def get_oqparam(job_ini, pkg=None, calculators=None):
"""
Parse a dictionary of parameters from one or more INI-style config file.
:param job_ini:
Path to configuration file/archive or dictionary of parameters
:param pkg:
Python package where to find the configuration file (optional)
:param calculators:
Sequence of calculator names (optional) used to restrict the
valid choices for `calculation_mode`
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
# UGLY: this is here to avoid circular imports
from openquake.commonlib.calculators import base
OqParam.calculation_mode.validator.choices = tuple(
calculators or base.calculators)
if isinstance(job_ini, dict):
oqparam = OqParam(**job_ini)
else:
basedir = os.path.dirname(pkg.__file__) if pkg else ''
inis = [os.path.join(basedir, ini) for ini in job_ini.split(',')]
oqparam = OqParam(**get_params(inis))
oqparam.validate()
return oqparam
def test_missing_export_dir(self):
oq = OqParam(
calculation_mode='event_based', inputs={},
sites='0.1 0.2',
intensity_measure_types='PGA',
maximum_distance=400)
oq.validate()
self.assertEqual(oq.export_dir, os.path.expanduser('~'))
def test_imts_and_imtls(self):
oq = OqParam(
calculation_mode='event_based', inputs={},
intensity_measure_types_and_levels="{'PGA': [0.1, 0.2]}",
intensity_measure_types='PGV', sites='0.1 0.2',
maximum_distance=400)
oq.validate()
self.assertEqual(list(oq.imtls.keys()), ['PGA'])
def test_missing_export_dir(self):
oq = OqParam(
calculation_mode='event_based', inputs=GST,
sites='0.1 0.2',
intensity_measure_types='PGA',
reference_vs30_value='200',
maximum_distance='400')
oq.validate()
self.assertEqual(oq.export_dir, os.getcwd())
def show(calc_id, key=None, rlzs=None):
"""
Show the content of a datastore.
:param calc_id: numeric calculation ID; if 0, show all calculations
:param key: key of the datastore
:param rlzs: flag; if given, print out the realizations in order
"""
if not calc_id:
if not os.path.exists(datastore.DATADIR):
return
rows = []
for calc_id in datastore.get_calc_ids(datastore.DATADIR):
try:
oq = OqParam.from_(datastore.DataStore(calc_id).attrs)
cmode, descr = oq.calculation_mode, oq.description
except: # invalid datastore directory
logging.warn('Removed invalid calculation %d', calc_id)
shutil.rmtree(os.path.join(
datastore.DATADIR, 'calc_%s' % calc_id))
else:
rows.append((calc_id, cmode, descr))
for row in sorted(rows, key=lambda row: row[0]): # by calc_id
print('#%d %s: %s' % row)
return
ds = datastore.DataStore(calc_id)
if key:
if key in datastore.view:
print(datastore.view(key, ds))
return
obj = ds[key]
if hasattr(obj, 'value'): # an array
print(write_csv(io.StringIO(), obj.value))
else:
print(obj)
return
# print all keys
oq = OqParam.from_(ds.attrs)
print(oq.calculation_mode, 'calculation (%r) saved in %s contains:' %
(oq.description, ds.hdf5path))
for key in ds:
print(key, humansize(ds.getsize(key)))
# this part is experimental and not tested on purpose
if rlzs and 'curves_by_trt_gsim' in ds:
min_value = 0.01 # used in rmsep
curves_by_rlz, mean_curves = combined_curves(ds)
