def test_get_travel_time_df():
datapath = os.path.join('data', 'testdata', 'travel_times')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc1 = StreamCollection.from_directory(os.path.join(datadir, 'ci37218996'))
sc2 = StreamCollection.from_directory(os.path.join(datadir, 'ci38461735'))
scs = [sc1, sc2]
df1, catalog = create_travel_time_dataframe(
sc1, os.path.join(datadir, 'catalog_test_traveltimes.csv'), 5, 0.1,
'iasp91')
df2, catalog = create_travel_time_dataframe(
sc2, os.path.join(datadir, 'catalog_test_traveltimes.csv'), 5, 0.1,
'iasp91')
model = TauPyModel('iasp91')
for dfidx, df in enumerate([df1, df2]):
for staidx, sta in enumerate(df):
for eqidx, time in enumerate(df[sta]):
sta_coords = scs[dfidx][staidx][0].stats.coordinates
event = catalog[eqidx]
dist = locations2degrees(sta_coords['latitude'],
sta_coords['longitude'],
event.latitude, event.longitude)
if event.depth_km < 0:
depth = 0
else:
depth = event.depth_km
travel_time = model.get_travel_times(depth, dist,
['p', 'P', 'Pn'])[0].time
abs_time = event.time + travel_time
np.testing.assert_almost_equal(abs_time, time, decimal=1)
def test_colocated():
datapath = os.path.join('data', 'testdata', 'colocated_instruments')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(datadir)
sc.select_colocated()
assert sc.n_passed == 7
assert sc.n_failed == 4
# What if no preference is matched?
sc = StreamCollection.from_directory(datadir)
sc.select_colocated(preference=["XX"])
assert sc.n_passed == 3
assert sc.n_failed == 8
def test_metrics2():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
config['metrics']['output_imts'].append('Arias')
config['metrics']['output_imcs'].append('arithmetic_mean')
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
workspace.calcMetrics(event.id, labels=['processed'])
etable, imc_tables1 = workspace.getTables('processed')
etable2, imc_tables2 = workspace.getTables('processed', config=config)
assert 'ARITHMETIC_MEAN' not in imc_tables1
assert 'ARITHMETIC_MEAN' in imc_tables2
assert 'ARIAS' in imc_tables2['ARITHMETIC_MEAN']
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_colocated():
eventid = 'ci38445975'
datafiles, event = read_data_dir('fdsn', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config_file = os.path.join(datadir, 'test_config.yml')
with open(config_file, 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
processed_streams = process_streams(raw_streams, event, config=config)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label='raw')
ws.addStreams(event, processed_streams, label='processed')
ws.calcMetrics(eventid, labels=['processed'], config=config)
stasum = ws.getStreamMetrics(eventid, 'CI', 'MIKB', 'processed')
np.testing.assert_allclose(
stasum.get_pgm('duration', 'geometric_mean'), 38.94480068)
ws.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_get_status():
dpath = os.path.join('data', 'testdata', 'status')
directory = pkg_resources.resource_filename('gmprocess', dpath)
sc = StreamCollection.from_directory(directory)
# Manually fail some of the streams
sc.select(station='BSAP')[0][0].fail('Failure 0')
sc.select(station='CPE')[0][0].fail('Failure 1')
sc.select(station='MIKB', instrument='HN')[0][0].fail('Failure 2')
sc.select(network='PG', station='PSD')[0][0].fail('Failure 3')
# Test results from 'short', 'long', and 'net
short = sc.get_status('short')
assert (short == 1).all()
long = sc.get_status('long')
assert long.at['AZ.BSAP.HN'] == 'Failure 0'
assert long.at['AZ.BZN.HN'] == ''
assert long.at['AZ.CPE.HN'] == 'Failure 1'
assert long.at['CI.MIKB.BN'] == ''
assert long.at['CI.MIKB.HN'] == 'Failure 2'
assert long.at['CI.PSD.HN'] == ''
assert long.at['PG.PSD.HN'] == 'Failure 3'
