E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140109060707 I
OP: CVUA-IA/AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: S Fossa Hirondelle,SZ18,FE95 405 3
2014-01-09-0606-43S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
SET2 EZ EP 6 7 14.10 34 59 -0.2510 42.4 70
SET2 EZ ES 6 7 20.26 59 0.5510 42.4 70
PFET EZ EP 6 7 14.10 33 59 -0.3510 43.1 74
PFET EZ ES 6 7 19.81 59 -0.0610 43.1 74
SET4 EZ EP 6 7 14.08 36 59 -0.5710 44.3 66
SET4 EZ ES 6 7 20.58 59 0.3610 44.3 66
PSAN EZ EP 6 7 14.96 36 59 -0.4810 49.4 72
PSAN EZ ES 6 7 20.98 59 -0.6310 49.4 72
FAC EZ EP 6 7 16.25 30 50 0.1210 54.1 83
FAC EZ ES 6 7 22.63 50 -0.1910 54.1 83
CML EZ EP 6 7 17.38 39 50 -0.0310 63.2 84
CML EZ ES 6 7 25.28 50 0.2210 63.2 84
CMLA B2 ES 4 6 7 24.33 50 -1.17 0 65.0 85
PVER EN ES 6 7 25.52 50 -0.2610 66.4 82
PRCH EZ EP 6 7 18.23 34 50 0.0710 68.7 88
PRCH EN ES 6 7 26.56 50 0.1910 68.7 88
PRCH EZ IAML 6 7 27.56 149.5 0.19 68.7 88
LFA EZ EP 6 7 18.54 33 50 0.3110 68.8 84
PMAT EZ EP 6 7 18.33 24 50 -0.0610 70.4 80
PMAT EN ES 6 7 26.55 50 -0.2310 70.4 80
PMAT EZ IAML 6 7 26.62 84.8 0.16 70.4 80
MESC EN ES 6 7 27.35 50 0.0010 72.3 83
MESC EZ IAML 6 7 28.22 30.4 0.19 72.3 83
VIF EN ES 6 7 27.75 50 0.4010 72.4 87
VIF EZ IAML 6 7 28.20 43.6 0.25 72.4 87
PCNG EN ES 6 7 28.88 43 0.5910 76.4 85
FRA1 EN ES 6 7 29.08 43 0.0010 80.0 87
FRA1 EZ IAML 6 7 30.04 47.2 0.24 80.0 87
PFAV EN ES 6 7 40.40 43 0.0010 132 327
PFAV EZ IAML 6 7 41.27 17.2 0.27 132 327
PRIB EN ES 6 7 40.76 43 0.1110 133 323
PRIB EZ IAML 6 7 41.85 25.3 0.28 133 323
PVNV EN ES 6 7 42.35 43 0.1610 140 326
2014 1 9 1503 59.5 LQ 38.063 -26.236 0.0 *AZO 20 0.3 2.3LAZO 2.7CAZO 1
GAP=183 0.83 11.8 9.6 3.7 0.1125E+03 -0.2100E+02 -0.2738E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140109150359 I
OP: CVUA-BC/RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa Hirondelle,SZ18,FE95 405 3
2014-01-09-1503-33S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
SET4 EZ EP 15 4 6.75 53 59 -0.0610 43.7 118
SET4 EZ ES 15 4 12.56 59 0.2510 43.7 118
SET2 EZ EP 15 4 7.11 59 0.1110 44.9 123
PFET EZ EP 15 4 7.36 36 59 -0.0810 47.8 125
PFET EZ ES 15 4 13.61 59 0.2010 47.8 125
PSAN EZ EP 15 4 7.63 43 59 -0.2510 50.6 118
PSAN EZ ES 15 4 14.44 59 0.2510 50.6 118
CML EZ EP 15 4 10.11 50 -0.3210 68.8 118
PVER EN ES 15 4 18.74 50 -0.1710 70.0 115
CMLA BZ EP 9 15 4 9.53 54 50 -1.19 0 70.8 118
CMLA B1 ES 15 4 18.27 0.3010 70.8 118
PMAT EZ EP 15 4 10.60 44 50 -0.3610 72.6 112
PMAT EN ES 15 4 19.36 50 -0.2110 72.6 112
PMAT EZ IAML 15 4 20.21 258.3 0.17 72.6 112
LFA EZ IP 15 4 10.76 54 50 -0.3910 73.6 116
MESC EZ EP 15 4 10.99 54 50 -0.4610 75.6 113
MESC EN ES 15 4 20.08 50 -0.3510 75.6 113
MESC EZ IAML 15 4 20.34 71.7 0.19 75.6 113
PRCH EN ES 15 4 20.71 43 0.2910 76.2 119
VIF EZ EP 15 4 12.18 44 43 0.4010 78.4 116
VIF EN ES 15 4 21.37 43 0.3610 78.4 116
VIF EZ IAML 15 4 22.08 64.5 0.20 78.4 116
PCNG EN ES 15 4 21.78 43 0.2610 80.8 114
FRA1 EN ES 15 4 22.94 43 0.4210 85.4 114
FRA1 EZ IAML 15 4 23.65 69.7 0.16 85.4 114
MIRA EZ EP 15 4 12.78 35 43 -0.4110 89.6 110
MIRA EZ ES 15 4 23.52 43 0.0410 89.6 110
PFAV EN ES 15 4 26.99 43 0.6510 103 314
PRIB EE ES 15 4 26.99 43 -0.0310 106 310
PRIB EZ IAML 15 4 27.46 63.8 0.14 106 310
PVNV EN ES 15 4 27.95 43 -0.2210 112 314
PVNV EZ IAML 15 4 28.81 33.2 0.19 112 314
PPAD EN ES 15 4 29.18 43 0.0410 116 308
PPAD EZ IAML 15 4 29.49 18.7 0.12 116 308
ASBA EN ES 15 4 30.19 43 0.0110 120 308
2014 1 9 2224 43.4 LQ 37.442 -24.806 0.7 *AZO 18 0.3 2.9LAZO 3.2CAZO 1
GAP=247 0.72 3.5 14.6 9.2 0.4637E+02 0.1281E+03 0.2725E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140109222443 I
OP: CVUA-RM 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa da Povoacao,SZ13,FE95 404 3
2014-01-09-2224-00S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
MIRA EZ EP 2224 52.17 65 50 -0.4610 56.5 313
FRA1 EZ EP 2224 53.37 85 50 0.4710 58.7 305
FRA1 EN ES 2224 59.43 50 -0.6110 58.7 305
FRA1 EZ IAML 2225 1.59 391.7 0.23 58.7 305
PSPR EZ EP 2224 52.91 82 50 -0.0210 59.1 200
PSPR EN ES 2225 0.33 50 0.2510 59.1 200
PSPR EZ IAML 2225 2.73 296.7 0.33 59.1 200
PVPO EZ EP 2224 53.14 93 50 -0.2410 62.5 210
PVPO EN ES 2225 0.70 50 -0.1710 62.5 210
PVPO EZ IAML 2225 1.00 201.2 0.19 62.5 210
PCNG EZ EP 2224 53.90 63 50 0.3410 63.3 305
PCNG EN ES 2225 0.94 50 -0.2410 63.3 305
PCNG EZ IAML 2225 3.22 125.7 0.48 63.3 305
VIF EZ EP 2224 53.87 82 50 0.0410 65.3 301
VIF EN ES 2225 1.85 50 0.1910 65.3 301
VIF EZ IAML 2225 2.96 778.3 0.20 65.3 301
PRCH EZ EP 2224 53.97 74 50 -0.1110 67.4 298
PRCH EN ES 2225 2.25 50 0.1510 67.4 298
PRCH EZ IAML 2225 3.45 898.9 0.19 67.4 298
MESC EZ EP 2224 54.12 96 50 -0.1910 68.6 305
MESC EN ES 2225 2.42 50 -0.0810 68.6 305
MESC EZ IAML 2225 4.09 420.2 0.23 68.6 305
LFA EZ EP 2224 54.48 86 50 -0.0310 70.1 302
PMAT EZ EP 2224 54.42 76 50 -0.2810 71.9 305
PMAT EN ES 2225 3.60 50 0.4210 71.9 305
PMAT EZ IAML 2225 4.03 381.7 0.20 71.9 305
CMLA BZ EP 9 2224 53.80 79 50 -1.04 0 72.8 300
CMLA B2 ES 2225 2.56 0.1810 72.8 300
PVER EZ EP 2224 55.25 76 50 0.2810 73.9 303
PVER EN ES 2225 3.53 50 -0.1210 73.9 303
PVER EZ IAML 2225 4.69 259.7 0.38 73.9 303
CML EZ EP 2224 54.98 77 50 -0.1410 74.8 300
CML EZ ES 2225 4.11 50 0.2010 74.8 300
FAC EZ EP 4 2224 56.89 71 43 0.75 0 83.1 297
FAC EZ ES 2225 5.55 43 -0.1510 83.1 297
PSAN EZ EP 2224 57.50 68 43 0.1010 93.0 300
PSAN EZ ES 2225 8.31 43 0.4010 93.0 300
PFET EZ EP 2224 57.99 66 43 0.1910 96.2 296
PFET EZ ES 2225 8.56 43 -0.0410 96.2 296
SET2 EZ EP 2224 58.55 70 43 0.3910 98.9 297
SET2 EZ ES 2225 9.05 43 -0.1810 98.9 297
SET4 EZ EP 2224 58.16 69 43 -0.1010 99.9 299
SET4 EZ ES 2225 9.18 43 -0.2410 99.9 299
2014 111 1337 24.9 LQ 37.605 -25.179 6.6 *AZO 14 0.2 2.0LAZO 2.3CAZO 1
GAP=317 0.69 4.0 4.5 2.7 0.8869E+01 0.7143E+01 0.6380E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140111133724 I
OP: CVUA-IA/RC 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa da Povoacao,SZ13,FE95 405 3
2014-01-11-1336-29S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
