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 AttributeError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
try:
inslimit = conversions.insuranceLimit
except AttributeError:
inslimit = Node('insuranceLimit', text=True)
try:
deductible = conversions.deductible
except AttributeError:
deductible = Node('deductible', text=True)
try:
area = conversions.area
except AttributeError:
# 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))
cost_types = numpy.array(cost_types, cost_type_dt)
insurance_limit_is_absolute = inslimit.attrib.get('isAbsolute', True)
deductible_is_absolute = deductible.attrib.get('isAbsolute', True)
time_events = set()
cc = riskmodels.CostCalculator({}, {}, {}, deductible_is_absolute,
insurance_limit_is_absolute)
for ct in cost_types:
name = ct['name'] # structural, nonstructural, ...
cc.cost_types[name] = ct['type'] # aggregated, per_asset, per_area
cc.area_types[name] = area['type']
cc.units[name] = ct['unit']
exp = Exposure(exposure['id'], exposure['category'], ~description,
cost_types, time_events, insurance_limit_is_absolute,
deductible_is_absolute, area.attrib, [], set(), [], cc)
return exp, exposure.assets
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