def _init_header(self, path):
config = _load_config(path)
metadata = config['metadata']
header = Stats()
config_key = self._config['header_sampling_rate_key']
try:
header.sampling_rate = float(metadata[config_key])
except KeyError:
raise KeyError("The following key was not found in the metadata: " +
"{}. Did you set the correct ".format(config_key) +
"'sample rate metadata key' in PAL H5 Output module?")
header.npts = int(metadata[self._config['header_samples_per_record_key']]) - 1
try:
if self._config['dd_300']:
header.calib = metadata['dd_300_calibration']
elif self._config['dd_900']:
header.calib = metadata['dd_900_calibration']
elif self._config['vd_08']:
header.calib = metadata['vd_08_calibration']
elif self._config['vd_09']:
header.calib = metadata['vd_09_calibration']
except KeyError:
pass
header.comments = str(config['comments'])
header.place = metadata
if self._config['header_extra1_name'] != '' and self._config['header_extra1_val'] != '':
header[self._config['header_extra1_name']] = self._config['header_extra1_val']
if self._config['header_extra2_name'] != '' and self._config['header_extra2_val'] != '':
header[self._config['header_extra2_name']] = self._config['header_extra2_val']
return header
def read_IMS_ASCII(path, net='', **kwargs):
"""
read a IMS_ASCII seismogram from a single station
:param path: path to file
:return: uquake.core.Stream
"""
data = np.loadtxt(path, delimiter=',', skiprows=1)
stats = Stats()
with open(path) as fid:
field = fid.readline().split(',')
stats.sampling_rate = float(field[1])
timetmp = datetime.fromtimestamp(float(field[5])) \
+ timedelta(
seconds=float(field[6]) / 1e6) # trigger time in second
trgtime_UTC = UTCDateTime(timetmp)
stats.starttime = trgtime_UTC - float(field[10]) / stats.sampling_rate
stats.npts = len(data)
stats.station = field[8]
stats.network = net
traces = []
component = np.array(['X', 'Y', 'Z'])
std = np.std(data, axis=0)
mstd = np.max(std)
for k, dt in enumerate(data.T):
stats.channel = '%s' % (component[k])
traces.append(Trace(data=np.array(dt), header=stats))
return Stream(traces=traces)
def convert_to_obspy(signal, fsp):
# type: (np.array, float) -> obspy.core.trace
"""
Function to convert a numpy array from any infrasound reading into Obspy format.
This could help us to take advantage of Obspy optimization routines.
:param signal: A numpy array containg the signal we want to compute.
:param fsp: The sampling frequency of the signal.
:return:
"""
# we substract the mean
stats = Stats()
stats.sampling_rate = float(fsp)
stats.npts = signal.shape[0]
return Trace(data=signal.reshape(signal.shape[0], ), header=stats)
def _init_header(self, path):
config = _load_config(path)
metadata = config['metadata']
header = Stats()
if 'ATS9440' in config['plugins'].keys():
ats_config = config['plugins']['ATS9440']['config']
elif 'ATS660' in config['plugins'].keys():
ats_config = config['plugins']['ATS660']['config']
else:
raise KeyError('Cannot locate trace config data')
if 'Polytec' in config['plugins'].keys():
polytec_config = config['plugins']['Polytec']['config']
else:
raise KeyError('Cannot locate vibrometer config data')
header.sampling_rate = _calc_sampling_rate(ats_config['sample_rate'])
header.npts = int(ats_config['pre_trigger_samples'] +
ats_config['post_trigger_samples']) - 1
if polytec_config['dd_300']:
header.calib = float(
re.findall(_NUMBER, polytec_config['dd_300_range'])[0])
elif polytec_config['dd_900']:
header.calib = float(
re.findall(_NUMBER, polytec_config['dd_900_range'])[0])
elif polytec_config['vd_08']:
header.calib = float(
re.findall(_NUMBER, polytec_config['vd_08_range'])[0])
elif polytec_config['vd_09']:
header.calib = float(
re.findall(_NUMBER, polytec_config['vd_09_range'])[0])
else:
raise KeyError('Cannot locate vibrometer calibration data')
header.comments = str(config['comments'])
header.place = metadata
if self._config['header_extra1_name'] != '' and self._config[
'header_extra1_val'] != '':
header[self._config['header_extra1_name']] = self._config[
'header_extra1_val']
if self._config['header_extra2_name'] != '' and self._config[
'header_extra2_val'] != '':
header[self._config['header_extra2_name']] = self._config[
