def build_loss_tables(dstore):
"""
Compute the total losses by rupture and losses by rlzi.
"""
oq = dstore['oqparam']
R = dstore['csm_info'].get_num_rlzs()
lbe = dstore['losses_by_event'][()]
loss = lbe['loss']
shp = (R, ) + lbe.dtype['loss'].shape
lbr = numpy.zeros(shp, F32) # losses by rlz
losses_by_rlz = fast_agg(lbe['rlzi'], loss)
lbr[:len(losses_by_rlz)] = losses_by_rlz
dstore['losses_by_rlzi'] = lbr
rup_id = dstore['events']['rup_id']
if len(shp) > 2:
loss = loss.sum(axis=tuple(range(1, len(shp) - 1)))
losses_by_rupid = fast_agg(rup_id[lbe['event_id']], loss)
lst = [('rup_id', U32)] + [(name, F32) for name in oq.loss_names]
tbl = numpy.zeros(len(losses_by_rupid), lst)
tbl['rup_id'] = numpy.arange(len(tbl))
for li, name in enumerate(oq.loss_names):
tbl[name] = losses_by_rupid[:, li]
tbl.sort(order=oq.loss_names[0])
dstore['rup_loss_table'] = tbl
def aggregate_by(self, tagnames, array):
"""
:param tagnames: a list of valid tag names
:param array: an array with the same length as the asset collection
:returns: an array of aggregate values with the proper shape
"""
missing = set(tagnames) - set(self.tagcol.tagnames)
if missing:
raise ValueError('Unknown tagname(s) %s' % missing)
A, *shp = array.shape
if A != len(self):
raise ValueError('The array must have length %d, got %d' %
(len(self), A))
if not tagnames:
return array.sum(axis=0)
elif len(tagnames) == 1:
# fast track for single-tag aggregation
# for the Canada exposure it is 30x faster
# fast_agg(assets['taxonomy'], values) => 47.6 ms
# fast_agg2(assets[['taxonomy']], values) => 1.4 s
[tagname] = tagnames
avalues = general.fast_agg(self.array[tagname], array)[1:]
tags = [(i + 1, ) for i in range(len(avalues))]
else: # multi-tag aggregation
tags, avalues = general.fast_agg2(self.array[tagnames], array)
shape = [
len(getattr(self.tagcol, tagname)) - 1 for tagname in tagnames
]
arr = numpy.zeros(shape, (F32, tuple(shp)) if shp else F32)
for tag, aval in zip(tags, avalues):
arr[tuple(i - 1 for i in tag)] = aval
return arr
def view_events_by_mag(token, dstore):
"""
Show how many events there are for each magnitude
"""
rups = dstore['ruptures'][()]
num_evs = fast_agg(dstore['events']['rup_id'])
counts = {}
for mag, grp in group_array(rups, 'mag').items():
counts[mag] = sum(num_evs[rup_id] for rup_id in grp['id'])
return numpy.array(list(counts.items()), dt('mag num_events'))
def view_mean_perils(token, dstore):
"""
For instance `oq show mean_perils`
"""
oq = dstore['oqparam']
pdcols = dstore.get_attr('gmf_data', '__pdcolumns__').split()
perils = [col for col in pdcols[2:] if not col.startswith('gmv_')]
N = len(dstore['sitecol/sids'])
sid = dstore['gmf_data/sid'][:]
out = numpy.zeros(N, [(per, float) for per in perils])
if oq.number_of_logic_tree_samples:
E = len(dstore['events'])
for peril in perils:
out[peril] = fast_agg(sid, dstore['gmf_data/' + peril][:]) / E
else:
rlz_weights = dstore['weights'][:]
ev_weights = rlz_weights[dstore['events']['rlz_id']]
totw = ev_weights.sum() # num_gmfs
for peril in perils:
data = dstore['gmf_data/' + peril][:]
weights = ev_weights[dstore['gmf_data/eid'][:]]
out[peril] = fast_agg(sid, data * weights) / totw
return out
def build_loss_tables(dstore):
"""
Compute the total losses by rupture
"""
oq = dstore['oqparam']
R = dstore['full_lt'].get_num_rlzs()
lbe = dstore['losses_by_event'][()]
loss = lbe['loss'] # shape (E, L, T...)
shp = (R, ) + lbe.dtype['loss'].shape
rup_id = dstore['events']['rup_id']
if len(shp) > 2:
loss = loss.sum(axis=tuple(range(2, len(shp)))) # shape (E, L)
losses_by_rupid = general.fast_agg(rup_id[lbe['event_id']], loss)
lst = [('rup_id', U32)] + [(name, F32) for name in oq.loss_names]
tbl = numpy.zeros(len(losses_by_rupid), lst)
tbl['rup_id'] = numpy.arange(len(tbl))
for li, name in enumerate(oq.loss_names):
tbl[name] = losses_by_rupid[:, li]
tbl.sort(order=oq.loss_names[0])
dstore['rup_loss_table'] = tbl
