#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# 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/>.
import shutil
import numpy
import pandas
from openquake.hazardlib import (
valid, nrml, sourceconverter, sourcewriter, logictree)
from openquake.baselib import general
[docs]def save_bak(fname, node, num_nodes, total):
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
with open(fname, 'wb') as f:
nrml.write(node, f, xmlns=node['xmlns'])
print('Extracted %d nodes out of %d' % (num_nodes, total))
[docs]def reduce_source_model(fname, reduction_factor, itime):
"""
Reduce the source model by sampling the sources; as a special case,
multiPointSources are split in pointSources and then sampled.
"""
conv = sourceconverter.SourceConverter(area_source_discretization=20.,
investigation_time=itime)
[smodel] = nrml.read_source_models([fname], conv)
grp = smodel.src_groups[0]
if any(src.code == b'M' for src in grp): # multiPoint
for src in grp:
if src.code == b'M':
grp.sources = general.random_filter(src, reduction_factor)
print('Extracted %d point sources out of %d' %
(len(grp), len(src)))
break
elif any(src.code == b'F' for src in grp): # multiFault
for src in grp:
if src.code == b'F':
rids = numpy.arange(src.count_ruptures())
ok = general.random_filter(rids, reduction_factor)
src.mags = src.mags[ok]
src.rakes = src.rakes[ok]
src.probs_occur = src.probs_occur[ok]
src._rupture_idxs = [src._rupture_idxs[o] for o in ok]
print('Extracted %d ruptures out of %d' %
(len(ok), len(rids)))
break
else:
total = len(grp)
grp.sources = general.random_filter(grp, reduction_factor)
print('Extracted %d nodes out of %d' % (len(grp), total))
smodel.src_groups = [grp]
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
sourcewriter.write_source_model(fname, smodel)
[docs]def main(fname, reduction_factor: valid.probability,
investigation_time: valid.positivefloat = 50.):
"""
Produce a submodel from `fname` by sampling the nodes randomly.
Supports source models, site models and exposure models. As a special
case, it is also able to reduce .csv files by sampling the lines.
This is a debugging utility to reduce large computations to small ones.
"""
if fname.endswith('.csv'):
df = pandas.read_csv(fname, dtype=str)
idxs = general.random_filter(numpy.arange(len(df)), reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
df.loc[idxs].to_csv(fname, index=False,
lineterminator='\r\n', na_rep='nan')
print('Extracted %d lines out of %d' % (len(idxs), len(df)))
return
elif fname.endswith('.npy'):
array = numpy.load(fname)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
arr = numpy.array(general.random_filter(array, reduction_factor))
numpy.save(fname, arr)
print('Extracted %d rows out of %d' % (len(arr), len(array)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = general.random_filter(
model.assets, reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = general.random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
reduce_source_model(fname, reduction_factor, investigation_time)
return
elif model.tag.endswith('logicTree'):
for smpath in logictree.collect_info(fname).smpaths:
reduce_source_model(smpath, reduction_factor, investigation_time)
return
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
save_bak(fname, node, num_nodes, total)
main.fname = 'path to the model file'
main.reduction_factor = 'reduction factor in the range 0..1'
main.investigation_time = 'investigation_time used in read_source_models'
