def rot_12_NE(st: Stream, meta):
'''
Performs a 12 -> NE rotation.
:param st: A stream containing the traces to rotate.
:param dict meta: Dictionary contaning the metadata for all streams.
:return: The rotated streams.
'''
st2 = st.select(channel="??1")
for tr in st2:
id1 = tr.id
id2 = id1[:-1] + '2'
tr1 = tr
try:
tr2 = st.select(id=id2)[0]
except IndexError:
st.remove(tr)
logger.warning("%s Channel 2 not found. Impossible to rotate",
tr.id)
continue
timeA = max(tr1.stats.starttime, tr2.stats.starttime)
timeB = min(tr1.stats.endtime, tr2.stats.endtime)
tr1.trim(timeA, timeB)
tr2.trim(timeA, timeB)
azi = meta[id1]['azimuth']
tr1.data, tr2.data = rot2D(tr1.data, tr2.data, -azi)
return st
def test_xyzalgorithm_uneccesary_channel_empty():
"""XYZAlgorithm_test.test_xyzalgorithm_uneccesary_channel_gaps()
confirms the process will run when an uneccesary channel is input
but contains gaps or is completely empty. ie. gaps in 'Z' channel
or and empty 'F' channel. This also makes sure the 'Z' and 'F' channels
are passed without any modification.
"""
algorithm = XYZAlgorithm("obs", "mag")
timeseries = Stream()
timeseries += __create_trace("H", [1, 1])
timeseries += __create_trace("E", [1, 1])
timeseries += __create_trace("Z", [1, np.NaN])
timeseries += __create_trace("F", [np.NaN, np.NaN])
outstream = algorithm.process(timeseries)
assert_equal(
outstream.select(channel="Z")[0].data.all(),
timeseries.select(channel="Z")[0].data.all(),
)
assert_equal(
outstream.select(channel="F")[0].data.all(),
timeseries.select(channel="F")[0].data.all(),
)
ds = outstream.select(channel="D")
# there is 1 trace
assert_equal(len(ds), 1)
d = ds[0]
# d has 2 values (same as input)
assert_equal(len(d.data), 2)
# d has no NaN values
assert_equal(np.isnan(d).any(), False)
def test_xyzalgorithm_uneccesary_channel_empty():
"""XYZAlgorithm_test.test_xyzalgorithm_uneccesary_channel_gaps()
confirms the process will run when an uneccesary channel is input
but contains gaps or is completely empty. ie. gaps in 'Z' channel
or and empty 'F' channel. This also makes sure the 'Z' and 'F' channels
are passed without any modification.
"""
algorithm = XYZAlgorithm('obs', 'mag')
timeseries = Stream()
timeseries += __create_trace('H', [1, 1])
timeseries += __create_trace('E', [1, 1])
timeseries += __create_trace('Z', [1, np.NaN])
timeseries += __create_trace('F', [np.NaN, np.NaN])
outstream = algorithm.process(timeseries)
assert_equals(outstream.select(channel='Z')[0].data.all(),
timeseries.select(channel='Z')[0].data.all())
assert_equals(outstream.select(channel='F')[0].data.all(),
timeseries.select(channel='F')[0].data.all())
ds = outstream.select(channel='D')
# there is 1 trace
assert_equals(len(ds), 1)
d = ds[0]
# d has 2 values (same as input)
assert_equals(len(d.data), 2)
# d has no NaN values
assert_equals(np.isnan(d).any(), False)
def save_raw(saved: dict, st: Stream, rawloc: str, inv: Inventory,
saveasdf: bool):
"""
Save the raw waveform data in the desired format.
The point of this function is mainly that the waveforms will be saved
with the correct associations and at the correct locations.
:param saved: Dictionary holding information about the original streams
to identify them afterwards.
:type saved: dict
:param st: obspy stream holding all data (from various stations)
:type st: Stream
:param rawloc: Parental directory (with phase) to save the files in.
:type rawloc: str
:param inv: The inventory holding all the station information
:type inv: Inventory
:param saveasdf: If True the data will be saved in asdf format.
:type saveasdf: bool
"""
# Just use the same name
for evt, startt, endt, net, stat in zip(saved['event'], saved['startt'],
saved['endt'], saved['net'],
saved['stat']):
# earlier we downloaded all locations, but we don't really want
# to have several, so let's just keep one
try:
sst = st.select(network=net, station=stat)
# This might actually be empty if so, let's just skip
if sst.count() == 0:
logging.debug(f'No trace of {net}.{stat} in Stream.')
