def test_corner_freqs():
event_time = UTCDateTime('2001-02-28T18:54:32')
ALCT_tr = read(os.path.join(datadir, 'ALCTENE.UW..sac'))[0]
ALCT_dist = 75.9559
corners_1 = process.get_corner_frequencies(ALCT_tr, event_time, ALCT_dist,
split_method='velocity')
# np.testing.assert_allclose(corners_1, [0.036, 50.0], atol=0.001)
np.testing.assert_allclose(corners_1, [3.281554e-02, 50.0], atol=0.001)
ALCT_tr.stats.starttime += 300
corners_2 = process.get_corner_frequencies(ALCT_tr, event_time, ALCT_dist,
split_method='velocity')
assert corners_2 == [-1, -1]
event_time = UTCDateTime('2016-10-22T17:17:05')
ALKI_tr = read(os.path.join(datadir, 'ALKIENE.UW..sac'))[0]
ALKI_dist = 37.87883
corners_3 = process.get_corner_frequencies(ALKI_tr, event_time, ALKI_dist,
ratio=100000.0,
split_method='velocity')
assert corners_3 == [-2, -2]
corners_4 = process.get_corner_frequencies(ALKI_tr, event_time, ALKI_dist,
split_method='velocity')
# assert corners_4 == [-3, -3]
assert corners_4 == [-2, -2]
def _test_horizontal_frequencies():
config = get_config()
event_time = UTCDateTime('2001-02-28T18:54:32')
ALCT_tr1 = read(os.path.join(datadir, 'ALCTENE.UW..sac'))[0]
ALCT_tr2 = read(os.path.join(datadir, 'ALCTENN.UW..sac'))[0]
stream = [ALCT_tr1, ALCT_tr2]
ALCT_dist = 75.9559
processed = process.process_config(
stream, config=config,
event_time=event_time, epi_dist=ALCT_dist)
for trace in processed:
corners = trace.stats.processing_parameters.corners
# assert corners['default_high_frequency'] == 50
np.testing.assert_allclose([corners['default_high_frequency']], [50.0])
# assert corners['default_low_frequency'] == 0.018310546875
assert corners['default_low_frequency'] == 0.01595909725588508
stream[0].stats.channel = 'Z'
processed = process.process_config(
stream, config=config,
event_time=event_time, epi_dist=ALCT_dist)
corners1 = processed[0].stats.processing_parameters.corners
high1 = corners1['default_high_frequency']
low1 = corners1['default_low_frequency']
assert np.allclose([high1], [50.0])
# assert low1 == 0.0244140625
assert low1 == 0.02155036612037732
corners2 = processed[1].stats.processing_parameters.corners
high2 = corners2['default_high_frequency']
low2 = corners2['default_low_frequency']
# assert high2 == 48.4619140625
assert high2 == 48.52051157467704
# assert low2 == 0.018310546875
assert low2 == 0.01595909725588508
def test_get_stream(mock_ntf, data):
mseeddata = data.read('trace_GE.APE.mseed')
segment = MockSegment(mseeddata)
tobspy = time.time()
stream_obspy = read(BytesIO(mseeddata))
tobspy = time.time() - tobspy
tme = time.time()
stream_me = get_stream(segment)
tme = time.time() - tme
# assert we are faster (actually that calling read with format='MSEED' is faster than
# calling with format=None)
assert tme < tobspy
assert (stream_obspy[0].data == stream_me[0].data).all()
assert not mock_ntf.called
with pytest.raises(TypeError):
stream_obspy = read(BytesIO(mseeddata[:5]))
assert mock_ntf.called
mock_ntf.reset_mock()
segment = MockSegment(mseeddata[:5])
with pytest.raises(ValueError):
stream_me = get_stream(segment)
assert not mock_ntf.called
def test_plotMultipleTraces(self):
"""
Plots multiple traces underneath.
"""
# 1 trace
st = read()[1]
with ImageComparison(self.path, 'waveform_1_trace.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 3 traces
st = read()
with ImageComparison(self.path, 'waveform_3_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 5 traces
st = st[1] * 5
with ImageComparison(self.path, 'waveform_5_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces
st = st[1] * 10
with ImageComparison(self.path, 'waveform_10_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces - huge numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10 ** i
with ImageComparison(self.path, 'waveform_10_traces_huge.png') as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
# 10 traces - tiny numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10 ** i)
with ImageComparison(self.path, 'waveform_10_traces_tiny.png') as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
def test_plot_multiple_traces(self):
"""
Plots multiple traces underneath.
"""
# 1 trace
st = read()[1]
with ImageComparison(self.path, 'waveform_1_trace.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 3 traces
st = read()
with ImageComparison(self.path, 'waveform_3_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 5 traces
st = st[1] * 5
with ImageComparison(self.path, 'waveform_5_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces
st = st[1] * 10
with ImageComparison(self.path, 'waveform_10_traces.png') as ic:
st.plot(outfile=ic.name, automerge=False)
# 10 traces - huge numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10**i
with ImageComparison(self.path, 'waveform_10_traces_huge.png') as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
# 10 traces - tiny numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10**i)
with ImageComparison(self.path, 'waveform_10_traces_tiny.png') as ic:
st.plot(outfile=ic.name, automerge=False, equal_scale=False)
def test_plotMultipleTraces(self):
"""
Plots multiple traces underneath.
"""
# 1 trace
st = read()[0]
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False)
expected_image = os.path.join(self.path, 'waveform_1_trace.png')
compare_images(tf.name, expected_image, 0.001)
# 3 traces
st = read()
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False)
expected_image = os.path.join(self.path, 'waveform_3_traces.png')
compare_images(tf.name, expected_image, 0.001)
# 5 traces
st = st[1] * 5
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False)
expected_image = os.path.join(self.path, 'waveform_5_traces.png')
compare_images(tf.name, expected_image, 0.001)
# 10 traces
st = st[1] * 10
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False)
expected_image = os.path.join(self.path, 'waveform_10_traces.png')
compare_images(tf.name, expected_image, 0.001)
# 10 traces - huge numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10**i
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False, equal_scale=False)
expected_image = os.path.join(self.path,
'waveform_10_traces_huge.png')
compare_images(tf.name, expected_image, 0.001)
# 10 traces - tiny numbers
st = st[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10**i)
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name, automerge=False, equal_scale=False)
expected_image = os.path.join(self.path,
'waveform_10_traces_tiny.png')
compare_images(tf.name, expected_image, 0.001)
def test_plot_multiple_traces_1_trace(self, image_path):
"""
Plots multiple traces underneath.
"""
# 1 trace
st = read()[1]
st.plot(outfile=image_path, automerge=False)
def test_split():
tr1 = read(os.path.join(datadir, 'CN.BBB..BHE.sac'))[0]
success = False
try:
process.split_signal_and_noise(tr1, split_method='p_arrival')
success = True
except ValueError:
pass
assert success is False
success = False
try:
process.split_signal_and_noise(tr1, split_method='velocity')
success = True
except ValueError:
pass
assert success is False
success = False
try:
process.split_signal_and_noise(tr1, split_method='invalid')
success = True
except ValueError:
pass
assert success is False
def is_obspy(filename, config=None):
"""Check to see if file is a format supported by Obspy (not KNET).