FRA1 EZ EP 1337 28.15 104 -0.4910 21.6 315
FRA1 EN ES 1337 31.78 104 0.3310 21.6 315
FRA1 EZ IAML 1337 32.57 310.2 0.27 21.6 315
MIRA EZ IP C 1337 28.96 30 104 0.2510 22.0 337
MIRA EZ ES 1337 31.36 104 -0.2110 22.0 337
PCNG EZ EP 1337 28.85 24 101 -0.5010 26.3 314
PCNG EN ES 1337 32.76 101 0.0710 26.3 314
PCNG EZ IAML 1337 33.38 37.6 0.14 26.3 314
VIF EZ EP 1337 29.51 45 100 -0.0710 27.7 304
VIF EN ES 1337 33.36 100 0.2610 27.7 304
VIF EZ IAML 1337 34.08 245.8 0.17 27.7 304
PRCH EZ EP 1337 30.08 29 63 0.2310 29.8 297
PRCH EN ES 1337 33.42 63 -0.1510 29.8 297
PRCH EZ IAML 1337 34.57 1059.5 0.33 29.8 297
MESC EZ EP 1337 30.30 63 0.1510 31.4 312
MESC EN ES 1337 34.14 63 0.0610 31.4 312
MESC EZ IAML 1337 34.50 194.6 0.14 31.4 312
LFA EZ EP 1337 30.59 31 63 0.2910 32.6 305
PMAT EZ EP 1337 30.62 24 63 0.0810 34.8 312
PMAT EN ES 1337 34.62 63 -0.1610 34.8 312
PMAT EZ IAML 1337 35.76 173.7 0.20 34.8 312
CMLA B1 ES 4 1337 33.99 63 -0.90 0 35.1 300
CML EZ EP 1337 31.16 46 63 0.2710 37.2 300
CML EZ ES 1337 35.29 63 -0.0910 37.2 300
FAC EZ EP 1337 31.83 39 63 -0.2010 45.5 295
FAC EZ ES 1337 36.98 63 -0.4110 45.5 295
PSAN EZ EP 1337 33.25 30 63 -0.1710 55.4 300
PSAN EZ ES 1337 40.11 63 0.2910 55.4 300
PFET EZ E 1337 34.11 58.7 294
PFET EZ ES 1337 40.74 63 0.1310 58.7 294
SET2 EZ EP 1337 34.48 44 63 0.2210 61.3 296
SET2 EZ ES 1337 41.03 63 -0.2610 61.3 296
SET4 EZ E 1337 34.58 62.3 299
SET4 EZ ES 1337 41.57 63 0.0810 62.3 299
2014 114 0756 0.2 LQ 38.530 -29.435 0.0 *AZO 7 0.1 2.4LAZO 2.3CAZO 1
GAP=344 0.36 6.4 8.4 6.1 -0.3015E+02 -0.4733E+02 0.1987E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140114075600 I
OP: CVUA-RC 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: W Faial,SZ31,FE95 405 3
2014-01-14-0755-37S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PTCA EZ EP 756 9.49 26 50 0.1610 55.9 84
PTCA EN ES 756 16.27 50 0.0610 55.9 84
PTCA EZ IAML 756 17.45 222.3 0.28 55.9 84
PCTB EZ EP 756 9.85 31 50 -0.1010 60.5 90
PCTB EN ES 756 17.15 50 -0.1410 60.5 90
PCTB EZ IAML 756 18.81 194.3 0.14 60.5 90
PCUT EN ES 756 18.19 50 0.0110 64.0 80
PCUT EZ IAML 756 18.94 183.6 0.25 64.0 80
PCND EN ES 756 23.06 43 -0.0910 85.1 94
PCND EZ IAML 756 27.57 53.7 0.60 85.1 94
PBOI EZ EP 756 14.21 33 43 -0.0710 93.0 96
PBOI EN ES 756 25.00 43 0.1410 93.0 96
PBOI EZ IAML 756 26.39 60.8 0.41 93.0 96
PTEI EN ES 756 25.28 43 0.1110 94.1 92
ROSA BE ES 4 756 26.52 43 -1.13 0 106 78
PAMA EE ES 756 28.58 43 -0.0810 111 81
2014 115 0550 8.7 LQ 37.476 -24.668 5.1 *AZO 14 0.3 2.3LAZO 2.1CAZO 1
GAP=266 0.71 3.5 17.7 15.4 0.5281E+02 0.2528E+03 0.3904E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140115055008 I
OP: CVUA-RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa da Povoacao,SZ13,FE95 404 3
2014-01-15-0550-00S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
MIRA EZ ES 550 25.51 63 -0.4510 63.9 303
FRA1 EN E 550 30.95 67.3 296
PSPR EZ EP 550 19.20 21 63 0.1710 67.6 209
PSPR EN ES 550 26.97 63 0.2010 67.6 209
PSPR EZ IAML 550 27.68 66.4 0.22 67.6 209
PCNG EN ES 550 27.49 53 -0.3110 71.9 297
PVPO EZ EP 550 19.46 22 53 -0.1610 72.5 217
PVPO EN ES 550 27.44 53 -0.3710 72.5 217
PVPO EZ IAML 550 28.05 49.1 0.27 72.5 217
VIF EN ES 550 28.87 53 0.5210 74.4 294
PRCH EN ES 550 28.89 53 0.0710 77.0 292
PRCH EZ IAML 550 30.14 115.1 0.37 77.0 292
MESC EN ES 550 29.09 53 0.1010 77.1 297
LFA EZ EP 550 20.58 22 53 0.0510 79.1 295
PMAT EZ EP 550 20.51 53 -0.1010 80.3 298
PMAT EZ IAML 550 29.39 65.1 0.16 80.3 298
PMAT EN ES 550 29.47 53 -0.0810 80.3 298
CMLA B2 ES 4 550 30.27 53 0.31 0 82.1 293
CML EZ EP 550 21.22 27 53 0.1210 84.1 293
FAC EZ ES 550 32.51 53 0.2410 92.8 291
PSAN EZ E 550 35.07 102 294
PFET EZ E 550 36.42 106 291
2014 115 1021 18.1 LQ 38.714 -29.090 0.1 *AZO 8 0.2 2.0LAZO 2.2CAZO 1
GAP=329 0.46 3.9 6.1 4.6 0.1389E+02 -0.2205E+02 -0.7152E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140115102118 I
OP: CVUA-AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: W Faial,SZ31,FE95 405 3
2014-01-15-1020-46S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PTCA EZ IP D 1021 23.13 40 59 -0.0310 29.3 119
PTCA EN IS 1021 27.00 59 0.0410 29.3 119
PTCA EZ IAML 1021 27.21 150.9 0.13 29.3 119
PCUT EZ EP 1021 23.88 27 59 -0.0810 34.4 106
PCUT EN IS 1021 28.25 59 -0.1110 34.4 106
PCUT EZ IAML 1021 28.70 388.3 0.11 34.4 106
PCTB EZ EP 1021 24.00 23 59 -0.2910 36.6 124
PCTB EN ES 1021 29.13 59 0.1910 36.6 124
PLGR EZ EP 1021 25.31 36 59 0.1110 42.2 109
PLGR EN ES 1021 30.62 59 0.0910 42.2 109
PLGR EZ IAML 1021 30.98 60.7 0.10 42.2 109
PCND EN E 1021 36.15 61.1 116
PTEI EZ IAML 1021 37.04 28.2 0.18 68.4 111
PTEI EN ES 1021 37.09 50 -0.1910 68.4 111
PBOI EN ES 1021 37.55 50 0.0610 69.5 116
PBOI EZ IAML 1021 37.87 26.2 0.11 69.5 116
ROSA BN ES 4 1021 36.96 50 -1.53 0 73.8 89
PAMA EZ EP 1021 30.74 43 0.2710 79.3 92
PAMA EN ES 1021 39.68 43 -0.0710 79.3 92
PAMA EZ IAML 1021 40.35 86.0 0.24 79.3 92
2014 115 1037 53.7 LQ 39.126 -28.466 10.6 *AZO 12 0.3 2.6LAZO 2.6CAZO 1
GAP=251 0.90 13.1 4.6 21.8 0.3363E+02 0.7146E+02 0.2738E+03E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140115103753 I
OP: CVUA-AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: W Graciosa,SZ27,FE95 405 3
2014-01-15-1036-47S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PVIA EZ EP 1037 59.24 30 97 -0.1610 36.1 100
PVIA EN IS 1038 3.84 97 0.1410 36.1 100
PVIA EZ IAML 1038 4.13 97.3 0.23 36.1 100
STGR EZ EP 1037 59.80 36 96 -0.2110 40.5 97
STGR EN ES 1038 4.28 96 -0.4910 40.5 97
STGR EZ IAML 1038 4.91 426.1 0.28 40.5 97
PLUZ EZ EP 1038 1.00 35 95 0.5010 44.0 106
PLUZ EN ES 1038 5.71 95 0.0910 44.0 106
PLUZ EZ IAML 1038 6.67 342.2 0.31 44.0 106
ROSA BN ES 4 1038 5.17 94 -1.73 0 49.1 157
PAMA EZ EP 1038 2.27 57 64 0.2110 55.4 153
PAMA EN ES 1038 8.20 64 -0.1510 55.4 153
PAMA EZ IAML 1038 8.31 89.6 0.08 55.4 153
PCUT EZ IP D 1038 2.39 33 64 -0.1410 59.3 201
PCUT EN IS 1038 9.41 64 0.2310 59.3 201
PCUT EZ IAML 1038 9.57 309.6 0.24 59.3 201
PLGR EZ IP C 1038 2.56 43 64 -0.2210 61.0 194
