def find_seismogram(top_level_dir,
starttime,
endtime,
stations=['S12', 'S14', 'S15', 'S16'],
channels=['MH1', 'MH2', 'MHZ'],
dir_type='pdart_dir'):
for station in stations:
stream = Stream()
channel = '*'
if dir_type == 'processed_dir':
dir = find_processed_dir(top_level_dir, starttime.year, station)
filename = '*%s.%s.%s.%s.%s.%03d*.gz' % (
'XA', station, '*', channel, str(
starttime.year), starttime.julday)
else:
dir = find_dir(top_level_dir, starttime.year, station, channel)
filename = '%s.%s.%s.%s.%s.%03d.gz' % ('XA', station, '*', channel,
str(starttime.year),
starttime.julday)
filename = os.path.join(dir, filename)
try:
stream += read(filename)
except Exception as e:
print(str(e))
if starttime.julday != endtime.julday:
if dir_type == 'processed_dir':
dir = find_processed_dir(top_level_dir, endtime.year, station)
filename = '*%s.%s.%s.%s.%s.%03d*.gz' % (
'XA', station, '*', channel, str(
endtime.year), endtime.julday)
else:
dir = find_dir(top_level_dir, endtime.year, station, channel)
filename = '*%s.%s.%s.%s.%s.%03d*.gz' % (
'XA', station, '*', channel, str(
endtime.year), endtime.julday)
filename = os.path.join(dir, filename)
try:
stream += read(filename)
except Exception as e:
print(str(e))
# print('Before ', stream)
stream = stream.trim(starttime=starttime, endtime=endtime)
# print('After ', stream)
if stream is not None and len(stream) > 0:
for tr in stream:
tr.stats.location = ''
if tr.stats.channel not in channels:
stream.remove(tr)
stream.merge()
return stream
def get_streams_gema(networks,
stations,
starttime,
endtime,
only_vertical_channel=False,
local_dir_name=None):
if not local_dir_name:
local_dir_name = "%s/archive" % (os.getenv("HOME"))
if only_vertical_channel:
channels = "*Z"
else:
channels = "*"
# READ ARCHIVE DATABASE
st = Stream()
this_day = UTCDateTime(starttime.strftime("%Y-%m-%d"))
last_day = UTCDateTime(endtime.strftime("%Y-%m-%d"))
while this_day <= last_day:
for network, station in zip(networks, stations):
pattern = '%s/%s/%s/%s/%s*' % (local_dir_name,
this_day.strftime("%Y"), network,
station, channels)
paths_ch = sorted(glob.glob(pattern))
for path in paths_ch:
pattern = "%s/*%s" % (path, this_day.strftime("%Y.%03j"))
msfile_list = glob.glob(pattern)
if len(msfile_list) > 0:
for msfile in msfile_list:
st += read(msfile,
starttime=starttime,
endtime=endtime)
this_day += 86400
# PATCH PROBLEM DIFFERENT SAMPLING RATES IN LONQ STATION FROM SCREAM
for tr in st.select(station="LONQ"):
if tr.stats.sampling_rate != 50:
st.remove(tr)
# EXPORT GAPS AND MERGE STREAM
gaps = st.get_gaps()
if len(st) > 0: # and len(gaps)>0
st.trim(starttime, endtime)
st.merge(method=1, interpolation_samples=-1, fill_value='interpolate')
return st, gaps
def preprocess(db, stations, comps, goal_day, params, responses=None):
"""
Fetches data for each ``stations`` and each ``comps`` using the
data_availability table in the database.
To correct for instrument responses, make sure to set ``remove_response``
to "Y" in the config and to provide the ``responses`` DataFrame.
:Example:
>>> from msnoise.api import connect, get_params, preload_instrument_responses
>>> from msnoise.preprocessing import preprocess
>>> db = connect()
>>> params = get_params(db)
>>> responses = preload_instrument_responses(db)
>>> st = preprocess(db, ["YA.UV06","YA.UV10"], ["Z",], "2010-09-01", params, responses)
>>> st
2 Trace(s) in Stream:
YA.UV06.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
YA.UV10.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
:type db: :class:`sqlalchemy.orm.session.Session`
:param db: A :class:`~sqlalchemy.orm.session.Session` object, as
obtained by :func:`msnoise.api.connect`.
:type stations: list of str
:param stations: a list of station names, in the format NET.STA.
:type comps: list of str
:param comps: a list of component names, in Z,N,E,1,2.
:type goal_day: str
:param goal_day: the day of data to load, ISO 8601 format: e.g. 2016-12-31.
:type params: class
:param params: an object containing the config parameters, as obtained by
:func:`msnoise.api.get_params`.
:type responses: :class:`pandas.DataFrame`
:param responses: a DataFrame containing the instrument responses, as
obtained by :func:`msnoise.api.preload_instrument_responses`.
:rtype: :class:`obspy.core.stream.Stream`
:return: A Stream object containing all traces.
"""
datafiles = {}
output = Stream()
for station in stations:
datafiles[station] = {}
net, sta = station.split('.')
gd = datetime.datetime.strptime(goal_day, '%Y-%m-%d')
files = get_data_availability(db,
net=net,
sta=sta,
starttime=gd,
endtime=gd)
for comp in comps:
datafiles[station][comp] = []
for file in files:
if file.comp[-1] not in comps:
continue
fullpath = os.path.join(file.path, file.file)
datafiles[station][file.comp[-1]].append(fullpath)
for istation, station in enumerate(stations):
net, sta = station.split(".")
for comp in comps:
files = eval("datafiles['%s']['%s']" % (station, comp))
if len(files) != 0:
logging.debug("%s.%s Reading %i Files" %
(station, comp, len(files)))
stream = Stream()
for file in sorted(files):
try:
st = read(file,
dytpe=np.float,
starttime=UTCDateTime(gd),
endtime=UTCDateTime(gd) + 86400)
except:
logging.debug("ERROR reading file %s" % file)
continue
for tr in st:
if len(tr.stats.channel) == 2:
tr.stats.channel += tr.stats.location
tr.stats.location = "00"
tmp = st.select(network=net, station=sta, component=comp)
if not len(tmp):
for tr in st:
tr.stats.network = net
st = st.select(network=net,
station=sta,
component=comp)
else:
st = tmp
for tr in st:
tr.data = tr.data.astype(np.float)
tr.stats.network = tr.stats.network.upper()
tr.stats.station = tr.stats.station.upper()
tr.stats.channel = tr.stats.channel.upper()
stream += st
del st
stream.sort()
try:
# HACK not super clean... should find a way to prevent the
# same trace id with different sps to occur
stream.merge(method=1,
interpolation_samples=3,
fill_value=None)
except:
logging.debug("Error while merging...")
traceback.print_exc()
continue
stream = stream.split()
if not len(stream):
continue
logging.debug("%s Checking sample alignment" % stream[0].id)
for i, trace in enumerate(stream):
stream[i] = check_and_phase_shift(trace)
logging.debug("%s Checking Gaps" % stream[0].id)
if len(getGaps(stream)) > 0:
max_gap = params.preprocess_max_gap * stream[
0].stats.sampling_rate
only_too_long = False
while getGaps(stream) and not only_too_long:
too_long = 0
gaps = getGaps(stream)
for gap in gaps:
if int(gap[-1]) <= max_gap:
try:
stream[gap[0]] = stream[gap[0]].__add__(
stream[gap[1]],
method=1,
fill_value="interpolate")
stream.remove(stream[gap[1]])
except:
stream.remove(stream[gap[1]])
break
else:
too_long += 1
if too_long == len(gaps):
only_too_long = True
stream = stream.split()
for tr in stream:
if tr.stats.sampling_rate < (params.goal_sampling_rate -
1):
stream.remove(tr)
taper_length = 20.0 # seconds
for trace in stream:
if trace.stats.npts < 4 * taper_length * trace.stats.sampling_rate:
stream.remove(trace)
else:
trace.detrend(type="demean")
trace.detrend(type="linear")
trace.taper(max_percentage=None, max_length=1.0)
if not len(stream):
logging.debug(" has only too small traces, skipping...")
continue
for trace in stream:
logging.debug("%s Highpass at %.2f Hz" %
(trace.id, params.preprocess_highpass))
trace.filter("highpass",
freq=params.preprocess_highpass,
zerophase=True)
if trace.stats.sampling_rate != params.goal_sampling_rate:
logging.debug("%s Lowpass at %.2f Hz" %
(trace.id, params.preprocess_lowpass))
trace.filter("lowpass",
freq=params.preprocess_lowpass,
zerophase=True,
corners=8)
if params.resampling_method == "Resample":
logging.debug(
"%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = resample(
trace.data, params.goal_sampling_rate /
trace.stats.sampling_rate, 'sinc_fastest')
elif params.resampling_method == "Decimate":
decimation_factor = trace.stats.sampling_rate / params.goal_sampling_rate
if not int(decimation_factor) == decimation_factor:
logging.warning(
"%s CANNOT be decimated by an integer factor, consider using Resample or Lanczos methods"
" Trace sampling rate = %i ; Desired CC sampling rate = %i"
% (trace.id, trace.stats.sampling_rate,
params.goal_sampling_rate))
sys.stdout.flush()
sys.exit()
logging.debug("%s Decimate by a factor of %i" %
(trace.id, decimation_factor))
trace.data = trace.data[::int(decimation_factor)]
elif params.resampling_method == "Lanczos":
logging.debug(
"%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = np.array(trace.data)
trace.interpolate(
method="lanczos",
sampling_rate=params.goal_sampling_rate,
a=1.0)
trace.stats.sampling_rate = params.goal_sampling_rate
if params.remove_response:
logging.debug('%s Removing instrument response' %
stream[0].id)
response = responses[responses["channel_id"] ==
stream[0].id]
if len(response) > 1:
response = response[
response["start_date"] <= UTCDateTime(gd)]
if len(response) > 1:
response = response[
response["end_date"] >= UTCDateTime(gd)]
elif len(response) == 0:
logging.info("No instrument response information "
"for %s, skipping" % stream[0].id)
continue
try:
datalesspz = response["paz"].values[0]
except:
logging.error("Bad instrument response information "
"for %s, skipping" % stream[0].id)
continue
stream.simulate(
paz_remove=datalesspz,
remove_sensitivity=True,
pre_filt=params.response_prefilt,
paz_simulate=None,
)
for tr in stream:
tr.data = tr.data.astype(np.float32)
output += stream
del stream
del files
clean_scipy_cache()
return output
def preprocess(db, stations, comps, goal_day, params, responses=None):
datafiles = {}
output = Stream()
for station in stations:
datafiles[station] = {}
net, sta = station.split('.')
