# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2023 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import io
import os
import time
import zlib
import pickle
import psutil
import logging
import operator
import numpy
import pandas
from PIL import Image
from openquake.baselib import (
performance, parallel, hdf5, config, python3compat)
from openquake.baselib.general import (
AccumDict, DictArray, block_splitter, groupby, humansize)
from openquake.hazardlib import valid, InvalidFile
from openquake.hazardlib.contexts import read_cmakers, get_maxsize
from openquake.hazardlib.calc.hazard_curve import classical as hazclassical
from openquake.hazardlib.calc import disagg
from openquake.hazardlib.map_array import MapArray, rates_dt
from openquake.commonlib import calc
from openquake.calculators import base, getters
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
I64 = numpy.int64
TWO24 = 2 ** 24
TWO32 = 2 ** 32
BUFFER = 1.5 # enlarge the pointsource_distance sphere to fix the weight;
# with BUFFER = 1 we would have lots of apparently light sources
# collected together in an extra-slow task, as it happens in SHARE
# with ps_grid_spacing=50
get_weight = operator.attrgetter('weight')
slice_dt = numpy.dtype([('idx', U32), ('start', int), ('stop', int)])
# NB: using 32 bit ratemaps
[docs]def get_pmaps_gb(dstore):
"""
:returns: memory required on the master node to keep the pmaps
"""
N = len(dstore['sitecol'])
L = dstore['oqparam'].imtls.size
full_lt = dstore['full_lt'].init()
all_trt_smrs = dstore['trt_smrs'][:]
trt_rlzs = full_lt.get_trt_rlzs(all_trt_smrs)
gids = full_lt.get_gids(all_trt_smrs)
return len(trt_rlzs) * N * L * 4 / 1024**3, trt_rlzs, gids
[docs]def build_slices(idxs, offset=0):
"""
Convert an array of site IDs (with repetitions) into an array slice_dt
"""
arr = performance.idx_start_stop(idxs)
sbs = numpy.zeros(len(arr), slice_dt)
sbs['idx'] = arr[:, 0]
sbs['start'] = arr[:, 1] + offset
sbs['stop'] = arr[:, 2] + offset
return sbs
[docs]class Set(set):
__iadd__ = set.__ior__
[docs]def store_ctxs(dstore, rupdata_list, grp_id):
"""
Store contexts in the datastore
"""
for rupdata in rupdata_list:
nr = len(rupdata)
known = set(rupdata.dtype.names)
for par in dstore['rup']:
if par == 'grp_id':
hdf5.extend(dstore['rup/grp_id'], numpy.full(nr, grp_id))
elif par == 'probs_occur':
dstore.hdf5.save_vlen('rup/probs_occur', rupdata[par])
elif par in known:
hdf5.extend(dstore['rup/' + par], rupdata[par])
else:
hdf5.extend(dstore['rup/' + par], numpy.full(nr, numpy.nan))
[docs]def to_rates(pnemap, gid, tiling, disagg_by_src):
"""
:returns: dictionary if tiling is True, else MapArray with rates
"""
rates = pnemap.to_rates()
if tiling:
return rates.to_dict(gid)
if disagg_by_src:
return rates
return rates.remove_zeros()
# ########################### task functions ############################ #
[docs]def classical(sources, sitecol, cmaker, dstore, monitor):
"""
Call the classical calculator in hazardlib
"""
# NB: removing the yield would cause terrible slow tasks
cmaker.init_monitoring(monitor)
