def get_vulnerability_functions(fname):
"""
:param fname:
path of the vulnerability filter
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
# NB: the vulnerabilitySetID is not an unique ID!
# it is right to have several vulnerability sets with the same ID
# the IMTs can also be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
node = nrml.read(fname)
if node['xmlns'] == 'http://openquake.org/xmlns/nrml/0.5':
vmodel = node[0]
for vfun in vmodel[1:]: # the first node is the description
imt = vfun.imls['imt']
imls = numpy.array(~vfun.imls)
taxonomy = vfun['id']
loss_ratios, probs = [], []
for probabilities in vfun[1:]:
loss_ratios.append(probabilities['lr'])
probs.append(valid.probabilities(~probabilities))
probs = numpy.array(probs)
assert probs.shape == (len(loss_ratios), len(imls))
vf_dict[imt, taxonomy] = scientific.VulnerabilityFunctionWithPMF(
taxonomy, imt, imls, numpy.array(loss_ratios), probs)
return vf_dict
# otherwise, read the old format (NRML 0.4)
for vset in read_nodes(fname, filter_vset,
nodefactory['vulnerabilityModel']):
imt_str, imls, min_iml, max_iml, imlUnit = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vf_dict
def get_consequence_model(node, fname):
with context(fname, node):
description = ~node.description # make sure it is there
limitStates = ~node.limitStates # make sure it is there
# ASK: is the 'id' mandatory?
node['assetCategory'] # make sure it is there
node['lossCategory'] # make sure it is there
cfs = node[2:]
functions = {}
for cf in cfs:
with context(fname, cf):
params = []
if len(limitStates) != len(cf):
raise ValueError(
'Expected %d limit states, got %d' %
(len(limitStates), len(cf)))
for ls, param in zip(limitStates, cf):
with context(fname, param):
if param['ls'] != ls:
raise ValueError('Expected %r, got %r' %
(ls, param['ls']))
params.append((param['mean'], param['stddev']))
functions[cf['id']] = scientific.ConsequenceFunction(
cf['id'], cf['dist'], params)
attrs = node.attrib.copy()
attrs.update(description=description, limitStates=limitStates)
cmodel = scientific.ConsequenceModel(**attrs)
cmodel.update(functions)
return cmodel
def get_vulnerability_functions_05(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path of the vulnerability filter
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
taxonomies = set()
vmodel = scientific.VulnerabilityModel(**node.attrib)
# imt, taxonomy -> vulnerability function
for vfun in node.getnodes('vulnerabilityFunction'):
with context(fname, vfun):
imt = vfun.imls['imt']
imls = numpy.array(~vfun.imls)
taxonomy = vfun['id']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
if vfun['dist'] == 'PM':
loss_ratios, probs = [], []
for probabilities in vfun[1:]:
loss_ratios.append(probabilities['lr'])
probs.append(valid.probabilities(~probabilities))
probs = numpy.array(probs)
assert probs.shape == (len(loss_ratios), len(imls))
vmodel[imt, taxonomy] = (
scientific.VulnerabilityFunctionWithPMF(
taxonomy, imt, imls, numpy.array(loss_ratios),
probs)) # the seed will be set by readinput.get_risk_model
else:
with context(fname, vfun):
loss_ratios = ~vfun.meanLRs
coefficients = ~vfun.covLRs
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.meanLRs.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, '
'line %d' % (len(coefficients), len(imls), fname,
vfun.covLRs.lineno))
with context(fname, vfun):
vmodel[imt, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt, imls, loss_ratios, coefficients,
vfun['dist'])
return vmodel
def ffconvert(fname, limit_states, ff):
"""
Convert a fragility function into a numpy array plus a bunch
of attributes.
:param fname: path to the fragility model file
:param limit_states: expected limit states
:param ff: fragility function node
:returns: a pair (array, dictionary)
"""
with context(fname, ff):
ffs = ff[1:]
imls = ff.imls
with context(fname, imls):
attrs = dict(format=ff['format'],
imt=imls['imt'],
nodamage=imls.attrib.get('noDamageLimit'))
LS = len(limit_states)
if LS != len(ffs):
with context(fname, ff):
raise InvalidFile('expected %d limit states, found %d' %
(LS, len(ffs)))
if ff['format'] == 'continuous':
attrs['minIML'] = float(imls['minIML'])
attrs['maxIML'] = float(imls['maxIML'])
array = numpy.zeros(LS, [('mean', F64), ('stddev', F64)])
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
if ls != node['ls']:
with context(fname, node):
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
array['mean'][i] = node['mean']
array['stddev'][i] = node['stddev']
elif ff['format'] == 'discrete':
attrs['imls'] = valid.positivefloats(~imls)
valid.check_levels(attrs['imls'], attrs['imt'])
num_poes = len(attrs['imls'])
array = numpy.zeros((LS, num_poes))
for i, ls, node in zip(range(LS), limit_states, ff[1:]):
with context(fname, node):
if ls != node['ls']:
raise InvalidFile('expected %s, found' %
(ls, node['ls']))
poes = (~node if isinstance(~node, list)
else valid.probabilities(~node))
if len(poes) != num_poes:
raise InvalidFile('expected %s, found' %
(num_poes, len(poes)))
array[i, :] = poes
# NB: the format is constrained in nrml.FragilityNode to be either
# discrete or continuous, there is no third option
return array, attrs
def get_vulnerability_functions_04(fname):
"""
Parse the vulnerability model in NRML 0.4 format.
