# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2017 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 division
import os
import csv
import gzip
import zipfile
import logging
import operator
import tempfile
import collections
import numpy
from shapely import wkt, geometry
from openquake.baselib.general import groupby, AccumDict, writetmp
from openquake.baselib.python3compat import configparser, encode
from openquake.baselib.node import Node, context
from openquake.baselib import hdf5
from openquake.hazardlib import (
geo, site, imt, valid, sourceconverter, nrml, InvalidFile)
from openquake.hazardlib.calc.hazard_curve import zero_curves
from openquake.risklib import riskmodels, riskinput, read_nrml
from openquake.commonlib import datastore
from openquake.commonlib.oqvalidation import OqParam
from openquake.commonlib import logictree
from openquake.commonlib.riskmodels import get_risk_models
from openquake.commonlib import source
read_nrml.update_validators()
# the following is quite arbitrary, it gives output weights that I like (MS)
NORMALIZATION_FACTOR = 1E-2
MAX_SITE_MODEL_DISTANCE = 5 # km, given by Graeme Weatherill
F32 = numpy.float32
[docs]class DuplicatedPoint(Exception):
"""
Raised when reading a CSV file with duplicated (lon, lat) pairs
"""
[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 = []
for fname in os.listdir(dirpath):
fullname = os.path.join(dirpath, fname)
if os.path.isdir(fullname): # navigate inside
files.extend(collect_files(fullname))
else: # collect files
if cond(fullname):
files.append(fullname)
return files
[docs]def get_params(job_inis):
"""
Parse one or more INI-style config files.
:param job_inis:
List of configuration files (or list containing a single zip archive)
:returns:
A dictionary of parameters
"""
if len(job_inis) == 1 and job_inis[0].endswith('.zip'):
job_inis = extract_from_zip(
job_inis[0], ['job_hazard.ini', 'job_haz.ini',
'job.ini', 'job_risk.ini'])
not_found = [ini for ini in job_inis if not os.path.exists(ini)]
if not_found: # something was not found
raise IOError('File not found: %s' % not_found[0])
cp = configparser.ConfigParser()
cp.read(job_inis)
# drectory containing the config files we're parsing
job_ini = os.path.abspath(job_inis[0])
base_path = os.path.dirname(job_ini)
params = dict(base_path=base_path, inputs={'job_ini': job_ini})
for sect in cp.sections():
for key, value in cp.items(sect):
if key.endswith(('_file', '_csv')):
input_type, _ext = key.rsplit('_', 1)
path = value if os.path.isabs(value) else os.path.join(
base_path, value)
params['inputs'][input_type] = possibly_gunzip(path)
else:
params[key] = value
# populate the 'source' list
smlt = params['inputs'].get('source_model_logic_tree')
if smlt:
params['inputs']['source'] = [
os.path.join(base_path, src_path)
for src_path in sorted(source.collect_source_model_paths(smlt))]
return params
[docs]def get_oqparam(job_ini, pkg=None, calculators=None, hc_id=None):
"""
Parse a dictionary of parameters from an INI-style config file.
:param job_ini:
Path to configuration file/archive or dictionary of parameters
:param pkg:
Python package where to find the configuration file (optional)
:param calculators:
Sequence of calculator names (optional) used to restrict the
valid choices for `calculation_mode`
:param hc_id:
Not None only when called from a post calculation
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate 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.