gd = datetime.datetime.strptime(goal_day, '%Y-%m-%d')
files = get_data_availability(db,
net=net,
sta=sta,
starttime=gd,
endtime=gd)
for comp in comps:
datafiles[station][comp] = []
for file in files:
if file.comp[-1] not in comps:
continue
fullpath = os.path.join(file.path, file.file)
datafiles[station][file.comp[-1]].append(fullpath)
for istation, station in enumerate(stations):
net, sta = station.split(".")
for comp in comps:
files = eval("datafiles['%s']['%s']" % (station, comp))
if len(files) != 0:
logging.debug("%s.%s Reading %i Files" %
(station, comp, len(files)))
stream = Stream()
for file in sorted(files):
st = read(file,
dytpe=np.float,
starttime=UTCDateTime(gd),
endtime=UTCDateTime(gd) + 86400)
tmp = st.select(network=net, station=sta, component=comp)
if not len(tmp):
for tr in st:
tr.stats.network = net
st = st.select(network=net,
station=sta,
component=comp)
else:
st = tmp
for tr in st:
tr.data = tr.data.astype(np.float)
stream += st
del st
stream.sort()
stream.merge(method=1,
interpolation_samples=3,
fill_value=None)
stream = stream.split()
logging.debug("Checking sample alignment")
for i, trace in enumerate(stream):
stream[i] = check_and_phase_shift(trace)
logging.debug("Checking Gaps")
if len(getGaps(stream)) > 0:
max_gap = 10
only_too_long = False
while getGaps(stream) and not only_too_long:
too_long = 0
gaps = getGaps(stream)
for gap in gaps:
if int(gap[-1]) <= max_gap:
stream[gap[0]] = stream[gap[0]].__add__(
stream[gap[1]],
method=1,
fill_value="interpolate")
stream.remove(stream[gap[1]])
break
else:
too_long += 1
if too_long == len(gaps):
only_too_long = True
stream = stream.split()
taper_length = 20.0 # seconds
for trace in stream:
if trace.stats.npts < 4 * taper_length * trace.stats.sampling_rate:
stream.remove(trace)
else:
trace.detrend(type="demean")
trace.detrend(type="linear")
trace.taper(max_percentage=None, max_length=1.0)
if not len(stream):
logging.debug(" has only too small traces, skipping...")
continue
for trace in stream:
logging.debug("%s.%s Highpass at %.2f Hz" %
(station, comp, params.preprocess_highpass))
trace.filter("highpass",
freq=params.preprocess_highpass,
zerophase=True)
if trace.stats.sampling_rate != params.goal_sampling_rate:
logging.debug(
"%s.%s Lowpass at %.2f Hz" %
(station, comp, params.preprocess_lowpass))
trace.filter("lowpass",
freq=params.preprocess_lowpass,
zerophase=True,
corners=8)
if params.resampling_method == "Resample":
logging.debug(
"%s.%s Downsample to %.1f Hz" %
(station, comp, params.goal_sampling_rate))
trace.data = resample(
trace.data, params.goal_sampling_rate /
trace.stats.sampling_rate, 'sinc_fastest')
elif params.resampling_method == "Decimate":
decimation_factor = trace.stats.sampling_rate / params.goal_sampling_rate
if not int(decimation_factor) == decimation_factor:
logging.warning(
"%s.%s CANNOT be decimated by an integer factor, consider using Resample or Lanczos methods"
" Trace sampling rate = %i ; Desired CC sampling rate = %i"
%
(station, comp, trace.stats.sampling_rate,
params.goal_sampling_rate))
sys.stdout.flush()
sys.exit()
logging.debug("%s.%s Decimate by a factor of %i" %
(station, comp, decimation_factor))
trace.data = trace.data[::int(decimation_factor)]
elif params.resampling_method == "Lanczos":
logging.debug(
"%s.%s Downsample to %.1f Hz" %
(station, comp, params.goal_sampling_rate))
trace.data = np.array(trace.data)
trace.interpolate(
method="lanczos",
sampling_rate=params.goal_sampling_rate,
a=1.0)
trace.stats.sampling_rate = params.goal_sampling_rate
if get_config(db, 'remove_response', isbool=True):
logging.debug('%s Removing instrument response' %
stream[0].id)
response_prefilt = eval(get_config(db, 'response_prefilt'))
response = responses[responses["channel_id"] ==
stream[0].id]
if len(response) > 1:
response = response[
response["start_date"] < UTCDateTime(gd)]
response = response[
response["end_date"] > UTCDateTime(gd)]
elif len(response) == 0:
logging.info("No instrument response information "
"for %s, exiting" % stream[0].id)
sys.exit()
datalesspz = response["paz"].values[0]
stream.simulate(
paz_remove=datalesspz,
remove_sensitivity=True,
pre_filt=response_prefilt,
paz_simulate=None,
)
for tr in stream:
tr.data = tr.data.astype(np.float32)
output += stream
del stream
del files
clean_scipy_cache()
return 0, output
def preprocess(db, stations, comps, goal_day, params, responses=None):
"""
Fetches data for each ``stations`` and each ``comps`` using the
data_availability table in the database.
To correct for instrument responses, make sure to set ``remove_response``
to "Y" in the config and to provide the ``responses`` DataFrame.
:Example:
>>> from msnoise.api import connect, get_params, preload_instrument_responses
>>> from msnoise.preprocessing import preprocess
>>> db = connect()
>>> params = get_params(db)
>>> responses = preload_instrument_responses(db)
>>> st = preprocess(db, ["YA.UV06","YA.UV10"], ["Z",], "2010-09-01", params, responses)
>>> st
2 Trace(s) in Stream:
YA.UV06.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
YA.UV10.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
:type db: :class:`sqlalchemy.orm.session.Session`
:param db: A :class:`~sqlalchemy.orm.session.Session` object, as
obtained by :func:`msnoise.api.connect`.
:type stations: list of str
:param stations: a list of station names, in the format NET.STA.
:type comps: list of str
:param comps: a list of component names, in Z,N,E,1,2.
:type goal_day: str
:param goal_day: the day of data to load, ISO 8601 format: e.g. 2016-12-31.
:type params: class
:param params: an object containing the config parameters, as obtained by
:func:`msnoise.api.get_params`.
:type responses: :class:`pandas.DataFrame`
:param responses: a DataFrame containing the instrument responses, as
obtained by :func:`msnoise.api.preload_instrument_responses`.
:rtype: :class:`obspy.core.stream.Stream`
:return: A Stream object containing all traces.
"""
datafiles = {}
output = Stream()
MULTIPLEX = False
MULTIPLEX_files = {}
for station in stations:
datafiles[station] = {}
net, sta, loc = station.split('.')
gd = datetime.datetime.strptime(goal_day, '%Y-%m-%d')
files = get_data_availability(db,
net=net,
sta=sta,
loc=loc,
starttime=gd,
endtime=gd)
for comp in comps:
datafiles[station][comp] = []
for file in files:
if file.sta != "MULTIPLEX":
if file.chan[-1] not in comps:
continue
fullpath = os.path.join(file.path, file.file)
datafiles[station][file.chan[-1]].append(fullpath)
else:
MULTIPLEX = True
print("Mutliplex mode, reading the files")
fullpath = os.path.join(file.path, file.file)
multiplexed = sorted(glob.glob(fullpath))
for comp in comps:
for fn in multiplexed:
if fn in MULTIPLEX_files:
_ = MULTIPLEX_files[fn]
else:
# print("Reading %s" % fn)
_ = read(fn, format=params.archive_format or None)
traces = []
for tr in _:
if "%s.%s" % (
tr.stats.network, tr.stats.station
) in stations and tr.stats.channel[-1] in comps:
traces.append(tr)
del _
_ = Stream(traces=traces)
MULTIPLEX_files[fn] = _
datafiles[station][comp].append(_)
for istation, station in enumerate(stations):
net, sta, loc = station.split(".")