tiling = not hasattr(sources, '__iter__') # passed gid
disagg_by_src = cmaker.disagg_by_src
with dstore:
if tiling: # tiling calculator, read the sources from the datastore
gid = sources
with monitor('reading sources'): # fast, but uses a lot of RAM
arr = dstore.getitem('_csm')[cmaker.grp_id]
sources = pickle.loads(zlib.decompress(arr.tobytes()))
else: # regular calculator
gid = 0
sitecol = dstore['sitecol'] # super-fast
if disagg_by_src and not getattr(sources, 'atomic', False):
# in case_27 (Japan) we do NOT enter here;
# disagg_by_src still works since the atomic group contains a single
# source 'case' (mutex combination of case:01, case:02)
for srcs in groupby(sources, valid.basename).values():
pmap = MapArray(
sitecol.sids, cmaker.imtls.size, len(cmaker.gsims)).fill(
cmaker.rup_indep)
result = hazclassical(srcs, sitecol, cmaker, pmap)
result['pnemap'] = to_rates(~pmap, gid, tiling, disagg_by_src)
yield result
else:
# size_mb is the maximum size of the pmap array in GB
size_mb = (len(cmaker.gsims) * cmaker.imtls.size * len(sitecol)
* 8 / 1024**2)
if config.distribution.compress:
size_mb /= 5 # produce 5x less tiles
# NB: the parameter config.memory.pmap_max_mb avoids the hanging
# of oq1 due to too large zmq packets
itiles = int(numpy.ceil(size_mb / cmaker.pmap_max_mb))
for sites in sitecol.split_in_tiles(itiles):
pmap = MapArray(
sites.sids, cmaker.imtls.size, len(cmaker.gsims)).fill(
cmaker.rup_indep)
result = hazclassical(sources, sites, cmaker, pmap)
result['pnemap'] = to_rates(~pmap, gid, tiling, disagg_by_src)
yield result
# for instance for New Zealand G~1000 while R[full_enum]~1_000_000
# i.e. passing the gweights reduces the data transfer by 1000 times
[docs]def fast_mean(pgetter, gweights, monitor):
"""
:param pgetter: a :class:`openquake.commonlib.getters.MapGetter`
:param gweights: an array of G weights
:returns: a dictionary kind -> MapArray
"""
with monitor('reading rates', measuremem=True):
pgetter.init()
with monitor('compute stats', measuremem=True):
hcurves = pgetter.get_fast_mean(gweights)
pmap_by_kind = {'hcurves-stats': [hcurves]}
if pgetter.poes:
with monitor('make_hmaps', measuremem=False):
pmap_by_kind['hmaps-stats'] = calc.make_hmaps(
pmap_by_kind['hcurves-stats'], pgetter.imtls, pgetter.poes)
return pmap_by_kind
[docs]def postclassical(pgetter, weights, wget, hstats, individual_rlzs,
max_sites_disagg, amplifier, monitor):
"""
:param pgetter: a :class:`openquake.commonlib.getters.MapGetter`
:param weights: a list of ImtWeights
:param wget: function (weights[:, :], imt) -> weights[:]
:param hstats: a list of pairs (statname, statfunc)
:param individual_rlzs: if True, also build the individual curves
:param max_sites_disagg: if there are less sites than this, store rup info
:param amplifier: instance of Amplifier or None
:param monitor: instance of Monitor
:returns: a dictionary kind -> MapArray
The "kind" is a string of the form 'rlz-XXX' or 'mean' of 'quantile-XXX'
used to specify the kind of output.
