def oq():
args = set(sys.argv[1:])
if 'engine' not in args and 'dbserver' not in args:
# oq engine and oq dbserver define their own log levels
level = logging.DEBUG if 'debug' in args else logging.INFO
logging.basicConfig(level=level)
sap.run(commands, prog='oq')
def oq():
args = set(sys.argv[1:])
if 'engine' not in args and 'dbserver' not in args:
# oq engine and oq dbserver define their own log levels
level = logging.DEBUG if 'debug' in args else logging.INFO
logging.basicConfig(level=level)
warnings.simplefilter( # make sure we do not make efficiency errors
"error",
category=sparse.base.SparseEfficiencyWarning)
sap.run(commands, prog='oq')
gdf_poly = gpd.GeoDataFrame(df, geometry='Polygon', crs='epsg:4326')
within = gpd.sjoin(gdf, gdf_poly, op='within')
if len(within.magnitude) < 1:
continue
mmax = np.max(within.magnitude.to_numpy())
lab = 'mmax_{:s}'.format(cat_lab)
model['sources'][str(poly.id)][lab] = float(mmax)
print('{:10s}: {:5.2f}'.format(src_id, mmax))
# Saving results into the config file
with open(fname_conf, 'w') as f:
f.write(toml.dumps(model))
print('Updated {:s}'.format(fname_conf))
descr = 'The name of the shapefile with the polygons'
mmax_per_zone.fname_poly = descr
descr = 'The name of the .csv file with the catalogue'
mmax_per_zone.fname_cat = descr
descr = 'The name of configuration file'
mmax_per_zone.fname_conf = descr
descr = 'The label used to identify the catalogue'
mmax_per_zone.cat_lab = descr
mmax_per_zone.use = 'A list with the ID of sources that should be considered'
msg = 'A list with the ID of sources that should not be considered'
mmax_per_zone.skip = msg
if __name__ == '__main__':
sap.run(mmax_per_zone)
tmfd = ged(src.mfd, nmfd.bin_width)
nmfd.stack(tmfd)
occ = np.array(nmfd.get_annual_occurrence_rates())
bgr = model["sources"][src_id]["bgr_weichert"]
agr = model["sources"][src_id]["agr_weichert"]
tmp = occ[:, 0] - binw
mfd = 10.0**(agr - bgr * tmp[:-1]) - 10.0**(agr - bgr *
(tmp[:-1] + binw))
_ = plt.figure(figsize=(8, 6))
plt.plot(occ[:, 0], occ[:, 1], 'o')
plt.plot(tmp[:-1] + binw / 2, mfd, 'x')
print(mfd)
plt.title(fname)
plt.xlabel('Magnitude')
plt.ylabel('Annual occurrence rate')
plt.yscale('log')
plt.show()
check_mfds.fname_input_pattern = "Pattern for input .xml files"
check_mfds.fname_config = "Name of the configuration file"
check_mfds.src_id = "The ID of the source to use"
if __name__ == '__main__':
sap.run(check_mfds)
mean_ref, std_ref = pmap_ref[site_id_ref[lonlat]].array.T
err = numpy.sqrt(std**2 + std_ref**2)
for imt in imtls:
sl = imtls(imt)
ok = (numpy.abs(mean[sl] - mean_ref[sl]) < nsigma * err[sl]).all()
if not ok:
md = (numpy.abs(mean[sl] - mean_ref[sl])).max()
plt.title('point=%s, imt=%s, maxdiff=%.2e' % (lonlat, imt, md))
plt.loglog(imtls[imt],
mean_ref[sl] + std_ref[sl],
label=str(calc_ref),
color='black')
plt.loglog(imtls[imt],
mean_ref[sl] - std_ref[sl],
color='black')
plt.loglog(imtls[imt],
mean[sl] + std[sl],
label=str(calc),
color='red')
plt.loglog(imtls[imt], mean[sl] - std[sl], color='red')
plt.legend()
plt.show()
compare_mean_curves.calc_ref = 'first calculation'
compare_mean_curves.calc = 'second calculation'
compare_mean_curves.nsigma = 'tolerance as number of sigma'
if __name__ == '__main__':
sap.run(compare_mean_curves)
f = h5py.File(fname, 'r')
keys = sorted(f.keys())
print('Total number of earthquakes: {:d}'.format(len(f[keys[0]])))
for i1, k1 in enumerate(keys):
for i2 in range(i1, len(keys)):
k2 = keys[i2]
if k1 == k2:
continue
chk = np.logical_and(f[k1], f[k2])
if any(chk):
print('{:s} and {:s} have common eqks'.format(k1, k2))
print(len(chk))
# Check total numbers
tot = 0
for key in f:
smm = sum(f[key])
print('{:10s}: {:5d}'.format(key, smm))
tot += smm
print(tot, '/', len(f[key]))
# Close hdf5
f.close()
check.fname = "Name of the .hdf5 file wiith the catalogue classification"
if __name__ == "__main__":
sap.run(check)
tr_fname=tr_fname,
subcatalogues=subcatalogues,
olab='_uh',
save_af=True,
fix_defaults=True)
declustering_meth = 'GardnerKnopoffType1'
declustering_params = {
'time_distance_window': 'GruenthalWindow',
'fs_time_prop': 0.9
}
_ = decluster(fname,
declustering_meth,
declustering_params,
output_folder,
labels=labels,
tr_fname=tr_fname,
subcatalogues=subcatalogues,
olab='_gr',
save_af=True,
fix_defaults=True)
catalogue_declustering.fname = 'Name of the .csv formatted catalogue'
catalogue_declustering.output_folder = 'Path to the output folder'
msg = 'Boolean, when true it creates subcatalogues'
catalogue_declustering.subcatalogues = msg
if __name__ == '__main__':
sap.run(catalogue_declustering)
dct = {key: idx for idx, key in enumerate(cat.data['eventID'])}
idx = []
targ = []
for line in open(eqlist, 'r'):
eqid, target = line.split(',')
idx.append(dct[eqid])
targ.append(target)
f = h5py.File(treg, "r")
f2 = h5py.File(treg2, "w")
for key in f.keys():
tmp = f[key][:]
for eid, tar in zip(idx, targ):
if re.search(key, tar):
tmp[eid] = True
else:
tmp[eid] = False
f2[key] = tmp
f.close()
f2.close()
change.cat_pickle_filename = 'pickled catalogue'
change.treg = 'TR hdf5 filename'
msg = 'list of events to change. format <eventID>,<target class>'
change.eqlist = msg
if __name__ == "__main__":
sap.run(change)
for src_id in model['sources']:
if exclude is not None and src_id in exclude:
continue
else:
print("src_id:", src_id, " ", method)
if use == "*" or src_id in use:
output['sources'][src_id]['bgr'] = \
output['sources'][src_id][labb]
output['sources'][src_id]['agr'] = \
output['sources'][src_id][laba]
# Saving results into the config file
with open(fname_conf, 'w') as f:
f.write(toml.dumps(output))
print('Updated {:s}'.format(fname_conf))
descr = 'The name of configuration file'
set_gr_params.fname_conf = descr
descr = 'Can be either a string with * or with source IDs separated by commas'
set_gr_params.use = descr
descr = 'The label with the method used to infer these parameters: '
descr += 'e.g. weichert, counting'
set_gr_params.method = descr
descr = 'A string with source IDs separated by commas'
set_gr_params.skip = descr
if __name__ == '__main__':
sap.run(set_gr_params)
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
from openquake.baselib import sap
from openquake.risklib.countries import code2country
# example: utils/addcol.py country=VEN Exposure_Res_Venezuela.csv
def addcol(namevalue, fnames):
name, value = namevalue.split('=')
if name == 'country':
assert value in code2country, value
for fname in fnames:
header, *lines = open(fname).readlines()
out = [header.rstrip() + ',' + name]
for line in lines:
out.append(line.rstrip() + ',' + value)
with open(fname, 'w') as f:
for line in out:
f.write(line + '\n')
print('Added %s to %s' % (namevalue, fname))
addcol.namevalue = 'string of the form column_name=column_value'
addcol.fnames = dict(help='CSV files to update', nargs='+')
if __name__ == '__main__':
sap.run(addcol)
# Get catalogue
catalogue = get_catalogue(cat_pickle_fname, treg_filename, label)
