# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2018 GEM Foundation
#
# OpenQuake is free software: you can redistribute it and/or modify it
# under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# OpenQuake is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with OpenQuake. If not, see <http://www.gnu.org/licenses/>.
import os
import logging
import functools
import multiprocessing
import numpy
from openquake.baselib import datastore
from openquake.baselib.general import DictArray
from openquake.hazardlib.imt import from_string
from openquake.hazardlib import correlation, stats
from openquake.hazardlib import valid, InvalidFile
from openquake.commonlib import logictree
from openquake.risklib.riskmodels import get_risk_files
GROUND_MOTION_CORRELATION_MODELS = ['JB2009']
TWO16 = 2 ** 16 # 65536
F32 = numpy.float32
F64 = numpy.float64
[docs]class OqParam(valid.ParamSet):
siteparam = dict(
vs30measured='reference_vs30_type',
vs30='reference_vs30_value',
z1pt0='reference_depth_to_1pt0km_per_sec',
z2pt5='reference_depth_to_2pt5km_per_sec',
backarc='reference_backarc')
asset_loss_table = valid.Param(valid.boolean, False)
area_source_discretization = valid.Param(
valid.NoneOr(valid.positivefloat), None)
asset_correlation = valid.Param(valid.NoneOr(valid.FloatRange(0, 1)), 0)
asset_life_expectancy = valid.Param(valid.positivefloat)
avg_losses = valid.Param(valid.boolean, True)
base_path = valid.Param(valid.utf8, '.')
calculation_mode = valid.Param(valid.Choice(), '') # -> get_oqparam
coordinate_bin_width = valid.Param(valid.positivefloat)
compare_with_classical = valid.Param(valid.boolean, False)
concurrent_tasks = valid.Param(
valid.positiveint, multiprocessing.cpu_count() * 3) # by M. Simionato
conditional_loss_poes = valid.Param(valid.probabilities, [])
continuous_fragility_discretization = valid.Param(valid.positiveint, 20)
cross_correlation = valid.Param(
valid.Choice('cross', 'no correlation', 'full correlation'), 'cross')
description = valid.Param(valid.utf8_not_empty)
disagg_by_src = valid.Param(valid.boolean, False)
disagg_outputs = valid.Param(valid.disagg_outputs, None)
distance_bin_width = valid.Param(valid.positivefloat)
mag_bin_width = valid.Param(valid.positivefloat)
export_dir = valid.Param(valid.utf8, '.')
export_multi_curves = valid.Param(valid.boolean, False)
exports = valid.Param(valid.export_formats, ())
prefilter_sources = valid.Param(valid.Choice('rtree', 'numpy', 'no'),
'rtree')
filter_distance = valid.Param(valid.Choice('rjb', 'rrup'), None)
ground_motion_correlation_model = valid.Param(
valid.NoneOr(valid.Choice(*GROUND_MOTION_CORRELATION_MODELS)), None)
ground_motion_correlation_params = valid.Param(valid.dictionary)
ground_motion_fields = valid.Param(valid.boolean, False)
gsim = valid.Param(valid.gsim, valid.FromFile())
hazard_calculation_id = valid.Param(valid.NoneOr(valid.positiveint), None)
hazard_curves_from_gmfs = valid.Param(valid.boolean, False)
hazard_output_id = valid.Param(valid.NoneOr(valid.positiveint))
hazard_maps = valid.Param(valid.boolean, False)
hypocenter = valid.Param(valid.point3d)
ignore_missing_costs = valid.Param(valid.namelist, [])
ignore_covs = valid.Param(valid.boolean, False)
iml_disagg = valid.Param(valid.floatdict, {}) # IMT -> IML
inputs = valid.Param(dict, {})
insured_losses = valid.Param(valid.boolean, False)
intensity_measure_types = valid.Param(valid.intensity_measure_types, None)
intensity_measure_types_and_levels = valid.Param(
valid.intensity_measure_types_and_levels, None)
interest_rate = valid.Param(valid.positivefloat)
investigation_time = valid.Param(valid.positivefloat, None)
lrem_steps_per_interval = valid.Param(valid.positiveint, 0)
steps_per_interval = valid.Param(valid.positiveint, 1)
master_seed = valid.Param(valid.positiveint, 0)
maximum_distance = valid.Param(valid.maximum_distance) # km
asset_hazard_distance = valid.Param(valid.positivefloat, 5) # km
