# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2023 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/>.
"""
Utilities to read the input files recognized by the OpenQuake engine.
"""
import os
import re
import ast
import copy
import zlib
import shutil
import zipfile
import pathlib
import logging
import tempfile
import functools
import configparser
import collections
import itertools
import numpy
import pandas
from scipy.spatial import cKDTree
from scipy.spatial.distance import cdist
import requests
from shapely import wkt, geometry
from openquake.baselib import config, hdf5, parallel, InvalidFile
from openquake.baselib.performance import Monitor
from openquake.baselib.general import (
random_filter, countby, get_duplicates, check_extension, gettemp, AccumDict)
from openquake.baselib.python3compat import zip, decode
from openquake.baselib.node import Node
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.geo.packager import fiona
from openquake.hazardlib.calc.filters import getdefault
from openquake.hazardlib.calc.gmf import CorrelationButNoInterIntraStdDevs
from openquake.hazardlib import (
source, geo, site, imt, valid, sourceconverter, source_reader, nrml,
pmf, logictree, gsim_lt, get_smlt)
from openquake.hazardlib.map_array import MapArray
from openquake.hazardlib.geo.point import Point
from openquake.hazardlib.geo.utils import (
spherical_to_cartesian, geohash3, get_dist)
from openquake.risklib import asset, riskmodels, scientific, reinsurance
from openquake.risklib.riskmodels import get_risk_functions
from openquake.commonlib.oqvalidation import OqParam
from openquake.qa_tests_data import mosaic, global_risk
F32 = numpy.float32
F64 = numpy.float64
U8 = numpy.uint8
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
Site = collections.namedtuple('Site', 'sid lon lat')
[docs]class DuplicatedPoint(Exception):
"""
Raised when reading a CSV file with duplicated (lon, lat) pairs
"""
# used in extract_fom_zip
[docs]def collect_files(dirpath, cond=lambda fullname: True):
"""
Recursively collect the files contained inside dirpath.
:param dirpath: path to a readable directory
:param cond: condition on the path to collect the file
"""
files = set()
for fname in os.listdir(dirpath):
fullname = os.path.join(dirpath, fname)
if os.path.isdir(fullname): # navigate inside
files.update(collect_files(fullname))
else: # collect files
if cond(fullname):
files.add(fullname)
return sorted(files) # job_haz before job_risk
[docs]def unzip_rename(zpath, name):
"""
:param zpath: full path to a .zip archive
:param name: exposure.xml or ssmLT.xml
:returns: path to an .xml file with the same name of the archive
"""
xpath = zpath[:-4] + '.xml'
if os.path.exists(xpath):
# already unzipped
return xpath
dpath = os.path.dirname(zpath)
with zipfile.ZipFile(zpath) as archive:
for nam in archive.namelist():
fname = os.path.join(dpath, nam)
if os.path.exists(fname): # already unzipped
os.rename(fname, fname + '.bak')
logging.warning('Overriding %s with the file in %s',
fname, zpath)
logging.info('Unzipping %s', zpath)
archive.extractall(dpath)
xname = os.path.join(dpath, name)
if os.path.exists(xname):
os.rename(xname, xpath)
return xpath
[docs]def normpath(fnames, base_path):
vals = []
for fname in fnames:
val = os.path.normpath(os.path.join(base_path, fname))
if not os.path.exists(val):
raise OSError('No such file: %s' % val)
vals.append(val)
return vals
def _normalize(key, fnames, base_path):
# returns (input_type, filenames)
# check that all the fnames have the same extension
# NB: for consequences fnames is a list of lists
flatten = []
for fname in fnames:
if isinstance(fname, list):
flatten.extend(fname)
else:
flatten.append(fname)
check_extension(flatten)
input_type, _ext = key.rsplit('_', 1)
filenames = []
for val in fnames:
if isinstance(val, list):
val = normpath(val, base_path)
elif os.path.isabs(val):
raise ValueError('%s=%s is an absolute path' % (key, val))
elif val.endswith('.zip'):
zpath = os.path.normpath(os.path.join(base_path, val))
if key == 'exposure_file':
name = 'exposure.xml'
elif key == 'source_model_logic_tree_file':
name = 'ssmLT.xml'
else:
raise KeyError('Unknown key %s' % key)
val = unzip_rename(zpath, name)
elif val.startswith('${mosaic}/'):
if 'mosaic' in config.directory:
# support ${mosaic}/XXX/in/ssmLT.xml
val = val.format(**config.directory)[1:] # strip initial "$"
else:
continue
else:
val = os.path.normpath(os.path.join(base_path, val))
if isinstance(val, str) and not os.path.exists(val):
# tested in archive_err_2
raise OSError('No such file: %s' % val)
filenames.append(val)
return input_type, filenames
[docs]def update(params, items, base_path):
"""
Update a dictionary of string parameters with new parameters. Manages
correctly file parameters.
"""
for key, value in items:
if key in ('hazard_curves_csv', 'hazard_curves_file',
'gmfs_csv', 'gmfs_file',
'site_model_csv', 'site_model_file',
'exposure_csv', 'exposure_file'):
input_type, fnames = _normalize(key, value.split(), base_path)
params['inputs'][input_type] = fnames
elif key.endswith(('_file', '_csv', '_hdf5')):
if value.startswith('{'):
dic = ast.literal_eval(value) # name -> relpath
input_type, fnames = _normalize(key, dic.values(), base_path)
params['inputs'][input_type] = dict(zip(dic, fnames))
params[input_type] = ' '.join(dic)
elif value:
input_type, fnames = _normalize(key, [value], base_path)
assert len(fnames) in (0, 1)
for fname in fnames:
params['inputs'][input_type] = fname
else:
# remove the key if the value is empty
basekey, _file = key.rsplit('_', 1)
params['inputs'].pop(basekey, None)
elif (isinstance(value, str) and value.endswith('.hdf5')
and key != 'description'):
logging.warning('The [reqv] syntax has been deprecated, see '
'https://github.com/gem/oq-engine/blob/master/doc/'
'adv-manual/equivalent-distance-app for the new '
'syntax')
fname = os.path.normpath(os.path.join(base_path, value))
try:
reqv = params['inputs']['reqv']
except KeyError:
params['inputs']['reqv'] = {key: fname}
else:
reqv.update({key: fname})
else:
params[key] = value
[docs]def check_params(cp, fname):
params_sets = [
set(cp.options(section)) for section in cp.sections()]
for pair in itertools.combinations(params_sets, 2):
params_intersection = sorted(set.intersection(*pair))
if params_intersection:
raise InvalidFile(
f'{fname}: parameter(s) {params_intersection} is(are) defined'
' in multiple sections')
# NB: this function must NOT log, since it is called when the logging
# is not configured yet
[docs]def get_params(job_ini, kw={}):
"""
Parse a .ini file or a .zip archive
:param job_ini:
Configuration file | zip archive | URL
:param kw:
Optionally override some parameters
:returns:
A dictionary of parameters
"""
if isinstance(job_ini, pathlib.Path):
job_ini = str(job_ini)
if job_ini.startswith(('http://', 'https://')):
resp = requests.get(job_ini)
job_ini = gettemp(suffix='.zip')
with open(job_ini, 'wb') as f:
f.write(resp.content)
# directory containing the config files we're parsing
job_ini = os.path.abspath(job_ini)
base_path = os.path.dirname(job_ini)
params = dict(base_path=base_path, inputs={'job_ini': job_ini})
input_zip = None
if job_ini.endswith('.zip'):
input_zip = job_ini
job_inis = extract_from_zip(job_ini)
if not job_inis:
raise NameError('Could not find job.ini inside %s' % input_zip)
job_ini = job_inis[0]
if not os.path.exists(job_ini):
raise IOError('File not found: %s' % job_ini)
base_path = os.path.dirname(job_ini)
params = dict(base_path=base_path, inputs={'job_ini': job_ini})
cp = configparser.ConfigParser(interpolation=None)
cp.read([job_ini], encoding='utf-8-sig') # skip BOM on Windows
check_params(cp, job_ini)
dic = {}
for sect in cp.sections():
dic.update(cp.items(sect))
# put source_model_logic_tree_file on top of the items so that
# oq-risk-tests alaska, which has a smmLT.zip file works, since
# it is unzipped before and therefore the files can be read later
if 'source_model_logic_tree_file' in dic:
fname = dic.pop('source_model_logic_tree_file')
items = [('source_model_logic_tree_file', fname)] + list(dic.items())
else:
items = dic.items()
update(params, items, base_path)
if input_zip:
params['inputs']['input_zip'] = os.path.abspath(input_zip)
update(params, kw.items(), base_path) # override on demand
return params
[docs]def is_fraction(string):
"""
:returns: True if the string can be converted to a probability
"""
try:
f = float(string)
except (ValueError, TypeError):
return
return 0 < f < 1
[docs]def get_oqparam(job_ini, pkg=None, kw={}, validate=True):
"""
Parse a dictionary of parameters from an INI-style config file.