dists = []
for rlz in sorted(curves_by_rlz):
curves = curves_by_rlz[rlz]
dist = sum(rmsep(mean_curves[imt], curves[imt], min_value)
for imt in mean_curves.dtype.fields)
dists.append((dist, rlz))
for dist, rlz in sorted(dists):
print('rlz=%s, rmsep=%s' % (rlz, dist))
def export_ses_xml(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
fmt = ekey[-1]
oq = OqParam.from_(dstore.attrs)
try:
csm_info = dstore['rlzs_assoc'].csm_info
except AttributeError: # for scenario calculators don't export
return []
sescollection = dstore['sescollection']
col_id = 0
fnames = []
for sm in csm_info.source_models:
for trt_model in sm.trt_models:
sesruptures = list(sescollection[col_id].values())
col_id += 1
ses_coll = SESCollection(
groupby(sesruptures, operator.attrgetter('ses_idx')),
sm.path, oq.investigation_time)
smpath = '_'.join(sm.path)
fname = 'ses-%d-smltp_%s.%s' % (trt_model.id, smpath, fmt)
dest = os.path.join(dstore.export_dir, fname)
globals()['_export_ses_' + fmt](dest, ses_coll)
fnames.append(os.path.join(dstore.export_dir, fname))
return fnames
def export_agg_curve(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
cost_types = dstore['cost_types']
rlzs = dstore['rlzs_assoc'].realizations
agg_curve = dstore[ekey[0]]
fnames = []
L, R = len(cost_types), len(rlzs)
for ct in cost_types:
loss_type = ct['name']
array = agg_curve[loss_type].value
for ins in range(oq.insured_losses + 1):
for rlz in rlzs:
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (
rlz.uid, loss_type)
dest = dstore.export_path('agg_curve%s%s.%s' % (
suffix, '_ins' if ins else '', ekey[1]))
rec = array[rlz.ordinal, ins]
curve = AggCurve(rec['losses'], rec['poes'], rec['avg'], None)
risk_writers.AggregateLossCurveXMLWriter(
dest, oq.investigation_time, loss_type,
source_model_tree_path='_'.join(rlz.sm_lt_path),
gsim_tree_path='_'.join(rlz.gsim_lt_path),
unit=ct['unit']).serialize(curve)
fnames.append(dest)
return sorted(fnames)
def export_hcurves_xml_json(ekey, dstore):
export_type = ekey[1]
len_ext = len(export_type) + 1
oq = OqParam.from_(dstore.attrs)
sitemesh = dstore['sitemesh'].value
rlzs_assoc = dstore['rlzs_assoc']
fnames = []
writercls = (hazard_writers.HazardCurveGeoJSONWriter
if export_type == 'geojson' else
hazard_writers.HazardCurveXMLWriter)
rlzs = iter(rlzs_assoc.realizations)
for kind, curves in dstore[ekey[0]].items():
rlz = next(rlzs)
name = hazard_curve_name(
dstore, ekey, kind, rlzs_assoc, oq.number_of_logic_tree_samples)
for imt in oq.imtls:
fname = name[:-len_ext] + '-' + imt + '.' + export_type
data = [HazardCurve(Location(site), poes[imt])
for site, poes in zip(sitemesh, curves)]
writer = writercls(fname, investigation_time=oq.investigation_time,
imls=oq.imtls[imt],
smlt_path='_'.join(rlz.sm_lt_path),
gsimlt_path=rlz.gsim_rlz.uid)
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def pre_execute(self):
"""
Check if there is a pre_calculator or a previous calculation ID.
If yes, read the inputs by invoking the precalculator or by retrieving
the previous calculation; if not, read the inputs directly.