max_hazard_curves = valid.Param(valid.boolean, False)
mean_hazard_curves = valid.Param(valid.boolean, True)
max_loss_curves = valid.Param(valid.boolean, False)
mean_loss_curves = valid.Param(valid.boolean, True)
minimum_intensity = valid.Param(valid.floatdict, {}) # IMT -> minIML
number_of_ground_motion_fields = valid.Param(valid.positiveint)
number_of_logic_tree_samples = valid.Param(valid.positiveint, 0)
num_epsilon_bins = valid.Param(valid.positiveint)
poes = valid.Param(valid.probabilities, [])
poes_disagg = valid.Param(valid.probabilities, [])
quantile_hazard_curves = valid.Param(valid.probabilities, [])
quantile_loss_curves = valid.Param(valid.probabilities, [])
random_seed = valid.Param(valid.positiveint, 42)
reference_depth_to_1pt0km_per_sec = valid.Param(
valid.positivefloat, numpy.nan)
reference_depth_to_2pt5km_per_sec = valid.Param(
valid.positivefloat, numpy.nan)
reference_vs30_type = valid.Param(
valid.Choice('measured', 'inferred'), 'measured')
reference_vs30_value = valid.Param(
valid.positivefloat, numpy.nan)
reference_backarc = valid.Param(valid.boolean, False)
region = valid.Param(valid.wkt_polygon, None)
region_grid_spacing = valid.Param(valid.positivefloat, None)
optimize_same_id_sources = valid.Param(valid.boolean, False)
risk_imtls = valid.Param(valid.intensity_measure_types_and_levels, {})
risk_investigation_time = valid.Param(valid.positivefloat, None)
rupture_mesh_spacing = valid.Param(valid.positivefloat)
ruptures_per_block = valid.Param(valid.positiveint, 1000)
complex_fault_mesh_spacing = valid.Param(
valid.NoneOr(valid.positivefloat), None)
return_periods = valid.Param(valid.positiveints, None)
save_ruptures = valid.Param(valid.boolean, True)
ses_per_logic_tree_path = valid.Param(valid.positiveint, 1)
ses_seed = valid.Param(valid.positiveint, 42)
max_site_model_distance = valid.Param(valid.positivefloat, 5) # by Graeme
shakemap_id = valid.Param(valid.nice_string, None)
site_effects = valid.Param(valid.boolean, True) # shakemap amplification
sites = valid.Param(valid.NoneOr(valid.coordinates), None)
sites_disagg = valid.Param(valid.NoneOr(valid.coordinates), [])
sites_slice = valid.Param(valid.simple_slice, (None, None))
sm_lt_path = valid.Param(valid.logic_tree_path, None)
specific_assets = valid.Param(valid.namelist, [])
taxonomies_from_model = valid.Param(valid.boolean, False)
time_event = valid.Param(str, None)
truncation_level = valid.Param(valid.NoneOr(valid.positivefloat), None)
uniform_hazard_spectra = valid.Param(valid.boolean, False)
width_of_mfd_bin = valid.Param(valid.positivefloat, None)
@property
def risk_files(self):
try:
return self._risk_files
except AttributeError:
self._file_type, self._risk_files = get_risk_files(self.inputs)
return self._risk_files
@property
def file_type(self):
try:
return self._file_type
except AttributeError:
self._file_type, self._risk_files = get_risk_files(self.inputs)
return self._file_type
def __init__(self, **names_vals):
super().__init__(**names_vals)
job_ini = self.inputs['job_ini']
if 'calculation_mode' not in names_vals:
raise InvalidFile('Missing calculation_mode in %s' % job_ini)
if 'region_constraint' in names_vals:
if 'region' in names_vals:
raise InvalidFile('You cannot have both region and '
'region_constraint in %s' % job_ini)
logging.warn('region_constraint is obsolete, use region instead')
self.region = valid.wkt_polygon(
names_vals.pop('region_constraint'))
self.risk_investigation_time = (
self.risk_investigation_time or self.investigation_time)
if ('intensity_measure_types_and_levels' in names_vals and
'intensity_measure_types' in names_vals):
logging.warn('Ignoring intensity_measure_types since '
'intensity_measure_types_and_levels is set')
if 'iml_disagg' in names_vals:
self.hazard_imtls = self.iml_disagg
if 'intensity_measure_types_and_levels' in names_vals:
raise InvalidFile(
'Please remove the intensity_measure_types_and_levels '
'from %s: they will be inferred from the iml_disagg '
