def test_picks_without_phase_hints(self):
"""Check that things still run when phase_hints are missing."""
maximum_seperation = 1 # Maximum inter-event seperation in km
minimum_links = 8 # Minimum inter-event links to generate a pair
# We have to make an event list first
max_shift_len = 0.2
wavbase = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'WAV', 'TEST_')
testing_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'REA', 'TEST_')
sfile_list = glob.glob(os.path.join(testing_path, '*L.S??????'))
sfile_list = sfile_list[0:10]
sfile_list.append(
os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'sfile_wout_phase_hint'))
event_ids = list(range(len(sfile_list)))
event_list = zip(event_ids, sfile_list)
# In python 3.x this gives an error as zip is now an object...
event_list = list(event_list) # Do this for compatability
write_catalog(event_list=event_list, max_sep=maximum_seperation,
min_link=minimum_links)
self.assertTrue(os.path.isfile('dt.ct'))
write_correlations(event_list, wavbase, extract_len=2, pre_pick=0.5,
shift_len=max_shift_len, lowcut=2.0, highcut=10.0,
max_sep=1, min_link=8, cc_thresh=0.0, plotvar=False)
self.assertTrue(os.path.isfile('dt.cc'))
os.remove('dt.cc')
os.remove('dt.ct')
os.remove('phase.dat')
if os.path.isfile('dt.cc2'):
os.remove('dt.cc2')
if os.path.isfile('dt.ct2'):
os.remove('dt.ct2')
def test_write_catalog(self):
# Contents checked elsewhere
write_catalog(catalog=self.catalog,
event_id_mapper=None,
max_sep=8.,
min_link=8)
self.assertTrue(os.path.isfile("dt.ct"))
os.remove("dt.ct")
def setUpClass(cls):
cls.testing_path = os.path.join(
os.path.abspath(os.path.dirname(__file__)), 'test_data', 'REA',
'TEST_')
cls.wavbase = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'WAV', 'TEST_')
cls.sfile_list = glob.glob(os.path.join(cls.testing_path,
'*L.S??????'))[0:15]
cls.maximum_separation = 1 # Maximum inter-event separation in km
cls.minimum_links = 8 # Minimum inter-event links to generate a pair
# We have to make an event list first
cls.event_ids = list(range(len(cls.sfile_list)))
cls.event_list = zip(cls.event_ids, cls.sfile_list)
# In python 3.x this gives an error as zip is now an object...
cls.event_list = list(cls.event_list) # Do this for comparability
write_catalog(event_list=cls.event_list,
max_sep=cls.maximum_separation,
min_link=cls.minimum_links)
def test_write_catalog(self):
"""
Simple testing function for the write_catalogue function in \
catalog_to_dd.
"""
from eqcorrscan.utils.catalog_to_dd import write_catalog
from eqcorrscan.utils.mag_calc import dist_calc
from eqcorrscan.utils import sfile_util
import glob
import os
# Set forced variables
maximum_seperation = 1 # Maximum inter-event seperation in km
minimum_links = 8 # Minimum inter-event links to generate a pair
# We have to make an event list first
testing_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'REA', 'TEST_')
sfile_list = glob.glob(os.path.join(testing_path, '*L.S??????'))