continue
slst = sst.slice(startt, endt)
# Only write the prevelant location
locs = [tr.stats.location for tr in sst]
filtloc = max(set(locs), key=locs.count)
sslst = slst.select(location=filtloc)
if saveasdf:
sinv = inv.select(net, stat, starttime=startt, endtime=endt)
write_st(sslst, evt, rawloc, sinv)
else:
save_raw_mseed(evt, sslst, rawloc, net, stat)
except Exception as e:
logging.error(e)
def analyze_data(families, staloc, nhour, t1, duration, dt, ncpu, icpu):
"""
"""
nfamilies = int(ceil(len(families) / ncpu))
ibegin = icpu * nfamilies
iend = min((icpu + 1) * nfamilies, len(families))
for i in range(ibegin, iend):
# Create directory to store the LFEs times
namedir = 'LFEs/' + families['family'].iloc[i]
if not os.path.exists(namedir):
os.makedirs(namedir)
# File to write error messages
namedir = 'error'
if not os.path.exists(namedir):
os.makedirs(namedir)
errorfile = 'error/' + families['family'].iloc[i] + '.txt'
# Create dataframe to store LFE times
df = pd.DataFrame(columns=['year', 'month', 'day', 'hour', \
'minute', 'second', 'cc', 'nchannel'])
# Read the templates
stations = families['stations'].iloc[i].split(',')
templates = Stream()
for station in stations:
data = pickle.load(open(template_dir + '/' + families['family'].iloc[i] + \
'/' + station + '.pkl', 'rb'))
if (len(data) == 3):
EW = data[0]
NS = data[1]
UD = data[2]
EW.stats.station = station
NS.stats.station = station
EW.stats.channel = 'E'
NS.stats.channel = 'N'
templates.append(EW)
templates.append(NS)
else:
UD = data[0]
UD.stats.station = station
UD.stats.channel = 'Z'
templates.append(UD)
# Loop on hours of data
for hour in range(0, nhour):
nchannel = 0
Tstart = t1 + hour * 3600.0
Tend = t1 + (hour + 1) * 3600.0 + duration
delta = Tend - Tstart
ndata = int(delta / dt) + 1
# Get the data
data = []
for station in stations:
try:
D = read('tmp/' + station + '.mseed')
D = D.slice(Tstart, Tend)
namefile = 'tmp/' + station + '.pkl'
orientation = pickle.load(open(namefile, 'rb'))
# Get station metadata for reading response file
for ir in range(0, len(staloc)):
if (station == staloc['station'][ir]):
network = staloc['network'][ir]
channels = staloc['channels'][ir]
location = staloc['location'][ir]
server = staloc['server'][ir]
# Orientation of template
# Date chosen: April 1st 2008
mychannels = channels.split(',')
mylocation = location
if (mylocation == '--'):
mylocation = ''
response = '../data/response/' + network + '_' + station + '.xml'
inventory = read_inventory(response, format='STATIONXML')
reference = []
for channel in mychannels:
angle = inventory.get_orientation(network + '.' + \
station + '.' + mylocation + '.' + channel, \
UTCDateTime(2008, 4, 1, 0, 0, 0))
reference.append(angle)
# Append data to stream
if (type(D) == obspy.core.stream.Stream):
stationdata = fill_data(D, orientation, station, channels, reference)
if (len(stationdata) > 0):
for stream in stationdata:
data.append(stream)
except:
message = 'No data available for station {} '.format( \
station) + 'at time {}/{}/{} - {}:{}:{}\n'.format( \
Tstart.year, Tstart.month, Tstart.day, Tstart.hour, \
Tstart.minute, Tstart.second)
# Loop on channels
for channel in range(0, len(data)):
subdata = data[channel]
# Check whether we have a complete one-hour-long recording
if (len(subdata) == 1):
if (len(subdata[0].data) == ndata):
# Get the template
station = subdata[0].stats.station
component = subdata[0].stats.channel
template = templates.select(station=station, \
component=component)[0]
# Cross correlation
cctemp = correlate.optimized(template, subdata[0])
if (nchannel > 0):
cc = np.vstack((cc, cctemp))
else:
cc = cctemp
nchannel = nchannel + 1
if (nchannel > 0):
# Compute average cross-correlation across channels
meancc = np.mean(cc, axis=0)
if (type_threshold == 'MAD'):
MAD = np.median(np.abs(meancc - np.mean(meancc)))
index = np.where(meancc >= threshold * MAD)
elif (type_threshold == 'Threshold'):
index = np.where(meancc >= threshold)
else:
raise ValueError('Type of threshold must be MAD or Threshold')
times = np.arange(0.0, np.shape(meancc)[0] * dt, dt)
# Get LFE times
if np.shape(index)[1] > 0:
(time, cc) = clean_LFEs(index, times, meancc, dt, freq0)
# Add LFE times to dataframe
i0 = len(df.index)
for j in range(0, len(time)):
timeLFE = Tstart + time[j]
df.loc[i0 + j] = [int(timeLFE.year), int(timeLFE.month), \
int(timeLFE.day), int(timeLFE.hour), \
int(timeLFE.minute), timeLFE.second + \
timeLFE.microsecond / 1000000.0, cc[j], nchannel]
# Add to pandas dataframe and save
namefile = 'LFEs/' + families['family'].iloc[i] + '/catalog.pkl'
if os.path.exists(namefile):
df_all = pickle.load(open(namefile, 'rb'))
df_all = pd.concat([df_all, df], ignore_index=True)
else:
df_all = df
df_all = df_all.astype(dtype={'year':'int32', 'month':'int32', \
'day':'int32', 'hour':'int32', 'minute':'int32', \
'second':'float', 'cc':'float', 'nchannel':'int32'})
pickle.dump(df_all, open(namefile, 'wb'))
def find_LFEs(family_file, station_file, template_dir, tbegin, tend, \
TDUR, duration, filt, freq0, dt, nattempts, waittime, type_threshold='MAD', \
threshold=0.0075):
"""
Find LFEs with the temporary stations from FAME
using the templates from Plourde et al. (2015)
Input:
type family_file = string
family_file = File containing the list of LFE families
type station_file = string
station_file = File containing the list of stations
type template_dir = string
template_dir = Directory where to find the LFE templates
type tbegin = tuplet of 6 integers
tbegin = Time when we begin looking for LFEs
type tend = tuplet of 6 integers
tend = Time we stop looking for LFEs
type TDUR = float