"""
if self.pre_calculator is not None:
# the parameter hazard_calculation_id is only meaningful if
# there is a precalculator
precalc_id = self.oqparam.hazard_calculation_id
if precalc_id is None: # recompute everything
precalc = calculators[self.pre_calculator](
self.oqparam, self.monitor('precalculator'),
self.datastore.calc_id)
precalc.run(clean_up=False)
if 'scenario' not in self.oqparam.calculation_mode:
self.csm = precalc.csm
else: # read previously computed data
self.datastore.set_parent(datastore.DataStore(precalc_id))
# update oqparam with the attributes saved in the datastore
self.oqparam = OqParam.from_(self.datastore.attrs)
self.read_exposure_sitecol()
else: # we are in a basic calculator
self.read_exposure_sitecol()
self.read_sources()
self.datastore.hdf5.flush()
def export_gmf(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
sitecol = dstore['sitecol']
rlzs_assoc = dstore['rlzs_assoc']
rupture_by_tag = sum(dstore['sescollection'], AccumDict())
all_tags = dstore['tags'].value
oq = OqParam.from_(dstore.attrs)
investigation_time = (None if oq.calculation_mode == 'scenario'
else oq.investigation_time)
samples = oq.number_of_logic_tree_samples
fmt = ekey[-1]
gmfs = dstore[ekey[0]]
nbytes = gmfs.attrs['nbytes']
logging.info('Internal size of the GMFs: %s', humansize(nbytes))
if nbytes > GMF_MAX_SIZE:
logging.warn(GMF_WARNING, dstore.hdf5path)
fnames = []
for rlz, gmf_by_idx in zip(
rlzs_assoc.realizations, rlzs_assoc.combine_gmfs(gmfs)):
tags = all_tags[list(gmf_by_idx)]
gmfs = list(gmf_by_idx.values())
if not gmfs:
continue
ruptures = [rupture_by_tag[tag] for tag in tags]
fname = build_name(dstore, rlz, 'gmf', fmt, samples)
fnames.append(fname)
globals()['export_gmf_%s' % fmt](
('gmf', fmt), fname, sitecol,
ruptures, gmfs, rlz, investigation_time)
return fnames
def get_data_transfer(dstore):
"""
Determine the amount of data transferred from the controller node
to the workers and back in a classical calculation.
:param dstore: a :class:`openquake.commonlib.datastore.DataStore` instance
:returns: (block_info, to_send_forward, to_send_back)
"""
oqparam = OqParam.from_(dstore.attrs)
sitecol = dstore['sitecol']
rlzs_assoc = dstore['rlzs_assoc']
info = dstore['job_info']
sources = dstore['composite_source_model'].get_sources()
num_gsims_by_trt = groupby(rlzs_assoc, operator.itemgetter(0),
lambda group: sum(1 for row in group))
gsims_assoc = rlzs_assoc.gsims_by_trt_id
to_send_forward = 0
to_send_back = 0
block_info = []
for block in split_in_blocks(sources, oqparam.concurrent_tasks or 1,
operator.attrgetter('weight'),
operator.attrgetter('trt_model_id')):
num_gsims = num_gsims_by_trt.get(block[0].trt_model_id, 0)
back = info['n_sites'] * info['n_levels'] * info['n_imts'] * num_gsims
to_send_back += back * 8 # 8 bytes per float
args = (block, sitecol, gsims_assoc, PerformanceMonitor(''))
to_send_forward += sum(len(p) for p in parallel.pickle_sequence(args))
block_info.append((len(block), block.weight))
return numpy.array(block_info, block_dt), to_send_forward, to_send_back
def get_oqparam(self):
"""
Return an OqParam object as read from the database
"""
datadir = os.path.dirname(self.ds_calc_dir)
dstore = datastore.read(self.id, datadir=datadir)
oqparam = OqParam.from_(dstore.attrs)
return oqparam
def test_missing_maximum_distance(self):
with self.assertRaises(ValueError):
OqParam(
calculation_mode='classical_risk', inputs=dict(site_model=''),
hazard_calculation_id=None, hazard_output_id=None,
sites='0.1 0.2').validate()
with self.assertRaises(ValueError):
OqParam(
calculation_mode='classical_risk', inputs=dict(site_model=''),
hazard_calculation_id=None, hazard_output_id=None,
sites='0.1 0.2', maximum_distance='0').validate()
oq = OqParam(
calculation_mode='event_based', inputs=GST,
intensity_measure_types_and_levels="{'PGA': [0.1, 0.2]}",
intensity_measure_types='PGV', sites='0.1 0.2',
reference_vs30_value='200',
