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# -*- coding: utf-8 -*-
import json
import sqlite3
import warnings
from collections import defaultdict
import h5py
import numpy as np
from obspy import Trace, UTCDateTime
from utils.hdf5_waveform_dataset import (
DEFAULT_LOCATION,
apply_response_spectrum,
inventory_from_response_record,
load_response_json,
normalize_location,
response_from_json_record,
response_record_matches_time,
utc_or_none,
)
def parse_time_to_epoch(t):
return float(UTCDateTime(t).timestamp)
def epoch_to_utc(t):
return str(UTCDateTime(float(t)))
def wildcard_to_sql_like(pattern):
"""
Convert wildcard pattern to SQL LIKE pattern.
Examples:
"*" -> "%"
"BK" -> "BK"
"B*" -> "B%"
"*Z" -> "%Z"
"BH*" -> "BH%"
"""
if pattern is None or str(pattern).strip() == "":
return "%"
return str(pattern).strip().replace("*", "%")
def query_segments(
db_file,
network="*",
station="*",
location="*",
channel="*",
starttime=None,
endtime=None,
limit=None,
):
"""
Query waveform segments from SQLite index.
Parameters
----------
db_file : str
SQLite index database path.
network : str
Network code or wildcard pattern, e.g. "BK", "C*", "*".
station : str
Station code or wildcard pattern, e.g. "BDM", "BD*", "*".
location : str
Location code or wildcard pattern, e.g. "00", "--", "*".
channel : str
Channel code or wildcard pattern, e.g. "BHE", "BH*", "*Z", "*".
starttime : str
Query start time.
endtime : str
Query end time.
limit : int or None
Optional maximum number of returned segments.
Returns
-------
rows : list[dict]
Matched waveform segment metadata.
"""
if starttime is None or endtime is None:
raise ValueError("starttime and endtime are required.")
query_start = parse_time_to_epoch(starttime)
query_end = parse_time_to_epoch(endtime)
sql = """
SELECT *
FROM waveform_segments
WHERE network LIKE ?
AND station LIKE ?
AND location LIKE ?
AND channel LIKE ?
AND end_epoch >= ?
AND start_epoch <= ?
ORDER BY network, station, location, channel, start_epoch
"""
params = [
wildcard_to_sql_like(network),
wildcard_to_sql_like(station),
wildcard_to_sql_like(location),
wildcard_to_sql_like(channel),
query_start,
query_end,
]
if limit is not None:
sql += " LIMIT ?"
params.append(int(limit))
conn = sqlite3.connect(db_file)
conn.row_factory = sqlite3.Row
try:
cur = conn.cursor()
cur.execute(sql, params)
rows = [dict(r) for r in cur.fetchall()]
finally:
conn.close()
return rows
def read_hdf5_segment(row):
"""
Read a single waveform segment from HDF5 using h5_file and dataset_path.
"""
with h5py.File(row["h5_file"], "r") as h5:
data = h5[row["dataset_path"]][()]
return np.asarray(data)
def trim_segment_to_window(data, row, starttime, endtime):
"""
Trim one segment to the requested time window.
"""
sr = float(row["sampling_rate"])
seg_start = float(row["start_epoch"])
seg_end = float(row["end_epoch"])
query_start = parse_time_to_epoch(starttime)
query_end = parse_time_to_epoch(endtime)
use_start = max(seg_start, query_start)
use_end = min(seg_end, query_end)
if use_end < use_start:
return data[:0], use_start, use_end
i0 = int(round((use_start - seg_start) * sr))
i1 = int(round((use_end - seg_start) * sr)) + 1
i0 = max(i0, 0)
i1 = min(i1, len(data))
return data[i0:i1], use_start, use_end
def merge_segments(
rows,
starttime,
endtime,
fill_value=0.0,
dtype=np.float32,
):
"""
Merge multiple waveform segments into continuous arrays.
Segments are grouped by:
network.station.location.channel
Missing samples are filled by fill_value.
Returns
-------
merged : dict
{
"BK.BDM.00.BHE": {
"data": np.ndarray,
"network": "BK",
"station": "BDM",
"location": "00",
"channel": "BHE",
"starttime": "...",
"endtime": "...",
"sampling_rate": 100.0,
"npts": 360001,
"segments": [...]
},
...