event_ids = list(range(len(sfile_list)))
event_list = zip(event_ids, sfile_list)
write_catalog(event_list=event_list,
max_sep=maximum_seperation,
min_link=minimum_links)
self.assertTrue(os.path.isfile('dt.ct'))
# Check dt.ct file, should contain only a few linked events
dt_file_out = open('dt.ct', 'r')
event_pairs = []
for i, line in enumerate(dt_file_out):
if line[0] == '#':
if i != 0:
# Check the number of links
self.assertTrue(len(event_links) >= minimum_links)
# Check the distance between events
event_1_name = [event[1] for event in event_list
if event[0] ==
int(event_pair.split()[1])][0]
event_2_name = [event[1] for event in event_list
if event[0] ==
int(event_pair.split()[2])][0]
event_1 = sfile_util.readheader(event_1_name)
event_2 = sfile_util.readheader(event_2_name)
event_1_location = (event_1.origins[0].latitude,
event_1.origins[0].longitude,
event_1.origins[0].depth / 1000)
event_2_location = (event_2.origins[0].latitude,
event_2.origins[0].longitude,
event_2.origins[0].depth / 1000)
hypocentral_seperation = dist_calc(event_1_location,
event_2_location)
self.assertTrue(hypocentral_seperation <
maximum_seperation)
# Check that the differential times are accurate
event_1_picks = sfile_util.readpicks(event_1_name).picks
event_2_picks = sfile_util.readpicks(event_2_name).picks
for pick_pair in event_links:
station = pick_pair.split()[0]
event_1_travel_time_output = pick_pair.split()[1]
event_2_travel_time_output = pick_pair.split()[2]
weight = pick_pair.split()[3]
phase = pick_pair.split()[4]
# Extract the relevant pick information from the
# two sfiles
for pick in event_1_picks:
if pick.waveform_id.station_code == station:
if pick.phase_hint[0].upper() == phase:
event_1_pick = pick
for pick in event_2_picks:
if pick.waveform_id.station_code == station:
if pick.phase_hint[0].upper() == phase:
event_2_pick = pick
# Calculate the travel-time
event_1_travel_time_input = event_1_pick.time -\
event_1.origins[0].time
event_2_travel_time_input = event_2_pick.time -\
event_2.origins[0].time
self.assertEqual(event_1_travel_time_input,
float(event_1_travel_time_output))
self.assertEqual(event_2_travel_time_input,
float(event_2_travel_time_output))
event_pair = line
event_pairs.append(line)
event_links = []
else:
event_links.append(line)
self.assertTrue(os.path.isfile('phase.dat'))
dt_file_out.close()
os.remove('phase.dat')
os.remove('dt.ct')
if os.path.isfile('dt.ct2'):
os.remove('dt.ct2')
def test_write_correlations(self):
"""
Test that the write_correlations function works as it should.
Hard to test accurately...
"""
max_shift_len = 0.2
testing_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'REA', 'TEST_')
wavbase = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'WAV', 'TEST_')
sfile_list = glob.glob(os.path.join(testing_path, '*L.S??????'))
event_ids = list(range(len(sfile_list)))
event_list = list(zip(event_ids, sfile_list))
with Timer() as t:
write_correlations(event_list, wavbase, extract_len=2,
pre_pick=0.5, shift_len=max_shift_len,
lowcut=2.0, highcut=10.0, max_sep=1, min_link=8,
cc_thresh=0.0, plotvar=False)
msg = 'Running ' + str(len(list(event_list))) + \
' events took %s s' % t.secs
print(msg)
self.assertTrue(os.path.isfile('dt.cc'))
# Generate a complementary dt.ct file and check against that
write_catalog(event_list=event_list, max_sep=1, min_link=8)
cc = open('dt.cc', 'r')
cc_pairs = []
observations = []
pair = cc.readline().split()[1:3]
for line in cc:
if line[0] == '#':
# Append old observations to the previous pair and put in pairs
cc_pairs.append({'pair': pair, 'observations': observations})
pair = line.split()[1:3]
observations = []
else:
obs = line.split()
observations.append({'station': obs[0],
'diff_time': float(obs[1]),
'weight': float(obs[2]),
'phase': obs[3]})
cc.close()
ct = open('dt.ct', 'r')
ct_pairs = []
observations = []
pair = ct.readline().split()[1:3]
for line in ct:
if line[0] == '#':
# Append old observations to the previous pair and put in pairs
ct_pairs.append({'pair': pair,
'observations': observations})
pair = line.split()[1:3]
observations = []
else:
obs = line.split()
# for sub in line.split('-'):
# for item in sub.split():
# obs.append(item)
observations.append({'station': obs[0],
'diff_time': float(obs[1]) -
float(obs[2]),
'weight': float(obs[3]),
'phase': obs[4]})
ct.close()
# Everything is in memory, now we need to find matching pairs
for cc_pair in cc_pairs:
for ct_pair in ct_pairs:
if cc_pair['pair'] == ct_pair['pair']:
for cc_obs in cc_pair['observations']:
for ct_obs in ct_pair['observations']:
if cc_obs['station'] == ct_obs['station'] and\
cc_obs['phase'] == ct_obs['phase']:
corr_correction = abs(ct_obs['diff_time'] -
cc_obs['diff_time'])
self.assertTrue(corr_correction <
max_shift_len)
os.remove('dt.cc')
os.remove('dt.ct')
os.remove('phase.dat')
if os.path.isfile('dt.cc2'):
os.remove('dt.cc2')
if os.path.isfile('dt.ct2'):
os.remove('dt.ct2')
np.round(float(ev.preferred_origin().time.strftime('%Y%m%d%H%M%S.%f'))))
for i, ev in enumerate(cat)
]
seen = {}
result = []
for time in times:
if time[1] in seen:
continue
else:
seen[time[1]] = 1
result.append(time)
indices, times = zip(*result)
cat.events = [ev for i, ev in enumerate(cat) if i in indices]