:param fname:
path of the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function + vset
"""
categories = dict(assetCategory=set(),
lossCategory=set(),
vulnerabilitySetID=set())
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
for vset in read_nodes(fname, filter_vset,
nodefactory['vulnerabilityModel']):
categories['assetCategory'].add(vset['assetCategory'])
categories['lossCategory'].add(vset['lossCategory'])
categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])
imt_str, imls, min_iml, max_iml, imlUnit = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))
del categories['vulnerabilitySetID']
return vf_dict, categories
def get_fragility_functions(fname, continuous_fragility_discretization):
"""
:param fname:
path of the fragility file
:returns:
damage_states list and dictionary taxonomy -> functions
"""
[fmodel] = read_nodes(
fname, lambda el: el.tag.endswith('fragilityModel'),
nodefactory['fragilityModel'])
# ~fmodel.description is ignored
limit_states = ~fmodel.limitStates
tag = 'ffc' if fmodel['format'] == 'continuous' else 'ffd'
fragility_functions = AccumDict() # taxonomy -> functions
for ffs in fmodel.getnodes('ffs'):
nodamage = ffs.attrib.get('noDamageLimit')
taxonomy = ~ffs.taxonomy
imt_str, imls, min_iml, max_iml, imlUnit = ~ffs.IML
if continuous_fragility_discretization and not imls:
imls = numpy.linspace(min_iml, max_iml,
continuous_fragility_discretization + 1)
fragility_functions[taxonomy] = FragilityFunctionList(
[], imt=imt_str, imls=imls)
lstates = []
for ff in ffs.getnodes(tag):
ls = ff['ls'] # limit state
lstates.append(ls)
if tag == 'ffc':
with context(fname, ff):
mean_stddev = ~ff.params
fragility_functions[taxonomy].append(
scientific.FragilityFunctionContinuous(ls, *mean_stddev))
else: # discrete
with context(fname, ff):
poes = ~ff.poEs
if nodamage is None:
fragility_functions[taxonomy].append(
scientific.FragilityFunctionDiscrete(
ls, imls, poes, imls[0]))
else:
fragility_functions[taxonomy].append(
scientific.FragilityFunctionDiscrete(
ls, [nodamage] + imls, [0.0] + poes, nodamage))
if lstates != limit_states:
raise InvalidFile("Expected limit states %s, got %s in %s" %
(limit_states, lstates, fname))
fragility_functions.damage_states = ['no_damage'] + limit_states
return fragility_functions
def get_vulnerability_functions_04(fname):
"""
Parse the vulnerability model in NRML 0.4 format.
:param fname:
path of the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function + vset
"""
categories = dict(assetCategory=set(), lossCategory=set(),
vulnerabilitySetID=set())
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
for vset in nrml.read(fname).vulnerabilityModel:
categories['assetCategory'].add(vset['assetCategory'])
categories['lossCategory'].add(vset['lossCategory'])
categories['vulnerabilitySetID'].add(vset['vulnerabilitySetID'])
IML = vset.IML
imt_str = IML['IMT']
imls = ~IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
categories['id'] = '_'.join(sorted(categories['vulnerabilitySetID']))
del categories['vulnerabilitySetID']
return vf_dict, categories
def convert_mfdist(self, node):
"""
Convert the given node into a Magnitude-Frequency Distribution
object.
:param node: a node of kind incrementalMFD or truncGutenbergRichterMFD
:returns: a :class:`openquake.hazardlib.mdf.EvenlyDiscretizedMFD.` or
:class:`openquake.hazardlib.mdf.TruncatedGRMFD` instance
"""
with context(self.fname, node):
[mfd_node] = [
subnode for subnode in node if subnode.tag.endswith(("incrementalMFD", "truncGutenbergRichterMFD"))
]
if mfd_node.tag.endswith("incrementalMFD"):
return mfd.EvenlyDiscretizedMFD(
min_mag=mfd_node["minMag"], bin_width=mfd_node["binWidth"], occurrence_rates=~mfd_node.occurRates
)
elif mfd_node.tag.endswith("truncGutenbergRichterMFD"):
return mfd.TruncatedGRMFD(
a_val=mfd_node["aValue"],
b_val=mfd_node["bValue"],
min_mag=mfd_node["minMag"],
max_mag=mfd_node["maxMag"],
bin_width=self.width_of_mfd_bin,
)
def _get_exposure(fname, ok_cost_types, stop=None):
"""
:param fname:
path of the XML file containing the exposure
:param ok_cost_types:
a set of cost types (as strings)
:param stop:
node at which to stop parsing (or None)
:returns:
a pair (Exposure instance, list of asset nodes)
"""
[exposure] = nrml.read(fname, stop=stop)
description = exposure.description
try:
conversions = exposure.conversions
except NameError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except NameError:
inslimit = Node('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except NameError:
deductible = Node('deductible', text=True)
try:
area = conversions.area
except NameError:
# NB: the area type cannot be an empty string because when sending
# around the CostCalculator object we would run into this numpy bug
# about pickling dictionaries with empty strings:
# https://github.com/numpy/numpy/pull/5475
area = Node('area', dict(type='?'))
# read the cost types and make some check
cost_types = []
for ct in conversions.costTypes:
if ct['name'] in ok_cost_types:
with context(fname, ct):
cost_types.append(
(ct['name'], valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in ok_cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
time_events = set()
exp = Exposure(
exposure['id'], exposure['category'],
~description, numpy.array(cost_types, cost_type_dt), time_events,
inslimit.attrib.get('isAbsolute', True),
deductible.attrib.get('isAbsolute', True),
area.attrib, [], set(), [])
cc = riskmodels.CostCalculator(
{}, {}, {},
exp.deductible_is_absolute, exp.insurance_limit_is_absolute)
for ct in exp.cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = exp.area['type']
cc.units[name] = ct['unit']
return exp, exposure.assets, cc
def convert_complexFaultSource(self, node):
"""
Convert the given node into a complex fault object.
:param node: a node with tag areaGeometry
:returns: a :class:`openquake.hazardlib.source.ComplexFaultSource`
instance
"""
geom = node.complexFaultGeometry
edges = self.geo_lines(geom)
mfd = self.convert_mfdist(node)
msr = valid.SCALEREL[~node.magScaleRel]()
with context(self.fname, node):
cmplx = source.ComplexFaultSource(
source_id=node['id'],
name=node['name'],
tectonic_region_type=node['tectonicRegion'],
mfd=mfd,
rupture_mesh_spacing=self.complex_fault_mesh_spacing,
magnitude_scaling_relationship=msr,
rupture_aspect_ratio=~node.ruptAspectRatio,
edges=edges,
rake=~node.rake,
temporal_occurrence_model=self.tom)
return cmplx
def convert_complexFaultSource(self, node):
"""
Convert the given node into a complex fault object.
:param node: a node with tag areaGeometry
:returns: a :class:`openquake.hazardlib.source.ComplexFaultSource`
instance
"""
geom = node.complexFaultGeometry
edges = self.geo_lines(geom)
mfd = self.convert_mfdist(node)
msr = valid.SCALEREL[~node.magScaleRel]()
with context(self.fname, node):
cmplx = source.ComplexFaultSource(
source_id=node['id'],
name=node['name'],
tectonic_region_type=node['tectonicRegion'],
mfd=mfd,
rupture_mesh_spacing=self.complex_fault_mesh_spacing,
magnitude_scaling_relationship=msr,
rupture_aspect_ratio=~node.ruptAspectRatio,
edges=edges,
rake=~node.rake,
temporal_occurrence_model=self.tom)
return cmplx
def convert_mfdist(self, node):
"""
Convert the given node into a Magnitude-Frequency Distribution
object.
