Source code for openquake.commonlib.readinput

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2020 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 os
import re
import ast
import csv
import copy
import zlib
import shutil
import zipfile
import logging
import tempfile
import functools
import configparser
import collections
import numpy
import requests

from openquake.baselib import hdf5, parallel
from openquake.baselib.general import (
    random_filter, countby, group_array, get_duplicates, AccumDict)
from openquake.baselib.python3compat import decode, zip
from openquake.baselib.node import Node
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.calc.gmf import CorrelationButNoInterIntraStdDevs
from openquake.hazardlib import (
    source, geo, site, imt, valid, sourceconverter, nrml, InvalidFile)
from openquake.hazardlib.source import rupture
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.risklib import asset, riskmodels
from openquake.risklib.riskmodels import get_risk_models
from openquake.commonlib.oqvalidation import OqParam
from openquake.commonlib.source_reader import get_csm
from openquake.commonlib import logictree

# the following is quite arbitrary, it gives output weights that I like (MS)
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')
gsim_lt_cache = {}  # fname, trt1, ..., trtN -> GsimLogicTree instance

source_info_dt = numpy.dtype([
    ('source_id', hdf5.vstr),          # 0
    ('gidx', numpy.uint16),            # 1
    ('code', (numpy.string_, 1)),      # 2
    ('multiplicity', numpy.uint32),    # 3
    ('calc_time', numpy.float32),      # 4
    ('num_sites', numpy.uint32),       # 5
    ('eff_ruptures', numpy.uint32),    # 6
    ('checksum', numpy.uint32),        # 7
    ('serial', numpy.uint32),          # 8

[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 extract_from_zip(path, candidates): """ Given a zip archive and a function to detect the presence of a given filename, unzip the archive into a temporary directory and return the full path of the file. Raise an IOError if the file cannot be found within the archive. :param path: pathname of the archive :param candidates: list of names to search for """ temp_dir = tempfile.mkdtemp() with zipfile.ZipFile(path) as archive: archive.extractall(temp_dir) return [f for f in collect_files(temp_dir) if os.path.basename(f) in candidates]
[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)'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 normalize(key, fnames, base_path): input_type, _ext = key.rsplit('_', 1) filenames = [] for val in fnames: if '://' in val: # get the data from an URL resp = requests.get(val) _, val = val.rsplit('/', 1) with open(os.path.join(base_path, val), 'wb') as f: f.write(resp.content) elif os.path.isabs(val): raise ValueError('%s=%s is an absolute path' % (key, val)) if 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) else: val = os.path.normpath(os.path.join(base_path, val)) if not os.path.exists(val): # tested in archive_err_2 raise OSError('No such file: %s' % val) filenames.append(val) return input_type, filenames
def _update(params, items, base_path): for key, value in items: if key in ('hazard_curves_csv', 'site_model_file', '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, [fname] = normalize(key, [value], base_path) params['inputs'][input_type] = fname elif isinstance(value, str) and value.endswith('.hdf5'): logging.warning('The [reqv] syntax has been deprecated, see ' '' '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 get_params(job_inis, **kw): """ Parse one or more INI-style config files. :param job_inis: List of configuration files (or list containing a single zip archive) :param kw: Optionally override some parameters :returns: A dictionary of parameters """ input_zip = None if len(job_inis) == 1 and job_inis[0].endswith('.zip'): input_zip = job_inis[0] job_inis = extract_from_zip( job_inis[0], ['job_hazard.ini', 'job_haz.ini', 'job.ini', 'job_risk.ini']) if not job_inis: raise NameError('Could not find job.ini inside %s' % input_zip) 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(), encoding='utf8') # directory containing the config files we're parsing job_ini = os.path.abspath(job_inis[0]) base_path = decode(os.path.dirname(job_ini)) params = dict(base_path=base_path, inputs={'job_ini': job_ini}) if input_zip: params['inputs']['input_zip'] = os.path.abspath(input_zip) for sect in cp.sections(): _update(params, cp.items(sect), base_path) _update(params, kw.items(), base_path) # override on demand if params['inputs'].get('reqv'): # using pointsource_distance=0 because of the reqv approximation params['pointsource_distance'] = '0' return params