PLGR EN IS 1038 9.65 64 0.0310 61.0 194
PLGR EZ IAML 1038 10.58 1597.1 0.22 61.0 194
PTCA EZ EP 1038 3.38 45 64 -0.0610 66.5 206
PTCA EN ES 1038 10.18 64 -0.5810 66.5 206
PTCA EZ IAML 1038 10.43 196.8 0.26 66.5 206
PCTB EZ EP 1038 3.70 30 64 -0.1910 70.3 200
PCTB EN ES 1038 12.09 64 0.5410 70.3 200
PCTB EZ IAML 1038 12.53 202.5 0.46 70.3 200
PTEI EZ EP 1038 4.43 43 64 0.4110 70.6 172
PTEI EN ES 1038 12.19 64 0.4010 70.6 172
PTEI EZ IAML 1038 12.56 81.7 0.43 70.6 172
PCND EZ EP 1038 4.29 55 64 0.0110 72.9 180
PCND EZ IAML 1038 11.39 255.3 0.40 72.9 180
PCND EN ES 1038 11.69 64 -0.5510 72.9 180
PBOI EZ EP 1038 5.17 58 64 0.4010 76.9 174
PBOI EN ES 1038 12.70 64 -0.4010 76.9 174
PBOI EZ IAML 1038 13.27 56.8 0.11 76.9 174
PIED EZ ES 1038 15.25 64 0.1810 86.2 155
2014 121 0321 25.9 LQ 38.637 -29.115 7.2 *AZO 7 0.1 2.1LAZO 2.3CAZO 1
GAP=338 0.37 4.4 5.2 4.9 0.5891E-02 -0.1925E+02 0.1545E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140121032125 I
OP: CVUA-IA/AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: W Faial,SZ31,FE95 405 3
2014-01-21-0321-03S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PTCA EZ IP D 321 30.77 33 63 0.1110 28.3 102
PTCA EN ES 321 34.29 63 0.1010 28.3 102
PTCA EZ IAML 321 34.99 143.3 0.23 28.3 102
PCUT EN ES 321 35.95 63 0.0810 35.2 92
PCUT EZ IAML 321 36.47 467.4 0.39 35.2 92
PLGR EZ EP 321 32.57 34 63 -0.0810 42.4 97
PLGR EN ES 321 37.45 63 -0.2210 42.4 97
PLGR EZ IAML 321 38.11 68.7 0.25 42.4 97
PCND EZ EP 321 35.04 32 63 -0.0410 59.8 108
PCND EN ES 321 41.98 63 0.0510 59.8 108
PCND EZ IAML 321 42.46 63.1 0.39 59.8 108
PTEI EZ E 321 37.76 67.9 103
PTEI EN ES 321 43.75 53 -0.1310 67.9 103
PBOI EN ES 321 43.94 53 0.0610 68.2 109
ROSA BN ES 4 321 44.37 53 -1.28 0 76.5 83
PAMA EN ES 321 46.82 53 0.0710 81.5 86
PAMA EZ IAML 321 47.72 103.4 0.25 81.5 86
2014 122 0737 8.6 LQ 36.996 -24.214 17.4 *AZO 15 0.2 2.8LAZO 3.0CAZO 1
GAP=312 0.64 5.2 11.8159.4 0.5013E+02 -0.1735E+04 -0.7225E+03E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140122073708 I
OP: CVUA-SA/RC 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Falha Gloria,SZ11,FE95 404 3
2014-01-22-0737-01S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PSPR EZ IP C 737 19.34 60 94 0.2610 73.4 266
PSPR EN ES 737 26.83 94 -0.0810 73.4 266
PSPR EZ IAML 737 28.09 233.0 0.27 73.4 266
PVPO EZ IP C 737 20.43 64 93 0.0010 84.4 267
PVPO EN ES 737 29.21 93 -0.0610 84.4 267
PVPO EZ IAML 737 29.84 73.6 0.23 84.4 267
MIRA EZ EP 737 25.79 49 92 -0.2710 129 313
MIRA EZ ES 737 38.84 92 -0.2810 129 313
FRA1 EN E 737 39.88 131 310
PCNG EN E 737 41.52 135 310
VIF EZ EP 737 26.85 49 92 -0.1910 137 308
VIF EN ES 737 40.80 92 -0.0410 137 308
VIF EZ IAML 737 40.89 40.7 0.28 137 308
PRCH EN ES 737 40.90 92 -0.2210 139 306
PRCH EZ IAML 737 41.93 248.5 0.34 139 306
MESC EZ EP 737 27.56 50 92 0.0410 140 309
MESC EN ES 737 42.01 92 0.3310 140 309
MESC EZ IAML 737 43.14 45.2 0.66 140 309
LFA EZ EP 737 27.61 54 92 -0.0510 142 308
PMAT EN ES 737 42.78 92 0.5010 144 310
PVER EN ES 737 42.62 92 -0.0110 146 308
CML EZ EP 737 28.14 49 92 -0.0110 146 307
CML EZ ES 737 42.92 92 0.1410 146 307
FAC EZ ES 737 44.63 91 0.1810 154 305
PSAN EZ EP 737 30.65 50 91 0.2310 164 306
PFET EZ ES 737 46.85 91 -0.4510 167 304
2014 122 1156 3.1 LQ 38.028 -25.812 0.0 *AZO 22 0.3 2.4LAZO 2.4CAZO 1
GAP=174 0.84 2.0 2.3 3.1 0.2948E+01 -0.1306E+00 -0.2479E+01E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140122115603 I
OP: CVUA-RC/RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa Hirondelle,SZ18,FE95 405 3
2014-01-22-1155-18S_SWARM.SEI 6
2014-01-22-1156-01S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
SET4 EZ EP 1156 6.43 36 91 0.2310 17.0 176
SET4 EZ ES 1156 8.83 91 0.3010 17.0 176
SET2 EZ ES 1156 8.96 59 -0.6410 20.5 179
PSAN EZ EP 1156 6.93 32 59 0.0410 21.0 159
PSAN EZ ES 1156 10.19 59 0.4610 21.0 159
PFET EZ IP C 1156 7.07 34 59 -0.1810 23.4 175
PFET EZ ES 1156 10.19 59 -0.1810 23.4 175
FAC EZ EP 1156 8.64 40 59 0.1210 31.6 153
FAC EZ ES 1156 12.82 59 0.2410 31.6 153
PVER EN ES 1156 14.26 59 0.3110 36.6 134
PVER EZ IAML 1156 15.61 353.0 0.42 36.6 134
CML EZ EP 1156 9.11 51 59 -0.2410 36.8 140
CML EZ ES 1156 14.06 59 0.0210 36.8 140
PMAT EZ IP C 1156 9.46 47 59 -0.0810 38.1 128
PMAT EN ES 1156 14.72 59 0.3510 38.1 128
PMAT EZ IAML 1156 15.90 599.4 0.39 38.1 128
LFA EZ IP C 1156 9.70 59 -0.2510 40.4 134
MESC EZ IP C 1156 9.90 40 59 -0.2110 41.4 129
MESC EN ES 1156 15.66 59 0.2910 41.4 129
MESC EZ IAML 1156 16.40 215.4 0.44 41.4 129
PRCH EZ EP 1156 10.19 38 59 -0.3110 44.3 139
PRCH EN ES 1156 15.63 59 -0.4310 44.3 139
PRCH EZ IAML 1156 16.98 800.4 0.27 44.3 139
VIF EZ EP 1156 10.69 37 59 -0.0210 45.3 134
VIF EN ES 1156 16.33 59 -0.0910 45.3 134
VIF EZ IAML 1156 17.18 160.3 0.20 45.3 134
PCNG EN ES 1156 17.03 59 0.2610 46.6 128
MIRA EZ EP 1156 11.66 50 -0.4110 54.0 120
MIRA EZ E 1156 17.57 54.0 120
PVPO EE E 1156 35.01 132 154
PFAV EZ E 1156 40.37 135 305
PRIB EN ES 1156 37.29 43 -0.4910 139 301
PSPR EE E 1156 37.06 139 150
PVNV EN ES 1156 38.91 43 0.2810 143 305
PPAD EN ES 1156 40.12 43 0.1310 149 301
PBIS EN E 1156 40.31 150 303
ASBA EN ES 1156 41.53 43 0.4910 153 301
2014 123 0244 55.0 LQ 38.098 -26.435 0.0 *AZO 16 0.2 2.2LAZO 2.5CAZO 1
GAP=193 0.67 12.7 11.0 3.5 0.1387E+03 -0.2793E+02 -0.3228E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140123024455 I
OP: CVUA-AP/RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: Fossa Hirondelle,SZ18,FE95 405 3
2014-01-23-0244-46S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
SET4 EZ EP 245 4.66 35 50 -0.2110 61.1 114
SET4 EZ ES 245 12.24 50 -0.0610 61.1 114
SET2 EZ EP 245 4.95 33 50 -0.0610 62.0 117
SET2 EZ ES 245 12.89 50 0.3510 62.0 117
PFET EZ EP 245 5.12 36 50 -0.2510 64.7 119
PFET EZ ES 245 13.66 50 0.4910 64.7 119
PSAN EZ EP 245 5.74 34 50 -0.0910 68.0 114
PSAN EZ ES 245 13.87 50 -0.1210 68.0 114
FAC EZ EP 245 7.43 33 43 0.3010 77.8 117
FAC EZ ES 245 16.00 43 -0.2510 77.8 117
CML EZ EP 245 8.04 30 43 -0.1510 86.1 115
CML EZ ES 245 17.88 43 -0.2210 86.1 115