for comp in comps:
files = eval("datafiles['%s']['%s']" % (station, comp))
if len(files) != 0:
logger.debug("%s.%s Reading %i Files" %
(station, comp, len(files)))
traces = []
for file in files:
if isinstance(file, Stream):
st = file.select(network=net,
station=sta,
component=comp).copy()
else:
try:
# print("Reading %s" % file)
# t= time.time()
st = read(file,
dytpe=np.float,
starttime=UTCDateTime(gd),
endtime=UTCDateTime(gd) + 86400,
station=sta,
format=params.archive_format or None)
# print("done in", time.time()-t)
except:
logger.debug("ERROR reading file %s" % file)
# TODO add traceback (optional?)
continue
for tr in st:
if len(tr.stats.channel) == 2:
tr.stats.channel += tr.stats.location
tr.stats.location = "00"
tmp = st.select(network=net, station=sta, component=comp)
if not len(tmp):
for tr in st:
tr.stats.network = net
st = st.select(network=net,
station=sta,
component=comp)
else:
st = tmp
for tr in st:
tr.data = tr.data.astype(np.float)
tr.stats.network = tr.stats.network.upper()
tr.stats.station = tr.stats.station.upper()
tr.stats.channel = tr.stats.channel.upper()
if tr.stats.location == "":
tr.stats.location = "--"
traces.append(tr)
del st
stream = Stream(traces=traces)
if not (len(stream)):
continue
f = io.BytesIO()
stream.write(f, format='MSEED')
f.seek(0)
stream = read(f, format="MSEED")
stream.sort()
# try:
# # HACK not super clean... should find a way to prevent the
# # same trace id with different sps to occur
# stream.merge(method=1, interpolation_samples=3, fill_value=None)
# except:
# logger.debug("Error while merging...")
# traceback.print_exc()
# continue
# stream = stream.split()
if not len(stream):
continue
logger.debug("%s Checking sample alignment" % stream[0].id)
for i, trace in enumerate(stream):
stream[i] = check_and_phase_shift(
trace, params.preprocess_taper_length)
logger.debug("%s Checking Gaps" % stream[0].id)
if len(getGaps(stream)) > 0:
max_gap = params.preprocess_max_gap * stream[
0].stats.sampling_rate
gaps = getGaps(stream)
while len(gaps):
too_long = 0
for gap in gaps:
if int(gap[-1]) <= max_gap:
try:
stream[gap[0]] = stream[gap[0]].__add__(
stream[gap[1]],
method=1,
fill_value="interpolate")
stream.remove(stream[gap[1]])
except:
stream.remove(stream[gap[1]])
break
else:
too_long += 1
if too_long == len(gaps):
break
gaps = getGaps(stream)
del gaps
stream = stream.split()
for tr in stream:
if tr.stats.sampling_rate < (params.goal_sampling_rate -
1):
stream.remove(tr)
taper_length = params.preprocess_taper_length # seconds
for trace in stream:
if trace.stats.npts < (4 * taper_length *
trace.stats.sampling_rate):
stream.remove(trace)
else:
trace.detrend(type="demean")
trace.detrend(type="linear")
trace.taper(max_percentage=None,
max_length=taper_length)
if not len(stream):
logger.debug(" has only too small traces, skipping...")
continue
for trace in stream:
logger.debug("%s Highpass at %.2f Hz" %
(trace.id, params.preprocess_highpass))
trace.filter("highpass",
freq=params.preprocess_highpass,
zerophase=True,
corners=4)
if trace.stats.sampling_rate != params.goal_sampling_rate:
logger.debug("%s Lowpass at %.2f Hz" %
(trace.id, params.preprocess_lowpass))
trace.filter("lowpass",
freq=params.preprocess_lowpass,
zerophase=True,
corners=8)
if params.resampling_method == "Resample":
logger.debug("%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = resample(
trace.data, params.goal_sampling_rate /
trace.stats.sampling_rate, 'sinc_fastest')
elif params.resampling_method == "Decimate":
decimation_factor = trace.stats.sampling_rate / params.goal_sampling_rate
if not int(decimation_factor) == decimation_factor:
logger.warning(
"%s CANNOT be decimated by an integer factor, consider using Resample or Lanczos methods"
" Trace sampling rate = %i ; Desired CC sampling rate = %i"
% (trace.id, trace.stats.sampling_rate,
params.goal_sampling_rate))
sys.stdout.flush()
sys.exit()
logger.debug("%s Decimate by a factor of %i" %
(trace.id, decimation_factor))
trace.data = trace.data[::int(decimation_factor)]
elif params.resampling_method == "Lanczos":
logger.debug("%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = np.array(trace.data)
trace.interpolate(
method="lanczos",
sampling_rate=params.goal_sampling_rate,
a=1.0)
trace.stats.sampling_rate = params.goal_sampling_rate
del trace
if params.remove_response:
logger.debug('%s Removing instrument response' %
stream[0].id)
try:
stream.attach_response(responses)
stream.remove_response(
pre_filt=params.response_prefilt, taper=False)
except:
logger.error("Bad or no instrument response "
"information for %s, skipping" %
stream[0].id)
continue
for tr in stream:
tr.data = tr.data.astype(np.float32)
output += stream
del stream
del files
clean_scipy_cache()
del MULTIPLEX_files
return output
def main():
print()
print(
"################################################################################"
)
print(
"# __ _ _ #"
)
print(
"# _ __ / _|_ __ _ _ | |__ __ _ _ __ _ __ ___ ___ _ __ (_) ___ ___ #"
)
print(
"# | '__| |_| '_ \| | | | | '_ \ / _` | '__| '_ ` _ \ / _ \| '_ \| |/ __/ __| #"
)
print(
"# | | | _| |_) | |_| | | | | | (_| | | | | | | | | (_) | | | | | (__\__ \ #"
)
print(
"# |_| |_| | .__/ \__, |___|_| |_|\__,_|_| |_| |_| |_|\___/|_| |_|_|\___|___/ #"
)
print(
"# |_| |___/_____| #"
)
print(
"# #"
)
print(
"################################################################################"
)
print()
# Run Input Parser
args = get_harmonics_arguments()
# Load Database
db, stkeys = stdb.io.load_db(fname=args.indb, keys=args.stkeys)
# Track processed folders
procfold = []
# Loop over station keys
for stkey in list(stkeys):
# Extract station information from dictionary
sta = db[stkey]
# Construct Folder Name
stfld = stkey
if not args.lkey:
stfld = stkey.split('.')[0] + "." + stkey.split('.')[1]
# Define path to see if it exists
if args.phase in ['P', 'PP', 'allP']:
datapath = Path('P_DATA') / stfld
elif args.phase in ['S', 'SKS', 'allS']:
datapath = Path('S_DATA') / stfld
if not datapath.is_dir():
print('Path to ' + str(datapath) + ' doesn`t exist - continuing')
continue
# Get search start time
if args.startT is None:
tstart = sta.startdate
else:
tstart = args.startT
# Get search end time
if args.endT is None:
tend = sta.enddate
else:
tend = args.endT
if tstart > sta.enddate or tend < sta.startdate:
continue
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
print(" ")
print(" ")
print("|===============================================|")
print("|===============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|===============================================|")
print("|===============================================|")
print("| Station: {0:>2s}.{1:5s} |".format(
sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Start time: {0:19s} |".format(
sta.startdate.strftime("%Y-%m-%d %H:%M:%S")))
print("| End time: {0:19s} |".format(
sta.enddate.strftime("%Y-%m-%d %H:%M:%S")))
print("|-----------------------------------------------|")
# Check for folder already processed
if stfld in procfold:
print(' {0} already processed...skipping '.format(stfld))
continue
rfRstream = Stream()
rfTstream = Stream()
datafiles = [x for x in datapath.iterdir() if x.is_dir()]
for folder in datafiles:
# Skip hidden folders
if folder.name.startswith('.'):
continue
date = folder.name.split('_')[0]
year = date[0:4]
month = date[4:6]
day = date[6:8]
dateUTC = UTCDateTime(year + '-' + month + '-' + day)
if dateUTC > tstart and dateUTC < tend:
filename = folder / "RF_Data.pkl"
if filename.is_file():
file = open(filename, "rb")
rfdata = pickle.load(file)
if rfdata[0].stats.snrh > args.snrh and \
rfdata[0].stats.snr and \
rfdata[0].stats.cc > args.cc:
rfRstream.append(rfdata[1])
rfTstream.append(rfdata[2])
file.close()
else:
continue
if args.no_outl:
# Remove outliers wrt variance
varR = np.array([np.var(tr.data) for tr in rfRstream])
# Calculate outliers
medvarR = np.median(varR)
madvarR = 1.4826 * np.median(np.abs(varR - medvarR))
robustR = np.abs((varR - medvarR) / madvarR)
outliersR = np.arange(len(rfRstream))[robustR > 2.]
for i in outliersR[::-1]:
rfRstream.remove(rfRstream[i])
rfTstream.remove(rfTstream[i])
# Do the same for transverse
varT = np.array([np.var(tr.data) for tr in rfTstream])
medvarT = np.median(varT)
madvarT = 1.4826 * np.median(np.abs(varT - medvarT))
robustT = np.abs((varT - medvarT) / madvarT)
outliersT = np.arange(len(rfTstream))[robustT > 2.]