net = sc.get_status('net')
assert net.at['AZ', 'number passed'] == 1
assert net.at['AZ', 'number failed'] == 2
assert net.at['CI', 'number passed'] == 2
assert net.at['CI', 'number failed'] == 1
assert net.at['PG', 'number passed'] == 0
assert net.at['PG', 'number failed'] == 1
def test_fit_spectra():
config = get_config()
datapath = os.path.join('data', 'testdata', 'demo', 'ci38457511', 'raw')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
event = get_event_object('ci38457511')
sc = StreamCollection.from_directory(datadir)
for st in sc:
st = signal_split(st, event)
end_conf = config['windows']['signal_end']
st = signal_end(st,
event_time=event.time,
event_lon=event.longitude,
event_lat=event.latitude,
event_mag=event.magnitude,
**end_conf)
st = compute_snr(st, 30)
st = get_corner_frequencies(st,
method='constant',
constant={
'highpass': 0.08,
'lowpass': 20.0
})
for st in sc:
spectrum.fit_spectra(st, event)
def test_metrics2():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
config['metrics']['output_imts'].append('Arias')
config['metrics']['output_imcs'].append('arithmetic_mean')
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
workspace.calcMetrics(event.id, labels=['processed'])
etable, imc_tables1, readmes1 = workspace.getTables('processed')
assert 'ARITHMETIC_MEAN' not in imc_tables1
assert 'ARITHMETIC_MEAN' not in readmes1
del workspace.dataset.auxiliary_data.WaveFormMetrics
del workspace.dataset.auxiliary_data.StationMetrics
workspace.calcMetrics(event.id, labels=['processed'], config=config)
etable2, imc_tables2, readmes2 = workspace.getTables('processed')
assert 'ARITHMETIC_MEAN' in imc_tables2
assert 'ARITHMETIC_MEAN' in readmes2
assert 'ARIAS' in imc_tables2['ARITHMETIC_MEAN']
testarray = readmes2['ARITHMETIC_MEAN']['Column header'].to_numpy()
assert 'ARIAS' in testarray
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_lowpass_max():
datapath = os.path.join('data', 'testdata', 'lowpass_max')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(datadir)
sc.describe()
conf = get_config()
update = {
'processing': [
{'detrend': {'detrending_method': 'demean'}},
{'remove_response': {
'f1': 0.001, 'f2': 0.005, 'f3': None, 'f4': None,
'output': 'ACC', 'water_level': 60}
},
# {'detrend': {'detrending_method': 'linear'}},
# {'detrend': {'detrending_method': 'demean'}},
{'get_corner_frequencies': {
'constant': {
'highpass': 0.08, 'lowpass': 20.0
},
'method': 'constant',
'snr': {'same_horiz': True}}
},
{'lowpass_max_frequency': {'fn_fac': 0.9}}
]
}
update_dict(conf, update)
update = {
'windows': {
'signal_end': {
'method': 'model',
'vmin': 1.0,
'floor': 120,
'model': 'AS16',
'epsilon': 2.0
},
'window_checks': {
'do_check': False,
'min_noise_duration': 1.0,
'min_signal_duration': 1.0
}
}
}
update_dict(conf, update)
edict = {
'id': 'ci38038071',
'time': UTCDateTime('2018-08-30 02:35:36'),
'lat': 34.136,
'lon': -117.775,
'depth': 5.5,
'magnitude': 4.4
}
event = get_event_object(edict)
test = process_streams(sc, event, conf)
for st in test:
for tr in st:
freq_dict = tr.getParameter('corner_frequencies')
np.testing.assert_allclose(freq_dict['lowpass'], 18.0)
def test_num_horizontals():
data_path = pkg_resources.resource_filename('gmprocess', 'data')
sc = StreamCollection.from_directory(os.path.join(
data_path, 'testdata', 'fdsn', 'uw61251926', 'strong_motion'))
st = sc.select(station='SP2')[0]
assert st.num_horizontal == 2
for tr in st:
tr.stats.channel = 'ENZ'
assert st.num_horizontal == 0
for tr in st:
tr.stats.channel = 'EN1'
assert st.num_horizontal == 3
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
stream1 = processed_streams[0]
stream2 = processed_streams[1]
summary1 = StationSummary.from_config(stream1)
