def test_append_sanity_checks(self):
"""
Testing sanity checks of append method.
"""
rtr = RtTrace()
ftr = Trace(data=np.array([0, 1]))
# sanity checks need something already appended
rtr.append(ftr)
# 1 - differing ID
tr = Trace(header={'network': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'station': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'location': 'xy'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'channel': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
# 2 - sample rate
tr = Trace(header={'sampling_rate': 100.0})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'delta': 0.25})
self.assertRaises(TypeError, rtr.append, tr)
# 3 - calibration factor
tr = Trace(header={'calib': 100.0})
self.assertRaises(TypeError, rtr.append, tr)
# 4 - data type
tr = Trace(data=np.array([0.0, 1.1]))
self.assertRaises(TypeError, rtr.append, tr)
# 5 - only Trace objects are allowed
self.assertRaises(TypeError, rtr.append, 1)
self.assertRaises(TypeError, rtr.append, "2323")
def test_appendSanityChecks(self):
"""
Testing sanity checks of append method.
"""
rtr = RtTrace()
ftr = Trace(data=np.array([0, 1]))
# sanity checks need something already appended
rtr.append(ftr)
# 1 - differing ID
tr = Trace(header={'network': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'station': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'location': 'xy'})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'channel': 'xyz'})
self.assertRaises(TypeError, rtr.append, tr)
# 2 - sample rate
tr = Trace(header={'sampling_rate': 100.0})
self.assertRaises(TypeError, rtr.append, tr)
tr = Trace(header={'delta': 0.25})
self.assertRaises(TypeError, rtr.append, tr)
# 3 - calibration factor
tr = Trace(header={'calib': 100.0})
self.assertRaises(TypeError, rtr.append, tr)
# 4 - data type
tr = Trace(data=np.array([0.0, 1.1]))
self.assertRaises(TypeError, rtr.append, tr)
# 5 - only Trace objects are allowed
self.assertRaises(TypeError, rtr.append, 1)
self.assertRaises(TypeError, rtr.append, "2323")
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 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_ne(self):
"""
Testing __ne__ method.
"""
tr = Trace()
tr2 = RtTrace()
tr3 = RtTrace()
# RtTrace should never be equal with Trace objects
self.assertTrue(tr2 != tr)
self.assertTrue(tr2.__ne__(tr))
self.assertFalse(tr2 != tr3)
self.assertFalse(tr2.__ne__(tr3))
def test_ne(self):
"""
Testing __ne__ method.
"""
tr = Trace()
tr2 = RtTrace()
tr3 = RtTrace()
# RtTrace should never be equal with Trace objects
self.assertNotEqual(tr2, tr)
self.assertTrue(tr2.__ne__(tr))
self.assertFalse(tr2 != tr3)
self.assertFalse(tr2.__ne__(tr3))
def test_append_overlap(self):
"""
Appending overlapping traces should raise a UserWarning/TypeError
"""
rtr = RtTrace()
tr = Trace(data=np.array([0, 1]))
rtr.append(tr)
# this raises UserWarning
with warnings.catch_warnings(record=True):
warnings.simplefilter('error', UserWarning)
self.assertRaises(UserWarning, rtr.append, tr)
# append with gap_overlap_check=True will raise a TypeError
self.assertRaises(TypeError, rtr.append, tr, gap_overlap_check=True)
def test_appendOverlap(self):
"""
Appending overlapping traces should raise a UserWarning/TypeError
"""
rtr = RtTrace()
tr = Trace(data=np.array([0, 1]))
rtr.append(tr)
# this raises UserWarning
with warnings.catch_warnings(record=True):
warnings.simplefilter('error', UserWarning)
self.assertRaises(UserWarning, rtr.append, tr)
# append with gap_overlap_check=True will raise a TypeError
self.assertRaises(TypeError, rtr.append, tr, gap_overlap_check=True)
def test_rt_gaussian_filter(self):
from am_signal import gaussian_filter
data_trace = self.data_trace.copy()
gauss5, tshift = gaussian_filter(1.0, 5.0, 0.01)
rt_trace = RtTrace()
rt_single = RtTrace()
for rtt in [rt_trace, rt_single]:
rtt.registerRtProcess('convolve', conv_signal=gauss5)
rt_single.append(data_trace, gap_overlap_check=True)
for tr in self.traces:
# pre-apply inversed time-shift before appending data
tr.stats.starttime -= tshift
rt_trace.append(tr, gap_overlap_check=True)
# test the waveforms are the same
diff = self.data_trace.copy()
diff.data = rt_trace.data - rt_single.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0)
# test the time-shifts
starttime_diff = rt_single.stats.starttime - self.data_trace.stats.starttime
self.assertAlmostEquals(starttime_diff, 0.0)
def test_rt_kurt_grad(self):
win = 3.0
data_trace = self.data_trace.copy()
sigma = float(np.std(data_trace.data))
fact = 1 / sigma
rt_trace = RtTrace()
rt_trace_single = RtTrace()
for rtt in [rt_trace, rt_trace_single]:
rtt.registerRtProcess('scale', factor=fact)
rtt.registerRtProcess('kurtosis', win=win)
rtt.registerRtProcess('boxcar', width=50)
rtt.registerRtProcess('differentiate')
rtt.registerRtProcess('neg_to_zero')
rt_trace_single.append(data_trace)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
diff = self.data_trace.copy()
diff.data = rt_trace_single.data - rt_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0, 5)
def test_rt_offset(self):
offset=500
rt_trace=RtTrace()
rt_trace.registerRtProcess('offset',offset=offset)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
diff=self.data_trace.copy()
diff.data=rt_trace.data-self.data_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)),offset)
def test_appendGap(self):
"""
Appending a traces with a time gap should raise a UserWarning/TypeError
"""
rtr = RtTrace()
tr = Trace(data=np.array([0, 1]))
tr2 = Trace(data=np.array([5, 6]))
tr2.stats.starttime = tr.stats.starttime + 10
rtr.append(tr)
# this raises UserWarning
with warnings.catch_warnings(record=True):
warnings.simplefilter('error', UserWarning)
self.assertRaises(UserWarning, rtr.append, tr2)
# append with gap_overlap_check=True will raise a TypeError
self.assertRaises(TypeError, rtr.append, tr2, gap_overlap_check=True)
def test_append_gap(self):
"""
Appending a traces with a time gap should raise a UserWarning/TypeError
"""
rtr = RtTrace()
tr = Trace(data=np.array([0, 1]))
tr2 = Trace(data=np.array([5, 6]))
tr2.stats.starttime = tr.stats.starttime + 10
rtr.append(tr)
# this raises UserWarning
with warnings.catch_warnings(record=True):
warnings.simplefilter('error', UserWarning)
self.assertRaises(UserWarning, rtr.append, tr2)
# append with gap_overlap_check=True will raise a TypeError
self.assertRaises(TypeError, rtr.append, tr2, gap_overlap_check=True)
def test_rt_neg_to_zero(self):
data_trace=self.data_trace.copy()
max_val=np.max(data_trace.data)
rt_trace=RtTrace()
rt_trace.registerRtProcess('neg_to_zero')
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
max_val_test=np.max(rt_trace.data)
min_val_test=np.min(rt_trace.data)
self.assertEqual(max_val, max_val_test)
self.assertEqual(0.0, min_val_test)
def test_rt_scale(self):
data_trace = self.data_trace.copy()
fact=1/np.std(data_trace.data)
data_trace.data *= fact
rt_trace=RtTrace()
rt_trace.registerRtProcess('scale',factor=fact)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
diff=self.data_trace.copy()
diff.data=rt_trace.data-data_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)),0.0)
def test_rt_variance(self):
win=10
data_trace=self.data_trace.copy()
rt_single=RtTrace()
rt_trace=RtTrace()
rt_trace.registerRtProcess('variance',win=win)
rt_single.registerRtProcess('variance',win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
rt_single.append(data_trace, gap_overlap_check = True)
assert_array_almost_equal(rt_single, rt_trace)
def _run_rt_process(self, process_list, max_length=None):
"""
Helper function to create a RtTrace, register all given process
functions and run the real time processing.
