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.
"""
return ArrayWrapper((), dstore.get_attrs('risk_model'))
def extract_disagg_layer(dstore, what):
"""
Extract a disaggregation output containing all sites
for the first realization or the mean.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/
disagg_layer?kind=Mag_Dist&imt=PGA&poe_id=0
"""
qdict = parse(what)
[label] = qdict['kind']
[imt] = qdict['imt']
poe_id = int(qdict['poe_id'][0])
grp = disagg_outputs(dstore, imt, 0, poe_id)[0]
dset = grp[label]
edges = {k: grp.attrs[k] for k in grp.attrs if k.endswith('_edges')}
dt = [('site_id', U32), ('lon', F32), ('lat', F32),
('poes', (dset.dtype, dset.shape))]
sitecol = dstore['sitecol']
out = numpy.zeros(len(sitecol), dt)
out[0] = (0, sitecol.lons[0], sitecol.lats[0], dset[()])
for sid, lon, lat, rec in zip(sitecol.sids, sitecol.lons, sitecol.lats,
out):
if sid > 0:
grp = disagg_outputs(dstore, imt, sid, poe_id)[0]
rec['site_id'] = sid
rec['lon'] = lon
rec['lat'] = lat
rec['poes'] = grp[label][()]
return ArrayWrapper(out, edges)
def extract_mfd(dstore, what):
"""
Display num_ruptures by magnitude for event based calculations.
Example: http://127.0.0.1:8800/v1/calc/30/extract/event_based_mfd?kind=mean
"""
oq = dstore['oqparam']
qdic = parse(what)
kind_mean = 'mean' in qdic.get('kind', [])
kind_by_group = 'by_group' in qdic.get('kind', [])
weights = dstore['csm_info/sm_data']['weight']
sm_idx = dstore['csm_info/sg_data']['sm_id']
grp_weight = weights[sm_idx]
duration = oq.investigation_time * oq.ses_per_logic_tree_path
dic = {'duration': duration}
dd = collections.defaultdict(float)
rups = dstore['ruptures']['grp_id', 'mag', 'n_occ']
mags = sorted(numpy.unique(rups['mag']))
magidx = {mag: idx for idx, mag in enumerate(mags)}
num_groups = rups['grp_id'].max() + 1
frequencies = numpy.zeros((len(mags), num_groups), float)
for grp_id, mag, n_occ in rups:
if kind_mean:
dd[mag] += n_occ * grp_weight[grp_id] / duration
if kind_by_group:
frequencies[magidx[mag], grp_id] += n_occ / duration
dic['magnitudes'] = numpy.array(mags)
if kind_mean:
dic['mean_frequency'] = numpy.array([dd[mag] for mag in mags])
if kind_by_group:
for grp_id, freqs in enumerate(frequencies.T):
dic['grp-%02d_frequency' % grp_id] = freqs
return ArrayWrapper((), dic)
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_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), ('multiplicity', U16), ('mag', F32),
('centroid_lon', F32), ('centroid_lat', F32),
('centroid_depth', F32), ('trt', '<S50'), ('strike', F32),
('dip', F32), ('rake', F32)]
rows = []
boundaries = []
sf = filters.SourceFilter(dstore['sitecol'], oq.maximum_distance)
for rgetter in getters.gen_rupture_getters(dstore):
rups = rgetter.get_ruptures(sf, min_mag)
rup_data = RuptureData(rgetter.trt, rgetter.rlzs_by_gsim)
for r, rup in zip(rup_data.to_array(rups), rups):
coords = ['%.5f %.5f' % xyz[:2] for xyz in zip(*r['boundaries'])]
boundaries.append('POLYGON((%s))' % ', '.join(coords))
rows.append(
(r['rup_id'], 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 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.
"""
name = dstore['oqparam'].risk_model
return ArrayWrapper(0, dstore.get_attrs(name))
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_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 None
allnames = []
allvalues = []
for dset in disagg_outputs(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
allnames.append(os.path.basename(dset.name))
allvalues.append(values)
if not allnames:
raise KeyError('No data for ' + what)
elif len(allnames) == 1:
return ArrayWrapper(values, qdict)
else:
qdict['names'] = allnames
return ArrayWrapper(numpy.array(allvalues), qdict)
def get_output(self, assets, data_by_lt, epsgetter):
"""
returns an ArrayWrapper of shape (L, ...)
