def extract_aggregate_by(dstore, what):
"""
/extract/aggregate_by/taxonomy,occupancy/curves/structural
yield pairs (<stat>, <array of shape (T, O, S, P)>)
/extract/aggregate_by/taxonomy,occupancy/avg_losses/structural
yield pairs (<stat>, <array of shape (T, O, S)>)
"""
try:
tagnames, name, loss_type = what.split('/')
except ValueError: # missing '/' at the end
tagnames, name = what.split('/')
loss_type = ''
assert name in ('avg_losses', 'curves'), name
tagnames = tagnames.split(',')
assetcol = dstore['assetcol']
oq = dstore['oqparam']
dset, stats = _get(dstore, name)
for s, stat in enumerate(stats):
if loss_type:
array = dset[:, s, oq.lti[loss_type]]
else:
array = dset[:, s]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {})
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
if not loss_type:
aw.extra = ('loss_type', ) + oq.loss_dt().names
if name == 'curves':
aw.return_period = dset.attrs['return_periods']
aw.tagnames = encode(tagnames + ['return_period'])
else:
aw.tagnames = encode(tagnames)
yield decode(stat), aw
def extract_aggregate(dstore, what):
"""
/extract/aggregate/avg_losses?
kind=mean&loss_type=structural&tag=taxonomy&tag=occupancy
"""
name, qstring = what.split('?', 1)
info = get_info(dstore)
qdic = parse(qstring, info)
suffix = '-rlzs' if qdic['rlzs'] else '-stats'
tagnames = qdic.get('tag', [])
assetcol = dstore['assetcol']
loss_types = info['loss_types']
ltypes = qdic.get('loss_type', []) # list of indices
if ltypes:
lti = ltypes[0]
lt = [lt for lt, i in loss_types.items() if i == lti]
array = dstore[name + suffix][:, qdic['k'][0], lti]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {}, (lt, ))
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {},
loss_types)
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
aw.tagnames = encode(tagnames)
return aw
def extract_aggregate(dstore, what):
"""
/extract/aggregate/avg_losses?
kind=mean&loss_type=structural&tag=taxonomy&tag=occupancy
"""
name, qstring = what.split('?', 1)
info = get_info(dstore)
qdic = parse(qstring, info)
suffix = '-rlzs' if qdic['rlzs'] else '-stats'
tagnames = qdic.get('tag', [])
assetcol = dstore['assetcol']
ltypes = qdic.get('loss_type', [])
if ltypes:
array = dstore[name + suffix][:, qdic['k'][0], ltypes[0]]
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {})
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
aw.tagnames = encode(tagnames)
if not ltypes:
aw.extra = ('loss_type', ) + tuple(info['loss_types'])
return aw
def extract_aggregate(dstore, what):
"""
/extract/aggregate/avg_losses?
kind=mean&loss_type=structural&tag=taxonomy&tag=occupancy
"""
name, qstring = what.split('?', 1)
info = get_info(dstore)
qdic = parse(qstring, info)
suffix = '-rlzs' if qdic['rlzs'] else '-stats'
tagnames = qdic.get('tag', [])
assetcol = dstore['assetcol']
ltypes = qdic.get('loss_type', [])
if ltypes:
array = dstore[name + suffix][:, qdic['k'][0], ltypes[0]]
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregate_by(tagnames, array), {})
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname))
aw.tagnames = encode(tagnames)
if not ltypes:
aw.extra = ('loss_type',) + tuple(info['loss_types'])
return aw