maximum_distance='{"wrong TRT": 200}')
oq.inputs['source_model_logic_tree'] = 'something'
oq._gsims_by_trt = {'Active Shallow Crust': []}
self.assertFalse(oq.is_valid_maximum_distance())
self.assertIn('setting the maximum_distance for wrong TRT', oq.error)
oq._gsims_by_trt = {'Active Shallow Crust': [],
'Stable Continental Crust': []}
oq.maximum_distance = {'Active Shallow Crust': 200}
self.assertFalse(oq.is_valid_maximum_distance())
self.assertEqual('missing distance for Stable Continental Crust '
'and no default', oq.error)
def view_params(token, dstore):
oq = OqParam.from_(dstore.attrs)
params = ('calculation_mode', 'number_of_logic_tree_samples',
'maximum_distance', 'investigation_time',
'ses_per_logic_tree_path', 'truncation_level',
'rupture_mesh_spacing', 'complex_fault_mesh_spacing',
'width_of_mfd_bin', 'area_source_discretization',
'random_seed', 'master_seed', 'concurrent_tasks')
return rst_table([(param, getattr(oq, param)) for param in params])
def view_inputs(token, dstore):
inputs = OqParam.from_(dstore.attrs).inputs.copy()
try:
source_models = [('source', fname) for fname in inputs['source']]
del inputs['source']
except KeyError: # there is no 'source' in scenario calculations
source_models = []
return rst_table(
build_links(list(inputs.items()) + source_models),
header=['Name', 'File'])
def view_gmfs_total_size(name, dstore):
"""
:returns:
the total size of the GMFs as human readable string; it assumes
4 bytes for the rupture index, 4 bytes for the realization index
and 8 bytes for each float (there are num_imts floats per gmf)
"""
nbytes = 0
num_imts = len(OqParam.from_(dstore.attrs).imtls)
for counts in dstore['counts_per_rlz']:
nbytes += 8 * counts['gmf'] * (num_imts + 1)
return humansize(nbytes)
def get_oqparam(job_ini, pkg=None, calculators=None, hc_id=None, validate=1,
**kw):
"""
Parse a dictionary of parameters from an INI-style config file.
:param job_ini:
Path to configuration file/archive or dictionary of parameters
:param pkg:
Python package where to find the configuration file (optional)
:param calculators:
Sequence of calculator names (optional) used to restrict the
valid choices for `calculation_mode`
:param hc_id:
Not None only when called from a post calculation
:param validate:
Flag. By default it is true and the parameters are validated
:param kw:
String-valued keyword arguments used to override the job.ini parameters
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
# UGLY: this is here to avoid circular imports
from openquake.calculators import base
OqParam.calculation_mode.validator.choices = tuple(
calculators or base.calculators)
if not isinstance(job_ini, dict):
basedir = os.path.dirname(pkg.__file__) if pkg else ''
job_ini = get_params([os.path.join(basedir, job_ini)])
if hc_id:
job_ini.update(hazard_calculation_id=str(hc_id))
job_ini.update(kw)
oqparam = OqParam(**job_ini)
if validate:
oqparam.validate()
return oqparam
def avglosses_data_transfer(token, dstore):
"""
Determine the amount of average losses transferred from the workers to the
controller node in a risk calculation.
"""
oq = OqParam.from_(dstore.attrs)
N = len(dstore['assetcol'])
R = len(dstore['rlzs_assoc'].realizations)
L = len(dstore['riskmodel'].loss_types)
ct = oq.concurrent_tasks
size_bytes = N * R * L * 2 * 8 * ct # two 8 byte floats, loss and ins_loss
return ('%d asset(s) x %d realization(s) x %d loss type(s) x 2 losses x '
'8 bytes x %d tasks = %s' % (N, R, L, ct, humansize(size_bytes)))
def test_disaggregation(self):
with self.assertRaises(ValueError) as ctx:
OqParam(
calculation_mode='disaggregation',
inputs=fakeinputs,
gsim='BooreAtkinson2008',
reference_vs30_value='200',
sites='0.1 0.2',
poes='0.2',
maximum_distance='400',
intensity_measure_types_and_levels="{'PGV': [0.1, 0.2, 0.3]}",
uniform_hazard_spectra='1')
self.assertIn("poes_disagg or iml_disagg must be set",
str(ctx.exception))