summary2 = StationSummary.from_config(stream2)
workspace.setStreamMetrics(event.id, 'processed', summary1)
workspace.setStreamMetrics(event.id, 'processed', summary2)
workspace.calcStationMetrics(event.id, labels=['processed'])
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.station,
'processed')
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
df = workspace.getMetricsTable(event.id)
cmp_series = {
'GREATER_OF_TWO_HORIZONTALS': 0.6787,
'H1': 0.3869,
'H2': 0.6787,
'Z': 0.7663
}
pga_dict = df.iloc[0]['PGA'].to_dict()
for key, value in pga_dict.items():
value2 = cmp_series[key]
np.testing.assert_almost_equal(value, value2, decimal=4)
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet',
eventid,
'*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = get_config()
# turn off sta/lta check and snr checks
newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
processed_streams = process_streams(raw_streams, event, config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, processed_streams, label='processed')
stream1 = processed_streams[0]
stream2 = processed_streams[1]
summary1 = StationSummary.from_config(stream1)
summary2 = StationSummary.from_config(stream2)
workspace.setStreamMetrics(event.id, 'processed', summary1)
workspace.setStreamMetrics(event.id, 'processed', summary2)
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.station,
'processed')
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].as_matrix()
array2 = s1_df_out['Result'].as_matrix()
np.testing.assert_almost_equal(array1, array2, decimal=4)
df = workspace.getMetricsTable(event.id)
cmp_series = {'GREATER_OF_TWO_HORIZONTALS': 0.6787,
'HN1': 0.3869,
'HN2': 0.6787,
'HNZ': 0.7663}
pga_dict = df.iloc[0]['PGA'].to_dict()
for key, value in pga_dict.items():
value2 = cmp_series[key]
np.testing.assert_almost_equal(value, value2, decimal=4)
workspace.close()
except Exception as e:
raise(e)
finally:
shutil.rmtree(tdir)
def test_allow_nans():
dpath = os.path.join('data', 'testdata', 'fdsn', 'uu60363602')
datadir = pkg_resources.resource_filename('gmprocess', dpath)
sc = StreamCollection.from_directory(datadir)
origin = read_event_json_files([os.path.join(datadir, 'event.json')])[0]
psc = process_streams(sc, origin)
st = psc[0]
ss = StationSummary.from_stream(
st, components=['quadratic_mean'], imts=['FAS(4.0)'], bandwidth=189,
allow_nans=True)
assert np.isnan(ss.pgms.Result).all()
ss = StationSummary.from_stream(
st, components=['quadratic_mean'], imts=['FAS(4.0)'], bandwidth=189,
allow_nans=False)
assert ~np.isnan(ss.pgms.Result).all()
def test_metrics():
eventid = 'usb000syza'
datafiles, event = read_data_dir('knet', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
# turn off sta/lta check and snr checks
# newconfig = drop_processing(config, ['check_sta_lta', 'compute_snr'])
# processed_streams = process_streams(raw_streams, event, config=newconfig)
newconfig = config.copy()
newconfig['processing'].append(
{'NNet_QA': {
'acceptance_threshold': 0.5,
'model_name': 'CantWell'
}})
processed_streams = process_streams(raw_streams.copy(),
event,
config=newconfig)
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
workspace = StreamWorkspace(tfile)
workspace.addEvent(event)
workspace.addStreams(event, raw_streams, label='raw')
workspace.addStreams(event, processed_streams, label='processed')
stream1 = raw_streams[0]
summary1 = StationSummary.from_config(stream1)
s1_df_in = summary1.pgms.sort_values(['IMT', 'IMC'])
array1 = s1_df_in['Result'].to_numpy()
workspace.calcMetrics(eventid, labels=['raw'])
pstreams2 = workspace.getStreams(event.id, labels=['processed'])
assert pstreams2[0].getStreamParamKeys() == ['nnet_qa']