# smoothing
values, smooth = smoothing(mlo, mla, mde, catalogue, hspa, vspa,
out_hdf5_smoothing_fname)
# Spatial index
r, proj = spatial_index(smooth)
# magnitude-frequency distribution
mfd = TruncatedGRMFD(min_mag=mmin,
max_mag=mmax,
bin_width=0.1,
a_val=agr,
b_val=bgr)
# Create all the ruptures - the probability of occurrence is for one year
# in this case
_ = create_ruptures(mfd, dips, sampling, msr, asprs, float_strike,
float_dip, r, values, ohs, 1., hdf5_filename,
uniform_fraction, proj, idl, align, True)
calculate_ruptures.ini_fname = '.ini filename'
calculate_ruptures.only_plt = 'Only plotting'
calculate_ruptures.ref_fdr = 'Reference folder for paths'
if __name__ == "__main__":
sap.run(calculate_ruptures)
model = toml.load(fname_conf)
output = copy.copy(model)
default = model['default'][key]
# Iterate over sources
for src_id in model['sources']:
if (len(use) and src_id not in use) or (src_id in skip):
continue
output['sources'][src_id][key] = default
# Saving results into the config file
with open(fname_conf, 'w') as f:
f.write(toml.dumps(output))
print('Updated {:s}'.format(fname_conf))
descr = 'The name of configuration file'
set_property.fname_conf = descr
descr = 'Key defining the property'
set_property.key = descr
descr = 'A list of source IDs to be used'
set_property.use = descr
descr = 'A list of source IDs to be skipped'
set_property.skip = descr
if __name__ == '__main__':
sap.run(set_property)
print('# earthquakes: {:d}'.format(len(catalogue.data['longitude'])))
# Sub-catalogue
csv_filename = out_cata + "_TR_unclassified.csv"
csv_filename = os.path.join(path_out, csv_filename)
# Write the purged catalogue
csvcat = CsvCatalogueWriter(csv_filename)
csvcat.write_file(catalogue)
print("Catalogue successfully written to %s" % csv_filename)
def create_sub_cata(pck_fname, treg_fname, *, out_cata='cat', path_out='.'):
"""
Create a subcatalogue
"""
aaa, alen = get_treg(treg_fname)
tot_lab = create_sub_catalogue(alen, aaa, pck_fname, treg_fname, out_cata,
path_out)
get_unclassified(tot_lab, pck_fname, out_cata, path_out)
create_sub_cata.pck_fname = 'Name of the pickle file with the catalogue'
msg = 'Name of the .hdf5 file with the catalogue classification'
create_sub_cata.treg_fname = msg
create_sub_cata.out_cata = 'Prefix in for the output files [cat]'
create_sub_cata.path_out = 'Output path [./]'
if __name__ == "__main__":
sap.run(create_sub_cata)
else:
fmclassification[mclass] = 1
dip_1[mclass] = [row['dip1']]
dip_2[mclass] = [row['dip2']]
strike_1[mclass] = [row['strike1']]
strike_2[mclass] = [row['strike2']]
title = "Source: {:s}".format(src_id)
_ = plot_histogram(gs0[0, 0], fmclassification, title)
plot_xx(gs0[0, 1], dip_1, dip_2, strike_1, strike_2)
stk1 = get_simpler(strike_1)
stk2 = get_simpler(strike_2)
dip1 = get_simpler(dip_1)
dip2 = get_simpler(dip_2)
plot_yy(gs0[1, 0], dip1, dip2, stk1, stk2)
plot_density_simple(gs0[1, 1], dip1, dip2, stk1, stk2)
plt.savefig(figure_name, format=ext)
plt.close()
return fmclassification
process_gcmt_datafames.fname_folder = 'Name of the folder with input files'
process_gcmt_datafames.folder_out = 'Name of the output folder'
if __name__ == '__main__':
sap.run(process_gcmt_datafames)
def process(contacts_shp,
outpath,
datafolder,
sidx_fname,
boundaries_shp,
imt_str,
inland_shp,
models_list=None,
only_buffers=False):
"""
This function processes all the models listed in the mosaic.DATA dictionary
and creates homogenised curves.