PVER EZ EP 245 8.21 33 43 -0.1310 87.5 112
PVER EN ES 245 18.50 43 0.1310 87.5 112
PVER EZ IAML 245 19.46 36.1 0.33 87.5 112
CMLA BZ EP 9 245 7.46 34 43 -0.99 0 88.2 115
CMLA B2 ES 245 17.72 0.1410 88.2 115
PMAT EZ EP 245 8.54 37 43 -0.1610 90.3 110
PMAT EN ES 245 19.16 43 0.1610 90.3 110
PRIB EZ EP 245 8.67 31 43 -0.0910 90.6 315
PRIB EN ES 245 19.03 43 -0.0810 90.6 315
PRIB EZ IAML 245 19.52 107.9 0.11 90.6 315
LFA EZ EP 245 8.47 32 43 -0.3910 91.1 113
LFA EZ ES 245 19.58 43 0.3010 91.1 113
MESC EZ EP 245 8.90 34 43 -0.2510 93.3 111
MESC EN ES 245 19.81 43 0.0310 93.3 111
MESC EZ IAML 245 20.14 22.7 0.56 93.3 111
PRCH EN ES 245 19.89 43 0.2010 93.4 116
PVNV EZ EP 245 9.30 34 43 -0.2210 96.9 320
PVNV EN ES 245 20.23 43 -0.2110 96.9 320
PVNV EZ IAML 245 21.15 56.3 0.09 96.9 320
PPAD EZ EP 245 10.42 30 43 0.4810 100 312
PPAD EN ES 245 21.42 43 0.2610 100 312
PPAD EZ IAML 245 22.29 22.1 0.25 100 312
ASBA EN ES 245 22.43 43 0.2410 104 312
ASBA EZ IAML 245 24.22 31.4 0.09 104 312
2014 123 0949 25.0 LQ 37.229 -24.555 33.9 *AZO 14 0.3 2.6LAZO 2.8CAZO 1
GAP=279 0.93 5.9 18.7 31.3 0.9577E+02 -0.5305E+03 -0.1659E+03E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140123094925 I
OP: CVUA-IA/RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: E Ilheus das Formigas,SZ13,FE95 404 3
2014-01-23-0949-09S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PSPR EZ IP C 949 33.50 53 118 -0.0810 53.3 233
PSPR EN ES 949 40.44 118 0.4310 53.3 233
PSPR EZ IAML 949 41.13 181.0 0.17 53.3 233
PVPO EZ IP C 949 34.21 48 114 -0.3010 61.8 241
PVPO EN ES 949 41.36 114 -0.2810 61.8 241
PVPO EZ IAML 949 42.41 53.2 0.36 61.8 241
MIRA EZ EP 949 37.27 40 106 -0.5310 88.9 314
MIRA EZ ES 949 46.77 106 -0.6210 88.9 314
FRA1 EN ES 949 47.83 106 0.1210 90.7 309
PCNG EN ES 949 49.10 105 0.4010 95.3 309
VIF EZ EP 949 39.08 48 105 0.3310 96.9 307
VIF EN ES 949 49.36 105 0.3010 96.9 307
VIF EZ IAML 949 49.60 65.6 0.38 96.9 307
PRCH EZ EP 949 39.15 58 104 0.2310 98.8 304
PRCH EN ES 949 49.15 104 -0.1910 98.8 304
PRCH EZ IAML 949 49.98 171.3 0.23 98.8 304
MESC EZ EP 949 39.31 46 104 0.1010 101 309
MESC EN ES 949 50.24 104 0.3810 101 309
MESC EZ IAML 949 50.79 71.9 0.45 101 309
LFA EZ EP 949 39.59 53 104 0.2410 102 307
LFA EZ ES 949 49.83 104 -0.2810 102 307
PMAT EN ES 949 50.73 103 0.2910 104 309
PMAT EZ IAML 949 51.41 87.4 0.25 104 309
CMLA B2 ES 4 949 48.90 103 -1.62 0 104 305
CML EZ EP 949 39.87 47 103 0.0310 106 305
CML EZ ES 949 50.38 103 -0.5710 106 305
FAC EZ E 949 52.85 114 302
PSAN EZ ES 4 949 55.79 101 0.94 0 124 304
PFET EZ E 949 53.55 127 301
SET2 EZ E 949 47.07 130 302
2014 126 0117 25.6 LQ 38.334 -26.843 8.1 *AZO 19 0.4 2.2LAZO 2.0CAZO 1
GAP=195 1.17 10.7 12.6 10.1 0.1311E+03 -0.6700E+02 -0.4421E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140126011725 I
OP: CVUA-RC 3
STATUS: OK 3
PUB: NAO 3
WEB: SIM 3
REGIAO: SE Terceira,SZ20,FE95 405 3
2014-01-26-0116-58S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PFAV EZ EP 117 32.38 22 63 -0.4010 46.9 333
PFAV EN ES 117 38.64 63 0.4410 46.9 333
PFAV EZ IAML 117 40.88 74.8 0.09 46.9 333
PRIB EZ EP 117 32.74 23 63 -0.1310 47.3 323
PRIB EN ES 117 38.73 63 0.3910 47.3 323
PRIB EZ IAML 117 39.35 173.9 0.16 47.3 323
PVNV EZ EP 117 33.38 23 63 -0.5110 54.8 330
PVNV EN ES 117 39.93 63 -0.2010 54.8 330
PVNV EZ IAML 117 42.88 77.9 0.14 54.8 330
PPAD EZ EP 117 33.54 22 63 -0.5610 56.2 317
PPAD EN ES 117 40.81 63 0.3210 56.2 317
PPAD EZ IAML 117 42.64 260.5 0.19 56.2 317
PBIS EN ES 117 41.19 63 -0.1110 59.4 323
PBIS EZ IAML 117 42.34 107.8 0.17 59.4 323
ASBA EZ EP 117 34.45 63 -0.2810 60.5 315
ASBA EN ES 117 41.79 63 0.1910 60.5 315
ASBA EZ IAML 117 43.42 125.9 0.31 60.5 315
SET4 EZ ES 117 51.18 53 -0.1410 105 119
PIED EZ E 117 44.70 106 275
SET2 EZ ES 117 51.74 53 0.1410 106 121
PFET EZ E 117 42.09 109 121
PSAN EZ ES 117 52.50 53 -0.3310 112 118
FAC EZ ES 117 54.86 53 -0.1210 122 120
PVER EE E 117 57.41 131 117
PMAT EN ES 117 57.31 53 -0.2310 133 115
LFA EZ EP 117 44.80 53 0.7510 135 117
MESC EN ES 117 57.87 53 -0.4810 136 116
PRCH EN E 117 59.16 137 119
VIF EE E 118 1.08 139 117
MIRA EZ EP 117 47.14 53 1.1410 150 113
MIRA EZ ES 118 1.43 53 0.1110 150 113
2014 126 0117 49.3 LQ 38.314 -26.853 1.0 *AZO 21 0.3 2.5LAZO 2.6CAZO 1
GAP=197 0.86 6.8 8.2 3.9 0.5466E+02 -0.1542E+02 -0.1079E+02E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140126011749 I
OP: CVUA-RC 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: SE Terceira,SZ20,FE95 405 3
2014-01-26-0116-58S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PFAV EZ EP 117 57.55 41 59 0.2610 48.5 335
PFAV EE ES 118 3.60 59 0.3310 48.5 335
PFAV EZ IAML 118 4.31 172.0 0.16 48.5 335
PRIB EZ EP 117 57.69 50 59 0.3710 48.6 325
PRIB EN ES 118 3.41 59 0.0910 48.6 325
PRIB EZ IAML 118 4.20 304.6 0.16 48.6 325
PVNV EZ EP 117 58.03 48 50 -0.4010 56.4 332
PVNV EN ES 118 4.75 50 -0.5110 56.4 332
PVNV EZ IAML 118 7.34 137.1 0.17 56.4 332
PPAD EZ IP C 117 58.45 40 50 -0.1210 57.3 319
PPAD EN ES 118 5.56 50 0.0510 57.3 319
PPAD EZ IAML 118 8.34 373.2 0.16 57.3 319
PBIS EN ES 118 6.23 50 -0.1510 60.7 325
PBIS EZ IAML 118 8.64 155.8 0.19 60.7 325
ASBA EN ES 118 6.77 50 0.1710 61.5 317
ASBA EZ IAML 118 8.69 272.3 0.19 61.5 317
SET4 EZ EP 118 4.62 36 43 -0.1110 104 117
SET4 EZ ES 118 16.40 43 0.1210 104 117
PIED EZ E 118 9.73 106 277
SET2 EZ EP 118 4.98 42 43 0.1110 106 119
SET2 EZ ES 118 16.43 43 -0.1110 106 119
PFET EZ EP 118 5.39 29 43 0.1910 108 120
PFET EZ ES 118 17.38 43 0.2610 108 120
PSAN EZ EP 118 5.48 30 43 -0.1110 111 117
PSAN EZ ES 118 17.05 43 -0.7410 111 117
FAC EZ ES 118 20.42 43 0.5010 121 119
CML EZ ES 118 22.33 43 0.6010 130 117
PVER EN ES 118 21.72 43 -0.2310 131 116
PVER EZ IAML 118 23.02 59.4 0.42 131 116
PMAT EN ES 118 22.94 43 0.4210 133 114
PMAT EZ IAML 118 24.45 47.4 0.16 133 114
LFA EZ EP 118 8.50 41 43 0.0010 134 116
MESC EZ EP 118 8.55 36 43 -0.2110 136 115
MESC EN ES 118 23.11 43 -0.2210 136 115
PRCH EE ES 118 22.97 43 -0.3610 137 118
VIF EN ES 118 24.06 43 0.1410 139 116