'dictionary' % job_ini)
elif 'intensity_measure_types_and_levels' in names_vals:
self.hazard_imtls = self.intensity_measure_types_and_levels
delattr(self, 'intensity_measure_types_and_levels')
elif 'intensity_measure_types' in names_vals:
self.hazard_imtls = dict.fromkeys(self.intensity_measure_types)
delattr(self, 'intensity_measure_types')
self._file_type, self._risk_files = get_risk_files(self.inputs)
self.check_source_model()
if self.hazard_precomputed() and self.job_type == 'risk':
self.check_missing('site_model', 'warn')
self.check_missing('gsim_logic_tree', 'warn')
self.check_missing('source_model_logic_tree', 'warn')
# check the gsim_logic_tree
if self.inputs.get('gsim_logic_tree'):
if not isinstance(self.gsim, valid.FromFile):
raise InvalidFile('%s: if `gsim_logic_tree_file` is set, there'
' must be no `gsim` key' % job_ini)
path = os.path.join(
self.base_path, self.inputs['gsim_logic_tree'])
gsim_lt = logictree.GsimLogicTree(path, ['*'])
# check the number of branchsets
branchsets = len(gsim_lt._ltnode)
if 'scenario' in self.calculation_mode and branchsets > 1:
raise InvalidFile(
'%s: %s for a scenario calculation must contain a single '
'branchset, found %d!' % (job_ini, path, branchsets))
# check the IMTs vs the GSIMs
self._gsims_by_trt = gsim_lt.values
for gsims in self._gsims_by_trt.values():
self.check_gsims(gsims)
elif self.gsim is not None:
self.check_gsims([self.gsim])
# checks for hazard outputs
if not self.hazard_stats():
if self.uniform_hazard_spectra:
raise InvalidFile(
'%(job_ini)s: uniform_hazard_spectra=true is inconsistent '
'with mean_hazard_curves=false' % self.inputs)
elif self.hazard_maps:
raise InvalidFile(
'%(job_ini)s: hazard_maps=true is inconsistent '
'with mean_hazard_curves=false' % self.inputs)
# checks for disaggregation
if self.calculation_mode == 'disaggregation':
if not self.poes_disagg and not self.iml_disagg:
raise InvalidFile('poes_disagg or iml_disagg must be set '
'in %(job_ini)s' % self.inputs)
elif self.poes_disagg and self.iml_disagg:
raise InvalidFile(
'%s: iml_disagg and poes_disagg cannot be set '
'at the same time' % job_ini)
for k in ('mag_bin_width', 'distance_bin_width',
'coordinate_bin_width', 'num_epsilon_bins'):
if k not in vars(self):
raise InvalidFile('%s must be set in %s' % (k, job_ini))
# checks for classical_damage
if self.calculation_mode == 'classical_damage':
if self.conditional_loss_poes:
raise InvalidFile(
'%s: conditional_loss_poes are not defined '
'for classical_damage calculations' % job_ini)
# checks for event_based_risk
if (self.calculation_mode == 'event_based_risk' and
self.asset_correlation not in (0, 1)):
raise ValueError('asset_correlation != {0, 1} is no longer'
' supported')
elif (self.calculation_mode == 'event_based_risk' and
self.conditional_loss_poes and not self.asset_loss_table):
raise InvalidFile(
'%s: asset_loss_table is not set, probably you want to remove'
' conditional_loss_poes' % job_ini)
# check for GMFs from file
if (self.inputs.get('gmfs', '').endswith('.csv') and not self.sites and
'sites' not in self.inputs):
raise InvalidFile('%s: You forgot sites|sites_csv' % job_ini)
elif (self.inputs.get('gmfs', '').endswith('.xml') and
'sites' in self.inputs):
raise InvalidFile('%s: You cannot have both sites_csv and '
'gmfs_file' % job_ini)
# checks for ucerf
if 'ucerf' in self.calculation_mode:
if self.ses_per_logic_tree_path >= TWO16:
raise ValueError('ses_per_logic_tree_path too big: %d' %
self.ses_per_logic_tree_path)
if self.number_of_logic_tree_samples >= TWO16:
raise ValueError('number_of_logic_tree_samples too big: %d' %
self.number_of_logic_tree_samples)
[docs] def check_gsims(self, gsims):
"""
:param gsims: a sequence of GSIM instances
"""
imts = set('SA' if imt.startswith('SA') else imt for imt in self.imtls)
for gsim in gsims:
restrict_imts = gsim.DEFINED_FOR_INTENSITY_MEASURE_TYPES
if restrict_imts:
names = set(cls.__name__ for cls in restrict_imts)
invalid_imts = ', '.join(imts - names)
if invalid_imts:
raise ValueError(
'The IMT %s is not accepted by the GSIM %s' %
(invalid_imts, gsim))
if 'site_model' not in self.inputs:
# look at the required sites parameters: they must have
# a valid value; the other parameters can keep a NaN
# value since they are not used by the calculator
for param in gsim.REQUIRES_SITES_PARAMETERS:
if param in ('lons', 'lats'): # no check
continue
param_name = self.siteparam[param]
param_value = getattr(self, param_name)
if (isinstance(param_value, float) and
numpy.isnan(param_value)):
raise ValueError(
'Please set a value for %r, this is required by '
'the GSIM %s' % (param_name, gsim))
@property
def tses(self):
"""
Return the total time as investigation_time * ses_per_logic_tree_path *
(number_of_logic_tree_samples or 1)
"""
return (self.investigation_time * self.ses_per_logic_tree_path *
(self.number_of_logic_tree_samples or 1))
@property
def ses_ratio(self):
"""
The ratio
risk_investigation_time / investigation_time / ses_per_logic_tree_path
"""
if self.investigation_time is None:
raise ValueError('Missing investigation_time in the .ini file')
return (self.risk_investigation_time or self.investigation_time) / (
self.investigation_time * self.ses_per_logic_tree_path)
@property
def imtls(self):
"""
Returns an OrderedDict with the risk intensity measure types and
levels, if given, or the hazard ones.
"""
imtls = getattr(self, 'hazard_imtls', None) or self.risk_imtls
return DictArray(imtls)
@property
def all_cost_types(self):
"""
Return the cost types of the computation (including `occupants`
if it is there) in order.
"""
costtypes = sorted(self.risk_files)
if not costtypes and self.hazard_calculation_id:
with datastore.read(self.hazard_calculation_id) as ds:
parent = ds['oqparam']
self._file_type, self._risk_files = get_risk_files(parent.inputs)
costtypes = sorted(self.risk_files)
return costtypes
[docs] def set_risk_imtls(self, risk_models):
"""
:param risk_models:
a dictionary taxonomy -> loss_type -> risk_function
Set the attribute risk_imtls.
"""
# NB: different loss types may have different IMLs for the same IMT
# in that case we merge the IMLs
imtls = {}
for taxonomy, risk_functions in risk_models.items():
for loss_type, rf in risk_functions.items():
imt = rf.imt
from_string(imt) # make sure it is a valid IMT
imls = list(rf.imls)
if imt in imtls and imtls[imt] != imls:
logging.debug(
'Different levels for IMT %s: got %s, expected %s',
imt, imls, imtls[imt])
imtls[imt] = sorted(set(imls + imtls[imt]))
else:
imtls[imt] = imls
self.risk_imtls = imtls
if self.uniform_hazard_spectra:
self.check_uniform_hazard_spectra()
[docs] def imt_dt(self):
"""
:returns: a numpy dtype {imt: float}
"""
return numpy.dtype([(imt, float) for imt in self.imtls])
@property
def lti(self):
"""
Dictionary extended_loss_type -> extended_loss_type index
"""
return {lt: i for i, (lt, dt) in enumerate(self.loss_dt_list())}
[docs] def loss_dt(self, dtype=F32):
"""
Return a composite dtype based on the loss types, including occupants
"""
return numpy.dtype(self.loss_dt_list(dtype))
[docs] def loss_dt_list(self, dtype=F32):
"""
Return a data type list [(loss_name, dtype), ...]
"""
loss_types = self.all_cost_types
dts = [(str(lt), dtype) for lt in loss_types]
if self.insured_losses:
for lt in loss_types:
dts.append((str(lt) + '_ins', dtype))
return dts
[docs] def loss_maps_dt(self, dtype=F32):
"""
Return a composite data type for loss maps
"""
ltypes = self.loss_dt(dtype).names
lst = [('poe-%s' % poe, dtype) for poe in self.conditional_loss_poes]
return numpy.dtype([(lt, lst) for lt in ltypes])
[docs] def no_imls(self):
"""
Return True if there are no intensity measure levels
"""
return all(numpy.isnan(ls).any() for ls in self.imtls.values())
[docs] def get_correl_model(self):
"""
Return a correlation object. See :mod:`openquake.hazardlib.correlation`
for more info.
