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seismo-relay/micromate/models.py
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"""
Micromate (Series IV / Thor) native data models.
These are the right-shaped dataclasses for Thor data — Thor measures
the microphone in dB(L) directly, so this model carries
``mic_pspl_dbl`` rather than the pseudo-``psi`` shoehorn that
``minimateplus.PeakValues`` uses for Series III BW data.
The ingest pipeline today goes:
.IDFW.txt → parse_idf_report() → dict
dict → IdfEvent.from_report() → IdfEvent (typed)
IdfEvent → IdfEvent.to_minimateplus_event() → shape DB / sidecar
machinery expects
The ``to_minimateplus_event()`` bridge is a temporary boundary — when we
crack the binary IDF codec and have richer per-event data to store, the
DB schema will grow Series-IV-specific columns and the bridge will
shrink or disappear.
"""
from __future__ import annotations
import datetime
from dataclasses import dataclass, field
from typing import Any, Dict, Optional, Tuple
# ── IdfReport ─────────────────────────────────────────────────────────────────
@dataclass
class IdfReport:
"""Typed wrapper around the dict returned by ``parse_idf_report``.
All fields optional — Thor's exporter is permissive and some IDF .txt
files (especially histograms) omit fields that waveform sidecars
include. Use ``.raw`` for any field this dataclass hasn't surfaced
yet (the parser keeps every recognised key in the raw dict).
"""
# Identity / kind
serial_number: Optional[str] = None
event_type: Optional[str] = None # "Full Waveform" | "Full Histogram"
event_datetime: Optional[datetime.datetime] = None
filename: Optional[str] = None # echoed by Thor's exporter
# Sampling / timing
sample_rate: Optional[int] = None # samples/sec
record_time_sec: Optional[float] = None
pre_trigger_sec: Optional[float] = None
# Geophone peaks (in/s)
tran_ppv: Optional[float] = None
vert_ppv: Optional[float] = None
long_ppv: Optional[float] = None
peak_vector_sum: Optional[float] = None
# Microphone — Thor's native unit is dB(L), NOT psi.
mic_pspl_dbl: Optional[float] = None
# Zero-crossing frequencies (Hz)
tran_zc_freq: Optional[float] = None
vert_zc_freq: Optional[float] = None
long_zc_freq: Optional[float] = None
mic_zc_freq: Optional[float] = None
# Per-channel time of peak (sec, since event start)
tran_time_of_peak: Optional[float] = None
vert_time_of_peak: Optional[float] = None
long_time_of_peak: Optional[float] = None
mic_time_of_peak: Optional[float] = None
# Derived per-channel motion
tran_peak_acceleration: Optional[float] = None # g
vert_peak_acceleration: Optional[float] = None
long_peak_acceleration: Optional[float] = None
tran_peak_displacement: Optional[float] = None # in
vert_peak_displacement: Optional[float] = None
long_peak_displacement: Optional[float] = None
# Operator-supplied strings (Thor's TitleString1..4 → semantic slots)
project: Optional[str] = None # TitleString1
client: Optional[str] = None # TitleString2
operator: Optional[str] = None # TitleString3
notes: Optional[str] = None # TitleString4 / PostEventNote
setup: Optional[str] = None # setup file name
# Sensor self-check results
tran_test_passed: Optional[bool] = None
vert_test_passed: Optional[bool] = None
long_test_passed: Optional[bool] = None
mic_test_passed: Optional[bool] = None
# Device-fixed metadata
firmware_version: Optional[str] = None
calibration_text: Optional[str] = None
battery_volts: Optional[float] = None
# Original parser dict — preserves every recognised key (including
# raw unit-suffixed strings) for forward-compatible field access.
raw: Dict[str, Any] = field(default_factory=dict, repr=False)
@classmethod
def from_dict(cls, d: Dict[str, Any]) -> "IdfReport":
"""Build an IdfReport from the dict returned by ``parse_idf_report``."""