"""
out = [
self(lt, assets, data, epsgetter)
for lt, data in zip(self.loss_types, data_by_lt)
]
return ArrayWrapper(numpy.array(out), {})
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]
tagidx = []
if tagnames:
tagcol = dstore['assetcol/tagcol']
for tagname, tagvalue in zip(tagnames, tagvalues):
values = list(getattr(tagcol, tagname)[1:])
tagidx.append(values.index(tagvalue))
tup = tuple([slice(None), k, l] + tagidx)
if qdic['rlzs']:
kinds = ['rlz-%d' % r for r in k]
arr = dstore['agg_curves-rlzs'][tup] # shape P, R
units = dstore.get_attr('agg_curves-rlzs', 'units')
rps = dstore.get_attr('agg_curves-rlzs', 'return_periods')
else:
kinds = list(info['stats'])
arr = dstore['agg_curves-stats'][tup] # shape P, S
units = dstore.get_attr('agg_curves-stats', 'units')
rps = dstore.get_attr('agg_curves-stats', 'return_periods')
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]:
aggname = '_'.join(['agg'] + tagnames)
tl = tuple(tagidx) + (l, )
evalue = dstore['exposed_values/' + aggname][tl] # 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'] = list(rps)
attrs['kind'] = kinds
attrs['units'] = 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, attrs)
def extract_rupture(dstore, rup_id):
"""
Extract information about the given event index.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/rupture/1066
"""
ridx = list(dstore['ruptures']['id']).index(int(rup_id))
[getter] = getters.gen_rupture_getters(dstore, slice(ridx, ridx + 1))
[ebr] = getter.get_ruptures()
return ArrayWrapper((), ebr.rupture.todict())
def get_output(self, assets, data_by_lt, epsgetter):
"""
:param assets: a list of assets with the same taxonomy
:param data_by_lt: hazards for each loss type
:param epsgetter: an epsilon getter function
:returns: an ArrayWrapper of shape (L, ...)
"""
out = [self(lt, assets, data, epsgetter)
for lt, data in zip(self.loss_types, data_by_lt)]
return ArrayWrapper(numpy.array(out), dict(assets=assets))
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_sources(dstore, what):
"""
Extract information about a source model.
Use it as /extract/sources?sm_id=0
"""
qdict = parse(what)
sm_id = int(qdict['sm_id'][0])
arr = dstore['source_info']['sm_id', 'source_id', 'eff_ruptures', 'wkt']
if sm_id not in numpy.unique(arr['sm_id']):
raise ValueError('There is no source model #%d' % sm_id)
return ArrayWrapper(get_array(arr, sm_id=sm_id), {'sm_id': sm_id})
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)
resp = self.sess.get(url)
if resp.status_code != 200:
raise WebAPIError(resp.text)
npz = numpy.load(io.BytesIO(resp.content))
attrs = {k: npz[k] for k in npz if k != 'array'}
return ArrayWrapper(npz['array'], attrs)
def extract_effect(dstore, what):
"""
Extracts the effect of ruptures. Use it as /extract/effect
"""
grp = dstore['effect_by_mag_dst_trt']
dist_bins = dict(grp.attrs)
ndists = len(dist_bins[next(iter(dist_bins))])
arr = numpy.zeros((len(grp), ndists, len(dist_bins)))
for i, mag in enumerate(grp):
arr[i] = dstore['effect_by_mag_dst_trt/' + mag][()]
return ArrayWrapper(arr, dict(dist_bins=dist_bins, ndists=ndists,
mags=[float(mag) for mag in grp]))
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)
src_id = dstore['disagg_by_src'].attrs['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=json.dumps(f)))
def make_iml4(R, imtls, iml_disagg, poes_disagg=(None,), curves=()):
"""
:returns: an ArrayWrapper over a 4D array of shape (N, R, M, P)
"""
N = len(curves) or 1
M = len(imtls)
P = len(poes_disagg)
arr = numpy.zeros((N, R, M, P))
imts = [from_string(imt) for imt in imtls]
for m, imt in enumerate(imtls):
imls = imtls[imt]
for p, poe in enumerate(poes_disagg):
for r in range(R):
arr[:, r, m, p] = _imls(curves, poe, imt, imls, r)
return ArrayWrapper(arr, dict(poes_disagg=poes_disagg, imts=imts))
def extract_sources(dstore, what):
"""
Extract information about a source model.
Use it as /extract/sources?sm_id=0&limit=10
or /extract/sources?sm_id=0&source_id=1&source_id=2
or /extract/sources?sm_id=0&code=A&code=B
"""
qdict = parse(what)
sm_id = int(qdict.get('sm_id', ['0'])[0])
limit = int(qdict.get('limit', ['100'])[0])
source_ids = qdict.get('source_id', None)
if source_ids is not None:
source_ids = [str(source_id) for source_id in source_ids]
codes = qdict.get('code', None)
if codes is not None:
codes = [code.encode('utf8') for code in codes]
info = dstore['source_info'][()]
info = info[info['sm_id'] == sm_id]
arrays = []
if source_ids is not None:
logging.info('Extracting sources with ids: %s', source_ids)
info = info[numpy.isin(info['source_id'], source_ids)]
if len(info) == 0:
raise NotFound('There is no source with id %s' % source_ids)
if codes is not None:
logging.info('Extracting sources with codes: %s', codes)
info = info[numpy.isin(info['code'], codes)]
if len(info) == 0:
raise NotFound('There is no source with code in %s' % codes)
for code, rows in general.group_array(info, 'code').items():
if limit < len(rows):
logging.info('Code %s: extracting %d sources out of %s', code,
limit, len(rows))
arrays.append(rows[:limit])
info = numpy.concatenate(arrays)
if len(info) == 0:
raise ValueError('There is no source model #%d' % sm_id)
wkt_gz = gzip.compress(';'.join(info['wkt']).encode('utf8'))
src_gz = gzip.compress(';'.join(info['source_id']).encode('utf8'))
oknames = [n for n in info.dtype.names if n not in ('source_id', 'wkt')]
arr = numpy.zeros(len(info), [(n, info.dtype[n]) for n in oknames])
for n in oknames:
arr[n] = info[n]
return ArrayWrapper(arr, {
'sm_id': sm_id,
'wkt_gz': wkt_gz,
'src_gz': src_gz
})
def extract_sources(dstore, what):
"""
Extract information about a source model.
