Source code for openquake.commonlib.readinput

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2014-2021 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 json
import zlib
import pickle
import shutil
import zipfile
import logging
import tempfile
import functools
import configparser
import collections

import numpy
import pandas
import requests

from openquake.baselib import hdf5, parallel
from openquake.baselib.general import (
    random_filter, countby, group_array, get_duplicates, gettemp)
from openquake.baselib.python3compat import zip
from openquake.baselib.node import Node
from openquake.hazardlib.const import StdDev
from openquake.hazardlib.calc.filters import SourceFilter
from openquake.hazardlib.calc.gmf import CorrelationButNoInterIntraStdDevs
from openquake.hazardlib import (
    source, geo, site, imt, valid, sourceconverter, nrml, InvalidFile, pmf)
from openquake.hazardlib.source import rupture
from openquake.hazardlib.calc.stochastic import rupture_dt
from openquake.hazardlib.probability_map import ProbabilityMap
from openquake.hazardlib.geo.utils import BBoxError, cross_idl
from openquake.risklib import asset, riskmodels
from openquake.risklib.riskmodels import get_risk_functions
from openquake.commonlib.oqvalidation import OqParam
from openquake.commonlib.source_reader import get_csm
from openquake.commonlib import logictree

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
smlt_cache = {}  # fname, seed, samples, meth -> SourceModelLogicTree instance

source_info_dt = numpy.dtype([
    ('source_id', hdf5.vstr),          # 0
    ('grp_id', numpy.uint16),          # 1
    ('code', (numpy.string_, 1)),      # 2
    ('calc_time', numpy.float32),      # 3
    ('num_sites', numpy.uint32),       # 4
    ('eff_ruptures', numpy.uint32),    # 5
    ('trti', numpy.uint8),             # 6
    ('task_no', numpy.uint16),         # 7

