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graficos, estatitsticas e filtros

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Shino
2025-12-11 14:18:03 -01:00
parent 490c88085a
commit 14dee58ab2
5 changed files with 286 additions and 69 deletions
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118
utils/parser.py
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@@ -1,6 +1,5 @@
# pyright: basic
import io
from collections import defaultdict
from datetime import datetime
@@ -11,40 +10,45 @@ DIST_IND = {"L": "Local", "R": "Regional", "D": "Distante"}
TYPE = {"Q": "Quake", "V": "Volcanic", "U": "Unknown", "E": "Explosion"}
# --- helper funcs ---
# --- helper funcs ---
def is_blank(l: str) -> bool:
return len(l.strip(" ")) == 0
def parse_flt(v:str) -> float | None:
def parse_flt(v: str) -> float | None:
try:
t = float(v)
return t
except ValueError:
return None
def parse_int(v:str) -> int | 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():
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))])
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:
def validate_no_stations(expected: int, stationsDF: pd.DataFrame) -> bool:
uniqueStations = stationsDF["Estacao"].nunique()
return expected == uniqueStations
@@ -55,29 +59,31 @@ def parse(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]])
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):
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))
boundaries.append((start, idx))
start = None
return boundaries
def parse_chunk(chunk_lines: list[str]):
hIdx = None
for (idx, l) in enumerate(chunk_lines):
for idx, l in enumerate(chunk_lines):
if l[-1] == "7":
hIdx = idx
break
@@ -89,6 +95,7 @@ def parse_chunk(chunk_lines: list[str]):
return _concat(preambleRet, phaseRet)
def _parse_preamble(hLines: list[str]):
aux = defaultdict(list)
@@ -111,7 +118,7 @@ def _parse_preamble(hLines: list[str]):
pass
headerDict = dict()
for (k,v) in aux.items():
for k, v in aux.items():
if len(v) != 0:
headerDict.update(FUNCS[k](v))
return headerDict
@@ -126,7 +133,7 @@ def _parse_type_1(data: list[str]):
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)
dt = datetime(y, mo, d, h, m, s, mil)
dist_ind = DIST_IND[aux[21]]
ev_type = TYPE[aux[22]]
@@ -135,18 +142,28 @@ def _parse_type_1(data: list[str]):
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()}
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]
m = line[base : base + 4]
mt = line[base + 4]
if not is_blank(m):
magnitudes.append({"Magnitude": m, "Tipo": mt})
base += 8
@@ -156,11 +173,24 @@ def _parse_mag(line: str):
def _parse_type_3(data: list[str]):
comments = {}
for line in data:
if line.startswith(" SENTIDO") or line.startswith(" REGIAO"):
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
@@ -173,21 +203,59 @@ def _parse_type_6(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),(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)
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])}
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])}
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}
FUNCS = {
1: _parse_type_1,
3: _parse_type_3,
6: _parse_type_6,
"E": _parse_type_e,
"I": _parse_type_i,
}