'header_extra2_val']
return header
def python2obspy(self):
from obspy.core.trace import Stats, Trace
from obspy.core.utcdatetime import UTCDateTime
s = Stats()
s.network = self.network
s.station = self.station
s.location = self.location
s.channel = self.channel
s.sampling_rate = self.sampling_rate
s.starttime = UTCDateTime(self.starttime)
s.npts = len(self.data)
misc_fields = dict()
if 'CALIB' in self.misc_fields:
s.calib = self.misc_fields.pop('CALIB')
s.update(self.misc_fields)
return Trace(self.data[:], header=s)
def python2obspy(self):
from obspy.core.trace import Stats, Trace
from obspy.core.utcdatetime import UTCDateTime
s = Stats()
s.network = self.network
s.station = self.station
s.location = self.location
s.channel = self.channel
s.sampling_rate = self.sampling_rate
s.starttime = UTCDateTime(self.starttime)
s.npts = len(self.data)
misc_fields = dict()
if 'CALIB' in self.misc_fields:
s.calib = self.misc_fields.pop('CALIB')
s.update(self.misc_fields)
return Trace(self.data[:], header=s)
def _init_header(self, path):
config = _load_config(path)
metadata = config['metadata']
header = Stats()
if 'ATS9440' in config['plugins'].keys():
ats_config = config['plugins']['ATS9440']['config']
elif 'ATS660' in config['plugins'].keys():
ats_config = config['plugins']['ATS660']['config']
else:
raise KeyError('Cannot locate trace config data')
if 'Polytec' in config['plugins'].keys():
polytec_config = config['plugins']['Polytec']['config']
else:
raise KeyError('Cannot locate vibrometer config data')
header.sampling_rate = _calc_sampling_rate(ats_config['sample_rate'])
header.npts = int(ats_config['pre_trigger_samples'] +
ats_config['post_trigger_samples']) - 1
if polytec_config['dd_300']:
header.calib = float(re.findall(
_NUMBER, polytec_config['dd_300_range'])[0])
elif polytec_config['dd_900']:
header.calib = float(re.findall(
_NUMBER, polytec_config['dd_900_range'])[0])
elif polytec_config['vd_08']:
header.calib = float(re.findall(
_NUMBER, polytec_config['vd_08_range'])[0])
elif polytec_config['vd_09']:
header.calib = float(re.findall(
_NUMBER, polytec_config['vd_09_range'])[0])
else:
raise KeyError('Cannot locate vibrometer calibration data')
header.comments = str(config['comments'])
header.place = metadata
if self._config['header_extra1_name'] != '' and self._config['header_extra1_val'] != '':
header[self._config['header_extra1_name']
] = self._config['header_extra1_val']
if self._config['header_extra2_name'] != '' and self._config['header_extra2_val'] != '':
header[self._config['header_extra2_name']
] = self._config['header_extra2_val']
return header
def to_sac_and_mseed(self, export_path, station_number, force_without_loc):
# Check if file exist
export_path_sac = export_path + self.get_export_file_name() + ".sac"
export_path_msd = export_path + self.get_export_file_name() + ".mseed"
#export_path_wav = export_path + self.get_export_file_name() + ".wav"
if os.path.exists(export_path_sac) and os.path.exists(export_path_msd):
return
# Check if the station location have been calculated
if self.station_loc is None and not force_without_loc:
print self.get_export_file_name() + ": Skip sac/mseed generation, wait the next ascent to compute location"
return
# Fill header info
stats = Stats()
stats.sampling_rate = self.decimated_fs
stats.network = "MH"
stats.station = station_number
stats.starttime = self.date
stats.sac = dict()
if not force_without_loc:
stats.sac["stla"] = self.station_loc.latitude
stats.sac["stlo"] = self.station_loc.longitude
stats.sac["stdp"] = self.depth
stats.sac["user0"] = self.snr
stats.sac["user1"] = self.criterion
stats.sac["iztype"] = 9 # 9 == IB in sac format
# Save data into a Stream object
trace = Trace()
trace.stats = stats
trace.data = self.data
stream = Stream(traces=[trace])
# Save stream object
print export_path_sac
stream.write(export_path_sac, format='SAC')
print export_path_msd
stream.write(export_path_msd, format='MSEED')
def synthetic_seismogram(Mw,
duration=0.1,
sampling_rate=10000,
vp=5000.0,
vs=3500.0,
rho=2400,
SSD=1,
pwave=True):
"""
Create a synthetic displacement pulse at the source seismogram based
on the brune model (Brune 1970).
