def test_missing_cost_types(self):
job_ini = general.gettemp('''\
[general]
description = Exposure with missing cost_types
calculation_mode = scenario
exposure_file = %s''' % os.path.basename(self.exposure4))
oqparam = readinput.get_oqparam(job_ini)
with self.assertRaises(InvalidFile) as ctx:
readinput.get_sitecol_assetcol(oqparam, cost_types=['structural'])
self.assertIn("is missing", str(ctx.exception))
def test_missing_cost_types(self):
job_ini = general.gettemp('''\
[general]
description = Exposure with missing cost_types
calculation_mode = scenario
exposure_file = %s''' % os.path.basename(self.exposure4))
oqparam = readinput.get_oqparam(job_ini)
with self.assertRaises(InvalidFile) as ctx:
readinput.get_sitecol_assetcol(oqparam, cost_types=['structural'])
self.assertIn("is missing", str(ctx.exception))
def test_grid_site_model_exposure(self):
oq = readinput.get_oqparam('job.ini', case_16)
oq.region_grid_spacing = 15
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 141) # 10 sites were discarded silently
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0) # no assets were discarded
def read_exposure(self, haz_sitecol): # after load_risk_model
"""
Read the exposure, the risk models and update the attributes
.sitecol, .assetcol
"""
oq = self.oqparam
with self.monitor('reading exposure'):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
oq, haz_sitecol, self.crmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not oq.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
self.policy_name = ''
self.policy_dict = {}
if oq.inputs.get('insurance'):
k, v = zip(*oq.inputs['insurance'].items())
self.load_insurance_data(k, v)
return readinput.exposure
def read_exposure(self, haz_sitecol=None): # after load_risk_model
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure
def read_exposure(self, haz_sitecol=None):
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(self.oqparam, haz_sitecol,
self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
msg = ('%d sites with assets were discarded; use '
'`oq plot_assets` to see them' % len(discarded))
if hasattr(self, 'rup') or self.oqparam.discard_assets:
# just log a warning in case of scenario from rupture
# or when discard_assets is set to True
logging.warn(msg)
else: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(msg)
readinput.exposure = None # reset the global
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
def test_exposure_only(self):
oq = readinput.get_oqparam('job.ini', case_16)
del oq.inputs['site_model']
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 148)
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0)
# test agg_value
arr = assetcol.agg_value()
assert_allclose(arr, [3.6306637e+09])
arr = assetcol.agg_value('taxonomy')
assert_allclose(arr,
[[4.9882240e+06],
[1.1328099e+08],
[4.2222912e+08],
[1.6412870e+07],
[5.0686808e+07],
[2.5343402e+07],
[1.5254313e+09],
[6.6375590e+06],
[8.3206810e+08],
[1.6412871e+07],
[3.9439158e+08],
[1.6734690e+07],
[6.7582400e+06],
[1.3613027e+08],
[4.3124016e+07],
[9.4132640e+06],
[1.0620092e+07]])
arr = assetcol.agg_value('occupancy')
assert_allclose(assetcol.agg_value('occupancy'), [[3.6306644e+09]])
arr = assetcol.agg_value('taxonomy', 'occupancy')
self.assertEqual(arr.shape, (17, 1, 1))
def test_grid_site_model_exposure(self):
oq = readinput.get_oqparam(
'job.ini', case_16, region_grid_spacing='15')
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 148) # 3 sites were discarded silently
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0) # no assets were discarded
def read_exposure(self, haz_sitecol=None): # after load_risk_model
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol, discarded = (
readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types))
if len(discarded):
self.datastore['discarded'] = discarded
if hasattr(self, 'rup'):
# this is normal for the case of scenario from rupture
logging.info('%d assets were discarded because too far '
'from the rupture; use `oq show discarded` '
'to show them and `oq plot_assets` to plot '
'them' % len(discarded))
elif not self.oqparam.discard_assets: # raise an error
self.datastore['sitecol'] = self.sitecol
self.datastore['assetcol'] = self.assetcol
raise RuntimeError(
'%d assets were discarded; use `oq show discarded` to'
' show them and `oq plot_assets` to plot them' %
len(discarded))
# reduce the riskmodel to the relevant taxonomies
taxonomies = set(taxo for taxo in self.assetcol.tagcol.taxonomy
if taxo != '?')