"""
process_maps(contacts_shp, outpath, datafolder, sidx_fname, boundaries_shp,
imt_str, inland_shp, models_list, only_buffers)
process.contacts_shp = 'Name of shapefile with contacts'
process.outpath = 'Output folder'
process.datafolder = 'Folder with the mosaic repository'
process.sidx_fname = 'Rtree spatial index file with ref. grid'
process.boundaries_shp = 'Name of shapefile with boundaries'
process.imt_str = 'String with the intensity measure type'
process.inland_shp = 'Name of shapefile with inland territories'
process.models_list = 'List of models to be processed'
if __name__ == "__main__":
sap.run(process)
# Read polygons
polygons_gdf = gpd.read_file(fname_polygons)
# Select point in polygon
columns = [
'eventID', 'year', 'month', 'day', 'magnitude', 'longitude',
'latitude', 'depth'
]
# Iterate over sources
for idx, poly in polygons_gdf.iterrows():
df = pd.DataFrame({'Name': [poly.id], 'Polygon': [poly.geometry]})
gdf_poly = gpd.GeoDataFrame(df, geometry='Polygon', crs='epsg:4326')
within = gpd.sjoin(gdf, gdf_poly, op='within')
# Create output file
if isinstance(poly.id, int):
fname = 'subcatalogue_zone_{:d}.csv'.format(poly.id)
else:
fname = 'subcatalogue_zone_{:s}.csv'.format(poly.id)
out_fname = os.path.join(folder_out, fname)
within.to_csv(out_fname, index=False, columns=columns)
create_subcatalogues.fname_polygons = 'Name of a shapefile with polygons'
create_subcatalogues.fname_cat = 'Name of the .csv file with the catalog'
create_subcatalogues.folder_out = 'Name of the output folder'
if __name__ == '__main__':
sap.run(create_subcatalogues)
if min_mmax is not None:
min_mmax = float(min_mmax)
mmax_delta = float(mmax_delta)
# Iterate over sources
for src_id in model['sources']:
mmax = 0.
for param in model['sources'][src_id]:
if re.search('^mmax_', param):
mmax = max(mmax, model['sources'][src_id][param])
if min_mmax is not None:
mmax = max(mmax, min_mmax)
output['sources'][src_id]['mmax'] = mmax + mmax_delta
# Saving results into the config file
with open(fname_conf, 'w') as f:
f.write(toml.dumps(output))
print('Updated {:s}'.format(fname_conf))
descr = 'The name of configuration file'
set_mmax_plus_delta.fname_conf = descr
descr = 'The increment to apply to mmax observed'
set_mmax_plus_delta.mmax_delta = descr
descr = 'The minimum mmax assigned'
set_mmax_plus_delta.min_mmax = descr
if __name__ == '__main__':
sap.run(set_mmax_plus_delta)
tlist.append([w, m])
var['hypocenter_distribution'] = tlist
if len(conf) > 0:
# Saving results into the config file
with open(conf, 'w') as fou:
fou.write(toml.dumps(model))
print('Updated {:s}'.format(conf))
analyze_hypocentral_depth.folder_subcat = 'The folder with the subcatalogues'
analyze_hypocentral_depth.depth_min = 'The minimum hypocentral depth [km]'
analyze_hypocentral_depth.depth_max = 'The maximum hypocentral depth [km]'
analyze_hypocentral_depth.depth_binw = 'The depth bin width [km]'
descr = "The name of the folder where to store figures"
analyze_hypocentral_depth.folder_out_figs = descr
descr = "[true/false] when true show figures on screen"
analyze_hypocentral_depth.show = descr
descr = "String with the bins limits. Overrides depth-min, depth-max, "
descr += "depth-binw"
analyze_hypocentral_depth.depth_bins = descr
descr = "A .toml file. When provided, updated with new info"
analyze_hypocentral_depth.conf = descr
descr = "Source IDs to use"
