Source code for openquake.commonlib.oqvalidation

# -*- coding: utf-8 -*-
# vim: tabstop=4 shiftwidth=4 softtabstop=4
#
# Copyright (C) 2014-2022 GEM Foundation
#
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import os
import re
import ast
import json
import inspect
import logging
import functools
import collections
import multiprocessing
import numpy

from openquake.baselib import __version__, hdf5, python3compat, config
from openquake.baselib.general import DictArray, AccumDict
from openquake.hazardlib.imt import from_string
from openquake.hazardlib.shakemap.maps import get_array
from openquake.hazardlib import correlation, cross_correlation, stats, calc
from openquake.hazardlib import valid, InvalidFile, site
from openquake.sep.classes import SecondaryPeril
from openquake.commonlib import logictree
from openquake.risklib.riskmodels import get_risk_files

__doc__ = """\
Full list of configuration parameters
=====================================

Engine Version: %s

Some parameters have a default that it is used when the parameter is
not specified in the job.ini file. Some other parameters have no default,
which means that not specifying them will raise an error when running
a calculation for which they are required.

aggregate_by:
  Used to compute aggregate losses and aggregate loss curves in risk
  calculations. Takes in input one or more exposure tags.
  Example: *aggregate_by = region, taxonomy*.
  Default: empty list

reaggregate_by:
  Used to perform additional aggregations in risk calculations. Takes in
  input a proper subset of the tags in the aggregate_by option.
  Example: *reaggregate_by = region*.
  Default: empty list

amplification_method:
  Used in classical PSHA calculations to amplify the hazard curves with
  the convolution or kernel method.
  Example: *amplification_method = convolution*.
  Default: None

area_source_discretization:
  Discretization parameters (in km) for area sources.
  Example: *area_source_discretization = 10*.
  Default: 10

ash_wet_amplification_factor:
  Used in volcanic risk calculations.
  Example: *ash_wet_amplification_factor=1.0*.
  Default: 1.0

asset_correlation:
  Used in risk calculations to take into account asset correlation. Accepts
  only the values 1 (full correlation) and 0 (no correlation).
  Example: *asset_correlation=1*.
  Default: 0

asset_hazard_distance:
  In km, used in risk calculations to print a warning when there are assets
  too distant from the hazard sites.
  Example: *asset_hazard_distance = 5*.
  Default: 15

asset_life_expectancy:
  Used in the classical_bcr calculator.
  Example: *asset_life_expectancy = 50*.
  Default: no default

assets_per_site_limit:
  INTERNAL

gmf_max_gb:
  If the size (in GB) of the GMFs is below this value, then compute avg_gmf
  Example: *gmf_max_gb = 1.*
  Default: 0.1

avg_losses:
  Used in risk calculations to compute average losses.
  Example: *avg_losses=false*.
  Default: True

base_path:
  INTERNAL

cachedir:
  INTERNAL

calculation_mode:
  One of classical, disaggregation, event_based, scenario, scenario_risk,
  scenario_damage, event_based_risk, classical_risk, classical_bcr.
  Example: *calculation_mode=classical*
  Default: no default

collapse_gsim_logic_tree:
  INTERNAL

collapse_level:
  INTERNAL

collect_rlzs:
  Collect all realizations into a single effective realization. If not given
  it is true for sampling and false for full enumeration.
  Example: *collect_rlzs=true*.
  Default: None

compare_with_classical:
  Used in event based calculation to perform also a classical calculation,
  so that the hazard curves can be compared.
  Example: *compare_with_classical = true*.
  Default: False

complex_fault_mesh_spacing:
  In km, used to discretize complex faults.
  Example: *complex_fault_mesh_spacing = 15*.
  Default: 5

concurrent_tasks:
  A hint to the engine for the number of tasks to generate. Do not set
  it unless you know what you are doing.
  Example: *concurrent_tasks = 100*.
  Default: twice the number of cores

conditional_loss_poes:
  Used in classical_risk calculations to compute loss curves.
  Example: *conditional_loss_poes = 0.01 0.02*.
  Default: empty list

cholesky_limit:
  When generating the GMFs from a ShakeMap the engine needs to perform a
  Cholesky decomposition of a matrix of size (M x N)^2, being M the number
  of intensity measure types and N the number of sites. The decomposition
  can become ultra-slow, run out of memory, or produce bogus negative
  eigenvalues, therefore there is a limit on the maximum size of M x N.
  Example: *cholesky_limit = 1000*.
  Default: 10,000

continuous_fragility_discretization:
  Used when discretizing continuuos fragility functions.
  Example: *continuous_fragility_discretization = 10*.
  Default: 20

coordinate_bin_width:
  Used in disaggregation calculations.
  Example: *coordinate_bin_width = 1.0*.
  Default: no default

cross_correlation:
  When used in Conditional Spectrum calculation is the name of a cross
  correlation class (i.e. "BakerJayaram2008").
  When used in ShakeMap calculations the valid choices are "yes", "no" "full",
  same as for *spatial_correlation*.
  Example: *cross_correlation = no*.
  Default: "yes"

description:
  A string describing the calculation.
  Example: *description = Test calculation*.
  Default: "no description"

disagg_by_src:
  Flag used to enable disaggregation by source when possible.
  Example: *disagg_by_src = true*.
  Default: False

disagg_outputs:
  Used in disaggregation calculations to restrict the number of exported
  outputs.
  Example: *disagg_outputs = Mag_Dist*
  Default: list of all possible outputs

discard_assets:
  Flag used in risk calculations to discard assets from the exposure.
  Example: *discard_assets = true*.
  Default: False

discard_trts:
  Used to discard tectonic region types that do not contribute to the hazard.
  Example: *discard_trts = Volcanic*.
  Default: empty list

discrete_damage_distribution:
  Make sure the damage distribution contain only integers (require the "number"
  field in the exposure to be integer).
  Example: *discrete_damage_distribution = true*
  Default: False

distance_bin_width:
  In km, used in disaggregation calculations to specify the distance bins.
  Example: *distance_bin_width = 20*.
  Default: no default

ebrisk_maxsize:
  INTERNAL

floating_x_step:
  Float, used in rupture generation for kite faults. indicates the fraction
  of fault length used to float ruptures along strike by the given float
  (i.e. "0.5" floats the ruptures at half the rupture length). Uniform
  distribution of the ruptures is maintained, such that if the mesh spacing
  and rupture dimensions prohibit the defined overlap fraction, the fraction
  is increased until uniform distribution is achieved. The minimum possible
  value depends on the rupture dimensions and the mesh spacing.
  If 0, standard rupture floating is used along-strike (i.e. no mesh nodes
  are skipped).
  Example: *floating_x_step = 0.5*
  Default: 0