}
"""
if len(rows) == 0:
return {}
query_start = parse_time_to_epoch(starttime)
query_end = parse_time_to_epoch(endtime)
groups = defaultdict(list)
for row in rows:
key = (
row["network"],
row["station"],
row["location"],
row["channel"],
)
groups[key].append(row)
merged = {}
for key, seg_rows in groups.items():
network, station, location, channel = key
seg_rows = sorted(seg_rows, key=lambda r: float(r["start_epoch"]))
sampling_rates = sorted(set(float(r["sampling_rate"]) for r in seg_rows))
if len(sampling_rates) != 1:
raise ValueError(
f"Multiple sampling rates found for {network}.{station}.{location}.{channel}: "
f"{sampling_rates}"
)
sr = sampling_rates[0]
npts = int(round((query_end - query_start) * sr)) + 1
out = np.full(npts, fill_value, dtype=dtype)
filled = np.zeros(npts, dtype=bool)
used_segments = []
for row in seg_rows:
data = read_hdf5_segment(row)
data, use_start, use_end = trim_segment_to_window(
data,
row,
starttime=starttime,
endtime=endtime,
)
if len(data) == 0:
continue
i0 = int(round((use_start - query_start) * sr))
i1 = i0 + len(data)
if i0 < 0:
data = data[-i0:]
i0 = 0
if i1 > npts:
data = data[: npts - i0]
i1 = npts
if i0 >= i1:
continue
target = slice(i0, i1)
mask = ~filled[target]
out[target][mask] = data[: i1 - i0][mask].astype(dtype, copy=False)
filled[target][mask] = True
used_segments.append(
{
"h5_file": row["h5_file"],
"dataset_path": row["dataset_path"],
"network": row["network"],
"station": row["station"],
"location": row["location"],
"channel": row["channel"],
"segment_starttime": row["starttime"],
"segment_endtime": row["endtime"],
"segment_start_epoch": float(row["start_epoch"]),
"segment_end_epoch": float(row["end_epoch"]),
"used_starttime": epoch_to_utc(use_start),
"used_endtime": epoch_to_utc(use_end),
"npts": int(len(data)),
}
)
out_key = f"{network}.{station}.{location}.{channel}"
merged[out_key] = {
"data": out,
"network": network,
"station": station,
"location": location,
"channel": channel,
"starttime": str(UTCDateTime(starttime)),
"endtime": str(UTCDateTime(endtime)),
"sampling_rate": sr,
"npts": int(len(out)),
"filled_ratio": float(filled.mean()) if len(filled) > 0 else 0.0,
"segments": used_segments,
}
return merged
class InstrumentResponseProcessor:
"""
Apply the same response-removal and response-simulation options used by the
PyTorch dataloader to waveforms returned by the SQLite query API.
"""
def __init__(
self,
instrument_response_json=None,
remove_instrument_response=False,
response_output="VEL",
response_pre_filt=None,
response_water_level=60,
response_zero_mean=True,
response_taper=True,
response_taper_fraction=0.05,
response_error_behavior="raise",
simulate_instrument_response=False,
simulation_response_json=None,
simulation_response_id=None,
simulation_response_selector=None,
simulation_paz=None,
simulation_output=None,
simulation_sensitivity=True,
default_location=DEFAULT_LOCATION,
dtype=np.float32,
):
if response_error_behavior not in {"raise", "warn", "skip"}:
raise ValueError("response_error_behavior must be 'raise', 'warn', or 'skip'")
self.instrument_response_json = (
str(instrument_response_json) if instrument_response_json is not None else None
)
self.remove_instrument_response = bool(remove_instrument_response)
self.response_output = str(response_output).upper() if response_output is not None else "VEL"
self.response_pre_filt = (
tuple(float(x) for x in response_pre_filt)
if response_pre_filt is not None else None
)
self.response_water_level = response_water_level
self.response_zero_mean = bool(response_zero_mean)
self.response_taper = bool(response_taper)
self.response_taper_fraction = float(response_taper_fraction)
self.response_error_behavior = response_error_behavior
self.simulate_instrument_response = bool(simulate_instrument_response)
self.simulation_response_json = (
str(simulation_response_json) if simulation_response_json is not None else None
)
self.simulation_response_id = (
str(simulation_response_id) if simulation_response_id is not None else None
)
self.simulation_response_selector = dict(simulation_response_selector or {})
self.simulation_paz = simulation_paz
self.simulation_output = (
str(simulation_output).upper()
if simulation_output is not None else self.response_output
)
self.simulation_sensitivity = bool(simulation_sensitivity)
self.default_location = default_location
self.dtype = dtype
if self.remove_instrument_response and self.instrument_response_json is None:
raise ValueError(
"instrument_response_json is required when remove_instrument_response is enabled."
