def to_node(self):
tset = self.tableset
dvs_node = record.nodedict(tset.tableDiscreteVulnerabilitySet)
dvf_node = record.nodedict(tset.tableDiscreteVulnerability)
for (set_id, vf_id), group in groupby(
tset.tableDiscreteVulnerabilityData,
['vulnerabilitySetID', 'vulnerabilityFunctionID']):
dvf = dvf_node[set_id, vf_id]
imt = dvs_node[(set_id,)].IML['IMT']
coeffs = []
ratios = []
imls = []
for row in group:
imls.append(row['IML'])
coeffs.append(row['coefficientsVariation'])
ratios.append(row['lossRatio'])
# check that we can instantiate a VulnerabilityFunction in risklib
scientific.VulnerabilityFunction(
imt, map(float, imls), map(float, ratios), map(float, coeffs))
dvf.lossRatio.text = ' '.join(ratios)
dvf.coefficientsVariation.text = ' '.join(coeffs)
dvs_node[(set_id,)].append(dvf)
dvs_node[(set_id,)].IML.text = ' '.join(imls)
return Node('vulnerabilityModel', nodes=dvs_node.values())
def to_node(self):
tset = self.tableset
dvs_node = record.nodedict(tset.tableDiscreteVulnerabilitySet)
dvf_node = record.nodedict(tset.tableDiscreteVulnerability)
for (set_id, vf_id), group in groupby(
tset.tableDiscreteVulnerabilityData,
['vulnerabilitySetID', 'vulnerabilityFunctionID']):
dvf = dvf_node[set_id, vf_id]
imt = dvs_node[(set_id, )].IML['IMT']
coeffs = []
ratios = []
imls = []
for row in group:
imls.append(row['IML'])
coeffs.append(row['coefficientsVariation'])
ratios.append(row['lossRatio'])
# check that we can instantiate a VulnerabilityFunction in risklib
scientific.VulnerabilityFunction(imt, map(float, imls),
map(float, ratios),
map(float, coeffs))
dvf.lossRatio.text = ' '.join(ratios)
dvf.coefficientsVariation.text = ' '.join(coeffs)
dvs_node[(set_id, )].append(dvf)
dvs_node[(set_id, )].IML.text = ' '.join(imls)
return Node('vulnerabilityModel', nodes=dvs_node.values())
def _to_node(self):
"""
Add to a gmfset node all the data from a file GmfData.csv of the form::
stochasticEventSetId,imtStr,ruptureId,lon,lat,gmv
1,SA(0.025),,0.0,0.0,0.2
1,SA(0.025),,1.0,0.0,1.4
1,SA(0.025),,0.0,1.0,0.6
1,PGA,,0.0,0.0,0.2
1,PGA,,1.0,0.0,1.4
1,PGA,,0.0,1.0,0.6
The rows are grouped by ses, imt, rupture.
"""
tset = self.tableset
gmfset_node = record.nodedict(tset.tableGmfSet)
for (ses, imt, rupture), rows in groupby(
tset.tableGmfData,
['stochasticEventSetId', 'imtStr', 'ruptureId']):
if imt.startswith('SA'):
attr = dict(IMT='SA', saPeriod=imt[3:-1], saDamping='5')
else:
attr = dict(IMT=imt)
if rupture:
attr['ruptureId'] = rupture
nodes = [records.GmfData(*r).to_node() for r in rows]
gmfset_node[(ses,)].append(Node('gmf', attr, nodes=nodes))
return gmfset_node
def to_node(self):
"""
Build a full discrete fragility node from CSV
"""
tset = self.tableset
frag = tset.tableFragilityDiscrete[0].to_node()
ffs_node = record.nodedict(tset.tableFFSetDiscrete)
nodamage = float(ffs_node['noDamageLimit']) \
if 'noDamageLimit' in ffs_node else None
frag.nodes.extend(ffs_node.values())
for (ls, ordinal), data in groupby(
tset.tableFFDataDiscrete, ['limitState', 'ffs_ordinal']):
data = list(data)
# check that we can instantiate a FragilityFunction in risklib
if nodamage:
scientific.FragilityFunctionDiscrete(
ls, [nodamage] + [rec.iml for rec in data],
[0.0] + [rec.poe for rec in data], nodamage)
else:
scientific.FragilityFunctionDiscrete(
ls, [rec.iml for rec in data],
[rec.poe for rec in data], nodamage)
imls = ' '.join(rec['iml'] for rec in data)
ffs_node[(ordinal,)].IML.text = imls
poes = ' '.join(rec['poe'] for rec in data)
n = Node('ffd', dict(ls=ls))
n.append(Node('poEs', text=poes))
ffs_node[(ordinal,)].append(n)
return frag
def _to_node(self):
"""
Add to a gmfset node all the data from a file GmfData.csv of the form::
stochasticEventSetId,imtStr,ruptureId,lon,lat,gmv
1,SA(0.025),,0.0,0.0,0.2
1,SA(0.025),,1.0,0.0,1.4
1,SA(0.025),,0.0,1.0,0.6
1,PGA,,0.0,0.0,0.2
1,PGA,,1.0,0.0,1.4
1,PGA,,0.0,1.0,0.6
The rows are grouped by ses, imt, rupture.
