def __call__(self, dstore, key):
if '/' in key:
k, v = key.split('/', 1)
data = self[k](dstore, v)
elif '?' in key:
k, v = key.split('?', 1)
data = self[k](dstore, v)
elif key in self:
data = self[key](dstore, '')
else:
data = extract_(dstore, key)
return ArrayWrapper.from_(data)
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 get(self, what):
"""
:param what: what to extract
:returns: an ArrayWrapper instance
"""
url = '%s/v1/calc/%d/extract/%s' % (self.server, self.calc_id, what)
logging.info('GET %s', url)
resp = self.sess.get(url)
if resp.status_code != 200:
raise WebAPIError(resp.text)
logging.info('Read %s of data' % general.humansize(len(resp.content)))
npz = numpy.load(io.BytesIO(resp.content))
attrs = {k: npz[k] for k in npz if k != 'array'}
try:
arr = npz['array']
except KeyError:
arr = ()
return ArrayWrapper(arr, attrs)
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, dic)
def extract_disagg_layer(dstore, what):
"""
Extract a disaggregation layer containing all sites and outputs
Example:
http://127.0.0.1:8800/v1/calc/30/extract/disagg_layer?
"""
qdict = parse(what)
oq = dstore['oqparam']
if 'kind' in qdict:
kinds = qdict['kind']
else:
kinds = oq.disagg_outputs
sitecol = dstore['sitecol']
poes_disagg = oq.poes_disagg or (None, )
edges, shapedic = disagg.get_edges_shapedic(oq, sitecol,
dstore['source_mags'])
dt = _disagg_output_dt(shapedic, kinds, oq.imtls, poes_disagg)
out = numpy.zeros(len(sitecol), dt)
realizations = numpy.array(dstore['full_lt'].get_realizations())
iml4 = dstore['iml4'][:]
best_rlzs = dstore['best_rlzs'][:]
arr = {kind: dstore['disagg/' + kind][:] for kind in kinds}
for sid, lon, lat, rec in zip(sitecol.sids, sitecol.lons, sitecol.lats,
out):
rlzs = realizations[best_rlzs[sid]]
rec['site_id'] = sid
rec['lon'] = lon
rec['lat'] = lat
rec['lon_bins'] = edges[2][sid]
rec['lat_bins'] = edges[3][sid]
for m, imt in enumerate(oq.imtls):
ws = numpy.array([rlz.weight[imt] for rlz in rlzs])
ws /= ws.sum() # normalize to 1
for p, poe in enumerate(poes_disagg):
for kind in kinds:
key = '%s-%s-%s' % (kind, imt, poe)
rec[key] = arr[kind][sid, m, p] @ ws
rec['iml-%s-%s' % (imt, poe)] = iml4[sid, m, p]
return ArrayWrapper(
out,
dict(mag=edges[0],
dist=edges[1],
eps=edges[-2],
trt=numpy.array(encode(edges[-1]))))
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))
names = [
name for name in dstore['assetcol/array'].dtype.names
if name.startswith(('value-', 'number',
'occupants_')) and not name.endswith('_None')
]
return ArrayWrapper(numpy.array(names), dic)
def extract_disagg_layer(dstore, what):
"""
Extract a disaggregation layer containing all sites and outputs
Example:
http://127.0.0.1:8800/v1/calc/30/extract/disagg_layer?