for i in outliersT[::-1]:
rfRstream.remove(rfRstream[i])
rfTstream.remove(rfTstream[i])
# Try binning if specified
if args.nbin is not None:
rf_tmp = binning.bin(rfRstream,
rfTstream,
typ='baz',
nbin=args.nbin + 1)
rfRstream = rf_tmp[0]
rfTstream = rf_tmp[1]
# Filter original streams
rfRstream.filter('bandpass',
freqmin=args.bp[0],
freqmax=args.bp[1],
corners=2,
zerophase=True)
rfTstream.filter('bandpass',
freqmin=args.bp[0],
freqmax=args.bp[1],
corners=2,
zerophase=True)
# Initialize the Harmonics object
harmonics = Harmonics(rfRstream, rfTstream)
# Stack with or without dip
if args.find_azim:
harmonics.dcomp_find_azim(xmin=args.trange[0], xmax=args.trange[1])
print("Optimal azimuth for trange between " + str(args.trange[0]) +
" and " + str(args.trange[1]) + " seconds is: " +
str(harmonics.azim))
else:
harmonics.dcomp_fix_azim(azim=args.azim)
if args.save_plot and not Path('FIGURES').is_dir():
Path('FIGURES').mkdir(parents=True)
if args.plot:
harmonics.plot(args.ymax, args.scale, args.save_plot, args.title,
args.form)
if args.save:
filename = datapath / (harmonics.hstream[0].stats.station +
".harmonics.pkl")
harmonics.save(filename)
# Update processed folders
procfold.append(stfld)
".." + channel + ".mseed")
if os.path.isfile(file):
st_temp += read(file)
# load 24h continuous waveforms only if template exists
for tt in st_temp:
station = tt.stats.station
channel = tt.stats.channel
file1 = cont_dir + sday + "." + station + "." + channel
# print(" file1 ===", file1)
if os.path.isfile(file1):
st_cont += read(file1)
else:
# remove from the template stream if continuous not exists
st_temp.remove(tt)
st_cont.filter("bandpass",
freqmin=bandpass[0],
freqmax=bandpass[1],
zerophase=True)
# define variables
# st1_temp = st_temp.select(channel=channel[0])
tt = Trace()
tc = Trace()
count = 0
nmin = 0
npanels = len(st_temp)
nfile = len(st_temp)
def main():
print()
print("#########################################")
print("# __ _ _ #")
print("# _ __ / _|_ __ _ _ | |__ | | __ #")
print("# | '__| |_| '_ \| | | | | '_ \| |/ / #")
print("# | | | _| |_) | |_| | | | | | < #")
print("# |_| |_| | .__/ \__, |___|_| |_|_|\_\ #")
print("# |_| |___/_____| #")
print("# #")
print("#########################################")
print()
# Run Input Parser
args = arguments.get_hk_arguments()
# Load Database
db = stdb.io.load_db(fname=args.indb)
# Construct station key loop
allkeys = db.keys()
sorted(allkeys)
# Extract key subset
if len(args.stkeys) > 0:
stkeys = []
for skey in args.stkeys:
stkeys.extend([s for s in allkeys if skey in s])
else:
stkeys = db.keys()
sorted(stkeys)
# Loop over station keys
for stkey in list(stkeys):
# Extract station information from dictionary
sta = db[stkey]
# Define path to see if it exists
if args.phase in ['P', 'PP', 'allP']:
datapath = Path('P_DATA') / stkey
elif args.phase in ['S', 'SKS', 'allS']:
datapath = Path('S_DATA') / stkey
if not datapath.is_dir():
print('Path to ' + str(datapath) + ' doesn`t exist - continuing')
continue
# Define save path
if args.save:
savepath = Path('HK_DATA') / stkey
if not savepath.is_dir():
print('Path to ' + str(savepath) +
' doesn`t exist - creating it')
savepath.mkdir(parents=True)
# Get search start time
if args.startT is None:
tstart = sta.startdate
else:
tstart = args.startT
# Get search end time
if args.endT is None:
tend = sta.enddate
else:
tend = args.endT
if tstart > sta.enddate or tend < sta.startdate:
continue
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
print(" ")
print(" ")
print("|===============================================|")
print("|===============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|===============================================|")
print("|===============================================|")
print("| Station: {0:>2s}.{1:5s} |".format(
sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Start time: {0:19s} |".format(
sta.startdate.strftime("%Y-%m-%d %H:%M:%S")))
print("| End time: {0:19s} |".format(
sta.enddate.strftime("%Y-%m-%d %H:%M:%S")))
print("|-----------------------------------------------|")
rfRstream = Stream()
datafiles = [x for x in datapath.iterdir() if x.is_dir()]
for folder in datafiles:
# Skip hidden folders
if folder.name.startswith('.'):
continue
date = folder.name.split('_')[0]
year = date[0:4]
month = date[4:6]
day = date[6:8]
dateUTC = UTCDateTime(year + '-' + month + '-' + day)
if dateUTC > tstart and dateUTC < tend:
# Load meta data
metafile = folder / "Meta_Data.pkl"
if not metafile.is_file():
continue
meta = pickle.load(open(metafile, 'rb'))
# Skip data not in list of phases
if meta.phase not in args.listphase:
continue
# QC Thresholding
if meta.snrh < args.snrh:
continue
if meta.snr < args.snr:
continue
if meta.cc < args.cc:
continue
# # Check bounds on data
# if meta.slow < args.slowbound[0] and meta.slow > args.slowbound[1]:
# continue
# if meta.baz < args.bazbound[0] and meta.baz > args.bazbound[1]:
# continue
# If everything passed, load the RF data
filename = folder / "RF_Data.pkl"
if filename.is_file():
file = open(filename, "rb")
rfdata = pickle.load(file)
rfRstream.append(rfdata[1])
file.close()
if rfdata[0].stats.npts != 1451:
print(folder)
if len(rfRstream) == 0:
continue
if args.no_outl:
t1 = 0.
t2 = 30.
varR = []
for i in range(len(rfRstream)):
taxis = rfRstream[i].stats.taxis
tselect = (taxis > t1) & (taxis < t2)
varR.append(np.var(rfRstream[i].data[tselect]))
varR = np.array(varR)
# Remove outliers wrt variance within time range
medvarR = np.median(varR)
madvarR = 1.4826 * np.median(np.abs(varR - medvarR))
robustR = np.abs((varR - medvarR) / madvarR)
outliersR = np.arange(len(rfRstream))[robustR > 2.5]
for i in outliersR[::-1]:
rfRstream.remove(rfRstream[i])
print('')
print("Number of radial RF data: " + str(len(rfRstream)))
print('')
# Try binning if specified
if args.calc_dip:
rf_tmp = binning.bin_baz_slow(rfRstream,
nbaz=args.nbaz + 1,
nslow=args.nslow + 1,
pws=args.pws)
rfRstream = rf_tmp[0]
else:
rf_tmp = binning.bin(rfRstream,
typ='slow',
nbin=args.nslow + 1,
pws=args.pws)
rfRstream = rf_tmp[0]
# Get a copy of the radial component and filter
if args.copy:
rfRstream_copy = rfRstream.copy()
rfRstream_copy.filter('bandpass',
freqmin=args.bp_copy[0],
freqmax=args.bp_copy[1],
corners=2,
zerophase=True)
# Check bin counts:
for tr in rfRstream:
if (tr.stats.nbin < args.binlim):
rfRstream.remove(tr)
# Continue if stream is too short
if len(rfRstream) < 5:
continue
if args.save_plot and not Path('HK_PLOTS').is_dir():
Path('HK_PLOTS').mkdir(parents=True)
print('')
print("Number of radial RF bins: " + str(len(rfRstream)))
print('')
# Filter original stream
rfRstream.filter('bandpass',
freqmin=args.bp[0],
freqmax=args.bp[1],
corners=2,
zerophase=True)
# Initialize the HkStack object
try:
hkstack = HkStack(rfRstream,
rfV2=rfRstream_copy,
strike=args.strike,
dip=args.dip,
vp=args.vp)
except:
hkstack = HkStack(rfRstream,
strike=args.strike,
dip=args.dip,
vp=args.vp)
# Update attributes
hkstack.hbound = args.hbound
hkstack.kbound = args.kbound
hkstack.dh = args.dh
hkstack.dk = args.dk
hkstack.weights = args.weights
# Stack with or without dip
if args.calc_dip:
hkstack.stack_dip()
else:
hkstack.stack()
# Average stacks
hkstack.average(typ=args.typ)
if args.plot:
hkstack.plot(args.save_plot, args.title, args.form)
if args.save:
filename = savepath / (hkstack.rfV1[0].stats.station + \
".hkstack."+args.typ+".pkl")
hkstack.save(file=filename)
class chunk(object):
def __init__(self, N, S, L, C, verbose = False):
self.N = N
self.S = S
self.L = L
self.C = C
self._verbose = verbose
self._S = Stream()
def id(self):
return "%s.%s.%s.%s" % (self.N, self.S, self.L, self.C)
def holdings(self):
print(self._S)
return
@staticmethod
def _syncronize(others, this):
others.append(this)
ss = reduce(max, map(lambda st: st[0].stats.starttime, others))
ee = reduce(min, map(lambda st: st[0].stats.endtime, others))
for st in others:
st.trim(ss, ee, nearest_sample = True)
mask = np.zeros(len(this[0]), dtype=bool)
for st in others:
if len(st) > 1: st.merge()
if not hasattr(st[0].data, "mask"): continue
mask += st[0].data.mask
for st in others:
st[0].data = np.ma.masked_where(mask , st[0].data)
return
def _clean(self, s, e = None):
toremove = []
for t in self._S:
if t.stats.endtime < s:
toremove.append(t)
for t in toremove:
self._S.remove(t)
if e is not None:
for tr in self._S:
tr.trim(starttime=s, endtime=e)
return
def _update(self, s, e):
raise NotImplemented("Please Implement Me!")
