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 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_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 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