TDUR = Time to add before and after the time window for tapering
type duration = float
duration = Duration of the LFE templates
type filt = tuple of floats
filt = Lower and upper frequencies of the filter
type freq0 = float
freq0 = Maximum frequency rate of LFE occurrence
type dt = float
dt = Time step for the LFE templates
type nattempts = integer
nattempts = Number of times we try to download data
type waittime = positive float
waittime = Type to wait between two attempts at downloading
type type_threshold = string
type_threshold = 'MAD' or 'Threshold'
type threshold = float
threshold = Cross correlation value must be higher than that
Output:
None
"""
# Get the network, channels, and location of the stations
staloc = pd.read_csv(station_file, \
sep=r'\s{1,}', header=None, engine='python')
staloc.columns = ['station', 'network', 'channels', 'location', \
'server', 'latitude', 'longitude', 'time_on', 'time_off']
# Begin and end time of analysis
t1 = UTCDateTime(year=tbegin[0], month=tbegin[1], \
day=tbegin[2], hour=tbegin[3], minute=tbegin[4], \
second=tbegin[5])
t2 = UTCDateTime(year=tend[0], month=tend[1], \
day=tend[2], hour=tend[3], minute=tend[4], \
second=tend[5])
# Number of hours of data to analyze
nhour = int(ceil((t2 - t1) / 3600.0))
# Begin and end time of downloading
Tstart = t1 - TDUR
Tend = t2 + duration + TDUR
# Temporary directory to store the data
namedir = 'tmp'
if not os.path.exists(namedir):
os.makedirs(namedir)
# Download the data from the stations
for ir in range(0, len(staloc)):
station = staloc['station'][ir]
network = staloc['network'][ir]
channels = staloc['channels'][ir]
location = staloc['location'][ir]
server = staloc['server'][ir]
time_on = staloc['time_on'][ir]
time_off = staloc['time_off'][ir]
# File to write error messages
namedir = 'error'
if not os.path.exists(namedir):
os.makedirs(namedir)
errorfile = 'error/' + station + '.txt'
# Check whether there are data for this period of time
year_on = int(time_on[0:4])
month_on = int(time_on[5:7])
day_on = int(time_on[8:10])
year_off = int(time_off[0:4])
month_off = int(time_off[5:7])
day_off = int(time_off[8:10])
if ((Tstart > UTCDateTime(year=year_on, month=month_on, day=day_on)) \
and (Tend < UTCDateTime(year=year_off, month=month_off, day=day_off))):
# First case: we can get the data from IRIS
if (server == 'IRIS'):
(D, orientation) = get_from_IRIS(station, network, channels, \
location, Tstart, Tend, filt, dt, nattempts, waittime, \
errorfile, DATADIR)
# Second case: we get the data from NCEDC
elif (server == 'NCEDC'):
(D, orientation) = get_from_NCEDC(station, network, channels, \
location, Tstart, Tend, filt, dt, nattempts, waittime, \
errorfile, DATADIR)
else:
raise ValueError(
'You can only download data from IRIS and NCEDC')
# Store the data into temporary files
if (type(D) == obspy.core.stream.Stream):
D.write('tmp/' + station + '.mseed', format='MSEED')
namefile = 'tmp/' + station + '.pkl'
pickle.dump(orientation, open(namefile, 'wb'))
# Loop on families
families = pd.read_csv(family_file, \
sep=r'\s{1,}', header=None, engine='python')
families.columns = ['family', 'stations']
for i in range(0, len(families)):
# Create directory to store the LFEs times
namedir = 'LFEs/' + families['family'].iloc[i]
if not os.path.exists(namedir):
os.makedirs(namedir)
# File to write error messages
namedir = 'error'
if not os.path.exists(namedir):
os.makedirs(namedir)
errorfile = 'error/' + families['family'].iloc[i] + '.txt'
# Create dataframe to store LFE times
df = pd.DataFrame(columns=['year', 'month', 'day', 'hour', \
'minute', 'second', 'cc', 'nchannel'])
# Read the templates
stations = families['stations'].iloc[i].split(',')
templates = Stream()
for station in stations:
templatefile = template_dir + '/' + \
families['family'].iloc[i] + '/' + station + '.pkl'
with open(templatefile, 'rb') as f:
data = pickle.load(f)
if (len(data) == 3):
EW = data[0]
NS = data[1]
UD = data[2]
EW.stats.station = station
NS.stats.station = station
EW.stats.channel = 'E'
NS.stats.channel = 'N'
templates.append(EW)
templates.append(NS)
else:
UD = data[0]
UD.stats.station = station
UD.stats.channel = 'Z'
templates.append(UD)
# Loop on hours of data
for hour in range(0, nhour):
nchannel = 0
Tstart = t1 + hour * 3600.0
Tend = t1 + (hour + 1) * 3600.0 + duration
delta = Tend - Tstart
ndata = int(delta / dt) + 1
# Get the data
data = []
for station in stations:
try:
D = read('tmp/' + station + '.mseed')
D = D.slice(Tstart, Tend)
namefile = 'tmp/' + station + '.pkl'
orientation = pickle.load(open(namefile, 'rb'))
# Get station metadata for reading response file
for ir in range(0, len(staloc)):
if (station == staloc['station'][ir]):
network = staloc['network'][ir]
channels = staloc['channels'][ir]
location = staloc['location'][ir]
server = staloc['server'][ir]
# Orientation of template
# Date chosen: April 1st 2008
mychannels = channels.split(',')
mylocation = location
if (mylocation == '--'):
mylocation = ''
response = os.path.join(
DATADIR,
'response/') + network + '_' + station + '.xml'
inventory = read_inventory(response, format='STATIONXML')
reference = []
for channel in mychannels:
angle = inventory.get_orientation(network + '.' + \
station + '.' + mylocation + '.' + channel, \
UTCDateTime(2020, 1, 1, 0, 0, 0))
reference.append(angle)
# Append data to stream
if (type(D) == obspy.core.stream.Stream):
stationdata = fill_data(D, orientation, station,
channels, reference)
if (len(stationdata) > 0):
for stream in stationdata:
data.append(stream)
except:
message = 'No data available for station {} '.format( \
station) + 'at time {}/{}/{} - {}:{}:{}\n'.format( \