analyze_hypocentral_depth.use = descr
descr = "Source IDs to skip"
analyze_hypocentral_depth.skip = descr
if __name__ == '__main__':
sap.run(analyze_hypocentral_depth)
from urllib import urlopen
except ImportError:
from urllib.request import urlopen
from openquake.baselib import sap
def viewlog(calc_id, host='localhost', port=8000):
"""
Extract the log of the given calculation ID from the WebUI
"""
base_url = 'http://%s:%s/v1/calc/' % (host, port)
start = 0
psize = 10 # page size
try:
while True:
url = base_url + '%d/log/%d:%d' % (calc_id, start, start + psize)
rows = json.load(urlopen(url))
for row in rows:
print(' '.join(row))
start += len(rows)
time.sleep(1)
except BaseException:
pass
if __name__ == '__main__':
viewlog.calc_id = 'calculation ID'
viewlog.host = 'hostname of the engine server'
viewlog.port = 'port of the engine server'
sap.run(viewlog)
print('src_id: {:s} '.format(src_id), end='')
if ('sources' in model and src_id in model['sources']
and 'completeness_table' in model['sources'][src_id]):
print(' source specific completeness')
ctab = numpy.array(model['sources'][src_id]['completeness_table'])
ctab = ctab.astype(numpy.float)
else:
print(' default completeness')
ctab = numpy.array(model['default']['completeness_table'])
ctab = ctab.astype(numpy.float)
print(ctab)
_plot_ctab(ctab)
ext = 'png'
figure_fname = os.path.join(outdir,
'fig_mtd_{:s}.{:s}'.format(src_id, ext))
plt.savefig(figure_fname, format=ext)
plt.close()
descr = 'Pattern for the .csv catalogue files'
subcatalogues_analysis.fname_input_pattern = descr
descr = 'Name of the .toml file with configuration parameters'
subcatalogues_analysis.fname_config = descr
subcatalogues_analysis.outdir = 'Name of the output folder'
subcatalogues_analysis.yealim = 'Year range used in the plot'
if __name__ == '__main__':
sap.run(subcatalogues_analysis)
dstore_small = datastore.read(calc_small)
sitecol_big = dstore_big['sitecol']
sitecol_small = dstore_small['sitecol']
site_id_big = {(lon, lat): sid for sid, lon, lat in zip(
sitecol_big.sids, sitecol_big.lons, sitecol_big.lats)}
site_id_small = {(lon, lat): sid for sid, lon, lat in zip(
sitecol_small.sids, sitecol_small.lons, sitecol_small.lats)}
common = set(site_id_big) & set(site_id_small)
if not common:
raise RuntimeError('There are no common sites between calculation '
'%d and %d' % (calc_big, calc_small))
sids_small = [site_id_small[lonlat] for lonlat in common]
pmap_big = PmapGetter(dstore_big).get_mean() # USA
pmap_small = PmapGetter(dstore_big, sids=sids_small).get_mean() # Cal
for lonlat in common:
pmap_big[site_id_big[lonlat]] |= pmap_small.get(
site_id_small[lonlat], 0)
out = 'combine_%d_%d.hdf5' % (calc_big, calc_small)
with hdf5.File(out, 'w') as h5:
h5['hcurves/mean'] = pmap_big
h5['oqparam'] = dstore_big['oqparam']
h5['sitecol'] = dstore_big['sitecol']
print('Generated %s' % out)
combine_mean_curves.calc_big = 'first calculation'
combine_mean_curves.calc_small = 'second calculation'
if __name__ == '__main__':
sap.run(combine_mean_curves)
import numpy as np
import pandas as pd
from openquake.baselib import sap
def fix_catalogue(fname):
df = pd.read_csv(fname)
print(df.head())
condition = ((df.month < 1) | (df.month > 12))
df.month = np.where(condition, 1, df.month)
condition = ((df.day < 1) | (df.day > 31))
df.day = np.where(condition, 1, df.day)
df.to_csv(fname)
if __name__ == '__main__':
sap.run(fix_catalogue)