summary1_a = workspace.getStreamMetrics(event.id,
stream1[0].stats.network,
stream1[0].stats.station,
'raw')
s1_df_out = summary1_a.pgms.sort_values(['IMT', 'IMC'])
array2 = s1_df_out['Result'].to_numpy()
np.testing.assert_almost_equal(array1, array2, decimal=4)
workspace.close()
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_zero_crossings():
datapath = os.path.join('data', 'testdata', 'zero_crossings')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(datadir)
sc.describe()
conf = get_config()
update = {
'processing': [{
'detrend': {
'detrending_method': 'demean'
}
}, {
'check_zero_crossings': {
'min_crossings': 1
}
}]
}
update_dict(conf, update)
edict = {
'id': 'ak20419010',
'time': UTCDateTime('2018-11-30T17:29:29'),
'lat': 61.346,
'lon': -149.955,
'depth': 46.7,
'magnitude': 7.1
}
event = get_event_object(edict)
test = process_streams(sc, event, conf)
for st in test:
for tr in st:
assert tr.hasParameter('ZeroCrossingRate')
np.testing.assert_allclose(
test[0][0].getParameter('ZeroCrossingRate')['crossing_rate'],
0.008888888888888889,
atol=1e-5)
def test_trim_multiple_events():
datapath = os.path.join('data', 'testdata', 'multiple_events')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(os.path.join(datadir, 'ci38457511'))
origin = get_event_object('ci38457511')
df, catalog = create_travel_time_dataframe(
sc, os.path.join(datadir, 'catalog.csv'), 5, 0.1, 'iasp91')
for st in sc:
st.detrend('demean')
remove_response(st, None, None)
st = corner_frequencies.constant(st)
lowpass_filter(st)
highpass_filter(st)
signal_split(st, origin)
signal_end(st,
origin.time,
origin.longitude,
origin.latitude,
origin.magnitude,
method='model',
model='AS16')
cut(st, 2)
trim_multiple_events(st, origin, catalog, df, 0.2, 0.7, 'B14',
{'vs30': 760}, {'rake': 0})
num_failures = sum([1 if not st.passed else 0 for st in sc])
assert num_failures == 1
failure = sc.select(station='WRV2')[0][0].getParameter('failure')
assert failure['module'] == 'trim_multiple_events'
assert failure['reason'] == ('A significant arrival from another event '
'occurs within the first 70.0 percent of the '
'signal window')
for tr in sc.select(station='JRC2')[0]:
np.testing.assert_almost_equal(
tr.stats.endtime, UTCDateTime('2019-07-06T03:20:38.7983Z'))
from gmprocess.streamcollection import StreamCollection
from gmprocess.config import get_config
from gmprocess.processing import process_streams
from gmprocess.event import get_event_object
gm_home = "/Users/gabriel/groundmotion-processing/"
# data = "/gmprocess/data/testdata/knet/us2000cnnl/"
data = "/data/testdata/demo/ci38457511/raw/"
output = "/Users/gabriel/groundmotion-processing/output/"
# Path to example data
datapath = os.path.join('data', 'testdata', 'demo', 'ci38457511', 'raw')
datadir = pkg_resources.resource_filename('gmprocess', datapath)
sc = StreamCollection.from_directory(datadir)
# Includes 3 StationStreams, each with 3 StationTraces
sc.describe()
# Get the default config file
conf = get_config()
# Get event object
event = get_event_object('ci38457511')
# Process the streams
psc = process_streams(sc, event, conf)
psc.describe()
# Save plots of processed records
def test_vs30_dist_metrics():
KNOWN_DISTANCES = {
'epicentral': 5.1,
'hypocentral': 10.2,
'rupture': 2.21,
'rupture_var': np.nan,
'joyner_boore': 2.21,
'joyner_boore_var': np.nan,
'gc2_rx': 2.66,
'gc2_ry': 3.49,
'gc2_ry0': 0.00,
'gc2_U': 34.34,
'gc2_T': 2.66
}
KNOWN_BAZ = 239.46
KNOWN_VS30 = 331.47
eventid = 'ci38457511'
datafiles, event = read_data_dir('fdsn', eventid, '*')
datadir = os.path.split(datafiles[0])[0]
raw_streams = StreamCollection.from_directory(datadir)
config = update_config(os.path.join(datadir, 'config_min_freq_0p2.yml'))