floating_y_step:
  Float, used in rupture generation for kite faults. indicates the fraction
  of fault width used to float ruptures down dip. (i.e. "0.5" floats the
  ruptures at half the rupture length). Uniform distribution of the ruptures
  is maintained, such that if the mesh spacing and rupture dimensions
  prohibit the defined overlap fraction, the fraction is increased until
  uniform distribution is achieved. The minimum possible value depends on
  the rupture dimensions and the mesh spacing.
  If 0, standard rupture floating is used along-strike (i.e. no mesh nodes
  on the rupture dimensions and the mesh spacing.
  Example: *floating_y_step = 0.5*
  Default: 0

ignore_encoding_errors:
  If set, skip characters with non-UTF8 encoding
  Example: *ignore_encoding_errors = true*.
  Default: False

ignore_master_seed:
  If set, estimate analytically the uncertainty on the losses due to the
  uncertainty on the vulnerability functions.
  Example: *ignore_master_seed = vulnerability*.
  Default: None

export_dir:
  Set the export directory.
  Example: *export_dir = /tmp*.
  Default: the current directory, "."

exports:
  Specify what kind of outputs to export by default.
  Example: *exports = csv, rst*.
  Default: empty list

ground_motion_correlation_model:
  Enable ground motion correlation.
  Example: *ground_motion_correlation_model = JB2009*.
  Default: None

ground_motion_correlation_params:
  To be used together with ground_motion_correlation_model.
  Example: *ground_motion_correlation_params = {"vs30_clustering": False}*.
  Default: empty dictionary

ground_motion_fields:
  Flag to turn on/off the calculation of ground motion fields.
  Example: *ground_motion_fields = false*.
  Default: True

gsim:
   Used to specify a GSIM in scenario or event based calculations.
   Example: *gsim = BooreAtkinson2008*.
   Default: "[FromFile]"

hazard_calculation_id:
  Used to specify a previous calculation from which the hazard is read.
  Example: *hazard_calculation_id = 42*.
  Default: None

hazard_curves:
   Used to disable the calculation of hazard curves when there are
   too many realizations.
   Example: *hazard_curves = false*
   Default: True

hazard_curves_from_gmfs:
  Used in scenario/event based calculations. If set, generates hazard curves
  from the ground motion fields.
  Example: *hazard_curves_from_gmfs = true*.
  Default: False

hazard_maps:
  Set it to true to export the hazard maps.
  Example: *hazard_maps = true*.
  Default: False

ignore_covs:
  Used in risk calculations to set all the coefficients of variation of the
  vulnerability functions to zero.
  Example *ignore_covs = true*
  Default: False

ignore_missing_costs:
  Accepts exposures with missing costs (by discarding such assets).
  Example: *ignore_missing_costs = nonstructural, business_interruption*.
  Default: False

iml_disagg:
  Used in disaggregation calculations to specify an intensity measure type
  and level.
  Example: *iml_disagg = {'PGA': 0.02}*.
  Default: no default

imls_ref:
  Reference intensity measure levels used together with the imt_ref parameter
  to compute the conditional spectrum
  Example: *imls_ref = 0.1 0.2*.
  Default: empty list

imt_ref:
  Reference intensity measure type used together with the imls_ref parameter
  to compute the conditional spectrum. The imt_ref must belong to the list
  of IMTs of the calculation.
  Example: *imt_ref = SA(0.15)*.
  Default: no default

individual_rlzs:
  When set, store the individual hazard curves and/or individual risk curves
  for each realization.
  Example: *individual_rlzs = true*.
  Default: False

individual_curves:
  Legacy name for `individual_rlzs`, it should not be used.
  Example: *individual_curves = true*.
  Default: False

inputs:
  INTERNAL. Dictionary with the input files paths.

intensity_measure_types:
  List of intensity measure types in an event based calculation.
  Example: *intensity_measure_types = PGA SA(0.1)*.
  Default: empty list

intensity_measure_types_and_levels:
  List of intensity measure types and levels in a classical calculation.
  Example: *intensity_measure_types_and_levels={"PGA": logscale(0.1, 1, 20)}*.
  Default: empty dictionary

interest_rate:
  Used in classical_bcr calculations.
  Example: *interest_rate = 0.05*.
  Default: no default

investigation_time:
  Hazard investigation time in years, used in classical and event based
  calculations.
  Example: *investigation_time = 50*.
  Default: no default

limit_states:
   Limit states used in damage calculations.
   Example: *limit_states = moderate, complete*
   Default: no default

lrem_steps_per_interval:
  Used in the vulnerability functions.
  Example: *lrem_steps_per_interval  = 1*.
  Default: 0

mag_bin_width:
  Width of the magnitude bin used in disaggregation calculations.
  Example: *mag_bin_width = 0.5*.
  Default: no default

master_seed:
  Seed used to control the generation of the epsilons, relevant for risk
  calculations with vulnerability functions with nonzero coefficients of
  variation.
  Example: *master_seed = 1234*.
  Default: 123456789

max:
  Compute the maximum across realizations. Akin to mean and quantiles.
  Example: *max = true*.
  Default: False

max_data_transfer:
  INTERNAL. Restrict the maximum data transfer in disaggregation calculations.

max_potential_gmfs:
  Restrict the product *num_sites * num_events*.
  Example: *max_potential_gmfs = 1E9*.
  Default: 2E11

max_potential_paths:
  Restrict the maximum number of realizations.
  Example: *max_potential_paths = 200*.
  Default: 15000

max_sites_disagg:
  Maximum number of sites for which to store rupture information.
  In disaggregation calculations with many sites you may be forced to raise
  *max_sites_disagg*, that must be greater or equal to the number of sites.
  Example: *max_sites_disagg = 100*
  Default: 10

max_sites_per_gmf:
  Restrict the maximum number of sites in event based calculation with GMFs.
  Example: *max_sites_per_gmf = 100_000*.
  Default: 65536

max_sites_per_tile:
  Used in classical calculations which are to big to run within the
  available memory. This effectively splits the calculation in homogeneous
  tiles with less than `max_sites_per_tile`. To be used as last resort.
  Example: *max_sites_per_tile = 50_000*
  Default: 500_000

max_weight:
  INTERNAL

maximum_distance:
  Integration distance. Can be give as a scalar, as a dictionary TRT -> scalar
  or as dictionary TRT -> [(mag, dist), ...]
  Example: *maximum_distance = 200*.
  Default: no default