"""
qdict = parse(what)
oq = dstore['oqparam']
if 'kind' in qdict:
kinds = qdict['kind']
else:
kinds = list(oq.disagg_outputs or disagg.pmf_map)
sitecol = dstore['sitecol']
poes_disagg = oq.poes_disagg or (None, )
edges, shapedic = disagg.get_edges_shapedic(oq, sitecol,
dstore['source_mags'])
dt = _disagg_output_dt(shapedic, kinds, oq.imtls, poes_disagg)
out = numpy.zeros(len(sitecol), dt)
try:
best_rlzs = dstore['best_rlzs']
except KeyError:
best_rlzs = numpy.zeros((len(sitecol), shapedic['Z']), U16)
for sid, lon, lat, rec in zip(sitecol.sids, sitecol.lons, sitecol.lats,
out):
rec['site_id'] = sid
rec['lon'] = lon
rec['lat'] = lat
rec['rlz_id'] = rlzs = best_rlzs[sid]
rec['lon_bins'] = edges[2][sid]
rec['lat_bins'] = edges[3][sid]
for kind in kinds:
for imt in oq.imtls:
for p, poe in enumerate(poes_disagg):
for rlz in rlzs:
key = '%s-%s-%s' % (kind, imt, poe)
label = 'disagg/rlz-%d-%s-sid-%d-poe-%s/%s' % (
rlz, imt, sid, p, kind)
rec[key] = dstore[label][()]
return ArrayWrapper(
out,
dict(mag=edges[0], dist=edges[1], eps=edges[-2],
trt=encode(edges[-1])))
def extract_agg_curves(dstore, what):
"""
Aggregate loss curves from the ebrisk calculator:
/extract/agg_curves?
kind=stats&absolute=1&loss_type=occupants&tagname=occupancy&tagvalue=RES
Returns an array of shape (P, S, T...) or (P, R, T...)
"""
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]
arr = dstore['agg_curves-rlzs'][:, k, l] # shape P, T...
rps = dstore.get_attr('agg_curves-rlzs', 'return_periods')
else:
kinds = list(info['stats'])
arr = dstore['agg_curves-stats'][:, k, l] # shape P, T...
rps = dstore.get_attr('agg_curves-stats', 'return_periods')
tagnames = qdic.get('tagname', [])
if set(tagnames) != set(info['tagnames']):
raise ValueError('Expected tagnames=%s, got %s' %
(info['tagnames'], tagnames))
tagvalues = qdic.get('tagvalue', [])
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]:
aggname = '_'.join(['agg'] + tagnames)
evalue = dstore['exposed_values/' + aggname][l] # shape T...
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'] = [numpy.nan] + list(rps)
attrs['kind'] = ['?'] + kinds
for tagname, tagvalue in zip(tagnames, tagvalues):
attrs[tagname] = [tagvalue]
return ArrayWrapper(arr, attrs)
def extract_rupture_info(dstore, what):
"""
Extract some information about the ruptures, including the boundary.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/rupture_info?min_mag=6
"""
qdict = parse(what)
if 'min_mag' in qdict:
[min_mag] = qdict['min_mag']
else:
min_mag = 0
oq = dstore['oqparam']
dtlist = [('rup_id', U32), ('occurrence_rate', F32), ('multiplicity', U16),
('mag', F32), ('centroid_lon', F32), ('centroid_lat', F32),
('centroid_depth', F32), ('trt', '<S50'), ('strike', F32),
('dip', F32), ('rake', F32)]
rows = []
boundaries = []
for rgetter in getters.gen_rgetters(dstore):
proxies = rgetter.get_proxies(min_mag)
rup_data = RuptureData(rgetter.trt, rgetter.samples,
rgetter.rlzs_by_gsim)
for r in rup_data.to_array(proxies):
coords = ['%.5f %.5f' % xyz[:2] for xyz in zip(*r['boundaries'])]
coordset = sorted(set(coords))
if len(coordset) < 4: # degenerate to line
boundaries.append('LINESTRING(%s)' % ', '.join(coordset))
else: # good polygon
boundaries.append('POLYGON((%s))' % ', '.join(coords))
rows.append((r['rup_id'], r['occurrence_rate'], r['multiplicity'],
r['mag'], r['lon'], r['lat'], r['depth'], rgetter.trt,
r['strike'], r['dip'], r['rake']))
arr = numpy.array(rows, dtlist)
geoms = gzip.compress('\n'.join(boundaries).encode('utf-8'))
return ArrayWrapper(
arr, dict(investigation_time=oq.investigation_time, boundaries=geoms))
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, dic)
def extract_disagg(dstore, what):
"""
Extract a disaggregation output
Example:
http://127.0.0.1:8800/v1/calc/30/extract/
