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', ) 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) asset_loss_table = valid.Param(valid.boolean, False) avg_losses = valid.Param(valid.boolean, False) base_path = valid.Param(valid.utf8, '.') calculation_mode = valid.Param(valid.Choice(*CALCULATORS), '') coordinate_bin_width = valid.Param(valid.positivefloat) compare_with_classical = valid.Param(valid.boolean, False) concurrent_tasks = valid.Param( valid.positiveint, parallel.executor.num_tasks_hint) conditional_loss_poes = valid.Param(valid.probabilities, []) continuous_fragility_discretization = valid.Param(valid.positiveint, 20) description = valid.Param(valid.utf8_not_empty) distance_bin_width = valid.Param(valid.positivefloat) mag_bin_width = valid.Param(valid.positivefloat) export_dir = valid.Param(valid.utf8, None) export_multi_curves = valid.Param(valid.boolean, False) exports = valid.Param(valid.export_formats, ()) filter_sources = valid.Param(valid.boolean, True) 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, None) 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, []) 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) loss_curve_resolution = valid.Param(valid.positiveint, 50) loss_ratios = valid.Param(valid.loss_ratios, ()) 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.floatdict) # km asset_hazard_distance = valid.Param(valid.positivefloat, 5) # km maximum_tile_weight = valid.Param(valid.positivefloat) mean_hazard_curves = valid.Param(valid.boolean, False) 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.coordinates, None) region_constraint = valid.Param(valid.wkt_polygon, None) region_grid_spacing = valid.Param(valid.positivefloat, None) 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, None) complex_fault_mesh_spacing = valid.Param( valid.NoneOr(valid.positivefloat), None) ses_per_logic_tree_path = valid.Param(valid.positiveint, 1) sites = valid.Param(valid.NoneOr(valid.coordinates), None) sites_disagg = valid.Param(valid.NoneOr(valid.coordinates), []) sites_per_tile = valid.Param(valid.positiveint, 1000) 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(OqParam, self).__init__(**names_vals) 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 '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) # check the IMTs vs the GSIMs if 'gsim_logic_tree' in self.inputs: if self.gsim: raise ValueError('If `gsim_logic_tree_file` is set, there ' 'must be no `gsim` key') path = os.path.join( self.base_path, self.inputs['gsim_logic_tree']) self._gsims_by_trt = logictree.GsimLogicTree(path, []).values for gsims in self._gsims_by_trt.values(): self.check_gsims(gsims) elif self.gsim is not None: self.check_gsims([self.gsim]) 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 """ 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 collections.OrderedDict(sorted(imtls.items())) @property def all_cost_types(self): """ Return the cost types of the computation (including `occupants` if it is there) in order. """ return sorted(self.risk_files) 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() def loss_dt(self, dtype=numpy.float32): """ Return a composite dtype based on the loss types, including occupants """ loss_types = self.all_cost_types dts = [(lt, dtype) for lt in loss_types] if self.insured_losses: for lt in loss_types: dts.append((lt + '_ins', dtype)) return numpy.dtype(dts) def no_imls(self): """ Return True if there are no intensity measure levels """ return all(ls is None for ls in self.imtls.values()) 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 def is_valid_region(self): """ If there is a region a region_grid_spacing must be given """ return self.region_grid_spacing if self.region else True 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 and exposure_file is set. You did set more than one, or nothing. """ if self.calculation_mode not in HAZARD_CALCULATORS: 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('gmvs', 0), region=bool(self.region), exposure=self.inputs.get('exposure', 0)) # 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( 'site_model') 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 def is_valid_maximum_distance(self): """ Invalid maximum_distance={maximum_distance}: {error} """ if 'source_model_logic_tree' not in self.inputs: 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 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 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 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 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 def is_valid_hazard_curves(self): """ You must set `hazard_curves_from_gmfs` if `mean_hazard_curves` or `quantile_hazard_curves` are set. """ if self.calculation_mode == 'event_based' and ( self.mean_hazard_curves or self.quantile_hazard_curves): return self.hazard_curves_from_gmfs return True 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) def is_valid_inputs(self): """ Invalid calculation_mode="{calculation_mode}" or missing fragility_file/vulnerability_file in the .ini file. """ if 'damage' in self.calculation_mode: return any(key.endswith('_fragility') for key in self.inputs) elif 'risk' in self.calculation_mode: return any(key.endswith('_vulnerability') for key in self.inputs) return True 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 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))
