def extract_tot_curves(dstore, what):
"""
Aggregate loss curves from the ebrisk calculator:
/extract/tot_curves?
kind=stats&absolute=1&loss_type=occupants
Returns an array of shape (P, S) or (P, R)
"""
info = get_info(dstore)
qdic = parse(what, info)
k = qdic['k'] # rlz or stat index
[l] = qdic['loss_type'] # loss type index
if qdic['rlzs']:
kinds = ['rlz-%d' % r for r in k]
name = 'agg_curves-rlzs'
else:
kinds = list(info['stats'])
name = 'agg_curves-stats'
shape_descr = hdf5.get_shape_descr(dstore.get_attr(name, 'json'))
units = dstore.get_attr(name, 'units')
rps = shape_descr['return_period']
K = shape_descr.get('K', 0)
arr = dstore[name][K, k, l].T # shape P, R
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]: # relative
arr /= dstore['agg_values'][K, l]
else:
raise ValueError('"absolute" must be 0 or 1 in %s' % what)
attrs = dict(shape_descr=['return_period', 'kind'])
attrs['return_period'] = rps
attrs['kind'] = kinds
attrs['units'] = list(units) # used by the QGIS plugin
return ArrayWrapper(arr, dict(json=hdf5.dumps(attrs)))
def crm_attrs(dstore, what):
"""
:returns:
the attributes of the risk model, i.e. limit_states, loss_types,
min_iml and covs, needed by the risk exporters.
"""
attrs = dstore.get_attrs('crm')
return ArrayWrapper((), dict(json=hdf5.dumps(attrs)))
def extract_agg_curves(dstore, what):
"""
Aggregate loss curves from the ebrisk calculator:
/extract/agg_curves?
kind=stats&absolute=1&loss_type=occupants&occupancy=RES
Returns an array of shape (P, S, 1...) or (P, R, 1...)
"""
info = get_info(dstore)
qdic = parse(what, info)
tagdict = qdic.copy()
for a in ('k', 'rlzs', 'kind', 'loss_type', 'absolute'):
del tagdict[a]
k = qdic['k'] # rlz or stat index
[l] = qdic['loss_type'] # loss type index
tagnames = sorted(tagdict)
if set(tagnames) != set(info['tagnames']):
raise ValueError('Expected tagnames=%s, got %s' %
(info['tagnames'], tagnames))
tagvalues = [tagdict[t][0] for t in tagnames]
idx = -1
if tagnames:
for i, tags in enumerate(dstore['agg_keys'][:][tagnames]):
if list(tags) == tagvalues:
idx = i
break
if qdic['rlzs']:
kinds = ['rlz-%d' % r for r in k]
name = 'agg_curves-rlzs'
else:
kinds = list(info['stats'])
name = 'agg_curves-stats'
units = dstore.get_attr(name, 'units')
shape_descr = hdf5.get_shape_descr(dstore.get_attr(name, 'json'))
units = dstore.get_attr(name, 'units')
rps = shape_descr['return_period']
tup = (idx, k, l)
arr = dstore[name][tup].T # shape P, R
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]:
evalue = dstore['agg_values'][idx, l] # shape K, L
arr /= evalue
else:
raise ValueError('"absolute" must be 0 or 1 in %s' % what)
attrs = dict(shape_descr=['return_period', 'kind'] + tagnames)
attrs['return_period'] = list(rps)
attrs['kind'] = kinds
attrs['units'] = list(units) # used by the QGIS plugin
for tagname, tagvalue in zip(tagnames, tagvalues):
attrs[tagname] = [tagvalue]
if tagnames:
arr = arr.reshape(arr.shape + (1,) * len(tagnames))
return ArrayWrapper(arr, dict(json=hdf5.dumps(attrs)))
def extract_gridded_sources(dstore, what):
"""
Extract information about the gridded sources (requires ps_grid_spacing)
Use it as /extract/gridded_sources?task_no=0.