def get(self, start, end, others, withgaps = False, incomplete = False,
nosync = False):
if isinstance(start, int):
s, _ = sol_span_in_utc(start)
_, e = sol_span_in_utc(end)
else:
s, e = start, end
self._update(s, e)
sliced_t = self._S.slice(s, e, keep_empty_traces = False,
nearest_sample = True).copy()
ntraces = len(sliced_t)
sliced_t = sliced_t.merge(method = 1,
interpolation_samples = 0,
fill_value = None)
if ntraces == 0: return False
if not incomplete and (sliced_t[0].stats.starttime > s or \
sliced_t[0].stats.endtime < e):
log(f" W:> Trace is incomplete: {sliced_t[0].stats.starttime} >" +\
f" {s} || {sliced_t[0].stats.endtime} < {e}.", level=1,
verbose=self._verbose)
return False
if not withgaps and ntraces > 1:
if self._verbose: print(" W:> Trace has %d gap%s." % (ntraces - 1,
"" if ntraces == 2 else "s"))
return False
if not nosync:
self._syncronize(others, sliced_t)
else:
others.append(sliced_t)
return True
def main():
print()
print("############################################")
print("# __ #")
print("# _ __ / _|_ __ _ _ ___ ___ _ __ #")
print("# | '__| |_| '_ \| | | | / __/ __| '_ \ #")
print("# | | | _| |_) | |_| | | (_| (__| |_) | #")
print("# |_| |_| | .__/ \__, |___\___\___| .__/ #")
print("# |_| |___/_____| |_| #")
print("# #")
print("############################################")
print()
# Run Input Parser
args = arguments.get_ccp_arguments()
# Load Database
db = stdb.io.load_db(fname=args.indb)
# Construct station key loop
allkeys = db.keys()
# Extract key subset
if len(args.stkeys) > 0:
stkeys = []
for skey in args.stkeys:
stkeys.extend([s for s in allkeys if skey in s])
else:
stkeys = db.keys()
if args.load:
# Check if CCPimage object exists and whether overwrite has been set
load_file = Path('CCP_load.pkl')
if load_file.is_file() and not args.ovr:
ccpfile = open(load_file, "rb")
ccpimage = pickle.load(ccpfile)
ccpfile.close()
else:
print()
print("|-----------------------------------------------|")
print("| Loading data |")
print("|-----------------------------------------------|")
print("| Gridding: ")
print("| start = {0:5.1f},{1:6.1f}".format(
args.coord_start[0], args.coord_start[1]))
print("| end = {0:5.1f},{1:6.1f}".format(
args.coord_end[0], args.coord_end[1]))
print("| dz = {0} (km)".format(str(args.dz)))
print("| dx = {0} (km)".format(str(args.dx)))
print()
# Initialize CCPimage object
ccpimage = CCPimage(coord_start=args.coord_start,
coord_end=args.coord_end,
dz=args.dz,
dx=args.dx)
# Loop over station keys
for stkey in list(stkeys):
# Extract station information from dictionary
sta = db[stkey]
# Define path to see if it exists
if args.phase in ['P', 'PP', 'allP']:
datapath = Path('P_DATA') / stkey
elif args.phase in ['S', 'SKS', 'allS']:
datapath = Path('S_DATA') / stkey
if not datapath.is_dir():
print('Path to ' + str(datapath) +
' doesn`t exist - continuing')
continue
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
rfRstream = Stream()
datafiles = [x for x in datapath.iterdir() if x.is_dir()]
for folder in datafiles:
# Skip hidden folders
if folder.name.startswith('.'):
continue
# Load meta data
filename = folder / "Meta_Data.pkl"
if not filename.is_file():
continue
metafile = open(filename, 'rb')
meta = pickle.load(metafile)
metafile.close()
# Skip data not in list of phases
if meta.phase not in args.listphase:
continue
# QC Thresholding
if meta.snrh < args.snrh:
continue
if meta.snr < args.snr:
continue
if meta.cc < args.cc:
continue
# If everything passed, load the RF data
filename = folder / "RF_Data.pkl"
if filename.is_file():
file = open(filename, "rb")
rfdata = pickle.load(file)
rfRstream.append(rfdata[1])
file.close()
if len(rfRstream) == 0:
continue
if args.no_outl:
t1 = 0.
t2 = 30.
varR = []
for i in range(len(rfRstream)):
taxis = rfRstream[i].stats.taxis
tselect = (taxis > t1) & (taxis < t2)
varR.append(np.var(rfRstream[i].data[tselect]))
varR = np.array(varR)
# Remove outliers wrt variance within time range
medvarR = np.median(varR)
madvarR = 1.4826 * np.median(np.abs(varR - medvarR))
robustR = np.abs((varR - medvarR) / madvarR)
outliersR = np.arange(len(rfRstream))[robustR > 2.5]
for i in outliersR[::-1]:
rfRstream.remove(rfRstream[i])
print("Station: {0:>2s}.{1:5s} - {2} traces loaded".format(
sta.network, sta.station, len(rfRstream)))
if len(rfRstream) == 0:
continue
ccpimage.add_rfstream(rfRstream)
if len(ccpimage.radialRF) > 0:
ccpimage.save("CCP_load.pkl")
ccpimage.is_ready_for_prep = True
print()
print("CCPimage saved to 'CCP_load.pkl'")
else:
ccpimage.is_ready_for_prep = False
else:
pass
if args.prep:
prep_file = Path("CCP_prep.pkl")
if prep_file.is_file() and not args.ovr:
ccpfile = open(prep_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
else:
load_file = Path('CCP_load.pkl')
if not load_file.is_file():
raise (Exception("No CCP_load.pkl file available - aborting"))
else:
print()
print("|-----------------------------------------------|")
print("| Preparing data before stacking |")
print("|-----------------------------------------------|")
print("| Frequencies: ")
print("| f1 = {0:4.2f} (Hz)".format(args.f1))
print("| f2ps = {0:4.2f} (Hz)".format(args.f2ps))
print("| f2pps = {0:4.2f} (Hz)".format(args.f2pps))
print("| f2pss = {0:4.2f} (Hz)".format(args.f2pss))
print("| Binning: ")
print("| nbaz = {0}".format(str(args.nbaz)))
print("| nslow = {0}".format(str(args.nslow)))
print()
ccpfile = open(load_file, "rb")
ccpimage = pickle.load(ccpfile)
ccpfile.close()
ccpimage.prep_data(f1=args.f1,
f2ps=args.f2ps,
f2pps=args.f2pps,
f2pss=args.f2pss,
nbaz=args.nbaz,
nslow=args.nslow)
ccpimage.is_ready_for_prestack = True
ccpimage.save(prep_file)
print()
print("CCPimage saved to {0}".format(str(prep_file)))
else:
pass
if args.prestack:
prestack_file = Path("CCP_prestack.pkl")
if prestack_file.is_file() and not args.ovr:
ccpfile = open(prestack_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
else:
prep_file = Path("CCP_prep.pkl")
if not prep_file.is_file():
raise (Exception("No CCP_prep.pkl file available - aborting"))
else:
print()
print("|-----------------------------------------------|")
print("| CCP pre-stacking each phase |")
print("|-----------------------------------------------|")
print()
ccpfile = open(prep_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
ccpimage.prestack()
ccpimage.save(prestack_file)
print()
print("CCPimage saved to {0}".format(str(prestack_file)))
else:
pass
if args.ccp:
ccp_file = Path("CCP_stack.pkl")
if ccp_file.is_file() and not args.ovr:
ccpfile = open(ccp_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
else:
prestack_file = Path("CCP_prestack.pkl")
if not prestack_file.is_file():
raise (
Exception("No CCP_prestack.pkl file available - aborting"))
else:
if args.linear:
print()
print("|-----------------------------------------------|")
print("| Linear CCP stack - all phases |")
print("|-----------------------------------------------|")
print()
elif args.pws:
print()
print("|-----------------------------------------------|")
print("| Phase-weighted CCP stack - all phases |")
print("|-----------------------------------------------|")
print()
ccpfile = open(prestack_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
ccpimage.ccp()
if args.linear:
if args.weights:
ccpimage.weights = args.weights
ccpimage.linear_stack(typ='ccp')
elif args.pws:
if args.weights:
ccpimage.weights = args.weights
ccpimage.phase_weighted_stack(typ='ccp')
ccpimage.save(ccp_file)
print()
print("CCPimage saved to {0}".format(str(ccp_file)))
if args.ccp_figure:
ccpimage.plot_ccp(save=args.save_figure,
fmt=args.fmt,
vmin=-1. * args.cbound,
vmax=args.cbound,
title=args.title)
else:
pass
if args.gccp:
gccp_file = Path("GCCP_stack.pkl")
if gccp_file.is_file() and not args.ovr:
ccpfile = open(gccp_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
else:
prestack_file = Path("CCP_prestack.pkl")
if not prestack_file.is_file():
raise (
Exception("No CCP_prestack.pkl file available - aborting"))
else:
if args.linear:
print()
print("|-----------------------------------------------|")
print("| Linear GCCP stack - all phases |")
print("|-----------------------------------------------|")
print()
elif args.pws:
print()
print("|-----------------------------------------------|")
print("| Phase-weighted GCCP stack - all phases |")
print("|-----------------------------------------------|")
print()
ccpfile = open(prestack_file, 'rb')
ccpimage = pickle.load(ccpfile)
ccpfile.close()
ccpimage.gccp(wlen=args.wlen)
if args.linear:
if args.weights:
ccpimage.weights = args.weights
ccpimage.linear_stack(typ='gccp')
elif args.pws:
if args.weights:
ccpimage.weights = args.weights
ccpimage.phase_weighted_stack(typ='gccp')
ccpimage.save(gccp_file)
print()
print("CCPimage saved to {0}".format(str(gccp_file)))
if args.ccp_figure:
ccpimage.plot_gccp(save=args.save_figure,
fmt=args.fmt,
vmin=-1. * args.cbound,
vmax=args.cbound,
title=args.title)
else:
pass
def preprocess(db, stations, comps, goal_day, params, responses=None):
datafiles = {}
output = Stream()
for station in stations:
datafiles[station] = {}
net, sta = station.split('.')
gd = datetime.datetime.strptime(goal_day, '%Y-%m-%d')
files = get_data_availability(
db, net=net, sta=sta, starttime=gd, endtime=gd)
for comp in comps:
datafiles[station][comp] = []
for file in files:
if file.comp[-1] not in comps:
continue
fullpath = os.path.join(file.path, file.file)
datafiles[station][file.comp[-1]].append(fullpath)
for istation, station in enumerate(stations):
net, sta = station.split(".")