Tstart.year, Tstart.month, Tstart.day, Tstart.hour, \
Tstart.minute, Tstart.second)
# Loop on channels
for channel in range(0, len(data)):
subdata = data[channel]
# Check whether we have a complete one-hour-long recording
if (len(subdata) == 1):
if (len(subdata[0].data) == ndata):
# Get the template
station = subdata[0].stats.station
component = subdata[0].stats.channel
template = templates.select(station=station, \
component=component)[0]
# Cross correlation
cctemp = correlate.optimized(template, subdata[0])
if (nchannel > 0):
cc = np.vstack((cc, cctemp))
else:
cc = cctemp
nchannel = nchannel + 1
if (nchannel > 0):
# Compute average cross-correlation across channels
meancc = np.mean(cc, axis=0)
if (type_threshold == 'MAD'):
MAD = np.median(np.abs(meancc - np.mean(meancc)))
index = np.where(meancc >= threshold * MAD)
elif (type_threshold == 'Threshold'):
index = np.where(meancc >= threshold)
else:
raise ValueError(
'Type of threshold must be MAD or Threshold')
times = np.arange(0.0, np.shape(meancc)[0] * dt, dt)
# Get LFE times
if np.shape(index)[1] > 0:
(time, cc) = clean_LFEs(index, times, meancc, dt, freq0)
# Add LFE times to dataframe
i0 = len(df.index)
for j in range(0, len(time)):
timeLFE = Tstart + time[j]
df.loc[i0 + j] = [int(timeLFE.year), int(timeLFE.month), \
int(timeLFE.day), int(timeLFE.hour), \
int(timeLFE.minute), timeLFE.second + \
timeLFE.microsecond / 1000000.0, cc[j], nchannel]
# Add to pandas dataframe and save
df_all = df
df_all = df_all.astype(dtype={'year':'int32', 'month':'int32', \
'day':'int32', 'hour':'int32', 'minute':'int32', \
'second':'float', 'cc':'float', 'nchannel':'int32'})
df_all.to_csv('LFEs/' + families['family'].iloc[i] + '/catalog_' + \
'{:04d}{:02d}{:02d}_{:02d}{:02d}{:02d}'.format(tbegin[0], \
tbegin[1], tbegin[2], tbegin[3], tbegin[4], tbegin[5]) + '.csv')
class GetIIData(object):
# initialize input vars
def __init__(self, year, startday, network, **kwargs):
# initialize year/start/net
# if statement to check for main args set QUERY=True
# else sys.exit(1)
if (year != "") and (startday != "") and (network != ""):
self.year = year
self.startday = startday
self.network = network
QUERY = True
else:
QUERY = False
# loop through **kwargs and initialize optargs
self.endday = "" # init endday string
self.station = "" # init station string
self.location = "" # init location string
self.channel = "" # init channel string
self.debug = False # init debug
self.archive = False # init archive
endday = self.endday
for key,val in kwargs.iteritems():
if key == "endday": self.endday = val
elif key == "station": self.station = val
elif key == "location": self.location = val
elif key == "channel": self.channel = val
elif key == "debug": self.debug = self.toBool(val)
elif key == "archive": self.archive = self.toBool(val)
# print arguments if 'debug' mode
if self.debug:
print "Year: " + self.year
print "Start Day: " + self.startday
print "End Day: " + self.endday
print "Network: " + self.network
print "Station: " + self.station
print "Location: " + self.location
print "Channel: " + self.channel
# handle wildcards
if self.location == "?":
self.location = "*"
if self.channel == "?":
self.channel = "*"
if self.station == "?":
self.station = "*"
# set start/end to UTCDateTime object
#--------------------------------------------------------------------
self.startTime = UTCDateTime(year + startday +"T00:00:00.000")
# If no end day in parser default to 1 day
if self.endday == "?":
self.endday = str(int(self.startday) + 1).zfill(3)
self.endTime = self.startTime + 24*60*60
else:
self.endTime = UTCDateTime(year + self.endday +"T00:00:00.000")
print "Here is our start time: " + self.startTime.formatIRISWebService()
print "Here is our end time: " + self.endTime.formatIRISWebService()
self.days = int(self.endday)- int(self.startday)
# there are 24, 1 hour increments in a day
self.hours = (int(self.endday)- int(self.startday)) * 24
# Will only run if main args are given
# check QUERY flag if True continue
if QUERY:
self.queryData()
else:
print '\nNo main args given.'
print 'Exiting\n'
sys.exit(1)
def queryData(self):
# code from IRIS client
# Here we pull the data
client = Client("IRIS")
DupStations = []
DupLocations = []
DupChannels = []
self.st = Stream()
self.STAWILD = False
self.LOCWILD = False
self.CHANWILD = False
try:
timeout = 300
socket.setdefaulttimeout(timeout)
# this needs to have a get_waveform that queries data 1 hour at a time
# data cant query right now if the data is too bulky
# also needs to include a timeout exception
for hourIndex in range(0,self.hours): #this cant be days... has to be hours
self.startTime1 = self.startTime + (hourIndex)*1*60*60
self.endTime1 = self.startTime + (hourIndex+1)*1*60*60
requestArray = [(self.network,self.station,self.location, \
self.channel,self.startTime1,self.endTime1)]
self.st1 = client.get_waveforms_bulk(requestArray)
self.st += self.st1
print self.st
print
#self.st = client.get_waveforms_bulk(timeout=10,requestArray)
for self.tr in self.st:
#Here we remove the M data quality and go with D
self.tr.stats.mseed['dataquality'] = 'D'
if self.debug:
if self.station == '*':
self.STAWILD = True
DupStations.append(self.tr.stats.station)
elif self.station != '*':
self.STAWILD = False
if self.location == '*':
self.LOCWILD = True
DupLocations.append(self.tr.stats.location)
elif self.location != '*':
self.LOCWILD = False
if self.channel == '*':
self.CHANWILD = True
DupChannels.append(self.tr.stats.channel)
elif self.channel != '*':
self.CHANWILD = False
#except TimeoutError:
#print 'Get waveform timeout, exiting...'
