def get_gmfs_from_txt(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
the full path of the CSV file
:returns:
a composite array of shape (N, R) read from a CSV file with format
`etag indices [gmv1 ... gmvN] * num_imts`
"""
with open(fname) as csvfile:
firstline = next(csvfile)
try:
coords = valid.coordinates(firstline)
except:
raise InvalidFile(
'The first line of %s is expected to contain comma separated'
'ordered coordinates, got %s instead' % (fname, firstline))
sitecol = sitecol_from_coords(oqparam, coords)
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
imts = list(oqparam.imtls)
imt_dt = numpy.dtype([(bytes(imt), F32) for imt in imts])
num_gmfs = oqparam.number_of_ground_motion_fields
gmf_by_imt = numpy.zeros((num_gmfs, len(sitecol)), imt_dt)
etags = []
for lineno, line in enumerate(csvfile, 2):
row = line.split(',')
try:
indices = list(map(valid.positiveint, row[1].split()))
except:
raise InvalidFile(
'The second column in %s is expected to contain integer '
'indices, got %s' % (fname, row[1]))
r_sites = (
sitecol if not indices else
site.FilteredSiteCollection(indices, sitecol))
for i in range(len(imts)):
try:
array = numpy.array(valid.positivefloats(row[i + 2]))
# NB: i + 2 because the first 2 fields are etag and indices
except:
raise InvalidFile(
'The column #%d in %s is expected to contain positive '
'floats, got %s instead' % (i + 3, fname, row[i + 2]))
gmf_by_imt[imts[i]][lineno - 2] = r_sites.expand(array, 0)
etags.append(row[0])
if lineno < num_gmfs + 1:
raise InvalidFile('%s contains %d rows, expected %d' % (
fname, lineno, num_gmfs + 1))
if etags != sorted(etags):
raise InvalidFile('The etags in %s are not ordered: %s' % (fname, etags))
return sitecol, numpy.array(etags, '|S100'), gmf_by_imt.T
def get_gmfs_from_txt(oqparam, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param fname:
the full path of the CSV file
:returns:
a composite array of shape (N, R) read from a CSV file with format
`etag indices [gmv1 ... gmvN] * num_imts`
"""
with open(fname) as csvfile:
firstline = next(csvfile)
try:
coords = valid.coordinates(firstline)
except:
raise InvalidFile(
'The first line of %s is expected to contain comma separated'
'ordered coordinates, got %s instead' % (fname, firstline))
sitecol = sitecol_from_coords(oqparam, coords)
if not oqparam.imtls:
oqparam.set_risk_imtls(get_risk_models(oqparam))
imts = list(oqparam.imtls)
imt_dt = numpy.dtype([(bytes(imt), F32) for imt in imts])
num_gmfs = oqparam.number_of_ground_motion_fields
gmf_by_imt = numpy.zeros((num_gmfs, len(sitecol)), imt_dt)
etags = []
for lineno, line in enumerate(csvfile, 2):
row = line.split(',')
try:
indices = list(map(valid.positiveint, row[1].split()))
except:
raise InvalidFile(
'The second column in %s is expected to contain integer '
'indices, got %s' % (fname, row[1]))
r_sites = (sitecol if not indices else site.FilteredSiteCollection(
indices, sitecol))
for i in range(len(imts)):
try:
array = numpy.array(valid.positivefloats(row[i + 2]))
# NB: i + 2 because the first 2 fields are etag and indices
except:
raise InvalidFile(
'The column #%d in %s is expected to contain positive '
'floats, got %s instead' % (i + 3, fname, row[i + 2]))
gmf_by_imt[imts[i]][lineno - 2] = r_sites.expand(array, 0)
etags.append(row[0])
if lineno < num_gmfs + 1:
raise InvalidFile('%s contains %d rows, expected %d' %
(fname, lineno, num_gmfs + 1))
if etags != sorted(etags):
raise InvalidFile('The etags in %s are not ordered: %s' %
(fname, etags))
return sitecol, numpy.array(etags, '|S100'), gmf_by_imt.T
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 validate_uncertainty_value(self, node, branchset, value):
"""
See superclass' method for description and signature specification.
Checks that the following conditions are met:
* For uncertainty of type "sourceModel": referenced file must exist
and be readable. This is checked in :meth:`collect_source_model_data`
along with saving the source model information.
* For uncertainty of type "abGRAbsolute": value should be two float
values.
* For both absolute uncertainties: the source (only one) must
be referenced in branchset's filter "applyToSources".
* For all other cases: value should be a single float value.
