Source code for openquake.calculators.extract
# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2017-2022 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
from urllib.parse import parse_qs
from unittest.mock import Mock
from functools import lru_cache
import logging
import json
import gzip
import ast
import io
import requests
from h5py._hl.dataset import Dataset
from h5py._hl.group import Group
import numpy
import pandas
from scipy.cluster.vq import kmeans2
from openquake.baselib import config, hdf5, general, writers
from openquake.baselib.hdf5 import ArrayWrapper
from openquake.baselib.general import group_array, println
from openquake.baselib.python3compat import encode, decode
from openquake.hazardlib.gsim.base import ContextMaker
from openquake.hazardlib.calc import disagg, stochastic, filters
from openquake.hazardlib.stats import calc_stats
from openquake.hazardlib.source import rupture
from openquake.risklib.asset import tagset
from openquake.commonlib import calc, util, oqvalidation, datastore, logictree
from openquake.calculators import getters
U16 = numpy.uint16
U32 = numpy.uint32
F32 = numpy.float32
F64 = numpy.float64
TWO32 = 2 ** 32
ALL = slice(None)
CHUNKSIZE = 4*1024**2 # 4 MB
SOURCE_ID = stochastic.rupture_dt['source_id']
memoized = lru_cache()
[docs]def lit_eval(string):
"""
`ast.literal_eval` the string if possible, otherwise returns it unchanged
"""
try:
return ast.literal_eval(string)
except (ValueError, SyntaxError):
return string
[docs]def get_info(dstore):
"""
:returns: {'stats': dic, 'loss_types': dic, 'num_rlzs': R}
"""
oq = dstore['oqparam']
stats = {stat: s for s, stat in enumerate(oq.hazard_stats())}
loss_types = {lt: li for li, lt in enumerate(oq.loss_dt().names)}
imt = {imt: i for i, imt in enumerate(oq.imtls)}
num_rlzs = len(dstore['weights'])
return dict(stats=stats, num_rlzs=num_rlzs, loss_types=loss_types,
imtls=oq.imtls, investigation_time=oq.investigation_time,
poes=oq.poes, imt=imt, uhs_dt=oq.uhs_dt(),
limit_states=oq.limit_states,
tagnames=tagset(oq.aggregate_by))
def _normalize(kinds, info):
a = []
b = []
stats = info['stats']
rlzs = False
for kind in kinds:
if kind.startswith('rlz-'):
rlzs = True
a.append(int(kind[4:]))
b.append(kind)
elif kind in stats:
a.append(stats[kind])
b.append(kind)
elif kind == 'stats':
a.extend(stats.values())
b.extend(stats)
elif kind == 'rlzs':
rlzs = True
a.extend(range(info['num_rlzs']))
b.extend(['rlz-%03d' % r for r in range(info['num_rlzs'])])
return a, b, rlzs
[docs]def parse(query_string, info={}):
"""
:returns: a normalized query_dict as in the following examples:
>>> parse('kind=stats', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean', 'max'], 'k': [0, 1], 'rlzs': False}
>>> parse('kind=rlzs', {'stats': {}, 'num_rlzs': 3})
{'kind': ['rlz-000', 'rlz-001', 'rlz-002'], 'k': [0, 1, 2], 'rlzs': True}
>>> parse('kind=mean', {'stats': {'mean': 0, 'max': 1}})
{'kind': ['mean'], 'k': [0], 'rlzs': False}
>>> parse('kind=rlz-3&imt=PGA&site_id=0', {'stats': {}})
{'kind': ['rlz-3'], 'imt': ['PGA'], 'site_id': [0], 'k': [3], 'rlzs': True}
"""
qdic = parse_qs(query_string)
loss_types = info.get('loss_types', [])
for key, val in sorted(qdic.items()):
# convert site_id to an int, loss_type to an int, etc
if key == 'loss_type':
qdic[key] = [loss_types[k] for k in val]
qdic['lt'] = val
else:
qdic[key] = [lit_eval(v) for v in val]
if info:
qdic['k'], qdic['kind'], qdic['rlzs'] = _normalize(qdic['kind'], info)
return qdic
[docs]def sanitize(query_string):
"""
Replace `/`, `?`, `&` characters with underscores and '=' with '-'
"""
return query_string.replace(
'/', '_').replace('?', '_').replace('&', '_').replace('=', '-')
[docs]def barray(iterlines):
"""
Array of bytes
"""
lst = [line.encode('utf-8') for line in iterlines]
arr = numpy.array(lst)
return arr
[docs]def extract_(dstore, dspath):
"""
Extracts an HDF5 path object from the datastore, for instance
extract(dstore, 'sitecol').
"""
obj = dstore[dspath]
if isinstance(obj, Dataset):
return ArrayWrapper(obj[()], obj.attrs)
elif isinstance(obj, Group):
return ArrayWrapper(numpy.array(list(obj)), obj.attrs)
else:
return obj
[docs]class Extract(dict):
"""
A callable dictionary of functions with a single instance called
`extract`. Then `extract(dstore, fullkey)` dispatches to the function
determined by the first part of `fullkey` (a slash-separated
string) by passing as argument the second part of `fullkey`.