"""
correl_name = self.ground_motion_correlation_model
if correl_name is None: # no correlation model
return
correl_model_cls = getattr(
correlation, '%sCorrelationModel' % correl_name)
return correl_model_cls(**self.ground_motion_correlation_params)
[docs] def hazard_stats(self):
"""
Return a list of item with the statistical functions defined for the
hazard calculation
"""
names = [] # name of statistical functions
funcs = [] # statistical functions of kind func(values, weights)
if self.mean_hazard_curves:
names.append('mean')
funcs.append(stats.mean_curve)
for q in self.quantile_hazard_curves:
names.append('quantile-%s' % q)
funcs.append(functools.partial(stats.quantile_curve, q))
if self.max_hazard_curves:
names.append('max')
funcs.append(stats.max_curve)
return list(zip(names, funcs))
[docs] def risk_stats(self):
"""
Return a list of items with the statistical functions defined for the
risk calculation
"""
names = [] # name of statistical functions
funcs = [] # statistical functions of kind func(values, weights)
if self.mean_loss_curves:
names.append('mean')
funcs.append(stats.mean_curve)
for q in self.quantile_loss_curves:
names.append('quantile-%s' % q)
funcs.append(functools.partial(stats.quantile_curve, q))
if self.max_loss_curves:
names.append('max')
funcs.append(stats.max_curve)
return list(zip(names, funcs))
@property
def job_type(self):
"""
'hazard' or 'risk'
"""
return 'risk' if ('risk' in self.calculation_mode or
'damage' in self.calculation_mode or
'bcr' in self.calculation_mode) else 'hazard'
[docs] def is_valid_shakemap(self):
"""
hazard_calculation_id must be set if shakemap_id is set
"""
return self.hazard_calculation_id if self.shakemap_id else True
[docs] def is_valid_truncation_level_disaggregation(self):
"""
Truncation level must be set for disaggregation calculations
"""
if self.calculation_mode == 'disaggregation':
return self.truncation_level is not None
else:
return True
[docs] def is_valid_geometry(self):
"""
It is possible to infer the geometry only if exactly
one of sites, sites_csv, hazard_curves_csv, gmfs_csv,
region is set. You did set more than one, or nothing.
"""
has_sites = (self.sites is not None or 'sites' in self.inputs
or 'site_model' in self.inputs)
if ('gmfs' in self.inputs and not has_sites and
not self.inputs['gmfs'].endswith('.xml')):
raise ValueError('Missing sites or sites_csv in the .ini file')
elif ('risk' in self.calculation_mode or
'damage' in self.calculation_mode or
'bcr' in self.calculation_mode):
return True # no check on the sites for risk
flags = dict(
sites=bool(self.sites),
sites_csv=self.inputs.get('sites', 0),
hazard_curves_csv=self.inputs.get('hazard_curves', 0),
gmfs_csv=self.inputs.get('gmfs', 0),
region=bool(self.region and self.region_grid_spacing))
# NB: below we check that all the flags
# are mutually exclusive
return sum(bool(v) for v in flags.values()) == 1 or self.inputs.get(
'exposure') or self.inputs.get('site_model')
[docs] def is_valid_poes(self):
"""
When computing hazard maps and/or uniform hazard spectra,
the poes list must be non-empty.
"""
if self.hazard_maps or self.uniform_hazard_spectra:
return bool(self.poes)
else:
return True
[docs] def is_valid_maximum_distance(self):
"""
Invalid maximum_distance={maximum_distance}: {error}
"""
if (not self.inputs.get('source_model_logic_tree') or not
self.inputs.get('gsim_logic_tree')):
return True # don't apply validation
gsim_lt = self.inputs['gsim_logic_tree']
trts = set(self.maximum_distance)
unknown = ', '.join(trts - set(self._gsims_by_trt) - set(['default']))
if unknown:
self.error = ('setting the maximum_distance for %s which is '
'not in %s' % (unknown, gsim_lt))
return False
for trt, val in self.maximum_distance.items():
if val <= 0:
self.error = '%s=%r < 0' % (trt, val)
return False
elif trt not in self._gsims_by_trt and trt != 'default':
self.error = 'tectonic region %r not in %s' % (trt, gsim_lt)
return False
if 'default' not in trts and trts < set(self._gsims_by_trt):
missing = ', '.join(set(self._gsims_by_trt) - trts)
self.error = 'missing distance for %s and no default' % missing
return False
return True
[docs] def is_valid_intensity_measure_types(self):
"""
If the IMTs and levels are extracted from the risk models,
they must not be set directly. Moreover, if
`intensity_measure_types_and_levels` is set directly,
`intensity_measure_types` must not be set.