:param node: a node of kind incrementalMFD or truncGutenbergRichterMFD
:returns: a :class:`openquake.hazardlib.mdf.EvenlyDiscretizedMFD.` or
:class:`openquake.hazardlib.mdf.TruncatedGRMFD` instance
"""
with context(self.fname, node):
[mfd_node] = [
subnode for subnode in node
if subnode.tag.endswith(('incrementalMFD',
'truncGutenbergRichterMFD'))
]
if mfd_node.tag.endswith('incrementalMFD'):
return mfd.EvenlyDiscretizedMFD(
min_mag=mfd_node['minMag'],
bin_width=mfd_node['binWidth'],
occurrence_rates=~mfd_node.occurRates)
elif mfd_node.tag.endswith('truncGutenbergRichterMFD'):
return mfd.TruncatedGRMFD(a_val=mfd_node['aValue'],
b_val=mfd_node['bValue'],
min_mag=mfd_node['minMag'],
max_mag=mfd_node['maxMag'],
bin_width=self.width_of_mfd_bin)
def get_fragility_model(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path to the vulnerability file
:returns:
a dictionary imt, taxonomy -> fragility function list
"""
with context(fname, node):
fid = node['id']
asset_category = node['assetCategory']
loss_type = node['lossCategory']
description = ~node.description
limit_states = ~node.limitStates
ffs = node[2:]
fmodel = scientific.FragilityModel(
fid, asset_category, loss_type, description, limit_states)
for ff in ffs:
imt_taxo = ff.imls['imt'], ff['id']
array, attrs = ffconvert(fname, limit_states, ff)
ffl = scientific.FragilityFunctionList(array)
vars(ffl).update(attrs)
fmodel[imt_taxo] = ffl
return fmodel
def get_vulnerability_functions_04(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path to the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
logging.warn('Please upgrade %s to NRML 0.5', fname)
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
taxonomies = set()
# imt, taxonomy -> vulnerability function
vmodel = scientific.VulnerabilityModel(**node.attrib)
for vset in node:
imt_str = vset.IML['IMT']
imls = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vmodel[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vmodel
def get_source_model_05(node, fname, converter):
converter.fname = fname
groups = [] # expect a sequence of sourceGroup nodes
for src_group in node:
with context(fname, src_group):
if 'sourceGroup' not in src_group.tag:
raise ValueError('expected sourceGroup')
groups.append(converter.convert_node(src_group))
return sorted(groups)
def get_vulnerability_functions_04(node, fname):
"""
:param node:
a vulnerabilityModel node
:param fname:
path to the vulnerability file
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
logging.warn('Please upgrade %s to NRML 0.5', fname)
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
taxonomies = set()
# imt, taxonomy -> vulnerability function
vmodel = scientific.VulnerabilityModel(**node.attrib)
for vset in node:
imt_str, imls, min_iml, max_iml, imlUnit = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vmodel[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vmodel
def geo_line(self, edge):
"""
Utility function to convert a node of kind edge
into a :class:`openquake.hazardlib.geo.Line` instance.
:param edge: a node describing an edge
"""
with context(self.fname, edge.LineString.posList) as plist:
coords = split_coords_2d(~plist)
return geo.Line([geo.Point(*p) for p in coords])
def geo_line(self, edge):
"""
Utility function to convert a node of kind edge
into a :class:`openquake.hazardlib.geo.Line` instance.
:param edge: a node describing an edge
"""
with context(self.fname, edge.LineString.posList) as plist:
coords = split_coords_2d(~plist)
return geo.Line([geo.Point(*p) for p in coords])
def convert_node(self, node):
"""
Convert the given node into a hazardlib source, depending
on the node tag.
:param node: a node representing a source
"""
with context(self.fname, node):
convert_source = getattr(self, 'convert_' + striptag(node.tag))
return convert_source(node)
def convert_hpdist(self, node):
"""
Convert the given node into a probability mass function for the
hypo depth distribution.
:param node: a hypoDepthDist node
:returns: a :class:`openquake.hazardlib.pmf.PMF` instance
"""
with context(self.fname, node):
return pmf.PMF([~hd for hd in node.hypoDepthDist])
def convert_hpdist(self, node):
"""
Convert the given node into a probability mass function for the
hypo depth distribution.
:param node: a hypoDepthDist node
:returns: a :class:`openquake.hazardlib.pmf.PMF` instance
"""
with context(self.fname, node):
return pmf.PMF([~hd for hd in node.hypoDepthDist])
def get_vulnerability_functions(fname):
"""
:param fname:
path of the vulnerability filter
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
# NB: the vulnerabilitySetID is not an unique ID!
# it is right to have several vulnerability sets with the same ID
# the IMTs can also be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
for vset in read_nodes(fname, filter_vset,
nodefactory['vulnerabilityModel']):
imt_str, imls, min_iml, max_iml, imlUnit = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vf_dict
def convert_node(self, node):
"""
Convert the given node into a hazardlib source, depending
on the node tag.
:param node: a node representing a source
"""
with context(self.fname, node):
convert_source = getattr(self, 'convert_' + striptag(node.tag))
return convert_source(node)
def get_exposure_lazy(fname, ok_cost_types):
"""
:param fname:
path of the XML file containing the exposure
:param ok_cost_types:
a set of cost types (as strings)
:returns:
a pair (Exposure instance, list of asset nodes)
"""
[exposure] = nrml.read_lazy(fname, ['assets'])
description = exposure.description
try:
conversions = exposure.conversions
except NameError:
conversions = LiteralNode('conversions',
nodes=[LiteralNode('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except NameError:
inslimit = LiteralNode('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except NameError:
deductible = LiteralNode('deductible', text=True)
try:
area = conversions.area
except NameError:
# NB: the area type cannot be an empty string because when sending
# around the CostCalculator object one runs into this numpy bug on
# pickling dictionaries with empty strings:
# https://github.com/numpy/numpy/pull/5475
area = LiteralNode('area', dict(type='?'))
# read the cost types and make some check
cost_types = []
for ct in conversions.costTypes:
if ct['name'] in ok_cost_types:
with context(fname, ct):
cost_types.append(
(ct['name'], valid_cost_type(ct['type']), ct['unit']))
if 'occupants' in ok_cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
time_events = set()
exp = Exposure(exposure['id'], exposure['category'], ~description,
numpy.array(cost_types, cost_type_dt), time_events,
~inslimit, ~deductible, area.attrib, [], set(), [])
cc = riskmodels.CostCalculator({}, {}, exp.deductible_is_absolute,
exp.insurance_limit_is_absolute)
for ct in exp.cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = exp.area['type']
return exp, exposure.assets, cc
def convert_node(self, node):
"""
Convert the given rupture node into a hazardlib rupture, depending
on the node tag.
