Source code for openquake.calculators.extract

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
# Copyright (C) 2017-2018 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 collections
import operator
import logging
from h5py._hl.dataset import Dataset
from import Group
import numpy
    from functools import lru_cache
except ImportError:
    from openquake.risklib.utils import memoized
    memoized = lru_cache(100)
from openquake.baselib.hdf5 import ArrayWrapper
from openquake.baselib.general import group_array
from openquake.baselib.python3compat import encode
from openquake.calculators import getters
from openquake.calculators.export.loss_curves import get_loss_builder
from openquake.commonlib import calc, util

F32 = numpy.float32
F64 = numpy.float64

[docs]def cast(loss_array, loss_dt): return loss_array.copy().view(loss_dt).squeeze()
[docs]def barray(iterlines): """ Array of bytes """ lst = [line.encode('utf-8') for line in iterlines] arr = numpy.array(lst) return arr
[docs]def extract_(dstore, dspath): """ Extracts an HDF5 path object from the datastore, for instance extract('sitecol', dstore). It is also possibly to extract the attributes, for instance with extract('sitecol.attrs', dstore). """ if dspath.endswith('.attrs'): return ArrayWrapper(0, dstore.get_attrs(dspath[:-6])) obj = dstore[dspath] if isinstance(obj, Dataset): return ArrayWrapper(obj.value, obj.attrs) elif isinstance(obj, Group): return ArrayWrapper(numpy.array(list(obj)), obj.attrs) else: return obj
[docs]class Extract(collections.OrderedDict): """ A callable dictionary of functions with a single instance called `extract`. Then `extract(dstore, fullkey)` dispatches to the function determined by the first part of `fullkey` (a slash-separated string) by passing as argument the second part of `fullkey`. For instance extract(dstore, 'sitecol), extract(dstore, 'asset_values/0') etc. """
[docs] def add(self, key, cache=False): def decorator(func): self[key] = memoized(func) if cache else func return func return decorator
def __call__(self, dstore, key): try: k, v = key.split('/', 1) except ValueError: # no slashes k, v = key, '' if k in self: return self[k](dstore, v) else: return extract_(dstore, key)
extract = Extract() # used by the QGIS plugin
[docs]@extract.add('realizations') def extract_realizations(dstore, dummy): """ Extract an array of realizations. Use it as /extract/realizations """ return dstore['csm_info'].rlzs
[docs]@extract.add('asset_values', cache=True) def extract_asset_values(dstore, sid): """ Extract an array of asset values for the given sid. Use it as /extract/asset_values/0 :returns: (aid, loss_type1, ..., loss_typeN) composite array """ if sid: return extract(dstore, 'asset_values')[int(sid)] assetcol = extract(dstore, 'assetcol') asset_refs = assetcol.asset_refs assets_by_site = assetcol.assets_by_site() lts = assetcol.loss_types time_event = assetcol.time_event dt = numpy.dtype([('aref', asset_refs.dtype), ('aid', numpy.uint32)] + [(str(lt), numpy.float32) for lt in lts]) data = [] for assets in assets_by_site: vals = numpy.zeros(len(assets), dt) for a, asset in enumerate(assets): vals[a]['aref'] = asset_refs[a] vals[a]['aid'] = asset.ordinal for lt in lts: vals[a][lt] = asset.value(lt, time_event) data.append(vals) return data
[docs]@extract.add('asset_tags') def extract_asset_tags(dstore, tagname): """ Extract an array of asset tags for the given tagname. Use it as /extract/asset_tags or /extract/asset_tags/taxonomy """ tagcol = dstore['assetcol/tagcol'] if tagname: yield tagname, barray(tagcol.gen_tags(tagname)) for tagname in tagcol.tagnames: yield tagname, barray(tagcol.gen_tags(tagname))
