def upgrade_file(path, multipoint):
"""Upgrade to the latest NRML version"""
node0 = nrml.read(path, chatty=False)[0]
shutil.copy(path, path + '.bak') # make a backup of the original file
tag = striptag(node0.tag)
gml = True
if tag == 'vulnerabilityModel':
vf_dict, cat_dict = get_vulnerability_functions_04(path)
# below I am converting into a NRML 0.5 vulnerabilityModel
node0 = Node(
'vulnerabilityModel', cat_dict,
nodes=[obj_to_node(val) for val in vf_dict.values()])
gml = False
elif tag == 'fragilityModel':
node0 = read_nrml.convert_fragility_model_04(
nrml.read(path)[0], path)
gml = False
elif tag == 'sourceModel':
node0 = nrml.read(path)[0]
dic = groupby(node0.nodes, operator.itemgetter('tectonicRegion'))
node0.nodes = [Node('sourceGroup',
dict(tectonicRegion=trt, name="group %s" % i),
nodes=srcs)
for i, (trt, srcs) in enumerate(dic.items(), 1)]
if multipoint:
sourceconverter.update_source_model(node0, path + '.bak')
with open(path, 'wb') as f:
nrml.write([node0], f, gml=gml)
def test_ill_formed_rupture(self):
rup_file = BytesIO(b'''\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns:gml="http://www.opengis.net/gml"
xmlns="http://openquake.org/xmlns/nrml/0.4">
<simpleFaultRupture>
<magnitude>7.65</magnitude>
<rake>15.0</rake>
<hypocenter lon="0.0" lat="91.0" depth="5.0"/>
<simpleFaultGeometry>
<gml:LineString>
<gml:posList>
-124.704 40.363
-124.977 41.214
-125.140 42.096
</gml:posList>
</gml:LineString>
<dip>50.0</dip>
<upperSeismoDepth>12.5</upperSeismoDepth>
<lowerSeismoDepth>19.5</lowerSeismoDepth>
</simpleFaultGeometry>
</simpleFaultRupture>
</nrml>
''')
# at line 7 there is an invalid depth="-5.0"
with self.assertRaises(ValueError) as ctx:
nrml.read(rup_file)
self.assertIn('line 7', str(ctx.exception))
def test_no_nrml(self):
fname = gettemp('''\
<?xml version="1.0" encoding="UTF-8"?>
<fragilityModel id="Ethiopia" assetCategory="buildings"
lossCategory="structural" />
''')
with self.assertRaises(ValueError) as ctx:
read(fname)
self.assertIn('expected a node of kind nrml, got fragilityModel',
str(ctx.exception))
def test_alternative_mfds(self):
converter = s.SourceConverter(
investigation_time=1.,
rupture_mesh_spacing=1, # km
complex_fault_mesh_spacing=5, # km
width_of_mfd_bin=0.1, # for Truncated GR MFDs
area_source_discretization=1.)
grp_nodes = nrml.read(ALT_MFDS_SRC_MODEL).sourceModel.nodes
[[sflt1, sflt2], [cplx1]] = map(converter.convert_node, grp_nodes)
# Check the values
# Arbitrary MFD
assert_close(cplx1.mfd.magnitudes, [8.6, 8.8, 9.0])
assert_close(cplx1.mfd.occurrence_rates, [0.0006, 0.0008, 0.0004])
# Youngs & Coppersmith from characteristic rate
self.assertAlmostEqual(sflt1.mfd.b_val, 1.0)
self.assertAlmostEqual(sflt1.mfd.a_val, 3.3877843113)
self.assertAlmostEqual(sflt1.mfd.char_mag, 7.0)
self.assertAlmostEqual(sflt1.mfd.char_rate, 0.005)
self.assertAlmostEqual(sflt1.mfd.min_mag, 5.0)
# Youngs & Coppersmith from total moment rate
self.assertAlmostEqual(sflt2.mfd.b_val, 1.0)
self.assertAlmostEqual(sflt2.mfd.a_val, 5.0800, 3)
self.assertAlmostEqual(sflt2.mfd.char_mag, 7.0)
self.assertAlmostEqual(sflt2.mfd.char_rate, 0.24615, 5)
self.assertAlmostEqual(sflt2.mfd.min_mag, 5.0)
def test_well_formed_ruptures(self):
converter = s.RuptureConverter(rupture_mesh_spacing=1.5,
complex_fault_mesh_spacing=1.5)
for fname in (SIMPLE_FAULT_RUPTURE, COMPLEX_FAULT_RUPTURE,
SINGLE_PLANE_RUPTURE, MULTI_PLANES_RUPTURE):
[node] = nrml.read(fname)
converter.convert_node(node)
def test_simple(self):
testfile = os.path.join(testdir, 'two-point-sources.xml')
sm = nrml.read(testfile).sourceModel
update_source_model(sm, testfile)
with io.BytesIO() as f:
nrml.write(sm, f)
got = f.getvalue().decode('utf-8')
self.assertEqual(got, expected)
def test_nonparametric_source_ok(self):
converter = s.SourceConverter(
investigation_time=50.,
rupture_mesh_spacing=1, # km
complex_fault_mesh_spacing=1, # km
width_of_mfd_bin=1., # for Truncated GR MFDs
area_source_discretization=1.)
[np] = nrml.read(NONPARAMETRIC_SOURCE).sourceModel
converter.convert_node(np)
def test_complex(self):
testfile = os.path.normpath(os.path.join(
testdir, '../../../qa_tests_data/classical/case_30/ssm/shallow/'
'gridded_seismicity_source_4.xml'))
sm = nrml.read(testfile).sourceModel
update_source_model(sm, testfile)
with io.BytesIO() as f:
nrml.write(sm, f)
got = f.getvalue().decode('utf-8')
self.assertEqual(got, multipoint)
def build_rupture_from_file(rupture_file, simple_mesh_spacing=1.0,
complex_mesh_spacing=1.0):
"""
Parses a rupture from the OpenQuake nrml 4.0 format and parses it to
an instance of :class: openquake.hazardlib.source.rupture.Rupture
"""
[rup_node] = nrml.read(rupture_file)
conv = RuptureConverter(simple_mesh_spacing,
complex_mesh_spacing)
return conv.convert_node(rup_node)
def test_invalid(self):
fname = gettemp('''\
<?xml version="1.0" encoding="UTF-8"?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.5">
<fragilityModel id="Ethiopia" assetCategory="buildings"
lossCategory="structural">
<description>structural_vul_ethiopia</description>
<limitStates> slight moderate extensive collapse</limitStates>
<fragilityFunction id="CR/LFINF/H:1,2" format="continuous" shape="logncdf">
<imls imt="SA" noDamageLimit="0.1" minIML="0.01" maxIML="1.2"/>
<params ls="slight" mean="0.184422723" stddev="0.143988438"/>
<params ls="moderate" mean="1.659007804" stddev="3.176361273"/>
<params ls="extensive" mean="9.747745727" stddev="38.54171001"/>
<params ls="collapse" mean="247.1792873" stddev="4014.774504"/>
</fragilityFunction>
</fragilityModel>
</nrml>''')
with self.assertRaises(ValueError) as ctx:
read(fname)
self.assertIn('Could not convert imt->intensity_measure_type: '
"Invalid IMT: 'SA', line 8", str(ctx.exception))
def fix(fname, outname=None):
root = nrml.read(fname)
xmlns = root['xmlns']
if xmlns == u'http://openquake.org/xmlns/nrml/0.4':
for src_node in root.sourceModel:
fix_source_node(src_node)
else: # nrml/0.5+
for src_grp in root.sourceModel:
for src_node in src_grp:
fix_source_node(src_node)
with open(outname or fname, 'wb') as out:
nrml.write([root.sourceModel], out, xmlns=xmlns)
def convert_xml_hdf5(input_file, output_file):
with hdf5.File(output_file, 'w') as out:
inp = nrml.read(input_file)
if inp['xmlns'].endswith('nrml/0.4'): # old version
d = os.path.dirname(input_file) or '.'
raise ValueError('Please upgrade with `oq upgrade_nrml %s`' % d)
elif inp['xmlns'].endswith('nrml/0.5'): # current version
sm = inp.sourceModel
else: # not a NRML
raise ValueError('Unknown NRML:' % inp['xmlns'])
out.save(node.node_to_dict(sm))
return output_file
def reduce(fname, reduction_factor):
"""
Produce a submodel from `fname` by sampling the nodes randomly.
