def _on_file_save(self): """ Creates a new obspy.core.event.Magnitude object and writes the moment magnitude to it. """ # Get the save filename. filename = QtGui.QFileDialog.getSaveFileName(caption="Save as...") filename = os.path.abspath(str(filename)) mag = Magnitude() mag.mag = self.final_result["moment_magnitude"] mag.magnitude_type = "Mw" mag.station_count = self.final_result["station_count"] mag.evaluation_mode = "manual" # Link to the used origin. mag.origin_id = self.current_state["event"].origins[0].resource_id mag.method_id = "Magnitude picker Krischer" # XXX: Potentially change once this program gets more stable. mag.evaluation_status = "preliminary" # Write the other results as Comments. mag.comments.append( \ Comment("Seismic moment in Nm: %g" % \ self.final_result["seismic_moment"])) mag.comments.append( \ Comment("Circular source radius in m: %.2f" % \ self.final_result["source_radius"])) mag.comments.append( \ Comment("Stress drop in Pa: %.2f" % \ self.final_result["stress_drop"])) mag.comments.append( \ Comment("Very rough Q estimation: %.1f" % \ self.final_result["quality_factor"])) event = copy.deepcopy(self.current_state["event"]) event.magnitudes.append(mag) cat = Catalog() cat.events.append(event) cat.write(filename, format="quakeml")
def _map_netmag2magnitude(self, db): """ Return an obspy Magnitude from an dict of CSS key/values corresponding to one record. Inputs ====== db : dict of key/values of CSS fields from the 'netmag' table Returns ======= obspy.core.event.Magnitude Notes ===== Any object that supports the dict 'get' method can be passed as input, e.g. OrderedDict, custom classes, etc. """ m = Magnitude() m.mag = db.get('magnitude') m.magnitude_type = db.get('magtype') m.mag_errors.uncertainty = db.get('uncertainty') m.station_count = db.get('nsta') posted_author = _str(db.get('auth')) mode, status = self.get_event_status(posted_author) m.evaluation_mode = mode m.evaluation_status = status m.creation_info = CreationInfo( creation_time = _utc(db.get('lddate')), agency_id = self.agency, version = db.get('magid'), author = posted_author, ) m.resource_id = self._rid(m) return m
def test_creating_minimal_quakeml_with_mt(self): """ Tests the creation of a minimal QuakeML containing origin, magnitude and moment tensor. """ # Rotate into physical domain lat, lon, depth, org_time = 10.0, -20.0, 12000, UTCDateTime(2012, 1, 1) mrr, mtt, mpp, mtr, mpr, mtp = 1E18, 2E18, 3E18, 3E18, 2E18, 1E18 scalar_moment = math.sqrt( mrr ** 2 + mtt ** 2 + mpp ** 2 + mtr ** 2 + mpr ** 2 + mtp ** 2) moment_magnitude = 0.667 * (math.log10(scalar_moment) - 9.1) # Initialise event ev = Event(event_type="earthquake") ev_origin = Origin(time=org_time, latitude=lat, longitude=lon, depth=depth, resource_id=ResourceIdentifier()) ev.origins.append(ev_origin) # populate event moment tensor ev_tensor = Tensor(m_rr=mrr, m_tt=mtt, m_pp=mpp, m_rt=mtr, m_rp=mpr, m_tp=mtp) ev_momenttensor = MomentTensor(tensor=ev_tensor) ev_momenttensor.scalar_moment = scalar_moment ev_momenttensor.derived_origin_id = ev_origin.resource_id ev_focalmechanism = FocalMechanism(moment_tensor=ev_momenttensor) ev.focal_mechanisms.append(ev_focalmechanism) # populate event magnitude ev_magnitude = Magnitude() ev_magnitude.mag = moment_magnitude ev_magnitude.magnitude_type = 'Mw' ev_magnitude.evaluation_mode = 'automatic' ev.magnitudes.append(ev_magnitude) # write QuakeML file cat = Catalog(events=[ev]) memfile = io.BytesIO() cat.write(memfile, format="quakeml", validate=IS_RECENT_LXML) memfile.seek(0, 0) new_cat = _read_quakeml(memfile) self.assertEqual(len(new_cat), 1) event = new_cat[0] self.assertEqual(len(event.origins), 1) self.assertEqual(len(event.magnitudes), 1) self.assertEqual(len(event.focal_mechanisms), 1) org = event.origins[0] mag = event.magnitudes[0] fm = event.focal_mechanisms[0] self.assertEqual(org.latitude, lat) self.assertEqual(org.longitude, lon) self.assertEqual(org.depth, depth) self.assertEqual(org.time, org_time) # Moment tensor. mt = fm.moment_tensor.tensor self.assertTrue((fm.moment_tensor.scalar_moment - scalar_moment) / scalar_moment < scalar_moment * 1E-10) self.assertEqual(mt.m_rr, mrr) self.assertEqual(mt.m_pp, mpp) self.assertEqual(mt.m_tt, mtt) self.assertEqual(mt.m_rt, mtr) self.assertEqual(mt.m_rp, mpr) self.assertEqual(mt.m_tp, mtp) # Mag self.assertAlmostEqual(mag.mag, moment_magnitude) self.assertEqual(mag.magnitude_type, "Mw") self.assertEqual(mag.evaluation_mode, "automatic")
def build(self): """ Build an obspy moment tensor focal mech event This makes the tensor output into an Event containing: 1) a FocalMechanism with a MomentTensor, NodalPlanes, and PrincipalAxes 2) a Magnitude of the Mw from the Tensor Which is what we want for outputting QuakeML using the (slightly modified) obspy code. Input ----- filehandle => open file OR str from filehandle.read() Output ------ event => instance of Event() class as described above """ p = self.parser event = Event(event_type='earthquake') origin = Origin() focal_mech = FocalMechanism() nodal_planes = NodalPlanes() moment_tensor = MomentTensor() principal_ax = PrincipalAxes() magnitude = Magnitude() data_used = DataUsed() creation_info = CreationInfo(agency_id='NN') ev_mode = 'automatic' ev_stat = 'preliminary' evid = None orid = None # Parse the entire file line by line. for n,l in enumerate(p.line): if 'REVIEWED BY NSL STAFF' in l: ev_mode = 'manual' ev_stat = 'reviewed' if 'Event ID' in l: evid = p._id(n) if 'Origin ID' in l: orid = p._id(n) if 'Ichinose' in l: moment_tensor.category = 'regional' if re.match(r'^\d{4}\/\d{2}\/\d{2}', l): ev = p._event_info(n) if 'Depth' in l: derived_depth = p._depth(n) if 'Mw' in l: magnitude.mag = p._mw(n) magnitude.magnitude_type = 'Mw' if 'Mo' in l and 'dyne' in l: moment_tensor.scalar_moment = p._mo(n) if 'Percent Double Couple' in l: moment_tensor.double_couple = p._percent(n) if 'Percent CLVD' in l: moment_tensor.clvd = p._percent(n) if 'Epsilon' in l: moment_tensor.variance = p._epsilon(n) if 'Percent Variance Reduction' in l: moment_tensor.variance_reduction = p._percent(n) if 'Major Double Couple' in l and 'strike' in p.line[n+1]: np = p._double_couple(n) nodal_planes.nodal_plane_1 = NodalPlane(*np[0]) nodal_planes.nodal_plane_2 = NodalPlane(*np[1]) nodal_planes.preferred_plane = 1 if 'Spherical Coordinates' in l: mt = p._mt_sphere(n) moment_tensor.tensor = Tensor( m_rr = mt['Mrr'], m_tt = mt['Mtt'], m_pp = mt['Mff'], m_rt = mt['Mrt'], m_rp = mt['Mrf'], m_tp = mt['Mtf'], ) if 'Eigenvalues and eigenvectors of the Major Double Couple' in l: ax = p._vectors(n) principal_ax.t_axis = Axis(ax['T']['trend'], ax['T']['plunge'], ax['T']['ev']) principal_ax.p_axis = Axis(ax['P']['trend'], ax['P']['plunge'], ax['P']['ev']) principal_ax.n_axis = Axis(ax['N']['trend'], ax['N']['plunge'], ax['N']['ev']) if 'Number of Stations' in l: data_used.station_count = p._number_of_stations(n) if 'Maximum' in l and 'Gap' in l: focal_mech.azimuthal_gap = p._gap(n) if re.match(r'^Date', l): creation_info.creation_time = p._creation_time(n) # Creation Time creation_info.version = orid # Fill in magnitude values magnitude.evaluation_mode = ev_mode magnitude.evaluation_status = ev_stat magnitude.creation_info = creation_info.copy() magnitude.resource_id = self._rid(magnitude) # Stub origin origin.time = ev.get('time') origin.latitude = ev.get('lat') origin.longitude = ev.get('lon') origin.depth = derived_depth * 1000. origin.depth_type = "from moment tensor inversion" origin.creation_info = creation_info.copy() # Unique from true origin ID _oid = self._rid(origin) origin.resource_id = ResourceIdentifier(str(_oid) + '/mt') del _oid # Make an id for the MT that references this origin ogid = str(origin.resource_id) doid = ResourceIdentifier(ogid, referred_object=origin) # Make an id for the moment tensor mag which references this mag mrid = str(magnitude.resource_id) mmid = ResourceIdentifier(mrid, referred_object=magnitude) # MT todo: could check/use URL for RID if parsing the php file moment_tensor.evaluation_mode = ev_mode moment_tensor.evaluation_status = ev_stat moment_tensor.data_used = data_used moment_tensor.moment_magnitude_id = mmid moment_tensor.derived_origin_id = doid moment_tensor.creation_info = creation_info.copy() moment_tensor.resource_id = self._rid(moment_tensor) # Fill in focal_mech values focal_mech.nodal_planes = nodal_planes focal_mech.moment_tensor = moment_tensor focal_mech.principal_axes = principal_ax focal_mech.creation_info = creation_info.copy() focal_mech.resource_id = self._rid(focal_mech) # add mech and new magnitude to event event.focal_mechanisms = [focal_mech] event.magnitudes = [magnitude] event.origins = [origin] event.creation_info = creation_info.copy() # If an MT was done, that's the preferred mag/mech event.preferred_magnitude_id = str(magnitude.resource_id) event.preferred_focal_mechanism_id = str(focal_mech.resource_id) if evid: event.creation_info.version = evid event.resource_id = self._rid(event) self.event = event
def calculate_moment_magnitudes(cat, output_file): """ :param cat: obspy.core.event.Catalog object. """ Mws = [] Mls = [] Mws_std = [] for event in cat: if not event.origins: print "No origin for event %s" % event.resource_id continue if not event.magnitudes: print "No magnitude for event %s" % event.resource_id continue origin_time = event.origins[0].time local_magnitude = event.magnitudes[0].mag #if local_magnitude < 1.0: #continue moments = [] source_radii = [] corner_frequencies = [] for pick in event.picks: # Only p phase picks. if pick.phase_hint.lower() == "p": radiation_pattern = 0.52 velocity = V_P k = 0.32 elif pick.phase_hint.lower() == "s": radiation_pattern = 0.63 velocity = V_S k = 0.21 else: continue distance = (pick.time - origin_time) * velocity if distance <= 0.0: continue stream = get_corresponding_stream(pick.waveform_id, pick.time, PADDING) if stream is None or len(stream) != 3: continue omegas = [] corner_freqs = [] for trace in stream: # Get the index of the pick. pick_index = int(round((pick.time - trace.stats.starttime) / \ trace.stats.delta)) # Choose date window 0.5 seconds before and 1 second after pick. data_window = trace.data[pick_index - \ int(TIME_BEFORE_PICK * trace.stats.sampling_rate): \ pick_index + int(TIME_AFTER_PICK * trace.stats.sampling_rate)] # Calculate the spectrum. spec, freq = mtspec.mtspec(data_window, trace.stats.delta, 2) try: fit = fit_spectrum(spec, freq, pick.time - origin_time, spec.max(), 10.0) except: continue if fit is None: continue Omega_0, f_c, err, _ = fit Omega_0 = np.sqrt(Omega_0) omegas.append(Omega_0) corner_freqs.append(f_c) M_0 = 4.0 * np.pi * DENSITY * velocity ** 3 * distance * \ np.sqrt(omegas[0] ** 2 + omegas[1] ** 2 + omegas[2] ** 2) / \ radiation_pattern r = 3 * k * V_S / sum(corner_freqs) moments.append(M_0) source_radii.append(r) corner_frequencies.extend(corner_freqs) if not len(moments): print "No moments could be calculated for event %s" % \ event.resource_id.resource_id continue # Calculate the seismic moment via basic statistics. moments = np.array(moments) moment = moments.mean() moment_std = moments.std() corner_frequencies = np.array(corner_frequencies) corner_frequency = corner_frequencies.mean() corner_frequency_std = corner_frequencies.std() # Calculate the source radius. source_radii = np.array(source_radii) source_radius = source_radii.mean() source_radius_std = source_radii.std() # Calculate the stress drop of the event based on the average moment and # source radii. stress_drop = (7 * moment) / (16 * source_radius ** 3) stress_drop_std = np.sqrt((stress_drop ** 2) * \ (((moment_std ** 2) / (moment ** 2)) + \ (9 * source_radius * source_radius_std ** 2))) if source_radius > 0 and source_radius_std < source_radius: print "Source radius:", source_radius, " Std:", source_radius_std print "Stress drop:", stress_drop / 1E5, " Std:", stress_drop_std / 1E5 Mw = 2.0 / 3.0 * (np.log10(moment) - 9.1) Mw_std = 2.0 / 3.0 * moment_std / (moment * np.log(10)) Mws_std.append(Mw_std) Mws.append(Mw) Mls.append(local_magnitude) calc_diff = abs(Mw - local_magnitude) Mw = ("%.3f" % Mw).rjust(7) Ml = ("%.3f" % local_magnitude).rjust(7) diff = ("%.3e" % calc_diff).rjust(7) ret_string = colorama.Fore.GREEN + \ "For event %s: Ml=%s | Mw=%s | " % (event.resource_id.resource_id, Ml, Mw) if calc_diff >= 1.0: ret_string += colorama.Fore.RED ret_string += "Diff=%s" % diff ret_string += colorama.Fore.GREEN ret_string += " | Determined at %i stations" % len(moments) ret_string += colorama.Style.RESET_ALL print ret_string mag = Magnitude() mag.mag = Mw mag.mag_errors.uncertainty = Mw_std mag.magnitude_type = "Mw" mag.origin_id = event.origins[0].resource_id mag.method_id = "smi:com.github/krischer/moment_magnitude_calculator/automatic/1" mag.station_count = len(moments) mag.evaluation_mode = "automatic" mag.evaluation_status = "preliminary" mag.comments.append(Comment( \ "Seismic Moment=%e Nm; standard deviation=%e" % (moment, moment_std))) mag.comments.append(Comment("Custom fit to Boatwright spectrum")) if source_radius > 0 and source_radius_std < source_radius: mag.comments.append(Comment( \ "Source radius=%.2fm; standard deviation=%.2f" % (source_radius, source_radius_std))) event.magnitudes.append(mag) print "Writing output file..." cat.write(output_file, format="quakeml")
def calculate_moment_magnitudes(cat, output_file): """ :param cat: obspy.core.event.Catalog object. """ Mws = [] Mls = [] Mws_std = [] for event in cat: if not event.origins: print "No origin for event %s" % event.resource_id continue if not event.magnitudes: print "No magnitude for event %s" % event.resource_id continue origin_time = event.origins[0].time local_magnitude = event.magnitudes[0].mag #if local_magnitude < 1.0: #continue moments = [] source_radii = [] corner_frequencies = [] for pick in event.picks: # Only p phase picks. if pick.phase_hint.lower() == "p": radiation_pattern = 0.52 velocity = V_P k = 0.32 elif pick.phase_hint.lower() == "s": radiation_pattern = 0.63 velocity = V_S k = 0.21 else: continue distance = (pick.time - origin_time) * velocity if distance <= 0.0: continue stream = get_corresponding_stream(pick.waveform_id, pick.time, PADDING) if stream is None or len(stream) != 3: continue omegas = [] corner_freqs = [] for trace in stream: # Get the index of the pick. pick_index = int(round((pick.time - trace.stats.starttime) / \ trace.stats.delta)) # Choose date window 0.5 seconds before and 1 second after pick. data_window = trace.data[pick_index - \ int(TIME_BEFORE_PICK * trace.stats.sampling_rate): \ pick_index + int(TIME_AFTER_PICK * trace.stats.sampling_rate)] # Calculate the spectrum. spec, freq = mtspec.mtspec(data_window, trace.stats.delta, 2) try: fit = fit_spectrum(spec, freq, pick.time - origin_time, spec.max(), 10.0) except: continue if fit is None: continue Omega_0, f_c, err, _ = fit Omega_0 = np.sqrt(Omega_0) omegas.append(Omega_0) corner_freqs.append(f_c) M_0 = 4.0 * np.pi * DENSITY * velocity ** 3 * distance * \ np.sqrt(omegas[0] ** 2 + omegas[1] ** 2 + omegas[2] ** 2) / \ radiation_pattern r = 3 * k * V_S / sum(corner_freqs) moments.append(M_0) source_radii.append(r) corner_frequencies.extend(corner_freqs) if