"""
# assemble real time trace
self.rt_trace = RtTrace(max_length=max_length)
for (process, options) in process_list:
self.rt_trace.register_rt_process(process, **options)
# append packet data to RtTrace
self.rt_appended_traces = []
for trace in self.orig_trace_chunks:
# process single trace
result = self.rt_trace.append(trace, gap_overlap_check=True)
# add to list of appended traces
self.rt_appended_traces.append(result)
def test_sw_kurtosis(self):
win=3.0
data_trace = self.data_trace.copy()
rt_trace=RtTrace()
rt_single=RtTrace()
rt_trace.registerRtProcess('sw_kurtosis',win=win)
rt_single.registerRtProcess('sw_kurtosis',win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
rt_single.append(data_trace)
diff=self.data_trace.copy()
diff.data=rt_trace.data-rt_single.data
self.assertAlmostEquals(np.mean(np.abs(diff)),0.0)
def test_rt_mean(self):
win=0.05
data_trace=self.data_trace.copy()
rt_single=RtTrace()
rt_trace=RtTrace()
rt_trace.registerRtProcess('mean',win=win)
rt_single.registerRtProcess('mean',win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
rt_single.append(data_trace, gap_overlap_check = True)
newtr=self.data_trace.copy()
newtr.data=newtr.data-rt_trace.data
assert_array_almost_equal(rt_single, rt_trace)
self.assertAlmostEqual(np.mean(newtr.data),0.0,0)
def test_rt_gaussian_filter(self):
from am_signal import gaussian_filter
data_trace = self.data_trace.copy()
gauss5,tshift = gaussian_filter(1.0, 5.0, 0.01)
rt_trace=RtTrace()
rt_single=RtTrace()
for rtt in [rt_trace, rt_single]:
rtt.registerRtProcess('convolve',conv_signal=gauss5)
rt_single.append(data_trace, gap_overlap_check = True)
for tr in self.traces:
# pre-apply inversed time-shift before appending data
tr.stats.starttime -= tshift
rt_trace.append(tr, gap_overlap_check = True)
# test the waveforms are the same
diff=self.data_trace.copy()
diff.data=rt_trace.data-rt_single.data
self.assertAlmostEquals(np.mean(np.abs(diff)),0.0)
# test the time-shifts
starttime_diff=rt_single.stats.starttime-self.data_trace.stats.starttime
self.assertAlmostEquals(starttime_diff,0.0)
def test_rt_kurt_grad(self):
win=3.0
data_trace = self.data_trace.copy()
sigma=float(np.std(data_trace.data))
fact = 1/sigma
rt_trace=RtTrace()
rt_trace_single = RtTrace()
for rtt in [rt_trace, rt_trace_single]:
rtt.registerRtProcess('scale',factor=fact)
rtt.registerRtProcess('kurtosis',win=win)
rtt.registerRtProcess('boxcar',width=50)
rtt.registerRtProcess('differentiate')
rtt.registerRtProcess('neg_to_zero')
rt_trace_single.append(data_trace)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
diff=self.data_trace.copy()
diff.data=rt_trace_single.data - rt_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0, 5)
def _runRtProcess(self, process_list, max_length=None):
"""
Helper function to create a RtTrace, register all given process
functions and run the real time processing.
"""
# assemble real time trace
self.rt_trace = RtTrace(max_length=max_length)
for (process, options) in process_list:
self.rt_trace.registerRtProcess(process, **options)
# append packet data to RtTrace
self.rt_appended_traces = []
for trace in self.orig_trace_chunks:
# process single trace
result = self.rt_trace.append(trace, gap_overlap_check=True)
# add to list of appended traces
self.rt_appended_traces.append(result)
def test_rt_offset(self):
offset = 500
rt_trace = RtTrace()
rt_trace.registerRtProcess('offset', offset=offset)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
diff = self.data_trace.copy()
diff.data = rt_trace.data - self.data_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)), offset)
def test_rt_neg_to_zero(self):
data_trace = self.data_trace.copy()
max_val = np.max(data_trace.data)
rt_trace = RtTrace()
rt_trace.registerRtProcess('neg_to_zero')
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
max_val_test = np.max(rt_trace.data)
min_val_test = np.min(rt_trace.data)
self.assertEqual(max_val, max_val_test)
self.assertEqual(0.0, min_val_test)
def test_rt_kurtosis(self):
win = 3.0
data_trace = self.data_trace.copy()
sigma = float(np.std(data_trace.data))
fact = 1 / sigma
dt = data_trace.stats.delta
C1 = dt / float(win)
x = data_trace.data
ktrace = data_trace.copy()
ktrace.data = rec_kurtosis(x * fact, C1)
rt_trace = RtTrace()
rt_trace.registerRtProcess('scale', factor=fact)
rt_trace.registerRtProcess('kurtosis', win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
diff = self.data_trace.copy()
diff.data = rt_trace.data - ktrace.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0)
def test_rt_scale(self):
data_trace = self.data_trace.copy()
fact = 1 / np.std(data_trace.data)
data_trace.data *= fact
rt_trace = RtTrace()
rt_trace.registerRtProcess('scale', factor=fact)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
diff = self.data_trace.copy()
diff.data = rt_trace.data - data_trace.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0)
def test_rt_kurtosis(self):
win=3.0
data_trace = self.data_trace.copy()
sigma=float(np.std(data_trace.data))
fact = 1/sigma
dt=data_trace.stats.delta
C1=dt/float(win)
x=data_trace.data
ktrace=data_trace.copy()
ktrace.data=rec_kurtosis(x*fact,C1)
rt_trace=RtTrace()
rt_trace.registerRtProcess('scale',factor=fact)
rt_trace.registerRtProcess('kurtosis',win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check = True)
diff=self.data_trace.copy()
diff.data=rt_trace.data-ktrace.data
self.assertAlmostEquals(np.mean(np.abs(diff)),0.0)
class RtMigrator(object):
"""
Class of objects for real-time migration.