Note: Currently only SAC and Miniseed are supported.
Args:
filename (str):
Path to possible Obspy format.
config (dict):
Dictionary containing configuration.
Returns:
bool: True if obspy supported, otherwise False.
"""
logging.debug("Checking if format is supported by obspy.")
metadir = config["read"]["metadata_directory"]
if config is None:
config = get_config()
if not os.path.isfile(filename):
return False
try:
stream = read(filename)
if stream[0].stats._format in IGNORE_FORMATS:
return False
if stream[0].stats._format in REQUIRES_XML:
xmlfile = _get_station_file(filename, stream, metadir)
if not os.path.isfile(xmlfile):
return False
return True
else:
return True
except BaseException:
return False
return False
def test_plot_multiple_traces_10_traces(self, image_path):
"""
10 traces
"""
# 10 traces
st = read()[1] * 10
st.plot(outfile=image_path, automerge=False)
def is_obspy(filename):
"""Check to see if file is a format supported by Obspy (not KNET).
Note: Currently only SAC and Miniseed are supported.
Args:
filename (str):
Path to possible Obspy format.
Returns:
bool: True if obspy supported, otherwise False.
"""
logging.debug("Checking if format is supported by obspy.")
if not os.path.isfile(filename):
return False
try:
stream = read(filename)
if stream[0].stats._format in IGNORE_FORMATS:
return False
if stream[0].stats._format in REQUIRES_XML:
xmlfile = _get_station_file(filename, stream)
if not os.path.isfile(xmlfile):
return False
return True
else:
return True
except BaseException:
return False
return False
def on_data(self, trace):
global i
global traces
global fn
global day
if i == 1:
print('Received traces. Checking for existing data...')
if (os.path.isfile(fn)):
print('Found %s, reading...' % fn)
traces = read(fn)
print('Done.')
else:
print('No data found. Creating new blank trace to write to...')
traces = Trace()
traces = trace
print('Trace %s: %s' % (i, trace))
else:
print('Trace %s: %s' % (i, trace))
traces += trace
traces.__add__(trace)
if (float(i) / 10. == int(float(i) / 10.)):
print('Saving %s traces to %s...' % (i, fn))
traces.write(fn, format='MSEED')
print('Done.')
i += 1
if (day != UTCDateTime.now().strftime('%Y.%j')):
day = UTCDateTime.now().strftime('%Y.%j')
fn = fn = '%s.%s.%s.%s.D.%s' % (net, sta, loc, cha, day)
i = 1
def test_plot_multiple_traces_10_traces_huge(self, image_path):
"""
10 traces - huge numbers
"""
st = read()[1] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10**i
st.plot(outfile=image_path, automerge=False, equal_scale=False)
def readallchannels(dataset, operation='eventdir'):
"""
wrapps obspy.core.stream.read so various seismic file
formats can be read in one pass.
Assumes data files are organized in event directories.
This can be improved
:type dataset: list
:param dataset: files to read.
:rtype: class obspy.core.stream
:return: class of data streams.
"""
# 1) Iterate over full files names
# 2) Get all available channel of each file
# 3) Read individually in stream.
# Reading the waveforms
m = 0
eventset = []
waveformset = Stream()
for e, eventfile in enumerate(dataset):
eventfile = eventfile.strip()
waveformset += read(eventfile)
eventset.append(e)
(root, ext) = os.path.splitext(eventfile)
channelset = glob.glob(
os.path.dirname(eventfile) + '/*' + waveformset[-1].stats.station +
'*' + ext)
for c, channelfile in enumerate(channelset):
if not eventfile == channelfile:
eventset.append(e)
waveformset += read(
channelfile
) # file with uncorrect metadata may be ignored (ex: YHHN in station metatdata)
#df = waveformset[-1].stats.sampling_rate
#cft = classicSTALTA(waveformset[-1].data, int(5 * df), int(10 * df))
#plotTrigger(waveformset[-1], cft, 1.5, 0.5)
return waveformset
def test_append_not_float32(self):
"""
Test for not using float32.
"""
tr = read()[0]
tr.data = np.require(tr.data, dtype=native_str('>f4'))
traces = tr / 3
rtr = RtTrace()
for trace in traces:
rtr.append(trace)
def test_appendNotFloat32(self):
"""
Test for not using float32.
"""
tr = read()[0]
tr.data = np.require(tr.data, dtype=native_str('>f4'))
traces = tr / 3
rtr = RtTrace()
for trace in traces:
rtr.append(trace)
def readallchannels(dataset, operation='eventdir'):
"""
wrapps obspy.core.stream.read so various seismic file
formats can be read in one pass.
Assumes data files are organized in event directories.
This can be improved
:type dataset: list
:param dataset: files to read.
:rtype: class obspy.core.stream
:return: class of data streams.
"""
# 1) Iterate over full files names
# 2) Get all available channel of each file
# 3) Read individually in stream.
# Reading the waveforms
m=0
eventset=[]
waveformset = Stream()
for e, eventfile in enumerate(dataset):
eventfile = eventfile.strip()
waveformset += read(eventfile)
eventset.append(e)
(root,ext)=os.path.splitext(eventfile)
channelset = glob.glob(os.path.dirname(eventfile)+'/*'+waveformset[-1].stats.station+'*'+ext)
for c, channelfile in enumerate(channelset):
if not eventfile == channelfile :
eventset.append(e)
waveformset += read(channelfile) # file with uncorrect metadata may be ignored (ex: YHHN in station metatdata)
#df = waveformset[-1].stats.sampling_rate
#cft = classicSTALTA(waveformset[-1].data, int(5 * df), int(10 * df))
#plotTrigger(waveformset[-1], cft, 1.5, 0.5)
return waveformset
def test_plotWithLabels(self):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
outfile = os.path.join(self.path, 'waveform_labels.png')
st.plot(outfile=outfile)
def test_plotWithLabels(self):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
outfile = os.path.join(self.path, 'waveform_labels.png')
st.plot(outfile=outfile)
def run(catalog_path, trace_folder, dump_folder):
catalog = Catalog.from_csv(catalog_path)
trace_writer = TraceWriter(dump_folder)
for filename in tqdm.tqdm(os.listdir(trace_folder)):
file_stream = stream.read(os.path.join(trace_folder, filename))
for trace in file_stream:
if get_trace_component(trace) != 'Z':
continue
if len(catalog.get_trace_picks(trace)) > 0:
trace_writer.write(trace)
def test_plot_with_labels(self, image_path):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
# create and compare image
st.plot(outfile=image_path)
def GetStream(path):
st=Stream()
if os.path.isfile(path) is True:
f=open(path,'r')
flst=f.readlines()
for line in flst:
sacfile=line.strip()
st += read(sacfile)
elif os.path.isdir(path) is True:
flst = os.listdir(path)
for item in flst:
sacfile = os.path.join(path,item)
st += read(sacfile)
else :
print("Parameter Error!")
exit(0)
return st
def test_plotWithLabels(self):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
# create and compare image
with ImageComparison(self.path, 'waveform_labels.png') as ic:
st.plot(outfile=ic.name)
def test_plot_with_labels(self):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
# create and compare image
with ImageComparison(self.path, 'waveform_labels.png') as ic:
st.plot(outfile=ic.name)
def read_fdsn(filename):
"""Read Obspy data file (SAC, MiniSEED, etc).
