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 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 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 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 read_TEXCEL_CSV(filename, **kwargs):
"""
Reads a texcel csv file and returns a uquake Stream object.
.. warning::
This function should NOT be called directly, it registers via the
uquake :func:`~uquake.core.stream.read` function, call this
instead.
:param filename: the path to the file
:param kwargs:
:return: ~uquake.core.stream.Stream
"""
with open(filename) as fle:
x = []
y = []
z = []
for k, line in enumerate(fle):
if k == 0:
if 'MICROPHONE' in line:
offset = 9
else:
offset = 8
# header
if k < 2:
continue
val = line.strip().split(',')
# relative time
if k == 3:
rt0 = timedelta(seconds=float(val[0]))
elif k == 6:
station = str(eval(val[offset]))
elif k == 7:
date = val[offset]
elif k == 8:
date_time = date + " " + val[offset]
datetime = parse(date_time)
starttime = datetime + rt0
elif k == 9:
site = val[offset]
elif k == 10:
location = val[offset]
elif k == 17:
sensitivity_x = float(val[offset])
sensitivity_y = float(val[offset + 1])
sensitivity_z = float(val[offset + 2])
elif k == 18:
range_x = float(val[offset])
range_y = float(val[offset + 1])
range_z = float(val[offset + 2])
elif k == 19:
trigger_x = float(val[offset])
trigger_y = float(val[offset + 1])
trigger_z = float(val[offset + 2])
elif k == 20:
si_x = float(val[offset])
si_y = float(val[offset + 1])
si_z = float(val[offset + 2])
elif k == 21:
sr_x = float(val[offset])
sr_y = float(val[offset + 1])
sr_z = float(val[offset + 2])
x.append(float(val[1]))
y.append(float(val[2]))
z.append(float(val[3]))
x = np.array(x)
y = np.array(y)
z = np.array(z)
stats = Stats()
stats.network = site
stats.delta = si_x / 1000.0
stats.npts = len(x)
stats.location = location
stats.station = station
stats.starttime = UTCDateTime(starttime)
stats.channel = 'radial'
tr_x = Trace(data=x / 1000.0, header=stats)
stats.delta = si_y / 1000.0
stats.channel = 'transverse'
tr_y = Trace(data=y / 1000.0, header=stats)
stats.delta = si_z / 1000.0
stats.channel = 'vertical'
tr_z = Trace(data=z / 1000.0, header=stats)
return Stream(traces=[tr_x, tr_y, tr_z])
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
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)
traceN.taper(0.05, side='right')
#----------------------#
stemp += Stream(traceN)
# for EW components