:param node: a node representing a rupture
"""
with context(self.fname, node):
convert_rupture = getattr(self, 'convert_' + striptag(node.tag))
mag = ~node.magnitude
rake = ~node.rake
hypocenter = ~node.hypocenter
return convert_rupture(node, mag, rake, hypocenter)
def convert_npdist(self, node):
"""
Convert the given node into a Nodal Plane Distribution.
:param node: a nodalPlaneDist node
:returns: a :class:`openquake.hazardlib.geo.NodalPlane` instance
"""
with context(self.fname, node):
npdist = []
for np in node.nodalPlaneDist:
prob, strike, dip, rake = ~np
npdist.append((prob, geo.NodalPlane(strike, dip, rake)))
return pmf.PMF(npdist)
def geo_lines(self, edges):
"""
Utility function to convert a list of edges into a list of
:class:`openquake.hazardlib.geo.Line` instances.
:param edge: a node describing an edge
"""
lines = []
for edge in edges:
with context(self.fname, edge):
coords = split_coords_3d(~edge.LineString.posList)
lines.append(geo.Line([geo.Point(*p) for p in coords]))
return lines
def geo_lines(self, edges):
"""
Utility function to convert a list of edges into a list of
:class:`openquake.hazardlib.geo.Line` instances.
:param edge: a node describing an edge
"""
lines = []
for edge in edges:
with context(self.fname, edge):
coords = split_coords_3d(~edge.LineString.posList)
lines.append(geo.Line([geo.Point(*p) for p in coords]))
return lines
def convert_npdist(self, node):
"""
Convert the given node into a Nodal Plane Distribution.
:param node: a nodalPlaneDist node
:returns: a :class:`openquake.hazardlib.geo.NodalPlane` instance
"""
with context(self.fname, node):
npdist = []
for np in node.nodalPlaneDist:
prob, strike, dip, rake = ~np
npdist.append((prob, geo.NodalPlane(strike, dip, rake)))
return pmf.PMF(npdist)
def convert_node(self, node):
"""
Convert the given rupture node into a hazardlib rupture, depending
on the node tag.
:param node: a node representing a rupture
"""
with context(self.fname, node):
convert_rupture = getattr(self, "convert_" + striptag(node.tag))
mag = ~node.magnitude
rake = ~node.rake
h = node.hypocenter
hypocenter = geo.Point(h["lon"], h["lat"], h["depth"])
return convert_rupture(node, mag, rake, hypocenter)
def convert_node(self, node):
"""
Convert the given rupture node into a hazardlib rupture, depending
on the node tag.
:param node: a node representing a rupture
"""
with context(self.fname, node):
convert_rupture = getattr(self, 'convert_' + striptag(node.tag))
mag = ~node.magnitude
rake = ~node.rake
h = node.hypocenter
hypocenter = geo.Point(h['lon'], h['lat'], h['depth'])
return convert_rupture(node, mag, rake, hypocenter)
def convert_fragility_model_04(node, fname, fmcounter=itertools.count(1)):
"""
:param node:
an :class:`openquake.commonib.node.LiteralNode` in NRML 0.4
:param fname:
path of the fragility file
:returns:
an :class:`openquake.commonib.node.LiteralNode` in NRML 0.5
"""
convert_type = {"lognormal": "logncdf"}
new = LiteralNode('fragilityModel',
dict(assetCategory='building',
lossCategory='structural',
id='fm_%d_converted_from_NRML_04' %
next(fmcounter)))
with context(fname, node):
fmt = node['format']
descr = ~node.description
limit_states = ~node.limitStates
new.append(LiteralNode('description', {}, descr))
new.append((LiteralNode('limitStates', {}, ' '.join(limit_states))))
for ffs in node[2:]:
IML = ffs.IML
# NB: noDamageLimit = None is different than zero
nodamage = ffs.attrib.get('noDamageLimit')
ff = LiteralNode('fragilityFunction', {'format': fmt})
ff['id'] = ~ffs.taxonomy
ff['shape'] = convert_type[ffs.attrib.get('type', 'lognormal')]
if fmt == 'continuous':
with context(fname, IML):
attr = dict(imt=IML['IMT'],
minIML=IML['minIML'],
maxIML=IML['maxIML'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(LiteralNode('imls', attr))
for ffc in ffs[2:]:
with context(fname, ffc):
ls = ffc['ls']
param = ffc.params
with context(fname, param):
m, s = param['mean'], param['stddev']
ff.append(LiteralNode('params', dict(ls=ls, mean=m, stddev=s)))
else: # discrete
with context(fname, IML):
imls = ' '.join(map(str, (~IML)[1]))
attr = dict(imt=IML['IMT'])
if nodamage is not None:
attr['noDamageLimit'] = nodamage
ff.append(LiteralNode('imls', attr, imls))
for ffd in ffs[2:]:
ls = ffd['ls']
with context(fname, ffd):
poes = ' '.join(map(str, ~ffd.poEs))
ff.append(LiteralNode('poes', dict(ls=ls), poes))
new.append(ff)
return new
def convert_mfdist(self, node):
"""
Convert the given node into a Magnitude-Frequency Distribution
object.
:param node: a node of kind incrementalMFD or truncGutenbergRichterMFD
:returns: a :class:`openquake.hazardlib.mdf.EvenlyDiscretizedMFD.` or
:class:`openquake.hazardlib.mdf.TruncatedGRMFD` instance
"""
with context(self.fname, node):
[mfd_node] = [
subnode for subnode in node
if subnode.tag.endswith(('incrementalMFD',
'truncGutenbergRichterMFD',
'arbitraryMFD',
'YoungsCoppersmithMFD'))
]
if mfd_node.tag.endswith('incrementalMFD'):
return mfd.EvenlyDiscretizedMFD(
min_mag=mfd_node['minMag'],
bin_width=mfd_node['binWidth'],
occurrence_rates=~mfd_node.occurRates)
elif mfd_node.tag.endswith('truncGutenbergRichterMFD'):
return mfd.TruncatedGRMFD(a_val=mfd_node['aValue'],
b_val=mfd_node['bValue'],
min_mag=mfd_node['minMag'],
max_mag=mfd_node['maxMag'],
bin_width=self.width_of_mfd_bin)
elif mfd_node.tag.endswith('arbitraryMFD'):
return mfd.ArbitraryMFD(magnitudes=~mfd_node.magnitudes,
occurrence_rates=~mfd_node.occurRates)
elif mfd_node.tag.endswith('YoungsCoppersmithMFD'):
if "totalMomentRate" in mfd_node.attrib.keys():
# Return Youngs & Coppersmith from the total moment rate
return mfd.YoungsCoppersmith1985MFD.from_total_moment_rate(
min_mag=mfd_node["minMag"],
b_val=mfd_node["bValue"],
char_mag=mfd_node["characteristicMag"],
total_moment_rate=mfd_node["totalMomentRate"],
bin_width=mfd_node["binWidth"])
elif "characteristicRate" in mfd_node.attrib.keys():
# Return Youngs & Coppersmith from the total moment rate
return mfd.YoungsCoppersmith1985MFD.\
from_characteristic_rate(
min_mag=mfd_node["minMag"],
b_val=mfd_node["bValue"],
char_mag=mfd_node["characteristicMag"],
char_rate=mfd_node["characteristicRate"],
bin_width=mfd_node["binWidth"])
def collect_source_model_paths(smlt):
"""
Given a path to a source model logic tree or a file-like, collect all of
the soft-linked path names to the source models it contains and return them
as a uniquified list (no duplicates).