not len(moments): print "No moments could be calculated for event %s" % \ event.resource_id.resource_id continue # Calculate the seismic moment via basic statistics. moments = np.array(moments) moment = moments.mean() moment_std = moments.std() corner_frequencies = np.array(corner_frequencies) corner_frequency = corner_frequencies.mean() corner_frequency_std = corner_frequencies.std() # Calculate the source radius. source_radii = np.array(source_radii) source_radius = source_radii.mean() source_radius_std = source_radii.std() # Calculate the stress drop of the event based on the average moment and # source radii. stress_drop = (7 * moment) / (16 * source_radius ** 3) stress_drop_std = np.sqrt((stress_drop ** 2) * \ (((moment_std ** 2) / (moment ** 2)) + \ (9 * source_radius * source_radius_std ** 2))) if source_radius > 0 and source_radius_std < source_radius: print "Source radius:", source_radius, " Std:", source_radius_std print "Stress drop:", stress_drop / 1E5, " Std:", stress_drop_std / 1E5 Mw = 2.0 / 3.0 * (np.log10(moment) - 9.1) Mw_std = 2.0 / 3.0 * moment_std / (moment * np.log(10)) Mws_std.append(Mw_std) Mws.append(Mw) Mls.append(local_magnitude) calc_diff = abs(Mw - local_magnitude) Mw = ("%.3f" % Mw).rjust(7) Ml = ("%.3f" % local_magnitude).rjust(7) diff = ("%.3e" % calc_diff).rjust(7) ret_string = colorama.Fore.GREEN + \ "For event %s: Ml=%s | Mw=%s | " % (event.resource_id.resource_id, Ml, Mw) if calc_diff >= 1.0: ret_string += colorama.Fore.RED ret_string += "Diff=%s" % diff ret_string += colorama.Fore.GREEN ret_string += " | Determined at %i stations" % len(moments) ret_string += colorama.Style.RESET_ALL print ret_string mag = Magnitude() mag.mag = Mw mag.mag_errors.uncertainty = Mw_std mag.magnitude_type = "Mw" mag.origin_id = event.origins[0].resource_id mag.method_id = "Custom fit to Boatwright spectrum" mag.station_count = len(moments) mag.evaluation_mode = "automatic" mag.evaluation_status = "preliminary" mag.comments.append(Comment( \ "Seismic Moment=%e Nm; standard deviation=%e" % (moment, moment_std))) if source_radius > 0 and source_radius_std < source_radius: mag.comments.append(Comment( \ "Source radius=%.2fm; standard deviation=%.2f" % (source_radius, source_radius_std))) event.magnitudes.append(mag) print "Writing output file..." cat.write(output_file, format="quakeml")
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
def write_qml(config, sourcepar): if not config.options.qml_file: return qml_file = config.options.qml_file cat = read_events(qml_file) evid = config.hypo.evid try: ev = [e for e in cat if evid in str(e.resource_id)][0] except Exception: logging.warning('Unable to find evid "{}" in QuakeML file. ' 'QuakeML output will not be written.'.format(evid)) origin = ev.preferred_origin() if origin is None: origin = ev.origins[0] origin_id = origin.resource_id origin_id_strip = origin_id.id.split('/')[-1] origin_id_strip = origin_id_strip.replace(config.smi_strip_from_origin_id, '') # Common parameters ssp_version = get_versions()['version'] method_id = config.smi_base + '/sourcespec/' + ssp_version cr_info = CreationInfo() cr_info.agency_id = config.agency_id if config.author is None: author = '{}@{}'.format(getuser(), gethostname()) else: author = config.author cr_info.author = author cr_info.creation_time = UTCDateTime() means = sourcepar.means_weight errors = sourcepar.errors_weight stationpar = sourcepar.station_parameters # Magnitude mag = Magnitude() _id = config.smi_magnitude_template.replace('$SMI_BASE', config.smi_base) _id = _id.replace('$ORIGIN_ID', origin_id_strip) mag.resource_id = ResourceIdentifier(id=_id) mag.method_id = ResourceIdentifier(id=method_id) mag.origin_id = origin_id mag.magnitude_type = 'Mw' mag.mag = means['Mw'] mag_err = QuantityError() mag_err.uncertainty = errors['Mw'] mag_err.confidence_level = 68.2 mag.mag_errors = mag_err mag.station_count = len([_s for _s in stationpar.keys()]) mag.evaluation_mode = 'automatic' mag.creation_info = cr_info # Seismic moment -- It has to be stored in a MomentTensor object # which, in turn, is part of a FocalMechanism object mt = MomentTensor() _id = config.smi_moment_tensor_template.replace('$SMI_BASE', config.smi_base) _id = _id.replace('$ORIGIN_ID', origin_id_strip) mt.resource_id = ResourceIdentifier(id=_id) mt.derived_origin_id = origin_id mt.moment_magnitude_id = mag.resource_id mt.scalar_moment = means['Mo'] mt_err = QuantityError() mt_err.lower_uncertainty = errors['Mo'][0] mt_err.upper_uncertainty = errors['Mo'][1] mt_err.confidence_level = 68.2 mt.scalar_moment_errors = mt_err mt.method_id = method_id mt.creation_info = cr_info # And here is the FocalMechanism object fm = FocalMechanism() _id = config.smi_focal_mechanism_template.replace('$SMI_BASE', config.smi_base) _id = _id.replace('$ORIGIN_ID', origin_id_strip) fm.resource_id = ResourceIdentifier(id=_id) fm.triggering_origin_id = origin_id fm.method_id = ResourceIdentifier(id=method_id) fm.moment_tensor = mt fm.creation_info = cr_info ev.focal_mechanisms.append(fm) # Station magnitudes for statId in sorted(stationpar.keys()): par = stationpar[statId] st_mag = StationMagnitude() seed_id = statId.split()[0] _id = config.smi_station_magnitude_template.replace( '$SMI_MAGNITUDE_TEMPLATE', config.smi_magnitude_template) _id = _id.replace('$ORIGIN_ID', origin_id_strip) _id = _id.replace('$SMI_BASE', config.smi_base) _id = _id.replace('$WAVEFORM_ID', seed_id) st_mag.resource_id = ResourceIdentifier(id=_id) st_mag.origin_id = origin_id st_mag.mag = par['Mw'] st_mag.station_magnitude_type = 'Mw' st_mag.method_id = mag.method_id st_mag.creation_info = cr_info st_mag.waveform_id = WaveformStreamID(seed_string=seed_id) st_mag.extra = SSPExtra() st_mag.extra.moment = SSPTag(par['Mo']) st_mag.extra.corner_frequency = SSPTag(par['fc']) st_mag.extra.t_star = SSPTag(par['t_star']) ev.station_magnitudes.append(st_mag) st_mag_contrib = StationMagnitudeContribution() st_mag_contrib.station_magnitude_id = st_mag.resource_id mag.station_magnitude_contributions.append(st_mag_contrib) ev.magnitudes.append(mag) # Write other average parameters as custom tags ev.extra = SSPExtra() ev.extra.corner_frequency = SSPContainerTag() ev.extra.corner_frequency.value.value = SSPTag(means['fc']) ev.extra.corner_frequency.value.lower_uncertainty =\ SSPTag(errors['fc'][0]) ev.extra.corner_frequency.value.upper_uncertainty =\ SSPTag(errors['fc'][1]) ev.extra.corner_frequency.value.confidence_level = SSPTag(68.2) ev.extra.t_star = SSPContainerTag() ev.extra.t_star.value.value = SSPTag(means['t_star']) ev.extra.t_star.value.uncertainty = SSPTag(errors['t_star']) ev.extra.t_star.value.confidence_level = SSPTag(68.2) ev.extra.source_radius = SSPContainerTag() ev.extra.source_radius.value.value = SSPTag(means['ra']) ev.extra.source_radius.value.lower_uncertainty =\ SSPTag(errors['ra'][0]) ev.extra.source_radius.value.upper_uncertainty =\ SSPTag(errors['ra'][1]) ev.extra.source_radius.value.confidence_level = SSPTag(68.2) ev.extra.stress_drop = SSPContainerTag() ev.extra.stress_drop.value.value = SSPTag(means['bsd']) ev.extra.stress_drop.value.lower_uncertainty =\ SSPTag(errors['bsd'][0]) ev.extra.stress_drop.value.upper_uncertainty =\ SSPTag(errors['bsd'][1]) ev.extra.stress_drop.value.confidence_level = SSPTag(68.2) if config.set_preferred_magnitude: ev.preferred_magnitude_id = mag.resource_id.id qml_file_out = os.path.join(config.options.outdir, evid + '.xml') ev.write(qml_file_out, format='QUAKEML') logging.info('QuakeML file written to: ' + qml_file_out)