def get_phase_dataframe(detail, catalog='preferred'):
"""Return a Pandas DataFrame consisting of Phase arrival data.
Args:
detail (DetailEvent): DetailEvent object.
catalog (str): Source network ('us','ak', etc. ,or 'preferred'.)
Returns:
DataFrame: Pandas DataFrame containing columns:
- Channel: Network.Station.Channel.Location (NSCL) style station
description. ("--" indicates missing information)
- Distance: Distance (kilometers) from epicenter to station.
- Azimuth: Azimuth (degrees) from epicenter to station.
- Phase: Name of the phase (Pn,Pg, etc.)
- Arrival Time: Pick arrival time (UTC).
- Status: "manual" or "automatic".
- Residual: Arrival time residual.
- Weight: Arrival weight.
- Agency: Agency ID.
Raises:
AttributeError: If input DetailEvent does not have a phase-data product
for the input catalog.
"""
if catalog is None:
catalog = 'preferred'
df = pd.DataFrame(columns=['Channel', 'Distance', 'Azimuth',
'Phase', 'Arrival Time', 'Status',
'Residual', 'Weight', 'Agency'])
phasedata = detail.getProducts('phase-data', source=catalog)[0]
quakeurl = phasedata.getContentURL('quakeml.xml')
try:
fh = urlopen(quakeurl, timeout=TIMEOUT)
data = fh.read()
fh.close()
except Exception:
return None
unpickler = Unpickler()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
catalog = unpickler.loads(data)
catevent = catalog.events[0]
for pick in catevent.picks:
phaserow = _get_phaserow(pick, catevent)
if phaserow is None:
continue
df = df.append(phaserow, ignore_index=True)
return df
def _read_sc3ml(filename, id_prefix='smi:org.gfz-potsdam.de/geofon/'):
"""
Read a SC3ML file and returns a :class:`~obspy.core.event.Catalog`.
An XSLT file is used to convert the SC3ML file to a QuakeML file. The
catalog is then generated using the QuakeML module.
.. warning::
This function should NOT be called directly, it registers via the
the :meth:`~obspy.core.event.catalog.Catalog.write` method of an
ObsPy :class:`~obspy.core.event.catalog.Catalog` object, call this
instead.
:type filename: str
:param filename: SC3ML file to be read.
:type id_prefix: str
:param id_prefix: ID prefix. SC3ML does not enforce any particular ID
restriction, this ID prefix allows to convert the IDs to a well
formatted QuakeML ID. You can modify the default ID prefix with the
reverse DNS name of your institute.
:rtype: :class:`~obspy.core.event.Catalog`
:return: An ObsPy Catalog object.
.. rubric:: Example
>>> from obspy import read_events
>>> cat = read_events('/path/to/iris_events.sc3ml')
>>> print(cat)
2 Event(s) in Catalog:
2011-03-11T05:46:24.120000Z | +38.297, +142.373
2006-09-10T04:26:33.610000Z | +9.614, +121.961
"""
sc3ml_doc = _xml_doc_from_anything(filename)
match = re.match(
r'{http://geofon\.gfz-potsdam\.de/ns/seiscomp3-schema/([-+]?'
r'[0-9]*\.?[0-9]+)}', sc3ml_doc.tag)
try:
version = match.group(1)
except AttributeError:
raise ValueError("Not a SC3ML compatible file or string.")
else:
if version not in SCHEMA_VERSION:
message = ("Can't read SC3ML version %s, ObsPy can deal with "
"versions [%s].") % (version, ', '.join(SCHEMA_VERSION))
raise ValueError(message)
xslt_filename = os.path.join(os.path.dirname(__file__), 'data',
'sc3ml_%s__quakeml_1.2.xsl' % version)
transform = etree.XSLT(etree.parse(xslt_filename))
quakeml_doc = transform(sc3ml_doc,
ID_PREFIX=etree.XSLT.strparam(id_prefix))
return Unpickler().load(io.BytesIO(quakeml_doc))
def _read_sc3ml(filename, id_prefix='smi:org.gfz-potsdam.de/geofon/'):
"""
Read a 0.9 SC3ML file and returns a :class:`~obspy.core.event.Catalog`.