for comp in comps:
files = eval("datafiles['%s']['%s']" % (station, comp))
if len(files) != 0:
logging.debug("%s.%s Reading %i Files" %
(station, comp, len(files)))
stream = Stream()
for file in sorted(files):
st = read(file, dytpe=np.float,
starttime=UTCDateTime(gd),
endtime=UTCDateTime(gd)+86400)
tmp = st.select(network=net, station=sta, component=comp)
if not len(tmp):
for tr in st:
tr.stats.network = net
st = st.select(network=net, station=sta, component=comp)
else:
st = tmp
for tr in st:
tr.data = tr.data.astype(np.float)
stream += st
del st
stream.sort()
stream.merge(method=1, interpolation_samples=3, fill_value=None)
stream = stream.split()
logging.debug("Checking sample alignment")
for i, trace in enumerate(stream):
stream[i] = check_and_phase_shift(trace)
logging.debug("Checking Gaps")
if len(getGaps(stream)) > 0:
max_gap = 10
only_too_long = False
while getGaps(stream) and not only_too_long:
too_long = 0
gaps = getGaps(stream)
for gap in gaps:
if int(gap[-1]) <= max_gap:
stream[gap[0]] = stream[gap[0]].__add__(stream[gap[1]], method=1,
fill_value="interpolate")
stream.remove(stream[gap[1]])
break
else:
too_long += 1
if too_long == len(gaps):
only_too_long = True
stream = stream.split()
taper_length = 20.0 # seconds
for trace in stream:
if trace.stats.npts < 4 * taper_length * trace.stats.sampling_rate:
stream.remove(trace)
else:
trace.detrend(type="demean")
trace.detrend(type="linear")
trace.taper(max_percentage=None, max_length=1.0)
if not len(stream):
logging.debug(" has only too small traces, skipping...")
continue
for trace in stream:
logging.debug(
"%s.%s Highpass at %.2f Hz" % (station, comp, params.preprocess_highpass))
trace.filter("highpass", freq=params.preprocess_highpass, zerophase=True)
if trace.stats.sampling_rate != params.goal_sampling_rate:
logging.debug(
"%s.%s Lowpass at %.2f Hz" % (station, comp, params.preprocess_lowpass))
trace.filter("lowpass", freq=params.preprocess_lowpass, zerophase=True, corners=8)
if params.resampling_method == "Resample":
logging.debug("%s.%s Downsample to %.1f Hz" %
(station, comp, params.goal_sampling_rate))
trace.data = resample(
trace.data, params.goal_sampling_rate / trace.stats.sampling_rate, 'sinc_fastest')
elif params.resampling_method == "Decimate":
decimation_factor = trace.stats.sampling_rate / params.goal_sampling_rate
if not int(decimation_factor) == decimation_factor:
logging.warning("%s.%s CANNOT be decimated by an integer factor, consider using Resample or Lanczos methods"
" Trace sampling rate = %i ; Desired CC sampling rate = %i" %
(station, comp, trace.stats.sampling_rate, params.goal_sampling_rate))
sys.stdout.flush()
sys.exit()
logging.debug("%s.%s Decimate by a factor of %i" %
(station, comp, decimation_factor))
trace.data = trace.data[::int(decimation_factor)]
elif params.resampling_method == "Lanczos":
logging.debug("%s.%s Downsample to %.1f Hz" %
(station, comp, params.goal_sampling_rate))
trace.data = np.array(trace.data)
trace.interpolate(method="lanczos", sampling_rate=params.goal_sampling_rate, a=1.0)
trace.stats.sampling_rate = params.goal_sampling_rate
if get_config(db, 'remove_response', isbool=True):
logging.debug('%s Removing instrument response'%stream[0].id)
response_prefilt = eval(get_config(db, 'response_prefilt'))
response = responses[responses["channel_id"] == stream[0].id]
if len(response) > 1:
response = response[response["start_date"]<UTCDateTime(gd)]
response = response[response["end_date"]>UTCDateTime(gd)]
elif len(response) == 0:
logging.info("No instrument response information "
"for %s, exiting" % stream[0].id)
sys.exit()
datalesspz = response["paz"].values[0]
stream.simulate(paz_remove=datalesspz,
remove_sensitivity=True,
pre_filt=response_prefilt,
paz_simulate=None, )
for tr in stream:
tr.data = tr.data.astype(np.float32)
output += stream
del stream
del files
clean_scipy_cache()
return 0, output
def get_waveforms(
eqinfo: Event,
META,
wp_tw_factor=15,
t_beforeP=1500., # seconds
t_afterWP=60., # seconds
client=None,
dist_range=(5., 90.), # degrees
add_ptime=True,
bulk_chunk_len=200,
prune_cutoffs=(1., 2., 3., 4., 5., 5.),
decimate=True,
reject_incomplete=False,
req_times=None,
waveforms=None,
save_path=None):
'''
This function will get the waveforms associated with an event to
perform a WP inversion.
:param float wp_tw_factor: Defines the Wphase time window. this is used as
:math:`[t_p, t_p + wp_tw_factor Delta]`.
:param float dist_range: A pair of floats specifying the min/max epicentral
distance (in degrees) to be considered; i.e. stations outside of this
range will be excluded.
:param bool add_ptime: Should the p arrival time (with respect to the origin
time) be included in each stations metadata. Including this should
result in a significant speedup.
:param int bulk_chunk_len: Maximum number of entries (channels) to be
included in a single request to a metadata service.
:param prune_cutoffs: List of pruning (distance) cutoffs passed to
:py:func:`station_pruningNEZ` :type prune_cutoffs: list of floats
:param bool decimate: Perform decimation of BH channels. **This should
always be true when running Wphase**.
:param dict req_times: Dictionary keyed by channel id containing the start
and end times of the time window required by Wphase.
:param client: An Obspy FDSN client.
:return: If *add_ptime* is *True*, then return a two element tuple containing:
#. An :py:class:`obspy.core.stream.Stream` containing the data.
#. A new metadata dictionary containing the p arrival time for each trace.
If *add_ptime* is *False* return the stream only.
'''
# Obtaining stations within the distance range only. trlist_in_dist_range will contain
# the ids of channels which are within the specified distance range.
trlist_in_dist_range = []
tr_dists_in_range = []
for trid, stmeta in META.items():
stlat, stlon = stmeta["latitude"], stmeta["longitude"]
dist = locations2degrees(eqinfo.latitude, eqinfo.longitude, stlat,
stlon)
if dist >= dist_range[0] and dist <= dist_range[1]:
trlist_in_dist_range.append(trid)
tr_dists_in_range.append(dist)
# Determining times for the waveform request. req_times will contain time series
# window for each channel.
# {
# <channel-id>: (<obspy.core.utcdatetime.UTCDateTime>, <obspy.core.utcdatetime.UTCDateTime>)
# }
#
# new_META:
# {
# <channel-id>: original metadata for channel and extra key 'ptime'
# }
if req_times is None:
req_times = {}
if add_ptime:
new_META = {}
for i_trid, trid in enumerate(trlist_in_dist_range):
stmeta = META[trid]
dist = tr_dists_in_range[i_trid]
t_p = getPtime(dist, eqinfo.depth)
t_p_UTC = eqinfo.time + t_p
t_wp_end = wp_tw_factor * dist
t_wp_end_UTC = t_p_UTC + t_wp_end
t1 = t_p_UTC - t_beforeP
t2 = t_wp_end_UTC + t_afterWP
if add_ptime:
new_META[trid] = stmeta
new_META[trid]['ptime'] = t_p
req_times[trid] = [t1, t2]
# ---------------get waveforms--------------
# Station pruning
if prune_cutoffs is not None:
trlist_in_dist_range = station_pruningNEZ(trlist_in_dist_range,
new_META, prune_cutoffs)
#reject incomplete traces:
if reject_incomplete:
now = UTCDateTime()
trlist_in_dist_range = [
trid for trid in trlist_in_dist_range if req_times[trid][1] < now
]
if waveforms:
# waveforms provided as input, just clean them
if isinstance(waveforms, str):
waveforms = obspy.read(waveforms)
logger.info('%d traces provided as input', len(waveforms))
st = waveforms
else:
# fetch waveforms from server
logger.info('fetching data from %s', client.base_url)
st = Stream()
# Create the subsets for each request
bulk = []
for trid in trlist_in_dist_range:
net, sta, loc, cha = trid.split('.')
t1, t2 = req_times[trid]
bulk.append([net, sta, loc, cha, t1, t2])
bulk_chunks = [
bulk[i_chunk:i_chunk + bulk_chunk_len]
for i_chunk in range(0, len(bulk), bulk_chunk_len)
]
# make a call for each subset
# TODO: One might want to do this in parallel.
for chunk in bulk_chunks:
# TODO: Do want to try/catch here?
try:
st += client.get_waveforms_bulk(chunk)
except Exception as e:
logger.error('Problem with request from server %s:\n%s',
client.base_url, str(e))
continue
if save_path:
logger.info("Saving waveforms in %s", save_path)
st.write(save_path, format='MSEED')
# Removing gappy traces (that is channel ids that are repeated)
st = remove_gappy_traces(st)
logger.info('%s traces remaining after throwing out gappy ones', len(st))
# Decimating BH channels. This can be done in parallel.
if decimate:
st_B_cha_list = [tr for tr in st if tr.id.split('.')[-1][0] == 'B']
for tr in st_B_cha_list:
try:
decimateTo1Hz(tr)
except CannotDecimate as e:
logger.info("Removing trace %s - %s", tr.id, e)
st.remove(tr)
# Creating contigous arrays for the traces. This may speed up things later.
for tr in st:
tr.data = np.ascontiguousarray(tr.data)
if add_ptime:
return st, new_META
else:
return st
def GetData(
eqinfo,
META,
wp_tw_factor=15,
t_beforeP=1500., # seconds
t_afterWP=60., # seconds
server="http://rhe-eqm-seiscomp-dev.dev.lan:8081/",
dist_range=(5., 90.), # degrees
add_ptime=True,
bulk_chunk_len=200,
prune_cutoffs=(1., 2., 3., 4., 5., 5.),
decimate=True,
reject_incomplete=False,
req_times=None):
'''
This function will get the waveforms associated with an event to
perform a WP inversion.