:param job_ini:
Path to configuration file/archive or
dictionary of parameters with a key "calculation_mode"
:param pkg:
Python package where to find the configuration file (optional)
:param kw:
Dictionary of strings to override the job parameters
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validated and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
if not isinstance(job_ini, dict):
basedir = os.path.dirname(pkg.__file__) if pkg else ''
job_ini = get_params(os.path.join(basedir, job_ini), kw)
re = os.environ.get('OQ_REDUCE') # debugging facility
if is_fraction(re):
# reduce the imtls to the first imt
# reduce the logic tree to one random realization
# reduce the sites by a factor of `re`
# reduce the ses by a factor of `re`
os.environ['OQ_SAMPLE_SITES'] = re
ses = job_ini.get('ses_per_logic_tree_path')
if ses:
ses = int(numpy.ceil(int(ses) * float(re)))
job_ini['ses_per_logic_tree_path'] = str(ses)
imtls = job_ini.get('intensity_measure_types_and_levels')
if imtls:
imtls = valid.intensity_measure_types_and_levels(imtls)
imt = next(iter(imtls))
job_ini['intensity_measure_types_and_levels'] = repr(
{imt: imtls[imt]})
oqparam = OqParam(**job_ini)
oqparam._input_files = get_input_files(oqparam)
if validate: # always true except from oqzip
oqparam.validate()
return oqparam
[docs]def get_mesh_exp(oqparam, h5=None):
"""
Extract the mesh of points to compute from the sites,
the sites_csv, the region, the site model, the exposure in this order.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
a pair (mesh, exposure) both of which can be None
"""
exposure = get_exposure(oqparam, h5)
if oqparam.aristotle:
sm = get_site_model(oqparam, h5)
mesh = geo.Mesh(sm['lon'], sm['lat'])
return mesh, exposure
if oqparam.sites:
mesh = geo.Mesh.from_coords(oqparam.sites)
return mesh, exposure
elif 'hazard_curves' in oqparam.inputs:
fname = oqparam.inputs['hazard_curves']
if isinstance(fname, list): # for csv
mesh, _pmap = get_pmap_from_csv(oqparam, fname)
return mesh, exposure
raise NotImplementedError('Reading from %s' % fname)
elif oqparam.region_grid_spacing:
if oqparam.region:
poly = geo.Polygon.from_wkt(oqparam.region)
elif exposure:
# in case of implicit grid the exposure takes precedence over
# the site model
poly = exposure.mesh.get_convex_hull()
elif 'site_model' in oqparam.inputs:
# this happens in event_based/case_19, where there is an implicit
# grid over the site model
sm = get_site_model(oqparam) # do not store in h5!
poly = geo.Mesh(sm['lon'], sm['lat']).get_convex_hull()
else:
raise InvalidFile('There is a grid spacing but not a region, '
'nor a site model, nor an exposure in %s' %
oqparam.inputs['job_ini'])
try:
logging.info('Inferring the hazard grid')
mesh = poly.dilate(oqparam.region_grid_spacing).discretize(
oqparam.region_grid_spacing)
return geo.Mesh.from_coords(zip(mesh.lons, mesh.lats)), exposure
except Exception:
raise ValueError(
'Could not discretize region with grid spacing '
'%(region_grid_spacing)s' % vars(oqparam))
# the site model has the precedence over the exposure, see the
# discussion in https://github.com/gem/oq-engine/pull/5217
elif 'site_model' in oqparam.inputs:
logging.info('Extracting the hazard sites from the site model')
sm = get_site_model(oqparam, h5)
mesh = geo.Mesh(sm['lon'], sm['lat'])
elif 'exposure' in oqparam.inputs:
mesh = exposure.mesh
else:
mesh = None
return mesh, exposure
[docs]def get_poor_site_model(fname):
"""
:returns: a poor site model with only lon, lat fields
"""
with open(fname, encoding='utf-8-sig') as f:
data = [ln.replace(',', ' ') for ln in f]
coords = sorted(valid.coordinates(','.join(data)))
# sorting the coordinates so that event_based do not depend on the order
dt = [('lon', float), ('lat', float), ('depth', float)]
return numpy.array(coords, dt)
[docs]def rup_radius(rup):
"""
Maximum distance from the rupture mesh to the hypocenter
"""
hypo = rup.hypocenter
xyz = spherical_to_cartesian(hypo.x, hypo.y, hypo.z).reshape(1, 3)
radius = cdist(rup.surface.mesh.xyz, xyz).min(axis=0)
return radius
[docs]def filter_site_array_around(array, rup, dist):
"""
:param array: array with fields 'lon', 'lat'
:param rup: a rupture object
:param dist: integration distance in km
:returns: slice to the rupture
"""
hypo = rup.hypocenter
x, y, z = hypo.x, hypo.y, hypo.z
xyz_all = spherical_to_cartesian(array['lon'], array['lat'], 0)
xyz = spherical_to_cartesian(x, y, z)
# first raw filtering
tree = cKDTree(xyz_all)
# NB: on macOS query_ball returns the indices in a different order
# than on linux and windows, hence the need to sort
idxs = tree.query_ball_point(xyz, dist + rup_radius(rup), eps=.001)
idxs.sort()
# then fine filtering
array = array[idxs]
idxs, = numpy.where(get_dist(xyz_all[idxs], xyz) < dist)