:param smlt: source model logic tree file
"""
for blevel in nrml.read(smlt).logicTree:
with node.context(smlt, blevel):
for bset in blevel:
for br in bset:
smfname = br.uncertaintyModel.text
if smfname:
yield smfname
def geo_planar(self, surface):
"""
Utility to convert a PlanarSurface node with subnodes
topLeft, topRight, bottomLeft, bottomRight into a
:class:`openquake.hazardlib.geo.PlanarSurface` instance.
:param surface: PlanarSurface node
"""
with context(self.fname, surface):
top_left = geo.Point(*~surface.topLeft)
top_right = geo.Point(*~surface.topRight)
bottom_left = geo.Point(*~surface.bottomLeft)
bottom_right = geo.Point(*~surface.bottomRight)
return geo.PlanarSurface.from_corner_points(
self.rupture_mesh_spacing,
top_left, top_right, bottom_right, bottom_left)
def convert_complexFaultRupture(self, node, mag, rake, hypocenter):
"""
Convert a complexFaultRupture node.
:param node: the rupture node
:param mag: the rupture magnitude
:param rake: the rupture rake angle
:param hypocenter: the rupture hypocenter
"""
with context(self.fname, node):
surfaces = [node.complexFaultGeometry]
rupt = source.rupture.Rupture(
mag=mag, rake=rake, tectonic_region_type=None,
hypocenter=geo.Point(*hypocenter),
surface=self.convert_surfaces(surfaces),
source_typology=source.ComplexFaultSource)
return rupt
def convert_multiPlanesRupture(self, node, mag, rake, hypocenter):
"""
Convert a multiPlanesRupture node.
:param node: the rupture node
:param mag: the rupture magnitude
:param rake: the rupture rake angle
:param hypocenter: the rupture hypocenter
"""
with context(self.fname, node):
surfaces = list(node.getnodes('planarSurface'))
hrupt = source.rupture.Rupture(
mag=mag, rake=rake,
tectonic_region_type=None,
hypocenter=geo.Point(*hypocenter),
surface=self.convert_surfaces(surfaces),
source_typology=source.NonParametricSeismicSource)
return hrupt
def convert_complexFaultRupture(self, node, mag, rake, hypocenter):
"""
Convert a complexFaultRupture node.
:param node: the rupture node
:param mag: the rupture magnitude
:param rake: the rupture rake angle
:param hypocenter: the rupture hypocenter
"""
with context(self.fname, node):
surfaces = [node.complexFaultGeometry]
rupt = source.rupture.Rupture(
mag=mag, rake=rake, tectonic_region_type=None,
hypocenter=hypocenter,
surface=self.convert_surfaces(surfaces),
source_typology=source.ComplexFaultSource,
surface_nodes=surfaces)
return rupt
def get_exposure_lazy(fname, ok_cost_types):
"""
:param fname:
path of the XML file containing the exposure
:param ok_cost_types:
a set of cost types (as strings)
:returns:
a pair (Exposure instance, list of asset nodes)
"""
[exposure] = nrml.read_lazy(fname, ['assets'])
description = exposure.description
try:
conversions = exposure.conversions
except NameError:
conversions = LiteralNode('conversions',
nodes=[LiteralNode('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except NameError:
inslimit = LiteralNode('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except NameError:
deductible = LiteralNode('deductible', text=True)
try:
area = conversions.area
except NameError:
area = LiteralNode('area', dict(type=''))
# read the cost types and make some check
cost_types = []
for ct in conversions.costTypes:
if ct['name'] in ok_cost_types:
with context(fname, ct):
cost_types.append(
(ct['name'], valid_cost_type(ct['type']), ct['unit']))
if 'occupants' in ok_cost_types:
cost_types.append(('occupants', 'per_area', 'people'))
cost_types.sort(key=operator.itemgetter(0))
time_events = set()
return Exposure(
exposure['id'], exposure['category'],
~description, numpy.array(cost_types, cost_type_dt), time_events,
~inslimit, ~deductible, area.attrib, [], set()), exposure.assets
def convert_mfdist(self, node):
"""
Convert the given node into a Magnitude-Frequency Distribution
object.
:param node: a node of kind incrementalMFD or truncGutenbergRichterMFD
:returns: a :class:`openquake.hazardlib.mdf.EvenlyDiscretizedMFD.` or
:class:`openquake.hazardlib.mdf.TruncatedGRMFD` instance
"""
with context(self.fname, node):
[mfd_node] = [subnode for subnode in node
if subnode.tag.endswith(
('incrementalMFD', 'truncGutenbergRichterMFD',
'arbitraryMFD', 'YoungsCoppersmithMFD'))]
if mfd_node.tag.endswith('incrementalMFD'):
return mfd.EvenlyDiscretizedMFD(
min_mag=mfd_node['minMag'], bin_width=mfd_node['binWidth'],
occurrence_rates=~mfd_node.occurRates)
elif mfd_node.tag.endswith('truncGutenbergRichterMFD'):
return mfd.TruncatedGRMFD(
a_val=mfd_node['aValue'], b_val=mfd_node['bValue'],
min_mag=mfd_node['minMag'], max_mag=mfd_node['maxMag'],
bin_width=self.width_of_mfd_bin)
elif mfd_node.tag.endswith('arbitraryMFD'):
return mfd.ArbitraryMFD(
magnitudes=~mfd_node.magnitudes,
occurrence_rates=~mfd_node.occurRates)
elif mfd_node.tag.endswith('YoungsCoppersmithMFD'):
if "totalMomentRate" in mfd_node.attrib.keys():
# Return Youngs & Coppersmith from the total moment rate
return mfd.YoungsCoppersmith1985MFD.from_total_moment_rate(
min_mag=mfd_node["minMag"], b_val=mfd_node["bValue"],
char_mag=mfd_node["characteristicMag"],
total_moment_rate=mfd_node["totalMomentRate"],
bin_width=mfd_node["binWidth"])
elif "characteristicRate" in mfd_node.attrib.keys():
# Return Youngs & Coppersmith from the total moment rate
return mfd.YoungsCoppersmith1985MFD.\
from_characteristic_rate(
min_mag=mfd_node["minMag"],
b_val=mfd_node["bValue"],
char_mag=mfd_node["characteristicMag"],
char_rate=mfd_node["characteristicRate"],
bin_width=mfd_node["binWidth"])
def convert_singlePlaneRupture(self, node, mag, rake, hypocenter):
"""
Convert a singlePlaneRupture node.