An XSLT file is used to convert the SC3ML file to a QuakeML file. The
catalog is then generated using the QuakeML module.
.. warning::
This function should NOT be called directly, it registers via the
the :meth:`~obspy.core.event.catalog.Catalog.write` method of an
ObsPy :class:`~obspy.core.event.catalog.Catalog` object, call this
instead.
:type filename: str
:param filename: SC3ML file to be read.
:type id_prefix: str
:param id_prefix: ID prefix. SC3ML does not enforce any particular ID
restriction, this ID prefix allows to convert the IDs to a well
formatted QuakeML ID. You can modify the default ID prefix with the
reverse DNS name of your institute.
:rtype: :class:`~obspy.core.event.Catalog`
:return: An ObsPy Catalog object.
.. rubric:: Example
>>> from obspy import read_events
>>> cat = read_events('/path/to/iris_events.sc3ml')
>>> print(cat)
2 Event(s) in Catalog:
2011-03-11T05:46:24.120000Z | +38.297, +142.373
2006-09-10T04:26:33.610000Z | +9.614, +121.961
"""
xslt_filename = os.path.join(os.path.dirname(__file__), 'data',
'sc3ml_0.9__quakeml_1.2.xsl')
transform = etree.XSLT(etree.parse(xslt_filename))
sc3ml_doc = _xml_doc_from_anything(filename)
quakeml_doc = transform(sc3ml_doc,
ID_PREFIX=etree.XSLT.strparam(id_prefix))
return Unpickler().load(io.BytesIO(quakeml_doc))
# -*- coding: utf-8 -*-
"""
Parse data from GHA Kermadec Spreadsheet or csv of eventIDs and produce plots of data/analysis
"""
import argparse
import datetime
from io import BytesIO
import math
import matplotlib
import matplotlib.pyplot as plt
from obspy.io.quakeml.core import Unpickler
import pycurl
import subprocess
quakeml_reader = Unpickler()
def get_event(eventID):
"""
Get a single event from the GeoNet FDSN service
:param eventID: GeoNet eventID
:return: FDSN event
"""
query = "https://service.geonet.org.nz/fdsnws/dataselect/1/query?network=NZ&" + eventID
queryresult = curl(query)
event = quakeml_reader.loads(queryresult)
return event
def get_magnitude_data_frame(detail, catalog, magtype):
"""Return a Pandas DataFrame consisting of magnitude data.
Args:
detail (DetailEvent): DetailEvent object.
catalog (str): Source catalog ('us','ak', etc. ,or 'preferred'.)
magtype (str): Magnitude type (mb, ml, etc.)
Returns:
DataFrame: Pandas DataFrame containing columns:
- Channel: Network.Station.Channel.Location (NSCL) style station
description. ("--" indicates missing information)
- Type: Magnitude type.
- Amplitude: Amplitude of seismic wave at each station (m).
- Period: Period of seismic wave at each station (s).
- Status: "manual" or "automatic".
- Magnitude: Locally determined magnitude.
- Weight: Magnitude weight.
Raises:
AttributeError if input DetailEvent does not have a phase-data product
for the input catalog.