processed_streams = process_streams(raw_streams, event, config=config)
rupture_file = get_rupture_file(datadir)
grid_file = os.path.join(datadir, 'test_grid.grd')
config['metrics']['vs30'] = {
'vs30': {
'file': grid_file,
'column_header': 'GlobalVs30',
'readme_entry': 'GlobalVs30',
'units': 'm/s'
}
}
tdir = tempfile.mkdtemp()
try:
tfile = os.path.join(tdir, 'test.hdf')
ws = StreamWorkspace(tfile)
ws.addEvent(event)
ws.addStreams(event, raw_streams, label='raw')
ws.addStreams(event, processed_streams, label='processed')
ws.calcMetrics(event.id,
rupture_file=rupture_file,
labels=['processed'],
config=config)
sta_sum = ws.getStreamMetrics(event.id, 'CI', 'CLC', 'processed')
for dist in sta_sum.distances:
np.testing.assert_allclose(sta_sum.distances[dist],
KNOWN_DISTANCES[dist],
rtol=0.01)
np.testing.assert_allclose(sta_sum._back_azimuth, KNOWN_BAZ, rtol=0.01)
np.testing.assert_allclose(sta_sum._vs30['vs30']['value'],
KNOWN_VS30,
rtol=0.01)
event_df, imc_tables, readme_tables = ws.getTables('processed')
check_cols = set([
'EpicentralDistance', 'HypocentralDistance', 'RuptureDistance',
'RuptureDistanceVar', 'JoynerBooreDistance',
'JoynerBooreDistanceVar', 'GC2_rx', 'GC2_ry', 'GC2_ry0', 'GC2_U',
'GC2_T', 'GlobalVs30', 'BackAzimuth'
])
assert check_cols.issubset(set(readme_tables['Z']['Column header']))
assert check_cols.issubset(set(imc_tables['Z'].columns))
except Exception as e:
raise (e)
finally:
shutil.rmtree(tdir)
def test_StreamCollection():
# read usc data
dpath = os.path.join('data', 'testdata', 'usc', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
usc_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(usc_streams) == 7
usc_sc = StreamCollection(usc_streams)
# Use print method
print(usc_sc)
# Use len method
assert len(usc_sc) == 3
# Use nonzero method
assert bool(usc_sc)
# Slice
lengths = [len(usc_sc[0]), len(usc_sc[1]), len(usc_sc[2])]
sort_lengths = np.sort(lengths)
assert sort_lengths[0] == 1
assert sort_lengths[1] == 3
assert sort_lengths[2] == 3
# read dmg data
dpath = os.path.join('data', 'testdata', 'dmg', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
dmg_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(dmg_streams) == 1
dmg_sc = StreamCollection(dmg_streams)
# Has one station
assert len(dmg_sc) == 1
# With 3 channels
assert len(dmg_sc[0]) == 3
# So this should have 4 stations
test1 = dmg_sc + usc_sc
assert len(test1) == 4
test_copy = dmg_sc.copy()
assert test_copy[0][0].stats['standard']['process_level'] == \
'uncorrected physical units'
stream1 = test_copy[0]
test_append = usc_sc.append(stream1)
assert len(test_append) == 4
# Change back to unique values for station/network
for tr in dmg_sc[0]:
tr.stats['network'] = 'LALALA'
tr.stats['station'] = '575757'
stream2 = dmg_sc[0]
test_append = usc_sc.append(stream2)
assert len(test_append) == 4
# Check the from_directory method
sc_test = StreamCollection.from_directory(directory)
assert len(sc_test) == 1
# Test to_dataframe
jsonfile = os.path.join(directory, 'event.json')
with open(jsonfile, 'rt') as f:
origin = json.load(f)
dmg_df = sc_test.to_dataframe(origin)
np.testing.assert_allclose(dmg_df['H1']['PGA'], 0.145615, atol=1e5)
# Check the from_traces method
traces = []
for st in sc_test:
for tr in st:
traces.append(tr)
sc_test = StreamCollection.from_traces(traces)
assert len(sc_test) == 1
def test_StreamCollection():
# read usc data
dpath = os.path.join('data', 'testdata', 'usc', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
usc_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(usc_streams) == 7
usc_sc = StreamCollection(usc_streams)
# Use print method
print(usc_sc)
# Use len method
assert len(usc_sc) == 3
# Use nonzero method
assert bool(usc_sc)