ed = d.get("event_datetime")
if isinstance(ed, str):
try:
ed = datetime.datetime.fromisoformat(ed)
except ValueError:
ed = None
return cls(
serial_number = d.get("serial_number"),
event_type = d.get("event_type"),
event_datetime = ed if isinstance(ed, datetime.datetime) else None,
filename = d.get("filename"),
sample_rate = d.get("sample_rate"),
record_time_sec = d.get("record_time_sec"),
pre_trigger_sec = d.get("pre_trigger_sec"),
tran_ppv = d.get("tran_ppv"),
vert_ppv = d.get("vert_ppv"),
long_ppv = d.get("long_ppv"),
peak_vector_sum = d.get("peak_vector_sum"),
mic_pspl_dbl = d.get("mic_ppv"), # parser names it mic_ppv (legacy)
tran_zc_freq = d.get("tran_zc_freq"),
vert_zc_freq = d.get("vert_zc_freq"),
long_zc_freq = d.get("long_zc_freq"),
mic_zc_freq = d.get("mic_zc_freq"),
tran_time_of_peak = d.get("tran_time_of_peak"),
vert_time_of_peak = d.get("vert_time_of_peak"),
long_time_of_peak = d.get("long_time_of_peak"),
mic_time_of_peak = d.get("mic_time_of_peak"),
tran_peak_acceleration = d.get("tran_peak_acceleration"),
vert_peak_acceleration = d.get("vert_peak_acceleration"),
long_peak_acceleration = d.get("long_peak_acceleration"),
tran_peak_displacement = d.get("tran_peak_displacement"),
vert_peak_displacement = d.get("vert_peak_displacement"),
long_peak_displacement = d.get("long_peak_displacement"),
project = d.get("project"),
client = d.get("client"),
operator = d.get("operator"),
notes = d.get("notes"),
setup = d.get("setup"),
tran_test_passed = d.get("tran_test_passed"),
vert_test_passed = d.get("vert_test_passed"),
long_test_passed = d.get("long_test_passed"),
mic_test_passed = d.get("mic_test_passed"),
firmware_version = d.get("version"),
calibration_text = d.get("calibration_text"),
battery_volts = d.get("battery_volts"),
raw = d,
)
# ── IdfPeaks / IdfProjectInfo / IdfSensorCheck (narrow grouping types) ───────
@dataclass
class IdfPeaks:
"""Geophone + mic peak values for one Thor event. Native Thor units.
Thor stores the mic peak in two parallel forms — ``mic_pspl_dbl`` is
what the sidecar's top-level ``MicPSPL`` header field carries (dB(L)),
used in the report header. ``mic_pspl_psi`` is the psi value derived
either from the IDFW sample table / IDFH interval column 9, or from
the binary mic counts (~2.14e-6 psi/count). Needed because the
BW-shaped ``PeakValues.micl`` consumed by ``event_hdf5.write_event_hdf5``
expects psi — feeding it dB(L) makes the h5 mic-chart scale factor
blow up.
"""
transverse_ips: Optional[float] = None # in/s
vertical_ips: Optional[float] = None # in/s
longitudinal_ips: Optional[float] = None # in/s
peak_vector_sum_ips: Optional[float] = None # in/s
mic_pspl_dbl: Optional[float] = None # dB(L)
mic_pspl_psi: Optional[float] = None # psi
@dataclass
class IdfProjectInfo:
"""Operator-supplied strings from Thor's TitleString1..4."""
project: Optional[str] = None
client: Optional[str] = None
operator: Optional[str] = None
notes: Optional[str] = None
setup: Optional[str] = None
@dataclass
class IdfSensorCheck:
"""Per-channel pass/fail from Thor's self-test."""
tran: Optional[bool] = None
vert: Optional[bool] = None
long: Optional[bool] = None
mic: Optional[bool] = None
# ── IdfEvent ─────────────────────────────────────────────────────────────────
@dataclass
class IdfEvent:
"""A single Thor / Micromate Series IV event.
Built from a parsed .IDFW.txt or .IDFH.txt sidecar via
``IdfEvent.from_report()``. The filename is the authoritative
source for serial + timestamp + kind; the .txt provides
device-authoritative peak values, frequencies, project strings,
sensor self-check, firmware, calibration.
"""
# Identity
serial: str
timestamp: datetime.datetime
kind: str # "Waveform" | "Histogram"
filename: str # device-native binary filename, e.g. "UM11719_20231219163444.IDFW"
# Sampling / timing
sample_rate: Optional[int] = None
record_time_sec: Optional[float] = None
pre_trigger_sec: Optional[float] = None
# Peaks
peaks: IdfPeaks = field(default_factory=IdfPeaks)
# Per-channel frequencies (Hz)
tran_zc_freq: Optional[float] = None
vert_zc_freq: Optional[float] = None
long_zc_freq: Optional[float] = None
mic_zc_freq: Optional[float] = None
# Project strings
project_info: IdfProjectInfo = field(default_factory=IdfProjectInfo)
# Sensor self-check
sensor_check: IdfSensorCheck = field(default_factory=IdfSensorCheck)
# Device-fixed
firmware_version: Optional[str] = None
calibration_text: Optional[str] = None
battery_volts: Optional[float] = None
# The full parsed report — preserves anything not surfaced as a typed field
report: IdfReport = field(default_factory=IdfReport)
@classmethod
def from_report(
cls,
report: Any,
filename: str,
) -> "IdfEvent":
"""Build an IdfEvent from a parsed report (dict or IdfReport) and
the device-native binary filename.
The filename is authoritative for serial + timestamp + kind:
Thor's filenames are literal ``<SERIAL>_<YYYYMMDDHHMMSS>.<KIND>``
and the device's own clock is the canonical event timestamp.
If the report carries an ``event_datetime`` that differs from
what's in the filename, the report wins (it has finer-grained
device-reported time-of-trigger semantics).