For instance extract(dstore, 'sitecol').
"""
[docs] def add(self, key, cache=False):
def decorator(func):
self[key] = memoized(func) if cache else func
return func
return decorator
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)
if isinstance(data, pandas.DataFrame):
return data
return ArrayWrapper.from_(data)
extract = Extract()
[docs]@extract.add('oqparam')
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})
# used by the QGIS plugin in scenario
[docs]@extract.add('realizations')
def extract_realizations(dstore, dummy):
"""
Extract an array of realizations. Use it as /extract/realizations
"""
dt = [('rlz_id', U32), ('branch_path', '<S100'), ('weight', F32)]
oq = dstore['oqparam']
scenario = 'scenario' in oq.calculation_mode
rlzs = dstore['full_lt'].rlzs
# NB: branch_path cannot be of type hdf5.vstr otherwise the conversion
# to .npz (needed by the plugin) would fail
arr = numpy.zeros(len(rlzs), dt)
arr['rlz_id'] = rlzs['ordinal']
arr['weight'] = rlzs['weight']
if scenario:
gsims = dstore.getitem('full_lt/gsim_lt')['uncertainty']
if 'shakemap' in oq.inputs:
gsims = ["[FromShakeMap]"]
arr['branch_path'] = ['"%s"' % repr(gsim)[1:-1].replace('"', '""')
for gsim in gsims] # quotes Excel-friendly
else:
arr['branch_path'] = rlzs['branch_path']
return arr
[docs]@extract.add('weights')
def extract_weights(dstore, what):
"""
Extract the realization weights
"""
rlzs = dstore['full_lt'].get_realizations()
return numpy.array([rlz.weight['weight'] for rlz in rlzs])
[docs]@extract.add('gsims_by_trt')
def extract_gsims_by_trt(dstore, what):
"""
Extract the dictionary gsims_by_trt
"""
return ArrayWrapper((), dstore['full_lt'].gsim_lt.values)
[docs]@extract.add('exposure_metadata')
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)))
[docs]@extract.add('assets')
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)))
[docs]@extract.add('asset_risk')
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)))
[docs]@extract.add('asset_tags')
def extract_asset_tags(dstore, tagname):
"""
Extract an array of asset tags for the given tagname. Use it as
/extract/asset_tags or /extract/asset_tags/taxonomy
"""
tagcol = dstore['assetcol/tagcol']
if tagname:
yield tagname, barray(tagcol.gen_tags(tagname))
for tagname in tagcol.tagnames:
yield tagname, barray(tagcol.gen_tags(tagname))
[docs]def get_sites(sitecol, complete=True):
"""
:returns:
a lon-lat or lon-lat-depth array depending if the site collection
is at sea level or not; if there is a custom_site_id, prepend it
"""
sc = sitecol.complete if complete else sitecol
if sc.at_sea_level():
fields = ['lon', 'lat']
else:
fields = ['lon', 'lat', 'depth']
if 'custom_site_id' in sitecol.array.dtype.names:
fields.insert(0, 'custom_site_id')
return sitecol[fields]
[docs]def hazard_items(dic, sites, *extras, **kw):
"""
:param dic: dictionary of arrays of the same shape
:param sites: a sites array with lon, lat fields of the same length
:param extras: optional triples (field, dtype, values)
:param kw: dictionary of parameters (like investigation_time)
:returns: a list of pairs (key, value) suitable for storage in .npz format
"""
for item in kw.items():
yield item
try:
field = next(iter(dic))
except StopIteration:
return
arr = dic[field]
dtlist = [(str(field), arr.dtype) for field in sorted(dic)]
for field, dtype, values in extras:
dtlist.append((str(field), dtype))
array = numpy.zeros(arr.shape, dtlist)
for field in dic:
array[field] = dic[field]
for field, dtype, values in extras:
array[field] = values
yield 'all', util.compose_arrays(sites, array)
def _get_dict(dstore, name, imtls, stats):
dic = {}
dtlist = []
for imt, imls in imtls.items():
dt = numpy.dtype([(str(iml), F32) for iml in imls])
dtlist.append((imt, dt))
for s, stat in enumerate(stats):
dic[stat] = dstore[name][:, s].flatten().view(dtlist)
return dic
[docs]@extract.add('sitecol')
def extract_sitecol(dstore, what):
"""
Extracts the site collection array (not the complete object, otherwise it
would need to be pickled).