"""
# UGLY: this is here to avoid circular imports
from openquake.calculators import base
OqParam.calculation_mode.validator.choices = tuple(
calculators or base.calculators)
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)])
oqparam = OqParam(**job_ini)
oqparam.validate()
return oqparam
[docs]def get_mesh(oqparam):
"""
Extract the mesh of points to compute from the sites,
the sites_csv, or the region.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
if oqparam.sites:
lons, lats = zip(*sorted(oqparam.sites))
return geo.Mesh(numpy.array(lons), numpy.array(lats))
elif 'sites' in oqparam.inputs:
csv_data = open(oqparam.inputs['sites'], 'U').read()
coords = valid.coordinates(
csv_data.strip().replace(',', ' ').replace('\n', ','))
lons, lats = zip(*sorted(coords))
return geo.Mesh(numpy.array(lons), numpy.array(lats))
elif oqparam.region:
# close the linear polygon ring by appending the first
# point to the end
firstpoint = geo.Point(*oqparam.region[0])
points = [geo.Point(*xy) for xy in oqparam.region] + [firstpoint]
try:
mesh = geo.Polygon(points).discretize(oqparam.region_grid_spacing)
lons, lats = zip(*sorted(zip(mesh.lons, mesh.lats)))
return geo.Mesh(numpy.array(lons), numpy.array(lats))
except:
raise ValueError(
'Could not discretize region %(region)s with grid spacing '
'%(region_grid_spacing)s' % vars(oqparam))
elif 'gmfs' in oqparam.inputs:
return get_gmfs(oqparam)[0].mesh
elif oqparam.hazard_calculation_id:
sitecol = datastore.read(oqparam.hazard_calculation_id)['sitecol']
return geo.Mesh(sitecol.lons, sitecol.lats)
elif 'exposure' in oqparam.inputs:
# the mesh is extracted from get_sitecol_assets
return
elif 'site_model' in oqparam.inputs:
coords = [(param.lon, param.lat) for param in get_site_model(oqparam)]
lons, lats = zip(*sorted(coords))
return geo.Mesh(numpy.array(lons), numpy.array(lats))
[docs]def sitecol_from_coords(oqparam, coords):
"""
Return a SiteCollection instance from an ordered set of coordinates
"""
lons, lats = zip(*coords)
return site.SiteCollection.from_points(lons, lats, oqparam)
[docs]def get_site_model(oqparam):
"""
Convert the NRML file into an iterator over 6-tuple of the form
(z1pt0, z2pt5, measured, vs30, lon, lat)
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
for node in nrml.read(oqparam.inputs['site_model']).siteModel:
yield valid.site_param(**node.attrib)
[docs]def get_site_collection(oqparam, mesh=None, site_model_params=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
:param mesh:
a mesh of hazardlib points; if None the mesh is
determined by invoking get_mesh
:param site_model_params:
object with a method .get_closest returning the closest site
model parameters
"""
if mesh is None:
mesh = get_mesh(oqparam)
if mesh is None:
return
if oqparam.inputs.get('site_model'):
if site_model_params is None:
# read the parameters directly from their file
site_model_params = geo.geodetic.GeographicObjects(
get_site_model(oqparam))
sitecol = []
for pt in mesh:
# NB: the mesh, when read from the datastore, is a 32 bit array;
# however, the underlying C library expects 64 bit floats, thus
# we have to cast float(pt.longitude), float(pt.latitude);
# we should change the geodetic speedups instead
param, dist = site_model_params.\
get_closest(float(pt.longitude), float(pt.latitude))
if dist >= MAX_SITE_MODEL_DISTANCE:
logging.warn('The site parameter associated to %s came from a '
'distance of %d km!' % (pt, dist))
sitecol.append(
site.Site(pt, param.vs30, param.measured,
param.z1pt0, param.z2pt5, param.backarc))
return site.SiteCollection(sitecol)
# else use the default site params
return site.SiteCollection.from_points(mesh.lons, mesh.lats, oqparam)
[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)
return gsim_lt
[docs]def get_gsims(oqparam):
"""
Return an ordered list of GSIM instances from the gsim name in the
configuration file or from the gsim logic tree file.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
return [valid.gsim(str(rlz)) for rlz in get_gsim_lt(oqparam)]
[docs]def get_rupture(oqparam):
"""
Returns a hazardlib rupture by reading the `rupture_model` file.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
rup_model = oqparam.inputs['rupture_model']
[rup_node] = nrml.read(rup_model)
conv = sourceconverter.RuptureConverter(
oqparam.rupture_mesh_spacing, oqparam.complex_fault_mesh_spacing)
rup = conv.convert_node(rup_node)
rup.tectonic_region_type = '*' # there is not TRT for scenario ruptures
return rup
[docs]def get_source_model_lt(oqparam):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
a :class:`openquake.commonlib.logictree.SourceModelLogicTree`
instance
"""
fname = oqparam.inputs['source_model_logic_tree']
return logictree.SourceModelLogicTree(
fname, validate=False, seed=oqparam.random_seed,
num_samples=oqparam.number_of_logic_tree_samples)
[docs]def possibly_gunzip(fname):
"""
A file can be .gzipped to save space (this happens
in the debian package); in that case, let's gunzip it.