if len(idxs) < len(array):
logging.info('Filtered %d/%d sites', len(idxs), len(array))
return array[idxs]
[docs]def get_site_model_around(site_model_hdf5, rup, dist):
"""
:param site_model_hdf5: path to an HDF5 file containing a 'site_model'
:param rup: a rupture object
:param dist: integration distance in km
:returns: site model close to the rupture
"""
with hdf5.File(site_model_hdf5) as f:
sm = f['site_model'][:]
return filter_site_array_around(sm, rup, dist)
def _smparse(fname, oqparam, arrays, sm_fieldsets):
# check if the file is a list of lon,lat without header
with open(fname, encoding='utf-8-sig') as f:
lon, _rest = next(f).split(',', 1)
try:
valid.longitude(lon)
except ValueError: # has a header
sm = hdf5.read_csv(fname, site.site_param_dt).array
else:
sm = get_poor_site_model(fname)
sm_fieldsets[fname] = set(sm.dtype.names)
# make sure site_id starts from 0, if given
if 'site_id' in sm.dtype.names:
if (sm['site_id'] != numpy.arange(len(sm))).any():
raise InvalidFile('%s: site_id not sequential from zero'
% fname)
# round coordinates and check for duplicate points
sm['lon'] = numpy.round(sm['lon'], 5)
sm['lat'] = numpy.round(sm['lat'], 5)
dupl = get_duplicates(sm, 'lon', 'lat')
if dupl:
raise InvalidFile(
'Found duplicate sites %s in %s' % (dupl, fname))
# used global parameters is local ones are missing
params = sorted(set(sm.dtype.names) | set(oqparam.req_site_params))
z = numpy.zeros(
len(sm), [(p, site.site_param_dt[p]) for p in params])
for name in z.dtype.names:
try:
z[name] = sm[name]
except ValueError: # missing, use the global parameter
# exercised in the test classical/case_28_bis
z[name] = check_site_param(oqparam, name)
arrays.append(z)
[docs]def check_site_param(oqparam, name):
"""
Extract the value of the given parameter
"""
longname = site.param[name] # vs30 -> reference_vs30_value
value = getattr(oqparam, longname, None)
if value is None:
raise InvalidFile('Missing site_model_file specifying the parameter %s'
% name)
if isinstance(value, float) and numpy.isnan(value):
raise InvalidFile(
f"{oqparam.inputs['job_ini']}: "
f"{site.param[name]} not specified")
elif name == 'vs30measured': # special case
value = value == 'measured'
return value
[docs]def get_site_model(oqparam, h5=None):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an array with fields lon, lat, vs30, ...
"""
if h5 and 'site_model' in h5:
return h5['site_model'][:]
if oqparam.aristotle:
# read the site model close to the rupture
rup = get_rupture(oqparam)
dist = oqparam.maximum_distance('*')(rup.mag)
sm = get_site_model_around(oqparam.inputs['exposure'][0], rup, dist)
if h5:
h5['site_model'] = sm
return sm
arrays = []
sm_fieldsets = {}
for fname in oqparam.inputs['site_model']:
if isinstance(fname, str) and not fname.endswith('.xml'):
# parsing site_model.csv and populating arrays
_smparse(fname, oqparam, arrays, sm_fieldsets)
continue
# parsing site_model.xml
nodes = nrml.read(fname).siteModel
params = [valid.site_param(node.attrib) for node in nodes]
missing = set(oqparam.req_site_params) - set(params[0])
if 'vs30measured' in missing: # use a default of False
missing -= {'vs30measured'}
for param in params:
param['vs30measured'] = False
if 'backarc' in missing: # use a default of False
missing -= {'backarc'}
for param in params:
param['backarc'] = False
if 'ampcode' in missing: # use a default of b''
missing -= {'ampcode'}
for param in params:
param['ampcode'] = b''
if missing:
raise InvalidFile('%s: missing parameter %s' %
(oqparam.inputs['site_model'],
', '.join(missing)))
# NB: the sorted in sorted(params[0]) is essential, otherwise there is
# an heisenbug in scenario/test_case_4
site_model_dt = numpy.dtype([(p, site.site_param_dt[p])
for p in sorted(params[0])])
sm = numpy.array([tuple(param[name] for name in site_model_dt.names)
for param in params], site_model_dt)
dupl = "\n".join(
'%s %s' % loc for loc, n in countby(sm, 'lon', 'lat').items()
if n > 1)
if dupl:
raise InvalidFile('There are duplicated sites in %s:\n%s' %
(fname, dupl))
arrays.append(sm)
# all source model input files must have the same fields
for this_sm_fname in sm_fieldsets:
for other_sm_fname in sm_fieldsets:
if other_sm_fname == this_sm_fname:
continue
this_fieldset = sm_fieldsets[this_sm_fname]
other_fieldset = sm_fieldsets[other_sm_fname]
fieldsets_diff = this_fieldset - other_fieldset
if fieldsets_diff:
raise InvalidFile(
f'Fields {fieldsets_diff} present in'
f' {this_sm_fname} were not found in {other_sm_fname}')
sm = numpy.concatenate(arrays, dtype=arrays[0].dtype)
if h5:
h5['site_model'] = sm
return sm
[docs]def debug_site(oqparam, haz_sitecol):
"""
Reduce the site collection to the custom_site_id specified in
OQ_DEBUG_SITE. For conditioned GMFs, keep the stations.