"""
# attributes
x=np.array([])
y=np.array([])
z=np.array([])
ttimes_matrix=np.empty((0,0), dtype=float)
npts=0
nsta=0
sta_list=[]
obs_rt_list=[]
point_rt_list=[]
stack_list=[]
max_out=None
x_out=None
y_out=None
z_out=None
last_common_end_stack=[]
last_common_end_max=None
dt=1.0
filter_shift=0.0
def __init__(self,waveloc_options):
"""
Initialize from a set of travel-times as hdf5 files
"""
wo=waveloc_options
# initialize the travel-times
#############################
ttimes_fnames=glob.glob(wo.ttimes_glob)
# get basic lengths
f=h5py.File(ttimes_fnames[0],'r')
# copy the x, y, z data over
self.x = np.array(f['x'][:])
self.y = np.array(f['y'][:])
self.z = np.array(f['z'][:])
f.close()
# read the files
ttimes_list = []
self.sta_list=[]
for fname in ttimes_fnames:
f=h5py.File(fname,'r')
# update the list of ttimes
ttimes_list.append(np.array(f['ttimes']))
sta=f['ttimes'].attrs['station']
f.close()
# update the dictionary of station names
self.sta_list.append(sta)
# stack the ttimes into a numpy array
self.ttimes_matrix=np.vstack(ttimes_list)
(self.nsta,self.npts) = self.ttimes_matrix.shape
# initialize the RtTrace(s)
##########################
max_length = wo.opdict['max_length']
self.safety_margin = wo.opdict['safety_margin']
self.dt = wo.opdict['dt']
# need a RtTrace per station
self.obs_rt_list=[RtTrace() for sta in self.sta_list]
# register pre-processing
self._register_preprocessing(wo)
# need nsta streams for each point we test (nsta x npts)
# for shifted waveforms
self.point_rt_list=[[RtTrace(max_length=max_length) \
for ista in xrange(self.nsta)] for ip in xrange(self.npts)]
# register processing of point-streams here
for sta_list in self.point_rt_list:
for rtt in sta_list:
# This is where we would scale for distance (given pre-calculated
# distances from each point to every station)
rtt.registerRtProcess('scale', factor=1.0)
# need npts streams to store the point-stacks
self.stack_list=[RtTrace(max_length=max_length) for ip in xrange(self.npts)]
# register stack procesing here
for rtt in self.stack_list:
# This is where we would add or lower weights if we wanted to
rtt.registerRtProcess('scale', factor=1.0)
# need 4 output streams (max, x, y, z)
self.max_out = RtTrace()
self.x_out = RtTrace()
self.y_out = RtTrace()
self.z_out = RtTrace()
if not wo.is_syn:
self.max_out.registerRtProcess('boxcar', width=50)
# need a list of common start-times
self.last_common_end_stack = [UTCDateTime(1970,1,1) for i in xrange(self.npts)]
self.last_common_end_max = UTCDateTime(1970,1,1)
def _register_preprocessing(self, waveloc_options):
wo=waveloc_options
# if this is a synthetic
if wo.is_syn:
# do dummy processing only
for rtt in self.obs_rt_list:
rtt.registerRtProcess('scale', factor=1.0)
else:
# get gaussian filtering parameters
f0, sigma, dt = wo.gauss_filter
gauss, self.filter_shift = gaussian_filter(f0, sigma, dt)
# get kwin
# for now just use one window
kwin = wo.opdict['kwin']
# register pre-processing of data here
for rtt in self.obs_rt_list:
rtt.registerRtProcess('convolve', conv_signal=gauss)
rtt.registerRtProcess('sw_kurtosis', win=kwin)
rtt.registerRtProcess('boxcar', width=50)
rtt.registerRtProcess('differentiate')
rtt.registerRtProcess('neg_to_zero')
def updateData(self, tr_list):
"""
Adds a list of traces (one per station) to the system
"""
t_copy=0.0
t_append=0.0
t_append_proc=0.0
t0_update=time.time()
for tr in tr_list:
if (self.dt!=tr.stats.delta):
msg = 'Value of dt from options file %.2f does not match dt from data %2f'%(self.dt, tr.stats.delta)
raise ValueError()
# pre-correct for filter_shift
#tr.stats.starttime -= np.round(self.filter_shift/self.dt) * self.dt
tr.stats.starttime -= self.filter_shift
sta=tr.stats.station
ista=self.sta_list.index(sta)
# make dtype of data float if it is not already
tr.data=tr.data.astype(np.float32)
t0=time.time()
pp_data = self.obs_rt_list[ista].append(tr, gap_overlap_check = True)
t_append_proc += time.time() - t0
# loop over points
for ip in xrange(self.npts):
# do time shift and append
t0=time.time()
pp_data_tmp = pp_data.copy()
t_copy += time.time() - t0
pp_data_tmp.stats.starttime -= np.round(self.ttimes_matrix[ista,ip]/self.dt) * self.dt
t0=time.time()
self.point_rt_list[ip][ista].append(pp_data_tmp, gap_overlap_check = True)
t_append += time.time() - t0
print "In updateData : %.2f s in process and %.2f s in data copy and %.2f s in append and a total of %.2f s" % (t_append_proc, t_copy, t_append, time.time()-t0_update)
def updateStacks(self):
npts=self.npts
for ip in xrange(npts):
self._updateStack(ip)
def _updateStack(self,ip):
UTCDateTime.DEFAULT_PRECISION=2
nsta=self.nsta
# get common start-time for this point
common_start=max([self.point_rt_list[ip][ista].stats.starttime \
for ista in xrange(nsta)])
common_start=max(common_start,self.last_common_end_stack[ip])
# get list of stations for which the end-time is compatible
# with the common_start time and the safety buffer
ista_ok=[ista for ista in xrange(nsta) if (self.point_rt_list[ip][ista].stats.endtime - common_start) > self.safety_margin]
# get common end-time
common_end=min([ self.point_rt_list[ip][ista].stats.endtime for ista in ista_ok])
self.last_common_end_stack[ip]=common_end+self.dt
# stack
c_list=[]
for ista in ista_ok:
tr=self.point_rt_list[ip][ista].copy()
tr.trim(common_start, common_end)
c_list.append(np.array(tr.data[:]))
tr_common=np.vstack(c_list)
# prepare trace for passing up
stack_data = np.sum(tr_common, axis=0)
stats={'station':'STACK', 'npts':len(stack_data), 'delta':self.dt, \
'starttime':common_start}
tr=Trace(data=stack_data,header=stats)
#import pdb; pdb.set_trace()
# append to appropriate stack_list
self.stack_list[ip].append(tr, gap_overlap_check = True)
def updateMax(self):
npts=self.npts
nsta=self.nsta
# now extract maximum etc from stacks
# get common start-time for this point
common_start=max([self.stack_list[ip].stats.starttime \
for ip in xrange(npts)])
common_start=max(common_start,self.last_common_end_max)
# get list of points for which the end-time is compatible
# with the common_start time and the safety buffer
ip_ok=[ip for ip in xrange(npts) if (self.stack_list[ip].stats.endtime - common_start) > self.safety_margin]
common_end=min([self.stack_list[ip].stats.endtime for ip in ip_ok ])
self.last_common_end_max=common_end+self.dt
# stack
c_list=[]
for ip in ip_ok:
tr=self.stack_list[ip].copy()
tr.trim(common_start, common_end)
c_list.append(tr.data)
tr_common=np.vstack(c_list)
# get maximum and the corresponding point
max_data = np.max(tr_common, axis=0)
argmax_data = np.argmax(tr_common, axis=0)
# prepare traces for passing up
# max
stats={'station':'Max', 'npts':len(max_data), 'delta':self.dt, \
'starttime':common_start}
tr_max=Trace(data=max_data,header=stats)
self.max_out.append(tr_max, gap_overlap_check = True)
# x coordinate
stats['station'] = 'xMax'
tr_x=Trace(data=self.x[argmax_data],header=stats)
self.x_out.append(tr_x, gap_overlap_check = True)
# y coordinate
stats['station'] = 'yMax'
tr_y=Trace(data=self.y[argmax_data],header=stats)
self.y_out.append(tr_y, gap_overlap_check = True)
# z coordinate
stats['station'] = 'zMax'
tr_z=Trace(data=self.z[argmax_data],header=stats)
self.z_out.append(tr_z, gap_overlap_check = True)
def test_kwin_bank(self):
win_list = [1.0, 3.0, 9.0]
n_win = len(win_list)
data_trace = self.data_trace.copy()
sigma = float(np.std(data_trace.data))
fact = 1 / sigma
# One RtTrace for processing before the kurtosis
rt_trace = RtTrace()
rt_trace.registerRtProcess('scale', factor=fact)
# One RtTrace per kurtosis window
kurt_traces = []
for i in xrange(n_win):
rtt = RtTrace()
rtt.registerRtProcess('kurtosis', win=win_list[i])
kurt_traces.append(rtt)
# One RrTrace for post-processing the max kurtosis window
max_kurt = RtTrace()
max_kurt.registerRtProcess('differentiate')
max_kurt.registerRtProcess('neg_to_zero')
for tr in self.traces:
# prepare memory for kurtosis
kurt_tr = tr.copy()
# do initial processing
proc_trace = rt_trace.append(tr, gap_overlap_check=True)
kurt_output = []
for i in xrange(n_win):
# pass output of initial processing to the kwin bank
ko = kurt_traces[i].append(proc_trace, gap_overlap_check=True)
# append the output to the kurt_output list
kurt_output.append(ko.data)
# stack the output of the kwin bank and find maximum
kurt_stack = np.vstack(tuple(kurt_output))
kurt_tr.data = np.max(kurt_stack, axis=0)
# append to the max_kurt RtTrace for post-processing
max_kurt.append(kurt_tr)
def test_copy(self):
"""
Testing copy of RtTrace object.