Use it as /extract/sources?limit=10
or /extract/sources?source_id=1&source_id=2
or /extract/sources?code=A&code=B
"""
qdict = parse(what)
limit = int(qdict.get('limit', ['100'])[0])
source_ids = qdict.get('source_id', None)
if source_ids is not None:
source_ids = [str(source_id) for source_id in source_ids]
codes = qdict.get('code', None)
if codes is not None:
codes = [code.encode('utf8') for code in codes]
fields = 'source_id code multiplicity num_sites eff_ruptures'
info = dstore['source_info'][()][fields.split()]
wkt = dstore['source_wkt'][()]
arrays = []
if source_ids is not None:
logging.info('Extracting sources with ids: %s', source_ids)
info = info[numpy.isin(info['source_id'], source_ids)]
if len(info) == 0:
raise NotFound('There is no source with id %s' % source_ids)
if codes is not None:
logging.info('Extracting sources with codes: %s', codes)
info = info[numpy.isin(info['code'], codes)]
if len(info) == 0:
raise NotFound('There is no source with code in %s' % codes)
for code, rows in general.group_array(info, 'code').items():
if limit < len(rows):
logging.info('Code %s: extracting %d sources out of %s', code,
limit, len(rows))
arrays.append(rows[:limit])
if not arrays:
raise ValueError('There no sources')
info = numpy.concatenate(arrays)
wkt_gz = gzip.compress(';'.join(wkt).encode('utf8'))
src_gz = gzip.compress(';'.join(info['source_id']).encode('utf8'))
oknames = [
name for name in info.dtype.names # avoid pickle issues
if name not in ('source_id', 'grp_ids')
]
arr = numpy.zeros(len(info), [(n, info.dtype[n]) for n in oknames])
for n in oknames:
arr[n] = info[n]
return ArrayWrapper(arr, {'wkt_gz': wkt_gz, 'src_gz': src_gz})
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 not name.endswith('_None')]
return ArrayWrapper((), dict(json=dumps(dic)))
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)
npz = numpy.load(io.BytesIO(resp.content), allow_pickle=True)
attrs = {k: npz[k] for k in npz if k != 'array'}
try:
arr = npz['array']
except KeyError:
arr = ()
return ArrayWrapper(arr, attrs)
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']
oq.maximum_distance = filters.MagDepDistance(oq.maximum_distance)
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())
hmap4 = dstore['hmap4'][:]
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)] = hmap4[sid, m, p]
return ArrayWrapper(
out,
dict(mag=edges[0],
dist=edges[1],
eps=edges[-2],
trt=numpy.array(encode(edges[-1]))))
def read_composite_array(fname, sep=','):
r"""
Convert a CSV file with header into an ArrayWrapper object.
>>> from openquake.baselib.general import gettemp
>>> fname = gettemp('PGA:3,PGV:2,avg:1\n'
... '.1 .2 .3,.4 .5,.6\n')
>>> print(read_composite_array(fname).array) # array of shape (1,)
[([0.1, 0.2, 0.3], [0.4, 0.5], [0.6])]
"""
with open(fname) as f:
header = next(f)
if header.startswith('#'): # the first line is a comment, skip it
attrs = dict(parse_comment(header[1:]))
header = next(f)
else:
attrs = {}
transheader = htranslator.read(header.split(sep))
fields, dtype = parse_header(transheader)
ts_pairs = [] # [(type, shape), ...]
for name in fields:
dt = dtype.fields[name][0]
ts_pairs.append(
(dt.subdtype[0].type if dt.subdtype else dt.type, dt.shape))
col_ids = list(range(1, len(ts_pairs) + 1))
num_columns = len(col_ids)
records = []
col, col_id = '', 0
for i, line in enumerate(f, 2):
row = line.split(sep)
if len(row) != num_columns:
raise InvalidFile(
'expected %d columns, found %d in file %s, line %d' %
(num_columns, len(row), fname, i))
try:
record = []
for (ntype, shape), col, col_id in zip(ts_pairs, row, col_ids):
record.append(_cast(col, ntype, shape, i, fname))
records.append(tuple(record))
except Exception as e:
raise InvalidFile(
'Could not cast %r in file %s, line %d, column %d '
'using %s: %s' % (col, fname, i, col_id,
(ntype.__name__, ) + shape, e))
return ArrayWrapper(numpy.array(records, dtype), 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 = 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)