"""
with monitor('reading rates', measuremem=True):
pgetter.init()
if amplifier:
with hdf5.File(pgetter.filename, 'r') as f:
ampcode = f['sitecol'].ampcode
imtls = DictArray({imt: amplifier.amplevels
for imt in pgetter.imtls})
else:
imtls = pgetter.imtls
poes, sids = pgetter.poes, U32(pgetter.sids)
M = len(imtls)
L = imtls.size
L1 = L // M
R = pgetter.R
S = len(hstats)
pmap_by_kind = {}
if R == 1 or individual_rlzs:
pmap_by_kind['hcurves-rlzs'] = [
MapArray(sids, M, L1).fill(0) for r in range(R)]
if hstats:
pmap_by_kind['hcurves-stats'] = [
MapArray(sids, M, L1).fill(0) for r in range(S)]
combine_mon = monitor('combine pmaps', measuremem=False)
compute_mon = monitor('compute stats', measuremem=False)
hmaps_mon = monitor('make_hmaps', measuremem=False)
sidx = MapArray(sids, 1, 1).fill(0).sidx
for sid in sids:
idx = sidx[sid]
with combine_mon:
pc = pgetter.get_hcurve(sid) # shape (L, R)
if amplifier:
pc = amplifier.amplify(ampcode[sid], pc)
# NB: the hcurve have soil levels != IMT levels
if pc.sum() == 0: # no data
continue
with compute_mon:
if R == 1 or individual_rlzs:
for r in range(R):
pmap_by_kind['hcurves-rlzs'][r].array[idx] = (
pc[:, r].reshape(M, L1))
if hstats:
for s, (statname, stat) in enumerate(hstats.items()):
sc = getters.build_stat_curve(
pc, imtls, stat, weights, wget, pgetter.use_rates)
arr = sc.reshape(M, L1)
pmap_by_kind['hcurves-stats'][s].array[idx] = arr
if poes and (R == 1 or individual_rlzs):
with hmaps_mon:
pmap_by_kind['hmaps-rlzs'] = calc.make_hmaps(
pmap_by_kind['hcurves-rlzs'], imtls, poes)
if poes and hstats:
with hmaps_mon:
pmap_by_kind['hmaps-stats'] = calc.make_hmaps(
pmap_by_kind['hcurves-stats'], imtls, poes)
return pmap_by_kind
[docs]def make_hmap_png(hmap, lons, lats):
"""
:param hmap:
a dictionary with keys calc_id, m, p, imt, poe, inv_time, array
:param lons: an array of longitudes
:param lats: an array of latitudes
:returns: an Image object containing the hazard map
"""
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
ax.grid(True)
ax.set_title('hmap for IMT=%(imt)s, poe=%(poe)s\ncalculation %(calc_id)d,'
'inv_time=%(inv_time)dy' % hmap)
ax.set_ylabel('Longitude')
coll = ax.scatter(lons, lats, c=hmap['array'], cmap='jet')
plt.colorbar(coll)
bio = io.BytesIO()
plt.savefig(bio, format='png')
return dict(img=Image.open(bio), m=hmap['m'], p=hmap['p'])
[docs]class Hazard:
"""
Helper class for storing the rates
"""
def __init__(self, dstore, srcidx, gids):
self.datastore = dstore
oq = dstore['oqparam']
self.itime = oq.investigation_time
self.weig = dstore['_rates/weig'][:]
self.imtls = oq.imtls
self.sids = dstore['sitecol/sids'][:]
self.srcidx = srcidx
self.gids = gids
self.N = len(dstore['sitecol/sids'])
self.M = len(oq.imtls)
self.L = oq.imtls.size
self.L1 = self.L // self.M
self.sites_per_task = int(numpy.ceil(
self.N / (oq.concurrent_tasks or 1)))
self.acc = AccumDict(accum={})
self.offset = 0
# used in in disagg_by_src
[docs] def get_rates(self, pmap, grp_id):
"""
:param pmap: a MapArray
:returns: an array of rates of shape (N, M, L1)
"""
gids = self.gids[grp_id]
rates = pmap.array @ self.weig[gids] / self.itime
return rates.reshape((self.N, self.M, self.L1))
[docs] def store_rates(self, pnemap):
"""
Store pnes inside the _rates dataset
"""
if isinstance(pnemap, dict): # already converted (tiling)
rates = pnemap
else:
rates = pnemap.to_dict()
if len(rates['sid']) == 0: # happens in case_60
return self.offset * 12
hdf5.extend(self.datastore['_rates/sid'], rates['sid'])
hdf5.extend(self.datastore['_rates/gid'], rates['gid'])
hdf5.extend(self.datastore['_rates/lid'], rates['lid'])