This model is extensively used and generally agrees with observations from
many different setting and over a large range of magnitude.
The displacement time function, u(t), is expressed as follows:
u(t) = A_0 x t x omega_0 x H(t) * exp(-t x omega_0) ,
where t the time, omega_0, the angular frequency and H(t) is the
heavyside function. Note that the angular frequency is calculated from
the static stress drop (SSD). A0 is given by the following
equation:
A0 = M0 / (4 * pi * rho * v ** 3)
References for further reading:
- Routine data processing in earthquake seismology
- Relating Peak Particle Velocity and Acceleration to Moment Magnitude
in Passive (Micro-) Seismic Monitoring
(www.bcengineers.com/images/BCE_Technical_Note_3.pdf)
:param Mw: the moment magnitude of the seismic event (default: -1),
this value determine the wave amplitude and the
frequency content
:type Mw: float
:param noise_level: level of gaussian noise to add to the synthetic
seismogram (default: 1e-5)
:type noise_level: float
:param duration: duration of the seismogram in seconds (default: 0.1),
the pulse is centered at zero
:type duration: float
:param sampling_rate: sampling rate in Hz of the generated time series (
default: 10000)
:type sampling_rate: int
:param vp: P-wave velocity of the material at the source (default: 5000 m/s)
:type vp: float
:param vs: S-wave velocity of the material at the source (default: 3500 m/s)
:type vs: float
:param rho: density of the material at the source in kg/m**3 (default:
2400 kg/m**3)
:param SSD: Static stress drop in "bar" (default: 1 bar)
:type SSD: float
:param pwave: Return P-wave displacement seismogram if True and S-wave
displacement seismogram if false
:rparam: tuple Obspy Trace containing the seismogram
:rtype: Obspy Trace
.. note::
The velocity and acceleration can easily be obtained by
differentiating the trace using the Obspy Trace method
differentiate.
Example
>>> tr = synthetic_seismogram(-1)
>>> tr.differentiate() # this creates a velocity trace
>>> tr.differentiate() # this creates an acceleration trace
This operation is performed in place on the actual data arrays. The
raw data is not accessible anymore afterwards. To keep your
original data, use :meth:`~obspy.core.trace.Trace.copy` to create
a copy of your trace object.
This also makes an entry with information on the applied processing
in ``stats.processing`` of this trace.