if len(self.riskmodel.taxonomies) > len(taxonomies):
logging.info('Reducing risk model from %d to %d taxonomies',
len(self.riskmodel.taxonomies), len(taxonomies))
self.riskmodel = self.riskmodel.reduce(taxonomies)
return readinput.exposure
def test_exposure_only(self):
oq = readinput.get_oqparam('job.ini', case_16)
del oq.inputs['site_model']
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 148)
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0)
def read_exposure(self, haz_sitecol=None):
"""
Read the exposure, the riskmodel and update the attributes
.sitecol, .assetcol
"""
with self.monitor('reading exposure', autoflush=True):
self.sitecol, self.assetcol = readinput.get_sitecol_assetcol(
self.oqparam, haz_sitecol, self.riskmodel.loss_types)
readinput.exposure = None # reset the global
def read_exposure(self):
"""
Read the exposure, the riskmodel and update the attributes .exposure,
.sitecol, .assets_by_site, .taxonomies.
"""
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assetcol = (readinput.get_sitecol_assetcol(
self.oqparam, self.exposure))
# NB: using hdf5.vstr would fail for large exposures;
# the datastore could become corrupt, and also ultra-strange things
# may happen (i.e. having the sitecol saved inside asset_refs!!)
arefs = numpy.array(self.exposure.asset_refs)
self.datastore['asset_refs'] = arefs
self.datastore.set_attrs('asset_refs', nbytes=arefs.nbytes)
def read_config_file(cfg):
gmf_file = cfg['input']['gmf_file']
gmf_file_gmpe_rate = cfg['input']['gmf_file_gmpe_rate']
job_ini = cfg['input']['job_ini']
oq_param = get_oqparam(job_ini)
get_risk_model(oq_param) # read risk functions and set imtls
haz_sitecol = get_site_collection(oq_param)
sites, assets_by_site, _ = get_sitecol_assetcol(oq_param, haz_sitecol)
gsimlt = get_gsim_lt(oq_param)
gsim_list = [br.uncertainty for br in gsimlt.branches]
cinfo = source.CompositionInfo.fake(gsimlt)
mean_shift_inter_residuals = float(
cfg['input']['mean_shift_inter_residuals'])
realizations_inter = int(cfg['input']['realizations_inter'])
realizations_intra = int(cfg['input']['realizations_intra'])
intra_files_name = cfg['input']['intra_files_name']
intra_files = cfg['input']['intra_files'].split()
csv_rate_gmf_file = cfg['output']['csv_rate_gmf_file']
seed = int(cfg['input']['seed'])
return (gmf_file, gmf_file_gmpe_rate, sites, gsim_list, cinfo, oq_param,
mean_shift_inter_residuals, realizations_inter, realizations_intra,
intra_files_name, intra_files, csv_rate_gmf_file, seed)
def test_exposure_only(self):
oq = readinput.get_oqparam('job.ini', case_16)
del oq.inputs['site_model']
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 148)
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0)
# test agg_value
arr = assetcol.agg_value()
assert_allclose(arr, [3.6306637e+09])
arr = assetcol.agg_value('taxonomy')
assert_allclose(arr,
[[0.0000000e+00], [4.9882240e+06], [1.1328099e+08],
[4.2222912e+08], [1.6412870e+07], [5.0686808e+07],
[2.5343402e+07], [1.5254313e+09], [6.6375590e+06],
[8.3206810e+08], [1.6412871e+07], [3.9439158e+08],
[1.6734690e+07], [6.7582400e+06], [1.3613027e+08],
[4.3124016e+07], [9.4132640e+06], [1.0620092e+07]])
arr = assetcol.agg_value('occupancy')
assert_allclose(assetcol.agg_value('occupancy'),
[[0.0000000e+00], [3.6306644e+09]])
arr = assetcol.agg_value('taxonomy', 'occupancy')