def test_invalid_export_dir(self):
# FIXME: apparently this fails only when --with-doctest is set
raise unittest.SkipTest
with self.assertRaises(ValueError) as ctx:
OqParam(
calculation_mode='event_based', inputs=GST,
sites='0.1 0.2',
maximum_distance='400',
reference_vs30_value='200',
intensity_measure_types='PGA',
export_dir='/non/existing',
).validate()
self.assertIn('The `export_dir` parameter must refer to a '
'directory', str(ctx.exception))
def test_unknown_parameter(self):
# if the job.ini file contains an unknown parameter, print a warning
with mock.patch('logging.warn') as w:
OqParam(
calculation_mode='classical', inputs=GST,
hazard_calculation_id=None, hazard_output_id=None,
maximum_distance='10', sites='0.1 0.2',
reference_vs30_value='200',
not_existing_param='XXX', export_dir=TMP,
intensity_measure_types_and_levels="{'PGA': [0.1, 0.2]}",
rupture_mesh_spacing='1.5').validate()
self.assertEqual(
w.call_args[0][0],
"The parameter 'not_existing_param' is unknown, ignoring")
def test_geometry(self):
# you cannot have both region and sites
with self.assertRaises(ValueError):
OqParam(
calculation_mode='classical_risk',
hazard_calculation_id=None,
hazard_output_id=None,
maximum_distance='10',
inputs=fakeinputs,
region_grid_spacing='5',
region='-78.182 15.615, -78.152 15.615, -78.152 15.565, '
'-78.182 15.565',
sites='0.1 0.2',
).validate()
def test_create_export_dir(self):
# FIXME: apparently this fails only when --with-doctest is set
raise unittest.SkipTest
EDIR = os.path.join(TMP, 'nonexisting')
OqParam(
calculation_mode='event_based',
sites='0.1 0.2',
reference_vs30_value='200',
intensity_measure_types='PGA',
inputs=GST,
maximum_distance='400',
export_dir=EDIR,
).validate()
self.assertTrue(os.path.exists(EDIR))
def test_required_site_param(self):
with self.assertRaises(ValueError) as ctx:
OqParam(
calculation_mode='scenario',
gsim='AbrahamsonSilva2008',
sites='0.1 0.2',
maximum_distance='400',
reference_vs30_value='760',
intensity_measure_types='PGA',
inputs=fakeinputs,
).validate()
self.assertIn(
"Please set a value for 'reference_depth_to_1pt0km_per_sec', this "
"is required by the GSIM AbrahamsonSilva2008", str(ctx.exception))
def test_event_based_risk(self):
with self.assertRaises(InvalidFile) as ctx:
OqParam(
calculation_mode='event_based_risk',
inputs=fakeinputs,
gsim='BooreAtkinson2008',
reference_vs30_value='200',
sites='0.1 0.2',
poes='0.2',
maximum_distance='400',
intensity_measure_types_and_levels="{'PGV': [0.1, 0.2, 0.3]}",
conditional_loss_poes='0.02')
self.assertIn("asset_loss_table is not set, probably you want to "
"remove conditional_loss_poes", str(ctx.exception))
def get_oqparam(job_ini, pkg=None, calculators=None, hc_id=None, validate=1):
"""
Parse a dictionary of parameters from an INI-style config file.
:param job_ini:
Path to configuration file/archive or dictionary of parameters
:param pkg:
Python package where to find the configuration file (optional)
:param calculators:
Sequence of calculator names (optional) used to restrict the
valid choices for `calculation_mode`
:param hc_id:
Not None only when called from a post calculation
:param validate:
Flag. By default it is true and the parameters are validated
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
# UGLY: this is here to avoid circular imports
from openquake.calculators import base
OqParam.calculation_mode.validator.choices = tuple(calculators
or base.calculators)
if not isinstance(job_ini, dict):
basedir = os.path.dirname(pkg.__file__) if pkg else ''
job_ini = get_params([os.path.join(basedir, job_ini)])
if hc_id:
job_ini.update(hazard_calculation_id=str(hc_id))
oqparam = OqParam(**job_ini)
if validate:
oqparam.validate()
BaseSeismicSource.min_mag = oqparam.minimum_magnitude
return oqparam
def get_oqparam(job_ini, pkg=None, calculators=None, hc_id=None, validate=1):
"""
Parse a dictionary of parameters from an INI-style config file.
