# Source code for openquake.commands.reduce

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2015-2017 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/>.
from __future__ import print_function
import random
import shutil
from openquake.hazardlib import valid, nrml
from openquake.baselib.python3compat import encode
from openquake.baselib import sap
[docs]def random_filter(objects, reduction_factor, seed=42):
"""
Given a list of objects, returns a sublist by extracting randomly
some elements. The reduction factor (< 1) tells how small is the extracted
list compared to the original list.
"""
assert 0 < reduction_factor <= 1, reduction_factor
rnd = random.Random(seed)
out = []
for obj in objects:
if rnd.random() <= reduction_factor:
out.append(obj)
return out
@sap.Script
def reduce(fname, reduction_factor):
"""
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'):
with open(fname) as f:
all_lines = f.readlines()
lines = random_filter(all_lines, reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
with open(fname, 'wb') as f:
for line in lines:
f.write(encode(line))
print('Extracted %d lines out of %d' % (len(lines), len(all_lines)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = random_filter(model.assets, reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
total = len(model)
model.nodes = random_filter(model, reduction_factor)
num_nodes = len(model)
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
with open(fname, 'wb') as f:
nrml.write([model], f, xmlns=node['xmlns'])
print('Extracted %d nodes out of %d' % (num_nodes, total))
reduce.arg('fname', 'path to the model file')
reduce.arg('reduction_factor', 'reduction factor in the range 0..1',
type=valid.probability)
```