"""
siteid = os.environ.get('OQ_DEBUG_SITE')
if siteid:
complete = copy.copy(haz_sitecol.complete)
ok = haz_sitecol['custom_site_id'] == siteid.encode('ascii')
if not ok.any():
raise ValueError('There is no custom_site_id=%s', siteid)
if 'station_data' in oqparam.inputs:
# keep the stations while restricting to the specified site
sdata, _imts = get_station_data(oqparam, haz_sitecol)
ok |= numpy.isin(haz_sitecol.sids, sdata.site_id.to_numpy())
haz_sitecol.array = haz_sitecol[ok]
haz_sitecol.complete = complete
oqparam.concurrent_tasks = 0
[docs]def get_site_collection(oqparam, h5=None):
"""
Returns a SiteCollection instance by looking at the points and the
site model defined by the configuration parameters.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
if h5 and 'sitecol' in h5:
return h5['sitecol']
mesh, exp = get_mesh_exp(oqparam, h5)
if mesh is None and oqparam.ground_motion_fields:
raise InvalidFile('You are missing sites.csv or site_model.csv in %s'
% oqparam.inputs['job_ini'])
elif mesh is None:
# a None sitecol is okay when computing the ruptures only
return None
else: # use the default site params
if ('gmfs' in oqparam.inputs or 'hazard_curves' in oqparam.inputs
or 'shakemap' in oqparam.inputs):
req_site_params = set() # no parameters are required
else:
req_site_params = oqparam.req_site_params
if h5 and 'site_model' in h5:
sm = h5['site_model'][:]
elif oqparam.aristotle and (
not oqparam.infrastructure_connectivity_analysis):
# filter the far away sites
rup = get_rupture(oqparam)
dist = oqparam.maximum_distance('*')(rup.mag)
[expo_hdf5] = oqparam.inputs['exposure']
sm = get_site_model_around(expo_hdf5, rup, dist)
elif (not h5 and 'site_model' in oqparam.inputs and
'exposure' not in oqparam.inputs):
# tested in test_with_site_model
sm = get_site_model(oqparam, h5)
if len(sm) > len(mesh): # the association will happen in base.py
sm = oqparam
elif 'site_model' not in oqparam.inputs:
# check the required site parameters are not NaN
sm = oqparam
for req_site_param in req_site_params:
if req_site_param in site.param:
check_site_param(oqparam, req_site_param)
else:
sm = oqparam
sitecol = site.SiteCollection.from_points(
mesh.lons, mesh.lats, mesh.depths, sm, req_site_params)
if ('vs30' in sitecol.array.dtype.names and
not numpy.isnan(sitecol.vs30).any()):
assert sitecol.vs30.max() < 32767, sitecol.vs30.max()
if oqparam.tile_spec:
if 'custom_site_id' not in sitecol.array.dtype.names:
gh = sitecol.geohash(6)
assert len(numpy.unique(gh)) == len(gh), 'geohashes are not unique'
sitecol.add_col('custom_site_id', 'S6', gh)
tileno, ntiles = oqparam.tile_spec
assert len(sitecol) > ntiles, (len(sitecol), ntiles)
mask = sitecol.sids % ntiles == tileno - 1
oqparam.max_sites_disagg = 1
sitecol = sitecol.filter(mask)
sitecol.make_complete()
ss = os.environ.get('OQ_SAMPLE_SITES')
if ss:
# debugging tip to reduce the size of a calculation
# OQ_SAMPLE_SITES=.1 oq engine --run job.ini
# will run a computation with 10 times less sites
sitecol.array = numpy.array(random_filter(sitecol.array, float(ss)))
sitecol.make_complete()
sitecol.array['lon'] = numpy.round(sitecol.lons, 5)
sitecol.array['lat'] = numpy.round(sitecol.lats, 5)
sitecol.exposure = exp
# add custom_site_id in risk calculations (or GMF calculations)
custom_site_id = any(x in oqparam.calculation_mode
for x in ('scenario', 'event_based',
'risk', 'damage'))
if custom_site_id and 'custom_site_id' not in sitecol.array.dtype.names:
gh = sitecol.geohash(8)
if len(numpy.unique(gh)) < len(gh):
logging.error('geohashes are not unique')
sitecol.add_col('custom_site_id', 'S8', gh)
if sitecol is not sitecol.complete:
# tested in scenario_risk/test_case_8
gh = sitecol.complete.geohash(8)
sitecol.complete.add_col('custom_site_id', 'S8', gh)
debug_site(oqparam, sitecol)
if h5:
h5['sitecol'] = sitecol
return sitecol
[docs]def get_gsim_lt(oqparam, trts=('*',)):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param trts:
a sequence of tectonic region types as strings; trts=['*']
means that there is no filtering
:returns:
a GsimLogicTree instance obtained by filtering on the provided
tectonic region types.
"""
if 'gsim_logic_tree' not in oqparam.inputs:
return logictree.GsimLogicTree.from_(oqparam.gsim)
gsim_file = os.path.join(
oqparam.base_path, oqparam.inputs['gsim_logic_tree'])
gsim_lt = logictree.GsimLogicTree(gsim_file, trts)
gmfcorr = oqparam.correl_model
for trt, gsims in gsim_lt.values.items():
for gsim in gsims:
# NB: gsim.DEFINED_FOR_TECTONIC_REGION_TYPE can be != trt,
# but it is not an error, it is actually the most common case!
if gmfcorr and (gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES ==
{StdDev.TOTAL}) and not oqparam.with_betw_ratio:
raise CorrelationButNoInterIntraStdDevs(gmfcorr, gsim)
imt_dep_w = any(len(branch.weight.dic) > 1 for branch in gsim_lt.branches)
if oqparam.number_of_logic_tree_samples and imt_dep_w:
logging.error('IMT-dependent weights in the logic tree cannot work '
'with sampling, because they would produce different '
'GMPE paths for each IMT that cannot be combined, so '
'I am using the default weights')
for branch in gsim_lt.branches:
for k, w in sorted(branch.weight.dic.items()):
if k != 'weight':
logging.debug(
'Using weight=%s instead of %s for %s %s',
branch.weight.dic['weight'], w, branch.gsim, k)
del branch.weight.dic[k]
if oqparam.collapse_gsim_logic_tree:
logging.info('Collapsing the gsim logic tree')
gsim_lt = gsim_lt.collapse(oqparam.collapse_gsim_logic_tree)
return gsim_lt
[docs]def get_rupture(oqparam):
"""
Read the `rupture_model` XML file or the `rupture_dict` dictionary
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an hazardlib rupture
"""
rupture_model = oqparam.inputs.get('rupture_model')
if rupture_model:
[rup_node] = nrml.read(oqparam.inputs['rupture_model'])
conv = sourceconverter.RuptureConverter(oqparam.rupture_mesh_spacing)
rup = conv.convert_node(rup_node)
rup.tectonic_region_type = '*' # there is no TRT for scenario ruptures
else: # assume rupture_dict
r = oqparam.rupture_dict
hypo = Point(r['lon'], r['lat'], r['dep'])
rup = source.rupture.build_planar(
hypo, r['mag'], r.get('rake'),
r.get('strike', 0), r.get('dip', 90), r.get('trt', '*'))
return rup
[docs]def get_source_model_lt(oqparam):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
a :class:`openquake.hazardlib.logictree.SourceModelLogicTree`
instance
"""
smlt = get_smlt(vars(oqparam))
srcids = set(smlt.source_data['source'])
for src in oqparam.reqv_ignore_sources:
if src not in srcids:
raise NameError('The source %r in reqv_ignore_sources does '
'not exist in the source model(s)' % src)
if len(oqparam.source_id) == 1: # reduce to a single source
return smlt.reduce(oqparam.source_id[0])
return smlt
[docs]def get_full_lt(oqparam):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
a :class:`openquake.hazardlib.logictree.FullLogicTree`
instance
"""
source_model_lt = get_source_model_lt(oqparam)
trts = source_model_lt.tectonic_region_types
trts_lower = {trt.lower() for trt in trts}
reqv = oqparam.inputs.get('reqv', {})
for trt in reqv:
if trt in oqparam.discard_trts.split(','):
continue
elif trt.lower() not in trts_lower:
logging.warning('Unknown TRT=%s in [reqv] section' % trt)
gsim_lt = get_gsim_lt(oqparam, trts or ['*'])
oversampling = oqparam.oversampling