"""
rtr = RtTrace()
rtr.copy()
# register predefined function
rtr.register_rt_process('integrate', test=1, muh='maeh')
rtr.copy()
# register ObsPy function call
rtr.register_rt_process(signal.filter.bandpass, freqmin=0, freqmax=1,
df=0.1)
rtr.copy()
# register NumPy function call
rtr.register_rt_process(np.square)
rtr.copy()
class RtMigrator(object):
"""
Class of objects for real-time migration.
"""
# attributes
x = np.array([])
y = np.array([])
z = np.array([])
ttimes_matrix = np.empty((0, 0), dtype=float)
npts = 0
nsta = 0
sta_list = []
obs_rt_list = []
point_rt_list = []
stack_list = []
max_out = None
x_out = None
y_out = None
z_out = None
last_common_end_stack = []
last_common_end_max = None
dt = 1.0
filter_shift = 0.0
def __init__(self, waveloc_options):
"""
Initialize from a set of travel-times as hdf5 files
"""
wo = waveloc_options
# initialize the travel-times
#############################
ttimes_fnames = glob.glob(wo.ttimes_glob)
# get basic lengths
f = h5py.File(ttimes_fnames[0], 'r')
# copy the x, y, z data over
self.x = np.array(f['x'][:])
self.y = np.array(f['y'][:])
self.z = np.array(f['z'][:])
f.close()
# read the files
ttimes_list = []
self.sta_list = []
for fname in ttimes_fnames:
f = h5py.File(fname, 'r')
# update the list of ttimes
ttimes_list.append(np.array(f['ttimes']))
sta = f['ttimes'].attrs['station']
f.close()
# update the dictionary of station names
self.sta_list.append(sta)
# stack the ttimes into a numpy array
self.ttimes_matrix = np.vstack(ttimes_list)
(self.nsta, self.npts) = self.ttimes_matrix.shape
# initialize the RtTrace(s)
##########################
max_length = wo.opdict['max_length']
self.safety_margin = wo.opdict['safety_margin']
self.dt = wo.opdict['dt']
# need a RtTrace per station
self.obs_rt_list = [RtTrace() for sta in self.sta_list]
# register pre-processing
self._register_preprocessing(wo)
# need nsta streams for each point we test (nsta x npts)
# for shifted waveforms
self.point_rt_list=[[RtTrace(max_length=max_length) \
for ista in xrange(self.nsta)] for ip in xrange(self.npts)]
# register processing of point-streams here
for sta_list in self.point_rt_list:
for rtt in sta_list:
# This is where we would scale for distance (given pre-calculated
# distances from each point to every station)
rtt.registerRtProcess('scale', factor=1.0)
# need npts streams to store the point-stacks
self.stack_list = [
RtTrace(max_length=max_length) for ip in xrange(self.npts)
]
# register stack procesing here
for rtt in self.stack_list:
# This is where we would add or lower weights if we wanted to
rtt.registerRtProcess('scale', factor=1.0)
# need 4 output streams (max, x, y, z)
self.max_out = RtTrace()
self.x_out = RtTrace()
self.y_out = RtTrace()
self.z_out = RtTrace()
if not wo.is_syn:
self.max_out.registerRtProcess('boxcar', width=50)
# need a list of common start-times
self.last_common_end_stack = [
UTCDateTime(1970, 1, 1) for i in xrange(self.npts)
]
self.last_common_end_max = UTCDateTime(1970, 1, 1)
def _register_preprocessing(self, waveloc_options):
wo = waveloc_options
# if this is a synthetic
if wo.is_syn:
# do dummy processing only
for rtt in self.obs_rt_list:
rtt.registerRtProcess('scale', factor=1.0)
else:
# get gaussian filtering parameters
f0, sigma, dt = wo.gauss_filter
gauss, self.filter_shift = gaussian_filter(f0, sigma, dt)
# get kwin
# for now just use one window
kwin = wo.opdict['kwin']
# register pre-processing of data here
for rtt in self.obs_rt_list:
rtt.registerRtProcess('convolve', conv_signal=gauss)
rtt.registerRtProcess('sw_kurtosis', win=kwin)
rtt.registerRtProcess('boxcar', width=50)
rtt.registerRtProcess('differentiate')
rtt.registerRtProcess('neg_to_zero')
def updateData(self, tr_list):
"""
Adds a list of traces (one per station) to the system
"""
t_copy = 0.0
t_append = 0.0
t_append_proc = 0.0
t0_update = time.time()
for tr in tr_list:
if (self.dt != tr.stats.delta):
msg = 'Value of dt from options file %.2f does not match dt from data %2f' % (
self.dt, tr.stats.delta)
raise ValueError()
# pre-correct for filter_shift
#tr.stats.starttime -= np.round(self.filter_shift/self.dt) * self.dt
tr.stats.starttime -= self.filter_shift
sta = tr.stats.station
ista = self.sta_list.index(sta)
# make dtype of data float if it is not already
tr.data = tr.data.astype(np.float32)
t0 = time.time()
pp_data = self.obs_rt_list[ista].append(tr, gap_overlap_check=True)
t_append_proc += time.time() - t0
# loop over points
for ip in xrange(self.npts):
# do time shift and append
t0 = time.time()
pp_data_tmp = pp_data.copy()
t_copy += time.time() - t0
pp_data_tmp.stats.starttime -= np.round(
self.ttimes_matrix[ista, ip] / self.dt) * self.dt
t0 = time.time()
self.point_rt_list[ip][ista].append(pp_data_tmp,
gap_overlap_check=True)
t_append += time.time() - t0
print "In updateData : %.2f s in process and %.2f s in data copy and %.2f s in append and a total of %.2f s" % (
t_append_proc, t_copy, t_append, time.time() - t0_update)
def updateStacks(self):
npts = self.npts
for ip in xrange(npts):
self._updateStack(ip)
def _updateStack(self, ip):
UTCDateTime.DEFAULT_PRECISION = 2
nsta = self.nsta
# get common start-time for this point
common_start=max([self.point_rt_list[ip][ista].stats.starttime \
for ista in xrange(nsta)])
common_start = max(common_start, self.last_common_end_stack[ip])
# get list of stations for which the end-time is compatible
# with the common_start time and the safety buffer
ista_ok = [
ista for ista in xrange(nsta)
if (self.point_rt_list[ip][ista].stats.endtime -
common_start) > self.safety_margin
]
# get common end-time
common_end = min(
[self.point_rt_list[ip][ista].stats.endtime for ista in ista_ok])
self.last_common_end_stack[ip] = common_end + self.dt
# stack
c_list = []
for ista in ista_ok:
tr = self.point_rt_list[ip][ista].copy()
tr.trim(common_start, common_end)
c_list.append(np.array(tr.data[:]))
tr_common = np.vstack(c_list)
# prepare trace for passing up
stack_data = np.sum(tr_common, axis=0)
stats={'station':'STACK', 'npts':len(stack_data), 'delta':self.dt, \
'starttime':common_start}
tr = Trace(data=stack_data, header=stats)
#import pdb; pdb.set_trace()
# append to appropriate stack_list
self.stack_list[ip].append(tr, gap_overlap_check=True)
def updateMax(self):
npts = self.npts
nsta = self.nsta
# now extract maximum etc from stacks
# get common start-time for this point
common_start=max([self.stack_list[ip].stats.starttime \
for ip in xrange(npts)])
common_start = max(common_start, self.last_common_end_max)
# get list of points for which the end-time is compatible
# with the common_start time and the safety buffer
ip_ok = [
ip for ip in xrange(npts) if (self.stack_list[ip].stats.endtime -
common_start) > self.safety_margin
]
common_end = min([self.stack_list[ip].stats.endtime for ip in ip_ok])
self.last_common_end_max = common_end + self.dt
# stack
c_list = []
for ip in ip_ok:
tr = self.stack_list[ip].copy()
tr.trim(common_start, common_end)
c_list.append(tr.data)