:param fname: a file name (not zipped)
"""
is_gz = os.path.exists(fname) and fname.endswith('.gz')
there_is_gz = not os.path.exists(fname) and os.path.exists(fname + '.gz')
if is_gz:
return writetmp(gzip.open(fname).read())
elif there_is_gz:
return writetmp(gzip.open(fname + '.gz').read())
return fname
[docs]def get_source_models(oqparam, gsim_lt, source_model_lt, in_memory=True):
"""
Build all the source models generated by the logic tree.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param gsim_lt:
a :class:`openquake.commonlib.logictree.GsimLogicTree` instance
:param source_model_lt:
a :class:`openquake.commonlib.logictree.SourceModelLogicTree` instance
:param in_memory:
if True, keep in memory the sources, else just collect the TRTs
:returns:
an iterator over :class:`openquake.commonlib.source.SourceModel`
tuples
"""
converter = sourceconverter.SourceConverter(
oqparam.investigation_time,
oqparam.rupture_mesh_spacing,
oqparam.complex_fault_mesh_spacing,
oqparam.width_of_mfd_bin,
oqparam.area_source_discretization)
parser = nrml.SourceModelParser(converter)
gsim_file = oqparam.inputs.get('gsim_logic_tree')
# consider only the effective realizations
for sm in source_model_lt.gen_source_models(gsim_lt):
fname = possibly_gunzip(os.path.join(oqparam.base_path, sm.name))
if in_memory:
apply_unc = source_model_lt.make_apply_uncertainties(sm.path)
try:
src_groups = parser.parse_src_groups(fname, apply_unc)
except ValueError as e:
if str(e) in ('Surface does not conform with Aki & '
'Richards convention',
'Edges points are not in the right order'):
raise InvalidFile('''\
%s: %s. Probably you are using an obsolete model.
In that case you can fix the file with the command
python -m openquake.engine.tools.correct_complex_sources %s
''' % (fname, e, fname))
else:
raise
else: # just collect the TRT models
smodel = nrml.read(fname).sourceModel
src_groups = sourceconverter.SourceGroup.collect(smodel)
sm.src_groups = src_groups
trts = [mod.trt for mod in src_groups]
source_model_lt.tectonic_region_types.update(trts)
logging.info('Processed source model %d with %d gsim path(s)',
sm.ordinal + 1, sm.num_gsim_paths)
if gsim_file: # check TRTs
for src_group in src_groups:
if src_group.trt not in gsim_lt.values:
raise ValueError(
"Found in %r a tectonic region type %r inconsistent "
"with the ones in %r" % (sm, src_group.trt, gsim_file))
yield sm
# log if some source file is being used more than once
for fname, hits in parser.fname_hits.items():
if hits > 1:
logging.info('%s has been considered %d times', fname, hits)
[docs]def get_composite_source_model(oqparam, in_memory=True):
"""
Parse the XML and build a complete composite source model in memory.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param in_memory:
if False, just parse the XML without instantiating the sources
"""
source_model_lt = get_source_model_lt(oqparam)
smodels = []
grp_id = 0
idx = 0
def getid(src):
try:
return src.source_id
except:
return src['id']
gsim_lt = get_gsim_lt(oqparam)
for source_model in get_source_models(
oqparam, gsim_lt, source_model_lt, in_memory=in_memory):
for src_group in source_model.src_groups:
src_group.sources = sorted(src_group, key=getid)
src_group.id = grp_id
for src in src_group:
# there are two cases depending on the flag in_memory:
# 1) src is a hazardlib source and has a src_group_id
# attribute; in that case the source has to be numbered
# 2) src is a Node object, then nothing must be done
if isinstance(src, Node):
continue
src.src_group_id = grp_id
src.id = idx
idx += 1
grp_id += 1
smodels.append(source_model)
csm = source.CompositeSourceModel(
gsim_lt, source_model_lt, smodels, in_memory)
return csm