Args:
filename (str):
Path to data file.
kwargs (ref):
Other arguments will be ignored.
Returns:
Stream: StationStream object.
"""
logging.debug("Starting read_fdsn.")
if not is_fdsn(filename):
raise Exception('%s is not a valid Obspy file format.' % filename)
streams = []
tstream = read(filename)
xmlfile = _get_station_file(filename, tstream)
inventory = read_inventory(xmlfile)
traces = []
for ttrace in tstream:
trace = StationTrace(data=ttrace.data,
header=ttrace.stats,
inventory=inventory)
location = ttrace.stats.location
trace.stats.channel = get_channel_name(
trace.stats.sampling_rate, trace.stats.channel[1] == 'N',
inventory.get_orientation(trace.id)['dip'] in [90, -90]
or trace.stats.channel[2] == 'Z',
is_channel_north(inventory.get_orientation(trace.id)['azimuth']))
if trace.stats.location == '':
trace.stats.location = '--'
network = ttrace.stats.network
if network in LOCATION_CODES:
codes = LOCATION_CODES[network]
if location in codes:
sdict = codes[location]
if sdict['free_field']:
trace.stats.standard.structure_type = 'free_field'
else:
trace.stats.standard.structure_type = sdict['description']
head, tail = os.path.split(filename)
trace.stats['standard']['source_file'] = tail or os.path.basename(head)
traces.append(trace)
stream = StationStream(traces=traces)
streams.append(stream)
return streams
def test_plotWithLabels(self):
"""
Plots with labels.
"""
st = read()
st.label = u"Title #1 üöä?"
st[0].label = 'Hello World!'
st[1].label = u'Hällö Wörld & Marß'
st[2].label = '*' * 80
# create and compare image
with NamedTemporaryFile(suffix='.png') as tf:
st.plot(outfile=tf.name)
# compare images
expected_image = os.path.join(self.path, 'waveform_labels.png')
compare_images(tf.name, expected_image, 0.001)
def run(catalog_path, signal_folder, output_folder, window_size, event_start_offset): window_writer = WindowWriter( folder=output_folder, window_size=window_size, event_start_offset=event_start_offset) catalog = Catalog.from_csv(catalog_path) for signal_filename in tqdm.tqdm(os.listdir(signal_folder)): # Read stream st = stream.read(os.path.join(signal_folder, signal_filename)) # Streams are expected to only have one trace if they have multiple only the first one is used trace = st[0] window_writer.write_windows(trace, catalog)
def test_amp_check_trim():
# read the two sac files for testing
# one is unedited with a standard maximum amplitude
# the second has been multiplied so that it fails the amplitude check
NOWS_tr = read(os.path.join(datadir, 'NOWSENR.sac'))[0]
NOWS_tr_mul = Trace(data=NOWS_tr.data * 10e9, header=NOWS_tr.stats)
assert process.check_max_amplitude(NOWS_tr) is True
assert process.check_max_amplitude(NOWS_tr_mul) is False
# Check that our trim and window function doesn't affect the ending time
# of this record
org_time = UTCDateTime('2001-02-14T22:03:58')
trim = process.trim_total_window(NOWS_tr, org_time, 32.7195).stats.endtime
assert NOWS_tr.stats.endtime == trim
def _process(self, input):
stations = defaultdict(lambda: [])
# first group all files by station
for filename in os.listdir(input):
m = self.pattern.match(filename)
if m:
stations[m.group(1)].append(m.group(2))
# now read and create an obspy stream from each group
for s, fs in stations.items():
streaming = obspy.Stream()
for f in fs:
filename = os.path.join(input,
'{}.{}.{}'.format(s, f, self.ext))
streaming = streaming + read(filename)
self.write('output', [streaming, self.label])
def requestEventWaveformTraces(self, event):
"""
Method for requesting event data from waveformLocation. Implement this to fetch event waveform data
"""
streams = Stream()
if self.__checkStatus() and event.waveform_h:
wfdisc_file_start_i = int(
event.waveform_h[0].waveform_info.lower().find('.wfdisc'))
wfdisc_file = event.waveform_h[0].waveform_info.lower(
)[wfdisc_file_start_i - 13:wfdisc_file_start_i + 7]
year = wfdisc_file[:4]
yearjul = wfdisc_file[:7]
if os.path.exists('{0}/{1}/{2}/{3}'.format(self.__file_path, year,
yearjul, wfdisc_file)):
streams += read('{0}/{1}/{2}/{3}'.format(
self.__file_path, year, yearjul, wfdisc_file))
return streams
def read_fdsn(filename):
"""Read Obspy data file (SAC, MiniSEED, etc).
Args:
filename (str):
Path to data file.
kwargs (ref):
Other arguments will be ignored.
Returns:
Stream: StationStream object.
"""
logging.debug("Starting read_fdsn.")
if not is_fdsn(filename):
raise Exception('%s is not a valid Obspy file format.' % filename)
streams = []
tstream = read(filename)
xmlfile = _get_station_file(filename, tstream)
inventory = read_inventory(xmlfile)
traces = []
for ttrace in tstream:
trace = StationTrace(data=ttrace.data,
header=ttrace.stats,
inventory=inventory)
location = ttrace.stats.location
network = ttrace.stats.network
if network in LOCATION_CODES:
codes = LOCATION_CODES[network]
if location in codes:
sdict = codes[location]
if sdict['free_field']:
trace.stats.standard.structure_type = 'free_field'
else:
trace.stats.standard.structure_type = sdict['description']
head, tail = os.path.split(filename)
trace.stats['standard']['source_file'] = tail or os.path.basename(head)
traces.append(trace)
stream = StationStream(traces=traces)
streams.append(stream)
return streams
def extractDataFromArchive(t1,
t2,
fileNames,
fileTimes,
wantedStaChas=[['*', '*']]):
# Return nothing if there is no data
if len(fileTimes) == 0:
return EmptyStream()
# Catch the case where the asked time range is completely outside the archive data availability
if t1 > fileTimes[-1, 1] or t2 < fileTimes[0, 0]:
return EmptyStream()
# Figure out what set of files are wanted
collectArgs = np.where((fileTimes[:, 0] <= t2)
& (fileTimes[:, 1] >= t1))[0]
stream = EmptyStream()
flagged = False
# Read in all of the information
for aFile in fileNames[collectArgs]:
# Flag to user if the archive structure has changed
if not os.path.exists(aFile):
flagged = True
continue
aStream = read(aFile)
for aSta, aCha in wantedStaChas:
stream += aStream.select(station=aSta, channel=aCha)
if flagged:
print('Archive structure as changed, reload the current archive')
# Merge traces which are adjacent
try:
stream.merge(method=1)
except:
stream = RemoveOddRateTraces(stream)
stream.merge(method=1)
# If any trace has masked values, split
if True in [isinstance(tr.data, np.ma.masked_array) for tr in stream]:
stream = stream.split()
# Trim to wanted times
stream.trim(UTCDateTime(t1), UTCDateTime(t2))
return stream
def is_fdsn(filename):
"""Check to see if file is a format supported by Obspy (not KNET).
