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 src.data]
else:
srcmags = [
'%.2f' % item[0]
for item in src.get_annual_occurrence_rates()
]
mags[sg.trt].update(srcmags)
logging.info('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
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
"""
full_lt = get_full_lt(oqparam)
if oqparam.csm_cache and not oqparam.is_ucerf():
csm = _get_csm_cached(oqparam, full_lt, h5)
else:
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 = -1
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:
if src.source_id in data:
multiplicity = data[src.source_id][MULTIPLICITY] + 1
else:
multiplicity = 1
ns += 1
src.gidx = gidx[tuple(src.grp_ids)]
row = [src.source_id, src.gidx, src.code,
multiplicity, 0, 0, 0, src.checksum, src.serial or ns,
full_lt.trti[src.tectonic_region_type]]
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 src.data]
else:
srcmags = ['%.2f' % item[0] for item in
src.get_annual_occurrence_rates()]
mags[sg.trt].update(srcmags)
logging.info('There are %d sources', ns + 1)
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)
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 # src_id -> row
if os.environ.get('OQ_CHECK_INPUT'):
source.check_complex_faults(csm.get_sources())
return csm
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
"""
logging.info('Reading the CompositeSourceModel')
full_lt = get_full_lt(oqparam)
if oqparam.cachedir and not oqparam.is_ucerf():
csm = _get_cachedir(oqparam, full_lt, h5)
else:
csm = get_csm(oqparam, full_lt, h5)
et_ids = csm.get_et_ids()
logging.info('%d effective smlt realization(s)', len(full_lt.sm_rlzs))
data = {} # src_id -> row
mags_by_trt = csm.get_mags_by_trt()
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,
full_lt.trti[src.tectonic_region_type], 0
]
wkts.append(src._wkt)
data[src.id] = row
logging.info('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))
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'] = et_ids
for trt in mags_by_trt:
h5['source_mags/' + trt] = numpy.array(mags_by_trt[trt])
oqparam.maximum_distance.interp(mags_by_trt)
csm.gsim_lt.check_imts(oqparam.imtls)
csm.source_info = data # src_id -> row
if os.environ.get('OQ_CHECK_INPUT'):
source.check_complex_faults(csm.get_sources())
return csm
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
try:
sitecol = get_site_collection(oqparam, h5) # already stored
except Exception: # missing sites.csv in test_case_1_ruptures
sitecol = None
csm.sitecol = sitecol
if sitecol is None:
return
srcfilter = SourceFilter(sitecol, oqparam.maximum_distance)
logging.info('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!?')
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
"""
full_lt = get_full_lt(oqparam)
if oqparam.cachedir and not oqparam.is_ucerf():
csm = _get_cachedir(oqparam, full_lt, h5)
else:
csm = get_csm(oqparam, full_lt, h5)
et_ids = csm.get_et_ids()
logging.info('%d effective smlt realization(s)', len(full_lt.sm_rlzs))
grp_id = {tuple(arr): i for i, arr in enumerate(et_ids)}
data = {} # src_id -> row
mags = AccumDict(accum=set()) # trt -> mags
wkts = []
lens = []
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:
lens.append(len(src.et_ids))
src.grp_id = grp_id[tuple(src.et_ids)]
row = [
src.source_id, src.grp_id, src.code, 0, 0, 0, src.id,
full_lt.trti[src.tectonic_region_type]
]
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 src.data]
else:
srcmags = [
'%.2f' % item[0]
for item in src.get_annual_occurrence_rates()
]
mags[sg.trt].update(srcmags)
logging.info('There are %d groups and %d sources with len(et_ids)=%.1f',
len(csm.src_groups), sum(len(sg) for sg in csm.src_groups),
numpy.mean(lens))
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'] = et_ids
mags_by_trt = {}
for trt in mags:
mags_by_trt[trt] = arr = numpy.array(sorted(mags[trt]))
h5['source_mags/' + trt] = arr
oqparam.maximum_distance.interp(mags_by_trt)
csm.gsim_lt.check_imts(oqparam.imtls)
csm.source_info = data # src_id -> row
if os.environ.get('OQ_CHECK_INPUT'):
source.check_complex_faults(csm.get_sources())
return csm