:param node: the rupture node
:param mag: the rupture magnitude
:param rake: the rupture rake angle
:param hypocenter: the rupture hypocenter
"""
with context(self.fname, node):
surfaces = [node.planarSurface]
rupt = source.rupture.Rupture(
mag=mag, rake=rake,
tectonic_region_type=None,
hypocenter=hypocenter,
surface=self.convert_surfaces(surfaces),
source_typology=source.NonParametricSeismicSource,
surface_nodes=surfaces)
return rupt
def convert_multiPlanesRupture(self, node, mag, rake, hypocenter):
"""
Convert a multiPlanesRupture node.
:param node: the rupture node
:param mag: the rupture magnitude
:param rake: the rupture rake angle
:param hypocenter: the rupture hypocenter
"""
with context(self.fname, node):
surfaces = list(node.getnodes('planarSurface'))
rupt = source.rupture.Rupture(
mag=mag, rake=rake,
tectonic_region_type=None,
hypocenter=hypocenter,
surface=self.convert_surfaces(surfaces),
source_typology=source.NonParametricSeismicSource,
surface_nodes=surfaces)
return rupt
def geo_planar(self, surface):
"""
Utility to convert a PlanarSurface node with subnodes
topLeft, topRight, bottomLeft, bottomRight into a
:class:`openquake.hazardlib.geo.PlanarSurface` instance.
:param surface: PlanarSurface node
"""
with context(self.fname, surface):
tl = surface.topLeft
top_left = geo.Point(tl['lon'], tl['lat'], tl['depth'])
tr = surface.topRight
top_right = geo.Point(tr['lon'], tr['lat'], tr['depth'])
bl = surface.bottomLeft
bottom_left = geo.Point(bl['lon'], bl['lat'], bl['depth'])
br = surface.bottomRight
bottom_right = geo.Point(br['lon'], br['lat'], br['depth'])
return geo.PlanarSurface.from_corner_points(self.rupture_mesh_spacing,
top_left, top_right,
bottom_right, bottom_left)
def geo_planar(self, surface):
"""
Utility to convert a PlanarSurface node with subnodes
topLeft, topRight, bottomLeft, bottomRight into a
:class:`openquake.hazardlib.geo.PlanarSurface` instance.
:param surface: PlanarSurface node
"""
with context(self.fname, surface):
tl = surface.topLeft
top_left = geo.Point(tl['lon'], tl['lat'], tl['depth'])
tr = surface.topRight
top_right = geo.Point(tr['lon'], tr['lat'], tr['depth'])
bl = surface.bottomLeft
bottom_left = geo.Point(bl['lon'], bl['lat'], bl['depth'])
br = surface.bottomRight
bottom_right = geo.Point(br['lon'], br['lat'], br['depth'])
return geo.PlanarSurface.from_corner_points(
self.rupture_mesh_spacing,
top_left, top_right, bottom_right, bottom_left)
def collect_source_model_paths(smlt):
"""
Given a path to a source model logic tree or a file-like, collect all of
the soft-linked path names to the source models it contains and return them
as a uniquified list (no duplicates).
:param smlt: source model logic tree file
"""
n = nrml.read(smlt)
try:
blevels = n.logicTree
except:
raise InvalidFile('%s is not a valid source_model_logic_tree_file'
% smlt)
for blevel in blevels:
with node.context(smlt, blevel):
for bset in blevel:
for br in bset:
smfname = br.uncertaintyModel.text.strip()
if smfname:
yield smfname
def convert_simpleFaultSource(self, node):
"""
Convert the given node into a simple fault object.
:param node: a node with tag areaGeometry
:returns: a :class:`openquake.hazardlib.source.SimpleFaultSource`
instance
"""
geom = node.simpleFaultGeometry
msr = valid.SCALEREL[~node.magScaleRel]()
fault_trace = self.geo_line(geom)
mfd = self.convert_mfdist(node)
with context(self.fname, node):
try:
hypo_list = valid.hypo_list(node.hypoList)
except NameError:
hypo_list = ()
try:
slip_list = valid.slip_list(node.slipList)
except NameError:
slip_list = ()
simple = source.SimpleFaultSource(
source_id=node["id"],
name=node["name"],
tectonic_region_type=node["tectonicRegion"],
mfd=mfd,
rupture_mesh_spacing=self.rupture_mesh_spacing,
magnitude_scaling_relationship=msr,
rupture_aspect_ratio=~node.ruptAspectRatio,
upper_seismogenic_depth=~geom.upperSeismoDepth,
lower_seismogenic_depth=~geom.lowerSeismoDepth,
fault_trace=fault_trace,
dip=~geom.dip,
rake=~node.rake,
temporal_occurrence_model=self.tom,
hypo_list=hypo_list,
slip_list=slip_list,
)
return simple
def convert_simpleFaultSource(self, node):
"""
Convert the given node into a simple fault object.