Use it as /extract/sitecol?field=vs30
"""
qdict = parse(what)
if 'field' in qdict:
[f] = qdict['field']
return dstore['sitecol'][f]
return dstore['sitecol'].array
def _items(dstore, name, what, info):
params = parse(what, info)
filt = {}
if 'site_id' in params:
filt['site_id'] = params['site_id'][0]
if 'imt' in params:
[imt] = params['imt']
filt['imt'] = imt
if params['rlzs']:
for k in params['k']:
filt['rlz_id'] = k
yield 'rlz-%03d' % k, dstore.sel(name + '-rlzs', **filt)[:, 0]
else:
stats = list(info['stats'])
for k in params['k']:
filt['stat'] = stat = stats[k]
yield stat, dstore.sel(name + '-stats', **filt)[:, 0]
yield from params.items()
[docs]@extract.add('hcurves')
def extract_hcurves(dstore, what):
"""
Extracts hazard curves. Use it as /extract/hcurves?kind=mean&imt=PGA or
/extract/hcurves?kind=rlz-0&imt=SA(1.0)
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
sites = get_sites(sitecol, complete=False)
dic = _get_dict(dstore, 'hcurves-stats', info['imtls'], info['stats'])
yield from hazard_items(
dic, sites, investigation_time=info['investigation_time'])
return
yield from _items(dstore, 'hcurves', what, info)
[docs]@extract.add('hmaps')
def extract_hmaps(dstore, what):
"""
Extracts hazard maps. Use it as /extract/hmaps?imt=PGA
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
sites = get_sites(sitecol, complete=False)
dic = _get_dict(dstore, 'hmaps-stats',
{imt: info['poes'] for imt in info['imtls']},
info['stats'])
yield from hazard_items(
dic, sites, investigation_time=info['investigation_time'])
return
yield from _items(dstore, 'hmaps', what, info)
[docs]@extract.add('uhs')
def extract_uhs(dstore, what):
"""
Extracts uniform hazard spectra. Use it as /extract/uhs?kind=mean or
/extract/uhs?kind=rlz-0, etc
"""
info = get_info(dstore)
if what == '': # npz exports for QGIS
sitecol = dstore['sitecol']
sites = get_sites(sitecol, complete=False)
dic = {}
for stat, s in info['stats'].items():
hmap = dstore['hmaps-stats'][:, s] # shape (N, M, P)
dic[stat] = calc.make_uhs(hmap, info)
yield from hazard_items(
dic, sites, investigation_time=info['investigation_time'])
return
for k, v in _items(dstore, 'hmaps', what, info): # shape (N, M, P)
if hasattr(v, 'shape') and len(v.shape) == 3:
yield k, calc.make_uhs(v, info)
else:
yield k, v
[docs]@extract.add('effect')
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]))
[docs]@extract.add('rups_by_mag_dist')
def extract_rups_by_mag_dist(dstore, what):
"""
Extracts the number of ruptures by mag, dist.
Use it as /extract/rups_by_mag_dist
"""
return extract_effect(dstore, 'rups_by_mag_dist')
[docs]@extract.add('source_data')
def extract_source_data(dstore, what):
"""
Extract performance information about the sources.
Use it as /extract/source_data?
"""
qdict = parse(what)
if 'taskno' in qdict:
sel = {'taskno': int(qdict['taskno'][0])}
else:
sel = {}
df = dstore.read_df('source_data', 'src_id', sel=sel).sort_values('ctimes')
dic = {col: df[col].to_numpy() for col in df.columns}
return ArrayWrapper(df.index.to_numpy(), dic)
[docs]@extract.add('sources')
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 num_sites eff_ruptures'
info = dstore['source_info'][()][fields.split()]
wkt = decode(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 getters.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 getters.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(decode(info['source_id'])).encode('utf8'))
oknames = [name for name in info.dtype.names # avoid pickle issues
if name not in ('source_id', 'trt_smrs')]
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})
[docs]@extract.add('gridded_sources')
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)})
[docs]@extract.add('task_info')
def extract_task_info(dstore, what):
"""
Extracts the task distribution. Use it as /extract/task_info?kind=classical
"""
dic = group_array(dstore['task_info'][()], 'taskname')
if 'kind' in what:
name = parse(what)['kind'][0]
yield name, dic[encode(name)]
return
for name in dic:
yield decode(name), dic[name]
def _agg(losses, idxs):
shp = losses.shape[1:]
if not idxs:
# no intersection, return a 0-dim matrix
return numpy.zeros((0,) + shp, losses.dtype)
# numpy.array wants lists, not sets, hence the sorted below
return losses[numpy.array(sorted(idxs))].sum(axis=0)
def _filter_agg(assetcol, losses, selected, stats=''):
# losses is an array of shape (A, ..., R) with A=#assets, R=#realizations
aids_by_tag = assetcol.get_aids_by_tag()
idxs = set(range(len(assetcol)))
tagnames = []
for tag in selected:
tagname, tagvalue = tag.split('=', 1)
if tagvalue == '*':
tagnames.append(tagname)
else:
idxs &= aids_by_tag[tag]
if len(tagnames) > 1:
raise ValueError('Too many * as tag values in %s' % tagnames)
elif not tagnames: # return an array of shape (..., R)
return ArrayWrapper(
_agg(losses, idxs), dict(selected=encode(selected), stats=stats))
else: # return an array of shape (T, ..., R)
[tagname] = tagnames
_tags = list(assetcol.tagcol.gen_tags(tagname))
all_idxs = [idxs & aids_by_tag[t] for t in _tags]
