def _read_single_event(event_file, locate_dir, units, local_mag_ph): """ Parse an event file from QuakeMigrate into an obspy Event object. Parameters ---------- event_file : `pathlib.Path` object Path to .event file to read. locate_dir : `pathlib.Path` object Path to locate directory (contains "events", "picks" etc. directories). units : {"km", "m"} Grid projection coordinates for QM LUT (determines units of depths and uncertainties in the .event files). local_mag_ph : {"S", "P"} Amplitude measurement used to calculate local magnitudes. Returns ------- event : `obspy.Event` object Event object populated with all available information output by :class:`~quakemigrate.signal.scan.locate()`, including event locations and uncertainties, picks, and amplitudes and magnitudes if available. """ # Parse information from event file event_info = pd.read_csv(event_file).iloc[0] event_uid = str(event_info["EventID"]) # Set distance conversion factor (from units of QM LUT projection units). if units == "km": factor = 1e3 elif units == "m": factor = 1 else: raise AttributeError(f"units must be 'km' or 'm'; not {units}") # Create event object to store origin and pick information event = Event() event.extra = AttribDict() event.resource_id = str(event_info["EventID"]) event.creation_info = CreationInfo(author="QuakeMigrate", version=quakemigrate.__version__) # Add COA info to extra event.extra.coa = {"value": event_info["COA"], "namespace": ns} event.extra.coa_norm = {"value": event_info["COA_NORM"], "namespace": ns} event.extra.trig_coa = {"value": event_info["TRIG_COA"], "namespace": ns} event.extra.dec_coa = {"value": event_info["DEC_COA"], "namespace": ns} event.extra.dec_coa_norm = { "value": event_info["DEC_COA_NORM"], "namespace": ns } # Determine location of cut waveform data - add to event object as a # custom extra attribute. mseed = locate_dir / "raw_cut_waveforms" / event_uid event.extra.cut_waveforms_file = { "value": str(mseed.with_suffix(".m").resolve()), "namespace": ns } if (locate_dir / "real_cut_waveforms").exists(): mseed = locate_dir / "real_cut_waveforms" / event_uid event.extra.real_cut_waveforms_file = { "value": str(mseed.with_suffix(".m").resolve()), "namespace": ns } if (locate_dir / "wa_cut_waveforms").exists(): mseed = locate_dir / "wa_cut_waveforms" / event_uid event.extra.wa_cut_waveforms_file = { "value": str(mseed.with_suffix(".m").resolve()), "namespace": ns } # Create origin with spline location and set to preferred event origin. origin = Origin() origin.method_id = "spline" origin.longitude = event_info["X"] origin.latitude = event_info["Y"] origin.depth = event_info["Z"] * factor origin.time = UTCDateTime(event_info["DT"]) event.origins = [origin] event.preferred_origin_id = origin.resource_id # Create origin with gaussian location and associate with event origin = Origin() origin.method_id = "gaussian" origin.longitude = event_info["GAU_X"] origin.latitude = event_info["GAU_Y"] origin.depth = event_info["GAU_Z"] * factor origin.time = UTCDateTime(event_info["DT"]) event.origins.append(origin) ouc = OriginUncertainty() ce = ConfidenceEllipsoid() ce.semi_major_axis_length = event_info["COV_ErrY"] * factor ce.semi_intermediate_axis_length = event_info["COV_ErrX"] * factor ce.semi_minor_axis_length = event_info["COV_ErrZ"] * factor ce.major_axis_plunge = 0 ce.major_axis_azimuth = 0 ce.major_axis_rotation = 0 ouc.confidence_ellipsoid = ce ouc.preferred_description = "confidence