Supports source models, site models and exposure models. As a special
case, it is also able to reduce .csv files by sampling the lines.
This is a debugging utility to reduce large computations to small ones.
"""
if fname.endswith('.csv'):
with open(fname) as f:
line = f.readline() # read the first line
if csv.Sniffer().has_header(line):
header = line
all_lines = f.readlines()
else:
header = None
f.seek(0)
all_lines = f.readlines()
lines = general.random_filter(all_lines, reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
_save_csv(fname, lines, header)
print('Extracted %d lines out of %d' % (len(lines), len(all_lines)))
return
elif fname.endswith('.npy'):
array = numpy.load(fname)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
arr = numpy.array(general.random_filter(array, reduction_factor))
numpy.save(fname, arr)
print('Extracted %d rows out of %d' % (len(arr), len(array)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = general.random_filter(
model.assets, reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = general.random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
reduce_source_model(fname, reduction_factor)
return
elif model.tag.endswith('logicTree'):
for smpath in logictree.collect_info(fname).smpaths:
reduce_source_model(smpath, reduction_factor)
return
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
save_bak(fname, node, num_nodes, total)
def reduce_source_model(fname, reduction_factor):
node = nrml.read(fname)
if node['xmlns'] == 'http://openquake.org/xmlns/nrml/0.5':
total = sum(len(sg) for sg in node[0])
num_nodes = 0
for sg in node[0]:
sg.nodes = general.random_filter(sg, reduction_factor)
num_nodes += len(sg)
else: # nrml/0.4
total = len(node[0].nodes)
node[0].nodes = general.random_filter(node[0], reduction_factor)
num_nodes = len(node[0].nodes)
save_bak(fname, node, num_nodes, total)
def test_raises_useful_error_2(self):
area_file = BytesIO(b"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns:gml="http://www.opengis.net/gml"
xmlns="http://openquake.org/xmlns/nrml/0.4">
<sourceModel name="Some Source Model">
<areaSource id="1" name="Quito" tectonicRegion="Active Shallow Crust">
<areaGeometry>
<gml:Polygon>
<gml:exterior>
<gml:LinearRing>
<gml:posList>
-122.5 37.5
-121.5 37.5
-121.5 38.5
-122.5 38.5
</gml:posList>
</gml:LinearRing>
</gml:exterior>
</gml:Polygon>
<upperSeismoDepth>0.0</upperSeismoDepth>
<lowerSeismoDepth>10.0</lowerSeismoDepth>
</areaGeometry>
<magScaleRel>PeerMSR</magScaleRel>
<ruptAspectRatio>1.5</ruptAspectRatio>
<incrementalMFD minMag="6.55" binWidth="0.1">
<occurRates>0.0010614989 8.8291627E-4 7.3437777E-4
6.108288E-4 5.080653E-4
</occurRates>
</incrementalMFD>
<nodalPlanedist>
<nodalPlane probability="0.3" strike="0.0" dip="90.0" rake="0.0" />
<nodalPlane probability="0.7" strike="90.0" dip="45.0" rake="90.0" />
</nodalPlanedist>
<hypoDepthDist>
<hypoDepth probability="0.5" depth="4.0" />
<hypoDepth probability="0.5" depth="8.0" />
</hypoDepthDist>
</areaSource>
</sourceModel>
</nrml>
""")
[area] = nrml.read(area_file).sourceModel
with self.assertRaises(AttributeError) as ctx:
self.conv.convert_node(area)
self.assertIn(
"node areaSource: No subnode named 'nodalPlaneDist'"
" found in 'areaSource', line 5 of", str(ctx.exception))
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 get_rupture(oqparam):
"""
Read the `rupture_model` file and by filter the site collection
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an hazardlib rupture
"""
rup_model = oqparam.inputs['rupture_model']
[rup_node] = nrml.read(rup_model)
conv = sourceconverter.RuptureConverter(
oqparam.rupture_mesh_spacing, oqparam.complex_fault_mesh_spacing)
rup = conv.convert_node(rup_node)
rup.tectonic_region_type = '*' # there is not TRT for scenario ruptures
rup.serial = oqparam.random_seed
return rup
def get_site_model(oqparam):
"""
Convert the NRML file into an array of site parameters.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an array with fields lon, lat, vs30, ...
"""
req_site_params = get_gsim_lt(oqparam).req_site_params
arrays = []
for fname in oqparam.inputs['site_model']:
if isinstance(fname, str) and fname.endswith('.csv'):
sm = hdf5.read_csv(
fname, {None: float, 'vs30measured': bool}).array
if 'site_id' in sm.dtype.names:
raise InvalidFile('%s: you passed a sites.csv file instead of '
'a site_model.csv file!' % fname)
z = numpy.zeros(len(sm), sorted(sm.dtype.descr))
for name in z.dtype.names: # reorder the fields
z[name] = sm[name]
arrays.append(z)
continue
nodes = nrml.read(fname).siteModel
params = [valid.site_param(node.attrib) for node in nodes]
missing = req_site_params - set(params[0])
if 'vs30measured' in missing: # use a default of False
missing -= {'vs30measured'}
for param in params:
param['vs30measured'] = False
if 'backarc' in missing: # use a default of False
missing -= {'backarc'}
for param in params:
param['backarc'] = False
if missing:
raise InvalidFile('%s: missing parameter %s' %
(oqparam.inputs['site_model'],
', '.join(missing)))
# NB: the sorted in sorted(params[0]) is essential, otherwise there is
# an heisenbug in scenario/test_case_4
site_model_dt = numpy.dtype([(p, site.site_param_dt[p])
for p in sorted(params[0])])
sm = numpy.array([tuple(param[name] for name in site_model_dt.names)
for param in params], site_model_dt)
arrays.append(sm)
return numpy.concatenate(arrays)
def get_input_files(oqparam, hazard=False):
"""
:param oqparam: an OqParam instance
:param hazard: if True, consider only the hazard files
:returns: input path names in a specific order
"""
fnames = [] # files entering in the checksum
for key in oqparam.inputs:
fname = oqparam.inputs[key]
if hazard and key not in ('site_model', 'source_model_logic_tree',
'gsim_logic_tree', 'source'):
continue
# collect .hdf5 tables for the GSIMs, if any
elif key == 'gsim_logic_tree':
gsim_lt = get_gsim_lt(oqparam)
for gsims in gsim_lt.values.values():
for gsim in gsims:
table = getattr(gsim, 'GMPE_TABLE', None)
if table:
fnames.append(table)
fnames.append(fname)
elif key == 'source_model': # UCERF
f = oqparam.inputs['source_model']
fnames.append(f)
fname = nrml.read(f).sourceModel.UCERFSource['filename']
fnames.append(os.path.join(os.path.dirname(f), fname))
elif key == 'exposure': # fname is a list
for exp in asset.Exposure.read_headers(fname):
fnames.extend(exp.datafiles)
fnames.extend(fname)
elif isinstance(fname, dict):
fnames.extend(fname.values())
elif isinstance(fname, list):
for f in fname:
if f == oqparam.input_dir:
raise InvalidFile('%s there is an empty path in %s' %
(oqparam.inputs['job_ini'], key))
fnames.extend(fname)
elif key == 'source_model_logic_tree':
for smpaths in logictree.collect_info(fname).smpaths.values():
fnames.extend(smpaths)
fnames.append(fname)
else:
fnames.append(fname)
return sorted(fnames)
def tidy(fnames):
"""
Reformat a NRML file in a canonical form. That also means reducing the
precision of the floats to a standard value. If the file is invalid,
a clear error message is shown.