# NB: using a generator expression for all_idxs caused issues (?)
data, tags = [], []
for idxs, tag in zip(all_idxs, _tags):
agglosses = _agg(losses, idxs)
if len(agglosses):
data.append(agglosses)
tags.append(tag)
return ArrayWrapper(
numpy.array(data),
dict(selected=encode(selected), tags=encode(tags), stats=stats))
[docs]def get_loss_type_tags(what):
try:
loss_type, query_string = what.rsplit('?', 1)
except ValueError: # no question mark
loss_type, query_string = what, ''
tags = query_string.split('&') if query_string else []
return loss_type, tags
# probably not used
[docs]@extract.add('csq_curves')
def extract_csq_curves(dstore, what):
"""
Aggregate damages curves from the event_based_damage calculator:
/extract/csq_curves?agg_id=0&loss_type=occupants
Returns an ArrayWrapper of shape (P, D1) with attribute return_periods
"""
info = get_info(dstore)
qdic = parse(what + '&kind=mean', info)
[li] = qdic['loss_type'] # loss type index
[agg_id] = qdic.get('agg_id', [0])
df = dstore.read_df('aggcurves', 'return_period',
dict(agg_id=agg_id, loss_id=li))
cols = [col for col in df.columns if col not in 'agg_id loss_id']
return ArrayWrapper(df[cols].to_numpy(),
dict(return_period=df.index.to_numpy(),
consequences=cols))
[docs]@extract.add('agg_curves')
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...)
"""
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
lt = tagdict.pop('lt') # loss type string
[l] = qdic['loss_type'] # loss type index
tagnames = sorted(tagdict)
if set(tagnames) != info['tagnames']:
raise ValueError('Expected tagnames=%s, got %s' %
(info['tagnames'], tagnames))
tagvalues = [tagdict[t][0] for t in tagnames]
agg_id = -1
if tagnames:
lst = decode(dstore['agg_keys'][:])
agg_id = lst.index(','.join(tagvalues))
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 = (agg_id, k, l)
arr = dstore[name][tup].T # shape P, R
if qdic['absolute'] == [1]:
pass
elif qdic['absolute'] == [0]:
evalue, = dstore['agg_values'][agg_id][lt]
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)))
[docs]@extract.add('agg_losses')
def extract_agg_losses(dstore, what):
"""
Aggregate losses of the given loss type and tags. Use it as
/extract/agg_losses/structural?taxonomy=RC&custom_site_id=20126
/extract/agg_losses/structural?taxonomy=RC&custom_site_id=*
:returns:
an array of shape (T, R) if one of the tag names has a `*` value
an array of shape (R,), being R the number of realizations
an array of length 0 if there is no data for the given tags
"""
loss_type, tags = get_loss_type_tags(what)
if not loss_type:
raise ValueError('loss_type not passed in agg_losses/<loss_type>')
L = dstore['oqparam'].lti[loss_type]
if 'avg_losses-stats' in dstore:
stats = list(dstore['oqparam'].hazard_stats())
losses = dstore['avg_losses-stats'][:, :, L]
elif 'avg_losses-rlzs' in dstore:
stats = ['mean']
losses = dstore['avg_losses-rlzs'][:, :, L]
else:
raise KeyError('No losses found in %s' % dstore)
return _filter_agg(dstore['assetcol'], losses, tags, stats)
[docs]@extract.add('agg_damages')
def extract_agg_damages(dstore, what):
"""
Aggregate damages of the given loss type and tags. Use it as
/extract/agg_damages/structural?taxonomy=RC&custom_site_id=20126
:returns:
array of shape (R, D), being R the number of realizations and D the
number of damage states, or an array of length 0 if there is no data
for the given tags
"""
loss_type, tags = get_loss_type_tags(what)
if 'damages-rlzs' in dstore:
oq = dstore['oqparam']
lti = oq.lti[loss_type]
D = len(oq.limit_states) + 1
damages = dstore['damages-rlzs'][:, :, lti, :D]
else:
raise KeyError('No damages found in %s' % dstore)
return _filter_agg(dstore['assetcol'], damages, tags)
[docs]@extract.add('aggregate')
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.aggregateby(tagnames, array), {}, lt)
else:
array = dstore[name + suffix][:, qdic['k'][0]]
aw = ArrayWrapper(assetcol.aggregateby(tagnames, array), {},
loss_types)
for tagname in tagnames:
setattr(aw, tagname, getattr(assetcol.tagcol, tagname)[1:])
aw.shape_descr = tagnames
return aw
[docs]@extract.add('losses_by_asset')
