def __init__(self,
sac_files,
lat_min=-90,
lat_max=90,
lon_min=-180,
lon_max=180,
dep_min=0,
dep_max=800,
Mw_min=0,
Mw_max=10,
dist_min=0,
dist_max=180,
n_within_dist=10,
start_date=datetime(1970, 1, 1),
end_date=datetime(2050, 1, 1)):
self.sac_files = sac_files
self.lat_min = lat_min
self.lat_max = lat_max
self.lon_min = lon_min
self.lon_max = lon_max
self.dep_min = dep_min
self.dep_max = dep_max
self.Mw_min = Mw_min
self.Mw_max = Mw_max
self.dist_min = dist_min
self.dist_max = dist_max
self.n_within_dist = n_within_dist
self.start_date = start_date
self.end_date = end_date
self.dataset = Dataset.dataset_from_sac(sac_files, headonly=True)
def get_dataset(model,
tlen=1638.4,
nspc=64,
sampling_hz=20,
mode=0,
add_noise=False,
noise_normalized_std=1.):
#TODO fix outputs.us=NaN when event.latitude==station.latitude
event = get_ref_event()
events = [event]
stations = [
Station('{:03d}'.format(i), 'DSM', event.latitude + 5 + 0.5 * i,
event.longitude + 0.1) for i in range(61)
]
dataset = Dataset.dataset_from_arrays(events, [stations],
sampling_hz=sampling_hz)
pydsm_input = PyDSMInput.input_from_arrays(event, stations, model, tlen,
nspc, sampling_hz)
pydsm_output = compute(pydsm_input, mode=mode)
pydsm_output.to_time_domain()
dataset.data = np.zeros((1, ) + pydsm_output.us.shape, dtype=np.float64)
dataset.data[0] = pydsm_output.us
if add_noise:
noise_arr = white_noise(noise_normalized_std, dataset.data.shape)
npts_cut = int(dataset.data.shape[3] * 0.9)
norm = np.abs(dataset.data[:, :, :npts_cut]).max(axis=3, keepdims=True)
noise_arr *= norm
dataset.data += noise_arr
return dataset, pydsm_output
def main(path):
"""Compute synthetics in parallel.
Args:
path (str): path to an input file.
"""
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
# open log file
log = open('log', 'w', buffering=1)
if rank == 0:
input_file = PyDSMInputFile(path)
params = input_file.read()
seismic_model = SeismicModel.model_from_name(params['seismic_model'])
tlen = params['tlen']
nspc = params['nspc']
sampling_hz = params['sampling_hz']
mode = params['mode']
verbose = params['verbose']
start_time = time.time()
dataset = Dataset.dataset_from_sac(params['sac_files'],
verbose=verbose)
end_time = time.time()
if verbose >= 1:
log.write(
'Initalizing dataset finished in {} s\n'.format(end_time -
start_time))
else:
params = None
dataset = None
seismic_model = None
tlen = None
nspc = None
sampling_hz = None
mode = None
verbose = None
# run pydsm
start_time = time.time()
outputs = dsm.compute_dataset_parallel(dataset,
seismic_model,
tlen,
nspc,
sampling_hz,
mode=mode,
verbose=verbose,
log=log)
end_time = time.time()
log.write('rank {}: DSM finished in {} s\n'.format(rank,
end_time - start_time))
if rank == 0:
start_time = time.time()
for output in outputs:
output.set_source_time_function(None)
output.to_time_domain()
output.write(params['output_folder'], format='sac')
end_time = time.time()
log.write('finished FFT and writing in {} s\n'.format(
rank, end_time - start_time))
log.close()
return "Done!"
import dsmpy.utils.scardec as scardec
from dsmpy.utils.cmtcatalog import read_catalog
from dsmpy.dataset import Dataset
import glob
if __name__ == '__main__':
sac_files = glob.glob('/mnt/doremi/anpan/inversion/MTZ_JAPAN/DATA/2*/*Z')
dataset = Dataset.dataset_from_sac(sac_files)
for event in dataset.events:
print(event)
duration_scardec = scardec.get_duration(event)
if duration_scardec is None:
print('Scardec STF not found for {}'.format(event))
print('scardec_duration, gcmt_duration: {} {}'.format(
duration_scardec, 2 * event.source_time_function.half_duration))
distance_min = 10.
distance_max = 90.
dir_syn = '.'
t_before = 10.
t_after = 20.
buffer = 10.
catalog_path = 'stf_catalog.txt'
n_distinct_comp_phase = 1
for sac_files in sac_files_iterator(
'/home/anselme/Dropbox/Kenji/MTZ_JAPAN/DATA/20*/*T'):
logging.info('{} num sacs = {}\n'.format(rank, len(sac_files)))
if rank == 0:
logging.info('{} reading dataset\n'.format(rank))
dataset = Dataset.dataset_from_sac(sac_files, headonly=False)
logging.info('{} computing time windows\n'.format(rank))
windows_S = WindowMaker.windows_from_dataset(dataset,
'prem', ['S'],
[Component.T],
t_before=t_before,
t_after=t_after)
windows = windows_S
windows = [
window for window in windows
if (distance_min <= window.get_epicentral_distance() <=
distance_max)
]
else:
dataset = None
from dsmpy.dataset import Dataset
from dsmpy import rootdsm_psv
from dsmpy import rootdsm_psv, rootdsm_sh
if __name__ == '__main__':
parameter_files = [rootdsm_psv + 'test2.inf', rootdsm_psv + 'test3.inf']
dataset = Dataset.dataset_from_files(parameter_files)
counts, displacements = dataset.get_chunks_station(2)
counts_eq, displacements_eq = dataset.get_chunks_eq(2)
parameter_files_sh = [rootdsm_sh + 'AK135_SH.inf']
dataset_sh = Dataset.dataset_from_files(parameter_files_sh, mode=1)