[docs]@extract.add('hazard') def extract_hazard(dstore, what): """ Extracts hazard curves and possibly hazard maps and/or uniform hazard spectra. Use it as /extract/hazard/mean or /extract/hazard/rlz-0, etc """ oq = dstore['oqparam'] sitecol = dstore['sitecol'] rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() yield 'sitecol', sitecol yield 'oqparam', oq yield 'imtls', oq.imtls yield 'realizations', dstore['csm_info'].rlzs yield 'checksum32', dstore['/'].attrs['checksum32'] nsites = len(sitecol) M = len(oq.imtls) P = len(oq.poes) for statname, pmap in getters.PmapGetter(dstore, rlzs_assoc).items(what): for imt in oq.imtls: key = 'hcurves/%s/%s' % (imt, statname) arr = numpy.zeros((nsites, len(oq.imtls[imt]))) for sid in pmap: arr[sid] = pmap[sid].array[oq.imtls(imt), 0]'extracting %s', key) yield key, arr try: hmap = dstore['hmaps/' + statname] except KeyError: # for statname=rlz-XXX hmap = calc.make_hmap(pmap, oq.imtls, oq.poes) for p, poe in enumerate(oq.poes): key = 'hmaps/poe-%s/%s' % (poe, statname) arr = numpy.zeros((nsites, M)) idx = [m * P + p for m in range(M)] for sid in pmap: arr[sid] = hmap[sid].array[idx, 0]'extracting %s', key) yield key, arr
[docs]def get_mesh(sitecol, complete=True): """ :returns: a lon-lat or lon-lat-depth array depending if the site collection is at sea level or not """ sc = sitecol.complete if complete else sitecol if sc.at_sea_level(): mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64)]) mesh['lon'] = sc.lons mesh['lat'] = sc.lats else: mesh = numpy.zeros(len(sc), [('lon', F64), ('lat', F64), ('depth', F64)]) mesh['lon'] = sc.lons mesh['lat'] = sc.lats mesh['depth'] = sc.depths return mesh
[docs]def hazard_items(dic, mesh, *extras, **kw): """ :param dic: dictionary of arrays of the same shape :param mesh: a mesh array with lon, lat fields of the same length :param extras: optional triples (field, dtype, values) :param kw: dictionary of parameters (like investigation_time) :returns: a list of pairs (key, value) suitable for storage in .npz format """ for item in kw.items(): yield item arr = dic[next(iter(dic))] dtlist = [(str(field), arr.dtype) for field in sorted(dic)] for field, dtype, values in extras: dtlist.append((str(field), dtype)) array = numpy.zeros(arr.shape, dtlist) for field in dic: array[field] = dic[field] for field, dtype, values in extras: array[field] = values yield 'all', util.compose_arrays(mesh, array)
def _get_dict(dstore, name, imts, imls): dic = {} dtlist = [] for imt, imls in zip(imts, imls): dt = numpy.dtype([(str(iml), F32) for iml in imls]) dtlist.append((imt, dt)) for statname, curves in dstore[name].items(): dic[statname] = curves.value.view(dtlist).flatten() return dic
[docs]@extract.add('hcurves') def extract_hcurves(dstore, what): """ Extracts hazard curves. Use it as /extract/hcurves/mean or /extract/hcurves/rlz-0, /extract/hcurves/stats, /extract/hcurves/rlzs etc """ if 'hcurves' not in dstore: return [] oq = dstore['oqparam'] sitecol = dstore['sitecol'] mesh = get_mesh(sitecol, complete=False) dic = _get_dict(dstore, 'hcurves', oq.imtls, oq.imtls.values()) return hazard_items(dic, mesh, investigation_time=oq.investigation_time)
[docs]@extract.add('hmaps') def extract_hmaps(dstore, what): """ Extracts hazard maps. Use it as /extract/hmaps/mean or /extract/hmaps/rlz-0, etc """ oq = dstore['oqparam'] sitecol = dstore['sitecol'] mesh = get_mesh(sitecol) dic = _get_dict(dstore, 'hmaps', oq.imtls, [oq.poes] * len(oq.imtls)) return hazard_items(dic, mesh, investigation_time=oq.investigation_time)
[docs]@extract.add('uhs') def extract_uhs(dstore, what): """ Extracts uniform hazard spectra. Use it as /extract/uhs/mean or /extract/uhs/rlz-0, etc """ oq = dstore['oqparam'] mesh = get_mesh(dstore['sitecol']) rlzs_assoc = dstore['csm_info'].get_rlzs_assoc() dic = {} for name, hcurves in getters.PmapGetter(dstore, rlzs_assoc).items(what): dic[name] = calc.make_uhs(hcurves, oq.imtls, oq.poes, len(mesh)) return hazard_items(dic, mesh, investigation_time=oq.investigation_time)