[docs]def get_job_info(oqparam, csm, sitecol):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param csm:
a :class:`openquake.commonlib.source.CompositeSourceModel` instance
:param sitecol:
a :class:`openquake.hazardlib.site.SiteCollection` instance
:returns:
a dictionary with same parameters of the computation, in particular
the input and output weights
"""
info = {}
# The input weight is given by the number of ruptures generated
# by the sources; for point sources however a corrective factor
# given by the parameter `point_source_weight` is applied
input_weight = sum((src.weight or 0) * src_model.samples
for src_model in csm
for src_group in src_model.src_groups
for src in src_group)
imtls = oqparam.imtls
n_sites = len(sitecol) if sitecol else 0
# the imtls object has values [NaN] when the levels are unknown
# (this is a valid case for the event based hazard calculator)
n_imts = len(imtls)
n_levels = len(oqparam.imtls.array)
n_realizations = oqparam.number_of_logic_tree_samples or sum(
sm.num_gsim_paths for sm in csm)
# NB: in the event based case `n_realizations` can be over-estimated,
# if the method is called in the pre_execute phase, because
# some tectonic region types may have no occurrencies.
# The output weight is a pure number which is proportional to the size
# of the expected output of the calculator. For classical and disagg
# calculators it is given by
# n_sites * n_realizations * n_imts * n_levels;
# for the event based calculator is given by n_sites * n_realizations
# * n_levels * n_imts * (n_ses * investigation_time) * NORMALIZATION_FACTOR
output_weight = n_sites * n_imts * n_realizations
if oqparam.calculation_mode == 'event_based':
total_time = (oqparam.investigation_time *
oqparam.ses_per_logic_tree_path)
output_weight *= total_time * NORMALIZATION_FACTOR
else:
output_weight *= n_levels / n_imts
n_sources = csm.get_num_sources()
info['hazard'] = dict(input_weight=input_weight,
output_weight=output_weight,
n_imts=n_imts,
n_levels=n_levels,
n_sites=n_sites,
n_sources=n_sources,
n_realizations=n_realizations)
return info
[docs]def get_imts(oqparam):
"""
Return a sorted list of IMTs as hazardlib objects
"""
return list(map(imt.from_string, sorted(oqparam.imtls)))
[docs]def get_risk_model(oqparam):
"""
Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
"""
rmdict = get_risk_models(oqparam)
oqparam.set_risk_imtls(rmdict)
if oqparam.calculation_mode.endswith('_bcr'):
retro = get_risk_models(oqparam, 'vulnerability_retrofitted')
else:
retro = {}
return riskinput.CompositeRiskModel(oqparam, rmdict, retro)
# ########################### exposure ############################ #
cost_type_dt = numpy.dtype([('name', hdf5.vstr),
('type', hdf5.vstr),
('unit', hdf5.vstr)])
def _get_exposure(fname, ok_cost_types, stop=None):
"""
:param fname:
path of the XML file containing the exposure
:param ok_cost_types:
a set of cost types (as strings)
:param stop:
node at which to stop parsing (or None)
:returns:
a pair (Exposure instance, list of asset nodes)
"""
[exposure] = nrml.read(fname, stop=stop)
description = exposure.description
try:
conversions = exposure.conversions
except AttributeError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except AttributeError:
inslimit = Node('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except AttributeError:
deductible = Node('deductible', text=True)
try:
area = conversions.area
except AttributeError:
# NB: the area type cannot be an empty string because when sending
# around the CostCalculator object we would run into this numpy bug
# about pickling dictionaries with empty strings:
# https://github.com/numpy/numpy/pull/5475
area = Node('area', dict(type='?'))