mean:
  Flag to enable/disable the calculation of mean curves.
  Example: *mean = false*.
  Default: True

minimum_asset_loss:
  Used in risk calculations. If set, losses smaller than the
  *minimum_asset_loss* are consider zeros.
  Example: *minimum_asset_loss = {"structural": 1000}*.
  Default: empty dictionary

minimum_distance:
   If set, distances below the minimum are rounded up.
   Example: *minimum_distance = 5*
   Default: 0

minimum_intensity:
  If set, ground motion values below the *minimum_intensity* are
  considered zeros.
  Example: *minimum_intensity = {'PGA': .01}*.
  Default: empty dictionary

minimum_magnitude:
  If set, ruptures below the *minimum_magnitude* are discarded.
  Example: *minimum_magnitude = 5.0*.
  Default: 0

modal_damage_state:
  Used in scenario_damage calculations to export only the damage state
  with the highest probability.
  Example: *modal_damage_state = true*.
  Default: false

num_epsilon_bins:
  Number of epsilon bins in disaggregation calculations.
  Example: *num_epsilon_bins = 3*.
  Default: 1

num_rlzs_disagg:
  Used in disaggregation calculation to specify how many outputs will be
  generated.
  Example: *num_rlzs_disagg=1*.
  Default: None

number_of_ground_motion_fields:
  Used in scenario calculations to specify how many random ground motion
  fields to generate.
  Example: *number_of_ground_motion_fields = 100*.
  Default: no default

number_of_logic_tree_samples:
  Used to specify the number of realizations to generate when using logic tree
  sampling. If zero, full enumeration is performed.
  Example: *number_of_logic_tree_samples = 0*.
  Default: 0

poes:
  Probabilities of Exceedance used to specify the hazard maps or hazard spectra
  to compute.
  Example: *poes = 0.01 0.02*.
  Default: empty list

poes_disagg:
   Alias for poes.

pointsource_distance:
  Used in classical calculations to collapse the point sources. Can also be
  used in conjunction with *ps_grid_spacing*.
  Example: *pointsource_distance = 50*.
  Default: {'default': 1000}

ps_grid_spacing:
  Used in classical calculations to grid the point sources. Requires the
  *pointsource_distance* to be set too.
  Example: *ps_grid_spacing = 50*.
  Default: no default

quantiles:
  List of probabilities used to compute the quantiles across realizations.
  Example: quantiles = 9.15 0.50 0.85
  Default: empty list

random_seed:
  Seed used in the sampling of the logic tree.
  Example: *random_seed = 1234*.
  Default: 42

reference_backarc:
  Used when there is no site model to specify a global backarc parameter,
  used in some GMPEs. Can be True or False
  Example: *reference_backarc = true*.
  Default: False

reference_depth_to_1pt0km_per_sec:
  Used when there is no site model to specify a global z1pt0 parameter,
  used in some GMPEs.
  Example: *reference_depth_to_1pt0km_per_sec = 100*.
  Default: no default

reference_depth_to_2pt5km_per_sec:
  Used when there is no site model to specify a global z2pt5 parameter,
  used in some GMPEs.
  Example: *reference_depth_to_2pt5km_per_sec = 5*.
  Default: no default

reference_vs30_type:
  Used when there is no site model to specify a global vs30 type.
  The choices are "inferred" or "measured"
  Example: *reference_vs30_type = measured"*.
  Default: "inferred"

reference_vs30_value:
  Used when there is no site model to specify a global vs30 value.
  Example: *reference_vs30_value = 760*.
  Default: no default

region:
  A list of lon/lat pairs used to specify a region of interest
  Example: *region = 10.0 43.0, 12.0 43.0, 12.0 46.0, 10.0 46.0*
  Default: None

region_grid_spacing:
  Used together with the *region* option to generate the hazard sites.
  Example: *region_grid_spacing = 10*.
  Default: None

return_periods:
  Used in the computation of the loss curves.
  Example: *return_periods = 200 500 1000*.
  Default: empty list.

risk_imtls:
  INTERNAL. Automatically set by the engine.

risk_investigation_time:
  Used in risk calculations. If not specified, the (hazard) investigation_time
  is used instead.
  Example: *risk_investigation_time = 50*.
  Default: None

rlz_index:
  Used in disaggregation calculations to specify the realization from which
  to start the disaggregation.
  Example: *rlz_index = 0*.
  Default: None

rupture_mesh_spacing:
  Set the discretization parameter (in km) for rupture geometries.
  Example: *rupture_mesh_spacing = 2.0*.
  Default: 5.0

ruptures_per_block:
  INTERNAL

sampling_method:
  One of early_weights, late_weights, early_latin, late_latin)
  Example: *sampling_method = early_latin*.
  Default: 'early_weights'

save_disk_space:
 INTERNAL

sec_peril_params:
  INTERNAL

secondary_perils:
  INTERNAL

secondary_simulations:
  INTERNAL

sensitivity_analysis:
  Dictionary describing a sensitivity analysis.
  Example: *sensitivity_analysis = {'maximum_distance': [200, 300]}*.
  Default: empty dictionary

ses_per_logic_tree_path:
  Set the number of stochastic event sets per logic tree realization in
  event based calculations.
  Example: *ses_per_logic_tree_path = 100*.
  Default: 1

ses_seed:
  Seed governing the generation of the ground motion field.
  Example: *ses_seed = 123*.
  Default: 42

shakemap_id:
  Used in ShakeMap calculations to download a ShakeMap from the USGS site
  Example: *shakemap_id = usp000fjta*.
  Default: no default

shakemap_uri:
  Dictionary used in ShakeMap calculations to specify a ShakeMap. Must contain
  a key named "kind" with values "usgs_id", "usgs_xml" or "file_npy".
  Example: shakemap_uri = {
  "kind": "usgs_xml",
  "grid_url": "file:///home/michele/usp000fjta/grid.xml",
  "uncertainty_url": "file:///home/michele/usp000fjta/uncertainty.xml"}.
  Default: empty dictionary

shift_hypo:
  Used in classical calculations to shift the rupture hypocenter.
  Example: *shift_hypo = true*.
  Default: false

site_effects:
  Flag used in ShakeMap calculations to turn out GMF amplification
  Example: *site_effects = true*.
  Default: False

sites:
  Used to specify a list of sites.
  Example: *sites = 10.1 45, 10.2 45*.