)
if self.simulate_instrument_response and (
self.simulation_paz is None
and self.simulation_response_id is None
and not self.simulation_response_selector
and self.simulation_response_json is None
and self.instrument_response_json is None
):
raise ValueError(
"simulate_instrument_response=True requires simulation_paz, "
"simulation_response_id, simulation_response_selector, "
"simulation_response_json, or instrument_response_json."
)
self._response_store = None
self._simulation_response_store = None
self._response_object_cache = {}
self._inventory_cache = {}
self._simulation_response_record = None
self._simulation_response_object = None
@property
def enabled(self):
return self.remove_instrument_response or self.simulate_instrument_response
def _ensure_response_store(self):
if self._response_store is None:
if self.instrument_response_json is None:
raise ValueError("instrument_response_json is not configured")
self._response_store = load_response_json(self.instrument_response_json)
return self._response_store
def _ensure_simulation_response_store(self):
if self._simulation_response_store is None:
path = self.simulation_response_json or self.instrument_response_json
if path is None:
raise ValueError("No simulation response JSON is configured")
self._simulation_response_store = load_response_json(path)
return self._simulation_response_store
def _get_response_object(self, record):
response_id = str(record.get("response_id", ""))
cache_key = response_id or id(record)
if cache_key not in self._response_object_cache:
self._response_object_cache[cache_key] = response_from_json_record(record)
return self._response_object_cache[cache_key]
def _get_inventory(self, record):
response_id = str(record.get("response_id", ""))
cache_key = response_id or id(record)
if cache_key not in self._inventory_cache:
response = self._get_response_object(record)
self._inventory_cache[cache_key] = inventory_from_response_record(record, response)
return self._inventory_cache[cache_key]
def _find_response_record(self, network, station, location, channel, starttime, endtime=None):
store = self._ensure_response_store()
key = (
str(network),
str(station),
normalize_location(location, self.default_location),
str(channel),
)
for record in store["responses_by_key"].get(key, []):
if response_record_matches_time(record, starttime, endtime):
return record
return None
def _select_simulation_response_record(self):
if self._simulation_response_record is not None:
return self._simulation_response_record
store = self._ensure_simulation_response_store()
record = None
if self.simulation_response_id:
record = store["responses_by_id"].get(self.simulation_response_id)
if record is None:
raise KeyError(
f"simulation_response_id not found: {self.simulation_response_id}"
)
elif self.simulation_response_selector:
sel = self.simulation_response_selector
key = (
str(sel.get("network", "")),
str(sel.get("station", "")),
normalize_location(sel.get("location", self.default_location), self.default_location),
str(sel.get("channel", "")),
)
starttime = utc_or_none(sel.get("time")) or utc_or_none(sel.get("starttime"))
for item in store["responses_by_key"].get(key, []):
if response_record_matches_time(item, starttime):
record = item
break
if record is None:
raise KeyError(f"simulation_response_selector did not match any response: {sel}")
else:
records_by_id = store["responses_by_id"]
if len(records_by_id) != 1:
raise ValueError(
"simulation_response_json must contain exactly one response unless "
"simulation_response_id or simulation_response_selector is provided."
)
record = next(iter(records_by_id.values()))
self._simulation_response_record = record
self._simulation_response_object = self._get_response_object(record)
return record
def _handle_response_error(self, message):
if self.response_error_behavior == "raise":
raise RuntimeError(message)
if self.response_error_behavior == "warn":
warnings.warn(message, RuntimeWarning, stacklevel=2)
return None
def metadata_template(self):
return {
"remove_instrument_response": self.remove_instrument_response,
"simulate_instrument_response": self.simulate_instrument_response,
"response_output": self.response_output,
"simulation_output": self.simulation_output,
"response_id": "",
"response_epoch_start": "",
"response_epoch_end": "",
"simulation_response_id": "",
"error": "",
"processed": False,
}
def apply_to_item(self, item):
metadata = self.metadata_template()
item["instrument_processing"] = metadata
waveform = item.get("data")
if waveform is None or len(waveform) == 0 or not self.enabled:
return item
segments = item.get("segments") or []
first_segment = segments[0] if segments else {}
network = item.get("network") or first_segment.get("network", "")
station = item.get("station") or first_segment.get("station", "")