PCNG EN E 118 25.51 142 115
MIRA EZ EP 118 10.16 39 43 -0.3110 150 112
2014 127 1050 49.6 RQ 39.424 -29.951 10.6 *AZO 16 0.3 3.3LAZO 3.4CAZO 1
GAP=330 0.86 69.5 171.5302.4 -0.1183E+05 -0.5180E+05 0.2093E+05E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140127105049 I
OP: CVUA-BC/RS 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
OBS: Sismo sem qualidade para calculo MW 3
REGIAO: Crista Media Atlantica,SZ32,FE95 405 3
2014-01-27-1050-32S_SWARM.SEI 6
2014-01-27-1050-33S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PTCA EZ EP 1051 8.05 48 64 -0.1210 137 133
PCUT EN E 1051 22.08 139 129
PCTB EN ES 1051 24.04 64 0.3210 144 133
PLGR EZ EP 1051 9.18 51 64 -0.3210 147 129
PLGR EN ES 1051 23.50 64 -0.8910 147 129
PLGR EZ IAML 1051 25.05 310.0 0.28 147 129
PCND EZ EP 1051 12.06 82 64 0.0510 167 129
PCND EN ES 1051 28.72 64 -0.0710 167 129
ROSA BZ EP 9 1051 10.64 71 64 -1.37 0 167 117
ROSA BN ES 1051 27.64 0.2210 167 117
PCND EZ IAML 1051 30.10 115.6 0.26 167 129
PVIA EE ES 1051 28.76 64 -0.1810 168 103
PTEI EZ EP 1051 12.68 66 64 -0.0310 172 126
PTEI EZ IAML 1051 32.57 227.7 0.59 172 126
STGR EN E 1051 28.60 172 102
PTEI EN ES 1051 30.20 64 0.1810 172 126
PAMA EZ E 1051 17.05 174 118
PBOI EZ EP 1051 13.25 72 64 0.2310 175 128
PBOI EN ES 1051 31.06 64 0.5010 175 128
PBOI EZ IAML 1051 32.34 79.8 0.28 175 128
PLUZ EE E 1051 29.98 176 104
PPAD EZ EP 1051 21.80 64 0.1910 244 108
PVNV EZ EP 1051 22.72 49 64 0.0610 253 106
PRIB EZ EP 1051 22.77 50 64 -0.1510 255 108
PFAV EZ EP 1051 23.86 57 64 0.2410 260 107
2014 129 1950 45.0 LQ 37.276 -24.604 0.0 *AZO 12 0.3 2.2LAZO 2.6CAZO 1
GAP=272 0.94 4.5 42.0 27.5 -0.1606E+03 0.1136E+04 -0.1079E+03E
ACTION:UPD 24-11-22 11:01 OP:RS STATUS: ID:20140129195045 I
OP: CVUA-BC/AR 3
STATUS: OK 3
PUB: EPI 3
WEB: SIM 3
REGIAO: E Ilheus das Formigas,SZ10,FE95 404 3
2014-01-29-1950-17S_SWARM.SEI 6
STAT SP IPHASW D HRMM SECON CODA AMPLIT PERI AZIMU VELO AIN AR TRES W DIS CAZ7
PSPR EZ IP C 1950 53.77 48 50 -0.0210 53.3 226
PSPR EE ES 1951 0.94 50 0.5410 53.3 226
PSPR EZ IAML 1951 1.45 70.6 0.14 53.3 226
PVPO EZ EP 1950 54.42 44 50 -0.4010 60.9 234
PVPO EN ES 1951 1.85 50 -0.3610 60.9 234
PVPO EZ IAML 1951 2.72 36.4 0.12 60.9 234
MIRA EZ ES 1951 6.99 43 -0.3810 82.2 314
FRA1 EZ EP 1950 57.55 30 43 -0.4210 84.1 308
FRA1 EN ES 1951 7.73 43 0.0110 84.1 308
PCNG EN E 1951 9.55 88.7 308
VIF EN ES 1951 9.55 43 0.4210 90.4 305
PRCH EZ EP 1950 59.47 34 43 0.5110 92.3 303
PRCH EN ES 1951 9.38 43 -0.0710 92.3 303
PRCH EZ IAML 1951 10.33 143.0 0.31 92.3 303
MESC EZ EP 1950 59.46 45 43 0.2110 94.0 308
MESC EN ES 1951 10.38 43 0.4310 94.0 308
LFA EZ EP 1950 59.65 43 43 0.2510 95.3 306
LFA EZ E 1951 9.51 95.3 306
PMAT EN ES 1951 10.46 43 -0.0810 97.3 308
CMLA B2 ES 4 1951 9.71 43 -0.95 0 97.7 304
CML EZ EP 1950 59.67 45 43 -0.2310 99.8 304
CML EZ ES 1951 10.69 43 -0.4110 99.8 304
FAC EZ E 1951 13.79 108 301

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info/descricao.docx Normal file
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183
parser.py
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@@ -1,183 +0,0 @@
import io
import warnings
from collections import defaultdict
from datetime import datetime
import pandas as pd
def is_blank(l: str) -> bool:
return len(l.strip(" ")) == 0
def parse_flt(v:str) -> float | None:
try:
t = float(v)
return t
except ValueError:
return None
def parse_int(v:str) -> int | None:
try:
t = int(v)
return t
except ValueError:
return None
def into_dataframe(data) -> pd.DataFrame:
if len(data) == 0:
return pd.DataFrame()
aux = {k: [] for k in data.keys()}
for (k,v) in data.items():
aux[k].append(v)
return pd.DataFrame(data=aux)
# ------------ principal
def parse(fname="dados.txt"):
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)
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]):
boundaries = []
start = None
for (idx,l) in enumerate(data):
if start is None:
if not is_blank(l):
start = idx
else:
if is_blank(l):
boundaries.append((start,idx))
start = None
return boundaries
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])
phaseRet = parse_type_7(chunk_lines[hIdx:])
hDF = into_dataframe(headersRet)
hDF["ID"] = iD
phaseRet["ID"] = iD
return pd.concat([hDF, phaseRet])
def parse_header(hLines: list[str]):
aux = defaultdict(list)
for line in hLines:
match line[-1]:
case "1":
aux[1].append(line)
case "3":
aux[3].append(line)
case "6":
aux[6].append(line)
case "E":
aux["E"].append(line)
case "I":
aux["I"].append(line)
case "F":
aux["F"].append(line)
case unknown:
warnings.warn(f"header type not implemented: {unknown}")
headerDict = dict()
for (k,v) in aux.items():
if len(v) != 0:
headerDict.update(FUNCS[k](v))
return headerDict
def parse_mag(line: 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])
d = int(aux[8:10])
h = int(aux[11:13])
m = int(aux[13:15])
s = int(aux[16:18])
mil = int(aux[19]) * 10**5
dt = datetime(y,mo,d,h,m,s,mil)
dist_ind = aux[21]
eId = aux[22]
lat = float(aux[23:30])
long = float(aux[30:38])
depth = float(aux[38:43])
rep_ag = aux[45:48]
hypo = {"DateTime": dt.isoformat(), "Distance Indicator": dist_ind, "Event ID": eId, "Lat": lat, "Long": long, "Depth": depth, "Agency": rep_ag, "Magnitudes": list()}
for l in data:
hypo["Magnitudes"] = hypo["Magnitudes"] + parse_mag(l)
return hypo
def parse_type_3(data: list[str]):
comments = []
for line in data:
comments.append(line[:-2].strip())
return {"Comments": comments}
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]):
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)])
return dados
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]):
return {}
def parse_type_i(data: list[str]):
aux = data[0]
dt = datetime.strptime(aux[12:26], "%y-%m-%d %H:%M")
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}
parse()

3
requirements.txt
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@@ -1 +1,2 @@
pytest==8.4.2 numpy==2.3.4
pandas==2.3.3

31
test_data.txt
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@@ -1,31 +0,0 @@
