Source code for openquake.commands.sample

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2015-2020 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
# 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 <>.
import csv
import shutil
import numpy
from openquake.hazardlib import valid, nrml
from openquake.baselib.python3compat import encode
from openquake.baselib import sap, general
from openquake.commonlib import logictree

def _save_csv(fname, lines, header):
    with open(fname, 'wb') as f:
        if header:
        for line in lines:

[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): node = if node['xmlns'] == '': total = sum(len(sg) for sg in node[0]) num_nodes = 0 for sg in node[0]: sg.nodes = general.random_filter(sg, reduction_factor) num_nodes += len(sg) else: # nrml/0.4 total = len(node[0].nodes) node[0].nodes = general.random_filter(node[0], reduction_factor) num_nodes = len(node[0].nodes) save_bak(fname, node, num_nodes, total)
[docs]@sap.script def sample(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: line = f.readline() # read the first line if csv.Sniffer().has_header(line): header = line all_lines = f.readlines() else: header = None all_lines = f.readlines() lines = general.random_filter(all_lines, reduction_factor) shutil.copy(fname, fname + '.bak') print('Copied the original file in %s.bak' % fname) _save_csv(fname, lines, header) print('Extracted %d lines out of %d' % (len(lines), len(all_lines))) 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)), arr) print('Extracted %d rows out of %d' % (len(arr), len(array))) return node = 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) return elif model.tag.endswith('logicTree'): for smpath in logictree.collect_info(fname).smpaths: reduce_source_model(smpath, reduction_factor) return else: raise RuntimeError('Unknown model tag: %s' % model.tag) save_bak(fname, node, num_nodes, total)
sample.arg('fname', 'path to the model file') sample.arg('reduction_factor', 'reduction factor in the range 0..1', type=valid.probability)