Source code for openquake.commonlib.oqvalidation

# -*- 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
# 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 <>.

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

TWO16 = 2 ** 16  # 65536
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
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, True) 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 individual_curves = valid.Param(valid.boolean, True) 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) std_hazard_curves = valid.Param(valid.boolean, False) max_loss_curves = valid.Param(valid.boolean, False) mean_loss_curves = valid.Param(valid.boolean, True) minimum_intensity = valid.Param(valid.floatdict, {}) # IMT -> minIML minimum_magnitude = valid.Param(valid.positivefloat, 0) 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) 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, []) nodal_dist_collapsing_distance = valid.Param(valid.positivefloat, None) hypo_dist_collapsing_distance = valid.Param(valid.positivefloat, None) 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
[docs] def get_reqv(self): """ :returns: an instance of class:`RjbEquivalent` if reqv_hdf5 is set """ if 'reqv' in self.inputs: return valid.RjbEquivalent(self.inputs['reqv'])
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') # 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(from_string(imt).name 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. """ # rt has the form 'vulnerability/structural', 'fragility/...', ... costtypes = sorted(rt.rsplit('/')[1] for rt in self.risk_files) if not costtypes and self.hazard_calculation_id: with as ds: parent = ds['oqparam'] self._file_type, self._risk_files = get_risk_files(parent.inputs) costtypes = sorted(rt.rsplit('/')[1] for rt in 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 risk_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 gmf_data_dt(self): """ Return a composite data type for the GMFs """ return numpy.dtype( [('rlzi', U16), ('sid', U32), ('eid', U64), ('gmv', (F32, (len(self.imtls),)))])
[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())
@property def 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) if self.std_hazard_curves: names.append('std') funcs.append(stats.std_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_inputs(self): """ Invalid calculation_mode="{calculation_mode}" or missing fragility_file/vulnerability_file in the .ini file. """ if self.hazard_calculation_id: parent_datasets = set( else: parent_datasets = set() if 'damage' in self.calculation_mode: return any( key.endswith('_fragility') for key in self.inputs ) or 'composite_risk_model' in parent_datasets elif 'risk' in self.calculation_mode: return any( key.endswith('_vulnerability') for key in self.inputs ) or 'composite_risk_model' in parent_datasets return True
[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 is_valid_optimize_same_id_sources(self): """ The `optimize_same_id_sources` can be true only in the classical calculators. """ if (self.optimize_same_id_sources and 'classical' in self.calculation_mode or 'disagg' in self.calculation_mode): return True elif self.optimize_same_id_sources: return False else: return True
[docs] def check_uniform_hazard_spectra(self): ok_imts = [imt for imt in self.imtls if imt == 'PGA' or imt.startswith('SA')] if not ok_imts: raise ValueError('The `uniform_hazard_spectra` can be True only ' 'if the IMT set contains SA(...) or PGA, got %s' % list(self.imtls)) elif len(ok_imts) == 1: logging.warn( 'There is a single IMT, the uniform_hazard_spectra plot will ' 'contain a single point')
[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( return 'gmf_data' in parent or 'poes' in parent