# read the cost types and make some check
cost_types = []
for ct in conversions.costTypes:
if ct['name'] in ok_cost_types:
with context(fname, ct):
cost_types.append(
(ct['name'], valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in ok_cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
time_events = set()
exp = Exposure(
exposure['id'], exposure['category'],
~description, numpy.array(cost_types, cost_type_dt), time_events,
inslimit.attrib.get('isAbsolute', True),
deductible.attrib.get('isAbsolute', True),
area.attrib, [], set(), [])
cc = riskmodels.CostCalculator(
{}, {}, {},
exp.deductible_is_absolute, exp.insurance_limit_is_absolute)
for ct in exp.cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = exp.area['type']
cc.units[name] = ct['unit']
return exp, exposure.assets, cc
[docs]def get_cost_calculator(oqparam):
"""
Read the first lines of the exposure file and infers the cost calculator
"""
return _get_exposure(oqparam.inputs['exposure'],
set(oqparam.all_cost_types),
stop='assets')[-1]
[docs]def get_exposure(oqparam):
"""
Read the full exposure in memory and build a list of
:class:`openquake.risklib.riskmodels.Asset` instances.
If you don't want to keep everything in memory, use
get_exposure_lazy instead (for experts only).
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an :class:`Exposure` instance
"""
out_of_region = 0
if oqparam.region_constraint:
region = wkt.loads(oqparam.region_constraint)
else:
region = None
all_cost_types = set(oqparam.all_cost_types)
fname = oqparam.inputs['exposure']
exposure, assets_node, cc = _get_exposure(fname, all_cost_types)
relevant_cost_types = all_cost_types - set(['occupants'])
asset_refs = set()
ignore_missing_costs = set(oqparam.ignore_missing_costs)
for idx, asset in enumerate(assets_node):
values = {}
deductibles = {}
insurance_limits = {}
retrofitteds = {}
with context(fname, asset):
asset_id = asset['id'].encode('utf8')
if asset_id in asset_refs:
raise read_nrml.DuplicatedID(asset_id)
asset_refs.add(asset_id)
exposure.asset_refs.append(asset_id)
taxonomy = asset['taxonomy']
if 'damage' in oqparam.calculation_mode:
# calculators of 'damage' kind require the 'number'
# if it is missing a KeyError is raised
number = asset.attrib['number']
else:
# some calculators ignore the 'number' attribute;
# if it is missing it is considered 1, since we are going
# to multiply by it
try:
number = asset['number']
except KeyError:
number = 1
else:
if 'occupants' in all_cost_types:
values['occupants_None'] = number
location = asset.location['lon'], asset.location['lat']
if region and not geometry.Point(*location).within(region):
out_of_region += 1
continue
try:
costs = asset.costs
except AttributeError:
costs = Node('costs', [])
try:
occupancies = asset.occupancies
except AttributeError:
occupancies = Node('occupancies', [])
for cost in costs:
with context(fname, cost):
cost_type = cost['type']
if cost_type in relevant_cost_types:
values[cost_type] = cost['value']
retrovalue = cost.attrib.get('retrofitted')
if retrovalue is not None:
retrofitteds[cost_type] = retrovalue
if oqparam.insured_losses:
deductibles[cost_type] = cost['deductible']
insurance_limits[cost_type] = cost['insuranceLimit']
# check we are not missing a cost type
missing = relevant_cost_types - set(values)
if missing and missing <= ignore_missing_costs:
logging.warn(
'Ignoring asset %s, missing cost type(s): %s',
asset_id, ', '.join(missing))
for cost_type in missing:
values[cost_type] = None
elif missing and 'damage' not in oqparam.calculation_mode:
# missing the costs is okay for damage calculators
with context(fname, asset):
raise ValueError("Invalid Exposure. "
"Missing cost %s for asset %s" % (
missing, asset_id))
tot_occupants = 0
for occupancy in occupancies:
with context(fname, occupancy):
exposure.time_events.add(occupancy['period'])
occupants = 'occupants_%s' % occupancy['period']
values[occupants] = occupancy['occupants']
tot_occupants += values[occupants]
if occupancies: # store average occupants
values['occupants_None'] = tot_occupants / len(occupancies)
area = float(asset.attrib.get('area', 1))
ass = riskmodels.Asset(
idx, taxonomy, number, location, values, area,
deductibles, insurance_limits, retrofitteds, cc)
exposure.assets.append(ass)
exposure.taxonomies.add(taxonomy)
if region:
logging.info('Read %d assets within the region_constraint '
'and discarded %d assets outside the region',
len(exposure.assets), out_of_region)
if len(exposure.assets) == 0:
raise RuntimeError('Could not find any asset within the region!')