def extract_losses_by_asset(dstore, what):
loss_dt = dstore['oqparam'].loss_dt(F32)
rlzs = dstore['full_lt'].get_realizations()
assets = util.get_assets(dstore)
if 'losses_by_asset' in dstore:
losses_by_asset = dstore['losses_by_asset'][()]
for rlz in rlzs:
# I am exporting the 'mean' and ignoring the 'stddev'
losses = cast(losses_by_asset[:, rlz.ordinal]['mean'], loss_dt)
data = util.compose_arrays(assets, losses)
yield 'rlz-%03d' % rlz.ordinal, data
elif 'avg_losses-stats' in dstore:
aw = hdf5.ArrayWrapper.from_(dstore['avg_losses-stats'])
for s, stat in enumerate(aw.stat):
losses = cast(aw[:, s], loss_dt)
data = util.compose_arrays(assets, losses)
yield stat, data
elif 'avg_losses-rlzs' in dstore: # there is only one realization
avg_losses = dstore['avg_losses-rlzs'][()]
losses = cast(avg_losses, loss_dt)
data = util.compose_arrays(assets, losses)
yield 'rlz-000', data
def _gmf(df, num_sites, imts):
# convert data into the composite array expected by QGIS
gmfa = numpy.zeros(num_sites, [(imt, F32) for imt in imts])
for m, imt in enumerate(imts):
gmfa[imt][U32(df.sid)] = df[f'gmv_{m}']
return gmfa
# used by the QGIS plugin for a single eid
[docs]@extract.add('gmf_data')
def extract_gmf_npz(dstore, what):
oq = dstore['oqparam']
qdict = parse(what)
[eid] = qdict.get('event_id', [0]) # there must be a single event
rlzi = dstore['events'][eid]['rlz_id']
sites = get_sites(dstore['sitecol'])
n = len(sites)
try:
df = dstore.read_df('gmf_data', 'eid').loc[eid]
except KeyError:
# zero GMF
yield 'rlz-%03d' % rlzi, []
else:
gmfa = _gmf(df, n, oq.imtls)
yield 'rlz-%03d' % rlzi, util.compose_arrays(sites, gmfa)
[docs]@extract.add('avg_gmf')
def extract_avg_gmf(dstore, what):
qdict = parse(what)
info = get_info(dstore)
[imt] = qdict['imt']
imti = info['imt'][imt]
sitecol = dstore['sitecol']
avg_gmf = dstore['avg_gmf'][0, :, imti]
yield imt, avg_gmf[sitecol.sids]
yield 'sids', sitecol.sids
yield 'lons', sitecol.lons
yield 'lats', sitecol.lats
[docs]@extract.add('num_events')
def extract_num_events(dstore, what):
"""
:returns: the number of events (if any)
"""
yield 'num_events', len(dstore['events'])
[docs]def build_damage_dt(dstore):
"""
:param dstore: a datastore instance
:returns:
a composite dtype loss_type -> (ds1, ds2, ...)
"""
oq = dstore['oqparam']
attrs = json.loads(dstore.get_attr('damages-rlzs', 'json'))
limit_states = list(dstore.get_attr('crm', 'limit_states'))
csqs = attrs['dmg_state'][len(limit_states) + 1:] # consequences
dt_list = [(ds, F32) for ds in ['no_damage'] + limit_states + csqs]
damage_dt = numpy.dtype(dt_list)
loss_types = oq.loss_dt().names
return numpy.dtype([(lt, damage_dt) for lt in loss_types])
[docs]def build_csq_dt(dstore):
"""
:param dstore: a datastore instance
:returns:
a composite dtype loss_type -> (csq1, csq2, ...)
"""
oq = dstore['oqparam']
attrs = json.loads(dstore.get_attr('damages-rlzs', 'json'))
limit_states = list(dstore.get_attr('crm', 'limit_states'))
csqs = attrs['dmg_state'][len(limit_states) + 1:] # consequences
dt = numpy.dtype([(csq, F32) for csq in csqs])
loss_types = oq.loss_dt().names
return numpy.dtype([(lt, dt) for lt in loss_types])
[docs]def build_damage_array(data, damage_dt):
"""
:param data: an array of shape (A, L, D)
:param damage_dt: a damage composite data type loss_type -> states
:returns: a composite array of length N and dtype damage_dt
"""
A, L, D = data.shape
dmg = numpy.zeros(A, damage_dt)
for a in range(A):
for li, lt in enumerate(damage_dt.names):
dmg[lt][a] = tuple(data[a, li])
return dmg
[docs]@extract.add('damages-rlzs')
def extract_damages_npz(dstore, what):
oq = dstore['oqparam']
damage_dt = build_damage_dt(dstore)
rlzs = dstore['full_lt'].get_realizations()
if oq.collect_rlzs:
rlzs = [Mock(ordinal=0)]
data = dstore['damages-rlzs']
assets = util.get_assets(dstore)
for rlz in rlzs:
damages = build_damage_array(data[:, rlz.ordinal], damage_dt)
yield 'rlz-%03d' % rlz.ordinal, util.compose_arrays(
assets, damages)
# tested on oq-risk-tests event_based/etna
[docs]@extract.add('event_based_mfd')
def extract_mfd(dstore, what):
"""
Compare n_occ/eff_time with occurrence_rate.