Returns a json string id -> lonlats
"""
qdict = parse(what)
task_no = int(qdict.get('task_no', ['0'])[0])
dic = {}
for i, lonlats in enumerate(dstore['ps_grid/%02d' % task_no][()]):
dic[i] = numpy.round(F64(lonlats), 3)
return ArrayWrapper((), {'json': hdf5.dumps(dic)})
def extract_disagg_by_src(dstore, what):
"""
Extract the disagg_by_src information Example:
http://127.0.0.1:8800/v1/calc/30/extract/disagg_by_src?site_id=0&imt_id=0&rlz_id=0&lvl_id=-1
"""
qdict = parse(what)
dic = hdf5.get_shape_descr(dstore['disagg_by_src'].attrs['json'])
src_id = dic['src_id']
f = norm(qdict, 'site_id rlz_id lvl_id imt_id'.split())
poe = dstore['disagg_by_src'][
f['site_id'], f['rlz_id'], f['imt_id'], f['lvl_id']]
arr = numpy.zeros(len(src_id), [('src_id', '<S16'), ('poe', '<f8')])
arr['src_id'] = src_id
arr['poe'] = poe
arr.sort(order='poe')
return ArrayWrapper(arr[::-1], dict(json=hdf5.dumps(f)))
def extract_exposure_metadata(dstore, what):
"""
Extract the loss categories and the tags of the exposure.
Use it as /extract/exposure_metadata
"""
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
if 'asset_risk' in dstore:
dic['multi_risk'] = sorted(
set(dstore['asset_risk'].dtype.names) -
set(dstore['assetcol/array'].dtype.names))
dic['names'] = [name for name in dstore['assetcol/array'].dtype.names
if name.startswith(('value-', 'number', 'occupants'))
and name != 'value-occupants']
return ArrayWrapper((), dict(json=hdf5.dumps(dic)))
def extract_assets(dstore, what):
"""
Extract an array of assets, optionally filtered by tag.
Use it as /extract/assets?taxonomy=RC&taxonomy=MSBC&occupancy=RES
"""
qdict = parse(what)
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
arr = dstore['assetcol/array'][()]
for tag, vals in qdict.items():
cond = numpy.zeros(len(arr), bool)
for val in vals:
tagidx, = numpy.where(dic[tag] == val)
cond |= arr[tag] == tagidx
arr = arr[cond]
return ArrayWrapper(arr, dict(json=hdf5.dumps(dic)))
def extract_asset_risk(dstore, what):
"""
Extract an array of assets + risk fields, optionally filtered by tag.
Use it as /extract/asset_risk?taxonomy=RC&taxonomy=MSBC&occupancy=RES
"""
qdict = parse(what)
dic = {}
dic1, dic2 = dstore['assetcol/tagcol'].__toh5__()
dic.update(dic1)
dic.update(dic2)
arr = dstore['asset_risk'][()]
names = list(arr.dtype.names)
for i, name in enumerate(names):
if name == 'id':
names[i] = 'asset_id' # for backward compatibility
arr.dtype.names = names
for tag, vals in qdict.items():
cond = numpy.zeros(len(arr), bool)
for val in vals:
tagidx, = numpy.where(dic[tag] == val)
cond |= arr[tag] == tagidx
arr = arr[cond]
return ArrayWrapper(arr, dict(json=hdf5.dumps(dic)))
def test(self):
dic = dict(imts=numpy.array([0.1, 0.2, 0.3]))
self.assertEqual(dumps(dic), '{\n"imts": [0.1, 0.2, 0.3]}')
dic = dict(base_path=r"C:\Users\test")
self.assertEqual(dumps(dic), '{\n"base_path": "C:\\\\Users\\\\test"}')
def extract_oqparam(dstore, dummy):
"""
Extract job parameters as a JSON npz. Use it as /extract/oqparam
"""
js = hdf5.dumps(vars(dstore['oqparam']))
return ArrayWrapper((), {'json': js})