else:
logging.info('Read %d assets', len(exposure.assets))
# sanity check
values = any(len(ass.values) + ass.number for ass in exposure.assets)
assert values, 'Could not find any value??'
return exposure
Exposure = collections.namedtuple(
'Exposure', ['id', 'category', 'description', 'cost_types', 'time_events',
'insurance_limit_is_absolute', 'deductible_is_absolute',
'area', 'assets', 'taxonomies', 'asset_refs'])
[docs]def get_sitecol_assets(oqparam, exposure):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
two sequences of the same length: the site collection and an
array with the assets per each site, collected by taxonomy
"""
assets_by_loc = groupby(exposure.assets, key=lambda a: a.location)
lons, lats = zip(*sorted(assets_by_loc))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
sitecol = get_site_collection(oqparam, mesh)
assets_by_site = []
for lonlat in zip(sitecol.lons, sitecol.lats):
assets = assets_by_loc[lonlat]
assets_by_site.append(sorted(assets, key=operator.attrgetter('idx')))
return sitecol, numpy.array(assets_by_site)
[docs]def get_mesh_csvdata(csvfile, imts, num_values, validvalues):
"""
Read CSV data in the format `IMT lon lat value1 ... valueN`.
:param csvfile:
a file or file-like object with the CSV data
:param imts:
a list of intensity measure types
:param num_values:
dictionary with the number of expected values per IMT
:param validvalues:
validation function for the values
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
number_of_values = dict(zip(imts, num_values))
lon_lats = {imt: set() for imt in imts}
data = AccumDict() # imt -> list of arrays
check_imt = valid.Choice(*imts)
for line, row in enumerate(csv.reader(csvfile, delimiter=' '), 1):
try:
imt = check_imt(row[0])
lon_lat = valid.longitude(row[1]), valid.latitude(row[2])
if lon_lat in lon_lats[imt]:
raise DuplicatedPoint(lon_lat)
lon_lats[imt].add(lon_lat)
values = validvalues(' '.join(row[3:]))
if len(values) != number_of_values[imt]:
raise ValueError('Found %d values, expected %d' %
(len(values), number_of_values[imt]))
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
data += {imt: [numpy.array(values)]}
points = lon_lats.pop(imts[0])
for other_imt, other_points in lon_lats.items():
if points != other_points:
raise ValueError('Inconsistent locations between %s and %s' %
(imts[0], other_imt))
lons, lats = zip(*sorted(points))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.items()}
[docs]def get_gmfs(oqparam):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
sitecol, etags, gmf array
"""
fname = oqparam.inputs['gmfs']
if fname.endswith('.txt'):
return get_gmfs_from_txt(oqparam, fname)
elif fname.endswith('.xml'):
return get_scenario_from_nrml(oqparam, fname)
else:
raise NotImplemented('Reading from %s' % fname)
[docs]def get_hcurves(oqparam):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
sitecol, imtls, curve array
"""
fname = oqparam.inputs['hazard_curves']
if fname.endswith('.csv'):
return get_hcurves_from_csv(oqparam, fname)
elif fname.endswith('.xml'):
return get_hcurves_from_nrml(oqparam, fname)
else:
raise NotImplemented('Reading from %s' % fname)
[docs]def get_hcurves_from_csv(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