Example: http://127.0.0.1:8800/v1/calc/30/extract/event_based_mfd?
"""
oq = dstore['oqparam']
R = len(dstore['weights'])
eff_time = oq.investigation_time * oq.ses_per_logic_tree_path * R
rup_df = dstore.read_df('ruptures', 'id')
dic = dict(mag=[], freq=[], occ_rate=[])
for mag, df in rup_df.groupby('mag'):
dic['mag'].append(round(mag, 2))
dic['freq'].append(df.n_occ.sum() / eff_time)
dic['occ_rate'].append(df.occurrence_rate.sum())
return ArrayWrapper((), {k: numpy.array(v) for k, v in dic.items()})
[docs]@extract.add('mean_std_curves')
def extract_mean_std_curves(dstore, what):
"""
Yield imls/IMT and poes/IMT containg mean and stddev for all sites
"""
rlzs = dstore['full_lt'].get_realizations()
w = [rlz.weight for rlz in rlzs]
getter = getters.PmapGetter(dstore, w)
arr = getter.get_mean().array
for imt in getter.imtls:
yield 'imls/' + imt, getter.imtls[imt]
yield 'poes/' + imt, arr[:, getter.imtls(imt)]
[docs]@extract.add('composite_risk_model.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 _get(dstore, name):
try:
dset = dstore[name + '-stats']
return dset, list(dstore['oqparam'].hazard_stats())
except KeyError: # single realization
return dstore[name + '-rlzs'], ['mean']
[docs]@extract.add('events')
def extract_relevant_events(dstore, dummy=None):
"""
Extract the relevant events
Example:
http://127.0.0.1:8800/v1/calc/30/extract/events
"""
all_events = dstore['events'][:]
if 'relevant_events' not in dstore:
all_events.sort(order='id')
return all_events
rel_events = dstore['relevant_events'][:]
events = all_events[rel_events]
events.sort(order='id')
return events
[docs]@extract.add('event_info')
def extract_event_info(dstore, eidx):
"""
Extract information about the given event index.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/event_info/0
"""
event = dstore['events'][int(eidx)]
ridx = event['rup_id']
[getter] = getters.get_rupture_getters(dstore, slc=slice(ridx, ridx + 1))
rupdict = getter.get_rupdict()
rlzi = event['rlz_id']
full_lt = dstore['full_lt']
rlz = full_lt.get_realizations()[rlzi]
gsim = full_lt.gsim_by_trt(rlz)[rupdict['trt']]
for key, val in rupdict.items():
yield key, val
yield 'rlzi', rlzi
yield 'gsim', repr(gsim)
[docs]@extract.add('extreme_event')
def extract_extreme_event(dstore, eidx):
"""
Extract information about the given event index.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/extreme_event
"""
arr = dstore['gmf_data/gmv_0'][()]
idx = arr.argmax()
eid = dstore['gmf_data/eid'][idx]
dic = dict(extract_event_info(dstore, eid))
dic['gmv'] = arr[idx]
return dic
[docs]@extract.add('ruptures_within')
def get_ruptures_within(dstore, bbox):
"""
Extract the ruptures within the given bounding box, a string
minlon,minlat,maxlon,maxlat.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/ruptures_with/8,44,10,46
"""
minlon, minlat, maxlon, maxlat = map(float, bbox.split(','))
hypo = dstore['ruptures']['hypo'].T # shape (3, N)
mask = ((minlon <= hypo[0]) * (minlat <= hypo[1]) *
(maxlon >= hypo[0]) * (maxlat >= hypo[1]))
return dstore['ruptures'][mask]
[docs]@extract.add('disagg')
def extract_disagg(dstore, what):
"""
Extract a disaggregation output as a 2D array.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/
disagg?kind=Mag_Dist&imt=PGA&poe_id=0&site_id=1&traditional=1
"""
qdict = parse(what)
label = qdict['kind'][0]
imt = qdict['imt'][0]
poe_id = int(qdict['poe_id'][0])
sid = int(qdict['site_id'][0])
traditional = qdict.get('traditional')
def get(v, sid):
if len(v.shape) == 2:
return v[sid]
return v[:]
oq = dstore['oqparam']
imt2m = {imt: m for m, imt in enumerate(oq.imtls)}
bins = {k: get(v, sid) for k, v in dstore['disagg-bins'].items()}
m = imt2m[imt]
matrix = dstore['disagg/' + label][sid, m, poe_id] # shape (..., Z)
poe_agg = dstore['poe4'][sid, m, poe_id]
Z = len(poe_agg)
if traditional and traditional != '0':
if matrix.any(): # nonzero
matrix = numpy.log(1. - matrix) / numpy.log(1. - poe_agg)
# adapted from the nrml_converters
disag_tup = tuple(label.split('_'))
axis = [bins[k] for k in disag_tup]
# compute axis mid points
axis = [(ax[: -1] + ax[1:]) / 2. if ax.dtype == float
else ax for ax in axis]
if len(axis) == 1: # i.e. Mag or Dist
values = numpy.array([axis[0]] + list(matrix.T)) # i.e. shape (2, 3)
else: # i.e. Mag_Dist
# axis = [[5.5, 6.5, 7.5], [12.5, 37.5, 62.5, 87.5]]
grids = numpy.meshgrid(*axis, indexing='ij')
# with the 2 axis above there are 2 grids of shape (3, 4) each
values = [g.flatten() for g in grids] + [matrix[..., z].flatten()