[docs]def get_oqparam(job_ini, pkg=None, calculators=None, hc_id=None, validate=1, **kw): """ 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 :param validate: Flag. By default it is true and the parameters are validated :param kw: String-valued keyword arguments used to override the job.ini parameters :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)]) if hc_id: job_ini.update(hazard_calculation_id=str(hc_id)) job_ini.update(kw) oqparam = OqParam(**job_ini) if validate: oqparam.validate() return oqparam
pmap = None # set as side effect when the user reads hazard_curves from a file # the hazard curves format does not split the site locations from the data (an # unhappy legacy design choice that I fixed in the GMFs CSV format only) thus # this hack is necessary, otherwise we would have to parse the file twice exposure = None # set as side effect when the user reads the site mesh # this hack is necessary, otherwise we would have to parse the exposure twice gmfs, eids = None, None # set as a sided effect when reading gmfs.xml # this hack is necessary, otherwise we would have to parse the file twice
[docs]def get_csv_header(fname, sep=','): """ :param fname: a CSV file :param sep: the separator (default comma) :returns: the first line of fname """ with open(fname, encoding='utf-8-sig') as f: return next(f).split(sep)
[docs]def get_mesh(oqparam): """ 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 """ global pmap, exposure, gmfs, eids if 'exposure' in oqparam.inputs and exposure is None: # read it only once exposure = get_exposure(oqparam) if oqparam.sites: return geo.Mesh.from_coords(oqparam.sites) elif 'sites' in oqparam.inputs: fname = oqparam.inputs['sites'] header = get_csv_header(fname) if 'lon' in header: data = [] for i, row in enumerate( csv.DictReader(open(fname, encoding='utf-8-sig'))): if header[0] == 'site_id' and row['site_id'] != str(i): raise InvalidFile('%s: expected site_id=%d, got %s' % ( fname, i, row['site_id'])) data.append(' '.join([row['lon'], row['lat']])) elif 'gmfs' in oqparam.inputs: raise InvalidFile('Missing header in %(sites)s' % oqparam.inputs) else: data = [line.replace(',', ' ') for line in open(fname, encoding='utf-8-sig')] coords = valid.coordinates(','.join(data)) start, stop = oqparam.sites_slice c = (coords[start:stop] if header[0] == 'site_id' else sorted(coords[start:stop])) # NB: Notice the sort=False below # Calculations starting from ground motion fields input by the user # require at least two input files related to the gmf data: # 1. A sites.csv file, listing {site_id, lon, lat} tuples # 2. A gmfs.csv file, listing {event_id, site_id, gmv[IMT1], # gmv[IMT2], ...} tuples # The site coordinates defined in the sites file do not need to be in # sorted order. # We must only ensure uniqueness of the provided site_ids and # coordinates. # When creating the site mesh from the site coordinates read from # the csv file, the sort=False flag maintains the user-specified # site_ids instead of reassigning them after sorting. return geo.Mesh.from_coords(c, sort=False) 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) else: raise NotImplementedError('Reading from %s' % fname) return mesh 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) 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:'Inferring the hazard grid from the exposure') mesh = poly.dilate(oqparam.region_grid_spacing).discretize( oqparam.region_grid_spacing) return geo.Mesh.from_coords(zip(mesh.lons, mesh.lats)) 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 elif 'site_model' in oqparam.inputs:'Extracting the hazard sites from the site model') sm = get_site_model(oqparam) return geo.Mesh(sm['lon'], sm['lat']) elif 'exposure' in oqparam.inputs: return exposure.mesh
[docs]def get_site_model(oqparam): """ Convert the NRML file into an array of site parameters. :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance :returns: an array with fields lon, lat, vs30, ... """ req_site_params = get_gsim_lt(oqparam).req_site_params if 'amplification' in oqparam.inputs: req_site_params.add('ampcode') arrays = [] dtypedic = {None: float, 'vs30measured': numpy.uint8, 'ampcode': site.ampcode_dt} for fname in oqparam.inputs['site_model']: if isinstance(fname, str) and fname.endswith('.csv'): sm = hdf5.read_csv(fname, dtypedic).array 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)) if 'site_id' in sm.dtype.names: raise InvalidFile('%s: you passed a sites.csv file instead of ' 'a site_model.csv file!' % fname) z = numpy.zeros(len(sm), sorted(sm.dtype.descr)) for name in z.dtype.names: # reorder the fields z[name] = sm[name] arrays.append(z) continue nodes = params = [valid.site_param(node.attrib) for node in nodes] missing = 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) return numpy.concatenate(arrays)