a .txt file with format `IMT lon lat poe1 ... poeN`
:returns:
the site collection and the hazard curves read by the .txt file
"""
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
if not oqparam.imtls:
raise ValueError('Missing intensity_measure_types_and_levels in %s'
% oqparam.inputs['job_ini'])
num_values = list(map(len, list(oqparam.imtls.values())))
with open(oqparam.inputs['hazard_curves']) as csvfile:
mesh, hcurves_by_imt = get_mesh_csvdata(
csvfile, list(oqparam.imtls), num_values,
valid.decreasing_probabilities)
sitecol = get_site_collection(oqparam, mesh)
return sitecol, hcurves_by_imt
[docs]def get_hcurves_from_nrml(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
an XML file containing hazard curves
:returns:
sitecol, curve array
"""
hcurves_by_imt = {}
oqparam.hazard_imtls = imtls = collections.OrderedDict()
for hcurves in nrml.read(fname):
imt = hcurves['IMT']
oqparam.investigation_time = hcurves['investigationTime']
if imt == 'SA':
imt += '(%s)' % hcurves['saPeriod']
imtls[imt] = ~hcurves.IMLs
data = sorted((~node.Point.pos, ~node.poEs) for node in hcurves[1:])
hcurves_by_imt[imt] = numpy.array([d[1] for d in data])
n = len(hcurves_by_imt[imt])
curves = zero_curves(n, imtls)
for imt in imtls:
curves[imt] = hcurves_by_imt[imt]
lons, lats = [], []
for xy, poes in data:
lons.append(xy[0])
lats.append(xy[1])
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
sitecol = get_site_collection(oqparam, mesh)
return sitecol, curves
[docs]def get_gmfs_from_txt(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
the full path of the CSV file
:returns:
a composite array of shape (N, R) read from a CSV file with format
`etag indices [gmv1 ... gmvN] * num_imts`
"""
with open(fname) as csvfile:
firstline = next(csvfile)
try:
coords = valid.coordinates(firstline)
except:
raise InvalidFile(
'The first line of %s is expected to contain comma separated'
'ordered coordinates, got %s instead' % (fname, firstline))
sitecol = sitecol_from_coords(oqparam, coords)
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
imts = list(oqparam.imtls)
imt_dt = numpy.dtype([(imt, F32) for imt in imts])
num_gmfs = oqparam.number_of_ground_motion_fields
gmf_by_imt = numpy.zeros((num_gmfs, len(sitecol)), imt_dt)
etags = []
for lineno, line in enumerate(csvfile, 2):
row = line.split(',')
try:
indices = list(map(valid.positiveint, row[1].split()))
except:
raise InvalidFile(
'The second column in %s is expected to contain integer '
'indices, got %s' % (fname, row[1]))
r_sites = (
sitecol if not indices else
site.FilteredSiteCollection(indices, sitecol))
for i in range(len(imts)):
try:
array = numpy.array(valid.positivefloats(row[i + 2]))
# NB: i + 2 because the first 2 fields are etag and indices
except:
raise InvalidFile(
'The column #%d in %s is expected to contain positive '
'floats, got %s instead' % (i + 3, fname, row[i + 2]))
gmf_by_imt[imts[i]][lineno - 2][r_sites.sids] = array
etags.append(row[0])
if lineno < num_gmfs + 1:
raise InvalidFile('%s contains %d rows, expected %d' % (
fname, lineno, num_gmfs + 1))
if etags != sorted(etags):
raise InvalidFile('The etags in %s are not ordered: %s'
% (fname, etags))