:param node: a node with tag areaGeometry
:returns: a :class:`openquake.hazardlib.source.SimpleFaultSource`
instance
"""
geom = node.simpleFaultGeometry
msr = valid.SCALEREL[~node.magScaleRel]()
fault_trace = self.geo_line(geom)
mfd = self.convert_mfdist(node)
with context(self.fname, node):
try:
hypo_list = valid.hypo_list(node.hypoList)
except NameError:
hypo_list = ()
try:
slip_list = valid.slip_list(node.slipList)
except NameError:
slip_list = ()
simple = source.SimpleFaultSource(
source_id=node['id'],
name=node['name'],
tectonic_region_type=node['tectonicRegion'],
mfd=mfd,
rupture_mesh_spacing=self.rupture_mesh_spacing,
magnitude_scaling_relationship=msr,
rupture_aspect_ratio=~node.ruptAspectRatio,
upper_seismogenic_depth=~geom.upperSeismoDepth,
lower_seismogenic_depth=~geom.lowerSeismoDepth,
fault_trace=fault_trace,
dip=~geom.dip,
rake=~node.rake,
temporal_occurrence_model=self.tom,
hypo_list=hypo_list,
slip_list=slip_list)
return simple
def get_vulnerability_functions(fname):
"""
:param fname:
path of the vulnerability filter
:returns:
a dictionary imt, taxonomy -> vulnerability function
"""
# NB: the IMTs can be duplicated and with different levels, each
# vulnerability function in a set will get its own levels
imts = set()
taxonomies = set()
vf_dict = {} # imt, taxonomy -> vulnerability function
node = nrml.read(fname)
if node['xmlns'] == nrml.NRML05:
vmodel = node[0]
for vfun in vmodel.getnodes('vulnerabilityFunction'):
with context(fname, vfun):
imt = vfun.imls['imt']
imls = numpy.array(~vfun.imls)
taxonomy = vfun['id']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
if vfun['dist'] == 'PM':
loss_ratios, probs = [], []
for probabilities in vfun[1:]:
loss_ratios.append(probabilities['lr'])
probs.append(valid.probabilities(~probabilities))
probs = numpy.array(probs)
assert probs.shape == (len(loss_ratios), len(imls))
vf_dict[imt,
taxonomy] = (scientific.VulnerabilityFunctionWithPMF(
taxonomy, imt, imls, numpy.array(loss_ratios),
probs))
else:
with context(fname, vfun):
loss_ratios = ~vfun.meanLRs
coefficients = ~vfun.covLRs
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.meanLRs.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, '
'line %d' % (len(coefficients), len(imls), fname,
vfun.covLRs.lineno))
with context(fname, vfun):
vf_dict[imt, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt, imls, loss_ratios, coefficients,
vfun['dist'])
return vf_dict
# otherwise, read the old format (NRML 0.4)
for vset in read_nodes(fname, filter_vset,
nodefactory['vulnerabilityModel']):
imt_str, imls, min_iml, max_iml, imlUnit = ~vset.IML
imts.add(imt_str)
for vfun in vset.getnodes('discreteVulnerability'):
taxonomy = vfun['vulnerabilityFunctionID']
if taxonomy in taxonomies:
raise InvalidFile(
'Duplicated vulnerabilityFunctionID: %s: %s, line %d' %
(taxonomy, fname, vfun.lineno))
taxonomies.add(taxonomy)
with context(fname, vfun):
loss_ratios = ~vfun.lossRatio
coefficients = ~vfun.coefficientsVariation
if len(loss_ratios) != len(imls):
raise InvalidFile(
'There are %d loss ratios, but %d imls: %s, line %d' %
(len(loss_ratios), len(imls), fname,
vfun.lossRatio.lineno))
if len(coefficients) != len(imls):
raise InvalidFile(
'There are %d coefficients, but %d imls: %s, line %d' %
(len(coefficients), len(imls), fname,
vfun.coefficientsVariation.lineno))
with context(fname, vfun):
vf_dict[imt_str, taxonomy] = scientific.VulnerabilityFunction(
taxonomy, imt_str, imls, loss_ratios, coefficients,
vfun['probabilisticDistribution'])
return vf_dict
def get_exposure(oqparam):
"""
Read the full exposure in memory and build a list of
:class:`openquake.risklib.riskmodels.Asset` instances.
If you don't want to keep everything in memory, use
get_exposure_lazy instead (for experts only).
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an :class:`Exposure` instance
"""
out_of_region = 0
if oqparam.region_constraint:
region = wkt.loads(oqparam.region_constraint)
else:
region = None
all_cost_types = set(oqparam.all_cost_types)
fname = oqparam.inputs['exposure']
exposure, assets_node, cc = get_exposure_lazy(fname, all_cost_types)
relevant_cost_types = all_cost_types - set(['occupants'])
asset_refs = set()
ignore_missing_costs = set(oqparam.ignore_missing_costs)
for idx, asset in enumerate(assets_node):
values = {}
deductibles = {}
insurance_limits = {}
retrofitteds = {}
with context(fname, asset):
asset_id = asset['id'].encode('utf8')
if asset_id in asset_refs:
raise DuplicatedID(asset_id)
asset_refs.add(asset_id)
exposure.asset_refs.append(asset_id)
taxonomy = asset['taxonomy']
if 'damage' in oqparam.calculation_mode:
# calculators of 'damage' kind require the 'number'
# if it is missing a KeyError is raised
number = asset.attrib['number']
else:
# some calculators ignore the 'number' attribute;
# if it is missing it is considered 1, since we are going
# to multiply by it
try:
number = asset['number']
except KeyError:
number = 1
else:
if 'occupants' in all_cost_types:
values['occupants_None'] = number
location = asset.location['lon'], asset.location['lat']
if region and not geometry.Point(*location).within(region):
out_of_region += 1
continue
try:
costs = asset.costs
except NameError:
costs = LiteralNode('costs', [])
try:
occupancies = asset.occupancies
except NameError:
occupancies = LiteralNode('occupancies', [])
for cost in costs:
with context(fname, cost):
cost_type = cost['type']
if cost_type in relevant_cost_types:
values[cost_type] = cost['value']
retrovalue = cost.attrib.get('retrofitted')
if retrovalue is not None:
retrofitteds[cost_type] = retrovalue
if oqparam.insured_losses:
deductibles[cost_type] = cost['deductible']
insurance_limits[cost_type] = cost['insuranceLimit']
# check we are not missing a cost type
missing = relevant_cost_types - set(values)
if missing and missing <= ignore_missing_costs:
logging.warn('Ignoring asset %s, missing cost type(s): %s',
asset_id, ', '.join(missing))
for cost_type in missing:
values[cost_type] = None
elif missing and 'damage' not in oqparam.calculation_mode:
# missing the costs is okay for damage calculators
with context(fname, asset):
raise ValueError("Invalid Exposure. "
"Missing cost %s for asset %s" %
(missing, asset_id))
tot_occupants = 0
for occupancy in occupancies:
with context(fname, occupancy):
exposure.time_events.add(occupancy['period'])
occupants = 'occupants_%s' % occupancy['period']
values[occupants] = occupancy['occupants']
tot_occupants += values[occupants]
if occupancies: # store average occupants
values['occupants_None'] = tot_occupants / len(occupancies)
area = float(asset.attrib.get('area', 1))
ass = riskmodels.Asset(idx, taxonomy, number, location, values, area,
deductibles, insurance_limits, retrofitteds, cc)
exposure.assets.append(ass)
exposure.taxonomies.add(taxonomy)
if region:
logging.info(
'Read %d assets within the region_constraint '
'and discarded %d assets outside the region', len(exposure.assets),
out_of_region)
if len(exposure.assets) == 0:
raise RuntimeError('Could not find any asset within the region!')
else:
logging.info('Read %d assets', len(exposure.assets))
# sanity check
values = any(len(ass.values) + ass.number for ass in exposure.assets)
assert values, 'Could not find any value??'
return exposure
def get_exposure(oqparam):
"""
Read the full exposure in memory and build a list of
:class:`openquake.risklib.workflows.Asset` instances.