Args:
filename (str): Path to possible Obspy format.
Returns:
bool: True if obspy supported, otherwise False.
"""
logging.debug("Checking if format is Obspy.")
if not os.path.isfile(filename):
return False
try:
stream = read(filename)
if stream[0].stats._format in IGNORE_FORMATS:
return False
xmlfile = _get_station_file(filename, stream)
if not os.path.isfile(xmlfile):
return False
return True
except Exception:
return False
return False
def read(filename, format='MSEED', **kwargs):
if isinstance(filename, Path):
filename = str(filename)
if format in ENTRY_POINTS['waveform'].keys():
format_ep = ENTRY_POINTS['waveform'][format]
read_format = load_entry_point(
format_ep.dist.key, 'obspy.plugin.waveform.%s' % format_ep.name,
'readFormat')
st = Stream(stream=read_format(filename, **kwargs))
# making sure the channel names are upper case
trs = []
for tr in st:
tr.stats.channel = tr.stats.channel.upper()
trs.append(tr.copy())
st.traces = trs
return st
else:
return Stream(stream=obsstream.read(filename, format=format, **kwargs))
def is_fdsn(filename):
"""Check to see if file is a format supported by Obspy (not KNET).
Args:
filename (str): Path to possible Obspy format.
Returns:
bool: True if obspy supported, otherwise False.
"""
logging.debug("Checking if format is Obspy.")
if not os.path.isfile(filename):
return False
try:
stream = read(filename)
if stream[0].stats._format in IGNORE_FORMATS:
return False
xmlfile = _get_station_file(filename, stream)
if not os.path.isfile(xmlfile):
return False
return True
except Exception:
return False
return False
def test_plotMultipleTraces(self):
"""
Plots multiple traces underneath.
"""
st = read()
# 1 trace
outfile = os.path.join(self.path, 'waveform_1_trace.png')
st[0].plot(outfile=outfile, automerge=False)
# 3 traces
outfile = os.path.join(self.path, 'waveform_3_traces.png')
st.plot(outfile=outfile, automerge=False)
# 5 traces
st = st[0] * 5
outfile = os.path.join(self.path, 'waveform_5_traces.png')
st.plot(outfile=outfile, automerge=False)
# 10 traces
st = st[0] * 10
outfile = os.path.join(self.path, 'waveform_10_traces.png')
st.plot(outfile=outfile, automerge=False)
# 10 traces - huge numbers
st = st[0] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10 ** i
outfile = os.path.join(self.path, 'waveform_10_traces_huge.png')
st.plot(outfile=outfile, automerge=False, equal_scale=False)
# 10 traces - tiny numbers
st = st[0] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10 ** i)
outfile = os.path.join(self.path, 'waveform_10_traces_tiny.png')
st.plot(outfile=outfile, automerge=False, equal_scale=False)
# 20 traces
st = st[0] * 20
outfile = os.path.join(self.path, 'waveform_20_traces.png')
st.plot(outfile=outfile, automerge=False)
def test_plotMultipleTraces(self):
"""
Plots multiple traces underneath.
"""
st = read()
# 1 trace
outfile = os.path.join(self.path, 'waveform_1_trace.png')
st[0].plot(outfile=outfile, automerge=False)
# 3 traces
outfile = os.path.join(self.path, 'waveform_3_traces.png')
st.plot(outfile=outfile, automerge=False)
# 5 traces
st = st[0] * 5
outfile = os.path.join(self.path, 'waveform_5_traces.png')
st.plot(outfile=outfile, automerge=False)
# 10 traces
st = st[0] * 10
outfile = os.path.join(self.path, 'waveform_10_traces.png')
st.plot(outfile=outfile, automerge=False)
# 10 traces - huge numbers
st = st[0] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data * 10**i
outfile = os.path.join(self.path, 'waveform_10_traces_huge.png')
st.plot(outfile=outfile, automerge=False, equal_scale=False)
# 10 traces - tiny numbers
st = st[0] * 10
for i, tr in enumerate(st):
# scale data to have huge numbers
st[i].data = tr.data / (10**i)
outfile = os.path.join(self.path, 'waveform_10_traces_tiny.png')
st.plot(outfile=outfile, automerge=False, equal_scale=False)
# 20 traces
st = st[0] * 20
outfile = os.path.join(self.path, 'waveform_20_traces.png')
st.plot(outfile=outfile, automerge=False)
def getFileLims(self):
# Stop if no more files to load
if self.nextFileIdx >= self.nFiles:
self.cancelLoad()
return
nextFile = self.files[self.nextFileIdx]
# Try the quick read function
if not self.quickReadFail:
try:
startEnds = getMseedStartEnds(nextFile)
minTime, maxTime = np.min(startEnds[:,
0]), np.max(startEnds[:,
1])
except:
print('Quick MSEED reading failed on file: ' + nextFile)
self.quickReadFail = True
if self.quickReadFail:
try:
st = read(nextFile, headonly=True, format='MSEED')
stats = [tr.stats for tr in st]
# Get min and max times from file
minTime = np.min([stat.starttime for stat in stats]).timestamp
maxTime = np.max([stat.endtime for stat in stats]).timestamp
except:
print('Regular MSEED reading failed on file: ' + nextFile)
minTime, maxTime = 0, 0 # Assign 0,0 to be removed later
# Get a measure of progress and update the GUI
perc = int(100 * (self.nextFileIdx + 1) / self.nFiles)
if perc > self.progressbar.value():
self.progressbar.setValue(perc)
# Update the GUI every Xth loop
if self.nextFileIdx % 10 == 0:
QtWidgets.qApp.processEvents()
# Update the index, and add this min/max time
self.nextFileIdx += 1
self.fileMinMaxs.append([minTime, maxTime])
def section_plot(self, assoc_id, files, seconds_ahead = 5, record_length = 100, channel = 'Z'):
station=self.assoc_db.query(Candidate.sta).filter(Candidate.assoc_id==assoc_id).all()
sta_list=[]
for sta, in station:
sta_list.append(str(sta))
station_single = self.assoc_db.query(Pick.sta).filter(Pick.assoc_id==assoc_id).filter(Pick.locate_flag == None).all()
for sta, in station_single:
sta_list.append(str(sta))
#print sta_list
eve=self.assoc_db.query(Associated).filter(Associated.id==assoc_id).first()
# Earthquakes' epicenter
eq_lat = eve.latitude
eq_lon = eve.longitude
# Reading the waveforms
ST = Stream()
for file in files:
st = read(file)
ST += st
# in case of some seismometer use channel code like BH1, BH2 or BH3, resign the channel code as:
if channel=='E' or channel=='e':
Chan='E1'
elif channel=='N' or channel=='n':
Chan='N2'
elif channel=='Z' or channel=='z':
Chan='Z3'
else:
print('Please input component E, e, N, n, Z, or z, the default is Z')