"""
for fname in fnames:
try:
node = nrml.read(fname)
except ValueError as err:
print(err)
return
with open(fname + '.bak', 'wb') as f:
f.write(open(fname, 'rb').read())
with open(fname, 'wb') as f:
# make sure the xmlns i.e. the NRML version is unchanged
nrml.write(node.nodes, f, writers.FIVEDIGITS, xmlns=node['xmlns'])
print('Reformatted %s, original left in %s.bak' % (fname, fname))
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model
"""
found = 0
to_remove = set()
for paths in logictree.collect_info(smlt_file).smpaths.values():
for path in paths:
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
if src_node['id'] in source_ids:
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
if src_node['id'] in source_ids:
found += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
elif remove: # remove the files completely reduced
to_remove.add(path)
if found:
for path in to_remove:
os.remove(path)
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model
"""
found = 0
to_remove = []
for paths in logictree.collect_info(smlt_file).smpaths.values():
for path in paths:
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
if src_node['id'] in source_ids:
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
if src_node['id'] in source_ids:
found += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
elif remove: # remove the files completely reduced
to_remove.append(path)
if found:
for path in to_remove:
os.remove(path)
def collect_files(gsim_lt_path):
"""
Given a path to a gsim logic tree, collect all of the
path names it contains (relevent for tabular/file-dependent GSIMs).
"""
n = nrml.read(gsim_lt_path)
try:
blevels = n.logicTree
except Exception:
raise InvalidFile('%s is not a valid source_model_logic_tree_file' %
gsim_lt_path)
paths = set()
for blevel in blevels:
for bset in bsnodes(gsim_lt_path, blevel):
assert bset['uncertaintyType'] == 'gmpeModel', bset
for br in bset:
with context(gsim_lt_path, br):
relpaths = rel_paths(br.uncertaintyModel.text)
paths.update(abs_paths(gsim_lt_path, relpaths))
return sorted(paths)
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 info(calculators, gsims, views, exports, report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
logging.basicConfig(level=logging.INFO)
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
print(nrml.read(input_file).to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
def __init__(self, filename, seed=0, num_samples=0,
sampling_method='early_weights', test_mode=False):
self.filename = filename
self.basepath = os.path.dirname(filename)
# NB: converting the random_seed into an integer is needed on Windows
self.seed = int(seed)
self.num_samples = num_samples
self.sampling_method = sampling_method
self.test_mode = test_mode
self.branches = {} # branch_id -> branch
self.bsetdict = {}
self.previous_branches = []
self.tectonic_region_types = set()
self.source_types = set()
self.hdf5_files = set()
self.root_branchset = None
root = nrml.read(filename)
try:
tree = root.logicTree
except AttributeError:
raise LogicTreeError(
root, self.filename, "missing logicTree node")
self.shortener = {}
self.branchsets = []
self.parse_tree(tree)
# determine if the logic tree is source specific
dicts = list(self.bsetdict.values())[1:]
if not dicts:
self.is_source_specific = False
return
for dic in dicts:
ats = dic.get('applyToSources')
if not ats:
self.is_source_specific = False
return
elif len(ats.split()) != 1:
self.is_source_specific = False
return
self.is_source_specific = True
def nrml_to_pdf(file_nrml, include_ids=False, verbose=False):
'''
Convert NRML logic tree into a PDF diagram. Output file name is same
as input except with .pdf extension. An intermediate .tex file is
generated. Lualatex must be installed and present on the system path.
:param file_nrml: file name of NRML logic tree in XML format
:param include_ids: include or omit node ids from diagram
'''
if verbose:
print('Reading %s' % file_nrml)
root = nrml.read(file_nrml)
file_tex = file_nrml.replace('.xml', '') + '.tex'
if verbose:
print('Writing %s' % file_tex)
with open(file_tex, 'w+') as f:
with StreamingTexWriter(f, include_ids) as writer:
writer.serialize(root)
out_dir = 'build'
file_pdf = file_tex.replace('.tex', '.pdf')
build_pdf = os.path.join(out_dir, file_pdf)
if verbose:
print('Converting %s to %s' % (file_tex, build_pdf))
if not os.path.exists(out_dir):
os.mkdir(out_dir)
command = [
'lualatex', '-output-directory=' + out_dir, '-interaction=nonstopmode',
file_tex
]
print('Executing:' + ' '.join(command))
subprocess.call(command)
if verbose:
print('Moving %s to %s' % (build_pdf, file_pdf))
if os.path.exists(file_pdf):
os.remove(file_pdf)
os.rename(build_pdf, file_pdf)
def reduce(fname, reduction_factor):
"""
Produce a submodel from `fname` by sampling the nodes randomly.
Supports source models, site models and exposure models. As a special
case, it is also able to reduce .csv files by sampling the lines.
This is a debugging utility to reduce large computations to small ones.
"""
if fname.endswith('.csv'):
with open(fname) as f:
all_lines = f.readlines()
lines = random_filter(all_lines, reduction_factor)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
with open(fname, 'wb') as f:
for line in lines:
f.write(encode(line))
print('Extracted %d lines out of %d' % (len(lines), len(all_lines)))
return
node = nrml.read(fname)
model = node[0]
if model.tag.endswith('exposureModel'):
total = len(model.assets)
model.assets.nodes = random_filter(model.assets, reduction_factor)
num_nodes = len(model.assets)
elif model.tag.endswith('siteModel'):
total = len(model)
model.nodes = random_filter(model, reduction_factor)
num_nodes = len(model)
elif model.tag.endswith('sourceModel'):
total = len(model)
model.nodes = random_filter(model, reduction_factor)
num_nodes = len(model)
else:
raise RuntimeError('Unknown model tag: %s' % model.tag)
shutil.copy(fname, fname + '.bak')
print('Copied the original file in %s.bak' % fname)
with open(fname, 'wb') as f:
nrml.write([model], f, xmlns=node['xmlns'])
print('Extracted %d nodes out of %d' % (num_nodes, total))
def get_rupture(oqparam):
"""
Read the `rupture_model` file and by filter the site collection
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an hazardlib rupture
"""
rup_model = oqparam.inputs['rupture_model']
if rup_model.endswith('.csv'):
return rupture.from_array(hdf5.read_csv(rup_model))
if rup_model.endswith('.xml'):
[rup_node] = nrml.read(rup_model)
conv = sourceconverter.RuptureConverter(
oqparam.rupture_mesh_spacing, oqparam.complex_fault_mesh_spacing)
rup = conv.convert_node(rup_node)
else:
raise ValueError('Unrecognized ruptures model %s' % rup_model)
rup.tectonic_region_type = '*' # there is not TRT for scenario ruptures
rup.rup_id = oqparam.ses_seed
return rup
def read(self,
nrml_file,
validate=False,
simple_fault_spacing=1.0,
complex_mesh_spacing=5.0,
mfd_spacing=0.1):
"""
Build the source model from nrml format
"""
self.source_file = nrml_file
if validate:
converter = SourceConverter(1.0, simple_fault_spacing,
complex_mesh_spacing, mfd_spacing,
10.0)
converter.fname = nrml_file
src_nodes = nrml.read(nrml_file).sourceModel
sources = []
for no, src_node in enumerate(src_nodes, 1):
if validate:
print("Validating Source %s" % src_node.attrib["id"])
converter.convert_node(src_node)
sources.append(src_node)
return SourceModel(sources)
def main(what, fnames, chatty=False, *, outdir='.'):
"""
Convert source models into CSV files (or geopackages, if fiona is
installed).
