def test_all_pickers():
streams = get_streams()
picker_config = get_config(section='pickers')
methods = ['ar', 'baer', 'power', 'kalkan']
columns = ['Stream', 'Method', 'Pick_Time', 'Mean_SNR']
df = pd.DataFrame(columns=columns)
for stream in streams:
print(stream.get_id())
for method in methods:
try:
if method == 'ar':
loc, mean_snr = pick_ar(stream,
picker_config=picker_config)
elif method == 'baer':
loc, mean_snr = pick_baer(stream,
picker_config=picker_config)
elif method == 'power':
loc, mean_snr = pick_power(stream,
picker_config=picker_config)
elif method == 'kalkan':
loc, mean_snr = pick_kalkan(stream,
picker_config=picker_config)
elif method == 'yeck':
loc, mean_snr = pick_yeck(stream)
except GMProcessException:
loc = -1
mean_snr = np.nan
row = {
'Stream': stream.get_id(),
'Method': method,
'Pick_Time': loc,
'Mean_SNR': mean_snr
}
df = df.append(row, ignore_index=True)
stations = df['Stream'].unique()
cmpdict = {
'TW.ECU.BN': 'kalkan',
'TW.ELD.BN': 'power',
'TW.EGF.BN': 'ar',
'TW.EAS.BN': 'ar',
'TW.EDH.BN': 'ar',
'TK.4304.HN': 'ar',
'TK.0921.HN': 'ar',
'TK.5405.HN': 'ar',
'NZ.HSES.HN': 'baer',
'NZ.WTMC.HN': 'baer',
'NZ.THZ.HN': 'power'
}
for station in stations:
station_df = df[df['Stream'] == station]
max_snr = station_df['Mean_SNR'].max()
maxrow = station_df[station_df['Mean_SNR'] == max_snr].iloc[0]
method = maxrow['Method']
try:
assert cmpdict[station] == method
except Exception as e:
x = 1
def test_all_pickers():
streams = get_streams()
picker_config = get_config(section='pickers')
methods = ['ar', 'baer', 'power', 'kalkan']
columns = ['Stream', 'Method', 'Pick_Time', 'Mean_SNR']
df = pd.DataFrame(columns=columns)
for stream in streams:
print(stream.get_id())
for method in methods:
try:
if method == 'ar':
loc, mean_snr = pick_ar(
stream, picker_config=picker_config)
elif method == 'baer':
loc, mean_snr = pick_baer(
stream, picker_config=picker_config)
elif method == 'power':
loc, mean_snr = pick_power(
stream, picker_config=picker_config)
elif method == 'kalkan':
loc, mean_snr = pick_kalkan(stream,
picker_config=picker_config)
elif method == 'yeck':
loc, mean_snr = pick_yeck(stream)
except GMProcessException:
loc = -1
mean_snr = np.nan
row = {'Stream': stream.get_id(),
'Method': method,
'Pick_Time': loc,
'Mean_SNR': mean_snr}
df = df.append(row, ignore_index=True)
stations = df['Stream'].unique()
cmpdict = {'TW.ECU.BN': 'kalkan',
'TW.ELD.BN': 'ar',
'TW.EGF.BN': 'ar',
'TW.EAS.BN': 'ar',
'TW.EDH.BN': 'ar',
'TK.4304.HN': 'ar',
'TK.0921.HN': 'ar',
'TK.5405.HN': 'ar',
'NZ.HSES.HN': 'baer',
'NZ.WTMC.HN': 'baer',
'NZ.THZ.HN': 'power'}
for station in stations:
station_df = df[df['Stream'] == station]
max_snr = station_df['Mean_SNR'].max()
maxrow = station_df[station_df['Mean_SNR'] == max_snr].iloc[0]
method = maxrow['Method']
assert cmpdict[station] == method
def signal_split(st, origin, picker_config=None, config=None):
"""
This method tries to identifies the boundary between the noise and signal
for the waveform. The split time is placed inside the
'processing_parameters' key of the trace stats.
The P-wave arrival is used as the split between the noise and signal
windows. Multiple picker methods are suppored and can be configured in the
config file
'~/.gmprocess/picker.yml
Args:
st (StationStream):
Stream of data.
origin (ScalarEvent):
ScalarEvent object.
picker_config (dict):
Dictionary containing picker configuration information.
config (dict):
Dictionary containing system configuration information.
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_split'] dictionary.