# Calculating distance from headers lat/lon
ST_new = Stream()#;print ST
for tr in ST:
if tr.stats.channel[2] in Chan and tr.stats.station in sta_list:
if tr.stats.starttime.datetime < eve.ot and tr.stats.endtime.datetime > eve.ot:
tr.trim(UTCDateTime(eve.ot-timedelta(seconds=seconds_ahead)), UTCDateTime(eve.ot+timedelta(seconds=record_length)))
ST_new+=tr
#print ST_new.__str__(extended=True)
while True:
ST_new_sta=[]
for tr in ST_new:
ST_new_sta.append(tr.stats.station)
duplicate=list(set([tr for tr in ST_new_sta if ST_new_sta.count(tr)>1]))
if not duplicate:
break
index=[i for (i,j) in enumerate(ST_new_sta) if j==duplicate[-1]]
i=0
while True:
if ST_new[index[i]].stats.npts<ST_new[index[i+1]].stats.npts:
del ST_new[index[i]]
break
elif ST_new[index[i]].stats.npts>=ST_new[index[i+1]].stats.npts:
del ST_new[index[i+1]]
break
#print ST_new.__str__(extended=True)
ST_new.detrend('demean')
# ST_new.filter('bandpass', freqmin=0.1, freqmax=100)
factor=10
numRows=len(ST_new)
segs=[];ticklocs=[];sta=[];circle_x=[];circle_y=[];segs_picks=[];ticklocs_picks=[]
for tr in ST_new:
dmax=tr.data.max()
dmin=tr.data.min()
data=tr.data/(dmax-dmin)*factor
t=np.arange(0,round(tr.stats.npts/tr.stats.sampling_rate/tr.stats.delta))*tr.stats.delta # due to the float point arithmetic issue, can not use "t=np.arange(0,tr.stats.npts/tr.stats.sampling_rate,tr.stats.delta)"
segs.append(np.hstack((data[:,np.newaxis],t[:,np.newaxis])))
lon,lat = self.tt_stations_db_3D.query(Station3D.longitude,Station3D.latitude).filter(Station3D.sta==tr.stats.station).first()
distance = int(gps2DistAzimuth(lat,lon,eq_lat,eq_lon)[0]/1000.) #gps2DistAzimuth return in meters, convert to km by /1000
# distance=self.assoc_db.query(Candidate.d_km).filter(Candidate.assoc_id==assoc_id).filter(Candidate.sta==tr.stats.station).first()[0]#;print distance,tr.stats.station
ticklocs.append(distance)
sta.append(tr.stats.station)
# DOT plot where picks are picked, notice that for vertical trace plot p is queried from Pick table, s from PickModified table
# horizontal trace plot p and s queried from PickModified table
if channel=='Z3':
picks_p=self.assoc_db.query(Pick.time).filter(Pick.assoc_id==assoc_id).filter(Pick.sta==tr.stats.station).filter(Pick.chan==tr.stats.channel).filter(Pick.phase=='P').all()
if not picks_p:
picks_p=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).filter(PickModified.phase=='P').all()
picks_s=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).filter(PickModified.phase=='S').all()
# print picks_p,picks_s
else:
picks_p=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).filter(PickModified.phase=='P').all()
picks_s=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).filter(PickModified.phase=='S').all()
# print picks_p,picks_s
picks=picks_p+picks_s
# picks=self.assoc_db.query(PickModified.time).filter(PickModified.assoc_id==assoc_id).filter(PickModified.sta==tr.stats.station).all()
for pick, in picks:
index=int((pick-eve.ot+timedelta(seconds=seconds_ahead)).total_seconds()/tr.stats.delta)#;print pick,eve.ot,index,len(data)
circle_x.append(distance+data[index])
circle_y.append(t[index])
# BAR plot where picks are picked
t_picks=np.array([t[index],t[index]])
data_picks=np.array([data.min(),data.max()])
segs_picks.append(np.hstack((data_picks[:,np.newaxis],t_picks[:,np.newaxis])))
ticklocs_picks.append(distance)
tick_max=max(ticklocs)
tick_min=min(ticklocs)
offsets=np.zeros((numRows,2),dtype=float)
offsets[:,0]=ticklocs
offsets_picks=np.zeros((len(segs_picks),2),dtype=float)
offsets_picks[:,0]=ticklocs_picks
#lines=LineCollection(segs,offsets=offsets,transOffset=None,linewidths=.25,colors=[colorConverter.to_rgba(i) for i in ('b','g','r','c','m','y','k')]) #color='gray'
lines=LineCollection(segs,offsets=offsets,transOffset=None,linewidths=.25,color='gray')
#lines_picks=LineCollection(segs_picks,offsets=offsets_picks,transOffset=None,linewidths=1,color='r')
lines_picks=LineCollection(segs_picks,offsets=offsets_picks,transOffset=None,linewidths=1,color='k')
#print sta,ticklocs
fig=plt.figure(figsize=(15,8))
ax1 = fig.add_subplot(111)
#ax1.plot(circle_x,circle_y,'o') # blue dots indicating where to cross the waveforms
ax1.plot(circle_x,circle_y,'o',c='gray')
x0 = tick_min-(tick_max-tick_min)*0.1
x1 = tick_max+(tick_max-tick_min)*0.1
ylim(0,record_length);xlim(0,x1)
ax1.add_collection(lines)
ax1.add_collection(lines_picks)
ax1.set_xticks(ticklocs)
ax1.set_xticklabels(sta)
ax1.invert_yaxis()
ax1.xaxis.tick_top()
# ax2 = ax1.twiny()
# ax2.xaxis.tick_bottom()
plt.setp(plt.xticks()[1], rotation=45)
#xlabel('Station (km)')
xlabel('channel: '+channel, fontsize=18)
ylabel('Record Length (s)', fontsize=18)
# plt.title('Section Plot of Event at %s'%(tr.stats.starttime))
# plt.tight_layout()
plt.show()
from obspy.core.util import gps2DistAzimuth
host = 'http://examples.obspy.org/'
# Files (fmt: SAC)
files = ['TOK.2011.328.21.10.54.OKR01.HHN.inv',
'TOK.2011.328.21.10.54.OKR02.HHN.inv', 'TOK.2011.328.21.10.54.OKR03.HHN.inv',
'TOK.2011.328.21.10.54.OKR04.HHN.inv', 'TOK.2011.328.21.10.54.OKR05.HHN.inv',
'TOK.2011.328.21.10.54.OKR06.HHN.inv', 'TOK.2011.328.21.10.54.OKR07.HHN.inv',
'TOK.2011.328.21.10.54.OKR08.HHN.inv', 'TOK.2011.328.21.10.54.OKR09.HHN.inv',
'TOK.2011.328.21.10.54.OKR10.HHN.inv']
# Earthquakes' epicenter
eq_lat = 35.565
eq_lon = -96.792
# Reading the waveforms
st = Stream()
for waveform in files:
st += read(host + waveform)
# Calculating distance from SAC headers lat/lon
# (trace.stats.sac.stla and trace.stats.sac.stlo)
for tr in st:
tr.stats.distance = gps2DistAzimuth(tr.stats.sac.stla,
tr.stats.sac.stlo, eq_lat, eq_lon)[0]
# Setting Network name for plot title
tr.stats.network = 'TOK'
st.filter('bandpass', freqmin=0.1, freqmax=10)
# Plot
st.plot(type='section', plot_dx=20e3, recordlength=100,
time_down=True, linewidth=.25, grid_linewidth=.25)
def read_from_SDS(self, sds_root, net_name, sta_name, comp_name,
starttime=None, endtime=None, rmean=False, taper=False,
pad_value=None):
"""
Read waveform data from an SDS structured archive. Simple overlaps and
adjacent traces are merged if possile.