hdf5.extend(self.datastore['_rates/rate'], rates['rate'])
# NB: there is a genious idea here, to split in tasks by using
# the formula ``taskno = sites_ids // sites_per_task`` and then
# extracting a dictionary of slices for each taskno. This works
# since by construction the site_ids are sequential and there are
# at most G slices per task. For instance if there are 6 sites
# disposed in 2 groups and we want to produce 2 tasks we can use
# 012345012345 // 3 = 000111000111 and the slices are
# {0: [(0, 3), (6, 9)], 1: [(3, 6), (9, 12)]}
sbs = build_slices(rates['sid'] // self.sites_per_task, self.offset)
hdf5.extend(self.datastore['_rates/slice_by_idx'], sbs)
# slice_by_idx contains 3 slices in classical/case_22
self.offset += len(rates['sid'])
self.acc['nsites'] = self.offset
return self.offset * 12 # 4 + 2 + 2 + 4 bytes
[docs] def store_mean_rates_by_src(self, dic):
"""
Store data inside mean_rates_by_src with shape (N, M, L1, Ns)
"""
mean_rates_by_src = self.datastore['mean_rates_by_src/array'][()]
for key, rates in dic.items():
if isinstance(key, str):
# in case of mean_rates_by_src key is a source ID
idx = self.srcidx[valid.corename(key)]
mean_rates_by_src[..., idx] += rates
self.datastore['mean_rates_by_src/array'][:] = mean_rates_by_src
return mean_rates_by_src
[docs]@base.calculators.add('classical', 'ucerf_classical')
class ClassicalCalculator(base.HazardCalculator):
"""
Classical PSHA calculator
"""
core_task = classical
precalc = 'preclassical'
accept_precalc = ['preclassical', 'classical']
SLOW_TASK_ERROR = False
[docs] def agg_dicts(self, acc, dic):
"""
Aggregate dictionaries of hazard curves by updating the accumulator.
:param acc: accumulator dictionary
:param dic: dict with keys pmap, source_data, rup_data
"""
# NB: dic should be a dictionary, but when the calculation dies
# for an OOM it can become None, thus giving a very confusing error
if dic is None:
raise MemoryError('You ran out of memory!')
sdata = dic['source_data']
self.source_data += sdata
grp_id = dic.pop('grp_id')
self.rel_ruptures[grp_id] += sum(sdata['nrupts'])
cfactor = dic.pop('cfactor')
if cfactor[1] != cfactor[0]:
print('ctxs_per_mag = {:.0f}, cfactor_per_task = {:.1f}'.format(
cfactor[1] / cfactor[2], cfactor[1] / cfactor[0]))
self.cfactor += cfactor
# store rup_data if there are few sites
if self.few_sites and len(dic['rup_data']):
with self.monitor('saving rup_data'):
store_ctxs(self.datastore, dic['rup_data'], grp_id)
pnemap = dic['pnemap'] # probabilities of no exceedence
source_id = dic.pop('basename', '') # non-empty for disagg_by_src
if source_id:
# accumulate the rates for the given source
acc[source_id] += self.haz.get_rates(pnemap, grp_id)
G = pnemap.array.shape[2]
rates = self.pmap.array
sidx = self.pmap.sidx[pnemap.sids]
for i, gid in enumerate(self.gids[grp_id]):
rates[sidx, :, gid] += pnemap.array[:, :, i % G]
return acc
[docs] def create_rup(self):
"""
Create the rup datasets *before* starting the calculation
"""
params = {'grp_id', 'occurrence_rate', 'clon', 'clat', 'rrup',
'probs_occur', 'sids', 'src_id', 'rup_id', 'weight'}
for cm in self.cmakers:
params.update(cm.REQUIRES_RUPTURE_PARAMETERS)
params.update(cm.REQUIRES_DISTANCES)
if self.few_sites:
descr = [] # (param, dt)
for param in sorted(params):
if param == 'sids':
dt = U16 # storing only for few sites
elif param == 'probs_occur':
dt = hdf5.vfloat64
elif param in ('src_id', 'rup_id'):
dt = U32
elif param == 'grp_id':
dt = U16
else:
dt = F32
descr.append((param, dt))
self.datastore.create_df('rup', descr, 'gzip')
# NB: the relevant ruptures are less than the effective ruptures,
# which are a preclassical concept
[docs] def init_poes(self):