tr_common = np.vstack(c_list)
# get maximum and the corresponding point
max_data = np.max(tr_common, axis=0)
argmax_data = np.argmax(tr_common, axis=0)
# prepare traces for passing up
# max
stats={'station':'Max', 'npts':len(max_data), 'delta':self.dt, \
'starttime':common_start}
tr_max = Trace(data=max_data, header=stats)
self.max_out.append(tr_max, gap_overlap_check=True)
# x coordinate
stats['station'] = 'xMax'
tr_x = Trace(data=self.x[argmax_data], header=stats)
self.x_out.append(tr_x, gap_overlap_check=True)
# y coordinate
stats['station'] = 'yMax'
tr_y = Trace(data=self.y[argmax_data], header=stats)
self.y_out.append(tr_y, gap_overlap_check=True)
# z coordinate
stats['station'] = 'zMax'
tr_z = Trace(data=self.z[argmax_data], header=stats)
self.z_out.append(tr_z, gap_overlap_check=True)
def test_rt_kurtosis_dec(self):
win=5.0
data_trace=self.data_trace_filt.copy()
data_trace_dec=self.data_trace_filt.copy()
# no need to filter as we're using a pre-filtered trace
data_trace_dec.decimate(5,no_filter=True)
rt_trace=RtTrace()
rt_dec=RtTrace()
rt_trace.registerRtProcess('kurtosis',win=win)
rt_dec.registerRtProcess('kurtosis',win=win)
rt_trace.append(data_trace, gap_overlap_check = True)
rt_dec.append(data_trace_dec, gap_overlap_check = True)
newtr=rt_trace.copy()
newtr.decimate(5, no_filter=True)
#assert_array_almost_equal(rt_dec.data, newtr.data, 0)
diff=(np.max(rt_dec.data)-np.max(newtr.data)) / np.max(rt_dec.data)
self.assertAlmostEquals(np.abs(diff) , 0.0, 2)
def test_kwin_bank(self):
win_list=[1.0, 3.0, 9.0]
n_win = len(win_list)
data_trace = self.data_trace.copy()
sigma=float(np.std(data_trace.data))
fact = 1/sigma
# One RtTrace for processing before the kurtosis
rt_trace=RtTrace()
rt_trace.registerRtProcess('scale',factor=fact)
# One RtTrace per kurtosis window
kurt_traces=[]
for i in xrange(n_win):
rtt=RtTrace()
rtt.registerRtProcess('kurtosis',win=win_list[i])
kurt_traces.append(rtt)
# One RrTrace for post-processing the max kurtosis window
max_kurt=RtTrace()
max_kurt.registerRtProcess('differentiate')
max_kurt.registerRtProcess('neg_to_zero')
for tr in self.traces:
# prepare memory for kurtosis
kurt_tr=tr.copy()
# do initial processing
proc_trace=rt_trace.append(tr, gap_overlap_check = True)
kurt_output=[]
for i in xrange(n_win):
# pass output of initial processing to the kwin bank
ko=kurt_traces[i].append(proc_trace, gap_overlap_check = True)
# append the output to the kurt_output list
kurt_output.append(ko.data)
# stack the output of the kwin bank and find maximum
kurt_stack=np.vstack(tuple(kurt_output))
kurt_tr.data=np.max(kurt_stack,axis=0)
# append to the max_kurt RtTrace for post-processing
max_kurt.append(kurt_tr)
def __init__(self, waveloc_options):
"""
Initialize from a set of travel-times as hdf5 files
"""
wo = waveloc_options
# initialize the travel-times
#############################
ttimes_fnames = glob.glob(wo.ttimes_glob)
# get basic lengths
f = h5py.File(ttimes_fnames[0], 'r')
# copy the x, y, z data over
self.x = np.array(f['x'][:])
self.y = np.array(f['y'][:])
self.z = np.array(f['z'][:])
f.close()
# read the files
ttimes_list = []
self.sta_list = []
for fname in ttimes_fnames:
f = h5py.File(fname, 'r')
# update the list of ttimes
ttimes_list.append(np.array(f['ttimes']))
sta = f['ttimes'].attrs['station']
f.close()
# update the dictionary of station names
self.sta_list.append(sta)
# stack the ttimes into a numpy array
self.ttimes_matrix = np.vstack(ttimes_list)
(self.nsta, self.npts) = self.ttimes_matrix.shape
# initialize the RtTrace(s)
##########################
max_length = wo.opdict['max_length']
self.safety_margin = wo.opdict['safety_margin']
self.dt = wo.opdict['dt']
# need a RtTrace per station
self.obs_rt_list = [RtTrace() for sta in self.sta_list]
# register pre-processing
self._register_preprocessing(wo)
# need nsta streams for each point we test (nsta x npts)
# for shifted waveforms
self.point_rt_list=[[RtTrace(max_length=max_length) \
for ista in xrange(self.nsta)] for ip in xrange(self.npts)]
# register processing of point-streams here
for sta_list in self.point_rt_list:
for rtt in sta_list:
# This is where we would scale for distance (given pre-calculated
# distances from each point to every station)
rtt.registerRtProcess('scale', factor=1.0)
# need npts streams to store the point-stacks
self.stack_list = [
RtTrace(max_length=max_length) for ip in xrange(self.npts)
]
# register stack procesing here
for rtt in self.stack_list:
# This is where we would add or lower weights if we wanted to
rtt.registerRtProcess('scale', factor=1.0)
# need 4 output streams (max, x, y, z)
self.max_out = RtTrace()
self.x_out = RtTrace()
self.y_out = RtTrace()
self.z_out = RtTrace()
if not wo.is_syn:
self.max_out.registerRtProcess('boxcar', width=50)
# need a list of common start-times
self.last_common_end_stack = [
UTCDateTime(1970, 1, 1) for i in xrange(self.npts)
]
self.last_common_end_max = UTCDateTime(1970, 1, 1)
def test_rt_mean(self):
win = 0.05
data_trace = self.data_trace.copy()
rt_single = RtTrace()
rt_trace = RtTrace()
rt_trace.registerRtProcess('mean', win=win)
rt_single.registerRtProcess('mean', win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
rt_single.append(data_trace, gap_overlap_check=True)
newtr = self.data_trace.copy()
newtr.data = newtr.data - rt_trace.data
assert_array_almost_equal(rt_single, rt_trace)
self.assertAlmostEqual(np.mean(newtr.data), 0.0, 0)
class RealTimeSignalTestCase(unittest.TestCase):
"""
The obspy.realtime.signal test suite.
"""
@classmethod
def setUpClass(cls):
# read test data as float64
cls.orig_trace = read(os.path.join(os.path.dirname(__file__), 'data',
'II.TLY.BHZ.SAC'),
dtype=np.float64)[0]
# make really sure test data is float64
cls.orig_trace.data = np.require(cls.orig_trace.data, np.float64)
cls.orig_trace_chunks = cls.orig_trace / NUM_PACKETS
def setUp(self):
# clear results
self.filt_trace_data = None
self.rt_trace = None
self.rt_appended_traces = []
def tearDown(self):
# use results for debug plots if enabled
if PLOT_TRACES and self.filt_trace_data is not None and \
self.rt_trace is not None and self.rt_appended_traces:
self._plot_results()
def test_square(self):
"""
Testing np.square function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = np.square(trace)
# filtering real time
process_list = [(np.square, {})]
self._run_rt_process(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_integrate(self):
"""
Testing integrate function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = signal.integrate(trace)
# filtering real time
process_list = [('integrate', {})]
self._run_rt_process(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_differentiate(self):
"""
Testing differentiate function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = signal.differentiate(trace)
# filtering real time
process_list = [('differentiate', {})]
self._run_rt_process(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_boxcar(self):
"""
Testing boxcar function.