location = item.get("location") or first_segment.get("location", self.default_location)
channel = item.get("channel") or first_segment.get("channel", "")
sampling_rate = float(item.get("sampling_rate"))
starttime = UTCDateTime(item.get("starttime"))
endtime = UTCDateTime(item.get("endtime"))
try:
trace = Trace(
data=np.asarray(waveform, dtype=np.float64),
header={
"network": str(network),
"station": str(station),
"location": normalize_location(location, self.default_location),
"channel": str(channel),
"starttime": starttime,
"sampling_rate": sampling_rate,
},
)
if self.remove_instrument_response:
record = self._find_response_record(
network,
station,
location,
channel,
starttime,
endtime=endtime,
)
if record is None:
key = ".".join([
str(network),
str(station),
normalize_location(location, self.default_location),
str(channel),
])
raise KeyError(f"No response found for {key} at {starttime}")
metadata.update(
{
"response_id": record.get("response_id", ""),
"response_epoch_start": record.get("epoch_start", ""),
"response_epoch_end": record.get("epoch_end", ""),
}
)
trace.remove_response(
inventory=self._get_inventory(record),
output=self.response_output,
water_level=self.response_water_level,
pre_filt=self.response_pre_filt,
zero_mean=self.response_zero_mean,
taper=self.response_taper,
taper_fraction=self.response_taper_fraction,
)
if self.simulate_instrument_response:
if self.simulation_paz is not None:
trace.simulate(
paz_remove=None,
paz_simulate=self.simulation_paz,
remove_sensitivity=False,
simulate_sensitivity=self.simulation_sensitivity,
)
metadata["simulation_response_id"] = "simulation_paz"
else:
sim_record = self._select_simulation_response_record()
sim_response = self._simulation_response_object
trace.data = apply_response_spectrum(
trace.data,
sampling_rate=trace.stats.sampling_rate,
response=sim_response,
output=self.simulation_output,
)
metadata["simulation_response_id"] = sim_record.get("response_id", "")
metadata["processed"] = True
item["data"] = np.asarray(trace.data, dtype=self.dtype)
return item
except Exception as exc:
metadata["error"] = str(exc)
self._handle_response_error(str(exc))
item["data"] = np.asarray(waveform, dtype=self.dtype)
return item
def apply_instrument_processing_to_merged(merged, **kwargs):
processor = InstrumentResponseProcessor(**kwargs)
for item in merged.values():
processor.apply_to_item(item)
return merged
def query_and_merge(
db_file,
network="*",
station="*",
location="*",
channel="*",
starttime=None,
endtime=None,
fill_value=0.0,
dtype=np.float32,
limit=None,
instrument_response_json=None,
remove_instrument_response=False,
response_output="VEL",
response_pre_filt=None,
response_water_level=60,
response_zero_mean=True,
response_taper=True,
response_taper_fraction=0.05,
response_error_behavior="raise",
simulate_instrument_response=False,
simulation_response_json=None,
simulation_response_id=None,
simulation_response_selector=None,
simulation_paz=None,
simulation_output=None,
simulation_sensitivity=True,
default_location=DEFAULT_LOCATION,
):
"""
Query waveform segments and merge them into continuous waveforms.
"""
rows = query_segments(
db_file=db_file,
network=network,
station=station,
location=location,
channel=channel,
starttime=starttime,
endtime=endtime,
limit=limit,
)
merged = merge_segments(
rows=rows,
starttime=starttime,
endtime=endtime,
fill_value=fill_value,
dtype=dtype,
)
if remove_instrument_response or simulate_instrument_response:
apply_instrument_processing_to_merged(
merged,
instrument_response_json=instrument_response_json,
remove_instrument_response=remove_instrument_response,
response_output=response_output,
response_pre_filt=response_pre_filt,
response_water_level=response_water_level,
response_zero_mean=response_zero_mean,
response_taper=response_taper,
response_taper_fraction=response_taper_fraction,
response_error_behavior=response_error_behavior,
simulate_instrument_response=simulate_instrument_response,
simulation_response_json=simulation_response_json,
simulation_response_id=simulation_response_id,
simulation_response_selector=simulation_response_selector,
simulation_paz=simulation_paz,
simulation_output=simulation_output,
simulation_sensitivity=simulation_sensitivity,
default_location=default_location,
dtype=dtype,
)
return merged, rows
def save_merged_to_npz(merged, output_npz):
"""
Save merged waveforms and metadata to NPZ.
"""
arrays = {}
metadata = {}
for key, item in merged.items():
safe_key = key.replace(".", "_").replace("-", "_")
arrays[safe_key] = item["data"]
meta = dict(item)
meta.pop("data", None)
metadata[safe_key] = meta
arrays["metadata_json"] = np.array(json.dumps(metadata, ensure_ascii=False))
np.savez(output_npz, **arrays)
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