self.cmakers = read_cmakers(self.datastore, self.csm)
self.cfactor = numpy.zeros(3)
self.rel_ruptures = AccumDict(accum=0) # grp_id -> rel_ruptures
self.datastore.create_df(
'_rates', [(n, rates_dt[n]) for n in rates_dt.names], 'gzip')
self.datastore.create_dset('_rates/slice_by_idx', slice_dt,
compression='gzip')
oq = self.oqparam
if oq.disagg_by_src:
M = len(oq.imtls)
L1 = oq.imtls.size // M
sources = self.csm.get_basenames()
mean_rates_by_src = numpy.zeros((self.N, M, L1, len(sources)))
dic = dict(shape_descr=['site_id', 'imt', 'lvl', 'src_id'],
site_id=self.N, imt=list(oq.imtls),
lvl=L1, src_id=numpy.array(sources))
self.datastore['mean_rates_by_src'] = hdf5.ArrayWrapper(
mean_rates_by_src, dic)
[docs] def check_memory(self, N, L, maxw):
"""
Log the memory required to receive the largest MapArray,
assuming all sites are affected (upper limit)
"""
num_gs = [len(cm.gsims) for cm in self.cmakers]
max_gs = max(num_gs)
maxsize = get_maxsize(len(self.oqparam.imtls), max_gs)
logging.info('Considering {:_d} contexts at once'.format(maxsize))
size = max_gs * N * L * 4
avail = min(psutil.virtual_memory().available, config.memory.limit)
if avail < size:
raise MemoryError(
'You have only %s of free RAM' % humansize(avail))
[docs] def execute(self):
"""
Run in parallel `core_task(sources, sitecol, monitor)`, by
parallelizing on the sources according to their weight and
tectonic region type.
"""
oq = self.oqparam
if oq.hazard_calculation_id:
logging.info('Reading from parent calculation')
parent = self.datastore.parent
self.full_lt = parent['full_lt'].init()
self.csm = parent['_csm']
self.csm.init(self.full_lt)
self.datastore['source_info'] = parent['source_info'][:]
maxw = self.csm.get_max_weight(oq)
oq.mags_by_trt = {
trt: python3compat.decode(dset[:])
for trt, dset in parent['source_mags'].items()}
if '_rates' in parent:
self.build_curves_maps() # repeat post-processing
return {}
else:
maxw = self.max_weight
self.init_poes()
if oq.fastmean:
logging.info('Will use the fast_mean algorithm')
req_gb, self.trt_rlzs, self.gids = get_pmaps_gb(self.datastore)
self.datastore['_rates/weig'] = self.full_lt.g_weights(self.trt_rlzs)
srcidx = {name: i for i, name in enumerate(self.csm.get_basenames())}
self.haz = Hazard(self.datastore, srcidx, self.gids)
rlzs = self.R == 1 or oq.individual_rlzs
if not rlzs and not oq.hazard_stats():
raise InvalidFile('%(job_ini)s: you disabled all statistics',
oq.inputs)
self.source_data = AccumDict(accum=[])
if not performance.numba:
logging.warning('numba is not installed: using the slow algorithm')
t0 = time.time()
max_gb = float(config.memory.pmap_max_gb)
if oq.disagg_by_src or self.N < oq.max_sites_disagg or req_gb < max_gb:
self.check_memory(len(self.sitecol), oq.imtls.size, maxw)
self.execute_reg(maxw)
else:
self.execute_big(maxw * .75)
self.store_info()
if self.cfactor[0] == 0:
if self.N == 1:
logging.warning('The site is far from all seismic sources'
' included in the hazard model')
else:
raise RuntimeError('The sites are far from all seismic sources'
' included in the hazard model')
else:
logging.info('cfactor = {:_d}/{:_d} = {:.1f}'.format(
int(self.cfactor[1]), int(self.cfactor[0]),
self.cfactor[1] / self.cfactor[0]))
if '_rates' in self.datastore:
self.build_curves_maps()
if not oq.hazard_calculation_id:
self.classical_time = time.time() - t0
return True
[docs] def execute_reg(self, maxw):
"""
Regular case
"""
self.create_rup() # create the rup/ datasets BEFORE swmr_on()
acc = AccumDict(accum=0.) # src_id -> pmap
oq = self.oqparam
L = oq.imtls.size
Gt = len(self.trt_rlzs)