1996 6 7 1325 29.2 L 59.846 5.130 12.0F TES 12 .60 1.9LTES 2.2CTES 2.0LNAO1
GAP=177 2.78 4.5 12.80.000 0.2239E+02 0.6258E+03 -0.2817E+03E
1996 6 7 1325 30.5 L 59.763 5.396 29.2 NAO 2 1.0 2.0LNAO1
8.3 41.0 74.7 1 F
1996-06-07-1324-51S.TEST__009 6
ACTION:SPL 14-12-11 12:04 OP:jh STATUS: ID:19960607132529 L I
STAT COM NTLO IPHASE W HHMM SS.SSS PAR1 PAR2 AGA OPE AIN RES W DIS CAZ7
EGD HHZ NS IP 4 1325 35.950 C BER jh 120.0-1.131047.70 6
EGD HHZ NS END 1325 35.950 111.0 BER jh 0.0 47.70 6
EGD HHZ NS AMP 1325 35.950 11.1 33.3 BER jh 47.70 6
EGD HHN NS ES 1325 42.030 BER jh 70.0-.8901047.70 6
BER BHZ NS00 IP 1325 38.120 C kkk AUT -.9801061.00 11
BER BHZ NS00 END 1325 38.120 55.0 BER jh 4.8 61.00 11
BER BHN NS00 ES 1325 45.440 BER jh 70.0-.9901061.00 11
BER BHZ NS00 IAML A1325 46.710 31.7 0.20 BER jh 0.4 61.00 11
KMY BHZ NS10 IP 1325 40.260 C PPP Ajh 70.0 .3301070.90 175
KMY BHZ NS10 END 1325 40.260 62.0 BER jh 70.90 175
KMY BHN NS10 ES 1325 48.740 BER jh 70.0.3001070.90 175
KMY BHZ NS10 IAML 1325 48.920 83.6 0.20 BER jh 70.90 175
ASK SHZ NS EP 2 1325 39.590 D -1.031071.10 3
ASK SHZ NS END 1325 39.590 68.0 71.10 3
ASK SHZ NS ES 1325 48.070 -1.021071.10 3
ASK SHZ NS AMP 1325 48.070 333.3 2.20 71.10 3
ASK SHZ NS IAML 1325 50.900 111.0 0.30 71.10 3
NRA0 S Z Pn A1326 19.090 50.0-.05010368.0 72
NRA0 S Z END 1326 19.090 333.0 368.0 72
NRA0 S Z BAZ-P 1326 19.090 256.9 6.9 0. 368.0 72
NRA0 S Z Pg 1326 27.940 -.64010368.0 72
NRA0 S Z BAZ 1326 27.940 253.0 7.3 -3. 368.0 72
NRA0 S Z Lg 1327 10.540 -.89010368.0 72
NRA0 S Z BAZ 1327 10.540 266.6 4.1 9. 368.0 72

50
tests.py
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import pytest
import parser
def test_type_1():
test_data =[" 1996 6 7 1325 29.2 L 59.846 5.130 12.0F TES 12 .60 1.9LTES 2.2CTES 2.0LNAO1",
" 1996 6 7 1325 30.5 L 59.763 5.396 29.2 NAO 2 1.0 2.0LNAO1"]
expected = {"DateTime": "1996-06-07T13:25:29.200000", "Distance Indicator": "L", "Event ID": " ", 'Lat': 59.846, 'Long': 5.13,'Depth': 12.0, 'Agency': 'TES', 'Magnitudes': [{'M': ' 1.9', 'T': 'L'},{'M': ' 2.2', 'T': 'C'},{'M': ' 2.0', 'T': 'L'},{'M': ' 2.0', 'T': 'L'}]}
_ret = parser.parse_type_1(test_data)
for (k,v) in _ret.items():
assert _ret[k] == expected[k]
def test_type_3():
test_data = [" OP: CVUA-RM/RC 3",
" STATUS: OK SENTIDO 3",
" SENTIDO: II/III -Pico: S. Caetano 3",
" PUB: NAO 3",
" WEB: SIM 3",
" OBS: Por ordem do CT nao foi emitido novo comunicado 3",
" OBS: Sismo sobreposto 3",
" REGIAO: Pico,VZ14,SZ06,FE95 405 3"]
_ret = parser.parse_type_3(test_data)
assert len(_ret["Comments"]) == 8
def test_type_6():
test_data = [" 1996-06-03-2002-18S.TEST__012 6",
" 1996-06-03-1917-52S.TEST__002 6"]
expected = {"Wave": ["1996-06-03-2002-18S.TEST__012", "1996-06-03-1917-52S.TEST__002"]}
_ret = parser.parse_type_6(test_data)
for (k,v) in _ret.items():
assert _ret[k] == expected[k]
def test_type_i():
test_data = [" ACTION:SPL 08-10-02 10:19 OP:jh STATUS: ID:19960603195540 I"]
expected = {"Action": "SPL", "Action Extra": {"Date": '2008-10-02T10:19:00', "OP": "jh", "Status": "", "ID":19960603195540}}
_ret = parser.parse_type_i(test_data)
for (k,v) in _ret.items():
assert _ret[k] == expected[k]
def test_type_e():
test_data =[" GAP=348 2.88 999.9 999.9999.9 -0.1404E+08 -0.3810E+08 0.1205E+09E"]
expected = {"Gap": 348, "Origin": 2.88, "Error_lat": 999.9, "Error_long": 999.9, "Error_depth": 999.9, "Cov_xy": -14040000.0, "Cov_xz": -38100000.0, "Cov_yz": 120500000.0}
_ret = parser.parse_type_e(test_data)
for (k,v) in _ret.items():
assert _ret[k] == expected[k]

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# pyright: basic
import pandas as pd
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 150)
# -- globals
HEADER_COLS = ["Data", "Distancia", "Tipo Ev", "Lat", "Long", "Prof", "Magnitudes"]
TABLE_READ_RET = ["Estacao", "Hora", "Min", "Seg", "Componente", "Amplitude"]
# -- helper funcs
def _get_uniques(df) -> pd.DataFrame:
return df.get(["ID", "Data", "Regiao"]).drop_duplicates(subset="ID", keep="first")
def _show_events(df):
for (_, row) in df.iterrows():
print(f"{row["ID"]}: {row["Regiao"]}")
# -- main
def read_ids(df):
ids = _get_uniques(df)
_show_events(ids)
def get_unique_events_table(df):
return df.drop_duplicates(subset="ID", keep="first")
def read_header(df, event_id):
# Informações do header do evento
row = df[df["ID"] == event_id].iloc[0]
cols = list(df.columns)
# 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(HEADER_COLS):
info.append(f"{i+1} {col}: {row[col]}")
infoString = f"Header do evento {event_id}:\n" + "\n".join(info)
return infoString
def show_table(df, retCols=TABLE_READ_RET):
print(df.loc[:,retCols])
def get_table(df, event_id):
rows = df[df["ID"] == event_id]
return rows
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("Estacao")
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, row_line, new_data):
for key, value in new_data.items():
if key in df.columns:
df.loc[row_line, key] = value
return f"Linha {row_line} do evento 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)
print(f"Evento {event_id} apagado!")
return new_df
def delete_table_row(df, event_id, row_number):
# Apaga uma linha específica da tabela do evento
# Cria uma nova linha vazia no dataframe na posição insertion_point
matching_indices = df.index[df['ID'] == event_id].tolist()
first_event_row = matching_indices[0]
last_event_row = matching_indices[-1]
if row_number < first_event_row or row_number > last_event_row:
return df, f"Erro: A posição a apagar, {row_number} está fora do intervalo permitido para o evento {event_id}."
new_df = df.drop([row_number]).reset_index(drop=True)
return new_df, f"Linha {row_choice} apagada com sucesso!"
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, insertion_point):
# Cria uma nova linha vazia no dataframe na posição insertion_point
matching_indices = df.index[df['ID'] == event_id].tolist()
first_event_row = matching_indices[0]
last_event_row = matching_indices[-1]
if insertion_point < first_event_row or insertion_point > last_event_row + 1:
return df, f"Erro: A posição de inserção {insertion_point} está fora do intervalo permitido para o evento {event_id}"
new_row_df = pd.DataFrame(columns=df.columns, index=[0])
new_row_df['ID'] = event_id
new_row_df = new_row_df.fillna(0)
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 {insertion_point}"
def create_entire_database() -> pd.DataFrame:
pass
def create_table_row_old(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}."

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# pyright: basic
import io
from collections import defaultdict
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
def parse_flt(v: str) -> float | None:
try:
t = float(v)
return t
except ValueError:
return None
def parse_int(v: str) -> int | None:
try:
t = int(v)
return t
except ValueError:
return None
def into_dataframe(data) -> pd.DataFrame:
if len(data) == 0:
return pd.DataFrame()
aux = {k: [] for k in data.keys()}
for k, v in data.items():
aux[k].append(v)
return pd.DataFrame(data=aux)
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))])
return df
def validate_no_stations(expected: int, stationsDF: pd.DataFrame) -> bool:
uniqueStations = stationsDF["Estacao"].nunique()
return expected == uniqueStations
# --- 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]])
aux = pd.concat([df, a], axis=0, ignore_index=True)
df = aux
fp.close()
return df
def boundaries(data: list[str]):
boundaries = []
start = None
for idx, l in enumerate(data):
if start is None:
if not is_blank(l):
start = idx
else:
if is_blank(l):
boundaries.append((start, idx))
start = None
return boundaries
def parse_chunk(chunk_lines: list[str]):
hIdx = None
for idx, l in enumerate(chunk_lines):
if l[-1] == "7":
hIdx = idx
break
preambleRet = _parse_preamble(chunk_lines[:hIdx])
phaseRet = _parse_type_7(chunk_lines[hIdx:])
if not validate_no_stations(preambleRet["Estacoes"], phaseRet):
pass
return _concat(preambleRet, phaseRet)
def _parse_preamble(hLines: list[str]):
aux = defaultdict(list)
for line in hLines:
match line[-1]:
case "1":
aux[1].append(line)
case "3":
aux[3].append(line)
case "6":
aux[6].append(line)
case "E":
aux["E"].append(line)
case "I":
aux["I"].append(line)
case "F":
pass
# aux["F"].append(line)
case _:
pass
headerDict = dict()
for k, v in aux.items():
if len(v) != 0:
headerDict.update(FUNCS[k](v))
return headerDict
def _parse_type_1(data: list[str]):
aux = data[0]
y = int(aux[1:5])
mo = int(aux[6:8])
d = int(aux[8:10])
h = int(aux[11:13])
m = int(aux[13:15])
s = int(aux[16:18])
mil = int(aux[19]) * 10**5
dt = datetime(y, mo, d, h, m, s, mil)
dist_ind = DIST_IND[aux[21]]
ev_type = TYPE[aux[22]]
lat = float(aux[23:30])
long = float(aux[30:38])
depth = float(aux[38:43])
no_stat = int(aux[48:51])
hypo = {
"Data": dt.isoformat(),
"Distancia": dist_ind,
"Tipo Evento": ev_type,
"Latitude": lat,
"Longitude": long,
"Profundidade": depth,
"Estacoes": no_stat,
"Magnitudes": list(),
}
for l in data:
hypo["Magnitudes"] = hypo["Magnitudes"] + _parse_mag(l)
return hypo
def _parse_mag(line: str):
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:
if line.startswith(" SENTIDO"):
c, v = line[:-2].strip().split(": ", maxsplit=1)
v = v.split(",")[0]
comments[c.capitalize()] = v
elif line.startswith(" REGIAO"):
c, vals = line[:-2].strip().split(": ", maxsplit=1)
_d = {}
for v in vals.split(","):
if v.startswith("SZ"):
comments["SZ"] = int(v[2:])
elif v.startswith("VZ"):
comments["VZ"] = int(v[2:])
elif v.startswith("FE"):
comments["FZ"] = v[2:]
else:
comments["Regiao"] = v
return comments
def _parse_type_6(data: list[str]):
waves = []
for l in data:
waves.append(l.strip().split(" ")[0])
return {"Onda": waves}
def _parse_type_7(data: list[str]):
aux = io.StringIO("\n".join(data))
dados = pd.read_fwf(
aux,
colspecs=[
(1, 5),
(6, 8),
(10, 15),
(18, 20),
(20, 22),
(23, 28),
(34, 38),
(71, 75),
],
)
dados.rename(
columns={
"STAT": "Estacao",
"SP": "Componente",
"PHASW": "Tipo Onda",
"HR": "Hora",
"MM": "Min",
"SECON": "Seg",
"AMPL": "Amplitude",
" DIST": "Distancia Epicentro",
},
inplace=True,
)
return dados
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_i(data: list[str]):
aux = data[0]
return {"ID": int(aux[60:74])}
FUNCS = {
1: _parse_type_1,
3: _parse_type_3,
6: _parse_type_6,
"E": _parse_type_e,
"I": _parse_type_i,
}

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import collections
import datetime
import stats
from matplotlib import pyplot as plt
class Plotter:
def __init__(self, data):
self.raw_data = data
pass
def extract_info(self):
pass
def plot_events_day(self):
values = collections.Counter(stats._preprare_days(self.raw_data))
x = list(values.keys())
y = list(values.values())
fig, ax = plt.subplots(layout="constrained")
ax.bar(x, y)
plt.show()
def plot_events_month(self):
values = collections.Counter(stats._preprare_months(self.raw_data))
x = list(values.keys())
y = list(values.values())
fig, ax = plt.subplots(layout="constrained")
ax.bar(x, y)
plt.show()
if __name__ == "__main__":
import parser
asdf = parser.parse("../dados.txt")
a = Plotter(asdf)
# b = stats._filter_mags(a.raw_data, more_than=2.5, less_than=2.9)
c = stats.filter_date(
a.raw_data,
after=datetime.datetime(year=2014, month=1, day=6),
before=datetime.datetime(year=2014, month=1, day=12),
)
print(c)

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utils/stats.py Normal file
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# pyright: basic
import datetime
import numpy as np
import pandas as pd
import utils
def stats(df: pd.DataFrame) -> None:
"""Estatisticas para a DataFrame
:param df: DataFrame em questão"""
mags = mags_avg_std(df)
depth = depth_avg_std(df)
median_mags = median_mags(df)
def mags_avg_std(data: pd.DataFrame) -> tuple[np.floating, np.floating]:
"""Media e desvio-padrao das magnitudes
:param data: Dataframe com dados a filtrar
:returns: Tuple com a media e desvio-padrao
"""
filtered_data: pd.DataFrame = filter_mags(data)
vals = filtered_data["MagL"].to_numpy()