# Slice
lengths = [
len(usc_sc[0]),
len(usc_sc[1]),
len(usc_sc[2])
]
sort_lengths = np.sort(lengths)
assert sort_lengths[0] == 1
assert sort_lengths[1] == 3
assert sort_lengths[2] == 3
# read dmg data
dpath = os.path.join('data', 'testdata', 'dmg', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
dmg_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(dmg_streams) == 1
dmg_sc = StreamCollection(dmg_streams)
# Has one station
assert len(dmg_sc) == 1
# With 3 channels
assert len(dmg_sc[0]) == 3
# So this should have 4 stations
test1 = dmg_sc + usc_sc
assert len(test1) == 4
# Overwrite the dmg station and network to force it to be
# a duplicate of one of the stations in usc_sc to check if
# validation works with these addition methods
for tr in dmg_sc[0]:
tr.stats['network'] = 'LA'
tr.stats['station'] = '57'
test3 = dmg_sc + usc_sc
assert len(test3) == 3
# usc_sc has 1 channel for station 57 and the modified
# dmg_sc has 3 channels so the combined StreamCollection
# should have 4
assert len(test3[0]) == 4
test_copy = dmg_sc.copy()
assert test_copy[0][0].stats['standard']['process_level'] == \
'corrected physical units'
# Appending dmg should not add to length because of the
# overwriting of the station/network above
stream1 = test_copy[0]
test_append = usc_sc.append(stream1)
assert len(test_append) == 3
# Change back to unique values for station/network
for tr in dmg_sc[0]:
tr.stats['network'] = 'LALALA'
tr.stats['station'] = '575757'
stream2 = dmg_sc[0]
test_append = usc_sc.append(stream2)
assert len(test_append) == 4
# Check the from_directory method
sc_test = StreamCollection.from_directory(directory)
assert len(sc_test) == 1
# Test to_dataframe
jsonfile = os.path.join(directory, 'event.json')
with open(jsonfile, 'rt') as f:
origin = json.load(f)
dmg_df = sc_test.to_dataframe(origin)
np.testing.assert_allclose(
dmg_df['HN1']['PGA'],
0.145615,
atol=1e5)
def test_StreamCollection():
# read usc data
dpath = os.path.join('data', 'testdata', 'usc', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
usc_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(usc_streams) == 7
usc_sc = StreamCollection(usc_streams)
# Use print method
print(usc_sc)
# Use len method
assert len(usc_sc) == 3
# Use nonzero method
assert bool(usc_sc)
# Slice
lengths = [
len(usc_sc[0]),
len(usc_sc[1]),
len(usc_sc[2])
]
sort_lengths = np.sort(lengths)
assert sort_lengths[0] == 1
assert sort_lengths[1] == 3
assert sort_lengths[2] == 3
# read dmg data
dpath = os.path.join('data', 'testdata', 'dmg', 'ci3144585')
directory = pkg_resources.resource_filename('gmprocess', dpath)
dmg_streams, unprocessed_files, unprocessed_file_errors = \
directory_to_streams(directory)
assert len(dmg_streams) == 1
dmg_sc = StreamCollection(dmg_streams)
# Has one station
assert len(dmg_sc) == 1
# With 3 channels
assert len(dmg_sc[0]) == 3
# So this should have 4 stations
test1 = dmg_sc + usc_sc
assert len(test1) == 4
test_copy = dmg_sc.copy()
assert test_copy[0][0].stats['standard']['process_level'] == \
'corrected physical units'
stream1 = test_copy[0]
test_append = usc_sc.append(stream1)
assert len(test_append) == 4
# Change back to unique values for station/network
for tr in dmg_sc[0]:
tr.stats['network'] = 'LALALA'
tr.stats['station'] = '575757'
stream2 = dmg_sc[0]
test_append = usc_sc.append(stream2)
assert len(test_append) == 4
# Check the from_directory method
sc_test = StreamCollection.from_directory(directory)
assert len(sc_test) == 1
# Test to_dataframe
jsonfile = os.path.join(directory, 'event.json')
with open(jsonfile, 'rt') as f:
origin = json.load(f)
dmg_df = sc_test.to_dataframe(origin)
np.testing.assert_allclose(
dmg_df['HN1']['PGA'],
0.145615,
atol=1e5)