return sitecol, numpy.array([encode(e) for e in etags]), gmf_by_imt.T
# used in get_scenario_from_nrml
def _extract_etags_sitecounts(gmfset):
etags = set()
counter = collections.Counter()
for gmf in gmfset:
etags.add(gmf['ruptureId'])
for node in gmf:
counter[node['lon'], node['lat']] += 1
return numpy.array(sorted(etags), '|S100'), counter
[docs]def get_scenario_from_nrml(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
the NRML files containing the GMFs
:returns:
a triple (sitecol, etags, gmf array)
"""
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
imts = sorted(oqparam.imtls)
num_imts = len(imts)
imt_dt = numpy.dtype([(imt, F32) for imt in imts])
gmfset = nrml.read(fname).gmfCollection.gmfSet
etags, sitecounts = _extract_etags_sitecounts(gmfset)
oqparam.sites = sorted(sitecounts)
site_idx = {lonlat: i for i, lonlat in enumerate(oqparam.sites)}
oqparam.number_of_ground_motion_fields = num_events = len(etags)
sitecol = get_site_collection(oqparam)
num_sites = len(oqparam.sites)
gmf_by_imt = numpy.zeros((num_events, num_sites), imt_dt)
counts = collections.Counter()
for i, gmf in enumerate(gmfset):
if len(gmf) != num_sites: # there must be one node per site
raise InvalidFile('Expected %d sites, got %d nodes in %s, line %d'
% (num_sites, len(gmf), fname, gmf.lineno))
counts[gmf['ruptureId']] += 1
imt = gmf['IMT']
if imt == 'SA':
imt = 'SA(%s)' % gmf['saPeriod']
for node in gmf:
sid = site_idx[node['lon'], node['lat']]
gmf_by_imt[imt][i % num_events, sid] = node['gmv']
for etag, count in sorted(counts.items()):
if count < num_imts:
raise InvalidFile("Found a missing etag '%s' in %s" %
(etag, fname))
elif count > num_imts:
raise InvalidFile("Found a duplicated etag '%s' in %s" %
(etag, fname))
expected_gmvs_per_site = num_imts * len(etags)
for lonlat, counts in sitecounts.items():
if counts != expected_gmvs_per_site:
raise InvalidFile(
'%s: expected %d gmvs at location %s, found %d' %
(fname, expected_gmvs_per_site, lonlat, counts))
return sitecol, etags, gmf_by_imt.T
[docs]def get_mesh_hcurves(oqparam):
"""
Read CSV data in the format `lon lat, v1-vN, w1-wN, ...`.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
the mesh of points and the data as a dictionary
imt -> array of curves for each site
"""
imtls = oqparam.imtls
lon_lats = set()
data = AccumDict() # imt -> list of arrays
ncols = len(imtls) + 1 # lon_lat + curve_per_imt ...
csvfile = oqparam.inputs['hazard_curves']
for line, row in enumerate(csv.reader(csvfile), 1):
try:
if len(row) != ncols:
raise ValueError('Expected %d columns, found %d' %
ncols, len(row))
x, y = row[0].split()
lon_lat = valid.longitude(x), valid.latitude(y)
if lon_lat in lon_lats:
raise DuplicatedPoint(lon_lat)
lon_lats.add(lon_lat)
for i, imt_ in enumerate(imtls, 1):
values = valid.decreasing_probabilities(row[i])
if len(values) != len(imtls[imt_]):
raise ValueError('Found %d values, expected %d' %
(len(values), len(imtls([imt_]))))
data += {imt_: [numpy.array(values)]}
except (ValueError, DuplicatedPoint) as err:
raise err.__class__('%s: file %s, line %d' % (err, csvfile, line))
lons, lats = zip(*sorted(lon_lats))
mesh = geo.Mesh(numpy.array(lons), numpy.array(lats))
return mesh, {imt: numpy.array(lst) for imt, lst in data.items()}