"""
tset = self.tableset
gmfset_node = record.nodedict(tset.tableGmfSet)
for (ses, imt, rupture), rows in groupby(
tset.tableGmfData,
['stochasticEventSetId', 'imtStr', 'ruptureId']):
if imt.startswith('SA'):
attr = dict(IMT='SA', saPeriod=imt[3:-1], saDamping='5')
else:
attr = dict(IMT=imt)
if rupture:
attr['ruptureId'] = rupture
nodes = [records.GmfData(*r).to_node() for r in rows]
gmfset_node[(ses, )].append(Node('gmf', attr, nodes=nodes))
return gmfset_node
def to_node(self):
"""
Build a full discrete fragility node from CSV
"""
tset = self.tableset
frag = tset.tableFragilityDiscrete[0].to_node()
ffs_node = record.nodedict(tset.tableFFSetDiscrete)
nodamage = float(ffs_node['noDamageLimit']) \
if 'noDamageLimit' in ffs_node else None
frag.nodes.extend(ffs_node.values())
for (ls, ordinal), data in groupby(tset.tableFFDataDiscrete,
['limitState', 'ffs_ordinal']):
data = list(data)
# check that we can instantiate a FragilityFunction in risklib
if nodamage:
scientific.FragilityFunctionDiscrete(
[nodamage] + [rec.iml for rec in data],
[0.0] + [rec.poe for rec in data], nodamage)
else:
scientific.FragilityFunctionDiscrete([rec.iml for rec in data],
[rec.poe for rec in data],
nodamage)
imls = ' '.join(rec['iml'] for rec in data)
ffs_node[(ordinal, )].IML.text = imls
poes = ' '.join(rec['poe'] for rec in data)
n = Node('ffd', dict(ls=ls))
n.append(Node('poEs', text=poes))
ffs_node[(ordinal, )].append(n)
return frag
def _assetgenerator(self, assets, costtypes, periods):
"""
Convert assets into asset nodes.
:param assets: asset records
:param costtypes: the valid cost types
:param periods: the valid periods
:returns: an iterable over Node objects describing exposure assets
"""
# each asset contains the subnodes location, costs and occupancies
loc_dict = record.nodedict(self.tableset.tableLocation)
cost_dict = record.nodedict(self.tableset.tableCost)
occ_dict = record.nodedict(self.tableset.tableOccupancy)
for asset in assets:
ref = asset['asset_ref']
nodes = []
location = loc_dict[(asset['location_id'],)]
nodes.append(location)
costnodes = []
for ctype in costtypes:
cost = cost_dict.get((ref, ctype))
if cost is not None:
costnodes.append(cost)
if costnodes:
nodes.append(Node('costs', {}, nodes=costnodes))
occupancynodes = []
for period in periods:
occupancy = occ_dict.get((ref, period))
if occupancy is not None:
occupancynodes.append(occupancy)
if occupancynodes:
nodes.append(Node('occupancies', {}, nodes=occupancynodes))
assetnode = asset.to_node()
assetnode.nodes = nodes
yield assetnode
def _assetgenerator(self, assets, costtypes, periods):
"""
Convert assets into asset nodes.
:param assets: asset records
:param costtypes: the valid cost types
:param periods: the valid periods
:returns: an iterable over Node objects describing exposure assets
"""
# each asset contains the subnodes location, costs and occupancies
loc_dict = record.nodedict(self.tableset.tableLocation)
cost_dict = record.nodedict(self.tableset.tableCost)
occ_dict = record.nodedict(self.tableset.tableOccupancy)
for asset in assets:
ref = asset['asset_ref']
nodes = []
location = loc_dict[(asset['location_id'], )]
nodes.append(location)
costnodes = []
for ctype in costtypes:
cost = cost_dict.get((ref, ctype))
if cost is not None:
costnodes.append(cost)
if costnodes:
nodes.append(Node('costs', {}, nodes=costnodes))
occupancynodes = []
for period in periods:
occupancy = occ_dict.get((ref, period))
if occupancy is not None:
occupancynodes.append(occupancy)
if occupancynodes:
nodes.append(Node('occupancies', {}, nodes=occupancynodes))
assetnode = asset.to_node()
assetnode.nodes = nodes
yield assetnode
def to_node(self):
"""
Build a full continuous fragility node from CSV
"""
tset = self.tableset
frag = tset.tableFragilityContinuous[0].to_node()
ffs_node = record.nodedict(tset.tableFFSetContinuous)
frag.nodes.extend(ffs_node.values())
for (ls, ordinal), data in groupby(
tset.tableFFDataContinuous, ['limitState', 'ffs_ordinal']):
data = list(data)
n = Node('ffc', dict(ls=ls))
param = dict(row[2:] for row in data) # param, value
# check that we can instantiate a FragilityFunction in risklib
scientific.FragilityFunctionContinuous(
ls, float(param['mean']), float(param['stddev']))
n.append(Node('params', param))
ffs_node[(ordinal,)].append(n)
return frag
def to_node(self):
"""
Build a full continuous fragility node from CSV
"""
tset = self.tableset
frag = tset.tableFragilityContinuous[0].to_node()
ffs_node = record.nodedict(tset.tableFFSetContinuous)
frag.nodes.extend(ffs_node.values())
for (ls, ordinal), data in groupby(tset.tableFFDataContinuous,
['limitState', 'ffs_ordinal']):
data = list(data)
n = Node('ffc', dict(ls=ls))
param = dict(row[2:] for row in data) # param, value
# check that we can instantiate a FragilityFunction in risklib
scientific.FragilityFunctionContinuous(float(param['mean']),
float(param['stddev']))
n.append(Node('params', param))
ffs_node[(ordinal, )].append(n)
return frag