"""
if picker_config is None:
picker_config = get_config(section='pickers')
if config is None:
config = get_config()
loc, mean_snr = pick_travel(st, origin, picker_config=picker_config)
if loc > 0:
tsplit = st[0].stats.starttime + loc
preferred_picker = 'travel_time'
else:
pick_methods = ['ar', 'baer', 'power', 'kalkan']
columns = ['Stream', 'Method', 'Pick_Time', 'Mean_SNR']
df = pd.DataFrame(columns=columns)
for pick_method in pick_methods:
try:
if pick_method == 'ar':
loc, mean_snr = pick_ar(st,
picker_config=picker_config,
config=config)
elif pick_method == 'baer':
loc, mean_snr = pick_baer(st,
picker_config=picker_config,
config=config)
elif pick_method == 'power':
loc, mean_snr = pick_power(st,
picker_config=picker_config,
config=config)
elif pick_method == 'kalkan':
loc, mean_snr = pick_kalkan(st,
picker_config=picker_config,
config=config)
elif pick_method == 'yeck':
loc, mean_snr = pick_kalkan(st)
except Exception:
loc = -1
mean_snr = np.nan
row = {
'Stream': st.get_id(),
'Method': pick_method,
'Pick_Time': loc,
'Mean_SNR': mean_snr
}
df = df.append(row, ignore_index=True)
max_snr = df['Mean_SNR'].max()
if not np.isnan(max_snr):
maxrow = df[df['Mean_SNR'] == max_snr].iloc[0]
tsplit = st[0].stats.starttime + maxrow['Pick_Time']
preferred_picker = maxrow['Method']
else:
tsplit = -1
if tsplit >= st[0].stats.starttime:
# Update trace params
split_params = {
'split_time': tsplit,
'method': 'p_arrival',
'picker_type': preferred_picker
}
for tr in st:
tr.setParameter('signal_split', split_params)
return st
def signal_split(
st, origin,
picker_config=None,
config=None):
"""
This method tries to identifies the boundary between the noise and signal
for the waveform. The split time is placed inside the
'processing_parameters' key of the trace stats.
The P-wave arrival is used as the split between the noise and signal
windows. Multiple picker methods are suppored and can be configured in the
config file
'~/.gmprocess/picker.yml
Args:
st (StationStream):
Stream of data.
origin (ScalarEvent):
ScalarEvent object.
picker_config (dict):
Dictionary containing picker configuration information.
config (dict):
Dictionary containing system configuration information.
Returns:
trace with stats dict updated to include a
stats['processing_parameters']['signal_split'] dictionary.
"""
if picker_config is None:
picker_config = get_config(section='pickers')
if config is None:
config = get_config()
loc, mean_snr = pick_travel(st, origin,
picker_config=picker_config)
if loc > 0:
tsplit = st[0].stats.starttime + loc
preferred_picker = 'travel_time'
else:
pick_methods = ['ar', 'baer', 'power', 'kalkan']
columns = ['Stream', 'Method', 'Pick_Time', 'Mean_SNR']
df = pd.DataFrame(columns=columns)
for pick_method in pick_methods:
try:
if pick_method == 'ar':
loc, mean_snr = pick_ar(
st, picker_config=picker_config, config=config)
elif pick_method == 'baer':
loc, mean_snr = pick_baer(
st, picker_config=picker_config, config=config)
elif pick_method == 'power':
loc, mean_snr = pick_power(
st, picker_config=picker_config, config=config)
elif pick_method == 'kalkan':
loc, mean_snr = pick_kalkan(st,
picker_config=picker_config,
config=config)
elif pick_method == 'yeck':
loc, mean_snr = pick_kalkan(st)
except Exception:
loc = -1
mean_snr = np.nan
row = {'Stream': st.get_id(),
'Method': pick_method,
'Pick_Time': loc,
'Mean_SNR': mean_snr}
df = df.append(row, ignore_index=True)
max_snr = df['Mean_SNR'].max()
if not np.isnan(max_snr):
maxrow = df[df['Mean_SNR'] == max_snr].iloc[0]
tsplit = st[0].stats.starttime + maxrow['Pick_Time']
preferred_picker = maxrow['Method']
else:
tsplit = -1
if tsplit >= st[0].stats.starttime:
# Update trace params
split_params = {
'split_time': tsplit,
'method': 'p_arrival',
'picker_type': preferred_picker
}
for tr in st:
tr.setParameter('signal_split', split_params)
return st