"""
M0 = Mw2M0(Mw)
(f0p, f0s) = corner_frequency(Mw, vp, vs, SSD)
# duration = 5 / f0p
npts = duration * sampling_rate
t = np.arange(npts) / sampling_rate
if pwave:
W0 = 2 * np.pi * f0p
v = vp
else:
W0 = 2 * np.pi * f0s
v = vs
A0 = M0 / (4 * np.pi * rho * v**3)
data = A0 * t * W0**2 * np.exp(-t * W0)
data = np.roll(data, len(data) / 2)
stats = Stats()
stats.sampling_rate = sampling_rate
stats.npts = 2 * npts - 1
from uquake.core.util.cepstrum import minimum_phase
minphase_data = np.roll(minimum_phase(data, len(data)), len(data) / 2)
return Trace(data=data, header=stats)
def invert_raw():
######################################
# Binary
######################################
if mode == "Binary":
catch_files = []
files = glob.glob(file_path + "*")
for file in files:
catch = re.findall(
".*[0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}_[0-9]{2}_[0-9]{2}\.[0-9]{6}",
file)
if len(catch) > 0:
catch_files.append(file)
######################################
# Freq file
######################################
freq_file = glob.glob(file_path + "*_freq")
if len(freq_file) > 1:
print "warning : more than one freq file in folder"
if len(freq_file) == 0:
print "warning no freq file discovered use :" + str(sampling_freq)
else:
content = "40.000000"
with open(freq_file[0], "r") as f:
content = f.read()
sampling_freq = float(content)
print "Sampling used : " + str(sampling_freq)
files_nb = len(catch_files)
file_offset = 1
for catch_file in catch_files:
print catch_file
print "File nb : " + str(file_offset) + "/" + str(files_nb)
date = UTCDateTime(
re.findall(
".*([0-9]{4}-[0-9]{2}-[0-9]{2}T[0-9]{2}_[0-9]{2}_[0-9]{2}\.[0-9]{6})",
catch_file)[0])
rawdata = numpy.fromfile(catch_file, numpy.int32)
######################################
# Plot plotly file
######################################
# Add acoustic values to the graph
#data_line = graph.Scattergl(x=[date + i/sampling_freq for i in range(0,len(rawdata))],
# y=rawdata,
# name="counts",
# line=dict(color='blue', width=2),
# mode='lines')
#plotlydata = [data_line]
#layout = graph.Layout(title="Plot",
# xaxis=dict(title='Date', titlefont=dict(size=18)),
# yaxis=dict(title='Counts', titlefont=dict(size=18)),
# hovermode='closest'
# )
#plotly.plot({'data': plotlydata, 'layout': layout},
# filename=catch_file + ".html",
# auto_open=False)
######################################
# Create SAC file
######################################
# Fill header info
stats = Stats()
stats.sampling_rate = sampling_freq
stats.network = "test"
stats.station = 0
stats.starttime = date
stats.sac = dict()
# Save data into a Stream object
trace = Trace()
trace.stats = stats
trace.data = rawdata
stream = Stream(traces=[trace])
# Save stream object
stream.write(catch_file + ".sac", format='SAC')
stream.write(catch_file + ".mseed", format='MSEED')
file_offset = file_offset + 1
else:
######################################
# Text
######################################
#filename = "tool_invert_raw/1553771378.490936"
#date = UTCDateTime(1553771378.490936)
# text
#f = open(filename, 'r')
#rawdata = numpy.array(f.read().rstrip('\n').split('\n'))
#f.close()
# binary
######################################
# Plot plotly file
######################################
# Add acoustic values to the graph
data_line = graph.Scattergl(
x=[date + i / sampling_freq for i in range(0, len(rawdata))],
y=rawdata,
name="counts",
line=dict(color='blue', width=2),
mode='lines')
plotlydata = [data_line]
layout = graph.Layout(title="Plot",
xaxis=dict(title='Date',
titlefont=dict(size=18)),
yaxis=dict(title='Counts',
titlefont=dict(size=18)),
hovermode='closest')
plotly.plot({
'data': plotlydata,
'layout': layout
},
filename=filename + ".html",
auto_open=False)
######################################
# Create SAC file
######################################
# Fill header info
stats = Stats()
stats.sampling_rate = sampling_freq
stats.network = "test"
stats.station = 0
stats.starttime = date
stats.sac = dict()
# Save data into a Stream object
trace = Trace()
trace.stats = stats
trace.data = rawdata
stream = Stream(traces=[trace])
# Save stream object
stream.write(filename + ".sac", format='SAC')
def attach_obspy_trace_stats(self, kstnm, kinst, force_without_loc=False):
'''Attaches attribute: obspy_trace_stats, an obspy.core.trace.Stats instance.
obspy_trace_stats holds metadata common to both miniSEED and SAC formats.
obspy_trace_stats.sac holds extra metadata only found in the SAC format.
Floats are NOT converted to np.float32() in either case.