self.pmap = MapArray(self.sitecol.sids, L, Gt).fill(0, F32)
allargs = []
if 'sitecol' in self.datastore.parent:
ds = self.datastore.parent
else:
ds = self.datastore
for cm in self.cmakers:
sg = self.csm.src_groups[cm.grp_id]
cm.rup_indep = getattr(sg, 'rup_interdep', None) != 'mutex'
cm.pmap_max_mb = float(config.memory.pmap_max_mb)
if sg.atomic or sg.weight <= maxw:
blks = [sg]
else:
blks = block_splitter(sg, maxw, get_weight, sort=True)
for block in blks:
logging.debug('Sending %d source(s) with weight %d',
len(block), sg.weight)
allargs.append((block, None, cm, ds))
self.datastore.swmr_on() # must come before the Starmap
smap = parallel.Starmap(classical, allargs, h5=self.datastore.hdf5)
acc = smap.reduce(self.agg_dicts, acc)
with self.monitor('storing rates', measuremem=True):
self.haz.store_rates(self.pmap)
del self.pmap
if oq.disagg_by_src:
mrs = self.haz.store_mean_rates_by_src(acc)
if oq.use_rates and self.N == 1: # sanity check
self.check_mean_rates(mrs)
[docs] def check_mean_rates(self, mean_rates_by_src):
"""
The sum of the mean_rates_by_src must correspond to the mean_rates
"""
try:
exp = disagg.to_rates(self.datastore['hcurves-stats'][0, 0])
except KeyError: # if there are no ruptures close to the site
return
got = mean_rates_by_src[0].sum(axis=2) # sum over the sources
for m in range(len(got)):
# skipping large rates which can be wrong due to numerics
# (it happens in logictree/case_05 and in Japan)
ok = got[m] < 10.
numpy.testing.assert_allclose(got[m, ok], exp[m, ok], atol=1E-5)
[docs] def execute_big(self, maxw):
"""
Use parallel tiling
"""
oq = self.oqparam
assert not oq.disagg_by_src
assert self.N > self.oqparam.max_sites_disagg, self.N
allargs = []
self.ntiles = []
if '_csm' in self.datastore.parent:
ds = self.datastore.parent
else:
ds = self.datastore
for cm in self.cmakers:
sg = self.csm.src_groups[cm.grp_id]
cm.rup_indep = getattr(sg, 'rup_interdep', None) != 'mutex'
cm.pmap_max_mb = float(config.memory.pmap_max_mb)
gid = self.gids[cm.grp_id][0]
if sg.atomic or sg.weight <= maxw:
allargs.append((gid, self.sitecol, cm, ds))
else:
tiles = self.sitecol.split(numpy.ceil(sg.weight / maxw))
logging.info('Group #%d, %d tiles', cm.grp_id, len(tiles))
for tile in tiles:
allargs.append((gid, tile, cm, ds))
self.ntiles.append(len(tiles))
logging.warning('Generated at most %d tiles', max(self.ntiles))
self.datastore.swmr_on() # must come before the Starmap
mon = self.monitor('storing rates')
for dic in parallel.Starmap(classical, allargs, h5=self.datastore.hdf5):
self.cfactor += dic['cfactor']
with mon:
self.haz.store_rates(dic['pnemap'])
return {}
[docs] def store_info(self):
"""
Store full_lt, source_info and source_data
"""
self.store_rlz_info(self.rel_ruptures)
self.store_source_info(self.source_data)
df = pandas.DataFrame(self.source_data)
# NB: the impact factor is the number of effective ruptures;
# consider for instance a point source producing 200 ruptures
# for points within the pointsource_distance (n points) and
# producing 20 effective ruptures for the N-n points outside;
# then impact = (200 * n + 20 * (N-n)) / N; for n=1 and N=10
# it gives impact = 38, i.e. there are 38 effective ruptures
df['impact'] = df.nsites / self.N
self.datastore.create_df('source_data', df)
self.source_data.clear() # save a bit of memory
[docs] def collect_hazard(self, acc, pmap_by_kind):
"""
Populate hcurves and hmaps in the .hazard dictionary
:param acc: ignored
:param pmap_by_kind: a dictionary of MapArrays
"""
# this is practically instantaneous
if pmap_by_kind is None: # instead of a dict
raise MemoryError('You ran out of memory!')