:param float wp_tw_factor: Defines the Wphase time window. this is used as
:math:`[t_p, t_p + wp\_tw\_factor \Delta]`.
:param float dist_range: A pair of floats specifying the min/max epicentral
distance (in degrees) to be considered; i.e. stations outside of this
range will be excluded.
:param bool add_ptime: Should the p arrival time (with respect to the origin
time) be included in each stations metadata. Including this should
result in a significant speedup.
:param int bulk_chunk_len: Maximum number of entries (channels) to be
included in a single request to a metadata service.
:param prune_cutoffs: List of pruning (distance) cutoffs passed to
:py:func:`station_pruningNEZ` :type prune_cutoffs: list of floats
:param bool decimate: Perform decimation of BH channels. **This should
always be true when running Wphase**.
:param dict req_times: Dictionary keyed by channel id containing the start
and end times of the time window required by Wphase.
:return: If *add_ptime* is *True*, then return a two element tuple containing:
#. An :py:class:`obspy.core.stream.Stream` containing the data.
#. A new metadata dictionary containing the p arrival time for each trace.
If *add_ptime* is *False* return the stream only.
'''
# Sampling rates we will decimate. Channels with other sampling rates will be ignored.
decimable_BH_ch = [20., 40., 50.]
client = Client(server)
hyplat = eqinfo['lat']
hyplon = eqinfo['lon']
hypdep = eqinfo['dep']
otime = eqinfo['time']
# Obtaining stations within the distance range only. trlist_in_dist_range will contain
# the ids of channels which are within the specified distance range.
trlist_in_dist_range = []
tr_dists_in_range = []
for trid, stmeta in META.iteritems():
stlat, stlon = stmeta['latitude'], stmeta['longitude']
dist = locations2degrees(hyplat, hyplon, stlat, stlon)
if dist >= dist_range[0] and dist <= dist_range[1]:
trlist_in_dist_range.append(trid)
tr_dists_in_range.append(dist)
# Determining times for the waveform request. req_times will contain time series
# window for each channel.
# {
# <channel-id>: (<obspy.core.utcdatetime.UTCDateTime>, <obspy.core.utcdatetime.UTCDateTime>)
# }
#
# new_META:
# {
# <channel-id>: original metadata for channel and extra key 'ptime'
# }
if req_times is None:
req_times = {}
if add_ptime:
new_META = {}
for i_trid, trid in enumerate(trlist_in_dist_range):
stmeta = META[trid]
dist = tr_dists_in_range[i_trid]
t_p = getPtime(dist, hypdep)
t_p_UTC = otime + t_p
t_wp_end = wp_tw_factor*dist
t_wp_end_UTC = t_p_UTC + t_wp_end
t1 = t_p_UTC - t_beforeP
t2 = t_wp_end_UTC + t_afterWP
if add_ptime:
new_META[trid] = stmeta
new_META[trid]['ptime'] = t_p
req_times[trid] = [t1, t2]
# ---------------get waveforms--------------
# Station pruning
if prune_cutoffs is not None:
trlist_in_dist_range = station_pruningNEZ(
trlist_in_dist_range,
new_META, prune_cutoffs)
#reject incomplete traces:
if reject_incomplete:
now = UTCDateTime()
trlist_in_dist_range = [trid for trid in trlist_in_dist_range
if req_times[trid][1] < now]
st = Stream()
# Create the subsets for each request
bulk = []
for trid in trlist_in_dist_range:
net, sta, loc, cha = trid.split('.')
t1, t2 = req_times[trid]
bulk.append([net, sta, loc, cha, t1, t2])
bulk_chunks = [bulk[i_chunk:i_chunk + bulk_chunk_len]
for i_chunk in xrange(0, len(bulk), bulk_chunk_len)]
# make a call for each subset
# TODO: One might want to do this in parallel.
for chunk in bulk_chunks:
# TODO: Do want to try/catch here?
try:
st += client.get_waveforms_bulk(chunk)
except Exception as e:
print 'Problem with request from server: {}'.format(server)
print e
continue
# Removing gappy traces (that is channel ids that are repeated)
trlist_data = [tr.id for tr in st]
rep_ids = [trid for trid, nrep in Counter(trlist_data).items()
if nrep > 1]
st = Stream(tr for tr in st if tr.id not in rep_ids)
# Decimating BH channels. This can be done in parallel.
if decimate:
st_B_cha_list = [tr for tr in st if tr.id.split('.')[-1][0] == 'B']
for tr in st_B_cha_list:
samp_rate = tr.stats.sampling_rate
if samp_rate not in decimable_BH_ch:
st.remove(tr)
continue
if samp_rate == 20.:
tr = dec20to1(tr, fast=True)
elif samp_rate == 40:
tr = dec40to1(tr, fast=True)
elif samp_rate == 50:
tr = dec50to1(tr, fast=True)
# Creating contigous arrays for the traces. This may speed up things later.
for tr in st:
tr.data = np.ascontiguousarray(tr.data)
if add_ptime:
return st, new_META
else:
return st
snpts = 10
## Read in seismic data ##
st = Stream()
for day in range(stime.julday, etime.julday):
for staloc in stalocs:
try:
sta, loc = staloc.split('_')
st += read('/msd/' + net + '_' + sta + '/' + str(stime.year) +
'/' + str(stime.julday) + '/' + loc + '_LH*')
except:
pass
# Remove the vertical
for tr in st.select(channel="LHZ"):
st.remove(tr)
st.merge(fill_value=0)
st.sort()
if debug:
print(st)
## Creating Figure ##
fig = plt.figure(1, figsize=(19, 10))
plt.subplots_adjust(hspace=0.0)
## Setting font parameters ##
mpl.rc('font', family='serif')
mpl.rc('font', serif='Times')
mpl.rc('text', usetex=True)
mpl.rc('font', size=20)
def preprocess(db, stations, comps, goal_day, params, responses=None):
"""
Fetches data for each ``stations`` and each ``comps`` using the
data_availability table in the database.
To correct for instrument responses, make sure to set ``remove_response``
to "Y" in the config and to provide the ``responses`` DataFrame.
:Example:
>>> from msnoise.api import connect, get_params, preload_instrument_responses
>>> from msnoise.preprocessing import preprocess
>>> db = connect()
>>> params = get_params(db)
>>> responses = preload_instrument_responses(db)
>>> st = preprocess(db, ["YA.UV06","YA.UV10"], ["Z",], "2010-09-01", params, responses)
>>> st
2 Trace(s) in Stream:
YA.UV06.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
YA.UV10.00.HHZ | 2010-09-01T00:00:00.000000Z - 2010-09-01T23:59:59.950000Z | 20.0 Hz, 1728000 samples
:type db: :class:`sqlalchemy.orm.session.Session`
:param db: A :class:`~sqlalchemy.orm.session.Session` object, as
obtained by :func:`msnoise.api.connect`.
:type stations: list of str
:param stations: a list of station names, in the format NET.STA.
:type comps: list of str
:param comps: a list of component names, in Z,N,E,1,2.
:type goal_day: str
:param goal_day: the day of data to load, ISO 8601 format: e.g. 2016-12-31.
:type params: class
:param params: an object containing the config parameters, as obtained by
:func:`msnoise.api.get_params`.
:type responses: :class:`pandas.DataFrame`
:param responses: a DataFrame containing the instrument responses, as
obtained by :func:`msnoise.api.preload_instrument_responses`.
:rtype: :class:`obspy.core.stream.Stream`
:return: A Stream object containing all traces.
"""
datafiles = {}
output = Stream()
for station in stations:
datafiles[station] = {}
net, sta = station.split('.')
gd = datetime.datetime.strptime(goal_day, '%Y-%m-%d')
files = get_data_availability(
db, net=net, sta=sta, starttime=gd, endtime=gd)
for comp in comps:
datafiles[station][comp] = []
for file in files:
if file.comp[-1] not in comps:
continue
fullpath = os.path.join(file.path, file.file)
datafiles[station][file.comp[-1]].append(fullpath)
for istation, station in enumerate(stations):
net, sta = station.split(".")
for comp in comps:
files = eval("datafiles['%s']['%s']" % (station, comp))
if len(files) != 0:
logger.debug("%s.%s Reading %i Files" %
(station, comp, len(files)))
stream = Stream()
for file in sorted(files):
try:
st = read(file, dytpe=np.float,
starttime=UTCDateTime(gd),
endtime=UTCDateTime(gd)+86400)
except:
logger.debug("ERROR reading file %s" % file)
continue
for tr in st:
if len(tr.stats.channel) == 2:
tr.stats.channel += tr.stats.location
tr.stats.location = "00"
tmp = st.select(network=net, station=sta, component=comp)
if not len(tmp):
for tr in st:
tr.stats.network = net
st = st.select(network=net, station=sta, component=comp)
else:
st = tmp
for tr in st:
tr.data = tr.data.astype(np.float)
tr.stats.network = tr.stats.network.upper()
tr.stats.station = tr.stats.station.upper()
tr.stats.channel = tr.stats.channel.upper()
stream += st
del st
stream.sort()
try:
# HACK not super clean... should find a way to prevent the
# same trace id with different sps to occur
stream.merge(method=1, interpolation_samples=3, fill_value=None)
except:
logger.debug("Error while merging...")