"""
from .idf_ascii_report import parse_event_filename
# Normalise input to IdfReport
if isinstance(report, IdfReport):
rep = report
elif isinstance(report, dict):
rep = IdfReport.from_dict(report)
else:
raise TypeError(
f"report must be IdfReport or dict; got {type(report).__name__}"
)
# Filename → (serial, timestamp, kind). Required — fall back to
# report-supplied values only if filename parsing fails.
parsed = parse_event_filename(filename)
if parsed is not None:
fn_serial, fn_ts, fn_kind = parsed
kind = "Histogram" if fn_kind == "IDFH" else "Waveform"
else:
fn_serial = rep.serial_number or "UNKNOWN"
fn_ts = rep.event_datetime or datetime.datetime(1970, 1, 1)
kind = "Waveform" if (rep.event_type or "").lower().startswith("full waveform") else "Histogram"
# Prefer report's event_datetime (device-authoritative) over the filename.
ts = rep.event_datetime or fn_ts
serial = rep.serial_number or fn_serial
return cls(
serial=serial,
timestamp=ts,
kind=kind,
filename=filename,
sample_rate=rep.sample_rate,
record_time_sec=rep.record_time_sec,
pre_trigger_sec=rep.pre_trigger_sec,
peaks=IdfPeaks(
transverse_ips = rep.tran_ppv,
vertical_ips = rep.vert_ppv,
longitudinal_ips = rep.long_ppv,
peak_vector_sum_ips = rep.peak_vector_sum,
mic_pspl_dbl = rep.mic_pspl_dbl,
),
tran_zc_freq=rep.tran_zc_freq,
vert_zc_freq=rep.vert_zc_freq,
long_zc_freq=rep.long_zc_freq,
mic_zc_freq=rep.mic_zc_freq,
project_info=IdfProjectInfo(
project=rep.project,
client=rep.client,
operator=rep.operator,
notes=rep.notes,
setup=rep.setup,
),
sensor_check=IdfSensorCheck(
tran=rep.tran_test_passed,
vert=rep.vert_test_passed,
long=rep.long_test_passed,
mic=rep.mic_test_passed,
),
firmware_version=rep.firmware_version,
calibration_text=rep.calibration_text,
battery_volts=rep.battery_volts,
report=rep,
)
# ── Bridge to minimateplus shape (for the existing DB / sidecar paths) ──
def to_minimateplus_event(self, waveform_key: bytes) -> Any:
"""Project this Thor event into the shape ``minimateplus.Event``
carries, so it can flow through the existing
``SeismoDb.insert_events()`` and ``event_to_sidecar_dict()``
machinery without those code paths needing to know about Thor.
Caveats of the bridge:
- ``PeakValues.micl`` carries the mic peak in **psi** (matching
BW's convention) — set from :attr:`IdfPeaks.mic_pspl_psi`,
with a dB(L)→psi fallback when only the dB(L) value is
available. This is what the h5 writer's mic-scale-factor
logic needs. The dB(L) value still flows through
``bw_report.mic.pspl_dbl`` (set by the
``idf_to_bw_report`` adapter) and the renderer reads it
from there for the report header.
- Many Thor-specific fields (Peak Acceleration / Displacement,
sensor self-check, calibration) don't have a slot in
``Event``. The full IdfReport is preserved on the
``.sfm.json`` sidecar under ``extensions.idf_report`` via
``save_imported_idf`` — that's the source of truth for them.
"""
from minimateplus.models import (
Event, PeakValues, ProjectInfo, Timestamp,
)
ts_obj = Timestamp(
raw=bytes(9),
flag=0,
year=self.timestamp.year,
unknown_byte=0,
month=self.timestamp.month,
day=self.timestamp.day,
hour=self.timestamp.hour,
minute=self.timestamp.minute,
second=self.timestamp.second,
)
# Resolve mic peak as psi. Priority: binary-derived mic_pspl_psi
# (set by read_idf_file) > dB(L)→psi fallback via standard formula
# (psi = 2.9e-9 × 10^(dBL/20)) > None.
mic_psi = self.peaks.mic_pspl_psi
if mic_psi is None and self.peaks.mic_pspl_dbl is not None:
mic_psi = 2.9e-9 * (10.0 ** (self.peaks.mic_pspl_dbl / 20.0))
pv = PeakValues(
tran=self.peaks.transverse_ips,
vert=self.peaks.vertical_ips,
long=self.peaks.longitudinal_ips,
micl=mic_psi, # psi, matching BW's convention (h5 scaling depends on this)
peak_vector_sum=self.peaks.peak_vector_sum_ips,
)
pi = ProjectInfo(
setup_name=self.project_info.setup,
project=self.project_info.project,
client=self.project_info.client,
operator=self.project_info.operator,
sensor_location=None, # Thor folds location into project string
notes=self.project_info.notes,
)
ev = Event(
index=0,
timestamp=ts_obj,
sample_rate=self.sample_rate,
peak_values=pv,
project_info=pi,
record_type=self.kind,
rectime_seconds=self.record_time_sec,
)
ev._waveform_key = waveform_key
return ev