NB: the SAC header value shown to the world (e.g., "sac.delta"), and the private SAC header
written to disk (e.g., "sac._hf[0]"), differ in type. The relevant float header values that
actually get written to disk with sac.write are stored in the private "._hf" attribute,
which is not generated with initialization of the raw Stats() container. Therefore, if
printing those values to, e.g. a text file, ensure the relevant F (float) fields are cast to
np.float32 first.
For example:
>> from obspy.core.trace import Trace
>> from obspy.io.sac.sactrace import SACTrace
>> trace = Trace()
>> sac = SACTrace.from_obspy_trace(trace) <-- this gets called by sac.write (within stream.write)
>> sac.delta = 1/20
>> isinstance(sac.delta, float) <-- True: this is the public attr shown to the world
>> isinstance(sac.delta, np.float32) <-- False
>> isinstance(sac._hf[0], float) <-- False
>> isinstance(sac._hf[0], np.float32) <-- True: this is the private attr written to disk
For more detail see: http://www.adc1.iris.edu/files/sac-manual/manual/file_format.html
Update function `events.write_metadata` if the fields in this method are changed.
'''
# Fill metadata common to SAC and miniSEED formats
stats = Stats()
stats.network = utils.network()
stats.station = kstnm
stats.location = "00"
stats.channel = utils.band_code(
self.decimated_fs) + "DH" # SEED manual Appendix A
stats.starttime = self.corrected_starttime
stats.sampling_rate = self.decimated_fs
stats.npts = len(self.processed_data)
# Extra metadata, some of which is only written to SAC files
keys = [
'stla', 'stlo', 'stel', 'stdp', 'scale', 'cmpaz', 'cmpinc',
'user0', 'user1', 'user2', 'user3', 'kinst', 'kuser0', 'kuser1',
'kuser2'
]
def_float = -12345.
# Default SAC header (we may not will not fill all of these keys)
stats.sac = dict.fromkeys(keys, def_float)
# Fill station-location header fields.
if not force_without_loc:
stats.sac["stla"] = self.station_loc.latitude
stats.sac["stlo"] = self.station_loc.longitude
# Elevation is 0 (our reference is truly sea level)
stats.sac["stel"] = 0
# Add scaling factor to convert digital counts to Pa
stats.sac["scale"] = utils.sacpz_const()