:param sds_root: root of the SDS archive
:param net_name: network name
:param sta_name: station name
:param comp_name: component name
:param starttime: Start time of data to be read.
:param endtime: End time of data to be read.
:param rmean: If ``True`` removes the mean from the data upon reading.
If data are segmented, the mean will be removed from all segments
individually.
:param taper: If ``True`` applies a cosine taper to the data upon
reading. If data are segmented, tapers are applied to all segments
individually.
:param pad_value: If this parameter is set, points between
``starttime`` and the first point in the file, and points between
the last point in the file and ``endtime``, will be set to
``pad_value``. You may want to also use the ``rmean`` and
``taper`` parameters, depending on the nature of the data.
:type sds_root: string
:type net_name: string
:type sta_name: string
:type comp_name: string
:type starttime: ``obspy.core.utcdatetime.UTCDateTime`` object,
optional
:type endtime: ``obspy.core.utcdatetime.UTCDateTime`` object, optional
:type rmean: boolean, optional
:type taper: boolean, optional
:type pad_value: float, optional
:raises UserWarning: If there are no data between ``starttime`` and
``endtime``
"""
logging.info("Reading from SDS structure %s %s %s ..." %
(net_name, sta_name, comp_name))
# Get the complete file list. If a directory, get all the filenames.
filename = os.path.join(sds_root, net_name, sta_name,
"%s.D" % comp_name, "*")
logging.debug("Reading %s between %s and %s" %
(filename, starttime.isoformat(), endtime.isoformat()))
if os.path.isdir(glob.glob(filename)[0]):
filename = os.path.join(filename, "*")
file_glob = glob.glob(filename)
# read header from all files to keep only those within the time limits
fnames_within_times = []
for fname in file_glob:
st_head = stream.read(fname, headonly=True)
# retrieve first_start and last_end time for the stream
# without making any assumptions on order of traces
first_start = st_head[0].stats.starttime
last_end = st_head[0].stats.endtime
# find earliest start time and latest end time in stream
for tr in st_head:
if tr.stats.starttime < first_start:
first_start = tr.stats.starttime
if tr.stats.endtime > last_end:
last_end = tr.stats.endtime
# add to list if start or end time are within our requested limits
if (first_start < endtime and last_end > starttime):
fnames_within_times.append(fname)
logging.debug("Found %d files to read" % len(fnames_within_times))
# now read the full data only for the relevant files
st = Stream()
for fname in fnames_within_times:
st_tmp = read(fname, starttime=starttime, endtime=endtime)
for tr in st_tmp:
st.append(tr)
# and merge nicely
st.merge(method=-1)
if st.count() > 1: # There are gaps after sensible cleanup merging
logging.info("File contains gaps:")
st.printGaps()
# apply rmean if requested
if rmean:
logging.info("Removing the mean from single traces.")
st = stream_rmean(st)
# apply rmean if requested
if taper:
logging.info("Tapering single traces.")
st = stream_taper(st)
if not pad_value is None:
try:
first_tr = st.traces[0]
# save delta (to save typing)
delta = first_tr.stats.delta
if (not starttime is None) and \
((first_tr.stats.starttime - starttime) > delta):
logging.debug("Padding with value %f from %s to first\
point in file at %s." %
(pad_value,
starttime.isoformat(),
first_tr.stats.starttime.isoformat()))
# find the number of points from starttime to
# end of the first trace
npts_full_trace = \
int(np.floor((first_tr.stats.endtime -
starttime) / delta))+1
# find the number of points of the padding section
n_pad = npts_full_trace-first_tr.stats.npts
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[n_pad:] = first_tr.data[:]
first_tr.data = tr_pad
first_tr.stats.starttime = starttime
first_tr.stats.npts = npts_full_trace
st.traces[0] = first_tr
last_tr = st.traces[-1]
# save delta (to save typing)
delta = last_tr.stats.delta
if (not endtime is None) and \
((endtime - last_tr.stats.endtime) > delta):
logging.debug("Padding with value %f from last point\
in file at %s to %s." %
(pad_value,
last_tr.stats.endtime.isoformat(),
endtime.isoformat()))
# find the number of points from endtime to
# start of the last trace
npts_full_trace = \
int(np.floor((endtime -
last_tr.stats.starttime) / delta))+1
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[0:last_tr.stats.npts] = last_tr.data[:]
last_tr.data = tr_pad
last_tr.stats.npts = npts_full_trace
st.traces[-1] = last_tr
except IndexError:
logging.warning('No data within time limits requested')
raise UserWarning('No data within time limits requested.')
try:
self.stream = st
self.trace = st.traces[0]
self.proc = "None"
except IndexError:
raise UserWarning('No data within time limits requested.')
def read_from_file(self, filename, format=None, starttime=None,
endtime=None, rmean=False, taper=False, pad_value=None):
"""
Read waveform data from file. Multiple traces are merged if they
overlap exactly or are adjacent.
:param filename: Waveform filename
:param format: ``obspy`` format type (e.g. 'SAC', 'mseed'...)
:param starttime: Start time of data to be read.
:param endtime: End time of data to be read.
:param rmean: If ``True`` removes the mean from the data upon reading.
If data are segmented, the mean will be removed from all segments
individually.