"""
trace = self.orig_trace.copy()
options = {'width': 500}
# filtering manual
self.filt_trace_data = signal.boxcar(trace, **options)
# filtering real time
process_list = [('boxcar', options)]
self._run_rt_process(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertAlmostEqual(peak, 566974.214, 3)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_scale(self):
"""
Testing scale function.
"""
trace = self.orig_trace.copy()
options = {'factor': 1000}
# filtering manual
self.filt_trace_data = signal.scale(trace, **options)
# filtering real time
process_list = [('scale', options)]
self._run_rt_process(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertEqual(peak, 1045237000.0)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_offset(self):
"""
Testing offset function.
"""
trace = self.orig_trace.copy()
options = {'offset': 500}
# filtering manual
self.filt_trace_data = signal.offset(trace, **options)
# filtering real time
process_list = [('offset', options)]
self._run_rt_process(process_list)
# check results
diff = self.rt_trace.data - self.orig_trace.data
self.assertEqual(np.mean(diff), 500)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_kurtosis(self):
"""
Testing kurtosis function.
"""
trace = self.orig_trace.copy()
options = {'win': 5}
# filtering manual
self.filt_trace_data = signal.kurtosis(trace, **options)
# filtering real time
process_list = [('kurtosis', options)]
self._run_rt_process(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_abs(self):
"""
Testing np.abs function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = np.abs(trace)
# filtering real time
process_list = [(np.abs, {})]
self._run_rt_process(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertEqual(peak, 1045237)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_tauc(self):
"""
Testing tauc function.
"""
trace = self.orig_trace.copy()
options = {'width': 60}
# filtering manual
self.filt_trace_data = signal.tauc(trace, **options)
# filtering real time
process_list = [('tauc', options)]
self._run_rt_process(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertAlmostEqual(peak, 114.302, 3)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_mwp_integral(self):
"""
Testing mwpintegral functions.
"""
trace = self.orig_trace.copy()
options = {
'mem_time': 240,
'ref_time': trace.stats.starttime + 301.506,
'max_time': 120,
'gain': 1.610210e+09
}
# filtering manual
self.filt_trace_data = signal.mwpintegral(self.orig_trace.copy(),
**options)
# filtering real time
process_list = [('mwpintegral', options)]
self._run_rt_process(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_mwp(self):
"""
Testing Mwp calculation using two processing functions.
"""
trace = self.orig_trace.copy()
epicentral_distance = 30.0855
options = {
'mem_time': 240,
'ref_time': trace.stats.starttime + 301.506,
'max_time': 120,
'gain': 1.610210e+09
}
# filtering manual
trace.data = signal.integrate(trace)
self.filt_trace_data = signal.mwpintegral(trace, **options)
# filtering real time
process_list = [('integrate', {}), ('mwpintegral', options)]
self._run_rt_process(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
mwp = signal.calculate_mwp_mag(peak, epicentral_distance)
self.assertAlmostEqual(mwp, 8.78902911791, 5)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_combined(self):
"""
Testing combining integrate and differentiate functions.
"""
trace = self.orig_trace.copy()
# filtering manual
trace.data = signal.integrate(trace)
self.filt_trace_data = signal.differentiate(trace)
# filtering real time
process_list = [('int', {}), ('diff', {})]
self._run_rt_process(process_list)
# check results
trace = self.orig_trace.copy()
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
np.testing.assert_almost_equal(trace.data[1:], self.rt_trace.data[1:])
np.testing.assert_almost_equal(trace.data[1:],
self.filt_trace_data[1:])
def _run_rt_process(self, process_list, max_length=None):
"""
Helper function to create a RtTrace, register all given process
functions and run the real time processing.
"""
# assemble real time trace
self.rt_trace = RtTrace(max_length=max_length)
for (process, options) in process_list:
self.rt_trace.register_rt_process(process, **options)
# append packet data to RtTrace
self.rt_appended_traces = []
for trace in self.orig_trace_chunks:
# process single trace
result = self.rt_trace.append(trace, gap_overlap_check=True)
# add to list of appended traces
self.rt_appended_traces.append(result)
def _plot_results(self):
"""
Plots original, filtered original and real time processed traces into
a single plot.
"""
# plot only if test is started manually
if __name__ != '__main__':
return
# create empty stream
st = Stream()
st.label = self._testMethodName
# original trace
self.orig_trace.label = "Original Trace"
st += self.orig_trace
# use header information of original trace with filtered trace data
tr = self.orig_trace.copy()
tr.data = self.filt_trace_data
tr.label = "Filtered original Trace"
st += tr
# real processed chunks
for i, tr in enumerate(self.rt_appended_traces):
tr.label = "RT Chunk %02d" % (i + 1)
st += tr
# real time processed trace
self.rt_trace.label = "RT Trace"
st += self.rt_trace
st.plot(automerge=False, color='blue', equal_scale=False)
def test_registerRtProcess(self):
"""
Testing register_rt_process method.
"""
tr = RtTrace()
# 1 - function call
tr.register_rt_process(np.abs)
self.assertEqual(tr.processing, [(np.abs, {}, None)])
# 2 - predefined RT processing algorithm
tr.register_rt_process('integrate', test=1, muh='maeh')
self.assertEqual(tr.processing[1][0], 'integrate')
self.assertEqual(tr.processing[1][1], {'test': 1, 'muh': 'maeh'})
self.assertTrue(isinstance(tr.processing[1][2][0], RtMemory))
# 3 - contained name of predefined RT processing algorithm
tr.register_rt_process('in')
self.assertEqual(tr.processing[2][0], 'integrate')
tr.register_rt_process('integ')
self.assertEqual(tr.processing[3][0], 'integrate')
tr.register_rt_process('integr')
self.assertEqual(tr.processing[4][0], 'integrate')
# 4 - unknown functions
self.assertRaises(NotImplementedError,
tr.register_rt_process, 'integrate2')
self.assertRaises(NotImplementedError, tr.register_rt_process, 'xyz')
# 5 - module instead of function
self.assertRaises(NotImplementedError, tr.register_rt_process, np)
# check number off all processing steps within RtTrace
self.assertEqual(len(tr.processing), 5)
# check tr.stats.processing
self.assertEqual(len(tr.stats.processing), 5)
self.assertTrue(tr.stats.processing[0].startswith("realtime_process"))
self.assertIn('absolute', tr.stats.processing[0])
for i in range(1, 5):
self.assertIn('integrate', tr.stats.processing[i])
# check kwargs
self.assertIn("maeh", tr.stats.processing[1])
def test_register_rt_process(self):
"""
Testing register_rt_process method.
"""
tr = RtTrace()
# 1 - function call
tr.register_rt_process(np.abs)
self.assertEqual(tr.processing, [(np.abs, {}, None)])
# 2 - predefined RT processing algorithm
tr.register_rt_process('integrate', test=1, muh='maeh')
self.assertEqual(tr.processing[1][0], 'integrate')
self.assertEqual(tr.processing[1][1], {'test': 1, 'muh': 'maeh'})
self.assertTrue(isinstance(tr.processing[1][2][0], RtMemory))
# 3 - contained name of predefined RT processing algorithm
tr.register_rt_process('in')
self.assertEqual(tr.processing[2][0], 'integrate')
tr.register_rt_process('integ')
self.assertEqual(tr.processing[3][0], 'integrate')
tr.register_rt_process('integr')
self.assertEqual(tr.processing[4][0], 'integrate')
# 4 - unknown functions
self.assertRaises(NotImplementedError, tr.register_rt_process,
'integrate2')
self.assertRaises(NotImplementedError, tr.register_rt_process, 'xyz')
# 5 - module instead of function
self.assertRaises(NotImplementedError, tr.register_rt_process, np)
# check number off all processing steps within RtTrace
self.assertEqual(len(tr.processing), 5)
# check tr.stats.processing
self.assertEqual(len(tr.stats.processing), 5)
self.assertTrue(tr.stats.processing[0].startswith("realtime_process"))
self.assertIn('absolute', tr.stats.processing[0])
for i in range(1, 5):
self.assertIn('integrate', tr.stats.processing[i])
# check kwargs
self.assertIn("maeh", tr.stats.processing[1])
def test_missing_or_wrong_argument_in_rt_process(self):
"""
Tests handling of missing/wrong arguments.