full_lt = logictree.FullLogicTree(source_model_lt, gsim_lt, oversampling)
p = full_lt.source_model_lt.num_paths * gsim_lt.get_num_paths()
if full_lt.gsim_lt.has_imt_weights() and oqparam.use_rates:
raise ValueError('use_rates=true cannot be used with imtWeight')
if oqparam.number_of_logic_tree_samples:
if (oqparam.oversampling == 'forbid' and
oqparam.number_of_logic_tree_samples >= p
and 'event' not in oqparam.calculation_mode):
raise ValueError('Use full enumeration since there are only '
'{:_d} realizations'.format(p))
unique = numpy.unique(full_lt.rlzs['branch_path'])
logging.info('Considering {:_d} logic tree paths out of {:_d}, unique'
' {:_d}'.format(oqparam.number_of_logic_tree_samples, p,
len(unique)))
else: # full enumeration
if not oqparam.fastmean and p > oqparam.max_potential_paths:
raise ValueError(
'There are too many potential logic tree paths (%d):'
'raise `max_potential_paths`, use sampling instead of '
'full enumeration, or set use_rates=true ' % p)
elif (oqparam.is_event_based() and
(oqparam.ground_motion_fields or oqparam.hazard_curves_from_gmfs)
and p > oqparam.max_potential_paths / 100):
logging.warning(
'There are many potential logic tree paths (%d): '
'try to use sampling or reduce the source model' % p)
if source_model_lt.is_source_specific:
logging.info('There is a source specific logic tree')
return full_lt
[docs]def get_logic_tree(oqparam):
"""
:returns: a CompositeLogicTree instance
"""
flt = get_full_lt(oqparam)
return logictree.compose(flt.source_model_lt, flt.gsim_lt)
[docs]def check_min_mag(sources, minimum_magnitude):
"""
Raise an error if all sources are below the minimum_magnitude
"""
ok = 0
for src in sources:
min_mag = getdefault(minimum_magnitude, src.tectonic_region_type)
maxmag = src.get_min_max_mag()[1]
if min_mag <= maxmag:
ok += 1
if not ok:
raise RuntimeError('All sources were discarded by minimum_magnitude')
def _check_csm(csm, oqparam, h5):
# checks
csm.gsim_lt.check_imts(oqparam.imtls)
srcs = csm.get_sources()
check_min_mag(srcs, oqparam.minimum_magnitude)
if h5 and 'sitecol' in h5:
csm.sitecol = h5['sitecol']
else:
csm.sitecol = get_site_collection(oqparam, h5)
if csm.sitecol is None: # missing sites.csv (test_case_1_ruptures)
return
if os.environ.get('OQ_CHECK_INPUT'):
# slow checks
source.check_complex_faults(srcs)
# tested in test_mosaic
[docs]def get_cache_path(oqparam, h5=None):
"""
:returns: cache path of the form OQ_DATA/csm_<checksum>.hdf5
"""
if oqparam.cachedir:
checksum = get_checksum32(oqparam, h5)
return os.path.join(oqparam.cachedir, 'csm_%d.hdf5' % checksum)
return ''
[docs]def get_composite_source_model(oqparam, dstore=None):
"""
Parse the XML and build a complete composite source model in memory.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param dstore:
an open datastore where to save the source info
"""
logging.info('Reading %s', oqparam.inputs['source_model_logic_tree'])
h5 = dstore.hdf5 if dstore else None
with Monitor('building full_lt', measuremem=True, h5=h5):
full_lt = get_full_lt(oqparam) # builds the weights
path = get_cache_path(oqparam, h5)
if os.path.exists(path):
from openquake.commonlib import datastore # avoid circular import
with datastore.read(os.path.realpath(path)) as ds:
csm = ds['_csm']
csm.init(full_lt)
else:
csm = source_reader.get_csm(oqparam, full_lt, dstore)
_check_csm(csm, oqparam, dstore)
return csm
[docs]def get_imts(oqparam):
"""
Return a sorted list of IMTs as hazardlib objects
"""
return list(map(imt.from_string, sorted(oqparam.imtls)))
def _cons_coeffs(df, perils, loss_dt, limit_states):
# returns composite array peril -> loss_type -> coeffs
dtlist = [(peril, loss_dt) for peril in perils]
coeffs = numpy.zeros(len(limit_states), dtlist)
for loss_type in loss_dt.names:
for peril in perils:
the_df = df[(df.peril == peril) & (df.loss_type == loss_type)]
if len(the_df) == 1:
coeffs[peril][loss_type] = the_df[limit_states].to_numpy()[0]
elif len(the_df) > 1:
raise ValueError(f'Multiple consequences for {loss_type=}, {peril=}\n%s' % the_df)
return coeffs
[docs]def get_crmodel(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
if oqparam.aristotle:
with hdf5.File(oqparam.inputs['exposure'][0], 'r') as exp:
try:
crm = riskmodels.CompositeRiskModel.read(exp, oqparam)
except KeyError:
pass # missing crm in exposure.hdf5 in mosaic/case_01
else:
return crm
risklist = get_risk_functions(oqparam)
perils = numpy.array(sorted(set(rf.peril for rf in risklist)))
if not oqparam.limit_states and risklist.limit_states:
oqparam.limit_states = risklist.limit_states
elif 'damage' in oqparam.calculation_mode and risklist.limit_states:
assert oqparam.limit_states == risklist.limit_states
consdict = {}
if 'consequence' in oqparam.inputs:
if not risklist.limit_states:
raise InvalidFile('Missing fragility functions in %s' %
oqparam.inputs['job_ini'])
# build consdict of the form consequence_by_tagname -> tag -> array
loss_dt = oqparam.loss_dt()
for by, fnames in oqparam.inputs['consequence'].items():
if isinstance(fnames, str): # single file
fnames = [fnames]
# i.e. files collapsed.csv, fatalities.csv, ... with headers like
# taxonomy,consequence,slight,moderate,extensive
df = pandas.concat([pandas.read_csv(fname) for fname in fnames])
if 'loss_type' not in df.columns:
df['loss_type'] = 'structural'
if 'peril' not in df.columns:
df['peril'] = 'earthquake'
for consequence, group in df.groupby('consequence'):
if consequence not in scientific.KNOWN_CONSEQUENCES:
raise InvalidFile('Unknown consequence %s in %s' %
(consequence, fnames))
bytag = {
tag: _cons_coeffs(grp, perils, loss_dt, risklist.limit_states)
for tag, grp in group.groupby(by)}
consdict['%s_by_%s' % (consequence, by)] = bytag
# for instance consdict['collapsed_by_taxonomy']['W_LFM-DUM_H3']
# is [(0.05,), (0.2 ,), (0.6 ,), (1. ,)] for damage state and structural
crm = riskmodels.CompositeRiskModel(oqparam, risklist, consdict)
return crm
[docs]def get_exposure(oqparam, h5=None):
"""
Read the full exposure in memory and build a list of
:class:`openquake.risklib.asset.Asset` instances.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an :class:`Exposure` instance or a compatible AssetCollection
"""
oq = oqparam
if 'exposure' not in oq.inputs:
return
fnames = oq.inputs['exposure']
with Monitor('reading exposure', measuremem=True, h5=h5):
if oqparam.aristotle:
sm = get_site_model(oq, h5) # the site model around the rupture
gh3 = numpy.array(sorted(set(geohash3(sm['lon'], sm['lat']))))
exposure = asset.Exposure.read_around(fnames[0], gh3)
with hdf5.File(fnames[0]) as f:
if 'crm' in f:
loss_types = f['crm'].attrs['loss_types']
oq.all_cost_types = loss_types
oq.minimum_asset_loss = {lt: 0 for lt in loss_types}
else:
exposure = asset.Exposure.read_all(
oq.inputs['exposure'], oq.calculation_mode,
oq.ignore_missing_costs,
errors='ignore' if oq.ignore_encoding_errors else None,
infr_conn_analysis=oq.infrastructure_connectivity_analysis,
aggregate_by=oq.aggregate_by)
return exposure
[docs]def concat_if_different(values):
unique_values = values.dropna().unique().astype(str)
# If all values are identical, return the single unique value,
# otherwise join with "|"
return '|'.join(unique_values)
[docs]def read_df(fname, lon, lat, id, duplicates_strategy='error'):
"""
Read a DataFrame containing lon-lat-id fields.