#sys.exit(0)
except:
print 'Trouble getting data'
sys.exit(0)
# Takes duplicate stations out of list and
# makes station, location, and channel into an array
# for looping( probably easier way but it works)
self.stations = list(set(DupStations))
if self.station != '*':
self.stations.append(self.station)
self.locations = list(set(DupLocations))
if self.location != '*':
self.locations.append(self.location)
self.channels = list(set(DupChannels))
if self.channel != '*':
self.channels.append(self.channel)
print
print "Station(s) being pulled: " + str(self.stations)
print "Location(s) being pulled: " + str(self.locations)
print "Channel(s) being pulled: " + str(self.channels)
# Now call code to store streams in mseed files
self.storeMSEED()
def storeMSEED(self):
#Main program
#code for storing MSEED files
codepath = '/home/mkline/dev/getIIdataBackup/TEST_ARCHIVE/'
self.stFinal = Stream()
for self.channel in self.channels:
self.trace2 = self.st.select(channel = self.channel)
for self.location in self.locations:
self.trace1 = self.trace2.select(location = self.location)
for self.station in self.stations:
print
print "For station, location, and channel: " \
+ self.station +" "+ self.location +" "+ self.channel
trace = self.trace1.select(station = self.station)
trace.merge()
trace.sort()
trace.count()
for dayIndex in range(0,self.days):
print "Day properties: "
#startTime works better than trace[0].stats.starttime
trimStart = self.startTime + (dayIndex)*24*60*60
trimEnd = self.startTime + (dayIndex+1)*24*60*60
print "Start of day: " + str(trimStart)
print "End of day: " + str(trimEnd)
#Converting date into julian day to store in directory
timesplit = re.split('T', str(trimStart))
s = timesplit[0]
fmt = '%Y-%m-%d'
dt = datetime.datetime.strptime(s, fmt)
tt = dt.timetuple()
NewStartDay = str(tt.tm_yday).zfill(3)
self.stFinal = trace.copy()
self.stFinal.trim(starttime = trimStart, endtime = trimEnd)
# This if statement is used to make sure traces with no
# data dont get added to the directory structure
if not self.stFinal or str(self.stFinal[0].max()) == '--':
print "No trace for given day"
else:
#Added the directory structures in here since you won't want to
#add directory structures that you don't use
self.stFinal = self.stFinal.split()
if not os.path.exists(codepath + self.network + '_' + self.station + '/'):
os.mkdir(codepath + self.network + '_' + self.station + '/')
if not os.path.exists(codepath + self.network + '_' + self.station + '/' \
+ self.year + '/'):
os.mkdir(codepath + self.network + '_' + self.station + '/' \
+ self.year + '/')
stpath = codepath + self.network + '_' + self.station + '/' + self.year + \
'/' + self.year + '_' + NewStartDay + '/'
if not os.path.exists(stpath):
os.mkdir(stpath)
# Here we write the data using STEIM 2 and 512 record lengths
self.stFinal.write(stpath + self.stFinal[0].stats.location + '_' + \
self.stFinal[0].stats.channel + '.512.seed', format='MSEED', \
reclen = 512, encoding='STEIM2')
print self.stFinal
# convert optional boolean strings to boolean vars
def toBool(self, value):
"""
Converts 'string' to boolean. Raises exception for invalid formats
True values: 1, True, true, "1", "True", "true", "yes", "y", "t"
False values: 0, False, false, "0", "False", "false", "no", "n", "f"
"""
if str(value).lower() in ("true", "yes", "t", "y", "1"): return True
if str(value).lower() in ("false", "no", "f", "n", "0"): return False
raise Exception('Invalid value for boolean conversion: ' + str(value))
def find_LFEs(filename, stations, tbegin, tend, TDUR, filt, \
freq0, nattempts, waittime, draw=False, type_threshold='MAD', \
threshold=0.0075):
"""
Find LFEs with the temporary stations from FAME
using the templates from Plourde et al. (2015)
Input:
type filename = string
filename = Name of the template
type stations = list of strings
stations = name of the stations used for the matched-filter algorithm
type tebgin = tuplet of 6 integers
tbegin = Time when we begin looking for LFEs
type tend = tuplet of 6 integers
tend = Time we stop looking for LFEs
type TDUR = float
TDUR = Time to add before and after the time window for tapering
type filt = tuple of floats
filt = Lower and upper frequencies of the filter
type freq0 = float
freq0 = Maximum frequency rate of LFE occurrence
type nattempts = integer
nattempts = Number of times we try to download data
type waittime = positive float
waittime = Type to wait between two attempts at downloading
type draw = boolean
draw = Do we draw a figure of the cross-correlation?
type type_threshold = string
type_threshold = 'MAD' or 'Threshold'
type threshold = float
threshold = Cross correlation value must be higher than that
Output:
None
"""
# Get the network, channels, and location of the stations
staloc = pd.read_csv('../data/Ducellier/stations_permanent.txt', \
sep=r'\s{1,}', header=None, engine='python')
staloc.columns = ['station', 'network', 'channels', 'location', \
'server', 'latitude', 'longitude', 'time_on', 'time_off']
# Create directory to store the LFEs times
namedir = 'LFEs/' + filename
if not os.path.exists(namedir):
os.makedirs(namedir)
# File to write error messages
namedir = 'error'
if not os.path.exists(namedir):
os.makedirs(namedir)
errorfile = 'error/' + filename + '.txt'
# Read the templates
templates = Stream()
for station in stations:
data = pickle.load(open('templates_new/' + filename + \
'/' + station + '.pkl', 'rb'))
if (len(data) == 3):
EW = data[0]
NS = data[1]
UD = data[2]
EW.stats.station = station
NS.stats.station = station
EW.stats.channel = 'E'
NS.stats.channel = 'N'
templates.append(EW)
templates.append(NS)
else:
UD = data[0]
UD.stats.station = station
UD.stats.channel = 'Z'
templates.append(UD)
# Begin and end time of analysis
t1 = UTCDateTime(year=tbegin[0], month=tbegin[1], \
day=tbegin[2], hour=tbegin[3], minute=tbegin[4], \
second=tbegin[5])
t2 = UTCDateTime(year=tend[0], month=tend[1], \
day=tend[2], hour=tend[3], minute=tend[4], \
second=tend[5])
# Read the data
data = []
for station in stations:
# Get station metadata for downloading
for ir in range(0, len(staloc)):
if (station == staloc['station'][ir]):
network = staloc['network'][ir]
channels = staloc['channels'][ir]
location = staloc['location'][ir]
server = staloc['server'][ir]
# Duration of template
template = templates.select(station=station, component='Z')[0]