disagg?kind=Mag_Dist&imt=PGA&poe_id=0&site_id=1&rlz=0
"""
qdict = parse(what)
label = qdict['kind'][0]
imt = qdict['imt'][0]
poe_idx = int(qdict['poe_id'][0])
sid = int(qdict['site_id'][0])
rlz = (int(qdict['rlz'][0])
if 'rlz' in qdict else 0 if len(dstore['weights']) == 1 else None)
dset = disagg_output(dstore, imt, sid, poe_idx, rlz)
matrix = dset[label][()]
# adapted from the nrml_converters
disag_tup = tuple(label.split('_'))
if disag_tup == ('Mag', 'Lon', 'Lat'):
matrix = numpy.swapaxes(matrix, 0, 1)
matrix = numpy.swapaxes(matrix, 1, 2)
disag_tup = ('Lon', 'Lat', 'Mag')
axis = [dset.attrs[v.lower() + '_bin_edges'] for v in disag_tup]
# compute axis mid points
axis = [(ax[:-1] + ax[1:]) / 2. if ax.dtype == float else ax
for ax in axis]
values = None
if len(axis) == 1:
values = numpy.array([axis[0], matrix.flatten()]).T
else:
grids = numpy.meshgrid(*axis, indexing='ij')
values = [g.flatten() for g in grids]
values.append(matrix.flatten())
values = numpy.array(values).T
return ArrayWrapper(values, qdict)
def extract_curves(dstore, what, tot):
"""
Porfolio 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
tup = (slice(None), k, l)
if qdic['rlzs']:
kinds = ['rlz-%d' % r for r in k]
arr = dstore[tot + 'curves-rlzs'][tup] # shape P, R
units = dstore.get_attr(tot + 'curves-rlzs', 'units')
rps = dstore.get_attr(tot + 'curves-rlzs', 'return_periods')
else:
kinds = list(info['stats'])
arr = dstore[tot + 'curves-stats'][tup] # shape P, S
units = dstore.get_attr(tot + 'curves-stats', 'units')
rps = dstore.get_attr(tot + 'curves-stats', 'return_periods')
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]:
evalue = dstore['exposed_values/agg'][l]
arr /= evalue
else:
raise ValueError('"absolute" must be 0 or 1 in %s' % what)
attrs = dict(shape_descr=['return_period', 'kind'])
attrs['return_period'] = list(rps)
attrs['kind'] = kinds
attrs['units'] = units # used by the QGIS plugin
return ArrayWrapper(arr, attrs)
def extract_gsims_by_trt(dstore, what):
"""
Extract the dictionary gsims_by_trt
"""
return ArrayWrapper((), dstore['full_lt'].gsim_lt.values)
def extract_oqparam(dstore, dummy):
"""
Extract job parameters as a JSON npz. Use it as /extract/oqparam
"""
return ArrayWrapper((), {'json': json.dumps(vars(dstore['oqparam']))})
def disaggregation(sources,
site,
imt,
iml,
gsim_by_trt,
truncation_level,
n_epsilons,
mag_bin_width,
dist_bin_width,
coord_bin_width,
source_filter=filters.nofilter,
filter_distance='rjb'):
"""
Compute "Disaggregation" matrix representing conditional probability of an
intensity mesaure type ``imt`` exceeding, at least once, an intensity
measure level ``iml`` at a geographical location ``site``, given rupture
scenarios classified in terms of:
- rupture magnitude
- Joyner-Boore distance from rupture surface to site
- longitude and latitude of the surface projection of a rupture's point
closest to ``site``
- epsilon: number of standard deviations by which an intensity measure
level deviates from the median value predicted by a GSIM, given the
rupture parameters
- rupture tectonic region type
In other words, the disaggregation matrix allows to compute the probability
of each scenario with the specified properties (e.g., magnitude, or the
magnitude and distance) to cause one or more exceedences of a given hazard
level.
For more detailed information about the disaggregation, see for instance
"Disaggregation of Seismic Hazard", Paolo Bazzurro, C. Allin Cornell,
Bulletin of the Seismological Society of America, Vol. 89, pp. 501-520,
April 1999.
:param sources:
Seismic source model, as for
:mod:`PSHA <openquake.hazardlib.calc.hazard_curve>` calculator it
should be an iterator of seismic sources.
:param site:
:class:`~openquake.hazardlib.site.Site` of interest to calculate
disaggregation matrix for.