sites_slice:
  INTERNAL

soil_intensities:
  Used in classical calculations with amplification_method = convolution

source_id:
   Used for debugging purposes. When given, restricts the source model to the
   given source IDs.
   Example: *source_id = src001 src002*.
   Default: empty list

spatial_correlation:
  Used in the ShakeMap calculator. The choics are "yes", "no" and "full".
  Example: *spatial_correlation = full*.
  Default: "yes"

specific_assets:
  INTERNAL

split_sources:
  INTERNAL

outs_per_task:
  How many outputs per task to generate (honored in some calculators)
  Example: *outs_per_task = 3*
  Default: 4

std:
  Compute the standard deviation  across realizations. Akin to mean and max.
  Example: *std = true*.
  Default: False

steps_per_interval:
  Used in the fragility functions when building the intensity levels
  Example: *steps_per_interval = 4*.
  Default: 1

time_event:
  Used in scenario_risk calculations when the occupancy depend on the time.
  Valid choices are "day", "night", "transit".
  Example: *time_event = day*.
  Default: None

time_per_task:
  Used in calculatins with task splitting. If a task slice takes longer
  then *time_per_task* seconds, then spawn subtasks for the other slices.
  Example: *time_per_task=600*
  Default: 300

truncation_level:
  Truncation level used in the GMPEs.
  Example: *truncation_level = 0* to compute median GMFs.
  Default: None

uniform_hazard_spectra:
  Flag used to generated uniform hazard specta for the given poes
  Example: *uniform_hazard_spectra = true*.
  Default: False

vs30_tolerance:
  Used when amplification_method = convolution.
  Example: *vs30_tolerance = 20*.
  Default: 0

width_of_mfd_bin:
  Used to specify the width of the Magnitude Frequency Distribution.
  Example: *width_of_mfd_bin = 0.2*.
  Default: None
""" % __version__

try:
    PSDIST = config.performance.pointsource_distance
except AttributeError:
    PSDIST = 1000
GROUND_MOTION_CORRELATION_MODELS = ['JB2009', 'HM2018']
TWO16 = 2 ** 16  # 65536
TWO32 = 2 ** 32
U16 = numpy.uint16
U32 = numpy.uint32
U64 = numpy.uint64
F32 = numpy.float32
F64 = numpy.float64