dt = template.stats.delta
nt = template.stats.npts
duration = (nt - 1) * dt
Tstart = t1 - TDUR
Tend = t2 + duration + TDUR
delta = t2 + duration - t1
ndata = int(delta / dt) + 1
# Orientation of template
# Date chosen: April 1st 2008
mychannels = channels.split(',')
mylocation = location
if (mylocation == '--'):
mylocation = ''
response = '../data/response/' + network + '_' + station + '.xml'
inventory = read_inventory(response, format='STATIONXML')
reference = []
for channel in mychannels:
angle = inventory.get_orientation(network + '.' + \
station + '.' + mylocation + '.' + channel, \
UTCDateTime(2012, 1, 1, 0, 0, 0))
reference.append(angle)
# First case: we can get the data from IRIS
if (server == 'IRIS'):
(D, orientation) = get_from_IRIS(station, network, channels, \
location, Tstart, Tend, filt, dt, nattempts, waittime, \
errorfile)
# Second case: we get the data from NCEDC
elif (server == 'NCEDC'):
(D, orientation) = get_from_NCEDC(station, network, channels, \
location, Tstart, Tend, filt, dt, nattempts, waittime, \
errorfile)
else:
raise ValueError('You can only download data from IRIS and NCEDC')
# Append data to stream
if (type(D) == obspy.core.stream.Stream):
stationdata = fill_data(D, orientation, station, channels, reference)
if (len(stationdata) > 0):
for stream in stationdata:
data.append(stream)
# Number of hours of data to analyze
nhour = int(ceil((t2 - t1) / 3600.0))
# Create dataframe to store LFE times
df = pd.DataFrame(columns=['year', 'month', 'day', 'hour', \
'minute', 'second', 'cc', 'nchannel'])
# Loop on hours of data
for hour in range(0, nhour):
nchannel = 0
Tstart = t1 + hour * 3600.0
Tend = t1 + (hour + 1) * 3600.0 + duration
delta = Tend - Tstart
ndata = int(delta / dt) + 1
# Loop on channels
for channel in range(0, len(data)):
# Cut the data
subdata = data[channel]
subdata = subdata.slice(Tstart, Tend)
# Check whether we have a complete one-hour-long recording
if (len(subdata) == 1):
if (len(subdata[0].data) == ndata):
# Get the template
station = subdata[0].stats.station
component = subdata[0].stats.channel
template = templates.select(station=station, \
component=component)[0]
# Cross correlation
cctemp = correlate.optimized(template, subdata[0])
if (nchannel > 0):
cc = np.vstack((cc, cctemp))
else:
cc = cctemp
nchannel = nchannel + 1
if (nchannel > 0):
# Compute average cross-correlation across channels
meancc = np.mean(cc, axis=0)
if (type_threshold == 'MAD'):
MAD = np.median(np.abs(meancc - np.mean(meancc)))
index = np.where(meancc >= threshold * MAD)
elif (type_threshold == 'Threshold'):
index = np.where(meancc >= threshold)
else:
raise ValueError('Type of threshold must be MAD or Threshold')
times = np.arange(0.0, np.shape(meancc)[0] * dt, dt)
# Get LFE times
if np.shape(index)[1] > 0:
(time, cc) = clean_LFEs(index, times, meancc, dt, freq0)
# Add LFE times to dataframe
i0 = len(df.index)
for i in range(0, len(time)):
timeLFE = Tstart + time[i]
df.loc[i0 + i] = [int(timeLFE.year), int(timeLFE.month), \
int(timeLFE.day), int(timeLFE.hour), \
int(timeLFE.minute), timeLFE.second + \
timeLFE.microsecond / 1000000.0, cc[i], nchannel]
# Draw figure
if (draw == True):
params = {'xtick.labelsize':16,
'ytick.labelsize':16}
pylab.rcParams.update(params)
plt.figure(1, figsize=(20, 8))
if np.shape(index)[1] > 0:
for i in range(0, len(time)):
plt.axvline(time[i], linewidth=2, color='grey')
plt.plot(np.arange(0.0, np.shape(meancc)[0] * dt, \
dt), meancc, color='black')
if (type_threshold == 'MAD'):
plt.axhline(threshold * MAD, linewidth=2, color='red', \
label = '{:6.2f} * MAD'.format(threshold))
elif (type_threshold == 'Threshold'):
plt.axhline(threshold, linewidth=2, color='red', \
label = 'Threshold = {:8.4f}'.format(threshold))
else:
raise ValueError( \
'Type of threshold must be MAD or Threshold')
plt.xlim(0.0, (np.shape(meancc)[0] - 1) * dt)
plt.xlabel('Time (s)', fontsize=24)
plt.ylabel('Cross-correlation', fontsize=24)
plt.title('Average cross-correlation across stations', \
fontsize=30)
plt.legend(loc=2, fontsize=24)
plt.savefig('LFEs/' + filename + '/' + \
'{:04d}{:02d}{:02d}_{:02d}{:02d}{:02d}'.format( \
Tstart.year, Tstart.month, Tstart.day, Tstart.hour, \
Tstart.minute, Tstart.second) + '.png', format='png')
plt.close(1)
# Add to pandas dataframe and save
namefile = 'LFEs/' + filename + '/catalog.pkl'
if os.path.exists(namefile):
df_all = pickle.load(open(namefile, 'rb'))
df_all = pd.concat([df_all, df], ignore_index=True)
else:
df_all = df
df_all = df_all.astype(dtype={'year':'int32', 'month':'int32', \
'day':'int32', 'hour':'int32', 'minute':'int32', \
'second':'float', 'cc':'float', 'nchannel':'int32'})
pickle.dump(df_all, open(namefile, 'wb'))
def analyze_data(families, staloc, tbegin, tend, \
freq0, type_threshold, threshold, ncpu, icpu):
"""
"""
nfamilies = int(ceil(len(families) / ncpu))
ibegin = icpu * nfamilies
iend = min((icpu + 1) * nfamilies, len(families))
# Loop on families
for i in range(ibegin, iend):
# Create directory to store the LFEs times
namedir = 'LFEs/' + families['family'].iloc[i]
if not os.path.exists(namedir):
os.makedirs(namedir)
# File to write number of stations
namedir = 'nstations'
if not os.path.exists(namedir):
os.makedirs(namedir)
stationfile = 'nstations/' + families['family'].iloc[i] + '.txt'
# Create dataframe to store LFE times
df = pd.DataFrame(columns=['year', 'month', 'day', 'hour', \
'minute', 'second', 'cc', 'nchannel'])
# Read the templates
stations = families['stations'].iloc[i].split(',')
templates = Stream()
orientations = []
names = []
for station in stations:
subset = staloc.loc[staloc['station'] == station]
channels = subset['channels'].iloc[0]
mychannels = channels.split(',')
for channel in mychannels:
data = pickle.load(open(template_dir + '/' + \
families['family'].iloc[i] + '/' + station + '_' + \
channel + '.pkl', 'rb'))
template = data[0]
angle = data[1]
templates.append(template)
orientations.append(angle)
names.append(station + '_' + channel)
# Check the time step of the stations
subset = staloc.loc[staloc['station'].isin(stations)]
if len(subset['dt'].value_counts()) == 1:
dt = subset['dt'].iloc[0]
else:
raise ValueError('All stations must have the same time step')
# Number of hours of data to analyze
t1 = UTCDateTime(year=tbegin[0], month=tbegin[1], \