"""
t0 = time.time()
for fname in fnames:
logging.info('Reading %s', fname)
converter.fname = fname
name, _ext = os.path.splitext(os.path.basename(fname))
root = nrml.read(fname)
srcs = collections.defaultdict(list) # geom_index -> rows
if 'nrml/0.4' in root['xmlns']:
for srcnode in root.sourceModel:
appendrow(converter.convert_node(srcnode), srcs, chatty)
else:
for srcgroup in root.sourceModel:
trt = srcgroup['tectonicRegion']
for srcnode in srcgroup:
srcnode['tectonicRegion'] = trt
appendrow(converter.convert_node(srcnode), srcs, chatty)
if what == 'csv':
for kind, rows in srcs.items():
dest = os.path.join(outdir, '%s_%s.csv' % (name, kind))
logging.info('Saving %d sources on %s', len(rows), dest)
tups = []
for row in rows:
tup = row[:-2] + (to_wkt(*row[-2:]),)
tups.append(tup)
header = rows[0]._fields[:-2] + ('wkt',)
write_csv(dest, tups, header=header)
else: # gpkg
gpkg = GeoPackager(name + '.gpkg')
for kind, rows in srcs.items():
logging.info('Saving %d sources on layer %s', len(rows), kind)
gpkg.save_layer(kind, rows)
logging.info('Finished in %d seconds', time.time() - t0)
def upgrade_nrml(directory, dry_run, multipoint):
"""
Upgrade all the NRML files contained in the given directory to the latest
NRML version. Works by walking all subdirectories.
WARNING: there is no downgrade!
"""
for cwd, dirs, files in os.walk(directory):
for f in files:
path = os.path.join(cwd, f)
if f.endswith('.xml'):
ip = iterparse(path, events=('start',))
next(ip) # read node zero
try:
fulltag = next(ip)[1].tag # tag of the first node
xmlns, tag = fulltag.split('}')
except Exception: # not a NRML file
xmlns, tag = '', ''
if xmlns[1:] == NRML05: # already upgraded
if 'sourceModel' in tag and multipoint:
print('upgrading to multiPointSources', path)
node0 = nrml.read(path)[0]
sourceconverter.update_source_model(node0, path)
with open(path, 'wb') as f:
nrml.write([node0], f, gml=True)
elif 'nrml/0.4' in xmlns and (
'vulnerability' in tag or 'fragility' in tag or
'sourceModel' in tag):
if not dry_run:
print('Upgrading', path)
try:
upgrade_file(path, multipoint)
except Exception as exc:
raise
print(exc)
else:
print('Not upgrading', path)
def _get_ebruptures(fname, conv=None, ses_seed=None):
"""
:param fname: path to a rupture file (XML or CSV)
:param conv: RuptureConverter instanc, used for XML ruptures
:param ses_seed: used for XML ruptures
:returns: a list of one or more EBRuptures
"""
if fname.endswith('.xml'):
[rup_node] = nrml.read(fname)
rup = conv.convert_node(rup_node)
rup.tectonic_region_type = '*' # no TRT for scenario ruptures
rup.rup_id = ses_seed
ebrs = [EBRupture(rup, 'NA', 0, id=rup.rup_id, scenario=True)]
return ebrs
assert fname.endswith('.csv'), fname
aw = get_ruptures(fname)
ebrs = []
for i, rec in enumerate(aw.array):
rupture = _get_rupture(rec, aw.geoms[i], aw.trts[rec['trt_smr']])
ebr = EBRupture(rupture, rec['source_id'], rec['trt_smr'],
rec['n_occ'], rec['id'], rec['e0'])
ebrs.append(ebr)
return ebrs
def main(directory, dry_run=False, multipoint=False):
"""
Upgrade all the NRML files contained in the given directory to the latest
NRML version. Works by walking all subdirectories.
WARNING: there is no downgrade!
"""
for cwd, dirs, files in os.walk(directory):
for f in files:
path = os.path.join(cwd, f)
if f.endswith('.xml'):
ip = iterparse(path, events=('start', ))
next(ip) # read node zero
try:
fulltag = next(ip)[1].tag # tag of the first node
xmlns, tag = fulltag.split('}')
except Exception: # not a NRML file
xmlns, tag = '', ''
if xmlns[1:] == NRML05: # already upgraded
if 'sourceModel' in tag and multipoint:
print('upgrading to multiPointSources', path)
node0 = nrml.read(path)[0]
sourceconverter.update_source_model(node0, path)
with open(path, 'wb') as f:
nrml.write([node0], f, gml=True)
elif 'nrml/0.4' in xmlns and ('vulnerability' in tag
or 'fragility' in tag
or 'sourceModel' in tag):
if not dry_run:
print('Upgrading', path)
try:
upgrade_file(path, multipoint)
except Exception as exc:
raise
print(exc)
else:
print('Not upgrading', path)
def get_input_files(oqparam, hazard=False):
"""
:param oqparam: an OqParam instance
:param hazard: if True, consider only the hazard files
:returns: input path names in a specific order
"""
fnames = set() # files entering in the checksum
uri = oqparam.shakemap_uri
if isinstance(uri, dict) and uri:
# local files
for key, val in uri.items():
if key == 'fname' or key.endswith('_url'):
val = val.replace('file://', '')
fname = os.path.join(oqparam.base_path, val)
if os.path.exists(fname):
uri[key] = fname
fnames.add(fname)
# additional separate shapefiles
if uri['kind'] == 'shapefile' and not uri['fname'].endswith('.zip'):
fnames.update(get_shapefiles(os.path.dirname(fname)))
for key in oqparam.inputs:
fname = oqparam.inputs[key]
if hazard and key not in ('source_model_logic_tree', 'gsim_logic_tree',
'source'):
continue
# collect .hdf5 tables for the GSIMs, if any
elif key == 'gsim_logic_tree':
gsim_lt = get_gsim_lt(oqparam)
for gsims in gsim_lt.values.values():
for gsim in gsims:
for k, v in gsim.kwargs.items():
if k.endswith(('_file', '_table')):
fnames.add(v)
fnames.add(fname)
elif key == 'source_model': # UCERF
f = oqparam.inputs['source_model']
fnames.add(f)
fname = nrml.read(f).sourceModel.UCERFSource['filename']
fnames.add(os.path.join(os.path.dirname(f), fname))
elif key == 'exposure': # fname is a list
for exp in asset.Exposure.read_headers(fname):
fnames.update(exp.datafiles)
fnames.update(fname)
elif isinstance(fname, dict):
fnames.update(fname.values())
elif isinstance(fname, list):
for f in fname:
if f == oqparam.input_dir:
raise InvalidFile('%s there is an empty path in %s' %
(oqparam.inputs['job_ini'], key))
fnames.update(fname)
elif key == 'source_model_logic_tree':
args = (fname, oqparam.random_seed,
oqparam.number_of_logic_tree_samples,
oqparam.sampling_method)
try:
smlt = smlt_cache[args]
except KeyError:
smlt = smlt_cache[args] = logictree.SourceModelLogicTree(*args)
fnames.update(smlt.hdf5_files)
fnames.update(smlt.info.smpaths)
fnames.add(fname)
else:
fnames.add(fname)
return sorted(fnames)
def _get_exposure(fname, stop=None):
"""
:param fname:
path of the XML file containing the exposure
: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)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
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='?'))