:param taper: If ``True`` applies a cosine taper to the data upon
reading. If data are segmented, tapers are applied to all segments
individually.
:param pad_value: If this parameter is set, points between
``starttime`` and the first point in the file, and points between
the last point in the file and ``endtime``, will be set to
``pad_value``. You may want to also use the ``rmean`` and
``taper`` parameters, depending on the nature of the data.
:type format: string
:type starttime: ``obspy.core.utcdatetime.UTCDateTime`` object
:type endtime: ``obspy.core.utcdatetime.UTCDateTime`` object
:type rmean: boolean
:type taper: boolean
:type pad_value: float
:raises UserWarning: If there are no data between ``starttime`` and
``endtime``
"""
logging.debug("Reading from %s..." % filename)
if format is not None:
st = stream.read(filename, format, starttime=starttime,
endtime=endtime)
else:
st = stream.read(filename, starttime=starttime, endtime=endtime)
st.merge(method=-1)
if st.count() > 1: # There are gaps after intelligent merge
logging.info("File contains gaps:")
st.printGaps()
if rmean:
st = stream_rmean(st)
if taper:
st = stream_taper(st)
if not pad_value is None:
try:
first_tr = st.traces[0]
# save delta (to save typing)
delta = first_tr.stats.delta
if (not starttime is None) and \
((first_tr.stats.starttime - starttime) > delta):
logging.debug("Padding with value %f from %s to first\
point in file at %s." % (pad_value,
starttime.isoformat(),
first_tr.stats.starttime.isoformat()))
# find the number of points from starttime to
# end of the first trace
npts_full_trace = \
int(np.floor((first_tr.stats.endtime -
starttime) / delta))+1
# find the number of points of the padding section
n_pad = npts_full_trace-first_tr.stats.npts
# fill the full time range with padd value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[n_pad:] = first_tr.data[:]
first_tr.data = tr_pad
first_tr.stats.starttime = starttime
first_tr.stats.npts = npts_full_trace
st.traces[0] = first_tr
last_tr = st.traces[-1]
# save delta (to save typing)
delta = last_tr.stats.delta
if (not endtime is None) and \
((endtime - last_tr.stats.endtime) > delta):
logging.debug("Padding with value %f from last point \
in file at %s to %s." % (pad_value,
last_tr.stats.endtime.isoformat(),
endtime.isoformat()))
# find the number of points from endtime to
# start of the last trace
npts_full_trace = \
int(np.floor((endtime -
last_tr.stats.starttime) / delta))+1
# fill the full time range with pad value
tr_pad = np.zeros(npts_full_trace)+pad_value
# substitute in the data
tr_pad[0:last_tr.stats.npts] = last_tr.data[:]
last_tr.data = tr_pad
last_tr.stats.npts = npts_full_trace
st.traces[-1] = last_tr
except IndexError:
logging.warning('No data within time limits requested')
raise UserWarning('No data within time limits requested.')
try:
self.stream = st
self.trace = st.traces[0]
self.proc = "None"
except IndexError:
raise UserWarning('No data within time limits requested.')
def analyzeRemove(self, seedpath): # --------------------------------------- # Read MSEED files from query and analyze # --------------------------------------- print "------pullTraces() Start------\n" os.chdir(seedpath) filelist = sorted(os.listdir(seedpath), key=os.path.getctime) self.filelist = filelist filelen = len(filelist) stream = [0 for x in range(filelen)] # streams = [][] where the second entry denotes the trace index i = filelen - 1 while i >= 0: try: stream[i] = read(filelist[i]) # read MSEED files from query except Exception as e: print "Exception pullTraces() (read(MSEED)): " + str(e) sys.exit(0) i = i - 1 # Check for masked arrays and 0 fill to create np.ndarray types stream = removeMask(stream) # Remove traces with sample rate = 0.0Hz => NFFT = 0 try: print "Removing traces with 0.0Hz sampling rate from stream..." streamlen = len(stream) # number of streams (ie stream files) self.streamlen = streamlen RM = False i = 0 # reset indexing print "streamlen = %s\n" % str(streamlen) for i in range(streamlen): tracelen = stream[i].count() # number of traces in stream if tracelen == 1: tr = stream[i][0] # tmp trace if tr.stats['sampling_rate'] == 0.0: stream[i].remove(tr) elif tracelen > 1: j = 0 # stream will change sizes when trace is removed while j < range(stream[i].count()): if j == stream[i].count(): break # index = num traces tr = stream[i][j] # tmp trace if tr.stats['sampling_rate'] == 0.0: if not RM: print "Removing empty traces:" print stream[i] print RM = True stream[i].remove(tr) # rm empty trace j = 0 # reset index for new size else: j = j + 1 # mv to next element if RM: print "Final stream with removed traces:" print stream[i] print RM = False self.stream = stream # new stream with removed traces print "-------pullTraces() Complete-------\n\n" except KeyboardInterrupt: print "KeyboardInterrupt pullTraces(): terminating analyzeRemove() method" sys.exit(0) print "Method pullTraces() is terminated!" except Exception as e: print "Exception pullTraces(): " + str(e) sys.exit(0) print "Method pullTraces() is terminated!"
def process(pro,
seg,
cha,
sta,
evt,
dcen,
station_inventory,
amp_ratio_threshold,
arrival_time_delay,
savewindow_delta,
taper_max_percentage,
snr_window_length,
remove_response_output,
remove_response_water_level,
bandpass_corners,
bandpass_freq_max,
bandpass_max_nyquist_ratio,
multi_event_threshold1,
multi_event_threshold1_duration,
multi_event_threshold2,
coda_window_length,
coda_subwindow_length,
coda_subwindow_overlap,
coda_subwindow_amplitude_threshold,
**kwargs):
"""
Processes a single segment.
This function is supposed to perform calculation and set the attributes of the `pro`
object (it does not need to return it). These attributes are set in the `models` module
and the value types should match (meaning an attribute reflecting an integer database
column should be set with integer values only).
Exceptions are handled externally and should be consulted by looking at the log messages
stored in the output database (whose address is given in the `config.yaml` file)
:param pro: a dict-like object (whose keys can be accessed also as attributes, so
`pro['whatever]=6 is the same as `pro.whatever=4`) which has to be populated with values
resulting from processing the given segment.