day=tbegin[2], hour=tbegin[3], minute=tbegin[4], \
second=tbegin[5])
t2 = UTCDateTime(year=tend[0], month=tend[1], \
day=tend[2], hour=tend[3], minute=tend[4], \
second=tend[5])
nhour = int(ceil((t2 - t1) / 3600.0))
duration = families['duration'].iloc[i]
# To rotate components
swap = {'E': 'N', 'N': 'E', '1': '2', '2': '1'}
# Loop on hours of data
for hour in range(0, nhour):
Tstart = t1 + hour * 3600.0
Tend = t1 + (hour + 1) * 3600.0 + duration
delta = Tend - Tstart
ndata = int(delta / dt) + 1
# Get the data
data = []
for station in stations:
subset = staloc.loc[staloc['station'] == station]
channels = subset['channels'].iloc[0]
mychannels = channels.split(',')
for num, channel in enumerate(mychannels):
try:
D = read('tmp/' + station + '_' + channel + '.mseed')
D = D.slice(Tstart, Tend)
if (type(D) == obspy.core.stream.Stream):
namefile = 'tmp/' + station + '_' + channel + \
'.pkl'
orientation = pickle.load(open(namefile, 'rb')) \
[num]
index = names.index(station + '_' + channel)
reference = orientations[index]
# Rotate components
if (len(mychannels) > 1) and (num < 2):
if orientation != reference:
channel_new = channel[0:2] + \
swap[channel[2]]
D_new = read('tmp/' + station + '_' + \
channel_new + '.mseed')
D_new = D_new.slice(Tstart, Tend)
namefile = 'tmp/' + station + '_' + \
channel_new + '.pkl'
if num == 0:
orientation_new = pickle.load(open( \
namefile, 'rb'))[1]
else:
orientation_new = pickle.load(open( \
namefile, 'rb'))[0]
index = names.index(station + '_' + \
channel_new)
reference_new = orientations[index]
if channel[2] in ['E', '1']:
D = rotate_data(D, D_new, \
orientation, orientation_new, \
reference, reference_new, 'E')
else:
D = rotate_data(D_new, D, \
orientation_new, orientation, \
reference_new, reference, 'N')
# Append stream to data
data.append(D)
except:
message = 'No data available for station {}'.format( \
station) + ' and channel {}'.format(channel) + \
' at time {}/{}/{} - {}:{}:{}\n'.format( \
Tstart.year, Tstart.month, Tstart.day, \
Tstart.hour, Tstart.minute, Tstart.second)
# Loop on channels
nchannel = 0
for j in range(0, len(data)):
subdata = data[j]
# Check whether we have a complete one-hour-long recording
if (len(subdata) == 1):
if (len(subdata[0].data) == ndata):
# Get the template
station = subdata[0].stats.station
channel = subdata[0].stats.channel
template = templates.select(station=station, \
channel=channel)[0]
# Cross correlation
cctemp = correlate.optimized(template, subdata[0])
if (nchannel > 0):
cc = np.vstack((cc, cctemp))
else:
cc = cctemp
nchannel = nchannel + 1
# Write number of channels
with open(stationfile, 'a') as file:
file.write('{} {} {} {} {}\n'.format(Tstart.year, \
Tstart.month, Tstart.day, Tstart.hour, nchannel))
if (nchannel > 0):
# Compute average cross-correlation across channels
if len(np.shape(cc)) == 1:
meancc = cc
else:
meancc = np.mean(cc, axis=0)
if (type_threshold == 'MAD'):
MAD = np.median(np.abs(meancc - np.mean(meancc)))
index = np.where(meancc >= threshold * MAD)
elif (type_threshold == 'Threshold'):
index = np.where(meancc >= threshold)
else:
raise ValueError( \
'Type of threshold must be MAD or Threshold')
times = np.arange(0.0, np.shape(meancc)[0] * dt, dt)
# Get LFE times
if np.shape(index)[1] > 0:
(time, cc) = clean_LFEs(index, times, meancc, dt, freq0)
# Add LFE times to dataframe
i0 = len(df.index)
for j in range(0, len(time)):
timeLFE = Tstart + time[j]
df.loc[i0 + j] = [int(timeLFE.year), \
int(timeLFE.month), int(timeLFE.day), \
int(timeLFE.hour), int(timeLFE.minute), \
timeLFE.second + timeLFE.microsecond / 1000000.0, \
cc[j], nchannel]
# Add to pandas dataframe and save
namefile = 'LFEs/' + families['family'].iloc[i] + '/catalog.pkl'
if os.path.exists(namefile):
df_all = pickle.load(open(namefile, 'rb'))
df_all = pd.concat([df_all, df], ignore_index=True)
else:
df_all = df
df_all = df_all.astype(dtype={'year':'int32', 'month':'int32', \
'day':'int32', 'hour':'int32', 'minute':'int32', \
'second':'float', 'cc':'float', 'nchannel':'int32'})
pickle.dump(df_all, open(namefile, 'wb'))
class GetIIData(object):
# initialize input vars
def __init__(self, year, startday, network, **kwargs):
# initialize year/start/net
# if statement to check for main args set QUERY=True
# else sys.exit(1)
if (year != "") and (startday != "") and (network != ""):
self.year = year
self.startday = startday
self.network = network
QUERY = True
else:
QUERY = False
# loop through **kwargs and initialize optargs
self.endday = "" # init endday string
self.station = "" # init station string
self.location = "" # init location string
self.channel = "" # init channel string
self.debug = False # init debug
self.archive = False # init archive
endday = self.endday
for key, val in kwargs.iteritems():
if key == "endday": self.endday = val
elif key == "station": self.station = val
elif key == "location": self.location = val
elif key == "channel": self.channel = val
elif key == "debug": self.debug = self.toBool(val)
elif key == "archive": self.archive = self.toBool(val)
# print arguments if 'debug' mode
if self.debug:
print "Year: " + self.year
print "Start Day: " + self.startday
print "End Day: " + self.endday
print "Network: " + self.network
print "Station: " + self.station
print "Location: " + self.location
print "Channel: " + self.channel
# handle wildcards
if self.location == "?":
self.location = "*"
if self.channel == "?":
self.channel = "*"
if self.station == "?":
self.station = "*"
# set start/end to UTCDateTime object
#--------------------------------------------------------------------
self.startTime = UTCDateTime(year + startday + "T00:00:00.000")
# If no end day in parser default to 1 day
if self.endday == "?":
self.endday = str(int(self.startday) + 1).zfill(3)
self.endTime = self.startTime + 24 * 60 * 60
else:
self.endTime = UTCDateTime(year + self.endday + "T00:00:00.000")
print "Here is our start time: " + self.startTime.formatIRISWebService(
)
print "Here is our end time: " + self.endTime.formatIRISWebService()
self.days = int(self.endday) - int(self.startday)
# there are 24, 1 hour increments in a day
self.hours = (int(self.endday) - int(self.startday)) * 24
# Will only run if main args are given
# check QUERY flag if True continue
if QUERY:
self.queryData()
else:
print '\nNo main args given.'