[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, ext='.ini'): """ 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 ext: file extension 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).endswith(ext)]
[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 if 'reqv' in params['inputs']: params['pointsource_distance'] = '0' # 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 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()[job_ini], encoding='utf8') for sect in cp.sections(): _update(params, cp.items(sect), 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 get_oqparam(job_ini, pkg=None, calculators=None, kw={}, validate=1): """ 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 kw: Dictionary of strings to override the job parameters :param validate: Flag. By default it is true and the parameters are validated :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), kw) re = os.environ.get('OQ_REDUCE') # debugging facility if 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` # set save_disk_space = true os.environ['OQ_SAMPLE_SITES'] = re job_ini['number_of_logic_tree_samples'] = '1' ses = job_ini.get('ses_per_logic_tree_path') if ses: ses = str(int(numpy.ceil(int(ses) * float(re)))) job_ini['ses_per_logic_tree_path'] = 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]}) job_ini['save_disk_space'] = 'true' oqparam = OqParam(**job_ini) if validate and '_job_id' not in job_ini: oqparam.check_source_model() 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, 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 """ 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) # 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:'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)) 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) if h5: h5['site_model'] = sm mesh = geo.Mesh(sm['lon'], sm['lat']) return mesh 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 = [] for fname in oqparam.inputs['site_model']: if isinstance(fname, str) and fname.endswith('.csv'): sm = hdf5.read_csv(fname, site.site_param_dt).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, 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 = get_mesh(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 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') if h5 and 'site_model' in h5: # comes from a site_model.csv sm = h5['site_model'][:] else: sm = oqparam sitecol = site.SiteCollection.from_points( mesh.lons, mesh.lats, mesh.depths, sm, 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() 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']) key = (gsim_file,) + tuple(sorted(trts)) if key in gsim_lt_cache: return gsim_lt_cache[key] gsim_lt = logictree.GsimLogicTree(gsim_file, trts) 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:'Collapsing the gsim logic tree') gsim_lt = gsim_lt.collapse(oqparam.collapse_gsim_logic_tree) gsim_lt_cache[key] = gsim_lt return gsim_lt
[docs]def get_ruptures(fname_csv): """ Read ruptures in CSV format and return an ArrayWrapper """ if not rupture.BaseRupture._code: rupture.BaseRupture.init() # initialize rupture codes code = rupture.BaseRupture.str2code aw = hdf5.read_csv(fname_csv, rupture.rupture_dt) trts = aw.trts rups = [] geoms = [] n_occ = 1 for u, row in enumerate(aw.array): hypo = row['lon'], row['lat'], row['dep'] dic = json.loads(row['extra']) mesh = F32(json.loads(row['mesh'])) s1, s2 = mesh.shape[1:] rec = numpy.zeros(1, rupture_dt)[0] rec['seed'] = row['seed'] rec['minlon'] = minlon = mesh[0].min() rec['minlat'] = minlat = mesh[1].min() rec['maxlon'] = maxlon = mesh[0].max() rec['maxlat'] = maxlat = mesh[1].max() rec['mag'] = row['mag'] rec['hypo'] = hypo rate = dic.get('occurrence_rate', numpy.nan) tup = (u, row['seed'], 'no-source', trts.index(row['trt']), code[row['kind']], n_occ, row['mag'], row['rake'], rate, minlon, minlat, maxlon, maxlat, hypo, u, 0, 0) rups.append(tup) points = mesh.flatten() # lons + lats + deps # FIXME: extend to MultiSurfaces geoms.append(numpy.concatenate([[1], [s1, s2], points])) if not rups: return () dic = dict(geom=numpy.array(geoms, object)) # NB: PMFs for nonparametric ruptures are missing return hdf5.ArrayWrapper(numpy.array(rups, rupture_dt), dic)
[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('.csv'): return rupture.from_array(hdf5.read_csv(rup_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) else: raise ValueError('Unrecognized ruptures model %s' % rup_model) rup.tectonic_region_type = '*' # there is not TRT for scenario ruptures rup.rup_id = oqparam.ses_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'] args = (fname, oqparam.random_seed, oqparam.number_of_logic_tree_samples, oqparam.sampling_method) try: smlt = smlt_cache[args] except KeyError: smlt = smlt_cache[args] = logictree.SourceModelLogicTree(*args) 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)'Total 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 save_source_info(csm, h5): data = {} # src_id -> row wkts = [] lens = [] for sg in csm.src_groups: for src in sg: lens.append(len(src.et_ids)) row = [src.source_id, src.grp_id, src.code, 0, 0, 0, csm.full_lt.trti[src.tectonic_region_type], 0] wkts.append(src._wkt) data[] = row'There are %d groups and %d sources with len(et_ids)=%.2f', len(csm.src_groups), sum(len(sg) for sg in csm.src_groups), numpy.mean(lens)) csm.source_info = data # src_id -> row if h5: attrs = dict(atomic=any(grp.atomic for grp in csm.src_groups)) # avoid hdf5 damned bug by creating source_info in advance hdf5.create(h5, 'source_info', source_info_dt, attrs=attrs) h5['source_wkt'] = numpy.array(wkts, hdf5.vstr) h5['et_ids'] = csm.get_et_ids()