"""
if self.ground_motion_correlation_model:
for imt in self.imtls:
if not (imt.startswith('SA') or imt == 'PGA'):
raise ValueError(
'Correlation model %s does not accept IMT=%s' % (
self.ground_motion_correlation_model, imt))
if self.risk_files: # IMTLs extracted from the risk files
return (self.intensity_measure_types is None and
self.intensity_measure_types_and_levels is None)
elif not hasattr(self, 'hazard_imtls') and not hasattr(
self, 'risk_imtls'):
return False
return True
[docs] def is_valid_intensity_measure_levels(self):
"""
In order to compute hazard curves, `intensity_measure_types_and_levels`
must be set or extracted from the risk models.
"""
invalid = self.no_imls() and not self.risk_files and (
self.hazard_curves_from_gmfs or self.calculation_mode in
('classical', 'disaggregation'))
return not invalid
[docs] def is_valid_sites_disagg(self):
"""
The option `sites_disagg` (when given) requires `specific_assets` to
be set.
"""
if self.sites_disagg:
return self.specific_assets or 'specific_assets' in self.inputs
return True # a missing sites_disagg is valid
[docs] def is_valid_specific_assets(self):
"""
Read the special assets from the parameters `specific_assets` or
`specific_assets_csv`, if present. You cannot have both. The
concept is meaninful only for risk calculators.
"""
if self.specific_assets and 'specific_assets' in self.inputs:
return False
else:
return True
[docs] def is_valid_export_dir(self):
"""
The `export_dir` parameter must refer to a directory,
and the user must have the permission to write on it.
"""
if not self.export_dir:
self.export_dir = os.path.expanduser('~') # home directory
logging.warn('export_dir not specified. Using export_dir=%s'
% self.export_dir)
return True
elif not os.path.exists(self.export_dir):
# check that we can write on the parent directory
pdir = os.path.dirname(self.export_dir)
can_write = os.path.exists(pdir) and os.access(pdir, os.W_OK)
if can_write:
os.mkdir(self.export_dir)
return can_write
return os.path.isdir(self.export_dir) and os.access(
self.export_dir, os.W_OK)
[docs] def is_valid_complex_fault_mesh_spacing(self):
"""
The `complex_fault_mesh_spacing` parameter can be None only if
`rupture_mesh_spacing` is set. In that case it is identified with it.
"""
rms = getattr(self, 'rupture_mesh_spacing', None)
if rms and not getattr(self, 'complex_fault_mesh_spacing', None):
self.complex_fault_mesh_spacing = self.rupture_mesh_spacing
return True
[docs] def check_source_model(self):
if ('hazard_curves' in self.inputs or 'gmfs' in self.inputs or
self.calculation_mode.startswith('scenario')):
return
if 'source' not in self.inputs and not self.hazard_calculation_id:
raise ValueError('Missing source_model_logic_tree in %s '
'or missing --hc option' %
self.inputs.get('job_ini', 'job_ini'))
[docs] def check_missing(self, param, action):
"""
Make sure the given parameter is missing in the job.ini file
"""
assert action in ('warn', 'error'), action
if self.inputs.get(param):
msg = 'Please remove %s_file from %s, it makes no sense in %s' % (
param, self.inputs['job_ini'], self.calculation_mode)
if action == 'error':
raise InvalidFile(msg)
else:
logging.warn(msg)
[docs] def hazard_precomputed(self):
"""
:returns: True if the hazard is precomputed
"""
if 'gmfs' in self.inputs or 'hazard_curves' in self.inputs:
return True
elif self.hazard_calculation_id:
parent = list(datastore.read(self.hazard_calculation_id))
return 'gmf_data' in parent or 'poes' in parent