print 'Exiting\n'
sys.exit(1)
def queryData(self):
# code from IRIS client
# Here we pull the data
client = Client("IRIS")
DupStations = []
DupLocations = []
DupChannels = []
self.st = Stream()
self.STAWILD = False
self.LOCWILD = False
self.CHANWILD = False
try:
timeout = 300
socket.setdefaulttimeout(timeout)
# this needs to have a get_waveform that queries data 1 hour at a time
# data cant query right now if the data is too bulky
# also needs to include a timeout exception
for hourIndex in range(
0, self.hours): #this cant be days... has to be hours
self.startTime1 = self.startTime + (hourIndex) * 1 * 60 * 60
self.endTime1 = self.startTime + (hourIndex + 1) * 1 * 60 * 60
requestArray = [(self.network,self.station,self.location, \
self.channel,self.startTime1,self.endTime1)]
self.st1 = client.get_waveforms_bulk(requestArray)
self.st += self.st1
print self.st
print
#self.st = client.get_waveforms_bulk(timeout=10,requestArray)
for self.tr in self.st:
#Here we remove the M data quality and go with D
self.tr.stats.mseed['dataquality'] = 'D'
if self.debug:
if self.station == '*':
self.STAWILD = True
DupStations.append(self.tr.stats.station)
elif self.station != '*':
self.STAWILD = False
if self.location == '*':
self.LOCWILD = True
DupLocations.append(self.tr.stats.location)
elif self.location != '*':
self.LOCWILD = False
if self.channel == '*':
self.CHANWILD = True
DupChannels.append(self.tr.stats.channel)
elif self.channel != '*':
self.CHANWILD = False
#except TimeoutError:
#print 'Get waveform timeout, exiting...'
#sys.exit(0)
except:
print 'Trouble getting data'
sys.exit(0)
# Takes duplicate stations out of list and
# makes station, location, and channel into an array
# for looping( probably easier way but it works)
self.stations = list(set(DupStations))
if self.station != '*':
self.stations.append(self.station)
self.locations = list(set(DupLocations))
if self.location != '*':
self.locations.append(self.location)
self.channels = list(set(DupChannels))
if self.channel != '*':
self.channels.append(self.channel)
print
print "Station(s) being pulled: " + str(self.stations)
print "Location(s) being pulled: " + str(self.locations)
print "Channel(s) being pulled: " + str(self.channels)
# Now call code to store streams in mseed files
self.storeMSEED()
def storeMSEED(self):
#Main program
#code for storing MSEED files
codepath = '/home/mkline/dev/getIIdataBackup/TEST_ARCHIVE/'
self.stFinal = Stream()
for self.channel in self.channels:
self.trace2 = self.st.select(channel=self.channel)
for self.location in self.locations:
self.trace1 = self.trace2.select(location=self.location)
for self.station in self.stations:
print
print "For station, location, and channel: " \
+ self.station +" "+ self.location +" "+ self.channel
trace = self.trace1.select(station=self.station)
trace.merge()
trace.sort()
trace.count()
for dayIndex in range(0, self.days):
print "Day properties: "
#startTime works better than trace[0].stats.starttime
trimStart = self.startTime + (dayIndex) * 24 * 60 * 60
trimEnd = self.startTime + (dayIndex +
1) * 24 * 60 * 60
print "Start of day: " + str(trimStart)
print "End of day: " + str(trimEnd)
#Converting date into julian day to store in directory
timesplit = re.split('T', str(trimStart))
s = timesplit[0]
fmt = '%Y-%m-%d'
dt = datetime.datetime.strptime(s, fmt)
tt = dt.timetuple()
NewStartDay = str(tt.tm_yday).zfill(3)
self.stFinal = trace.copy()
self.stFinal.trim(starttime=trimStart, endtime=trimEnd)
# This if statement is used to make sure traces with no
# data dont get added to the directory structure
if not self.stFinal or str(
self.stFinal[0].max()) == '--':
print "No trace for given day"
else:
#Added the directory structures in here since you won't want to
#add directory structures that you don't use
self.stFinal = self.stFinal.split()
if not os.path.exists(codepath + self.network +
'_' + self.station + '/'):
os.mkdir(codepath + self.network + '_' +
self.station + '/')
if not os.path.exists(codepath + self.network + '_' + self.station + '/' \
+ self.year + '/'):
os.mkdir(codepath + self.network + '_' + self.station + '/' \
+ self.year + '/')
stpath = codepath + self.network + '_' + self.station + '/' + self.year + \
'/' + self.year + '_' + NewStartDay + '/'
if not os.path.exists(stpath):
os.mkdir(stpath)
# Here we write the data using STEIM 2 and 512 record lengths
self.stFinal.write(stpath + self.stFinal[0].stats.location + '_' + \
self.stFinal[0].stats.channel + '.512.seed', format='MSEED', \
reclen = 512, encoding='STEIM2')
print self.stFinal
# convert optional boolean strings to boolean vars
def toBool(self, value):
"""
Converts 'string' to boolean. Raises exception for invalid formats
True values: 1, True, true, "1", "True", "true", "yes", "y", "t"
False values: 0, False, false, "0", "False", "false", "no", "n", "f"
"""
if str(value).lower() in ("true", "yes", "t", "y", "1"): return True
if str(value).lower() in ("false", "no", "f", "n", "0"): return False
raise Exception('Invalid value for boolean conversion: ' + str(value))