try:
occupancy_periods = exposure.occupancyPeriods.text or ''
except AttributeError:
occupancy_periods = ''
try:
tagNames = exposure.tagNames
except AttributeError:
tagNames = Node('tagNames', text='')
tagnames = ~tagNames or []
if set(tagnames) & {'taxonomy', 'exposure', 'country'}:
raise InvalidFile('taxonomy, exposure and country are reserved names '
'you cannot use it in <tagNames>: %s' % fname)
tagnames.insert(0, 'taxonomy')
# read the cost types and make some check
cost_types = []
retrofitted = False
for ct in conversions.costTypes:
with context(fname, ct):
ctname = ct['name']
if ctname == 'structural' and 'retrofittedType' in ct.attrib:
if ct['retrofittedType'] != ct['type']:
raise ValueError(
'The retrofittedType %s is different from the type'
'%s' % (ct['retrofittedType'], ct['type']))
if ct['retrofittedUnit'] != ct['unit']:
raise ValueError(
'The retrofittedUnit %s is different from the unit'
'%s' % (ct['retrofittedUnit'], ct['unit']))
retrofitted = True
cost_types.append(
(ctname, valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in 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 = il = inslimit.get('isAbsolute')
deductible_is_absolute = de = deductible.get('isAbsolute')
cc = CostCalculator(
{}, {}, {},
True if de is None else de,
True if il is None else il,
{name: i for i, name in enumerate(tagnames)},
)
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']
assets = []
asset_refs = []
exp = Exposure(
exposure['id'], exposure['category'],
description.text, cost_types, occupancy_periods,
insurance_limit_is_absolute, deductible_is_absolute, retrofitted,
area.attrib, assets, asset_refs, cc, TagCollection(tagnames))
assets_text = exposure.assets.text.strip()
if assets_text:
# the <assets> tag contains a list of file names
dirname = os.path.dirname(fname)
exp.datafiles = [os.path.join(dirname, f) for f in assets_text.split()]
else:
exp.datafiles = []
return exp, exposure.assets
def main(what, report=False):
"""
Give information about the passed keyword or filename
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
if what == 'calculators':
for calc in sorted(base.calculators):
print(calc)
elif what == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif what == 'imts':
for im in gen_subclasses(imt.IMT):
print(im.__name__)
elif what == 'views':
for name in sorted(view):
print(name)
elif what == 'exports':
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
elif what == 'extracts':
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
elif hasattr(func, 'func'): # for partial objects
fm = FunctionMaker(func.func)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
elif what == 'parameters':
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
elif what == 'mfds':
for cls in gen_subclasses(BaseMFD):
print(cls.__name__)
elif what == 'sources':
for cls in gen_subclasses(BaseSeismicSource):
print(cls.__name__)
elif os.path.isdir(what) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(what)
print(mon)
elif what.endswith('.xml'):
node = nrml.read(what)
if node[0].tag.endswith('sourceModel'):
print(source_model_info([node]))
elif node[0].tag.endswith('logicTree'):
sm_nodes = []
for smpath in logictree.collect_info(what).smpaths:
sm_nodes.append(nrml.read(smpath))
print(source_model_info(sm_nodes))
else:
print(node.to_str())
elif what.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(what))
else:
print(readinput.get_oqparam(what).json())
if mon.duration > 1:
print(mon)
elif what:
print("No info for '%s'" % what)
def setUp(self):
self.expected_source = nrml.read(
os.path.join(BASE_DATA_PATH, "collapse_test_output.xml"))[0]
self.src = self.expected_source[0][0]
def get_source_models(oqparam, gsim_lt, source_model_lt, in_memory=True):
"""
Build all the source models generated by the logic tree.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param gsim_lt:
a :class:`openquake.commonlib.logictree.GsimLogicTree` instance
:param source_model_lt:
a :class:`openquake.commonlib.logictree.SourceModelLogicTree` instance
:param in_memory:
if True, keep in memory the sources, else just collect the TRTs
:returns:
an iterator over :class:`openquake.commonlib.logictree.SourceModel`
tuples
"""
converter = sourceconverter.SourceConverter(
oqparam.investigation_time, oqparam.rupture_mesh_spacing,
oqparam.complex_fault_mesh_spacing, oqparam.width_of_mfd_bin,
oqparam.area_source_discretization)
psr = nrml.SourceModelParser(converter)
# consider only the effective realizations
for sm in source_model_lt.gen_source_models(gsim_lt):
src_groups = []
for name in sm.name.split():
fname = possibly_gunzip(
os.path.abspath(os.path.join(oqparam.base_path, name)))
if in_memory:
apply_unc = source_model_lt.make_apply_uncertainties(sm.path)
try:
logging.info('Parsing %s', fname)
src_groups.extend(psr.parse_src_groups(fname, apply_unc))
except ValueError as e:
if str(e) in ('Surface does not conform with Aki & '
'Richards convention',
'Edges points are not in the right order'):
raise InvalidFile('''\
%s: %s. Probably you are using an obsolete model.
In that case you can fix the file with the command
python -m openquake.engine.tools.correct_complex_sources %s
''' % (fname, e, fname))
else:
raise
else: # just collect the TRT models
smodel = nrml.read(fname).sourceModel
if smodel[0].tag.endswith('sourceGroup'): # NRML 0.5 format
for sg_node in smodel:
sg = sourceconverter.SourceGroup(
sg_node['tectonicRegion'])
sg.sources = sg_node.nodes
src_groups.append(sg)
else: # NRML 0.4 format: smodel is a list of source nodes
src_groups.extend(
sourceconverter.SourceGroup.collect(smodel))
num_sources = sum(len(sg.sources) for sg in src_groups)
sm.src_groups = src_groups
trts = [mod.trt for mod in src_groups]
source_model_lt.tectonic_region_types.update(trts)
logging.info(
'Processed source model %d with %d potential gsim path(s) and %d '
'sources', sm.ordinal + 1, sm.num_gsim_paths, num_sources)
gsim_file = oqparam.inputs.get('gsim_logic_tree')
if gsim_file: # check TRTs
for src_group in src_groups:
if src_group.trt not in gsim_lt.values:
raise ValueError(
"Found in %r a tectonic region type %r inconsistent "
"with the ones in %r" % (sm, src_group.trt, gsim_file))
yield sm
# log if some source file is being used more than once
for fname, hits in psr.fname_hits.items():
if hits > 1:
logging.info('%s has been considered %d times', fname, hits)
for col_str in header:
col = col_str.strip().split(':')
n = len(col)
if n == 1: # default dtype and no shape
col = [col[0], 'float32', '']
elif n == 2:
if castable_to_int(col[1]): # default dtype and shape
col = [col[0], 'float32', col[1]]
else: # dtype and no shape
col = [col[0], col[1], '']
elif n > 3:
raise ValueError('Invalid column description: %s' % col_str)
field = col[0]
numpytype = col[1]
shape = () if not col[2].strip() else (int(col[2]),)
triples.append((field, numpytype, shape))
fields.append(field)
return fields, numpy.dtype(triples)
if __name__ == '__main__': # pretty print of NRML files
import sys
import shutil
from openquake.hazardlib import nrml
nrmlfiles = sys.argv[1:]
for fname in nrmlfiles:
node = nrml.read(fname)
shutil.copy(fname, fname + '.bak')
with open(fname, 'w') as out:
nrml.write(list(node), out)
def get_site_model(oqparam):
"""
Convert the NRML file into an array of site parameters.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:returns:
an array with fields lon, lat, vs30, ...