"""
_float_re = re.compile(r'^(\+|\-)?(\d+|\d*\.\d+)$')
if branchset.uncertainty_type == 'sourceModel':
self.collect_source_model_data(value)
elif branchset.uncertainty_type == 'abGRAbsolute':
ab = value.split()
if len(ab) == 2:
a, b = ab
if _float_re.match(a) and _float_re.match(b):
return
raise ValidationError(
node, self.filename,
'expected a pair of floats separated by space')
elif branchset.uncertainty_type == 'incrementalMFDAbsolute':
mbr = value.split(',')
if len(mbr) == 3:
min_mag, bin_width, rates = mbr
try:
rates = valid.positivefloats(rates)
except ValueError:
rates = []
if _float_re.match(min_mag) and _float_re.match(bin_width) and\
len(rates):
return
raise ValidationError(
node, self.filename,
'expected mfd in the form min_mag,bin_width,rate_1 rate_2 ...')
else:
if not _float_re.match(value):
raise ValidationError(node, self.filename,
'expected single float value')
def parse_uncertainty_value(self, node, branchset, value):
"""
See superclass' method for description and signature specification.
Doesn't change source model file name, converts other values to either
pair of floats or a single float depending on uncertainty type.
"""
if branchset.uncertainty_type == "sourceModel":
return value
elif branchset.uncertainty_type == "abGRAbsolute":
[a, b] = value.strip().split()
return float(a), float(b)
elif branchset.uncertainty_type == "incrementalMFDAbsolute":
min_mag, bin_width, rates = value.strip().split(",")
return float(min_mag), float(bin_width), valid.positivefloats(rates)
else:
return float(value)
def parse_uncertainty_value(self, node, branchset, value):
"""
See superclass' method for description and signature specification.
Doesn't change source model file name, converts other values to either
pair of floats or a single float depending on uncertainty type.
"""
if branchset.uncertainty_type == 'sourceModel':
return value
elif branchset.uncertainty_type == 'abGRAbsolute':
[a, b] = value.strip().split()
return float(a), float(b)
elif branchset.uncertainty_type == 'incrementalMFDAbsolute':
min_mag, bin_width, rates = value.strip().split(',')
return float(min_mag), float(bin_width),\
valid.positivefloats(rates)
else:
return float(value)
def get_gmfs(oqparam, sitecol):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param sitecol:
a SiteCollection instance with sites consistent with the CSV file
:returns:
a composite array of shape (N, R) read from a CSV file with format
`tag indices [gmv1 ... gmvN] * num_imts`
"""
imts = oqparam.imtls.keys()
imt_dt = numpy.dtype([(imt, float) for imt in imts])
num_gmfs = oqparam.number_of_ground_motion_fields
gmf_by_imt = numpy.zeros((num_gmfs, len(sitecol)), imt_dt)
tags = []
fname = oqparam.inputs['gmfs']
with open(fname) as csvfile:
for lineno, line in enumerate(csvfile, 1):
row = line.split(',')
try:
indices = map(valid.positiveint, row[1].split())
except:
raise InvalidFile(
'The second column in %s is expected to contain integer '
'indices, got %s instead' % (fname, row[1]))
r_sites = (
sitecol if not indices else
site.FilteredSiteCollection(indices, sitecol))
for i in range(len(imts)):
try:
array = numpy.array(valid.positivefloats(row[i + 2]))
# NB: i + 2 because the first 2 fields are tag and indices
except:
raise InvalidFile(
'The column #%d in %s is expected to contain positive '
'floats, got %s instead' % (i + 3, fname, row[i + 2]))
gmf_by_imt[imts[i]][lineno - 1, :] = r_sites.expand(array, 0)
tags.append(row[0])
if lineno < num_gmfs:
raise InvalidFile('%s contains %d rows, expected %d' % (
fname, lineno, num_gmfs))
if tags != sorted(tags):
raise InvalidFile('The tags in %s are not ordered: %s' % (fname, tags))
return gmf_by_imt.T
def get_gmfs_from_csv(oqparam, sitecol, fname):
"""
:param oqparam:
an :class:`openquake.commonlib.oqvalidation.OqParam` instance
:param sitecol:
a SiteCollection instance with sites consistent with the CSV file
:param fname:
the full path of the CSV file
:returns:
a composite array of shape (N, R) read from a CSV file with format
`tag indices [gmv1 ... gmvN] * num_imts`
"""
imts = list(oqparam.imtls)