# Add dip (CMPINC; "component incidence") in SAC dip convention, using as guide:
# https://github.com/iris-edu/mseed2sac/blob/master/doc/mseed2sac.md
#
# SAC dip convention: "degrees down from vertical up/outward",
# i.e., BHN, BHE = 90, BHZ = 0
#
# SEED dip convection: "degrees down from horizontal"
# i.e., BHN, BHE = 0, BHZ = -90
stats.sac["cmpinc"] = 0 # SAC dip
# Add azimuth: horizontal projection of component vector measured clockwise from north
# It is 0 for vertical components. Theoretically, BHN, BHZ = 90, BHE = 90
stats.sac["cmpaz"] = 0
# NB: I checked how IRIS serves up hydrophone data (in MATLAB):
# >> s = irisFetch.Stations('channel', '*', '*', '*', '?DH')
#
# For all 3233 channels from 2147 stations that were returned:
# dip = -90, 0, or 90
# azimuth = 0 or 360
#
# For dip = -90, I assume that is the SEED dip convention
# For dip = +90, I do not know; I thought perhaps it might be some(thing like a?)
# right-hand-rule convention, but not all +90 dips are associated with 360 azimuth
# REQ events do not record their depth at the time of acquisition, and because the onboard
# detection algorithm was not triggered there are no trigger parameters to report
if not self.is_requested:
stats.sac[
"stdp"] = self.depth # meters (from external pressure sensor; down is positive)
stats.sac["user0"] = self.snr
stats.sac["user1"] = self.criterion
stats.sac["user2"] = self.trig # sample index
# Clock drift correction, which is the 'Time correction' applied in the 48-byte
# fixed header in utils.set_mseed_time_correction()
stats.sac[
"user3"] = self.clockdrift_correction # = self.mseed_time_correction
# Generic instrument (e.g., '452.020')
stats.sac['kinst'] = kinst
# automaid version number
stats.sac["kuser0"] = self.__version__
# String describing detection/request status, and number of wavelet scales transmitted
# (e.g., 'DET.WLT5')
reqdet_scales = self.processed_file_name.split('.')[-2:]
stats.sac['kuser1'] = '.'.join(reqdet_scales)
# String detailing the type of (i)CDF24 transform: edge correction and
# normalization
stats.sac[
'kuser2'] = 'ec' + self.edges_correction + 'norm' + self.normalized
# Attach Stats to events object
self.obspy_trace_stats = stats
def dorange (self):
# load batches
print "mkms: loading batches.."
self.bdatas = []
for i in self.ids:
d = Dat ()
d.read (os.path.join (self.root, str(i) + '.DAT'))
self.bdatas.append (d.bdata)
# set up datastream
print "mkms: setting up stream for %s.." % self.station,
self.st = Stream ()
for bd in self.bdatas:
for b in bd.batches:
s = Stats ()
s.sampling_rate = self.sampling_rate
s.npts = b.length
s.network = self.network
s.location = self.location
s.station = self.station
s.channel = self.channel
s.starttime = UTCDateTime ((b.ref / 1000000.0))
t = Trace (data = numpy.array (b.samples_i, dtype = numpy.int32), header = s)
self.st.append (t)
print "done."
# generate file name
self.name = self.st[0].id.replace ('.', '_')
self.start = self.st[0].stats.starttime
self.name = self.start.strftime ("%Y-%m-%d-%H%M-%S") + '.' + self.name
if self.optplot:
self.plot ()
if not self.optnowrite:
print "mkms: writing %s.mseed.." % self.name,
if not os.path.exists (self.destdir):
os.makedirs (self.destdir)
self.st.write (os.path.join (self.destdir, self.name + '.mseed'), format = 'MSEED', encoding = 'INT32', byteorder = 1, flush = 1, verbose = 0)
print "done."
# write ids and refs
idsf = open (os.path.join (self.destdir, self.name + '.ids'), 'w')
refsf = open (os.path.join (self.destdir, self.name + '.refs'), 'w')
for bd in self.bdatas:
idsf.write ("%d,%d\n" % (bd.id, 1 if bd.e_sdlag else 0))
for b in bd.batches:
refsf.write ("%d,%d,%d,%d,%s,%s,%s,%s,%s,%d\n" % (bd.id, b.no, b.ref, b.status, b.latitude[:-2], b.latitude[-2:], b.longitude[:-2], b.longitude[-2:], b.checksum, 1 if b.checksum_pass else 0))
idsf.close ()
refsf.close ()
return (self.name + '.mseed', idsf, refsf)
else:
print "mkms: would write %s.mseed (disabled)." % os.path.join (self.destdir, self.name)
return None
traceN = Trace(ax)
traceE = Trace(az)
if PlotUnit == 'VEL':
#integrate accerelation if plotting velocity
traceN.integrate(method='cumtrapz')
traceE.integrate(method='cumtrapz')
plottheta = theta[thetacount] * 180 / np.pi
thetacount = thetacount + 1
# store stats
stationname = sta + '%04d' % i
channelnameN = cha + '%s' % 'N'
channelnameE = cha + '%s' % 'E'
# for NS components
statsN = Stats()
statsN.sampling_rate = 1.0 / sampling_rate_x
statsN.delta = sampling_rate_x
statsN.starttime = starttime
statsN.npts = len(traceN.data)
statsN.network = net
statsN.station = stationname
statsN.location = ''
statsN.channel = channelnameN
traceN.stats = statsN
traceN.stats.sac = obspy.core.AttribDict()
traceN.stats.sac.back_azimuth = plottheta # use this as azimuth of station
#---applying filters---#
traceN.filter('bandpass', freqmin=freqmin, freqmax=freqmax)
tN = traceN.stats.starttime
traceN.trim(starttime=tN, endtime=tN + trim_end_time)