"""
req_site_params = get_gsim_lt(oqparam).req_site_params
if 'amplification' in oqparam.inputs:
req_site_params.add('ampcode')
arrays = []
for fname in oqparam.inputs['site_model']:
if isinstance(fname, str) and fname.endswith('.csv'):
sm = hdf5.read_csv(fname, site.site_param_dt).array
sm['lon'] = numpy.round(sm['lon'], 5)
sm['lat'] = numpy.round(sm['lat'], 5)
dupl = get_duplicates(sm, 'lon', 'lat')
if dupl:
raise InvalidFile(
'Found duplicate sites %s in %s' % (dupl, fname))
if 'site_id' in sm.dtype.names:
raise InvalidFile('%s: you passed a sites.csv file instead of '
'a site_model.csv file!' % fname)
z = numpy.zeros(len(sm), sorted(sm.dtype.descr))
for name in z.dtype.names: # reorder the fields
z[name] = sm[name]
arrays.append(z)
continue
nodes = nrml.read(fname).siteModel
params = [valid.site_param(node.attrib) for node in nodes]
missing = req_site_params - set(params[0])
if 'vs30measured' in missing: # use a default of False
missing -= {'vs30measured'}
for param in params:
param['vs30measured'] = False
if 'backarc' in missing: # use a default of False
missing -= {'backarc'}
for param in params:
param['backarc'] = False
if 'ampcode' in missing: # use a default of b''
missing -= {'ampcode'}
for param in params:
param['ampcode'] = b''
if missing:
raise InvalidFile('%s: missing parameter %s' %
(oqparam.inputs['site_model'],
', '.join(missing)))
# NB: the sorted in sorted(params[0]) is essential, otherwise there is
# an heisenbug in scenario/test_case_4
site_model_dt = numpy.dtype([(p, site.site_param_dt[p])
for p in sorted(params[0])])
sm = numpy.array([tuple(param[name] for name in site_model_dt.names)
for param in params], site_model_dt)
dupl = "\n".join(
'%s %s' % loc for loc, n in countby(sm, 'lon', 'lat').items()
if n > 1)
if dupl:
raise InvalidFile('There are duplicated sites in %s:\n%s' %
(fname, dupl))
arrays.append(sm)
return numpy.concatenate(arrays)
def test_nrml(self):
# can read and write a NRML file converted into a Node object
xmlfile = io.BytesIO(b"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<exposureModel
id="my_exposure_model_for_population"
category="population"
taxonomySource="fake population datasource">
<description>
Sample population
</description>
<assets>
<asset id="asset_01" number="7" taxonomy="IT-PV">
<location lon="9.15000" lat="45.16667" />
</asset>
<asset id="asset_02" number="7" taxonomy="IT-CE">
<location lon="9.15333" lat="45.12200" />
</asset>
</assets>
</exposureModel>
</nrml>
""")
root = read(xmlfile)
tag, version = get_tag_version(root[0])
self.assertEqual(tag, 'exposureModel')
self.assertEqual(version, 'nrml/0.4')
outfile = io.BytesIO()
node_to_xml(root, outfile, {})
expected = b"""\
<?xml version="1.0" encoding="utf-8"?>
<nrml
xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml"
>
<exposureModel
category="population"
id="my_exposure_model_for_population"
taxonomySource="fake population datasource"
>
<description>
Sample population
</description>
<assets>
<asset
id="asset_01"
number="7.000000000E+00"
taxonomy="IT-PV"
>
<location lat="4.516667000E+01" lon="9.150000000E+00"/>
</asset>
<asset
id="asset_02"
number="7.000000000E+00"
taxonomy="IT-CE"
>
<location lat="4.512200000E+01" lon="9.153330000E+00"/>
</asset>
</assets>
</exposureModel>
</nrml>
"""
self.assertEqual(outfile.getvalue(), expected)
def _get_exposure(fname, stop=None):
"""
:param fname:
path of the XML file containing the exposure
: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)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
description = exposure.description
try:
conversions = exposure.conversions
except AttributeError:
conversions = Node('conversions', nodes=[Node('costTypes', [])])
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='?'))
try:
occupancy_periods = exposure.occupancyPeriods.text or ''
except AttributeError:
occupancy_periods = ''
try:
tagNames = exposure.tagNames
except AttributeError:
tagNames = Node('tagNames', text='')
tagnames = ['id'] + (~tagNames or [])
if set(tagnames) & {'taxonomy', 'exposure', 'country'}:
raise InvalidFile('taxonomy, exposure and country are reserved names '
'you cannot use it in <tagNames>: %s' % fname)
tagnames.insert(0, 'taxonomy')
# read the cost types and make some check
cost_types = []
retrofitted = False
for ct in conversions.costTypes:
with context(fname, ct):
ctname = ct['name']
if ctname == 'structural' and 'retrofittedType' in ct.attrib:
if ct['retrofittedType'] != ct['type']:
raise ValueError(
'The retrofittedType %s is different from the type'
'%s' % (ct['retrofittedType'], ct['type']))
if ct['retrofittedUnit'] != ct['unit']:
raise ValueError(
'The retrofittedUnit %s is different from the unit'
'%s' % (ct['retrofittedUnit'], ct['unit']))
retrofitted = True
cost_types.append(
(ctname, valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in 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)
cc = CostCalculator({}, {}, {},
{name: i
for i, name in enumerate(tagnames)})
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.text,
cost_types, occupancy_periods, retrofitted, area.attrib, [],
[], cc, TagCollection(tagnames))
assets_text = exposure.assets.text.strip()
if assets_text:
# the <assets> tag contains a list of file names
dirname = os.path.dirname(fname)
exp.datafiles = [os.path.join(dirname, f) for f in assets_text.split()]
else:
exp.datafiles = []
return exp, exposure.assets
def _get_exposure(fname, stop=None):
"""
:param fname:
path of the XML file containing the exposure
: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)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
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='?'))
try:
occupancy_periods = exposure.occupancyPeriods.text or ''
except AttributeError:
occupancy_periods = ''
try:
tagNames = exposure.tagNames
except AttributeError:
tagNames = Node('tagNames', text='')
tagnames = ~tagNames or []
tagnames.insert(0, 'taxonomy')
# read the cost types and make some check
cost_types = []
retrofitted = False
for ct in conversions.costTypes:
with context(fname, ct):
ctname = ct['name']
if ctname == 'structural' and 'retrofittedType' in ct.attrib:
if ct['retrofittedType'] != ct['type']:
raise ValueError(
'The retrofittedType %s is different from the type'
'%s' % (ct['retrofittedType'], ct['type']))
if ct['retrofittedUnit'] != ct['unit']:
raise ValueError(
'The retrofittedUnit %s is different from the unit'
'%s' % (ct['retrofittedUnit'], ct['unit']))
retrofitted = True
cost_types.append(
(ctname, valid.cost_type_type(ct['type']), ct['unit']))
if 'occupants' in 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 = il = inslimit.get('isAbsolute')
deductible_is_absolute = de = deductible.get('isAbsolute')
cc = CostCalculator(
{},
{},
{},
True if de is None else de,
True if il is None else il,
{name: i
for i, name in enumerate(tagnames)},
)
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']
assets = []
asset_refs = []
exp = Exposure(exposure['id'], exposure['category'], description.text,
cost_types, occupancy_periods, insurance_limit_is_absolute,
deductible_is_absolute, retrofitted, area.attrib, assets,
asset_refs, cc, TagCollection(tagnames))
return exp, exposure.assets
def info(calculators, gsims, views, exports, extracts, report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' %
(input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [
nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths
]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
def reduce_source_model(smlt_file, source_ids, remove=True):
"""
Extract sources from the composite source model.
