def Amp_pick_sfile(sfile,
datapath,
respdir,
chans=['Z'],
var_wintype=True,
winlen=0.9,
pre_pick=0.2,
pre_filt=True,
lowcut=1.0,
highcut=20.0,
corners=4):
"""
Function to read information from a SEISAN s-file, load the data and the \
picks, cut the data for the channels given around the S-window, simulate \
a Wood Anderson seismometer, then pick the maximum peak-to-trough \
amplitude.
Output will be put into a mag_calc.out file which will be in full S-file \
format and can be copied to a REA database.
:type sfile: string
:type datapath: string
:param datapath: Path to the waveform files - usually the path to the WAV \
directory
:type respdir: string
:param respdir: Path to the response information directory
:type chans: List of strings
:param chans: List of the channels to pick on, defaults to ['Z'] - should \
just be the orientations, e.g. Z,1,2,N,E
:type var_wintype: bool
:param var_wintype: If True, the winlen will be \
multiplied by the P-S time if both P and S picks are \
available, otherwise it will be multiplied by the \
hypocentral distance*0.34 - dervided using a p-s ratio of \
1.68 and S-velocity of 1.5km/s to give a large window, \
defaults to True
:type winlen: float
:param winlen: Length of window, see above parameter, if var_wintype is \
False then this will be in seconds, otherwise it is the \
multiplier to the p-s time, defaults to 0.5.
:type pre_pick: float
:param pre_pick: Time before the s-pick to start the cut window, defaults \
to 0.2
:type pre_filt: bool
:param pre_filt: To apply a pre-filter or not, defaults to True
:type lowcut: float
:param lowcut: Lowcut in Hz for the pre-filter, defaults to 1.0
:type highcut: float
:param highcut: Highcut in Hz for the pre-filter, defaults to 20.0
:type corners: int
:param corners: Number of corners to use in the pre-filter
"""
# Hardwire a p-s multiplier of hypocentral distance based on p-s ratio of
# 1.68 and an S-velocity 0f 1.5km/s, deliberately chosen to be quite slow
ps_multiplier = 0.34
from eqcorrscan.utils import Sfile_util
from obspy import read
from scipy.signal import iirfilter
from obspy.signal.invsim import paz2AmpValueOfFreqResp
import warnings
# First we need to work out what stations have what picks
event = Sfile_util.readpicks(sfile)[0]
# Convert these picks into a lists
stations = [] # List of stations
channels = [] # List of channels
picktimes = [] # List of pick times
picktypes = [] # List of pick types
distances = [] # List of hypocentral distances
picks_out = []
for pick in event.picks:
if pick.phase_hint in ['P', 'S']:
picks_out.append(pick) # Need to be able to remove this if there
# isn't data for a station!
stations.append(pick.waveform_id.station_code)
channels.append(pick.waveform_id.channel_code)
picktimes.append(pick.time)
picktypes.append(pick.phase_hint)
arrival = [
arrival for arrival in event.origins[0].arrivals
if arrival.pick_id == pick.resource_id
]
distances.append(arrival.distance)
# Read in waveforms
stream = read(datapath + '/' + Sfile_util.readwavename(sfile)[0])
if len(Sfile_util.readwavename(sfile)) > 1:
for wavfile in Sfile_util.readwavename(sfile):
stream += read(datapath + '/' + wavfile)
stream.merge() # merge the data, just in case!
# For each station cut the window
uniq_stas = list(set(stations))
del arrival
for sta in uniq_stas:
for chan in chans:
print 'Working on ' + sta + ' ' + chan
tr = stream.select(station=sta, channel='*' + chan)
if not tr:
# Remove picks from file
# picks_out=[picks_out[i] for i in xrange(len(picks))\
# if picks_out[i].station+picks_out[i].channel != \
# sta+chan]
warnings.warn('There is no station and channel match in the ' +
'wavefile!')