fxml = os.path.join(output_folder, '{:s}.xml'.format(mag))
# Set the source ID
mags = re.sub('\\.', 'pt', mag)
sid = 'src_{:s}_{:s}'.format(label, mags)
name = 'Ruptures for mag bin {:s}'.format(mags)
# Creates a non-parametric seismic source
src = create_source(grp, float(mag), sid, name, trt)
# Create source group
sgrp = SourceGroup(trt, [src])
# Create source model
name = 'Source model for {:s} magnitude {:s}'.format(label, mags)
mdl = SourceModel([sgrp], name, investigation_t)
# Write source model
write_source_model(fxml, mdl, mag)
f.close()
create.label = 'TR label'
create.rupture_hdf5_fname = 'hdf5 file with the ruptures'
create.output_folder = 'Name of the output folder'
create.investigation_t = 'Investigation time'
if __name__ == '__main__':
sap.run(create)
if subzones:
tmp = get_param(srcd_sz, model['default'], key)
npd = _get_nodal_plane_distribution(tmp)
loc = Point(pnt.lon, pnt.lat)
src = PointSource(sid, name, trt, mfd, rms, msr, rar, tom,
usd, lsd, loc, npd, hyd)
srcs.append(src)
# Write output file
fname_out = os.path.join(folder_out, 'src_{:s}.xml'.format(src_id))
write_source_model(fname_out, srcs, 'Zone {:s}'.format(src_id))
def get_param(dct, dct_default, key):
if key in dct:
return dct[key]
else:
return dct_default[key]
create_nrml_sources.fname_input_pattern = "Pattern for input .csv files"
create_nrml_sources.fname_config = "Name of the configuration file"
create_nrml_sources.folder_out = "Name of the output folder"
create_nrml_sources.fname_subzone_shp = "Name of the shapefile with subzones"
create_nrml_sources.fname_subzone_config = "Name of config file for subzones"
if __name__ == '__main__':
sap.run(create_nrml_sources)
Send a SIGTERM to all worker processes
"""
for sock in self.workers:
os.kill(sock.proc.pid, signal.SIGTERM)
for sock in self.workers:
sock.proc.join()
return 'WorkerPool %s stopped' % self.ctrl_url
def kill(self):
"""
Send a SIGKILL to all worker processes
"""
for sock in self.workers:
os.kill(sock.proc.pid, signal.SIGKILL)
for sock in self.workers:
sock.proc.join()
return 'WorkerPool %s killed' % self.ctrl_url
def workerpool(worker_url='tcp://0.0.0.0:1909', *, num_workers: int = -1):
# start a workerpool without a streamer
WorkerPool(worker_url, num_workers).start()
workerpool.worker_url = dict(
help='ZMQ address (tcp:///w.x.y.z:port) of the worker')
workerpool.num_workers = dict(help='number of cores to use')
if __name__ == '__main__':
sap.run(workerpool)
fout = open(fname_out, 'w')
for idx, poly in polygons_gdf.iterrows():
geojson_poly = eval(json.dumps(shapely.geometry.mapping(
poly.geometry)))
# Revert the positions of lons and lats
coo = [[c[1], c[0]] for c in geojson_poly['coordinates'][0]]
geojson_poly['coordinates'] = [coo]
# Discretizing
hexagons = list(h3.polyfill(geojson_poly, h3_level))
for hxg in hexagons:
if isinstance(poly.id, str):
fout.write("{:s},{:s}\n".format(hxg, poly.id))
else:
fout.write("{:s},{:d}\n".format(hxg, poly.id))
fout.close()
descr = 'The level of the H3 grid'
discretize_zones_with_h3_grid.h3_level = descr
descr = 'The name of the shapefile with polygons'
discretize_zones_with_h3_grid.fname_poly = descr
descr = 'The name of the folder where to save output'
discretize_zones_with_h3_grid.folder_out = descr
if __name__ == '__main__':
sap.run(discretize_zones_with_h3_grid)
A very important parameter in this section is the `priority` parameter.