In case of rows having the same coordinates, duplicates_strategy
determines how to manage duplicates:
- 'error': raise an error (default)
- 'keep_first': keep the first occurrence
- 'keep_last': keep the last occurrence
- 'avg': calculate the average numeric values
"""
assert duplicates_strategy in (
'error', 'keep_first', 'keep_last', 'avg'), duplicates_strategy
# NOTE: the id field has to be treated as a string even if it contains numbers
dframe = pandas.read_csv(fname, dtype={id: str})
dframe[lon] = numpy.round(dframe[lon].to_numpy(), 5)
dframe[lat] = numpy.round(dframe[lat].to_numpy(), 5)
duplicates = dframe[dframe.duplicated(subset=[lon, lat], keep=False)]
if not duplicates.empty:
msg = '%s: has duplicate sites %s' % (fname, list(duplicates[id]))
if duplicates_strategy == 'error':
raise InvalidFile(msg)
msg += f' (duplicates_strategy: {duplicates_strategy})'
logging.warning(msg)
if duplicates_strategy == 'keep_first':
dframe = dframe.drop_duplicates(subset=[lon, lat], keep='first')
elif duplicates_strategy == 'keep_last':
dframe = dframe.drop_duplicates(subset=[lon, lat], keep='last')
elif duplicates_strategy == 'avg':
string_columns = dframe.select_dtypes(include='object').columns
numeric_columns = dframe.select_dtypes(include='number').columns
# Group by lon and lat, averaging numeric columns and concatenating by "|"
# the different contents of string columns
dframe = dframe.groupby([lon, lat], as_index=False).agg(
{**{col: concat_if_different for col in string_columns},
**{col: 'mean' for col in numeric_columns}}
)
return dframe
[docs]def get_station_data(oqparam, sitecol, duplicates_strategy='error'):
"""
Read the station data input file and build a list of
ground motion stations and recorded ground motion values
along with their uncertainty estimates
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param sitecol:
the hazard site collection
:param duplicates_strategy: either 'error', 'keep_first', 'keep_last', 'avg'
:returns: station_data, observed_imts
"""
if parallel.oq_distribute() == 'zmq':
logging.error('Conditioned scenarios are not meant to be run '
' on a cluster')
# Read the station data and associate the site ID from longitude, latitude
df = read_df(oqparam.inputs['station_data'], 'LONGITUDE', 'LATITUDE', 'STATION_ID',
duplicates_strategy=duplicates_strategy)
lons = df['LONGITUDE'].to_numpy()
lats = df['LATITUDE'].to_numpy()
nsites = len(sitecol.complete)
sitecol.extend(lons, lats)
logging.info('Extended complete site collection from %d to %d sites',
nsites, len(sitecol.complete))
dic = {(lo, la): sid
for lo, la, sid in sitecol.complete[['lon', 'lat', 'sids']]}
sids = U32([dic[lon, lat] for lon, lat in zip(lons, lats)])
# Identify the columns with IM values
# Replace replace() with removesuffix() for pandas ≥ 1.4
imt_candidates = df.filter(regex="_VALUE$").columns.str.replace(
"_VALUE", "")
imts = [valid.intensity_measure_type(imt) for imt in imt_candidates]
im_cols = [imt + '_' + stat for imt in imts for stat in ["mean", "std"]]
cols = []
for im in imts:
stddev_str = "STDDEV" if im == "MMI" else "LN_SIGMA"
cols.append(im + '_VALUE')
cols.append(im + '_' + stddev_str)
for im_value_col in [im + '_VALUE' for im in imts]:
if (df[im_value_col] == 0).any():
file_basename = os.path.basename(oqparam.inputs['station_data'])
wrong_rows = df[['STATION_ID', im_value_col]].loc[
df.index[df[im_value_col] == 0]]
raise InvalidFile(
f"Please remove station data with zero intensity value from"
f" {file_basename}:\n"
f" {wrong_rows}")
station_data = pandas.DataFrame(df[cols].values, columns=im_cols)
station_data['site_id'] = sids
return station_data, imts
[docs]def get_sitecol_assetcol(oqparam, haz_sitecol=None, exp_types=(), h5=None):
"""
:param oqparam: calculation parameters
:param haz_sitecol: the hazard site collection
:param exp_types: the expected loss types
:returns: (site collection, asset collection, discarded, exposure)
"""
asset_hazard_distance = max(oqparam.asset_hazard_distance.values())
if haz_sitecol is None:
haz_sitecol = get_site_collection(oqparam, h5)
try:
exp = haz_sitecol.exposure
except AttributeError:
exp = get_exposure(oqparam)
if oqparam.region_grid_spacing:
haz_distance = oqparam.region_grid_spacing * 1.414
if haz_distance != asset_hazard_distance:
logging.debug('Using asset_hazard_distance=%d km instead of %d km',
haz_distance, asset_hazard_distance)
else:
haz_distance = asset_hazard_distance
# associate the assets to the hazard sites
# this is absurdely fast: 10 million assets can be associated in <10s
A = len(exp.assets)
N = len(haz_sitecol)
with Monitor('associating exposure', measuremem=True, h5=h5):
region = wkt.loads(oqparam.region) if oqparam.region else None
sitecol, discarded = exp.associate(haz_sitecol, haz_distance, region)
logging.info(
'Associated {:_d} assets (of {:_d}) to {:_d} sites'
' (of {:_d})'.format(len(exp.assets), A, len(sitecol), N))
assetcol = asset.AssetCollection(
exp, sitecol, oqparam.time_event, oqparam.aggregate_by)
u, c = numpy.unique(assetcol['taxonomy'], return_counts=True)
idx = c.argmax() # index of the most common taxonomy
tax = assetcol.tagcol.taxonomy[u[idx]]
logging.info('Found %d taxonomies with ~%.1f assets each',
len(u), len(assetcol) / len(u))
logging.info('The most common taxonomy is %s with %d assets', tax, c[idx])
# check on missing fields in the exposure
if 'risk' in oqparam.calculation_mode:
for exp_type in exp_types:
if not any(exp_type in name
for name in assetcol.array.dtype.names):
raise InvalidFile('The exposure %s is missing %s' %
(oqparam.inputs['exposure'], exp_type))
if (not oqparam.hazard_calculation_id and 'gmfs' not in oqparam.inputs
and 'hazard_curves' not in oqparam.inputs
and 'station_data' not in oqparam.inputs
and sitecol is not sitecol.complete):