:param seg: the segment (i.e., time series data) originating the processing. Its actual
data can be accessed via `loads(seg.data)` which returns a Stream object. Additional
fields are accessible via attributes and their names can be inspected via `seg.keys()`
FIXME: write detailed doc!
parameters and arguments must not conflict with imported functions
"""
# convert to UTCDateTime for operations later:
a_time = UTCDateTime(seg.arrival_time) + arrival_time_delay
mseed = read(StringIO(seg.data))
if get_gaps(mseed):
pro.has_gaps = True
else:
if len(mseed) != 1:
raise ValueError("Mseed has more than one Trace")
pro.has_gaps = False
# work on the trace now. All functions will return Traces or scalars, which is better
# so we can write them to database more easily
mseed = mseed[0]
ampratio = amp_ratio(mseed)
pro.amplitude_ratio = ampratio
if ampratio >= amp_ratio_threshold:
pro.is_saturated = True
else:
pro.is_saturated = False
mseed = bandpass(mseed, evt.magnitude, freq_max=bandpass_freq_max,
max_nyquist_ratio=bandpass_max_nyquist_ratio, corners=bandpass_corners)
inv_obj = station_inventory
mseed_acc = remove_response(mseed, inv_obj, output='ACC',
water_level=remove_response_water_level)
mseed_vel = remove_response(mseed, inv_obj, output='VEL',
water_level=remove_response_water_level)
mseed_disp = remove_response(mseed, inv_obj, output='DISP',
water_level=remove_response_water_level)
mseed_wa = simulate_wa(mseed_disp)
mseed_rem_resp = mseed_disp if remove_response_output == 'DISP' else \
(mseed_vel if remove_response_output == 'VEL' else mseed_acc)
mseed_cum = cumsum(mseed_rem_resp)
cum_times = cumtimes(mseed_cum, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
t05, t10, t90, t95 = cum_times[0], cum_times[1], cum_times[-2], \
cum_times[-1]
# mseed_acc_atime_95 = mseed_acc.slice(a_time, t95)
# mseed_vel_atime_t95 = mseed_vel.slice(a_time, t95)
# mseed_wa_atime_t95 = mseed_wa.slice(a_time, t95)
t_PGA, PGA = maxabs(mseed_acc, a_time, t95)
t_PGV, PGV = maxabs(mseed_vel, a_time, t95)
t_PWA, PWA = maxabs(mseed_wa, a_time, t95)
# instantiate the trace below cause it's used also later ...
mseed_rem_resp_t05_t95 = mseed_rem_resp.slice(t05, t95)
fft_rem_resp_s = fft(mseed_rem_resp_t05_t95, taper_max_percentage=taper_max_percentage)
fft_rem_resp_n = fft(mseed_rem_resp, fixed_time=a_time,
window_in_sec=t05-t95, # negative float (in seconds)
taper_max_percentage=taper_max_percentage)
# calculate the *real* start time
snr_rem_resp_t05_t95 = snr(fft_rem_resp_s, fft_rem_resp_n, signals_form='fft',
in_db=False)
fft_rem_resp_s2 = fft(mseed_rem_resp, t10, t90-t10,
taper_max_percentage=taper_max_percentage)
fft_rem_resp_n2 = fft(mseed_rem_resp, a_time, t10-t90,
taper_max_percentage=taper_max_percentage)
snr_rem_resp_t10_t90 = snr(fft_rem_resp_s2, fft_rem_resp_n2, signals_form='fft',
in_db=False)
fft_rem_resp_s3 = fft(mseed_rem_resp, a_time, snr_window_length,
taper_max_percentage=taper_max_percentage)
fft_rem_resp_n3 = fft(mseed_rem_resp, a_time, -snr_window_length,
taper_max_percentage=taper_max_percentage)
snr_rem_resp_fixed_window = snr(fft_rem_resp_s3, fft_rem_resp_n3,
signals_form='fft', in_db=False)
gme = get_multievent # rename func just to avoid line below is not too wide
double_evt = \
gme(mseed_cum, t05, t95,
threshold_inside_tmin_tmax_percent=multi_event_threshold1,
threshold_inside_tmin_tmax_sec=multi_event_threshold1_duration,
threshold_after_tmax_percent=multi_event_threshold2)
mseed_rem_resp_savewindow = mseed_rem_resp.slice(a_time-savewindow_delta,
t95+savewindow_delta).\
taper(max_percentage=taper_max_percentage)
wa_savewindow = mseed_wa.slice(a_time-savewindow_delta,
t95+savewindow_delta).\
taper(max_percentage=taper_max_percentage)
# deltafreq = dfreq(mseed_rem_resp_t05_t95)
# write stuff now to instance:
pro.mseed_rem_resp_savewindow = dumps(mseed_rem_resp_savewindow)
pro.fft_rem_resp_t05_t95 = dumps(fft_rem_resp_s)
pro.fft_rem_resp_until_atime = dumps(fft_rem_resp_n)
pro.wood_anderson_savewindow = dumps(wa_savewindow)
pro.cum_rem_resp = dumps(mseed_cum)
pro.pga_atime_t95 = PGA
pro.pgv_atime_t95 = PGV
pro.pwa_atime_t95 = PWA
pro.t_pga_atime_t95 = dtime(t_PGA)
pro.t_pgv_atime_t95 = dtime(t_PGV)
pro.t_pwa_atime_t95 = dtime(t_PWA)
pro.cum_t05 = dtime(t05)
pro.cum_t10 = dtime(t10)
pro.cum_t25 = dtime(cum_times[2])
pro.cum_t50 = dtime(cum_times[3])
pro.cum_t75 = dtime(cum_times[4])
pro.cum_t90 = dtime(t90)
pro.cum_t95 = dtime(t95)
pro.snr_rem_resp_fixedwindow = snr_rem_resp_fixed_window
pro.snr_rem_resp_t05_t95 = snr_rem_resp_t05_t95
pro.snr_rem_resp_t10_t90 = snr_rem_resp_t10_t90
# pro.amplitude_ratio = Column(Float)
# pro.is_saturated = Column(Boolean)
# pro.has_gaps = Column(Boolean)
pro.double_event_result = double_evt[0]
pro.secondary_event_time = dtime(double_evt[1])
# WITH JESSIE IMPLEMENT CODA ANALYSIS:
# pro.coda_tmax = Column(DateTime)
# pro.coda_length_sec = Column(Float)
return pro
'''
filelist = os.listdir(seedpath)
filelist = filter(lambda x: not os.path.isdir(x), filelist)
newest = max(filelist, key=lambda x: os.stat(x).st_mtime)
stream = read(newest)
print stream
'''
seedpath = "/home/agonzales/Documents/ObsPy/examples/agonzales/SeedFiles/"
os.chdir(seedpath)
filelist = sorted(os.listdir(seedpath), key=os.path.getctime)
filelen = len(filelist)
stream = [0 for x in range(filelen)] # multidim streams list, streams for each file contain multiple traces so streams = [][] where the second entry denotes the trace index
i = filelen-1
while i >= 0:
try:
stream[i] = read(filelist[i]) # read MSEED files from query
except Exception as e:
print "******Exception found = " + str(e)
i = i - 1
streamlen = len(stream) # number of streams (i.e. stream files)
trace = {} # dict of traces for each stream
nfft = 0 # number of fft points, necessary for some filtering
for i in range(streamlen):
strsel = stream[i]
tracelen = len(strsel) # number of traces in stream
index = str(i)
if tracelen == 1: # single trace stream
trace[index] = strsel[0] # trace 0 in stream i
nfft = trace[index].count()
else: # multiple trace stream