If you don't want to keep everything in memory, use
get_exposure_lazy instead (for experts only).
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an :class:`Exposure` instance
"""
out_of_region = 0
if oqparam.region_constraint:
region = wkt.loads(oqparam.region_constraint)
else:
region = None
fname = oqparam.inputs['exposure']
exposure, assets_node = get_exposure_lazy(fname)
cc = workflows.CostCalculator({}, {}, exposure.deductible_is_absolute,
exposure.insurance_limit_is_absolute)
for ct in exposure.cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = exposure.area['type']
all_cost_types = set(vulnerability_files(oqparam.inputs))
relevant_cost_types = all_cost_types - set(['occupants'])
asset_refs = set()
ignore_missing_costs = set(oqparam.ignore_missing_costs)
for asset in assets_node:
values = {}
deductibles = {}
insurance_limits = {}
retrofitting_values = {}
with context(fname, asset):
asset_id = asset['id'].encode('utf8')
if asset_id in asset_refs:
raise DuplicatedID(asset_id)
asset_refs.add(asset_id)
taxonomy = asset['taxonomy']
if 'damage' in oqparam.calculation_mode:
# calculators of 'damage' kind require the 'number'
# if it is missing a KeyError is raised
number = asset.attrib['number']
else:
# some calculators ignore the 'number' attribute;
# if it is missing it is considered 1, since we are going
# to multiply by it
try:
number = asset['number']
except KeyError:
number = 1
else:
if 'occupants' in all_cost_types:
values['fatalities_None'] = number
location = asset.location['lon'], asset.location['lat']
if region and not geometry.Point(*location).within(region):
out_of_region += 1
continue
try:
costs = asset.costs
except NameError:
costs = LiteralNode('costs', [])
try:
occupancies = asset.occupancies
except NameError:
occupancies = LiteralNode('occupancies', [])
with context(fname, costs):
for cost in costs:
cost_type = cost['type']
if cost_type not in relevant_cost_types:
continue
values[cost_type] = cost['value']
deduct = cost.attrib.get('deductible')
if deduct is not None:
deductibles[cost_type] = deduct
limit = cost.attrib.get('insuranceLimit')
if limit is not None:
insurance_limits[cost_type] = limit
# check we are not missing a cost type
missing = relevant_cost_types - set(values)
if missing and missing <= ignore_missing_costs:
logging.warn('Ignoring asset %s, missing cost type(s): %s',
asset_id, ', '.join(missing))
for cost_type in missing:
values[cost_type] = None
elif missing:
raise ValueError("Invalid Exposure. "
"Missing cost %s for asset %s" %
(missing, asset_id))
tot_fatalities = 0
for occupancy in occupancies:
with context(fname, occupancy):
fatalities = 'fatalities_%s' % occupancy['period']
values[fatalities] = occupancy['occupants']
tot_fatalities += values[fatalities]
if occupancies: # store average fatalities
values['fatalities_None'] = tot_fatalities / len(occupancies)
area = float(asset.attrib.get('area', 1))
ass = workflows.Asset(asset_id, taxonomy, number, location, values,
area, deductibles, insurance_limits,
retrofitting_values, cc)
exposure.assets.append(ass)
exposure.taxonomies.add(taxonomy)
if region:
logging.info(
'Read %d assets within the region_constraint '
'and discarded %d assets outside the region', len(exposure.assets),
out_of_region)
else:
logging.info('Read %d assets', len(exposure.assets))
# sanity check
values = any(len(ass.values) + ass.number for ass in exposure.assets)
assert values, 'Could not find any value??'
return exposure
def asset_gen():
# wrap the asset generation to get a nice error message
with context(fname, assets_node):
for asset in assets_node:
yield asset
def get_fragility_functions(fname,
continuous_fragility_discretization,
steps_per_interval=None):
"""
:param fname:
path of the fragility file
:param continuous_fragility_discretization:
continuous_fragility_discretization parameter
:param steps_per_interval:
steps_per_interval parameter
:returns:
damage_states list and dictionary taxonomy -> functions
"""
[fmodel] = read_nodes(fname, lambda el: el.tag.endswith('fragilityModel'),
nodefactory['fragilityModel'])
# ~fmodel.description is ignored
limit_states = ~fmodel.limitStates
tag = 'ffc' if fmodel['format'] == 'continuous' else 'ffd'
fragility_functions = AccumDict() # taxonomy -> functions
for ffs in fmodel.getnodes('ffs'):
add_zero_value = False
# NB: the noDamageLimit is only defined for discrete fragility
# functions. It is a way to set the starting point of the functions:
# if noDamageLimit is at the left of each IMLs, it means that the
# function starts at zero at the given point, so we need to add
# noDamageLimit to the list of IMLs and zero to the list of poes
nodamage = ffs.attrib.get('noDamageLimit')
taxonomy = ~ffs.taxonomy
imt_str, imls, min_iml, max_iml, imlUnit = ~ffs.IML
if fmodel['format'] == 'discrete':
if nodamage is not None and nodamage < imls[0]:
# discrete fragility
imls = [nodamage] + imls
add_zero_value = True
if steps_per_interval:
gen_imls = scientific.fine_graining(imls, steps_per_interval)
else:
gen_imls = imls
else: # continuous:
if min_iml is None:
raise InvalidFile('Missing attribute minIML, line %d' %
ffs.IML.lineno)
elif max_iml is None:
raise InvalidFile('Missing attribute maxIML, line %d' %
ffs.IML.lineno)
gen_imls = numpy.linspace(min_iml, max_iml,
continuous_fragility_discretization)
fragility_functions[taxonomy] = scientific.FragilityFunctionList(
[],
imt=imt_str,
imls=list(gen_imls),
no_damage_limit=nodamage,
continuous_fragility_discretization=
continuous_fragility_discretization,
steps_per_interval=steps_per_interval)
lstates = []
for ff in ffs.getnodes(tag):
ls = ff['ls'] # limit state
lstates.append(ls)
if tag == 'ffc':
with context(fname, ff):
mean_stddev = ~ff.params
fragility_functions[taxonomy].append(
scientific.FragilityFunctionContinuous(ls, *mean_stddev))
else: # discrete
with context(fname, ff):
poes = ~ff.poEs
if add_zero_value:
poes = [0.] + poes
fragility_functions[taxonomy].append(
scientific.FragilityFunctionDiscrete(
ls, imls, poes, nodamage))
if lstates != limit_states:
raise InvalidFile("Expected limit states %s, got %s in %s" %
(limit_states, lstates, fname))
fragility_functions.damage_states = ['no_damage'] + limit_states
return fragility_functions