# Write to HypoDD input files...
catalog_to_dd.write_catalog(cat)
####################################################
# Need a template dict for each located detection
# Read in catalog processed by above lines...
cat = read_events(
'/media/chet/hdd/seismic/NZ/catalogs/2015_dets_nlloc/2015_dets_nlloc_Sherburn_no_dups.xml'
)
temp_dir = '/media/chet/hdd/seismic/NZ/templates/rotnga_2015/2015_det2cats/*'
# temp_dir = '/projects/nesi00228/data/templates/nlloc_reloc/dayproc_4-27/*'
temp_files = glob(temp_dir)
template_dict = {}
for filename in temp_files:
if filename.split('/')[-1].rstrip('.mseed').split('_')[-1] == 'self':
uri_name = 'smi:local/' + \
def test_write_correlations(self):
"""
Test that the write_correlations function works as it should.
Hard to test accurately...
"""
from eqcorrscan.utils.catalog_to_dd import write_correlations
from eqcorrscan.utils.catalog_to_dd import write_catalog
from eqcorrscan.utils.timer import Timer
import os
import glob
max_shift_len = 0.2
testing_path = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'REA', 'TEST_')
wavbase = os.path.join(os.path.abspath(os.path.dirname(__file__)),
'test_data', 'WAV', 'TEST_')
sfile_list = glob.glob(os.path.join(testing_path, '*L.S??????'))
event_ids = list(range(len(sfile_list)))
event_list = zip(event_ids, sfile_list)
with Timer() as t:
write_correlations(event_list, wavbase, extract_len=2,
pre_pick=0.5, shift_len=max_shift_len,
lowcut=2.0, highcut=10.0, max_sep=1, min_link=8,
cc_thresh=0.0, plotvar=False)
msg = 'Running ' + str(len(list(event_list))) + \
' events took %s s' % t.secs
print(msg)
self.assertTrue(os.path.isfile('dt.cc'))
# Generate a complementary dt.ct file and check against that
write_catalog(event_list=event_list,
max_sep=1,
min_link=8)
cc = open('dt.cc', 'r')
cc_pairs = []
observations = []
pair = cc.readline().split()[1:3]
for line in cc:
if line[0] == '#':
# Append old observations to the previous pair and put in pairs
cc_pairs.append({'pair': pair,
'observations': observations})
pair = line.split()[1:3]
observations = []
else:
obs = line.split()
observations.append({'station': obs[0],
'diff_time': float(obs[1]),
'weight': float(obs[2]),
'phase': obs[3]})
cc.close()
ct = open('dt.ct', 'r')
ct_pairs = []
observations = []
pair = ct.readline().split()[1:3]
for line in ct:
if line[0] == '#':
# Append old observations to the previous pair and put in pairs
ct_pairs.append({'pair': pair,
'observations': observations})
pair = line.split()[1:3]
observations = []
else:
obs = line.split()
# for sub in line.split('-'):
# for item in sub.split():
# obs.append(item)
observations.append({'station': obs[0],
'diff_time': float(obs[1]) - float(obs[2]),
'weight': float(obs[3]),
'phase': obs[4]})
ct.close()
# Everything is in memory, now we need to find matching pairs
for cc_pair in cc_pairs:
for ct_pair in ct_pairs:
if cc_pair['pair'] == ct_pair['pair']:
for cc_obs in cc_pair['observations']:
for ct_obs in ct_pair['observations']:
if cc_obs['station'] == ct_obs['station'] and\
cc_obs['phase'] == ct_obs['phase']:
corr_correction = abs(ct_obs['diff_time'] -
cc_obs['diff_time'])
self.assertTrue(corr_correction <
max_shift_len)
os.remove('dt.cc')
os.remove('dt.ct')
os.remove('phase.dat')
if os.path.isfile('dt.cc2'):
os.remove('dt.cc2')
if os.path.isfile('dt.ct2'):
os.remove('dt.ct2')