for kind in pmap_by_kind: # hmaps-XXX, hcurves-XXX
pmaps = pmap_by_kind[kind]
if kind in self.hazard:
array = self.hazard[kind]
else:
dset = self.datastore.getitem(kind)
array = self.hazard[kind] = numpy.zeros(dset.shape, dset.dtype)
for r, pmap in enumerate(pmaps):
for idx, sid in enumerate(pmap.sids):
array[sid, r] = pmap.array[idx] # shape (M, P)
[docs] def post_execute(self, dummy):
"""
Check for slow tasks
"""
oq = self.oqparam
task_info = self.datastore.read_df('task_info', 'taskname')
try:
dur = task_info.loc[b'classical'].duration
except KeyError: # no data
pass
else:
slow_tasks = len(dur[dur > 3 * dur.mean()]) and dur.max() > 180
msg = 'There were %d slow task(s)' % slow_tasks
if slow_tasks and self.SLOW_TASK_ERROR and not oq.disagg_by_src:
raise RuntimeError('%s in #%d' % (msg, self.datastore.calc_id))
elif slow_tasks:
logging.info(msg)
def _create_hcurves_maps(self):
oq = self.oqparam
N = len(self.sitecol)
R = len(self.realizations)
if oq.individual_rlzs is None: # not specified in the job.ini
individual_rlzs = (N == 1) * (R > 1)
else:
individual_rlzs = oq.individual_rlzs
hstats = oq.hazard_stats()
# initialize datasets
P = len(oq.poes)
M = self.M = len(oq.imtls)
imts = list(oq.imtls)
if oq.soil_intensities is not None:
L = M * len(oq.soil_intensities)
else:
L = oq.imtls.size
L1 = self.L1 = L // M
S = len(hstats)
if R == 1 or individual_rlzs:
self.datastore.create_dset('hcurves-rlzs', F32, (N, R, M, L1))
self.datastore.set_shape_descr(
'hcurves-rlzs', site_id=N, rlz_id=R, imt=imts, lvl=L1)
if oq.poes:
self.datastore.create_dset('hmaps-rlzs', F32, (N, R, M, P))
self.datastore.set_shape_descr(
'hmaps-rlzs', site_id=N, rlz_id=R,
imt=list(oq.imtls), poe=oq.poes)
if hstats:
self.datastore.create_dset('hcurves-stats', F32, (N, S, M, L1))
self.datastore.set_shape_descr(
'hcurves-stats', site_id=N, stat=list(hstats),
imt=imts, lvl=numpy.arange(L1))
if oq.poes:
self.datastore.create_dset('hmaps-stats', F32, (N, S, M, P))
self.datastore.set_shape_descr(
'hmaps-stats', site_id=N, stat=list(hstats),
imt=list(oq.imtls), poe=oq.poes)
return N, S, M, P, L1, individual_rlzs
# called by execute before post_execute
[docs] def build_curves_maps(self):
"""
Compute and store hcurves-rlzs, hcurves-stats, hmaps-rlzs, hmaps-stats
"""
oq = self.oqparam
hstats = oq.hazard_stats()
N, S, M, P, L1, individual = self._create_hcurves_maps()
if '_rates' in set(self.datastore):
dstore = self.datastore
else:
dstore = self.datastore.parent
slicedic = AccumDict(accum=[])
for idx, start, stop in dstore['_rates/slice_by_idx'][:]:
slicedic[idx].append((start, stop))
if not slicedic:
# no hazard, nothing to do, happens in case_60
return
# using compactify improves the performance of `reading rates`;
# I have measured a 3.5x in the AUS model with 1 rlz
allslices = [calc.compactify(slices) for slices in slicedic.values()]