:param smlt_file: path to a source model logic tree file
:param source_ids: dictionary source_id -> records (src_id, code)
:param remove: if True, remove sm.xml files containing no sources
:returns: the number of sources satisfying the filter vs the total
"""
if isinstance(source_ids, dict): # in oq reduce_sm
def ok(src_node):
code = tag2code[re.search(r'\}(\w\w)', src_node.tag).group(1)]
arr = source_ids.get(src_node['id'])
if arr is None:
return False
return (arr['code'] == code).any()
else: # list of source IDs, in extract_source
def ok(src_node):
return src_node['id'] in source_ids
good, total = 0, 0
to_remove = set()
for paths in logictree.collect_info(smlt_file).smpaths.values():
for path in paths:
logging.info('Reading %s', path)
root = nrml.read(path)
model = Node('sourceModel', root[0].attrib)
origmodel = root[0]
if root['xmlns'] == 'http://openquake.org/xmlns/nrml/0.4':
for src_node in origmodel:
total += 1
if ok(src_node):
good += 1
model.nodes.append(src_node)
else: # nrml/0.5
for src_group in origmodel:
sg = copy.copy(src_group)
sg.nodes = []
weights = src_group.get('srcs_weights')
if weights:
assert len(weights) == len(src_group.nodes)
else:
weights = [1] * len(src_group.nodes)
src_group['srcs_weights'] = reduced_weigths = []
for src_node, weight in zip(src_group, weights):
total += 1
if ok(src_node):
good += 1
sg.nodes.append(src_node)
reduced_weigths.append(weight)
if sg.nodes:
model.nodes.append(sg)
shutil.copy(path, path + '.bak')
if model:
with open(path, 'wb') as f:
nrml.write([model], f, xmlns=root['xmlns'])
elif remove: # remove the files completely reduced
to_remove.add(path)
if good:
for path in to_remove:
os.remove(path)
return good, total
# this is simple and without error checking for the moment
def read_array(fname, sep=','):
r"""
Convert a CSV file without header into a numpy array of floats.
>>> from openquake.baselib.general import writetmp
>>> print(read_array(writetmp('.1 .2, .3 .4, .5 .6\n')))
[[[ 0.1 0.2]
[ 0.3 0.4]
[ 0.5 0.6]]]
"""
with open(fname) as f:
records = []
for line in f:
row = line.split(sep)
record = [list(map(float, col.split())) for col in row]
records.append(record)
return numpy.array(records)
if __name__ == '__main__': # pretty print of NRML files
import sys
import shutil
from openquake.hazardlib import nrml
nrmlfiles = sys.argv[1:]
for fname in nrmlfiles:
node = nrml.read(fname)
shutil.copy(fname, fname + '.bak')
with open(fname, 'w') as out:
nrml.write(list(node), out)
def test_nrml(self):
# can read and write a NRML file converted into a Node object
xmlfile = io.BytesIO(b"""\
<?xml version='1.0' encoding='utf-8'?>
<nrml xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml">
<exposureModel
id="my_exposure_model_for_population"
category="population"
taxonomySource="fake population datasource">
<description>
Sample population
</description>
<assets>
<asset id="asset_01" number="7" taxonomy="IT-PV">
<location lon="9.15000" lat="45.16667" />
</asset>
<asset id="asset_02" number="7" taxonomy="IT-CE">
<location lon="9.15333" lat="45.12200" />
</asset>
</assets>
</exposureModel>
</nrml>
""")
root = read(xmlfile)
tag, version = get_tag_version(root[0])
self.assertEqual(tag, 'exposureModel')
self.assertEqual(version, 'nrml/0.4')
outfile = io.BytesIO()
node_to_xml(root, outfile, {})
expected = b"""\
<?xml version="1.0" encoding="utf-8"?>
<nrml
xmlns="http://openquake.org/xmlns/nrml/0.4"
xmlns:gml="http://www.opengis.net/gml"
>
<exposureModel
category="population"
id="my_exposure_model_for_population"
taxonomySource="fake population datasource"
>
<description>
Sample population
</description>
<assets>
<asset
id="asset_01"
number="7.000000000E+00"
taxonomy="IT-PV"
>
<location lat="4.516667000E+01" lon="9.150000000E+00"/>
</asset>
<asset
id="asset_02"
number="7.000000000E+00"
taxonomy="IT-CE"
>
<location lat="4.512200000E+01" lon="9.153330000E+00"/>
</asset>
</assets>
</exposureModel>
</nrml>
"""
self.assertEqual(outfile.getvalue(), expected)
def expo2csv(job_ini):
"""
Convert an exposure in XML format into CSV format
"""
oq = readinput.get_oqparam(job_ini)
exposure = readinput.get_exposure(oq)
rows = []
header = ['id', 'lon', 'lat', 'number']
area = exposure.area['type'] != '?'
if area:
header.append('area')
for costname in exposure.cost_types['name']:
if costname != 'occupants':
header.append(costname)
if exposure.deductible_is_absolute is not None:
header.append(costname + '-deductible')
if exposure.insurance_limit_is_absolute is not None:
header.append(costname + '-insured_limit')
if exposure.retrofitted:
header.append('retrofitted')
header.extend(exposure.occupancy_periods)
header.extend(exposure.tagcol.tagnames)
for asset, asset_ref in zip(exposure.assets, exposure.asset_refs):
row = [asset_ref.decode('utf8'), asset.location[0], asset.location[1],
asset.number]
if area:
row.append(asset.area)
for costname in exposure.cost_types['name']:
if costname != 'occupants':
row.append(asset.values[costname])
if exposure.deductible_is_absolute is not None:
row.append(asset.deductibles[costname])
if exposure.insurance_limit_is_absolute is not None:
row.append(asset.insurance_limits[costname])
if exposure.retrofitted:
row.append(asset._retrofitted)
for time_event in exposure.occupancy_periods:
row.append(asset.values['occupants_' + time_event])
for tagname, tagidx in zip(exposure.tagcol.tagnames, asset.tagidxs):
tags = getattr(exposure.tagcol, tagname)
row.append(tags[tagidx])
rows.append(row)
with performance.Monitor('expo2csv') as mon:
# save exposure data as csv
csvname = oq.inputs['exposure'].replace('.xml', '.csv')
print('Saving %s' % csvname)
with codecs.open(csvname, 'wb', encoding='utf8') as f:
writer = csv.writer(f)
writer.writerow(header)
for row in rows:
writer.writerow(row)
# save exposure header as xml
head = nrml.read(oq.inputs['exposure'], stop='assets')
xmlname = oq.inputs['exposure'].replace('.xml', '-header.xml')
print('Saving %s' % xmlname)
head[0].assets.text = os.path.basename(csvname)
with open(xmlname, 'wb') as f:
nrml.write(head, f)
print(mon)
def get_source_models(oqparam, gsim_lt, source_model_lt, in_memory=True):
"""
Build all the source models generated by the logic tree.