The second (and the following sections) specify the parameters and the
data required to create tectonically uniform families of earthquakes.
The selection of crustal earthquakes requires a set of points (described in
terms of longitude, latitude and depth) describing the bottom surface of
the crust. Ideally, this set of points should be homogenously distributed
over a regular grid.
The selection of subduction interface earthquakes is perfomed by selecting
the ones located at less than a thershold distance [in km] from
surface describing the top of the subducted slab - interface part.
The threshold distance is defined in the `.ini` configuration file.
The selection of subduction inslab earthquakes is perfomed in a manner
similar to the one used for subduction interface seismicity.
"""
msg = 'Path to the configuration fname - typically a .ini file for tr'
classify.ini_fname = msg
msg = 'Flag defining if the calculation of distances'
classify.compute_distances = msg
msg = 'Root folder (path are relative to this in the .ini file)'
classify.rf = msg
if __name__ == "__main__":
sap.run(classify)
# lons, lats, poes, hea, imls = csvt.read_hazard_curve_csv(fname_csv)
# Compute the hazard maps
if pex is not None and iml is None:
dat = csvt.get_map_from_curves(imls, poes, pex)
elif pex is None and iml is not None:
raise ValueError('Not yet supported')
else:
raise ValueError('You cannot set both iml and pex')
# Save the hazard map
path_out = os.path.join(path_out, fname_out)
write_hazard_map(path_out, lons, lats, pex, dat, imt_str)
def map(path_in, prefix, fname_out, path_out, imt_str, pex=None, iml=None):
"""
Creates a hazard map from a set of hazard curves
"""
create_map(path_in, prefix, fname_out, path_out, imt_str, pex, iml)
map.path_in = 'Name of the file with input .json files'
map.prefix = 'Prefix for selecting files'
map.fname_out = 'Name output csv file'
map.path_out = 'Path to the output folder'
map.imt_str = 'String describing the IMT'
map.pex = 'Probability of exceedance'
map.iml = 'Intensity measure level used for building the maps'
if __name__ == "__main__":
sap.run(map)
#!/usr/bin/env python
# coding: utf-8
from openquake.baselib import sap
from openquake.wkf.ses import check_ses_vs_catalogue
def main(fname, example_flag=False):
"""
Confronts the seismicity contained in a stochastic event set generated by
the engine against a real earthquake catalogue.
"""
check_ses_vs_catalogue(fname, example_flag)
descr = 'Name of the .toml configuration file'
main.fname = descr
descr = 'A flag. When true the code prints an example of .toml configuration'
main.example_flag = descr
if __name__ == '__main__':
sap.run(main)
# Info
number_of_samples = numpy.ceil(lengths[longest_key] /
float(max_sampl_dist))
tmps = 'Number of subsegments for each profile: {:d}'
logging.info(tmps.format(int(number_of_samples)))
tmp = lengths[shortest_key] / number_of_samples
logging.info('Shortest sampling [%s]: %.4f' % (shortest_key, tmp))
tmp = lengths[longest_key] / number_of_samples
logging.info('Longest sampling [%s]: %.4f' % (longest_key, tmp))
# Resampled profiles
rsps = get_interpolated_profiles(sps, lengths, number_of_samples)
# Store new profiles
write_profiles_csv(rsps, out_path)
# Store computed edges
write_edges_csv(rsps, out_path)
build_complex_surface.in_path = 'Path to the input folder'
build_complex_surface.max_sampl_dist = 'Maximum profile sampling distance'
build_complex_surface.out_path = 'Path to the output folder'
build_complex_surface.upper_depth = 'Upper depth'
build_complex_surface.lower_depth = 'lower depth'
build_complex_surface.from_id = 'Index profile where to start the sampling'
build_complex_surface.to_id = 'Index profile where to stop the sampling'
if __name__ == "__main__":
sap.run(build_complex_surface)