# for predefined hazard you cannot reduce the site collection; instead
# you can in other cases, typically with a grid which is mostly empty
# (i.e. there are many hazard sites with no assets)
assetcol.reduce_also(sitecol)
return sitecol, assetcol, discarded, exp
[docs]def levels_from(header):
levels = []
for field in header:
if field.startswith('poe-'):
levels.append(float(field[4:]))
return levels
[docs]def aristotle_tmap(oqparam, taxidx):
"""
:returns: a taxonomy mapping dframe
"""
acc = AccumDict(accum=[]) # loss_type, taxi, risk_id, weight
with hdf5.File(oqparam.inputs['exposure'][0], 'r') as exp:
for key in exp['tmap']:
# tmap has fields conversion, taxonomy, weight
df = exp.read_df('tmap/' + key)
for taxo, risk_id, weight in zip(df.taxonomy, df.conversion, df.weight):
if taxo in taxidx:
acc['country'].append(key)
acc['peril'].append('earthquake')
acc['taxi'].append(taxidx[taxo])
acc['risk_id'].append(risk_id)
acc['weight'].append(weight)
return pandas.DataFrame(acc)
# tested in TaxonomyMappingTestCase
[docs]def taxonomy_mapping(oqparam, taxidx):
"""
:param oqparam: OqParam instance
:param taxidx: dictionary taxo:str -> taxi:int
:returns: a dictionary loss_type -> [[(riskid, weight), ...], ...]
"""
if oqparam.aristotle:
return aristotle_tmap(oqparam, taxidx)
elif 'taxonomy_mapping' not in oqparam.inputs: # trivial mapping
nt = len(taxidx) # number of taxonomies
df = pandas.DataFrame(dict(weight=numpy.ones(nt),
taxi=taxidx.values(),
risk_id=list(taxidx),
peril=['*']*nt,
country=['?']*nt))
return df
fname = oqparam.inputs['taxonomy_mapping']
return _taxonomy_mapping(fname, taxidx)
def _taxonomy_mapping(filename, taxidx):
try:
tmap_df = pandas.read_csv(filename, converters=dict(weight=float))
except Exception as e:
raise e.__class__('%s while reading %s' % (e, filename))
if 'weight' not in tmap_df:
tmap_df['weight'] = 1.
if 'peril' not in tmap_df:
tmap_df['peril'] = '*'
if 'country' not in tmap_df:
tmap_df['country'] = '?'
if 'conversion' in tmap_df.columns:
# conversion was the old name in the header for engine <= 3.12
tmap_df = tmap_df.rename(columns={'conversion': 'risk_id'})
assert set(tmap_df) == {'country', 'peril', 'taxonomy', 'risk_id', 'weight'}, set(tmap_df)
taxos = set()
for (taxo, per), df in tmap_df.groupby(['taxonomy', 'peril']):
taxos.add(taxo)
if abs(df.weight.sum() - 1.) > pmf.PRECISION:
raise InvalidFile('%s: the weights do not sum up to 1 for %s' %
(filename, taxo))
missing = set(taxidx) - taxos
if missing:
raise InvalidFile(
'The taxonomy strings %s are in the exposure but not in '
'the taxonomy mapping file %s' % (missing, filename))
tmap_df['taxi'] = [taxidx.get(taxo, -1) for taxo in tmap_df.taxonomy]
del tmap_df['taxonomy']
# NB: we are ignoring the taxonomies in the mapping but not in the exposure
# for instance in EventBasedRiskTestCase::test_case_5
return tmap_df[tmap_df.taxi != -1]
[docs]def assert_probabilities(array, fname):
"""
Check that the array contains valid probabilities
"""
for poe_field in (f for f in array.dtype.names if f.startswith('poe-')):
arr = array[poe_field]
if (arr > 1).any():
raise InvalidFile('%s: contains probabilities > 1: %s' %
(fname, arr[arr > 1]))
if (arr < 0).any():
raise InvalidFile('%s: contains probabilities < 0: %s' %
(fname, arr[arr < 0]))
[docs]def get_pmap_from_csv(oqparam, fnames):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fnames:
a space-separated list of .csv relative filenames
:returns:
the site mesh and the hazard curves read by the .csv files
"""
read = functools.partial(hdf5.read_csv, dtypedict={None: float})
imtls = {}
dic = {}
for fname in fnames:
wrapper = read(fname)
assert_probabilities(wrapper.array, fname)
dic[wrapper.imt] = wrapper.array
imtls[wrapper.imt] = levels_from(wrapper.dtype.names)
oqparam.hazard_imtls = imtls
oqparam.investigation_time = wrapper.investigation_time
array = wrapper.array
mesh = geo.Mesh(array['lon'], array['lat'])
N = len(mesh)
L = sum(len(imls) for imls in oqparam.imtls.values())
data = numpy.zeros((N, L))
level = 0
for im in oqparam.imtls:
arr = dic[im]
for poe in arr.dtype.names[3:]:
data[:, level] = arr[poe]
level += 1
for field in ('lon', 'lat', 'depth'): # sanity check
numpy.testing.assert_equal(arr[field], array[field])
pmap = MapArray(numpy.arange(N, dtype=U32), len(data), 1)
pmap.array = data.reshape(N, L, 1)
return mesh, pmap
tag2code = {'multiFaultSource': b'F',
'areaSource': b'A',
'multiPointSource': b'M',
'pointSource': b'P',
'simpleFaultSource': b'S',
'complexFaultSource': b'C',
'characteristicFaultSource': b'X',
'nonParametricSeismicSource': b'N'}
# tested in commands_test
[docs]def reduce_sm(paths, source_ids):
"""
:param paths: list of source_model.xml files
:param source_ids: dictionary src_id -> array[src_id, code]
:returns: dictionary with keys good, total, model, path, xmlns
NB: duplicate sources are not removed from the XML
"""
if isinstance(source_ids, dict): # in oq reduce_sm
def ok(src_node):
if src_node.tag.endswith('Surface'): # in geometrySections
return True
code = tag2code[re.search(r'\}(\w+)', src_node.tag).group(1)]
arr = source_ids.get(src_node['id'])
if arr is None:
return False
return (arr['code'] == code).any()
else: # list of source IDs, in extract_source
def ok(src_node):
return src_node['id'] in source_ids
for path in paths:
good = 0
total = 0
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
total += 1
if ok(src_node):
good += 1
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
reduced_weigths = []
for src_node, weight in zip(src_group, weights):
total += 1
if ok(src_node):
good += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
src_node.attrib.pop('tectonicRegion', None)
src_group['srcs_weights'] = reduced_weigths
if sg.nodes:
model.nodes.append(sg)
yield dict(good=good, total=total, model=model, path=path,
xmlns=root['xmlns'])
# used in oq reduce_sm and utils/extract_source
[docs]def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model.