[docs]def check_same_levels(imtls): """ :param imtls: a dictionary (or dict-like) imt -> imls :returns: the periods and the levels :raises: a ValueError if the levels are not the same across all IMTs """ if not imtls: raise ValueError('There are no intensity_measure_types_and_levels!') imls = imtls[next(iter(imtls))] for imt in imtls: if not imt.startswith(('PGA', 'SA')): raise ValueError('Site amplification works only with ' 'PGA and SA, got %s' % imt) if (imtls[imt] == 0).all(): raise ValueError( 'You forgot to set intensity_measure_types_and_levels!') elif len(imtls[imt]) != len(imls) or any( l1 != l2 for l1, l2 in zip(imtls[imt], imls)): raise ValueError('Site amplification works only if the ' 'levels are the same across all IMTs') periods = [from_string(imt).period for imt in imtls] return periods, imls
[docs]class OqParam(valid.ParamSet): _input_files = () # set in get_oqparam KNOWN_INPUTS = {'rupture_model', 'exposure', 'site_model', 'source_model', 'shakemap', 'gmfs', 'gsim_logic_tree', 'source_model_logic_tree', 'hazard_curves', 'insurance', 'sites', 'job_ini', 'multi_peril', 'taxonomy_mapping', 'fragility', 'consequence', 'reqv', 'input_zip', 'amplification', 'nonstructural_vulnerability', 'nonstructural_fragility', 'nonstructural_consequence', 'structural_vulnerability', 'structural_fragility', 'structural_consequence', 'contents_vulnerability', 'contents_fragility', 'contents_consequence', 'business_interruption_vulnerability', 'business_interruption_fragility', 'business_interruption_consequence', 'structural_vulnerability_retrofitted', 'occupants_vulnerability'} # old name => new name ALIASES = {'individual_curves': 'individual_rlzs', 'quantile_hazard_curves': 'quantiles', 'mean_hazard_curves': 'mean', 'max_hazard_curves': 'max'} hazard_imtls = {} aggregate_by = valid.Param(valid.namelist, []) reaggregate_by = valid.Param(valid.namelist, []) amplification_method = valid.Param( valid.Choice('convolution', 'kernel'), None) minimum_asset_loss = valid.Param(valid.floatdict, {'default': 0}) area_source_discretization = valid.Param( valid.NoneOr(valid.positivefloat), None) asset_correlation = valid.Param(valid.Choice('0', '1'), 0) asset_life_expectancy = valid.Param(valid.positivefloat) assets_per_site_limit = valid.Param(valid.positivefloat, 1000) avg_losses = valid.Param(valid.boolean, True) base_path = valid.Param(valid.utf8, '.') calculation_mode = valid.Param(valid.Choice()) # -> get_oqparam collapse_gsim_logic_tree = valid.Param(valid.namelist, []) collapse_level = valid.Param(valid.Choice('0', '1', '2', '3'), '0') collect_rlzs = valid.Param(valid.boolean, None) coordinate_bin_width = valid.Param(valid.positivefloat) compare_with_classical = valid.Param(valid.boolean, False) concurrent_tasks = valid.Param( valid.positiveint, multiprocessing.cpu_count() * 2) # by M. Simionato conditional_loss_poes = valid.Param(valid.probabilities, []) continuous_fragility_discretization = valid.Param(valid.positiveint, 20) cross_correlation = valid.Param(valid.utf8_not_empty, 'yes') cholesky_limit = valid.Param(valid.positiveint, 10_000) cachedir = valid.Param(valid.utf8, '') description = valid.Param(valid.utf8_not_empty, "no description") disagg_by_src = valid.Param(valid.boolean, False) disagg_outputs = valid.Param(valid.disagg_outputs, list(calc.disagg.pmf_map)) discard_assets = valid.Param(valid.boolean, False) discard_trts = valid.Param(str, '') # tested in the cariboo example discrete_damage_distribution = valid.Param(valid.boolean, False) distance_bin_width = valid.Param(valid.positivefloat) mag_bin_width = valid.Param(valid.positivefloat) floating_x_step = valid.Param(valid.positivefloat, 0) floating_y_step = valid.Param(valid.positivefloat, 0) ignore_encoding_errors = valid.Param(valid.boolean, False) ignore_master_seed = valid.Param(valid.boolean, False) export_dir = valid.Param(valid.utf8, '.') exports = valid.Param(valid.export_formats, ()) gmf_max_gb = valid.Param(valid.positivefloat, .1) 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.utf8, '[FromFile]') hazard_calculation_id = valid.Param(valid.NoneOr(valid.positiveint), None) hazard_curves = valid.Param(valid.boolean, True) hazard_curves_from_gmfs = valid.Param(valid.boolean, False) hazard_maps = valid.Param(valid.boolean, False) ignore_missing_costs = valid.Param(valid.namelist, []) ignore_covs = valid.Param(valid.boolean, False) iml_disagg = valid.Param(valid.floatdict, {}) # IMT -> IML imls_ref = valid.Param(valid.positivefloats, []) imt_ref = valid.Param(valid.intensity_measure_type) individual_rlzs = valid.Param(valid.boolean, None) inputs = valid.Param(dict, {}) ash_wet_amplification_factor = valid.Param(valid.positivefloat, 1.0) intensity_measure_types = valid.Param(valid.intensity_measure_types, '') 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) limit_states = valid.Param(valid.namelist, []) lrem_steps_per_interval = valid.Param(valid.positiveint, 0) steps_per_interval = valid.Param(valid.positiveint, 1) master_seed = valid.Param(valid.positiveint, 123456789) maximum_distance = valid.Param(valid.IntegrationDistance.new) # km asset_hazard_distance = valid.Param(valid.floatdict, {'default': 15}) # km max = valid.Param(valid.boolean, False) max_data_transfer = valid.Param(valid.positivefloat, 2E11) max_potential_gmfs = valid.Param(valid.positiveint, 2E11) max_potential_paths = valid.Param(valid.positiveint, 15_000) max_sites_per_gmf = valid.Param(valid.positiveint, 65536) max_sites_per_tile = valid.Param(valid.positiveint, 500_000) max_sites_disagg = valid.Param(valid.positiveint, 10) mean_hazard_curves = mean = valid.Param(valid.boolean, True) std = valid.Param(valid.boolean, False) minimum_distance = valid.Param(valid.positivefloat, 0) minimum_intensity = valid.Param(valid.floatdict, {}) # IMT -> minIML minimum_magnitude = valid.Param(valid.floatdict, {'default': 0}) # by TRT modal_damage_state = valid.Param(valid.boolean, False) 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, 1) num_rlzs_disagg = valid.Param(valid.positiveint, None) poes = valid.Param(valid.probabilities, []) poes_disagg = valid.Param(valid.probabilities, []) pointsource_distance = valid.Param(valid.floatdict, {'default': PSDIST}) ps_grid_spacing = valid.Param(valid.positivefloat, None) quantile_hazard_curves = quantiles = 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'), 'inferred') 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) risk_imtls = valid.Param(valid.intensity_measure_types_and_levels, {}) risk_investigation_time = valid.Param(valid.positivefloat, None) rlz_index = valid.Param(valid.positiveints, None) rupture_mesh_spacing = valid.Param(valid.positivefloat, 5.0) complex_fault_mesh_spacing = valid.Param( valid.NoneOr(valid.positivefloat), None) return_periods = valid.Param(valid.positiveints, []) ruptures_per_block = valid.Param(valid.positiveint, 500) # for UCERF sampling_method = valid.Param( valid.Choice('early_weights', 'late_weights', 'early_latin', 'late_latin'), 'early_weights') save_disk_space = valid.Param(valid.boolean, False) secondary_perils = valid.Param(valid.namelist, []) sec_peril_params = valid.Param(valid.dictionary, {}) secondary_simulations = valid.Param(valid.dictionary, {}) sensitivity_analysis = valid.Param(valid.dictionary, {}) ses_per_logic_tree_path = valid.Param( valid.compose(valid.nonzero, valid.positiveint), 1) ses_seed = valid.Param(valid.positiveint, 42) shakemap_id = valid.Param(valid.nice_string, None) shakemap_uri = valid.Param(valid.dictionary, {}) shift_hypo = valid.Param(valid.boolean, False) site_effects = valid.Param(valid.boolean, False) # shakemap amplification sites = valid.Param(valid.NoneOr(valid.coordinates), None) sites_slice = valid.Param(valid.simple_slice, None) soil_intensities = valid.Param(valid.positivefloats, None) source_id = valid.Param(valid.namelist, []) spatial_correlation = valid.Param(valid.Choice('yes', 'no', 'full'), 'yes') specific_assets = valid.Param(valid.namelist, []) split_sources = valid.Param(valid.boolean, True) outs_per_task = valid.Param(valid.positiveint, 4) ebrisk_maxsize = valid.Param(valid.positivefloat, 2E10) # used in ebrisk time_event = valid.Param(str, None) time_per_task = valid.Param(valid.positivefloat, 600) truncation_level = valid.Param(valid.NoneOr(valid.positivefloat), None) uniform_hazard_spectra = valid.Param(valid.boolean, False) vs30_tolerance = valid.Param(valid.positiveint, 0) width_of_mfd_bin = valid.Param(valid.positivefloat, None) @property def no_pointsource_distance(self): """ :returns: True if the pointsource_distance is 1000 km """ return set(self.pointsource_distance.values()) == {1000} @property def risk_files(self): try: return self._risk_files except AttributeError: self._risk_files = get_risk_files(self.inputs) return self._risk_files @property def input_dir(self): """ :returns: absolute path to where the job.ini is """ return os.path.abspath(os.path.dirname(self.inputs['job_ini']))
[docs] def get_input_size(self): """ :returns: the total size in bytes of the input files NB: this will fail if the files are not available, so it should be called only before starting the calculation. The same information is stored in the datastore. """ return sum(os.path.getsize(f) for f in self._input_files)
[docs] def get_reqv(self): """ :returns: an instance of class:`RjbEquivalent` if reqv_hdf5 is set """ if 'reqv' not in self.inputs: return return {key: valid.RjbEquivalent(value) for key, value in self.inputs['reqv'].items()}
def __init__(self, **names_vals): if '_log' in names_vals: # called from engine del names_vals['_log'] # support legacy names for name in list(names_vals): if name in self.ALIASES: # use the new name instead of the old one names_vals[self.ALIASES[name]] = names_vals.pop(name) super().__init__(**names_vals) if 'job_ini' not in self.inputs: self.inputs['job_ini'] = '<in-memory>' 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.warning( 'region_constraint is obsolete, use region instead') self.region = valid.wkt_polygon( names_vals.pop('region_constraint')) if ('intensity_measure_types_and_levels' in names_vals and 'intensity_measure_types' in names_vals): logging.warning('Ignoring intensity_measure_types since ' 'intensity_measure_types_and_levels is set') if 'iml_disagg' in names_vals: # normalize things like SA(0.10) -> SA(0.1) self.iml_disagg = {str(from_string(imt)): [iml] for imt, iml in self.iml_disagg.items()} 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') lens = set(map(len, self.hazard_imtls.values())) if len(lens) > 1: dic = {imt: len(ls) for imt, ls in self.hazard_imtls.items()} raise ValueError( 'Each IMT must have the same number of levels, instead ' 'you have %s' % dic) elif 'intensity_measure_types' in names_vals: self.hazard_imtls = dict.fromkeys( self.intensity_measure_types, [0]) delattr(self, 'intensity_measure_types') if ('ps_grid_spacing' in names_vals and 'pointsource_distance' not in names_vals): raise InvalidFile('%s: ps_grid_spacing requires setting a ' 'pointsource_distance!' % self.inputs['job_ini']) self._risk_files = get_risk_files(self.inputs) if self.risk_files: # checks for risk_files hc = self.hazard_calculation_id if 'damage' in self.calculation_mode and not hc: ok = any('fragility' in key for key in self._risk_files) if not ok: raise InvalidFile('Missing fragility files in %s' % self.inputs['job_ini']) elif ('risk' in self.calculation_mode and self.calculation_mode != 'multi_risk' and not hc): ok = any('vulnerability' in key for key in self._risk_files) if not ok: raise InvalidFile('Missing vulnerability files in %s' % self.inputs['job_ini']) if self.hazard_precomputed() and self.job_type == 'risk': self.check_missing('site_model', 'debug') self.check_missing('gsim_logic_tree', 'debug') self.check_missing('source_model_logic_tree', 'debug') # check investigation_time if (self.investigation_time and self.calculation_mode.startswith('scenario')): raise ValueError('%s: there cannot be investigation_time in %s' % (self.inputs['job_ini'], self.calculation_mode)) # check the gsim_logic_tree if self.inputs.get('gsim_logic_tree'): if self.gsim != '[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 IMTs vs the GSIMs self._trts = set(gsim_lt.values) for gsims in gsim_lt.values.values(): self.check_gsims(gsims) elif self.gsim: self.check_gsims([valid.gsim(self.gsim, self.base_path)]) # check inputs unknown = set(self.inputs) - self.KNOWN_INPUTS if unknown: raise ValueError('Unknown key %s_file in %s' % (unknown.pop(), self.inputs['job_ini'])) # check return_periods vs poes if self.return_periods and not self.poes and self.investigation_time: self.poes = 1 - numpy.exp( - self.investigation_time / numpy.array(self.return_periods)) # check for multi_risk if self.calculation_mode == 'multi_risk': # store input files, mandatory for the QGIS plugin self.save_disk_space = False # check for tiling if self.max_sites_disagg > self.max_sites_per_tile: raise ValueError( 'max_sites_disagg is larger than max_sites_per_tile! (%d>%d)' % (self.max_sites_disagg, self.max_sites_per_tile)) # checks for disaggregation if self.calculation_mode == 'disaggregation': if not self.poes_disagg and self.poes: self.poes_disagg = self.poes elif not self.poes and self.poes_disagg: self.poes = self.poes_disagg elif self.poes != self.poes_disagg: raise InvalidFile( 'poes_disagg != poes: %s!=%s in %s' % (self.poes_disagg, self.poes, self.inputs['job_ini'])) 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)) if self.disagg_outputs and not any( 'Eps' in out for out in self.disagg_outputs): self.num_epsilon_bins = 1 if (self.rlz_index is not None and self.num_rlzs_disagg is not None): raise InvalidFile('%s: you cannot set rlzs_index and ' 'num_rlzs_disagg at the same time' % job_ini) # checks for conditional_spectrum if self.calculation_mode == 'conditional_spectrum': if not self.imls_ref and not self.poes: raise InvalidFile("%s: you must specify poes or imls_ref" % job_ini) elif list(self.hazard_stats()) != ['mean']: raise InvalidFile('%s: only the mean is supported' % 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) if not self.investigation_time and not self.hazard_calculation_id: raise InvalidFile('%s: missing investigation_time' % job_ini) # check for GMFs from file if (self.inputs.get('gmfs', '').endswith('.csv') and 'sites' not in self.inputs and self.sites is None): raise InvalidFile('%s: You forgot sites|sites_csv' % job_ini) elif self.inputs.get('gmfs', '').endswith('.xml'): raise InvalidFile('%s: GMFs in XML are not supported anymore' % job_ini) # checks for event_based if 'event_based' in self.calculation_mode: if self.ps_grid_spacing: logging.warning('ps_grid_spacing is ignored in event_based ' 'calculations"') if self.ses_per_logic_tree_path >= TWO32: 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) # check grid + sites if self.region_grid_spacing and ('sites' in self.inputs or self.sites): raise ValueError('You are specifying grid and sites at the same ' 'time: which one do you want?') # check for amplification if ('amplification' in self.inputs and self.imtls and self.calculation_mode in ['classical', 'classical_risk', 'disaggregation']): check_same_levels(self.imtls) if ('amplification' in self.inputs and self.amplification_method == 'convolution' and not self.soil_intensities): raise InvalidFile('%s: The soil_intensities must be defined' % job_ini)