"""
trace = Trace(np.arange(100))
# 1- function scale needs no additional arguments
rt_trace = RtTrace()
rt_trace.register_rt_process('scale')
rt_trace.append(trace)
# adding arbitrary arguments should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('scale', muh='maeh')
self.assertRaises(TypeError, rt_trace.append, trace)
# 2- function tauc has one required argument
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', width=10)
rt_trace.append(trace)
# wrong argument should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', xyz='xyz')
self.assertRaises(TypeError, rt_trace.append, trace)
# missing argument width should raise an exception
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc')
self.assertRaises(TypeError, rt_trace.append, trace)
# adding arbitrary arguments should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', width=20, notexistingoption=True)
self.assertRaises(TypeError, rt_trace.append, trace)
def test_rt_kurtosis_dec(self):
win = 5.0
data_trace = self.data_trace_filt.copy()
data_trace_dec = self.data_trace_filt.copy()
# no need to filter as we're using a pre-filtered trace
data_trace_dec.decimate(5, no_filter=True)
rt_trace = RtTrace()
rt_dec = RtTrace()
rt_trace.registerRtProcess('kurtosis', win=win)
rt_dec.registerRtProcess('kurtosis', win=win)
rt_trace.append(data_trace, gap_overlap_check=True)
rt_dec.append(data_trace_dec, gap_overlap_check=True)
newtr = rt_trace.copy()
newtr.decimate(5, no_filter=True)
#assert_array_almost_equal(rt_dec.data, newtr.data, 0)
diff = (np.max(rt_dec.data) - np.max(newtr.data)) / np.max(rt_dec.data)
self.assertAlmostEquals(np.abs(diff), 0.0, 2)
def test_copy(self):
"""
Testing copy of RtTrace object.
"""
rtr = RtTrace()
rtr.copy()
# register predefined function
rtr.register_rt_process('integrate', test=1, muh='maeh')
rtr.copy()
# register ObsPy function call
rtr.register_rt_process(obspy.signal.filter.bandpass,
freqmin=0,
freqmax=1,
df=0.1)
rtr.copy()
# register NumPy function call
rtr.register_rt_process(np.square)
rtr.copy()
def test_sw_kurtosis(self):
win = 3.0
data_trace = self.data_trace.copy()
rt_trace = RtTrace()
rt_single = RtTrace()
rt_trace.registerRtProcess('sw_kurtosis', win=win)
rt_single.registerRtProcess('sw_kurtosis', win=win)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
rt_single.append(data_trace)
diff = self.data_trace.copy()
diff.data = rt_trace.data - rt_single.data
self.assertAlmostEquals(np.mean(np.abs(diff)), 0.0)
def test_missingOrWrongArgumentInRtProcess(self):
"""
Tests handling of missing/wrong arguments.
"""
trace = Trace(np.arange(100))
# 1- function scale needs no additional arguments
rt_trace = RtTrace()
rt_trace.register_rt_process('scale')
rt_trace.append(trace)
# adding arbitrary arguments should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('scale', muh='maeh')
self.assertRaises(TypeError, rt_trace.append, trace)
# 2- function tauc has one required argument
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', width=10)
rt_trace.append(trace)
# wrong argument should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', xyz='xyz')
self.assertRaises(TypeError, rt_trace.append, trace)
# missing argument width should raise an exception
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc')
self.assertRaises(TypeError, rt_trace.append, trace)
# adding arbitrary arguments should fail
rt_trace = RtTrace()
rt_trace.register_rt_process('tauc', width=20, notexistingoption=True)
self.assertRaises(TypeError, rt_trace.append, trace)
class RealTimeSignalTestCase(unittest.TestCase):
"""
The obspy.realtime.signal test suite.
"""
def __init__(self, *args, **kwargs):
super(RealTimeSignalTestCase, self).__init__(*args, **kwargs)
# read test data as float64
self.orig_trace = read(os.path.join(os.path.dirname(__file__), 'data',
'II.TLY.BHZ.SAC'), dtype='f8')[0]
# make really sure test data is float64
self.orig_trace.data = np.require(self.orig_trace.data, 'f8')
self.orig_trace_chunks = self.orig_trace / NUM_PACKETS
def setUp(self):
# clear results
self.filt_trace_data = None
self.rt_trace = None
self.rt_appended_traces = []
def tearDown(self):
# use results for debug plots if enabled
if PLOT_TRACES and self.filt_trace_data is not None and \
self.rt_trace is not None and self.rt_appended_traces:
self._plotResults()
def test_square(self):
"""
Testing np.square function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = np.square(trace)
# filtering real time
process_list = [(np.square, {})]
self._runRtProcess(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_integrate(self):
"""
Testing integrate function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = signal.integrate(trace)
# filtering real time
process_list = [('integrate', {})]
self._runRtProcess(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_differentiate(self):
"""
Testing differentiate function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = signal.differentiate(trace)
# filtering real time
process_list = [('differentiate', {})]
self._runRtProcess(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_boxcar(self):
"""
Testing boxcar function.
"""
trace = self.orig_trace.copy()
options = {'width': 500}
# filtering manual
self.filt_trace_data = signal.boxcar(trace, **options)
# filtering real time
process_list = [('boxcar', options)]
self._runRtProcess(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertAlmostEqual(peak, 566974.214, 3)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_scale(self):
"""
Testing scale function.
"""
trace = self.orig_trace.copy()
options = {'factor': 1000}
# filtering manual
self.filt_trace_data = signal.scale(trace, **options)
# filtering real time
process_list = [('scale', options)]
self._runRtProcess(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertEqual(peak, 1045237000.0)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_offset(self):
"""
Testing offset function.
"""
trace = self.orig_trace.copy()
options = {'offset': 500}
# filtering manual
self.filt_trace_data = signal.offset(trace, **options)
# filtering real time
process_list = [('offset', options)]
self._runRtProcess(process_list)
# check results
diff = self.rt_trace.data - self.orig_trace.data
self.assertEqual(np.mean(diff), 500)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_kurtosis(self):
"""
Testing kurtosis function.
"""
trace = self.orig_trace.copy()
options = {'win': 5}
# filtering manual
self.filt_trace_data = signal.kurtosis(trace, **options)
# filtering real time
process_list = [('kurtosis', options)]
self._runRtProcess(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_abs(self):
"""
Testing np.abs function.
"""
trace = self.orig_trace.copy()
# filtering manual
self.filt_trace_data = np.abs(trace)
# filtering real time
process_list = [(np.abs, {})]
self._runRtProcess(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertEqual(peak, 1045237)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_tauc(self):
"""
Testing tauc function.
"""
trace = self.orig_trace.copy()
options = {'width': 60}
# filtering manual
self.filt_trace_data = signal.tauc(trace, **options)
# filtering real time
process_list = [('tauc', options)]
self._runRtProcess(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
self.assertAlmostEqual(peak, 114.302, 3)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_mwpIntegral(self):
"""
Testing mwpIntegral functions.
"""
trace = self.orig_trace.copy()
options = {'mem_time': 240,
'ref_time': trace.stats.starttime + 301.506,
'max_time': 120,
'gain': 1.610210e+09}
# filtering manual
self.filt_trace_data = signal.mwpIntegral(self.orig_trace.copy(),
**options)
# filtering real time
process_list = [('mwpIntegral', options)]
self._runRtProcess(process_list)
# check results
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_mwp(self):
"""
Testing Mwp calculation using two processing functions.