[docs]def get_site_collection(oqparam): """ 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 """ mesh = get_mesh(oqparam) 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 else: # use the default site params req_site_params = get_gsim_lt(oqparam).req_site_params if 'amplification' in oqparam.inputs: req_site_params.add('ampcode') sitecol = site.SiteCollection.from_points( mesh.lons, mesh.lats, mesh.depths, oqparam, req_site_params) 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() 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']) key = (gsim_file,) + tuple(sorted(trts)) if key in gsim_lt_cache: return gsim_lt_cache[key] gsim_lt = logictree.GsimLogicTree(gsim_file, trts) gsim_lt_cache[key] = gsim_lt gmfcorr = oqparam.correl_model for trt, gsims in gsim_lt.values.items(): for gsim in gsims: if gmfcorr and (gsim.DEFINED_FOR_STANDARD_DEVIATION_TYPES == {StdDev.TOTAL}): 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.warning( '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: return gsim_lt.collapse(oqparam.collapse_gsim_logic_tree) 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 [rlz.value[0] for rlz in get_gsim_lt(oqparam)]
[docs]def get_rupture(oqparam): """ Read the `rupture_model` file and by filter the site collection :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance :returns: an hazardlib rupture """ rup_model = oqparam.inputs['rupture_model'] if rup_model.endswith('.xml'): [rup_node] = conv = sourceconverter.RuptureConverter( oqparam.rupture_mesh_spacing, oqparam.complex_fault_mesh_spacing) rup = conv.convert_node(rup_node) elif rup_model.endswith(('.txt', '.toml')): rup = rupture.from_toml(open(rup_model).read()) else: raise ValueError('Unrecognized ruptures model %s' % rup_model) rup.tectonic_region_type = '*' # there is not TRT for scenario ruptures rup.rup_id = oqparam.random_seed 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'] # NB: converting the random_seed into an integer is needed on Windows smlt = logictree.SourceModelLogicTree( fname, seed=int(oqparam.random_seed), num_samples=oqparam.number_of_logic_tree_samples) if oqparam.discard_trts: trts = set(trt.strip() for trt in oqparam.discard_trts.split(',')) # smlt.tectonic_region_types comes from applyToTectonicRegionType smlt.tectonic_region_types = smlt.tectonic_region_types - trts if 'ucerf' in oqparam.calculation_mode: smlt.tectonic_region_types = {'Active Shallow Crust'} return smlt
[docs]def get_full_lt(oqparam): """ :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance :returns: a :class:`openquake.commonlib.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: continue elif trt.lower() not in trts_lower: raise ValueError('Unknown TRT=%s in %s [reqv]' % (trt, oqparam.inputs['job_ini'])) gsim_lt = get_gsim_lt(oqparam, trts or ['*']) p = source_model_lt.num_paths * gsim_lt.get_num_paths() if oqparam.number_of_logic_tree_samples:'Considering {:_d} logic tree paths out of {:_d}'.format( oqparam.number_of_logic_tree_samples, p)) else: # full enumeration if (oqparam.is_event_based() and (oqparam.ground_motion_fields or oqparam.hazard_curves_from_gmfs) and p > oqparam.max_potential_paths): raise ValueError( 'There are too many potential logic tree paths (%d):' 'use sampling instead of full enumeration or reduce the ' 'source model with oq reduce_sm' % p)'Potential number of logic tree paths = {:_d}'.format(p)) if source_model_lt.on_each_source:'There is a logic tree on each source') full_lt = logictree.FullLogicTree(source_model_lt, gsim_lt) return full_lt
[docs]def get_composite_source_model(oqparam, full_lt=None, h5=None): """ Parse the XML and build a complete composite source model in memory. :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance :param full_lt: a :class:`openquake.commonlib.logictree.FullLogicTree` or None :param h5: an open hdf5.File where to store the source info """ if full_lt is None: full_lt = get_full_lt(oqparam) csm = get_csm(oqparam, full_lt, h5) grp_ids = csm.get_grp_ids() gidx = {tuple(arr): i for i, arr in enumerate(grp_ids)} if oqparam.is_event_based(): csm.init_serials(oqparam.ses_seed) data = {} # src_id -> row mags = AccumDict(accum=set()) # trt -> mags wkts = [] ns = 0 for sg in csm.src_groups: if hasattr(sg, 'mags'): # UCERF mags[sg.trt].update('%.2f' % mag for mag in sg.mags) for