for z in range(Z)]
# list of arrays of lenghts [12, 12, 12]
values = numpy.array(values) # shape (3, 12)
attrs = qdict.copy()
for k in disag_tup:
attrs[k] = bins[k]
attrs['kind'] = disag_tup
attrs['rlzs'] = dstore['best_rlzs'][sid]
weights = numpy.array([dstore['weights'][r] for r in attrs['rlzs']])
weights /= weights.sum()
attrs['weights'] = weights
return ArrayWrapper(values.T, attrs)
def _disagg_output_dt(shapedic, disagg_outputs, imts, poes_disagg):
dt = [('site_id', U32), ('lon', F32), ('lat', F32),
('lon_bins', (F32, shapedic['lon'] + 1)),
('lat_bins', (F32, shapedic['lat'] + 1))]
Z = shapedic['Z']
for out in disagg_outputs:
shp = tuple(shapedic[key] for key in out.lower().split('_'))
for imt in imts:
for poe in poes_disagg:
dt.append(('%s-%s-%s' % (out, imt, poe), (F32, shp)))
for imt in imts:
for poe in poes_disagg:
dt.append(('iml-%s-%s' % (imt, poe), (F32, (Z,))))
return dt
[docs]def norm(qdict, params):
dic = {}
for par in params:
dic[par] = int(qdict[par][0]) if par in qdict else 0
return dic
[docs]@extract.add('disagg_by_src')
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)))
[docs]@extract.add('disagg_layer')
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.IntegrationDistance(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]))))
# ######################### extracting ruptures ##############################
[docs]class RuptureData(object):
"""
Container for information about the ruptures of a given
tectonic region type.
"""
def __init__(self, trt, gsims):
self.trt = trt
self.cmaker = ContextMaker(trt, gsims, {'imtls': {}})
self.params = sorted(self.cmaker.REQUIRES_RUPTURE_PARAMETERS -
set('mag strike dip rake hypo_depth'.split()))
self.dt = numpy.dtype([
('rup_id', U32), ('source_id', SOURCE_ID), ('multiplicity', U32),
('occurrence_rate', F64),
('mag', F32), ('lon', F32), ('lat', F32), ('depth', F32),
('strike', F32), ('dip', F32), ('rake', F32),
('boundaries', hdf5.vfloat32)] +
[(param, F32) for param in self.params])
[docs] def to_array(self, proxies):
"""
Convert a list of rupture proxies into an array of dtype RuptureRata.dt
"""
data = []
for proxy in proxies:
ebr = proxy.to_ebr(self.trt)
rup = ebr.rupture
ctx = self.cmaker.make_rctx(rup)
ruptparams = tuple(getattr(ctx, param) for param in self.params)
point = rup.surface.get_middle_point()
boundaries = rup.surface.get_surface_boundaries_3d()
try:
rate = ebr.rupture.occurrence_rate
except AttributeError: # for nonparametric sources
rate = numpy.nan
data.append(
(ebr.id, ebr.source_id, ebr.n_occ, rate,
rup.mag, point.x, point.y, point.z, rup.surface.get_strike(),
rup.surface.get_dip(), rup.rake, boundaries) + ruptparams)
return numpy.array(data, self.dt)
[docs]@extract.add('rupture_info')
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', U32), ('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.get_rupture_getters(dstore):
proxies = rgetter.get_proxies(min_mag)
rup_data = RuptureData(rgetter.trt, 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['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))
[docs]@extract.add('ruptures')
def extract_ruptures(dstore, what):
"""
Extract the ruptures with their geometry as a big CSV string
Example:
http://127.0.0.1:8800/v1/calc/30/extract/ruptures?min_mag=6
"""
qdict = parse(what)
if 'min_mag' in qdict:
[min_mag] = qdict['min_mag']
else:
min_mag = 0
bio = io.StringIO()
first = True
trts = list(dstore.getitem('full_lt').attrs['trts'])
for rgetter in getters.get_rupture_getters(dstore):
rups = [rupture._get_rupture(proxy.rec, proxy.geom, rgetter.trt)
for proxy in rgetter.get_proxies(min_mag)]
arr = rupture.to_csv_array(rups)
if first:
header = None
comment = dict(trts=trts)
first = False
else:
header = 'no-header'
comment = None
writers.write_csv(bio, arr, header=header, comment=comment)
return bio.getvalue()
[docs]@extract.add('eids_by_gsim')
def extract_eids_by_gsim(dstore, what):
"""
Returns a dictionary gsim -> event_ids for the first TRT
Example:
http://127.0.0.1:8800/v1/calc/30/extract/eids_by_gsim
"""
rlzs = dstore['full_lt'].get_realizations()
gsims = [str(rlz.gsim_rlz.value[0]) for rlz in rlzs]
evs = extract_relevant_events(dstore)
df = pandas.DataFrame({'id': evs['id'], 'rlz_id': evs['rlz_id']})
for r, evs in df.groupby('rlz_id'):
yield gsims[r], numpy.array(evs['id'])
[docs]@extract.add('risk_stats')
def extract_risk_stats(dstore, what):
"""
Extract the risk statistics from a DataFrame.