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param gsim_lt:
a :class:`openquake.commonlib.logictree.GsimLogicTree` instance
:param source_model_lt:
a :class:`openquake.commonlib.logictree.SourceModelLogicTree` instance
:param in_memory:
if True, keep in memory the sources, else just collect the TRTs
:returns:
an iterator over :class:`openquake.commonlib.logictree.LtSourceModel`
tuples
"""
converter = sourceconverter.SourceConverter(
oqparam.investigation_time, oqparam.rupture_mesh_spacing,
oqparam.complex_fault_mesh_spacing, oqparam.width_of_mfd_bin,
oqparam.area_source_discretization)
psr = nrml.SourceModelParser(converter)
# consider only the effective realizations
smlt_dir = os.path.dirname(source_model_lt.filename)
for sm in source_model_lt.gen_source_models(gsim_lt):
src_groups = []
for name in sm.names.split():
fname = os.path.abspath(os.path.join(smlt_dir, name))
if in_memory:
apply_unc = source_model_lt.make_apply_uncertainties(sm.path)
logging.info('Reading %s', fname)
src_groups.extend(psr.parse_src_groups(fname, apply_unc))
else: # just collect the TRT models
smodel = nrml.read(fname).sourceModel
if smodel[0].tag.endswith('sourceGroup'): # NRML 0.5 format
for sg_node in smodel:
sg = sourceconverter.SourceGroup(
sg_node['tectonicRegion'])
sg.sources = sg_node.nodes
src_groups.append(sg)
else: # NRML 0.4 format: smodel is a list of source nodes
src_groups.extend(
sourceconverter.SourceGroup.collect(smodel))
num_sources = sum(len(sg.sources) for sg in src_groups)
sm.src_groups = src_groups
trts = [mod.trt for mod in src_groups]
source_model_lt.tectonic_region_types.update(trts)
logging.info(
'Processed source model %d with %d potential gsim path(s) and %d '
'sources', sm.ordinal + 1, sm.num_gsim_paths, num_sources)
gsim_file = oqparam.inputs.get('gsim_logic_tree')
if gsim_file: # check TRTs
for src_group in src_groups:
if src_group.trt not in gsim_lt.values:
raise ValueError(
"Found in %r a tectonic region type %r inconsistent "
"with the ones in %r" % (sm, src_group.trt, gsim_file))
yield sm
# check investigation_time
psr.check_nonparametric_sources(oqparam.investigation_time)
# log if some source file is being used more than once
dupl = 0
for fname, hits in psr.fname_hits.items():
if hits > 1:
logging.info('%s has been considered %d times', fname, hits)
if not psr.changed_sources:
dupl += hits
if dupl and not oqparam.optimize_same_id_sources:
logging.warn('You are doing redundant calculations: please make sure '
'that different sources have different IDs and set '
'optimize_same_id_sources=true in your .ini file')
def import_exposure_file(nrml_file):
"""
Import exposure from a NRML file
"""
return import_exposure_model(read(nrml_file).exposureModel, nrml_file)
def main(what, report=False):
"""
Give information about the passed keyword or filename
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
if what == 'calculators':
for calc in sorted(base.calculators):
print(calc)
elif what == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif what == 'portable_gsims':
for gs in gsim.get_portable_gsims():
print(gs)
elif what == 'imts':
for im in vars(imt).values():
if inspect.isfunction(im) and is_upper(im):
print(im.__name__)
elif what == 'views':
for name in sorted(view):
print(name)
elif what == 'exports':
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
items = [(DISPLAY_NAME.get(exporter, '?'), exporter, formats)
for exporter, formats in dic.items()]
n = 0
for dispname, exporter, formats in sorted(items):
print(dispname, '"%s"' % exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
elif what == 'extracts':
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
elif hasattr(func, 'func'): # for partial objects
fm = FunctionMaker(func.func)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
elif what == 'parameters':
docs = OqParam.docs()
names = set()
for val in vars(OqParam).values():
if hasattr(val, 'name'):
names.add(val.name)
params = sorted(names)
for param in params:
print(param)
print(docs[param])
elif what == 'mfds':
for cls in gen_subclasses(BaseMFD):
print(cls.__name__)
elif what == 'venv':
print(sys.prefix)
elif what == 'sources':
for cls in gen_subclasses(BaseSeismicSource):
print(cls.__name__)
elif what == 'consequences':
known = scientific.KNOWN_CONSEQUENCES
print('The following %d consequences are implemented:' % len(known))
for cons in known:
print(cons)
elif os.path.isdir(what) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(what)
print(mon)
elif what.endswith('.xml'):
node = nrml.read(what)
if node[0].tag.endswith('sourceModel'):
print(source_model_info([node]))
elif node[0].tag.endswith('logicTree'):
bset = node[0][0]
if bset.tag.endswith("logicTreeBranchingLevel"):
bset = bset[0]
if bset.attrib['uncertaintyType'] == 'sourceModel':
sm_nodes = []
for smpath in logictree.collect_info(what).smpaths:
sm_nodes.append(nrml.read(smpath))
print(source_model_info(sm_nodes))
elif bset.attrib['uncertaintyType'] == 'gmpeModel':
print(logictree.GsimLogicTree(what))
else:
print(node.to_str())
elif what.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(what))
else:
print(readinput.get_oqparam(what).json())
if mon.duration > 1:
print(mon)
elif what:
print("No info for '%s'" % what)
def info(calculators, gsims, views, exports, extracts, parameters,
report, input_file=''):
"""
Give information. You can pass the name of an available calculator,
a job.ini file, or a zip archive with the input files.
"""
if calculators:
for calc in sorted(base.calculators):
print(calc)
if gsims:
for gs in gsim.get_available_gsims():
print(gs)
if views:
for name in sorted(view):
print(name)
if exports:
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
n = 0
for exporter, formats in dic.items():
print(exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
if extracts:
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
if parameters:
params = []
for val in vars(OqParam).values():
if hasattr(val, 'name'):
params.append(val)
params.sort(key=lambda x: x.name)
for param in params:
print(param.name)
if os.path.isdir(input_file) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(input_file)
print(mon)
elif input_file.endswith('.xml'):
node = nrml.read(input_file)
if node[0].tag.endswith('sourceModel'):
if node['xmlns'].endswith('nrml/0.4'):
raise InvalidFile(
'%s is in NRML 0.4 format, please run the following '
'command:\noq upgrade_nrml %s' % (
input_file, os.path.dirname(input_file) or '.'))
print(source_model_info([node[0]]))
elif node[0].tag.endswith('logicTree'):
nodes = [nrml.read(sm_path)[0]
for sm_path in logictree.collect_info(input_file).smpaths]
print(source_model_info(nodes))
else:
print(node.to_str())
elif input_file.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(input_file))
else:
print_csm_info(input_file)
if mon.duration > 1:
print(mon)
elif input_file:
print("No info for '%s'" % input_file)
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)
if not exposure.tag.endswith('exposureModel'):
raise InvalidFile('%s: expected exposureModel, got %s' %
(fname, exposure.tag))
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