src in sg: ns += 1 if src.source_id in data: num_sources = data[src.source_id][3] + 1 else: num_sources = 1 row = [src.source_id, gidx[tuple(src.grp_ids)], src.code, num_sources, 0, 0, 0, src.checksum, src.serial] wkts.append(src._wkt) # this is a bit slow but okay data[src.source_id] = row if hasattr(src, 'mags'): # UCERF continue # already accounted for in sg.mags elif hasattr(src, 'data'): # nonparametric srcmags = ['%.2f' % item[0].mag for item in] else: srcmags = ['%.2f' % item[0] for item in src.get_annual_occurrence_rates()] mags[sg.trt].update(srcmags)'There are %d sources with %d unique IDs', ns, len(data)) if h5: hdf5.create(h5, 'source_info', source_info_dt) # avoid hdf5 damned bug h5['source_wkt'] = numpy.array(wkts, hdf5.vstr) for trt in mags: h5['source_mags/' + trt] = numpy.array(sorted(mags[trt])) h5['grp_ids'] = grp_ids csm.gsim_lt.check_imts(oqparam.imtls) csm.source_info = data if os.environ.get('OQ_CHECK_INPUT'): source.check_complex_faults(csm.get_sources()) 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)))
[docs]def get_amplification(oqparam): """ :returns: a composite array (amplification, param, imt0, imt1, ...) """ fname = oqparam.inputs['amplification'] aw = hdf5.read_csv(fname, {'ampcode': site.ampcode_dt, None: F64}) aw.fname = fname imls = () if 'level' in aw.dtype.names: for records in group_array(aw, 'ampcode').values(): if len(imls) == 0: imls = numpy.sort(records['level']) elif len(records['level']) != len(imls) or ( records['level'] != imls).any(): raise InvalidFile('%s: levels for %s %s instead of %s' % (fname, records['ampcode'][0], records['level'], imls)) return aw
def _cons_coeffs(records, limit_states): dtlist = [(lt, F32) for lt in records['loss_type']] coeffs = numpy.zeros(len(limit_states), dtlist) for rec in records: coeffs[rec['loss_type']] = [rec[ds] for ds in limit_states] return coeffs
[docs]def get_crmodel(oqparam): """ Return a :class:`openquake.risklib.riskinput.CompositeRiskModel` instance :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance """ riskdict = get_risk_models(oqparam) oqparam.set_risk_imtls(riskdict) if 'consequence' in oqparam.inputs: # build consdict of the form cname_by_tagname -> tag -> array consdict = {} for by, fname in oqparam.inputs['consequence'].items(): dtypedict = {by: str, 'cname': str, 'loss_type': str, None: float} dic = group_array(hdf5.read_csv(fname, dtypedict).array, 'cname') for cname, group in dic.items(): bytag = {tag: _cons_coeffs(grp, riskdict.limit_states) for tag, grp in group_array(group, by).items()} consdict['%s_by_%s' % (cname, by)] = bytag else: # legacy approach, extract the consequences from the risk models consdict = {'losses_by_taxonomy': {}} for taxo, dic in riskdict.items(): coeffs_by_lt = {lt: dic.pop((lt, kind)) for lt, kind in list(dic) if kind == 'consequence'} if coeffs_by_lt: dtlist = [(lt, F32) for lt in coeffs_by_lt] coeffs = numpy.zeros(len(riskdict.limit_states), dtlist) for lt, cf in coeffs_by_lt.items(): coeffs[lt] = cf consdict['losses_by_taxonomy'][taxo] = coeffs crm = riskmodels.CompositeRiskModel(oqparam, riskdict, consdict) return crm
[docs]def get_exposure(oqparam): """ 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 """ exposure = oqparam.inputs['exposure'], oqparam.calculation_mode, oqparam.region, oqparam.ignore_missing_costs, by_country='country' in oqparam.aggregate_by) exposure.mesh, exposure.assets_by_site = exposure.get_mesh_assets_by_site() return exposure
[docs]def get_sitecol_assetcol(oqparam, haz_sitecol=None, cost_types=()): """ :param oqparam: calculation parameters :param haz_sitecol: the hazard site collection :param cost_types: the expected cost types :returns: (site collection, asset collection, discarded) """ global exposure asset_hazard_distance = oqparam.asset_hazard_distance['default'] if exposure is None: # haz_sitecol not extracted from the exposure exposure = get_exposure(oqparam) if haz_sitecol is None: haz_sitecol = get_site_collection(oqparam) if oqparam.region_grid_spacing: haz_distance = oqparam.region_grid_spacing * 1.414 if haz_distance != asset_hazard_distance:'Using asset_hazard_distance=%d km instead of %d km', haz_distance, asset_hazard_distance) else: haz_distance = asset_hazard_distance if haz_sitecol.mesh != exposure.mesh: # associate the assets to the hazard sites sitecol, assets_by, discarded = geo.utils.assoc( exposure.assets_by_site, haz_sitecol, haz_distance, 'filter') assets_by_site = [[] for _ in sitecol.complete.sids] num_assets = 0 for sid, assets in zip(sitecol.sids, assets_by): assets_by_site[sid] = assets num_assets += len(assets) 