return (np.average(vals), np.std(vals))
def depth_avg_std(data: pd.DataFrame) -> tuple[np.floating, np.floating]:
"""Media e desvio-padrao das profundidades
:param data: Dataframe com dados a filtrar
:returns: Tuple com a media e desvio-padrao
"""
filtered_data: pd.DataFrame = filter_depth(data)
vals = np.average(filtered_data["Profundidade"].to_numpy())
return (np.average(vals), np.std(vals))
def median_mags(data: pd.DataFrame):
filtered_data: pd.DataFrame = filter_mags(data)
vals = sorted(filtered_data["MagL"].to_numpy())
quartil = len(vals) // 4
return (
filtered_data[quartil, :]["MagL"],
filtered_data[quartil * 2, :]["MagL"],
filtered_data[quartil * 3, :]["MagL"],
)
def filter_mags(data, more_than=None, less_than=None) -> pd.DataFrame:
"""Filters by magnitudes a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:param more_than(optional): Filter for magnitudes above threshold
:param after(optional): Filters for dates after set date
:returns: Returns a filtered pandas DataFrame
"""
v = data.drop_duplicates(subset="ID", keep="first")
_dict = {"Data": [], "MagL": []}
for idx, c in v.iterrows():
_dict["Data"].append(str(c.Data))
_dict["MagL"].append(utils.extract_mag_l(c.Magnitudes))
_df = pd.DataFrame.from_dict(_dict)
if more_than:
_df = _df[_df["MagL"] >= more_than]
if less_than:
_df = _df[_df["MagL"] <= less_than]
return _df
def filter_date(
data: pd.DataFrame,
before: datetime.datetime | None = None,
after: datetime.datetime | None = None,
) -> pd.DataFrame:
"""Filters by date a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:param before(optional): Filter for dates before set date
:param after(optional): Filters for dates after set date
:returns: Returns a filtered pandas DataFrame
"""
v = data
for idx, c in v.iterrows():
v.at[idx, "Data"] = datetime.datetime.fromisoformat(c.Data)
if after:
v = v[v["Data"] >= after]
if before:
v = v[v["Data"] >= before]
return v
def filter_depth(
data: pd.DataFrame,
less_than: float | None = None,
more_than: float | None = None,
) -> pd.DataFrame:
"""Filters by the depth a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:param less_than(optional): Filter for depths below the threshold
:param after(optional): Filters for depths deeper than threshold
:returns: Returns a filtered pandas DataFrame
"""
v = data.drop_duplicates(subset="ID", keep="first")
if more_than:
v = v[v["Profundidade"] >= more_than]
if less_than:
v = v[v["Profundidade"] >= less_than]
return v
def filter_gap(
data: pd.DataFrame,
threshold: int,
) -> pd.DataFrame:
"""Filters by the depth a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:param threshold: Filter for GAPS below the threshold
:returns: Returns a filtered pandas DataFrame
"""
v = data.drop_duplicates(subset="ID", keep="first")
v = v[v["Gap"] <= threshold]
return v
def filter_sz(
data: pd.DataFrame,
) -> pd.DataFrame:
"""Filters by SZ plane a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:returns: Returns a filtered pandas DataFrame
"""
v = data[data["SZ"].notna()]
return v
def filter_vz(
data: pd.DataFrame,
) -> pd.DataFrame:
"""Filters by VZ plane a DataFrame into a new Dataframe
:param data: Raw pandas DataFrame
:returns: Returns a filtered pandas DataFrame
"""
v = data[data["VZ"].notna()]
return v
def _preprare_days(data):
c = data.Data.to_list()
for idx, d in enumerate(c):
aux = datetime.datetime.fromisoformat(d)
c[idx] = datetime.datetime.strftime(aux, "%Y-%m-%d")
return c
def _preprare_months(data):
c = data.Data.to_list()
for idx, d in enumerate(c):
aux = datetime.datetime.fromisoformat(d)
c[idx] = datetime.datetime.strftime(aux, "%Y-%m")
return c

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#! /usr/bin/env python
# pyright: basic
import json
from datetime import time
from math import modf
from typing import Any
import numpy as np
import pandas as pd
def extract_mag_l(data) -> np.float64:
for v in data:
if v["Tipo"] == "L":
return np.float64(v["Magnitude"])
return np.float64(0.0)
def save_as_json(info: dict[str, Any]) -> bool:
with open("test.json", "w") as fp:
json.dump(info, fp)
return True
# TODO: passar os nomes das colunas, para não haver problemas no futuro, caso se altere os nomes da dataframe
def create_dict_struct(df: pd.DataFrame, event_cols, station_cols) -> dict[str, Any]:
# get all events by their id
uniqueIds = df["ID"].unique()
allEvents = {}
for id in uniqueIds:
filteredDf = df.loc[df["ID"] == id]
first_row = filteredDf.head(1)
allEvents[int(id)] = create_event_info(first_row, event_cols)
allEvents[int(id)].update(create_stations_info_1(filteredDf))
return allEvents
def create_event_info(info: pd.DataFrame, cols) -> dict[str, Any]:
informacoes = dict()
for v in cols:
if v == "Magnitudes":
informacoes[v] = create_mag_info(info.iloc[0][v])
elif v in {"Latitude", "Longitude", "Profundidade", "Gap"}:
informacoes[v] = float(info.iloc[0][v])
else:
informacoes[v] = info.iloc[0][v]
return informacoes
def create_stations_info_1(info: pd.DataFrame) -> dict[str, Any]:
stationsDict = {}
for idx in range(len(info)):
aux = info.iloc[idx]
micro, sec = tuple(map(int, modf(aux["Seg"])))
hms = time(
hour=aux["Hora"], minute=aux["Min"], second=sec, microsecond=micro
).strftime("%H:%M:%S.%f")
station = {
"Componente": aux["Componente"],
"Hora": hms,
"Distancia": float(aux["DIS"]),
}
if type(aux["Tipo Onda"]) != float:
station.update({"Tipo Onda": aux["Tipo Onda"]})
if aux["Tipo Onda"] == "IAML":
station.update({"Amplitude": float(aux["Amplitude"])})
if aux["Estacao"] not in stationsDict.keys():
stationsDict[aux["Estacao"]] = [station]
else:
stationsDict[aux["Estacao"]].append(station)
return {"Estacoes": stationsDict}
def create_mag_info(magnitudes):
mags = {}
for value in magnitudes:
mags[value["Tipo"]] = value["Magnitude"]
return mags
if __name__ == "__main__":
import parser
df = parser.parse("dados.txt")
a = create_dict_struct(df, None, None)
save_as_json(a)

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vis.py
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