self.assertEqual(arr.shape, (18, 2, 1))
def main(cfg_file):
startTime = datetime.now()
cfg = configparser.ConfigParser()
cfg.read(cfg_file)
(oq_param, source_model_file, matrixMagsMin, matrixMagsMax,
matrixMagsStep, matrixDistsMin, matrixDistsMax,
matrixDistsStep, limitIM, imt_filtering, trunc_level,
im_filter, gmf_file, gmf_file_gmpe_rate, rup_mesh_spac,
complex_mesh_spac, mfd_bin, area_discre, limit_max_mag,
limit_min_mag) = read_config_file(cfg)
# Set up the source model configuration
conv1 = SourceConverter(1.0, # Investigation time
rup_mesh_spac, # Rupture mesh spacing
complex_fault_mesh_spacing=complex_mesh_spac,
width_of_mfd_bin=mfd_bin,
area_source_discretization=area_discre)
# Parse the source Model
if source_model_file: # only one source model file
source_model = to_python(source_model_file, conv1)
else: # source model has many files (in this case 2 - adapt for more)
source_model_file2 = "demo_data/SA_RA_CATAL1_05.xml"
source_model2 = to_python(source_model_file2, conv1)
source_model = source_model+source_model2
# Calculate total number of ruptures in the erf
# num_rup = 0
# rate_rup = []
# for a in range(len(source_model)):
# model_trt = source_model[a]
# for b in range(len(model_trt)):
# num_rup = num_rup + len(list(model_trt[b].iter_ruptures()))
# for rup in model_trt[b].iter_ruptures():
# rate_rup.append(rup.occurrence_rate)
# print(num_rup)
# print(sum(rate_rup))
# print(rate_rup[0:10])
# If exposure model is provided:
haz_sitecol = get_site_collection(oq_param)
sites, assets_by_site, _ = get_sitecol_assetcol(oq_param, haz_sitecol)
# print(list(sites)[0:10])
# np.savetxt('sites.csv',list(zip(sites.lons, sites.lats)))
# If region coordinates are provided:
# sites = get_site_collection(oq_param)
gsimlt = get_gsim_lt(oq_param)
gsim_list = [br.uncertainty for br in gsimlt.branches]
GMPEmatrix = build_gmpe_table(matrixMagsMin, matrixMagsMax, matrixMagsStep,
matrixDistsMin, matrixDistsMax,
matrixDistsStep, imt_filtering, limitIM,
gsim_list, limit_max_mag, limit_min_mag)
# Calculate minimum distance between rupture and assets
# Import exposure from .ini file
depths = np.zeros(len(sites))
exposureCoords = Mesh(sites.lons, sites.lats, depths)
# To calculate Joyner Boore distance:
exposurePoints = (exposureCoords, exposureCoords)
recMeshExposure = RectangularMesh.from_points_list(exposurePoints)
imts = ['PGA', 'SA(0.3)']
cmake = ContextMaker(gsim_list)
filter1 = SourceFilter(sites, oq_param.maximum_distance)
if im_filter == 'True': # Here we consider the IM and the MaxDist filter
gmfs_median = calculate_gmfs_filter(source_model, gsimlt, filter1,
cmake, gsim_list, recMeshExposure,
matrixMagsMin, matrixMagsStep,
matrixDistsMin, matrixDistsStep,
GMPEmatrix, imts, trunc_level)
else: # No IM filter, just the MAxDist filter
gmfs_median = calc_gmfs_no_IM_filter(source_model, imts, gsim_list,
trunc_level, gsimlt,
filter1, cmake)
print("%s Ground Motion Fields" % len(gmfs_median))
save_gmfs(gmf_file, gmf_file_gmpe_rate, gmfs_median, exposureCoords,
gsim_list, imts)
print(datetime.now() - startTime)
def test_site_model_exposure(self):
oq = readinput.get_oqparam('job.ini', case_16)
sitecol, assetcol, discarded = readinput.get_sitecol_assetcol(oq)
self.assertEqual(len(sitecol), 148)
self.assertEqual(len(assetcol), 151)
self.assertEqual(len(discarded), 0)