imt_dt = numpy.dtype([(imt, float) for imt in imts])
num_gmfs = oqparam.number_of_ground_motion_fields
gmf_by_imt = numpy.zeros((num_gmfs, len(sitecol)), imt_dt)
tags = []
with open(fname) as csvfile:
for lineno, line in enumerate(csvfile, 1):
row = line.split(',')
try:
indices = list(map(valid.positiveint, row[1].split()))
except:
raise InvalidFile(
'The second column in %s is expected to contain integer '
'indices, got %s instead' % (fname, row[1]))
r_sites = (sitecol if not indices else site.FilteredSiteCollection(
indices, sitecol))
for i in range(len(imts)):
try:
array = numpy.array(valid.positivefloats(row[i + 2]))
# NB: i + 2 because the first 2 fields are tag and indices
except:
raise InvalidFile(
'The column #%d in %s is expected to contain positive '
'floats, got %s instead' % (i + 3, fname, row[i + 2]))
gmf_by_imt[imts[i]][lineno - 1] = r_sites.expand(array, 0)
tags.append(row[0])
if lineno < num_gmfs:
raise InvalidFile('%s contains %d rows, expected %d' %
(fname, lineno, num_gmfs))
if tags != sorted(tags):
raise InvalidFile('The tags in %s are not ordered: %s' % (fname, tags))
return sitecol, numpy.array(tags, '|S100'), gmf_by_imt.T
def validate_uncertainty_value(self, node, branchset, value):
"""
See superclass' method for description and signature specification.
Checks that the following conditions are met:
* For uncertainty of type "sourceModel": referenced file must exist
and be readable. This is checked in :meth:`collect_source_model_data`
along with saving the source model information.
* For uncertainty of type "abGRAbsolute": value should be two float
values.
* For both absolute uncertainties: the source (only one) must
be referenced in branchset's filter "applyToSources".
* For all other cases: value should be a single float value.
"""
_float_re = re.compile(r"^(\+|\-)?(\d+|\d*\.\d+)$")
if branchset.uncertainty_type == "sourceModel":
self.collect_source_model_data(value)
elif branchset.uncertainty_type == "abGRAbsolute":
ab = value.split()
if len(ab) == 2:
a, b = ab
if _float_re.match(a) and _float_re.match(b):
return
raise ValidationError(node, self.filename, "expected a pair of floats separated by space")
elif branchset.uncertainty_type == "incrementalMFDAbsolute":
mbr = value.split(",")
if len(mbr) == 3:
min_mag, bin_width, rates = mbr
try:
rates = valid.positivefloats(rates)
except ValueError:
rates = []
if _float_re.match(min_mag) and _float_re.match(bin_width) and len(rates):
return
raise ValidationError(node, self.filename, "expected mfd in the form min_mag,bin_width,rate_1 rate_2 ...")
else:
if not _float_re.match(value):
raise ValidationError(node, self.filename, "expected single float value")
class VulnerabilityNode(LiteralNode):
"""
Literal Node class used to validate discrete vulnerability functions
"""
validators = dict(
vulnerabilitySetID=str, # any ASCII string is fine
vulnerabilityFunctionID=str, # any ASCII string is fine
assetCategory=str, # any ASCII string is fine
# the assetCategory here has nothing to do with the category
# in the exposure model and it is not used by the engine
lossCategory=valid.utf8, # a description field
IML=valid.IML,
imls=lambda text, imt: valid.positivefloats(text),
lr=valid.probability,
lossRatio=valid.positivefloats,
coefficientsVariation=valid.positivefloats,
probabilisticDistribution=valid.Choice('LN', 'BT'),
dist=valid.Choice('LN', 'BT', 'PM'),
meanLRs=valid.positivefloats,
covLRs=valid.positivefloats,
)
class FragilityNode(LiteralNode):
"""
Literal Node class used to validate fragility functions and consequence
functions.
"""
validators = dict(
id=valid.utf8, # no constraints on the taxonomy
format=valid.ChoiceCI('discrete', 'continuous'),
assetCategory=valid.utf8,
dist=valid.Choice('LN'),
mean=valid.positivefloat,
stddev=valid.positivefloat,
lossCategory=valid.name,
poes=lambda text, **kw: valid.positivefloats(text),
IML=valid.IML,
minIML=valid.positivefloat,
maxIML=valid.positivefloat,
limitStates=valid.namelist,
description=valid.utf8_not_empty,
type=valid.ChoiceCI('lognormal'),
poEs=valid.probabilities,
noDamageLimit=valid.NoneOr(valid.positivefloat),
)