"""
columns = columns = [
'Channel', 'Type', 'Amplitude', 'Period', 'Status', 'Magnitude',
'Weight'
]
df = pd.DataFrame(columns=columns)
phasedata = detail.getProducts('phase-data', source=catalog)[0]
quakeurl = phasedata.getContentURL('quakeml.xml')
try:
fh = urlopen(quakeurl, timeout=TIMEOUT)
data = fh.read()
fh.close()
except Exception:
return None
fmt = '%s.%s.%s.%s'
unpickler = Unpickler()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
catalog = unpickler.loads(data)
catevent = catalog.events[0] # match this to input catalog
for magnitude in catevent.magnitudes:
if magnitude.magnitude_type.lower() != magtype.lower():
continue
for contribution in magnitude.station_magnitude_contributions:
row = {}
smag = contribution.station_magnitude_id.get_referred_object()
ampid = smag.amplitude_id
amp = None
if ampid is None:
waveid = smag.waveform_id
tpl = (waveid.network_code, waveid.station_code, '--',
'--')
else:
amp = ampid.get_referred_object()
if amp is None:
waveid = smag.waveform_id
tpl = (waveid.network_code, waveid.station_code, '--',
'--')
else:
waveid = amp.waveform_id
tpl = (waveid.network_code, waveid.station_code,
waveid.channel_code, waveid.location_code)
row['Channel'] = fmt % tpl
row['Type'] = smag.station_magnitude_type
if amp is not None:
row['Amplitude'] = amp.generic_amplitude
row['Period'] = amp.period
row['Status'] = amp.evaluation_mode
else:
row['Amplitude'] = np.nan
row['Period'] = np.nan
row['Status'] = 'automatic'
row['Magnitude'] = smag.mag
row['Weight'] = contribution.weight
df = df.append(row, ignore_index=True)
df = df[columns]
return df
def get_magnitude_data_frame(detail, catalog, magtype):
"""Return a Pandas DataFrame consisting of magnitude data.
Args:
detail (DetailEvent): DetailEvent object.
catalog (str): Source catalog ('us','ak', etc. ,or 'preferred'.)
magtype (str): Magnitude type (mb, ml, etc.)
Returns:
DataFrame: Pandas DataFrame containing columns:
- Channel: Network.Station.Channel.Location (NSCL) style station
description. ("--" indicates missing information)
- Type: Magnitude type.
- Amplitude: Amplitude of seismic wave at each station (m).
- Period: Period of seismic wave at each station (s).
- Status: "manual" or "automatic".
- Magnitude: Locally determined magnitude.
- Weight: Magnitude weight.
Raises:
AttributeError if input DetailEvent does not have a phase-data product
for the input catalog.
"""
columns = columns = ['Channel', 'Type', 'Amplitude',
'Period', 'Status', 'Magnitude',
'Weight']
df = pd.DataFrame(columns=columns)
phasedata = detail.getProducts('phase-data', source=catalog)[0]
quakeurl = phasedata.getContentURL('quakeml.xml')
try:
fh = urlopen(quakeurl, timeout=TIMEOUT)
data = fh.read()
fh.close()
except Exception:
return None
fmt = '%s.%s.%s.%s'
unpickler = Unpickler()
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=UserWarning)
catalog = unpickler.loads(data)
catevent = catalog.events[0] # match this to input catalog
for magnitude in catevent.magnitudes:
if magnitude.magnitude_type.lower() != magtype.lower():
continue
for contribution in magnitude.station_magnitude_contributions:
row = {}
smag = contribution.station_magnitude_id.get_referred_object()
ampid = smag.amplitude_id
amp = None
if ampid is None:
waveid = smag.waveform_id
tpl = (waveid.network_code,
waveid.station_code,
'--',
'--')
else:
amp = ampid.get_referred_object()
if amp is None:
waveid = smag.waveform_id
tpl = (waveid.network_code,
waveid.station_code,
'--',
'--')
else:
waveid = amp.waveform_id
tpl = (waveid.network_code,
waveid.station_code,
waveid.channel_code,
waveid.location_code)
row['Channel'] = fmt % tpl
row['Type'] = smag.station_magnitude_type
if amp is not None:
row['Amplitude'] = amp.generic_amplitude
row['Period'] = amp.period
row['Status'] = amp.evaluation_mode
else:
row['Amplitude'] = np.nan
row['Period'] = np.nan
row['Status'] = 'automatic'
row['Magnitude'] = smag.mag
row['Weight'] = contribution.weight
df = df.append(row, ignore_index=True)
df = df[columns]
return df