ellipsoid" # Set uncertainties for both as the gaussian uncertainties for origin in event.origins: origin.longitude_errors.uncertainty = kilometer2degrees( event_info["GAU_ErrX"] * factor / 1e3) origin.latitude_errors.uncertainty = kilometer2degrees( event_info["GAU_ErrY"] * factor / 1e3) origin.depth_errors.uncertainty = event_info["GAU_ErrZ"] * factor origin.origin_uncertainty = ouc # Add OriginQuality info to each origin? for origin in event.origins: origin.origin_type = "hypocenter" origin.evaluation_mode = "automatic" # --- Handle picks file --- pick_file = locate_dir / "picks" / event_uid if pick_file.with_suffix(".picks").is_file(): picks = pd.read_csv(pick_file.with_suffix(".picks")) else: return None for _, pickline in picks.iterrows(): station = str(pickline["Station"]) phase = str(pickline["Phase"]) wid = WaveformStreamID(network_code="", station_code=station) for method in ["modelled", "autopick"]: pick = Pick() pick.extra = AttribDict() pick.waveform_id = wid pick.method_id = method pick.phase_hint = phase if method == "autopick" and str(pickline["PickTime"]) != "-1": pick.time = UTCDateTime(pickline["PickTime"]) pick.time_errors.uncertainty = float(pickline["PickError"]) pick.extra.snr = { "value": float(pickline["SNR"]), "namespace": ns } elif method == "modelled": pick.time = UTCDateTime(pickline["ModelledTime"]) else: continue event.picks.append(pick) # --- Handle amplitudes file --- amps_file = locate_dir / "amplitudes" / event_uid if amps_file.with_suffix(".amps").is_file(): amps = pd.read_csv(amps_file.with_suffix(".amps")) i = 0 for _, ampsline in amps.iterrows(): wid = WaveformStreamID(seed_string=ampsline["id"]) noise_amp = ampsline["Noise_amp"] / 1000 # mm to m for phase in ["P_amp", "S_amp"]: amp = Amplitude() if pd.isna(ampsline[phase]): continue amp.generic_amplitude = ampsline[phase] / 1000 # mm to m amp.generic_amplitude_errors.uncertainty = noise_amp amp.unit = "m" amp.type = "AML" amp.method_id = phase amp.period = 1 / ampsline[f"{phase[0]}_freq"] amp.time_window = TimeWindow( reference=UTCDateTime(ampsline[f"{phase[0]}_time"])) # amp.pick_id = ? amp.waveform_id = wid # amp.filter_id = ? amp.magnitude_hint = "ML" amp.evaluation_mode = "automatic" amp.extra = AttribDict() try: amp.extra.filter_gain = { "value": ampsline[f"{phase[0]}_filter_gain"], "namespace": ns } amp.extra.avg_amp = { "value": ampsline[f"{phase[0]}_avg_amp"] / 1000, # m "namespace": ns } except KeyError: pass if phase[0] == local_mag_ph and not pd.isna(ampsline["ML"]): i += 1 stat_mag = StationMagnitude() stat_mag.extra = AttribDict() # stat_mag.origin_id = ? local_mag_loc stat_mag.mag = ampsline["ML"] stat_mag.mag_errors.uncertainty = ampsline["ML_Err"] stat_mag.station_magnitude_type = "ML" stat_mag.amplitude_id = amp.resource_id stat_mag.extra.picked = { "value": ampsline["is_picked"], "namespace": ns } stat_mag.extra.epi_dist = { "value": ampsline["epi_dist"], "namespace": ns } stat_mag.extra.z_dist = { "value": ampsline["z_dist"], "namespace": ns } event.station_magnitudes.append(stat_mag) event.amplitudes.append(amp) mag = Magnitude() mag.extra = AttribDict() mag.mag = event_info["ML"] mag.mag_errors.uncertainty = event_info["ML_Err"] mag.magnitude_type = "ML" # mag.origin_id = ? mag.station_count = i mag.evaluation_mode = "automatic" mag.extra.r2 = {"value": event_info["ML_r2"], "namespace": ns} event.magnitudes = [mag] event.preferred_magnitude_id = mag.resource_id return event