:param smlt_file: path to a source model logic tree file
:param source_ids: dictionary source_id -> records (src_id, code)
:param remove: if True, remove sm.xml files containing no sources
:returns: the number of sources satisfying the filter vs the total
"""
total = good = 0
to_remove = set()
paths = logictree.collect_info(smlt_file).smpaths
if isinstance(source_ids, dict):
source_ids = {decode(k): v for k, v in source_ids.items()}
for dic in parallel.Starmap.apply(reduce_sm, (paths, source_ids)):
path = dic['path']
model = dic['model']
good += dic['good']
total += dic['total']
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=dic['xmlns'])
elif remove: # remove the files completely reduced
to_remove.add(path)
if good:
for path in to_remove:
os.remove(path)
parallel.Starmap.shutdown()
return good, total
[docs]def read_delta_rates(fname, idx_nr):
"""
:param fname:
path to a CSV file with fields (source_id, rup_id, delta)
:param idx_nr:
dictionary source_id -> (src_id, num_ruptures) with Ns sources
:returns:
list of Ns floating point arrays of different lenghts
"""
delta_df = pandas.read_csv(fname, converters=dict(
source_id=str, rup_id=int, delta=float), index_col=0)
assert list(delta_df.columns) == ['rup_id', 'delta']
delta = [numpy.zeros(0) for _ in idx_nr]
for src, df in delta_df.groupby(delta_df.index):
idx, nr = idx_nr[src]
rupids = df.rup_id.to_numpy()
if len(numpy.unique(rupids)) < len(rupids):
raise InvalidFile('%s: duplicated rup_id for %s' % (fname, src))
drates = numpy.zeros(nr)
drates[rupids] = df.delta.to_numpy()
delta[idx] = drates
return delta
[docs]def get_shapefiles(dirname):
"""
:param dirname: directory containing the shapefiles
:returns: list of shapefiles
"""
out = []
extensions = ('.shp', '.dbf', '.prj', '.shx')
for fname in os.listdir(dirname):
if fname.endswith(extensions):
out.append(os.path.join(dirname, fname))
return out
[docs]def get_reinsurance(oqparam, assetcol=None):
"""
:returns: (policy_df, treaty_df, field_map)
"""
if assetcol is None:
_sitecol, assetcol, _discarded, _exp = get_sitecol_assetcol(oqparam)
[(_loss_type, fname)] = oqparam.inputs['reinsurance'].items()
# make sure the first aggregate by is policy
if oqparam.aggregate_by[0] != ['policy']:
raise InvalidFile('%s: aggregate_by=%s' %
(fname, oqparam.aggregate_by))
[(_key, fname)] = oqparam.inputs['reinsurance'].items()
p, t, f = reinsurance.parse(fname, assetcol.tagcol.policy_idx)
# check ideductible
arr = assetcol.array
for pol_no, deduc in zip(p.policy, p.deductible):
if deduc:
ideduc = arr[arr['policy'] == pol_no]['ideductible']
if ideduc.any():
pol = assetcol.tagcol.policy[pol_no]
raise InvalidFile('%s: for policy %s there is a deductible '
'also in the exposure!' % (fname, pol))
return p, t, f
def _checksum(fnames, checksum=0):
"""
:returns: the 32 bit checksum of a list of files
"""
for fname in (f for f in fnames if f != '<in-memory>'):
if not os.path.exists(fname):
zpath = os.path.splitext(fname)[0] + '.zip'
if not os.path.exists(zpath):
raise OSError('No such file: %s or %s' % (fname, zpath))
with open(zpath, 'rb') as f:
data = f.read()
else:
with open(fname, 'rb') as f:
data = f.read()
checksum = zlib.adler32(data, checksum)
return checksum
[docs]def get_checksum32(oqparam, h5=None):
"""
Build an unsigned 32 bit integer from the hazard input files
:param oqparam: an OqParam instance
"""
checksum = _checksum(oqparam._input_files)
hazard_params = []
for key, val in sorted(vars(oqparam).items()):
if key in ('rupture_mesh_spacing', 'complex_fault_mesh_spacing',
'width_of_mfd_bin', 'area_source_discretization',
'random_seed', 'number_of_logic_tree_samples',
'minimum_magnitude', 'source_id', 'sites',
'floating_x_step', 'floating_y_step'):
hazard_params.append('%s = %s' % (key, val))
data = '\n'.join(hazard_params).encode('utf8')
checksum = zlib.adler32(data, checksum)
if h5:
h5.attrs['checksum32'] = checksum
return checksum
# NOTE: we expect to call this for mosaic or global_risk, with buffer 0 or 0.1
[docs]@functools.lru_cache(maxsize=4)
def read_geometries(fname, code, buffer=0):
"""
:param fname: path of the file containing the geometries
:param code: name of the primary key field
:param buffer: shapely buffer in degrees
:returns: data frame with codes and geometries
"""
with fiona.open(fname) as f:
codes = []
geoms = []
for feature in f:
props = feature['properties']
codes.append(props[code])
geom = geometry.shape(feature['geometry'])
geoms.append(geom.buffer(buffer))
return pandas.DataFrame(dict(code=codes, geom=geoms))
[docs]def read_mosaic_df(buffer):
"""
:returns: a DataFrame of geometries for the mosaic models
"""
fname = os.path.join(os.path.dirname(mosaic.__file__),
'ModelBoundaries.shp')
return read_geometries(fname, 'code', buffer)
[docs]def read_countries_df(buffer=0.1):
"""
:returns: a DataFrame of geometries for the world countries
"""
fname = os.path.join(os.path.dirname(global_risk.__file__),
'geoBoundariesCGAZ_ADM0.shp')
return read_geometries(fname, 'shapeGroup', buffer)
[docs]def read_source_models(fnames, hdf5path='', **converterparams):
"""
:param fnames: a list of source model files
:param hdf5path: auxiliary .hdf5 file used to store the multifault sources
:param converterparams: a dictionary of parameters like rupture_mesh_spacing
:returns: a list of SourceModel instances
"""
converter = sourceconverter.SourceConverter()
vars(converter).update(converterparams)
smodels = list(nrml.read_source_models(fnames, converter))
smdict = dict(zip(fnames, smodels))
src_groups = [sg for sm in smdict.values() for sg in sm.src_groups]
secparams = source_reader.fix_geometry_sections(smdict, src_groups, hdf5path)
for smodel in smodels:
for sg in smodel.src_groups:
for src in sg:
if src.code == b'F': # multifault
src.set_msparams(secparams)
return smodels