Example:
http://127.0.0.1:8800/v1/calc/30/extract/risk_stats/aggcurves
"""
oq = dstore['oqparam']
stats = oq.hazard_stats()
df = dstore.read_df(what)
df['loss_type'] = [oq.loss_types[lid] for lid in df.loss_id]
del df['loss_id']
kfields = [f for f in df.columns if f in {
'agg_id', 'loss_type', 'return_period'}]
weights = dstore['weights'][:]
return calc_stats(df, kfields, stats, weights)
# ##################### extraction from the WebAPI ###################### #
[docs]class Extractor(object):
"""
A class to extract data from a calculation.
:param calc_id: a calculation ID
NB: instantiating the Extractor opens the datastore.
"""
def __init__(self, calc_id):
self.dstore = datastore.read(calc_id)
self.calc_id = self.dstore.calc_id
self.oqparam = self.dstore['oqparam']
[docs] def get(self, what, asdict=False):
"""
:param what: what to extract
:returns: an ArrayWrapper instance or a dictionary if asdict is True
"""
aw = extract(self.dstore, what)
if asdict:
return {k: v for k, v in vars(aw).items() if not k.startswith('_')}
return aw
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
[docs]class WebExtractor(Extractor):
"""
A class to extract data from the WebAPI.
:param calc_id: a calculation ID
:param server: hostname of the webapi server (can be '')
:param username: login username (can be '')
:param password: login password (can be '')
NB: instantiating the WebExtractor opens a session.
"""
def __init__(self, calc_id, server=None, username=None, password=None):
self.calc_id = calc_id
self.server = config.webapi.server if server is None else server
if username is None:
username = config.webapi.username
if password is None:
password = config.webapi.password
self.sess = requests.Session()
if username:
login_url = '%s/accounts/ajax_login/' % self.server
logging.info('POST %s', login_url)
resp = self.sess.post(
login_url, data=dict(username=username, password=password))
if resp.status_code != 200:
raise WebAPIError(resp.text)
url = '%s/v1/calc/%d/extract/oqparam' % (self.server, calc_id)
logging.info('GET %s', url)
resp = self.sess.get(url)
if resp.status_code == 404:
raise WebAPIError('Not Found: %s' % url)
elif resp.status_code != 200:
raise WebAPIError(resp.text)
self.oqparam = object.__new__(oqvalidation.OqParam)
js = bytes(numpy.load(io.BytesIO(resp.content))['json'])
vars(self.oqparam).update(json.loads(js))
[docs] 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)
[docs] def dump(self, fname):
"""
Dump the remote datastore on a local path.
"""
url = '%s/v1/calc/%d/datastore' % (self.server, self.calc_id)
resp = self.sess.get(url, stream=True)
down = 0
with open(fname, 'wb') as f:
logging.info('Saving %s', fname)
for chunk in resp.iter_content(CHUNKSIZE):
f.write(chunk)
down += len(chunk)
println('Downloaded {:,} bytes'.format(down))
print()
[docs]def clusterize(hmaps, rlzs, k):
"""
:param hmaps: array of shape (R, M, P)
:param rlzs: composite array of shape R
:param k: number of clusters to build
:returns: (array(K, MP), labels(R))
"""
R, M, P = hmaps.shape
hmaps = hmaps.transpose(0, 2, 1).reshape(R, M * P)
dt = [('label', U32), ('branch_paths', object), ('centroid', (F32, M*P))]
centroid, labels = kmeans2(hmaps, k, minit='++')
dic = dict(path=rlzs['branch_path'], label=labels)
df = pandas.DataFrame(dic)
tbl = []
for label, grp in df.groupby('label'):
paths = [encode(path) for path in grp['path']]
tbl.append((label, logictree.collect_paths(paths), centroid[label]))
return numpy.array(tbl, dt), labels
[docs]def read_ebrupture(dstore, rup_id):
"""
:param dstore: a DataStore instance
:param rup_id: an integer rupture ID
:returns: an EBRupture instance
"""
[getter] = getters.get_rupture_getters(
dstore, slc=slice(rup_id, rup_id + 1))
[proxy] = getter.get_proxies()
return proxy.to_ebr(getter.trt)