[docs] def validate(self): """ Set self.loss_types """ from openquake.commonlib import datastore # avoid circular import if self.hazard_calculation_id: with datastore.read(self.hazard_calculation_id) as ds: self._parent = ds['oqparam'] else: self._parent = None # set all_cost_types # rt has the form 'vulnerability/structural', 'fragility/...', ... costtypes = set(rt.rsplit('/')[1] for rt in self.risk_files) if not costtypes and self.hazard_calculation_id: try: self._risk_files = rfs = get_risk_files(self._parent.inputs) costtypes = set(rt.rsplit('/')[1] for rt in rfs) except OSError: # FileNotFound for wrong hazard_calculation_id pass self.all_cost_types = sorted(costtypes) # fix minimum_asset_loss self.minimum_asset_loss = { ln: calc.filters.getdefault(self.minimum_asset_loss, ln) for ln in self.loss_types} super().validate() self.check_source_model()
[docs] def check_gsims(self, gsims): """ :param gsims: a sequence of GSIM instances """ imts = set() for imt in self.imtls: im = from_string(imt) if imt.startswith("SA"): imts.add("SA") elif imt.startswith("EAS"): imts.add("EAS") elif imt.startswith("FAS"): imts.add("FAS") elif imt.startswith("DRVT"): imts.add("DRVT") else: imts.add(im.string) for gsim in gsims: if hasattr(gsim, 'weight'): # disable the check continue 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 (self.hazard_calculation_id is None and '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 ('lon', 'lat'): # no check continue elif param in site.param: # mandatory params param_name = site.param[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 time_ratio(self): """ The ratio risk_investigation_time / eff_investigation_time per rlz """ 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)
[docs] def risk_event_rates(self, num_events, num_haz_rlzs): """ :param num_events: the number of events per risk realization :param num_haz_rlzs the number of hazard realizations If risk_investigation_time is 1, returns the annual event rates for each realization as a list, possibly of 1 element. """ if self.investigation_time is None: # for scenarios there is no effective_time return [1] * len(num_events) else: # for event based compute the time_ratio time_ratio = self.time_ratio if self.collect_rlzs: time_ratio /= num_haz_rlzs return list(time_ratio * num_events)
@property def imtls(self): """ Returns a DictArray with the risk intensity measure types and levels, if given, or the hazard ones. """ imtls = self.hazard_imtls or self.risk_imtls return DictArray(imtls) if imtls else {} @property def min_iml(self): """ :returns: a dictionary of intensities, one per IMT """ mini = self.minimum_intensity if mini: for imt in self.imtls: try: mini[imt] = calc.filters.getdefault(mini, imt) except KeyError: mini[imt] = 0 if 'default' in mini: del mini['default'] return numpy.array([mini.get(imt) or 1E-10 for imt in self.imtls])
[docs] def levels_per_imt(self): """ :returns: the number of levels per IMT (a.ka. L1) """ return self.imtls.size // len(self.imtls)
[docs] def set_risk_imts(self, risklist): """ :param risklist: a list of risk functions with attributes .id, .loss_type, .kind Set the attribute risk_imtls. """ imtls = AccumDict(accum=[]) # imt -> imls for i, rf in enumerate(risklist): if not hasattr(rf, 'imt') or rf.kind.endswith('_retrofitted'): # for consequence or retrofitted continue if hasattr(rf, 'build'): # FragilityFunctionList rf = rf.build(risklist.limit_states, self.continuous_fragility_discretization, self.steps_per_interval) risklist[i] = rf from_string(rf.imt) # make sure it is a valid IMT imtls[rf.imt].extend(iml for iml in rf.imls if iml > 0) suggested = ['\nintensity_measure_types_and_levels = {'] risk_imtls = self.risk_imtls.copy() for imt, imls in imtls.items(): risk_imtls[imt] = list(valid.logscale(min(imls), max(imls), 20)) suggested.append(' %r: logscale(%s, %s, 20),' % (imt, min(imls), max(imls))) suggested[-1] += '}' self.risk_imtls = {imt: [min(ls)] for imt, ls in risk_imtls.items()} if self.uniform_hazard_spectra: self.check_uniform_hazard_spectra() if not self.hazard_imtls: if (self.calculation_mode.startswith('classical') or self.hazard_curves_from_gmfs): raise InvalidFile('%s: %s' % ( self.inputs['job_ini'], 'You must provide the ' 'intensity measure levels explicitly. Suggestion:' + '\n '.join(suggested))) if (len(self.imtls) == 0 and 'event_based' in self.calculation_mode and 'gmfs' not in self.inputs and not self.hazard_calculation_id and self.ground_motion_fields): raise ValueError('Please define intensity_measure_types in %s' % self.inputs['job_ini'])
[docs] def get_primary_imtls(self): """ :returns: IMTs and levels which are not secondary """ sec_imts = set(self.get_sec_imts()) return {imt: imls for imt, imls in self.imtls.items() if imt not in sec_imts}
[docs] def hmap_dt(self): # used for CSV export """ :returns: a composite dtype (imt, poe) """ return numpy.dtype([('%s-%s' % (imt, poe), F32) for imt in self.imtls for poe in self.poes])
[docs] def uhs_dt(self): # used for CSV and NPZ export """ :returns: a composity dtype (poe, imt) """ imts_dt = numpy.dtype([(imt, F32) for imt in self.imtls if imt.startswith(('PGA', 'SA'))]) return numpy.dtype([(str(poe), imts_dt) for poe in self.poes])
[docs] def imt_periods(self): """ :returns: the IMTs with a period, to be used in an UHS calculation """ imts = [] for im in self.imtls: imt = from_string(im) if imt.period or imt.string == 'PGA': imts.append(imt) return imts
[docs] def imt_dt(self, dtype=F64): """ :returns: a numpy dtype {imt: float} """ return numpy.dtype([(imt, dtype) 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())} @property def loss_types(self): """ :returns: list of loss types (empty for hazard) """ if not hasattr(self, "all_cost_types"): # for hazard return [] names = [] for lt in self.all_cost_types: names.append(lt) return names
[docs] def loss_dt(self, dtype=F64): """ :returns: 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=F64): """ :returns: a data type list [(loss_name, dtype), ...] """ dts = [(str(lt), dtype) for lt in self.loss_types] 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): """ :returns: a composite data type for the GMFs """ lst = [('sid', U32), ('eid', U32)] for m, imt in enumerate(self.get_primary_imtls()): lst.append((f'gmv_{m}', F32)) for out in self.get_sec_imts(): lst.append((out, F32)) return numpy.dtype(lst)
[docs] def all_imts(self): """ :returns: gmv_0, ... gmv_M, sec_imt... """ lst = [] for m, imt in enumerate(self.get_primary_imtls()): lst.append(f'gmv_{m}') for out in self.get_sec_imts(): lst.append(out) return lst
[docs] def get_sec_perils(self): """ :returns: a list of secondary perils """ return SecondaryPeril.instantiate(self.secondary_perils, self.sec_peril_params)
[docs] def get_sec_imts(self): """ :returns: a list of secondary outputs """ outs = [] for sp in self.get_sec_perils(): outs.extend(sp.outputs) return outs
[docs] def no_imls(self): """ Return True if there are no intensity measure levels """ return sum(sum(imls) for imls in self.imtls.values()) == 0