break
else:
tr = tr[0]
# Apply the pre-filter
if pre_filt:
try:
tr.detrend('simple')
except:
dummy = tr.split()
dummy.detrend('simple')
tr = dummy.merge()[0]
tr.filter('bandpass',
freqmin=lowcut,
freqmax=highcut,
corners=corners)
sta_picks = [
i for i in xrange(len(stations)) if stations[i] == sta
]
pick_id = event.picks[sta_picks[0]].resource_id
arrival = [
arrival for arrival in event.origins[0].arrivals
if arrival.pick_id == pick_id
]
hypo_dist = arrival.distance
CAZ = arrival.azimuth
if var_wintype:
if 'S' in [picktypes[i] for i in sta_picks] and\
'P' in [picktypes[i] for i in sta_picks]:
# If there is an S-pick we can use this :D
S_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'S'
]
S_pick = min(S_pick)
P_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'P'
]
P_pick = min(P_pick)
try:
tr.trim(starttime=S_pick - pre_pick,
endtime=S_pick + (S_pick - P_pick) * winlen)
except:
break
elif 'S' in [picktypes[i] for i in sta_picks]:
S_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'S'
]
S_pick = min(S_pick)
P_modelled = S_pick - hypo_dist * ps_multiplier
try:
tr.trim(starttime=S_pick - pre_pick,
endtime=S_pick +
(S_pick - P_modelled) * winlen)
except:
break
else:
# In this case we only have a P pick
P_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'P'
]
P_pick = min(P_pick)
S_modelled = P_pick + hypo_dist * ps_multiplier
try:
tr.trim(starttime=S_modelled - pre_pick,
endtime=S_modelled +
(S_modelled - P_pick) * winlen)
except:
break
# Work out the window length based on p-s time or distance
elif 'S' in [picktypes[i] for i in sta_picks]:
# If the window is fixed we still need to find the start time,
# which can be based either on the S-pick (this elif), or
# on the hypocentral distance and the P-pick
# Take the minimum S-pick time if more than one S-pick is
# available
S_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'S'
]
S_pick = min(S_pick)
try:
tr.trim(starttime=S_pick - pre_pick,
endtime=S_pick + winlen)
except:
break
else:
# In this case, there is no S-pick and the window length is
# fixed we need to calculate an expected S_pick based on the
# hypocentral distance, this will be quite hand-wavey as we
# are not using any kind of velocity model.
P_pick = [
picktimes[i] for i in sta_picks if picktypes[i] == 'P'
]
P_pick = min(P_pick)
hypo_dist = [
distances[i] for i in sta_picks if picktypes[i] == 'P'
][0]
S_modelled = P_pick + hypo_dist * ps_multiplier
try:
tr.trim(starttime=S_modelled - pre_pick,
endtime=S_modelled + winlen)
except:
break
# Find the response information
resp_info = _find_resp(tr.stats.station, tr.stats.channel,
tr.stats.network, tr.stats.starttime,
tr.stats.delta, respdir)
PAZ = []
seedresp = []
if resp_info and 'gain' in resp_info:
PAZ = resp_info
elif resp_info:
seedresp = resp_info
# Simulate a Wood Anderson Seismograph
if PAZ and len(tr.data) > 10:
# Set ten data points to be the minimum to pass
tr = _sim_WA(tr, PAZ, None, 10)
elif seedresp and len(tr.data) > 10:
tr = _sim_WA(tr, None, seedresp, 10)
elif len(tr.data) > 10:
warnings.warn('No PAZ for ' + tr.stats.station + ' ' +
tr.stats.channel + ' at time: ' +
str(tr.stats.starttime))
continue
if len(tr.data) <= 10:
# Should remove the P and S picks if len(tr.data)==0
warnings.warn('No data found for: ' + tr.stats.station)
# print 'No data in miniseed file for '+tr.stats.station+\
# ' removing picks'
# picks_out=[picks_out[i] for i in xrange(len(picks_out))\
# if i not in sta_picks]
break
# Get the amplitude
amplitude, period, delay = _max_p2t(tr.data, tr.stats.delta)
if amplitude == 0.0:
break
print 'Amplitude picked: ' + str(amplitude)