nslices = sum(len(slices) for slices in allslices)
logging.info('There are %.1f slices of rates per task',
nslices / len(slicedic))
if 'trt_smrs' not in dstore: # starting from hazard_curves.csv
trt_rlzs = self.full_lt.get_trt_rlzs([[0]])
else:
trt_rlzs = self.full_lt.get_trt_rlzs(dstore['trt_smrs'][:])
if oq.fastmean:
ws = self.datastore['weights'][:]
weights = numpy.array([ws[trs % TWO24].sum() for trs in trt_rlzs])
trt_rlzs = numpy.zeros(len(trt_rlzs)) # reduces the data transfer
else:
weights = self.full_lt.weights
wget = self.full_lt.wget
allargs = [
(getters.MapGetter(dstore.filename, trt_rlzs, self.R, slices, oq),
weights, wget, hstats, individual, oq.max_sites_disagg,
self.amplifier) for slices in allslices]
self.hazard = {} # kind -> array
hcbytes = 8 * N * S * M * L1
hmbytes = 8 * N * S * M * P if oq.poes else 0
if hcbytes:
logging.info('Producing %s of hazard curves', humansize(hcbytes))
if hmbytes:
logging.info('Producing %s of hazard maps', humansize(hmbytes))
if 'delta_rates' in oq.inputs:
pass # avoid an HDF5 error
else: # in all the other cases
self.datastore.swmr_on()
if oq.fastmean:
parallel.Starmap(
fast_mean, [args[0:2] for args in allargs],
distribute='no' if self.few_sites else None,
h5=self.datastore.hdf5,
).reduce(self.collect_hazard)
else:
parallel.Starmap(
postclassical, allargs,
distribute='no' if self.few_sites else None,
h5=self.datastore.hdf5,
).reduce(self.collect_hazard)
for kind in sorted(self.hazard):
logging.info('Saving %s', kind) # very fast
self.datastore[kind][:] = self.hazard.pop(kind)
fraction = os.environ.get('OQ_SAMPLE_SOURCES')
if fraction and hasattr(self, 'classical_time'):
total_time = time.time() - self.t0
delta = total_time - self.classical_time
est_time = self.classical_time / float(fraction) + delta
logging.info('Estimated time: %.1f hours', est_time / 3600)
if 'hmaps-stats' in self.datastore:
self.plot_hmaps()
[docs] def plot_hmaps(self):
"""
Generate hazard map plots if there are more the 1000 sites
"""
hmaps = self.datastore.sel('hmaps-stats', stat='mean') # NSMP
maxhaz = hmaps.max(axis=(0, 1, 3))
mh = dict(zip(self.oqparam.imtls, maxhaz))
logging.info('The maximum hazard map values are %s', mh)
if Image is None or not self.from_engine: # missing PIL
return
if self.N < 1000: # few sites, don't plot
return
M, P = hmaps.shape[2:]
logging.info('Saving %dx%d mean hazard maps', M, P)
inv_time = self.oqparam.investigation_time
allargs = []
for m, imt in enumerate(self.oqparam.imtls):
for p, poe in enumerate(self.oqparam.poes):
dic = dict(m=m, p=p, imt=imt, poe=poe, inv_time=inv_time,
calc_id=self.datastore.calc_id,
array=hmaps[:, 0, m, p])
allargs.append((dic, self.sitecol.lons, self.sitecol.lats))
smap = parallel.Starmap(make_hmap_png, allargs)
for dic in smap:
self.datastore['png/hmap_%(m)d_%(p)d' % dic] = dic['img']