@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) @property def cross_correl(self): """ Return a cross correlation object (or None). See :mod:`openquake.hazardlib.cross_correlation` for more info. """ try: cls = getattr(cross_correlation, self.cross_correlation) except AttributeError: return None return cls()
[docs] def get_kinds(self, kind, R): """ Yield 'rlz-000', 'rlz-001', ...', 'mean', 'quantile-0.1', ... """ stats = self.hazard_stats() if kind == 'stats': yield from stats return elif kind == 'rlzs': for r in range(R): yield 'rlz-%d' % r return elif kind: yield kind return # default: yield stats (and realizations if required) if R > 1 and self.individual_rlzs or not stats: for r in range(R): yield 'rlz-%03d' % r yield from stats
[docs] def hazard_stats(self): """ Return a dictionary stat_name -> stat_func """ names = [] # name of statistical functions funcs = [] # statistical functions of kind func(values, weights) if self.mean: names.append('mean') funcs.append(stats.mean_curve) if self.std: names.append('std') funcs.append(stats.std_curve) for q in self.quantiles: names.append('quantile-%s' % q) funcs.append(functools.partial(stats.quantile_curve, q)) if self.max: names.append('max') funcs.append(stats.max_curve) return dict(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_event_based(self): """ The calculation mode is event_based, event_based_risk or ebrisk """ return (self.calculation_mode in 'event_based_risk ebrisk event_based_damage ucerf_hazard')
[docs] def is_ucerf(self): """ :returns: True for UCERF calculations, False otherwise """ return 'source_model' in self.inputs
[docs] def is_valid_shakemap(self): """ hazard_calculation_id must be set if shakemap_id is set """ if self.shakemap_uri: kind = self.shakemap_uri['kind'] sig = inspect.signature(get_array[kind]) # parameters without default value params = [p.name for p in list( sig.parameters.values()) if p.default is p.empty] all_params = list(sig.parameters) if not all(p in list(self.shakemap_uri) for p in params) or \ not all(p in all_params for p in list(self.shakemap_uri)): raise ValueError( 'Error in shakemap_uri: Expected parameters %s, ' 'valid parameters %s, got %s' % (params, all_params, list(self.shakemap_uri))) return self.hazard_calculation_id if ( self.shakemap_id or self.shakemap_uri) else True
[docs] def is_valid_truncation_level(self): """ In presence of a correlation model the truncation level must be nonzero """ if self.ground_motion_correlation_model: return self.truncation_level != 0 else: return 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, region is set. You did set more than one, or nothing. """ if self.calculation_mode == 'preclassical': # disable the check return True if 'hazard_curves' in self.inputs and ( self.sites is not None or 'sites' in self.inputs or 'site_model' in self.inputs): return False has_sites = (self.sites is not None or 'sites' in self.inputs or 'site_model' in self.inputs) if not has_sites and not self.ground_motion_fields: # when generating only the ruptures you do not need the sites return True if ('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 'gsim_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 - self._trts - {'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 trt not in self._trts and trt != 'default': self.error = 'tectonic region %r not in %s' % (trt, gsim_lt) return False if 'default' not in trts and trts < self._trts: missing = ', '.join(self._trts - 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 == '' and self.intensity_measure_types_and_levels is None) elif not 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_ps_grid_spacing(self): """ `ps_grid_spacing` must be smaller than the `pointsource_distance` """ if self.ps_grid_spacing is None: return True return self.ps_grid_spacing <= calc.filters.getdefault( self.pointsource_distance, 'default')
[docs] def is_valid_soil_intensities(self): """ soil_intensities can be set only if amplification_method=convolution """ if self.amplification_method == 'convolution': return len(self.soil_intensities) > 1 else: return self.soil_intensities is None
[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_aggregate_by(self): """ At the moment only `aggregate_by=id` or `aggregate_by=site_id` are accepted """ if 'id' in self.aggregate_by and len(self.aggregate_by) > 1: return False elif 'site_id' in self.aggregate_by and len(self.aggregate_by) > 1: return False return True
[docs] def is_valid_export_dir(self): """ export_dir={export_dir} must refer to a directory, and the user must have the permission to write on it. """ if self.export_dir and not os.path.isabs(self.export_dir): self.export_dir = os.path.normpath( os.path.join(self.input_dir, self.export_dir)) if not self.export_dir: self.export_dir = os.path.expanduser('~') # home directory logging.warning('export_dir not specified. Using export_dir=%s' % self.export_dir) return True if not os.path.exists(self.export_dir): try: os.makedirs(self.export_dir) except PermissionError: return False return True 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 is_valid_collect_rlzs(self): """ sampling_method must be early_weights, only the mean is available, and number_of_logic_tree_samples must be greater than 1. """ if self.job_type == 'hazard': return True if self.collect_rlzs is None: self.collect_rlzs = self.number_of_logic_tree_samples > 1 if self.calculation_mode == 'event_based_damage': ini = self.inputs['job_ini'] if not self.investigation_time: raise InvalidFile('Missing investigation_time in %s' % ini) return True elif self.collect_rlzs is False: return True elif self.hazard_calculation_id: n = self._parent.number_of_logic_tree_samples if n and n != self.number_of_logic_tree_samples: raise ValueError('Please specify number_of_logic_tree_samples' '=%d' % n) hstats = list(self.hazard_stats()) nostats = not hstats or hstats == ['mean'] return nostats and self.number_of_logic_tree_samples > 1 and ( self.sampling_method == 'early_weights')
[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.warning( '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 'multi_peril' in self.inputs or 'rupture_model' in self.inputs or 'scenario' in self.calculation_mode): return if ('source_model_logic_tree' not in self.inputs and self.inputs['job_ini'] != '<in-memory>' 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 ('debug', 'info', 'warn', 'error'), action if self.inputs.get(param): msg = '%s_file in %s is ignored in %s' % ( param, self.inputs['job_ini'], self.calculation_mode) if action == 'error': raise InvalidFile(msg) else: getattr(logging, action)(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 return self.hazard_calculation_id
[docs] @classmethod def docs(cls): """ :returns: a dictionary parameter name -> parameter documentation """ dic = {} lst = re.split(r'\n([\w_]+):\n', __doc__) for name, doc in zip(lst[1::2], lst[2::2]): name = name.split()[-1] dic[name] = doc return dic
def __toh5__(self): return hdf5.dumps(vars(self)), {} def __fromh5__(self, array, attrs): if isinstance(array, numpy.ndarray): # old format <= 3.11 dd = collections.defaultdict(dict) for (name_, literal_) in array: name = python3compat.decode(name_) literal = python3compat.decode(literal_) if '.' in name: k1, k2 = name.split('.', 1) dd[k1][k2] = ast.literal_eval(literal) else: dd[name] = ast.literal_eval(literal) vars(self).update(dd) else: # new format >= 3.12 vars(self).update(json.loads(python3compat.decode(array)))