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 test_discrete(self):
hazard_imls = [0.05, 0.2, 0.4, 0.6, 0.8, 1, 1.2, 1.4]
fragility_functions = scientific.FragilityFunctionList(
[], imls=hazard_imls, format='discrete')
fragility_functions.extend([
scientific.FragilityFunctionDiscrete(
'slight', hazard_imls,
[0.0, 0.771, 0.95, 0.989, 0.997, 0.999, 1., 1.]),
scientific.FragilityFunctionDiscrete(
'moderate', hazard_imls,
[0, 0.5, 0.861, 0.957, 0.985, 0.994, 0.997, 0.999]),
scientific.FragilityFunctionDiscrete(
'extreme', hazard_imls,
[0.0, 0.231, 0.636, 0.837, 0.924, 0.962, .981, .989]),
scientific.FragilityFunctionDiscrete(
'complete', hazard_imls,
[0, 0.097, 0.414, 0.661, 0.806, 0.887, 0.933, 0.959]),
])
hazard_poes = numpy.array([
0.999999999997518,
0.077404949,
0.015530587,
0.004201327,
0.001284191,
0.000389925,
0.000127992,
0.000030350,
])
investigation_time = 50.
risk_investigation_time = 100.
poos = scientific.classical_damage(
fragility_functions, hazard_imls, hazard_poes,
investigation_time, risk_investigation_time)
aaae(poos, [1.0415184E-09, 1.4577245E-06, 1.9585762E-03, 6.9677521E-02,
9.2836244E-01])
def test_continuous(self):
hazard_imls = numpy.array([
0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6,
0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 0.95, 1, 1.05, 1.1, 1.15, 1.2,
1.25, 1.3, 1.35, 1.4
])
fragility_functions = scientific.FragilityFunctionList(
[
scientific.FragilityFunctionContinuous('slight', 0.160, 0.104),
scientific.FragilityFunctionContinuous('moderate', 0.225,
0.158),
scientific.FragilityFunctionContinuous('extreme', 0.400,
0.300),
scientific.FragilityFunctionContinuous('complete', 0.600,
0.480),
],
imls=hazard_imls,
steps_per_interval=None)
hazard_poes = numpy.array([
0.5917765421,
0.2482053921,
0.1298604374,
0.07718928965,
0.04912904516,
0.03262871528,
0.02226628376,
0.01553639696,
0.01101802934,
0.007905366815,
0.005741833876,
0.004199803178,
0.003088785556,
0.00229291494,
0.001716474683,
0.001284555773,
0.0009583846496,
0.0007102377096,
0.0005201223961,
0.0003899464723,
0.0002997724582,
0.0002287788496,
0.0001726083994,
0.0001279544769,
0.00009229282594,
0.00006368651249,
0.00004249201524,
0.00003033694903,
])
investigation_time = 50.
risk_investigation_time = 100.
poos = scientific.classical_damage(fragility_functions, hazard_imls,
hazard_poes, investigation_time,
risk_investigation_time)
aaae(poos, [0.56652127, 0.12513401, 0.1709355, 0.06555033, 0.07185889])
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