:param imt:
Instance of :mod:`intensity measure type <openquake.hazardlib.imt>`
class.
:param iml:
Intensity measure level. A float value in units of ``imt``.
:param gsim_by_trt:
Tectonic region type to GSIM objects mapping.
:param truncation_level:
Float, number of standard deviations for truncation of the intensity
distribution.
:param n_epsilons:
Integer number of epsilon histogram bins in the result matrix.
:param mag_bin_width:
Magnitude discretization step, width of one magnitude histogram bin.
:param dist_bin_width:
Distance histogram discretization step, in km.
:param coord_bin_width:
Longitude and latitude histograms discretization step,
in decimal degrees.
:param source_filter:
Optional source-site filter function. See
:mod:`openquake.hazardlib.calc.filters`.
:returns:
A tuple of two items. First is itself a tuple of bin edges information
for (in specified order) magnitude, distance, longitude, latitude,
epsilon and tectonic region types.
Second item is 6d-array representing the full disaggregation matrix.
Dimensions are in the same order as bin edges in the first item
of the result tuple. The matrix can be used directly by pmf-extractor
functions.
"""
trts = sorted(set(src.tectonic_region_type for src in sources))
trt_num = dict((trt, i) for i, trt in enumerate(trts))
rlzs_by_gsim = {gsim_by_trt[trt]: [0] for trt in trts}
by_trt = groupby(sources, operator.attrgetter('tectonic_region_type'))
bdata = {}
sitecol = SiteCollection([site])
iml2 = ArrayWrapper(numpy.array([[iml]]),
dict(imts=[imt], poes_disagg=[None], rlzi=0))
for trt, srcs in by_trt.items():
cmaker = ContextMaker(trt, rlzs_by_gsim,
source_filter.integration_distance,
{'filter_distance': filter_distance})
contexts.RuptureContext.temporal_occurrence_model = (
srcs[0].temporal_occurrence_model)
rupdata = contexts.RupData(cmaker, sitecol).from_srcs(srcs)
bdata[trt] = collect_bin_data(rupdata, sitecol, cmaker, iml2,
truncation_level, n_epsilons)
if sum(len(bd.mags) for bd in bdata.values()) == 0:
warnings.warn(
'No ruptures have contributed to the hazard at site %s' % site,
RuntimeWarning)
return None, None
min_mag = min(bd.mags.min() for bd in bdata.values())
max_mag = max(bd.mags.max() for bd in bdata.values())
mag_bins = mag_bin_width * numpy.arange(
int(numpy.floor(min_mag / mag_bin_width)),
int(numpy.ceil(max_mag / mag_bin_width) + 1))
min_dist = min(bd.dists.min() for bd in bdata.values())
max_dist = max(bd.dists.max() for bd in bdata.values())
dist_bins = dist_bin_width * numpy.arange(
int(numpy.floor(min_dist / dist_bin_width)),
int(numpy.ceil(max_dist / dist_bin_width) + 1))
bb = (min(bd.lons.min() for bd in bdata.values()),
min(bd.lats.min() for bd in bdata.values()),
max(bd.lons.max() for bd in bdata.values()),
max(bd.lats.max() for bd in bdata.values()))
lon_bins, lat_bins = lon_lat_bins(bb, coord_bin_width)
eps_bins = numpy.linspace(-truncation_level, truncation_level,
n_epsilons + 1)
bin_edges = (mag_bins, dist_bins, lon_bins, lat_bins, eps_bins)
matrix = numpy.zeros(
(len(mag_bins) - 1, len(dist_bins) - 1, len(lon_bins) - 1,
len(lat_bins) - 1, len(eps_bins) - 1, len(trts)))
for trt in bdata:
dic = build_disagg_matrix(bdata[trt], bin_edges)
if dic: # (poe, imt, rlzi) -> matrix
[mat] = dic.values()
matrix[..., trt_num[trt]] = mat
return bin_edges + (trts, ), matrix
def get(self, what):
"""
:param what: what to extract
:returns: an ArrayWrapper instance
"""
return ArrayWrapper.from_(extract(self.dstore, what))