class OqParam(valid.ParamSet): 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) base_path = valid.Param(valid.utf8, '.') calculation_mode = valid.Param(valid.Choice(*CALCULATORS), '') coordinate_bin_width = valid.Param(valid.positivefloat) compare_with_classical = valid.Param(valid.boolean, False) concurrent_tasks = valid.Param(valid.positiveint, parallel.executor.num_tasks_hint) conditional_loss_poes = valid.Param(valid.probabilities, []) continuous_fragility_discretization = valid.Param(valid.positiveint, 20) description = valid.Param(valid.utf8_not_empty) distance_bin_width = valid.Param(valid.positivefloat) mag_bin_width = valid.Param(valid.positivefloat) epsilon_sampling = valid.Param(valid.positiveint, 1000) export_dir = valid.Param(valid.utf8, None) export_multi_curves = valid.Param(valid.boolean, False) exports = valid.Param(valid.export_formats, ()) 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, None) 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, []) 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) # hazard_imtls = valid.Param(valid.intensity_measure_types_and_levels, {}) interest_rate = valid.Param(valid.positivefloat) investigation_time = valid.Param(valid.positivefloat, None) loss_curve_resolution = valid.Param(valid.positiveint, 50) loss_ratios = valid.Param(valid.loss_ratios, ()) lrem_steps_per_interval = valid.Param(valid.positiveint, 0) steps_per_interval = valid.Param(valid.positiveint, 0) master_seed = valid.Param(valid.positiveint, 0) maximum_distance = valid.Param(valid.positivefloat) # km asset_hazard_distance = valid.Param(valid.positivefloat, 5) # km maximum_tile_weight = valid.Param(valid.positivefloat) mean_hazard_curves = valid.Param(valid.boolean, False) number_of_ground_motion_fields = valid.Param(valid.positiveint, 0) 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, 1.) reference_depth_to_2pt5km_per_sec = valid.Param(valid.positivefloat, 1.) reference_vs30_type = valid.Param(valid.Choice('measured', 'inferred'), 'measured') reference_vs30_value = valid.Param(valid.positivefloat, 1.) reference_backarc = valid.Param(valid.boolean, False) region = valid.Param(valid.coordinates, None) region_constraint = valid.Param(valid.wkt_polygon, None) region_grid_spacing = valid.Param(valid.positivefloat, None) 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, None) complex_fault_mesh_spacing = valid.Param(valid.NoneOr(valid.positivefloat), None) ses_per_logic_tree_path = valid.Param(valid.positiveint, 1) sites = valid.Param(valid.NoneOr(valid.coordinates), None) sites_disagg = valid.Param(valid.NoneOr(valid.coordinates), []) specific_assets = valid.Param(valid.namelist, []) statistics = valid.Param(valid.boolean, True) 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) def __init__(self, **names_vals): super(OqParam, self).__init__(**names_vals) 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 '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') if vulnerability_files(self.inputs): self.risk_imtls = get_imtls_from_vulnerabilities(self.inputs) elif fragility_files(self.inputs): fname = self.inputs['fragility'] ffs = get_fragility_functions( fname, self.continuous_fragility_discretization) self.risk_imtls = {fset.imt: fset.imls for fset in ffs.values()} # check the IMTs vs the GSIMs if 'gsim_logic_tree' in self.inputs: if self.gsim: raise ValueError('If `gsim_logic_tree_file` is set, there ' 'must be no `gsim` key') path = os.path.join(self.base_path, self.inputs['gsim_logic_tree']) for gsims in logictree.GsimLogicTree(path, []).values.values(): self.check_imts_gsims(list(map(valid.gsim, gsims))) elif self.gsim is not None: self.check_imts_gsims([self.gsim]) def check_imts_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)) @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 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 collections.OrderedDict(sorted(imtls.items())) def no_imls(self): """ Return True if there are no intensity measure levels """ return all(ls is None for ls in self.imtls.values()) 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 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 and exposure_file is set. You did set more than one, or nothing. """ if self.calculation_mode not in HAZARD_CALCULATORS: 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('gmvs', 0), region=bool(self.region), exposure=self.inputs.get('exposure', 0)) # 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('site_model') 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 def is_valid_site_model(self): """ In absence of a site_model file the site model parameters must be all set. """ if self.calculation_mode in HAZARD_CALCULATORS and ( 'site_model' not in self.inputs): return (self.reference_vs30_type and self.reference_vs30_value and self.reference_depth_to_2pt5km_per_sec and self.reference_depth_to_1pt0km_per_sec) else: return True def is_valid_maximum_distance(self): """ The maximum_distance must be set for all hazard calculators """ return (self.calculation_mode not in HAZARD_CALCULATORS or getattr(self, 'maximum_distance', None)) 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 fragility_files(self.inputs) or vulnerability_files(self.inputs): 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 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 ( self.hazard_curves_from_gmfs or self.calculation_mode in ('classical', 'classical_tiling', 'disaggregation')) return not invalid 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 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 def is_valid_hazard_curves(self): """ You must set `hazard_curves_from_gmfs` if `mean_hazard_curves` or `quantile_hazard_curves` are set. """ if self.calculation_mode == 'event_based' and ( self.mean_hazard_curves or self.quantile_hazard_curves): return self.hazard_curves_from_gmfs return True 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) def is_valid_inputs(self): """ Invalid calculation_mode="{calculation_mode}" or missing fragility_file/vulnerability_file in the .ini file. """ if 'damage' in self.calculation_mode: return 'fragility' in self.inputs elif 'risk' in self.calculation_mode: return any(key.endswith('_vulnerability') for key in self.inputs) return True 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