"""
trace = self.orig_trace.copy()
epicentral_distance = 30.0855
options = {'mem_time': 240,
'ref_time': trace.stats.starttime + 301.506,
'max_time': 120,
'gain': 1.610210e+09}
# filtering manual
trace.data = signal.integrate(trace)
self.filt_trace_data = signal.mwpIntegral(trace, **options)
# filtering real time
process_list = [('integrate', {}), ('mwpIntegral', options)]
self._runRtProcess(process_list)
# check results
peak = np.amax(np.abs(self.rt_trace.data))
mwp = signal.calculateMwpMag(peak, epicentral_distance)
self.assertAlmostEqual(mwp, 8.78902911791, 5)
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
def test_combined(self):
"""
Testing combining integrate and differentiate functions.
"""
trace = self.orig_trace.copy()
# filtering manual
trace.data = signal.integrate(trace)
self.filt_trace_data = signal.differentiate(trace)
# filtering real time
process_list = [('int', {}), ('diff', {})]
self._runRtProcess(process_list)
# check results
trace = self.orig_trace.copy()
np.testing.assert_almost_equal(self.filt_trace_data,
self.rt_trace.data)
np.testing.assert_almost_equal(trace.data[1:], self.rt_trace.data[1:])
np.testing.assert_almost_equal(trace.data[1:],
self.filt_trace_data[1:])
def _runRtProcess(self, process_list, max_length=None):
"""
Helper function to create a RtTrace, register all given process
functions and run the real time processing.
"""
# assemble real time trace
self.rt_trace = RtTrace(max_length=max_length)
for (process, options) in process_list:
self.rt_trace.registerRtProcess(process, **options)
# append packet data to RtTrace
self.rt_appended_traces = []
for trace in self.orig_trace_chunks:
# process single trace
result = self.rt_trace.append(trace, gap_overlap_check=True)
# add to list of appended traces
self.rt_appended_traces.append(result)
def _plotResults(self):
"""
Plots original, filtered original and real time processed traces into
a single plot.
"""
# plot only if test is started manually
if __name__ != '__main__':
return
# create empty stream
st = Stream()
st.label = self._testMethodName
# original trace
self.orig_trace.label = "Original Trace"
st += self.orig_trace
# use header information of original trace with filtered trace data
tr = self.orig_trace.copy()
tr.data = self.filt_trace_data
tr.label = "Filtered original Trace"
st += tr
# real processed chunks
for i, tr in enumerate(self.rt_appended_traces):
tr.label = "RT Chunk %02d" % (i + 1)
st += tr
# real time processed trace
self.rt_trace.label = "RT Trace"
st += self.rt_trace
st.plot(automerge=False, color='blue', equal_scale=False)
def test_rt_dx2(self):
win = 10
data_trace = self.data_trace.copy()
rt_single = RtTrace()
rt_trace = RtTrace()
rt_trace.registerRtProcess('dx2', win=win)
rt_trace.registerRtProcess('boxcar', width=50)
rt_single.registerRtProcess('dx2', win=win)
rt_single.registerRtProcess('boxcar', width=50)
for tr in self.traces:
rt_trace.append(tr, gap_overlap_check=True)
rt_single.append(data_trace, gap_overlap_check=True)
assert_array_almost_equal(rt_single, rt_trace)
def __init__(self,waveloc_options):
"""
Initialize from a set of travel-times as hdf5 files
"""
wo=waveloc_options
# initialize the travel-times
#############################
ttimes_fnames=glob.glob(wo.ttimes_glob)
# get basic lengths
f=h5py.File(ttimes_fnames[0],'r')
# copy the x, y, z data over
self.x = np.array(f['x'][:])
self.y = np.array(f['y'][:])
self.z = np.array(f['z'][:])
f.close()
# read the files
ttimes_list = []
self.sta_list=[]
for fname in ttimes_fnames:
f=h5py.File(fname,'r')
# update the list of ttimes
ttimes_list.append(np.array(f['ttimes']))
sta=f['ttimes'].attrs['station']
f.close()
# update the dictionary of station names
self.sta_list.append(sta)
# stack the ttimes into a numpy array
self.ttimes_matrix=np.vstack(ttimes_list)
(self.nsta,self.npts) = self.ttimes_matrix.shape
# initialize the RtTrace(s)
##########################
max_length = wo.opdict['max_length']
self.safety_margin = wo.opdict['safety_margin']
self.dt = wo.opdict['dt']
# need a RtTrace per station
self.obs_rt_list=[RtTrace() for sta in self.sta_list]
# register pre-processing
self._register_preprocessing(wo)
# need nsta streams for each point we test (nsta x npts)
# for shifted waveforms
self.point_rt_list=[[RtTrace(max_length=max_length) \
for ista in xrange(self.nsta)] for ip in xrange(self.npts)]
# register processing of point-streams here
for sta_list in self.point_rt_list:
for rtt in sta_list:
# This is where we would scale for distance (given pre-calculated
# distances from each point to every station)
rtt.registerRtProcess('scale', factor=1.0)
# need npts streams to store the point-stacks
self.stack_list=[RtTrace(max_length=max_length) for ip in xrange(self.npts)]
# register stack procesing here
for rtt in self.stack_list:
# This is where we would add or lower weights if we wanted to
rtt.registerRtProcess('scale', factor=1.0)
# need 4 output streams (max, x, y, z)
self.max_out = RtTrace()
self.x_out = RtTrace()
self.y_out = RtTrace()
self.z_out = RtTrace()
if not wo.is_syn:
self.max_out.registerRtProcess('boxcar', width=50)
# need a list of common start-times
self.last_common_end_stack = [UTCDateTime(1970,1,1) for i in xrange(self.npts)]
self.last_common_end_max = UTCDateTime(1970,1,1)
def save_wave(self):
# Fetch a wave from Ring 0
wave = self.ring2buff.get_wave(0)
# if wave is empty return
if wave == {}:
return
# Lets try to buffer with python dictionaries and obspy
name = wave["station"] + '.' + wave["channel"] + '.' + wave[
"network"] + '.' + wave["location"]
if name in self.wave_buffer:
# Determine max samples for buffer
max_samp = wave["samprate"] * 60 * self.minutes
# Create a header:
wavestats = Stats()
wavestats.station = wave["station"]
wavestats.network = wave["network"]
wavestats.channel = wave["channel"]
wavestats.location = wave["location"]
wavestats.sampling_rate = wave["samprate"]
wavestats.starttime = UTCDateTime(wave['startt'])
# Create a trace
wavetrace = Trace(header=wavestats)
wavetrace.data = wave["data"]
# Try to append data to buffer, if gap shutdown.
try:
self.wave_buffer[name].append(wavetrace,
gap_overlap_check=True)
except TypeError as err:
logger.warning(err)
self.runs = False
except:
raise
self.runs = False
# Debug data
if self.debug:
logger.info("Station Channel combo is in buffer:")
logger.info(name)
logger.info("Size:")
logger.info(self.wave_buffer[name].count())
logger.debug("Data:")
logger.debug(self.wave_buffer[name])
else:
# First instance of data in buffer, create a header:
wavestats = Stats()
wavestats.station = wave["station"]
wavestats.network = wave["network"]
wavestats.channel = wave["channel"]
wavestats.location = wave["location"]
wavestats.sampling_rate = wave["samprate"]
wavestats.starttime = UTCDateTime(wave['startt'])
# Create a trace
wavetrace = Trace(header=wavestats)
wavetrace.data = wave["data"]
# Create a RTTrace
rttrace = RtTrace(int(self.minutes * 60))
self.wave_buffer[name] = rttrace
# Append data
self.wave_buffer[name].append(wavetrace, gap_overlap_check=True)
# Debug data
if self.debug:
logger.info("First instance of station/channel:")
logger.info(name)
logger.info("Size:")
logger.info(self.wave_buffer[name].count())
logger.debug("Data:")
logger.debug(self.wave_buffer[name])