'Associated %d assets to %d sites', num_assets, len(sitecol)) else: # asset sites and hazard sites are the same sitecol = haz_sitecol assets_by_site = exposure.assets_by_site discarded = []'Read %d sites and %d assets from the exposure', len(sitecol), sum(len(a) for a in assets_by_site)) assetcol = asset.AssetCollection( exposure, assets_by_site, oqparam.time_event) if assetcol.occupancy_periods: missing = set(cost_types) - set(exposure.cost_types['name']) - set( ['occupants']) else: missing = set(cost_types) - set(exposure.cost_types['name']) if missing and not oqparam.calculation_mode.endswith('damage'): raise InvalidFile('The exposure %s is missing %s' % (oqparam.inputs['exposure'], missing)) if (not oqparam.hazard_calculation_id and 'gmfs' not in oqparam.inputs and 'hazard_curves' 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
[docs]def levels_from(header): levels = [] for field in header: if field.startswith('poe-'): levels.append(float(field[4:])) return levels
[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 wrapper in map(read, fnames): dic[wrapper.imt] = wrapper.array imtls[wrapper.imt] = levels_from(wrapper.dtype.names) oqparam.hazard_imtls = imtls oqparam.set_risk_imtls(get_risk_models(oqparam)) array = wrapper.array mesh = geo.Mesh(array['lon'], array['lat']) num_levels = sum(len(imls) for imls in oqparam.imtls.values()) data = numpy.zeros((len(mesh), num_levels)) 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]) return mesh, ProbabilityMap.from_array(data, range(len(mesh)))
tag2code = {'ar': b'A', 'mu': b'M', 'po': b'P', 'si': b'S', 'co': b'C', 'ch': b'X', 'no': b'N'}
[docs]def reduce_sm(paths, source_ids): if isinstance(source_ids, dict): # in oq reduce_sm def ok(src_node): code = tag2code['\}(\w\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'Reading %s', path) root = model = Node('sourceModel', root[0].attrib) origmodel = root[0] if root['xmlns'] == '': 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 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 get_input_files(oqparam, hazard=False): """ :param oqparam: an OqParam instance :param hazard: if True, consider only the hazard files :returns: input path names in a specific order """ fnames = set() # files entering in the checksum for key in oqparam.inputs: fname = oqparam.inputs[key] if hazard and key not in ('site_model', 'source_model_logic_tree', 'gsim_logic_tree', 'source'): continue # collect .hdf5 tables for the GSIMs, if any elif key == 'gsim_logic_tree': gsim_lt = get_gsim_lt(oqparam) for gsims in gsim_lt.values.values(): for gsim in gsims: for k, v in gsim.kwargs.items(): if k.endswith(('_file', '_table')): fnames.add(v) fnames.add(fname) elif key == 'source_model': # UCERF f = oqparam.inputs['source_model'] fnames.add(f) fname =['filename'] fnames.add(os.path.join(os.path.dirname(f), fname)) elif key == 'exposure': # fname is a list for exp in asset.Exposure.read_headers(fname): fnames.update(exp.datafiles) fnames.update(fname) elif isinstance(fname, dict): fnames.update(fname.values()) elif isinstance(fname, list): for f in fname: if f == oqparam.input_dir: raise InvalidFile('%s there is an empty path in %s' % (oqparam.inputs['job_ini'], key)) fnames.update(fname) elif key == 'source_model_logic_tree': for smpath in logictree.collect_info(fname).smpaths: fnames.add(smpath) fnames.add(fname) else: fnames.add(fname) return sorted(fnames)
def _checksum(fname, checksum): 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 = else: with open(fname, 'rb') as f: data = return zlib.adler32(data, checksum)
[docs]def get_checksum32(oqparam, hazard=False): """ Build an unsigned 32 bit integer from the input files of a calculation. :param oqparam: an OqParam instance :param hazard: if True, consider only the hazard files :returns: the checkume """ # NB: using adler32 & 0xffffffff is the documented way to get a checksum # which is the same between Python 2 and Python 3 checksum = 0 for fname in get_input_files(oqparam, hazard): checksum = _checksum(fname, checksum) if hazard: hazard_params = [] for key, val in vars(oqparam).items(): if key in ('rupture_mesh_spacing', 'complex_fault_mesh_spacing', 'width_of_mfd_bin', 'area_source_discretization', 'random_seed', 'ses_seed', 'truncation_level', 'maximum_distance', 'investigation_time', 'number_of_logic_tree_samples', 'imtls', 'pointsource_distance', 'ses_per_logic_tree_path', 'minimum_magnitude', 'sites', 'filter_distance'): hazard_params.append('%s = %s' % (key, val)) data = '\n'.join(hazard_params).encode('utf8') checksum = zlib.adler32(data, checksum) & 0xffffffff return checksum