traceback.print_exc()
continue
stream = stream.split()
if not len(stream):
continue
logger.debug("%s Checking sample alignment" % stream[0].id)
for i, trace in enumerate(stream):
stream[i] = check_and_phase_shift(trace)
logger.debug("%s Checking Gaps" % stream[0].id)
if len(getGaps(stream)) > 0:
max_gap = params.preprocess_max_gap*stream[0].stats.sampling_rate
gaps = getGaps(stream)
while len(gaps):
too_long = 0
for gap in gaps:
if int(gap[-1]) <= max_gap:
try:
stream[gap[0]] = stream[gap[0]].__add__(stream[gap[1]], method=1,
fill_value="interpolate")
stream.remove(stream[gap[1]])
except:
stream.remove(stream[gap[1]])
break
else:
too_long += 1
if too_long == len(gaps):
break
gaps = getGaps(stream)
del gaps
stream = stream.split()
for tr in stream:
if tr.stats.sampling_rate < (params.goal_sampling_rate-1):
stream.remove(tr)
taper_length = 20.0 # seconds
for trace in stream:
if trace.stats.npts < 4 * taper_length * trace.stats.sampling_rate:
stream.remove(trace)
else:
trace.detrend(type="demean")
trace.detrend(type="linear")
trace.taper(max_percentage=None, max_length=1.0)
if not len(stream):
logger.debug(" has only too small traces, skipping...")
continue
for trace in stream:
logger.debug(
"%s Highpass at %.2f Hz" % (trace.id, params.preprocess_highpass))
trace.filter("highpass", freq=params.preprocess_highpass, zerophase=True, corners=4)
if trace.stats.sampling_rate != params.goal_sampling_rate:
logger.debug(
"%s Lowpass at %.2f Hz" % (trace.id, params.preprocess_lowpass))
trace.filter("lowpass", freq=params.preprocess_lowpass, zerophase=True, corners=8)
if params.resampling_method == "Resample":
logger.debug("%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = resample(
trace.data, params.goal_sampling_rate / trace.stats.sampling_rate, 'sinc_fastest')
elif params.resampling_method == "Decimate":
decimation_factor = trace.stats.sampling_rate / params.goal_sampling_rate
if not int(decimation_factor) == decimation_factor:
logger.warning("%s CANNOT be decimated by an integer factor, consider using Resample or Lanczos methods"
" Trace sampling rate = %i ; Desired CC sampling rate = %i" %
(trace.id, trace.stats.sampling_rate, params.goal_sampling_rate))
sys.stdout.flush()
sys.exit()
logger.debug("%s Decimate by a factor of %i" %
(trace.id, decimation_factor))
trace.data = trace.data[::int(decimation_factor)]
elif params.resampling_method == "Lanczos":
logger.debug("%s Downsample to %.1f Hz" %
(trace.id, params.goal_sampling_rate))
trace.data = np.array(trace.data)
trace.interpolate(method="lanczos", sampling_rate=params.goal_sampling_rate, a=1.0)
trace.stats.sampling_rate = params.goal_sampling_rate
del trace
if params.remove_response:
logger.debug('%s Removing instrument response'%stream[0].id)
response = responses[responses["channel_id"] == stream[0].id]
if len(response) > 1:
response = response[response["start_date"] <= UTCDateTime(gd)]
if len(response) > 1:
response = response[response["end_date"] >= UTCDateTime(gd)]
elif len(response) == 0:
logger.info("No instrument response information "
"for %s, skipping" % stream[0].id)
continue
try:
datalesspz = response["paz"].values[0]
except:
logger.error("Bad instrument response information "
"for %s, skipping" % stream[0].id)
continue
stream.simulate(paz_remove=datalesspz,
remove_sensitivity=True,
pre_filt=params.response_prefilt,
paz_simulate=None, )
for tr in stream:
tr.data = tr.data.astype(np.float32)
output += stream
del stream
del files
clean_scipy_cache()
return output
def main():
print()
print(
"################################################################################"
)
print(
"# __ _ _ #"
)
print(
"# _ __ / _|_ __ _ _ | |__ __ _ _ __ _ __ ___ ___ _ __ (_) ___ ___ #"
)
print(
"# | '__| |_| '_ \| | | | | '_ \ / _` | '__| '_ ` _ \ / _ \| '_ \| |/ __/ __| #"
)
print(
"# | | | _| |_) | |_| | | | | | (_| | | | | | | | | (_) | | | | | (__\__ \ #"
)
print(
"# |_| |_| | .__/ \__, |___|_| |_|\__,_|_| |_| |_| |_|\___/|_| |_|_|\___|___/ #"
)
print(
"# |_| |___/_____| #"
)
print(
"# #"
)
print(
"################################################################################"
)
print()
# Run Input Parser
(opts, indb) = options.get_harmonics_options()
# Load Database
db = stdb.io.load_db(fname=indb)
# Construct station key loop
allkeys = db.keys()
sorted(allkeys)
# Extract key subset
if len(opts.stkeys) > 0:
stkeys = []
for skey in opts.stkeys:
stkeys.extend([s for s in allkeys if skey in s])
else:
stkeys = db.keys()
sorted(stkeys)
# Loop over station keys
for stkey in list(stkeys):
# Extract station information from dictionary
sta = db[stkey]
# Define path to see if it exists
datapath = 'DATA/' + stkey
if not os.path.isdir(datapath):
raise (Exception('Path to ' + datapath +
' doesn`t exist - aborting'))
# Get search start time
if opts.startT is None:
tstart = sta.startdate
else:
tstart = opts.startT
# Get search end time
if opts.endT is None:
tend = sta.enddate
else:
tend = opts.endT
if tstart > sta.enddate or tend < sta.startdate:
continue
# Temporary print locations
tlocs = sta.location
if len(tlocs) == 0:
tlocs = ['']
for il in range(0, len(tlocs)):
if len(tlocs[il]) == 0:
tlocs[il] = "--"
sta.location = tlocs
# Update Display
print(" ")
print(" ")
print("|===============================================|")
print("|===============================================|")
print("| {0:>8s} |".format(
sta.station))
print("|===============================================|")
print("|===============================================|")
print("| Station: {0:>2s}.{1:5s} |".format(
sta.network, sta.station))
print("| Channel: {0:2s}; Locations: {1:15s} |".format(
sta.channel, ",".join(tlocs)))
print("| Lon: {0:7.2f}; Lat: {1:6.2f} |".format(
sta.longitude, sta.latitude))
print("| Start time: {0:19s} |".format(
sta.startdate.strftime("%Y-%m-%d %H:%M:%S")))
print("| End time: {0:19s} |".format(
sta.enddate.strftime("%Y-%m-%d %H:%M:%S")))
print("|-----------------------------------------------|")
rfRstream = Stream()
rfTstream = Stream()
for folder in os.listdir(datapath):
# Skip hidden folders
if folder.startswith('.'):
continue
date = folder.split('_')[0]
year = date[0:4]
month = date[4:6]
day = date[6:8]
dateUTC = UTCDateTime(year + '-' + month + '-' + day)
if dateUTC > tstart and dateUTC < tend:
filename = datapath + "/" + folder + "/RF_Data.pkl"
if os.path.isfile(filename):
file = open(filename, "rb")
rfdata = pickle.load(file)
if rfdata[0].stats.snrh > opts.snrh and rfdata[0].stats.snr and \
rfdata[0].stats.cc > opts.cc:
rfRstream.append(rfdata[1])
rfTstream.append(rfdata[2])
file.close()
else:
continue
if opts.no_outl:
# Remove outliers wrt variance
varR = np.array([np.var(tr.data) for tr in rfRstream])
# Calculate outliers
medvarR = np.median(varR)
madvarR = 1.4826 * np.median(np.abs(varR - medvarR))
robustR = np.abs((varR - medvarR) / madvarR)
outliersR = np.arange(len(rfRstream))[robustR > 2.]
for i in outliersR[::-1]:
rfRstream.remove(rfRstream[i])
rfTstream.remove(rfTstream[i])
# Do the same for transverse
varT = np.array([np.var(tr.data) for tr in rfTstream])
medvarT = np.median(varT)
madvarT = 1.4826 * np.median(np.abs(varT - medvarT))
robustT = np.abs((varT - medvarT) / madvarT)
outliersT = np.arange(len(rfTstream))[robustT > 2.]
for i in outliersT[::-1]:
rfRstream.remove(rfRstream[i])
rfTstream.remove(rfTstream[i])
# Try binning if specified
if opts.nbin is not None:
rf_tmp = binning.bin(rfRstream,
rfTstream,
typ='baz',
nbin=opts.nbin + 1)
rfRstream = rf_tmp[0]
rfTstream = rf_tmp[1]
# Filter original streams
rfRstream.filter('bandpass',
freqmin=opts.bp[0],
freqmax=opts.bp[1],
corners=2,
zerophase=True)
rfTstream.filter('bandpass',
freqmin=opts.bp[0],
freqmax=opts.bp[1],
corners=2,
zerophase=True)
# Initialize the HkStack object
harmonics = Harmonics(rfRstream, rfTstream)
# Stack with or without dip
if opts.find_azim:
harmonics.dcomp_find_azim(xmin=opts.trange[0], xmax=opts.trange[1])
print("Optimal azimuth for trange between "+\
str(opts.trange[0])+" and "+str(opts.trange[1])+\
"is: "+str(harmonics.azim))
else:
harmonics.dcomp_fix_azim(azim=opts.azim)
if opts.plot:
harmonics.plot(opts.ymax, opts.scale, opts.save_plot, opts.title,
opts.form)
if opts.save:
filename = datapath + "/" + hkstack.hstream[0].stats.station + \
".harmonics.pkl"
harmonics.save()