def _check_csm(csm, oqparam, h5): # checks csm.gsim_lt.check_imts(oqparam.imtls) srcs = csm.get_sources() if not srcs: raise RuntimeError('All sources were discarded!?') if os.environ.get('OQ_CHECK_INPUT'): source.check_complex_faults(srcs) # build a smart SourceFilter sitecol = get_site_collection(oqparam, h5) srcfilter = SourceFilter(sitecol, oqparam.maximum_distance) if sitecol: # missing in test_case_1_ruptures'Checking the sources bounding box') lons = [] lats = [] for src in srcs: try: box = srcfilter.get_enlarged_box(src) except BBoxError as exc: logging.error(exc) continue lons.append(box[0]) lats.append(box[1]) lons.append(box[2]) lats.append(box[3]) if cross_idl(*(list(sitecol.lons) + lons)): lons = numpy.array(lons) % 360 else: lons = numpy.array(lons) bbox = (lons.min(), min(lats), lons.max(), max(lats)) if bbox[2] - bbox[0] > 180: raise BBoxError( 'The bounding box of the sources is larger than half ' 'the globe: %d degrees' % (bbox[2] - bbox[0])) sids = sitecol.within_bbox(bbox) if len(sids) == 0: raise RuntimeError('All sources were discarded!?') csm.sitecol = sitecol
[docs]def get_composite_source_model(oqparam, h5=None): """ Parse the XML and build a complete composite source model in memory. :param oqparam: an :class:`openquake.commonlib.oqvalidation.OqParam` instance :param h5: an open hdf5.File where to store the source info """ # first read the logic tree full_lt = get_full_lt(oqparam) # then read the composite source model from the cache if possible if oqparam.cachedir and not os.path.exists(oqparam.cachedir): os.makedirs(oqparam.cachedir) if oqparam.cachedir and not oqparam.is_ucerf(): # for UCERF pickling the csm is slower checksum = get_checksum32(oqparam, h5) fname = os.path.join(oqparam.cachedir, 'csm_%s.pik' % checksum) if os.path.exists(fname):'Reading %s', fname) with open(fname, 'rb') as f: csm = pickle.load(f) csm.full_lt = full_lt if h5: # avoid errors with --reuse_hazard h5['et_ids'] = csm.get_et_ids() hdf5.create(h5, 'source_info', source_info_dt) _check_csm(csm, oqparam, h5) return csm # read and process the composite source model from the input files csm = get_csm(oqparam, full_lt, h5) save_source_info(csm, h5) if oqparam.cachedir and not oqparam.is_ucerf():'Saving %s', fname) with open(fname, 'wb') as f: pickle.dump(csm, f) _check_csm(csm, oqparam, h5) 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 DataFrame (ampcode, level, PGA, SA() ...) """ fname = oqparam.inputs['amplification'] df = hdf5.read_csv(fname, {'ampcode': site.ampcode_dt, None: F64}, index='ampcode') df.fname = fname return df
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 """ risklist = get_risk_functions(oqparam) consdict = {} if 'consequence' in oqparam.inputs: # build consdict of the form cname_by_tagname -> tag -> array 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, risklist.limit_states) for tag, grp in group_array(group, by).items()} consdict['%s_by_%s' % (cname, by)] = bytag crm = riskmodels.CompositeRiskModel(oqparam, risklist, 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 """ if oqparam.cachedir and not os.path.exists(oqparam.cachedir): os.makedirs(oqparam.cachedir) checksum = _checksum(oqparam.inputs['exposure']) fname = os.path.join(oqparam.cachedir, 'exp_%s.pik' % checksum) if os.path.exists(fname):'Reading %s', fname) with open(fname, 'rb') as f: return pickle.load(f) 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() if oqparam.cachedir:'Saving %s', fname) with open(fname, 'wb') as f: pickle.dump(exposure, f) 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, oqparam.aggregate_by) 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 taxonomy_mapping(oqparam, taxonomies): """ :param oqparam: OqParam instance :param taxonomies: array of strings tagcol.taxonomy :returns: a dictionary loss_type -> [[(taxonomy, weight), ...], ...] """ if 'taxonomy_mapping' not in oqparam.inputs: # trivial mapping lst = [[(taxo, 1)] for taxo in taxonomies] return {lt: lst for lt in oqparam.loss_names} dic = oqparam.inputs['taxonomy_mapping'] if isinstance(dic, str): # filename dic = {lt: dic for lt in oqparam.loss_names} return {lt: _taxonomy_mapping(dic[lt], taxonomies) for lt in oqparam.loss_names}
def _taxonomy_mapping(filename, taxonomies): tmap_df = pandas.read_csv(filename) if 'weight' not in tmap_df: tmap_df['weight'] = 1. assert set(tmap_df) == {'taxonomy', 'conversion', 'weight'} dic = dict(list(tmap_df.groupby('taxonomy'))) taxonomies = taxonomies[1:] # strip '?' missing = set(taxonomies) - set(dic) if missing: raise InvalidFile('The taxonomies %s are in the exposure but not in ' 'the taxonomy mapping %s' % (missing, filename)) lst = [[("?", 1)]] for taxo in taxonomies: recs = dic[taxo] if abs(recs['weight'].sum() - 1.) > pmf.PRECISION: raise InvalidFile('%s: the weights do not sum up to 1 for %s' % (filename, taxo)) lst.append([(rec['conversion'], rec['weight']) for r, rec in recs.iterrows()]) return lst
[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_imts(get_risk_functions(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'} # 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): 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 ('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': args = (fname, oqparam.random_seed, oqparam.number_of_logic_tree_samples, oqparam.sampling_method) try: smlt = smlt_cache[args] except KeyError: smlt = smlt_cache[args] = logictree.SourceModelLogicTree(*args) fnames.update(smlt.hdf5_files) fnames.update( fnames.add(fname) else: fnames.add(fname) return sorted(fnames)
def _checksum(fnames, checksum=0): """ :returns: the 32 bit checksum of a list of files """ for fname in fnames: 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 = 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(get_input_files(oqparam, hazard=True)) 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'): 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