def _agg(losses, idxs): shp = losses.shape[1:] if not idxs: # no intersection, return a 0-dim matrix return numpy.zeros((0,) + shp, losses.dtype) # numpy.array wants lists, not sets, hence the sorted below return losses[numpy.array(sorted(idxs))].sum(axis=0) def _filter_agg(assetcol, losses, selected, stats=''): # losses is an array of shape (A, ..., R) with A=#assets, R=#realizations aids_by_tag = assetcol.get_aids_by_tag() idxs = set(range(len(assetcol))) tagnames = [] for tag in selected: tagname, tagvalue = tag.split('=', 1) if tagvalue == '*': tagnames.append(tagname) else: idxs &= aids_by_tag[tag] if len(tagnames) > 1: raise ValueError('Too many * as tag values in %s' % tagnames) elif not tagnames: # return an array of shape (..., R) return ArrayWrapper( _agg(losses, idxs), dict(selected=encode(selected), stats=stats)) else: # return an array of shape (T, ..., R) [tagname] = tagnames _tags = list(assetcol.tagcol.gen_tags(tagname)) all_idxs = [idxs & aids_by_tag[t] for t in _tags] # NB: using a generator expression for all_idxs caused issues (?) data, tags = [], [] for idxs, tag in zip(all_idxs, _tags): agglosses = _agg(losses, idxs) if len(agglosses): data.append(agglosses) tags.append(tag) return ArrayWrapper( numpy.array(data), dict(selected=encode(selected), tags=encode(tags), stats=stats))
[docs]def get_loss_type_tags(what): try: loss_type, query_string = what.rsplit('?', 1) except ValueError: # no question mark loss_type, query_string = what, '' tags = query_string.split('&') if query_string else [] return loss_type, tags
[docs]@extract.add('agg_losses') def extract_agg_losses(dstore, what): """ Aggregate losses of the given loss type and tags. Use it as /extract/agg_losses/structural?taxonomy=RC&zipcode=20126 /extract/agg_losses/structural?taxonomy=RC&zipcode=* :returns: an array of shape (T, R) if one of the tag names has a `*` value an array of shape (R,), being R the number of realizations an array of length 0 if there is no data for the given tags """ loss_type, tags = get_loss_type_tags(what) if not loss_type: raise ValueError('loss_type not passed in agg_losses/<loss_type>') l = dstore['oqparam'].lti[loss_type] if 'losses_by_asset' in dstore: # scenario_risk stats = None losses = dstore['losses_by_asset'][:, :, l]['mean'] elif 'avg_losses-stats' in dstore: # event_based_risk, classical_risk stats = dstore['avg_losses-stats'].attrs['stats'] losses = dstore['avg_losses-stats'][:, :, l] elif 'avg_losses-rlzs' in dstore: # event_based_risk, classical_risk stats = None losses = dstore['avg_losses-rlzs'][:, :, l] else: raise KeyError('No losses found in %s' % dstore) return _filter_agg(dstore['assetcol'], losses, tags, stats)
[docs]@extract.add('agg_damages') def extract_agg_damages(dstore, what): """ Aggregate damages of the given loss type and tags. Use it as /extract/agg_damages/structural?taxonomy=RC&zipcode=20126 :returns: array of shape (R, D), being R the number of realizations and D the number of damage states or array of length 0 if there is no data for the given tags """ loss_type, tags = get_loss_type_tags(what) if 'dmg_by_asset' in dstore: # scenario_damage losses = dstore['dmg_by_asset'][loss_type]['mean'] else: raise KeyError('No damages found in %s' % dstore) return _filter_agg(dstore['assetcol'], losses, tags)
[docs]@extract.add('agg_curves') def extract_agg_curves(dstore, what): """ Aggregate loss curves of the given loss type and tags for event based risk calculations. Use it as /extract/agg_curves/structural?taxonomy=RC&zipcode=20126 :returns: array of shape (S, P), being P the number of return periods and S the number of statistics """ loss_type, tags = get_loss_type_tags(what) if 'curves-stats' in dstore: # event_based_risk losses = dstore['curves-stats'][loss_type] stats = dstore['curves-stats'].attrs['stats'] elif 'curves-rlzs' in dstore: # event_based_risk, 1 rlz losses = dstore['curves-rlzs'][loss_type] assert losses.shape[1] == 1, 'There must be a single realization' stats = [b'mean'] # suitable to be stored as hdf5 attribute else: raise KeyError('No curves found in %s' % dstore) res = _filter_agg(dstore['assetcol'], losses, tags, stats) cc = dstore['assetcol/cost_calculator'] res.units = cc.get_units(loss_types=[loss_type]) res.return_periods = get_loss_builder(dstore).return_periods return res