# Note, amplitude should be in meters at the moment!
# Remove the pre-filter response
if pre_filt:
# Generate poles and zeros for the filter we used earlier: this
# is how the filter is designed in the convenience methods of
# filtering in obspy.
z, p, k = iirfilter(corners, [
lowcut / (0.5 * tr.stats.sampling_rate), highcut /
(0.5 * tr.stats.sampling_rate)
],
btype='band',
ftype='butter',
output='zpk')
filt_paz = {
'poles': list(p),
'zeros': list(z),
'gain': k,
'sensitivity': 1.0
}
amplitude /= (paz2AmpValueOfFreqResp(filt_paz, 1 / period) *
filt_paz['sensitivity'])
# Convert amplitude to mm
if PAZ: # Divide by Gain to get to nm (returns pm? 10^-12)
# amplitude *=PAZ['gain']
amplitude /= 1000
if seedresp: # Seedresp method returns mm
amplitude *= 1000000
# Write out the half amplitude, approximately the peak amplitude as
# used directly in magnitude calculations
# Page 343 of Seisan manual:
# Amplitude (Zero-Peak) in units of nm, nm/s, nm/s^2 or counts
amplitude *= 0.5
# Generate a PICK type object for this pick
picks_out.append(
Sfile_util.PICK(station=tr.stats.station,
channel=tr.stats.channel,
impulsivity=' ',
phase='IAML',
weight='',
polarity=' ',
time=tr.stats.starttime + delay,
coda=999,
amplitude=amplitude,
peri=period,
azimuth=float('NaN'),
velocity=float('NaN'),
AIN=999,
SNR='',
azimuthres=999,
timeres=float('NaN'),
finalweight=999,
distance=hypo_dist,
CAZ=CAZ))
# Copy the header from the sfile to a new local S-file
fin = open(sfile, 'r')
fout = open('mag_calc.out', 'w')
for line in fin:
if not line[79] == '7':
fout.write(line)
else:
fout.write(line)
break
fin.close()
for pick in picks_out:
fout.write(pick)
# Note this uses the legacy pick class
fout.close()
# Write picks out to new s-file
for pick in picks_out:
print pick
# Sfile_util.populateSfile('mag_calc.out', picks_out)
return picks_out
def lag_calc(detections, detect_data, templates, shift_len=0.2, min_cc=0.4):
"""
Overseer function to take a list of detection objects, cut the data for
them to lengths of the same length of the template + shift_len on
either side. This will then write out SEISAN s-file for the detections
with pick times based on the lag-times found at the maximum correlation,
providing that correlation is above the min_cc.
:type detections: List of DETECTION
:param detections: List of DETECTION objects
:type detect_data: obspy.Stream
:param detect_data: All the data needed to cut from - can be a gappy Stream
:type templates: List of tuple of String, obspy.Stream
:param templates: List of the templates used as tuples of template name, template
:type shift_len: float
:param shift_len: Shift length allowed for the pick in seconds, will be
plus/minus this amount - default=0.2
:type min_cc: float
:param min_cc: Minimum cross-correlation value to be considered a pick,
default=0.4
"""
from eqcorrscan.utils import Sfile_util
from obspy import Stream
# First work out the delays for each template
delays = [] # List of tuples
for template in templates:
temp_delays = []
for tr in tempate[1]:
temp_delays.append((tr.stats.station, tr.stats.channel,\
tr.stats.starttime-template.sort['starttime'][0].stats.starttime))
delays.append((template[0], temp_delays))
detect_streams = []
for detection in detections:
detect_stream = []
for tr in detect_data:
tr_copy = tr.copy()
template = [
t for t in templates if t[0] == detection.template_name
][0]
template = template.select(station=tr.stats.station,
channel=tr.stats.channel)
if template:
template_len = len(template[0])
else:
continue # If there is no template-data match then skip the rest
# of the trace loop.
delay = [
delay for delay in delays
if delay[0] == detection.template_name
][0]
delay=[d for d in delay if d[0]==tr.stats.station and \
d[1]==tr.stats.channel][0]
detect_stream.append(tr_copy.trim(starttime=detection.detect_time-\
shift_len+delay, endtime=detection.detect_time+delay+\
shift_len+template_len))
detect_streams.append((detection.template_name, Stream(detect_stream)))
# Tuple of template name and data stream
# Segregate detections by template
lags = []
for template in templates:
template_detections=[detect[1] for detect in detect_streams\
if detect[0]==template[0]]
lags.append(day_loop(template_detections, template[1]))
# Write out the lags!
for event in lags:
# I think I have an old version of Sfile_util here
sfilename = Sfile_util.blanksfile(wavefile, 'L', 'PYTH', 'out', True)
picks = []
for pick in event:
picks.append(Sfile_util.PICK())
Sfile_util.populateSfile(sfilename, picks)