[docs]@extract.add('losses_by_asset') def extract_losses_by_asset(dstore, what): loss_dt = dstore['oqparam'].loss_dt() rlzs = dstore['csm_info'].get_rlzs_assoc().realizations assets = util.get_assets(dstore) if 'losses_by_asset' in dstore: losses_by_asset = dstore['losses_by_asset'].value for rlz in rlzs: # I am exporting the 'mean' and ignoring the 'stddev' losses = cast(losses_by_asset[:, rlz.ordinal]['mean'], loss_dt) data = util.compose_arrays(assets, losses) yield 'rlz-%03d' % rlz.ordinal, data elif 'avg_losses-stats' in dstore: avg_losses = dstore['avg_losses-stats'].value stats = dstore['avg_losses-stats'].attrs['stats'].split() for s, stat in enumerate(stats): losses = cast(avg_losses[:, s], loss_dt) data = util.compose_arrays(assets, losses) yield stat, data elif 'avg_losses-rlzs' in dstore: # there is only one realization avg_losses = dstore['avg_losses-rlzs'].value losses = cast(avg_losses, loss_dt) data = util.compose_arrays(assets, losses) yield 'rlz-000', data
[docs]@extract.add('losses_by_event') def extract_losses_by_event(dstore, what): dic = group_array(dstore['losses_by_event'].value, 'rlzi') for rlzi in dic: yield 'rlz-%03d' % rlzi, dic[rlzi]
def _gmf_scenario(data, num_sites, imts): # convert data into the composite array expected by QGIS eids = sorted(numpy.unique(data['eid'])) eid2idx = {eid: idx for idx, eid in enumerate(eids)} E = len(eid2idx) gmf_dt = numpy.dtype([(imt, (F32, (E,))) for imt in imts]) gmfa = numpy.zeros(num_sites, gmf_dt) for rec in data: arr = gmfa[rec['sid']] for imt, gmv in zip(imts, rec['gmv']): arr[imt][eid2idx[rec['eid']]] = gmv return gmfa, E
[docs]@extract.add('gmf_data') def extract_gmf_scenario_npz(dstore, what): oq = dstore['oqparam'] mesh = get_mesh(dstore['sitecol']) n = len(mesh) data_by_rlzi = group_array(dstore['gmf_data/data'].value, 'rlzi') for rlzi in data_by_rlzi: gmfa, e = _gmf_scenario(data_by_rlzi[rlzi], n, oq.imtls)'Exporting array of shape %s for rlz %d', (n, e), rlzi) yield 'rlz-%03d' % rlzi, util.compose_arrays(mesh, gmfa)
[docs]def build_damage_dt(dstore, mean_std=True): """ :param dstore: a datastore instance :param mean_std: a flag (default True) :returns: a composite dtype loss_type -> (mean_ds1, stdv_ds1, ...) or loss_type -> (ds1, ds2, ...) depending on the flag mean_std """ damage_states = ['no_damage'] + list( dstore.get_attr('composite_risk_model', 'limit_states')) dt_list = [] for ds in damage_states: ds = str(ds) if mean_std: dt_list.append(('%s_mean' % ds, F32)) dt_list.append(('%s_stdv' % ds, F32)) else: dt_list.append((ds, F32)) damage_dt = numpy.dtype(dt_list) loss_types = dstore.get_attr('composite_risk_model', 'loss_types') return numpy.dtype([(str(lt), damage_dt) for lt in loss_types])
[docs]def build_damage_array(data, damage_dt): """ :param data: an array of length N with fields 'mean' and 'stddev' :param damage_dt: a damage composite data type loss_type -> states :returns: a composite array of length N and dtype damage_dt """ L = len(data) if data.shape else 1 dmg = numpy.zeros(L, damage_dt) for lt in damage_dt.names: for i, ms in numpy.ndenumerate(data[lt]): if damage_dt[lt].names[0].endswith('_mean'): lst = [] for m, s in zip(ms['mean'], ms['stddev']): lst.append(m) lst.append(s) dmg[lt][i] = tuple(lst) else: dmg[lt][i] = ms['mean'] return dmg
[docs]@extract.add('dmg_by_asset') def extract_dmg_by_asset_npz(dstore, what): damage_dt = build_damage_dt(dstore) rlzs = dstore['csm_info'].get_rlzs_assoc().realizations data = dstore['dmg_by_asset'] assets = util.get_assets(dstore) for rlz in rlzs: dmg_by_asset = build_damage_array(data[:, rlz.ordinal], damage_dt) yield 'rlz-%03d' % rlz.ordinal, util.compose_arrays( assets, dmg_by_asset)
[docs]@extract.add('event_based_mfd') def extract_mfd(dstore, what): """ Display num_ruptures by magnitude for event based calculations. Example: """ dd = collections.defaultdict(int) for rup in dstore['ruptures'].value: dd[rup['mag']] += 1 dt = numpy.dtype([('mag', float), ('freq', int)]) magfreq = numpy.array(sorted(dd.items(), key=operator.itemgetter(0)), dt) return magfreq
[docs]@extract.add('mean_std_curves') def extract_mean_std_curves(dstore, what): """ Yield imls/IMT and poes/IMT containg mean and stddev for all sites """ getter = getters.PmapGetter(dstore) arr = getter.get_mean().array for imt in getter.imtls: yield 'imls/' + imt, getter.imtls[imt] yield 'poes/' + imt, arr[:, getter.imtls(imt)]