:param job_ini:
Path to configuration file/archive or dictionary of parameters
:param pkg:
Python package where to find the configuration file (optional)
:param calculators:
Sequence of calculator names (optional) used to restrict the
valid choices for `calculation_mode`
:param hc_id:
Not None only when called from a post calculation
:param validate:
Flag. By default it is true and the parameters are validated
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
# UGLY: this is here to avoid circular imports
from openquake.calculators import base
OqParam.calculation_mode.validator.choices = tuple(calculators
or base.calculators)
if not isinstance(job_ini, dict):
basedir = os.path.dirname(pkg.__file__) if pkg else ''
job_ini = get_params(os.path.join(basedir, job_ini))
if hc_id:
job_ini.update(hazard_calculation_id=str(hc_id))
re = os.environ.get('OQ_REDUCE') # debugging facility
if re:
# reduce the imtls to the first imt
# reduce the logic tree to one random realization
# reduce the sites by a factor of `re`
# reduce the ses by a factor of `re`
# set save_disk_space = true
os.environ['OQ_SAMPLE_SITES'] = str(1 / float(re))
job_ini['number_of_logic_tree_samples'] = 1
ses = job_ini.get('ses_per_logic_tree_path')
if ses:
ses = str(int(numpy.ceil(int(ses) / float(re))))
job_ini['ses_per_logic_tree_path'] = ses
imtls = job_ini.get('intensity_measure_types_and_levels')
if imtls:
imtls = valid.intensity_measure_types_and_levels(imtls)
imt = next(iter(imtls))
job_ini['intensity_measure_types_and_levels'] = repr(
{imt: imtls[imt]})
job_ini['save_disk_space'] = True
oqparam = OqParam(**job_ini)
if validate and '_job_id' not in job_ini:
oqparam.check_source_model()
oqparam.validate()
return oqparam
def test_uniform_hazard_spectra(self):
with self.assertRaises(ValueError) as ctx:
OqParam(
calculation_mode='classical',
gsim='BooreAtkinson2008',
reference_vs30_value='200',
sites='0.1 0.2',
poes='0.2',
maximum_distance='400',
intensity_measure_types_and_levels="{'PGV': [0.1, 0.2, 0.3]}",
uniform_hazard_spectra='1',
).set_risk_imtls({})
self.assertIn(
"The `uniform_hazard_spectra` can be True only if "
"the IMT set contains SA(...) or PGA", str(ctx.exception))
def export_loss_map_xml_geojson(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
unit_by_lt = {
riskmodels.cost_type_to_loss_type(ct['name']): ct['unit']
for ct in dstore['cost_types']
}
unit_by_lt['fatalities'] = 'people'
rlzs = dstore['rlzs_assoc'].realizations
loss_map = dstore[ekey[0]]
riskmodel = dstore['riskmodel']
assetcol = dstore['assetcol']
R = len(rlzs)
sitemesh = dstore['sitemesh']
L = len(riskmodel.loss_types)
fnames = []
export_type = ekey[1]
writercls = (risk_writers.LossMapGeoJSONWriter if export_type == 'geojson'
else risk_writers.LossMapXMLWriter)
loss_types = riskmodel.loss_types
for lt in loss_types:
alosses = loss_map[lt]
for ins in range(oq.insured_losses + 1):
means = alosses['mean' + ('_ins' if ins else '')]
stddevs = alosses['stddev' + ('_ins' if ins else '')]
for r in range(R):
rlz = rlzs[r]
unit = unit_by_lt[lt]
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (
rlz.uid, lt)
root = ekey[0][:-5] # strip -rlzs
name = '%s%s%s.%s' % (root, suffix, '_ins' if ins else '',
ekey[1])
fname = dstore.export_path(name)
data = []
for ass, mean, stddev in zip(assetcol, means[:, r],
stddevs[:, r]):
loc = Location(sitemesh[ass['site_id']])
lm = LossMap(loc, ass['asset_ref'], mean, stddev)
data.append(lm)
writer = writercls(fname,
oq.investigation_time,
poe=None,
loss_type=lt,
gsim_tree_path=rlz.uid,
unit=unit)
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def get_oq(text):
"""
Returns an OqParam instance from a configuration string. For instance:
>>> get_oq('maximum_distance=200')
<OqParam calculation_mode='classical', collapse_level=0, inputs={'job_ini': '<in-memory>'}, maximum_distance={'default': 200}, risk_investigation_time=None>
"""
# UGLY: this is here to avoid circular imports
from openquake.calculators import base
OqParam.calculation_mode.validator.choices = tuple(base.calculators)
cp = configparser.ConfigParser()
cp.read_string('[general]\ncalculation_mode=classical\n' + text)
dic = dict(cp['general'])
dic['inputs'] = dict(job_ini='<in-memory>')
oq = OqParam(**dic)
return oq
def export_avg_losses_stats(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = OqParam.from_(dstore.attrs)
avg_losses = dstore[ekey[0]].value
quantiles = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves]
assets = get_assets(dstore)
writer = writers.CsvWriter(fmt='%10.6E')
for i, quantile in enumerate(quantiles):
losses = avg_losses[:, i]
dest = dstore.export_path('avg_losses-%s.csv' % quantile)
data = compose_arrays(assets, losses)
writer.save(data, dest)
return writer.getsaved()
def test_duplicated_levels(self):
with self.assertRaises(ValueError) as ctx:
OqParam(
calculation_mode='classical', inputs={},
sites='0.1 0.2',
reference_vs30_type='measured',
reference_vs30_value='200',
reference_depth_to_2pt5km_per_sec='100',
reference_depth_to_1pt0km_per_sec='150',
maximum_distance='400',
intensity_measure_types_and_levels='{"PGA": [0.4, 0.4, 0.6]}',
).validate()
self.assertEqual(
str(ctx.exception),
'Found duplicated levels for PGA: [0.4, 0.4, 0.6]: could not '
'convert to intensity_measure_types_and_levels: '
'intensity_measure_types_and_levels={"PGA": [0.4, 0.4, 0.6]}')
def read_exposure_sitecol(self):
"""
Read the exposure (if any) and then the site collection, possibly
extracted from the exposure.
"""
logging.info('Reading the site collection')
with self.monitor('reading site collection', autoflush=True):
haz_sitecol = readinput.get_site_collection(self.oqparam)
inputs = self.oqparam.inputs
if 'exposure' in inputs:
logging.info('Reading the exposure')
with self.monitor('reading exposure', autoflush=True):
self.exposure = readinput.get_exposure(self.oqparam)
self.sitecol, self.assets_by_site = (
readinput.get_sitecol_assets(self.oqparam, self.exposure))
if len(self.exposure.cost_types):
self.cost_types = self.exposure.cost_types
self.taxonomies = numpy.array(
sorted(self.exposure.taxonomies), '|S100')
num_assets = self.count_assets()
if self.datastore.parent:
haz_sitecol = self.datastore.parent['sitecol']
if haz_sitecol is not None and haz_sitecol != self.sitecol:
with self.monitor('assoc_assets_sites'):
self.sitecol, self.assets_by_site = \
self.assoc_assets_sites(haz_sitecol.complete)
ok_assets = self.count_assets()
num_sites = len(self.sitecol)
logging.warn('Associated %d assets to %d sites, %d discarded',
ok_assets, num_sites, num_assets - ok_assets)
elif (self.datastore.parent and 'exposure' in
OqParam.from_(self.datastore.parent.attrs).inputs):
logging.info('Re-using the already imported exposure')
else: # no exposure
self.sitecol = haz_sitecol
# save mesh and asset collection
self.save_mesh()
if hasattr(self, 'assets_by_site'):
self.assetcol = riskinput.build_asset_collection(
self.assets_by_site, self.oqparam.time_event)
spec = set(self.oqparam.specific_assets)
unknown = spec - set(self.assetcol['asset_ref'])
if unknown:
raise ValueError('The specific asset(s) %s are not in the '
'exposure' % ', '.join(unknown))
def export_avg_losses_stats(ekey, dstore):
"""
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = OqParam.from_(dstore.attrs)
avg_losses = dstore[ekey[0]]
quantiles = ['mean'] + ['quantile-%s' % q for q in oq.quantile_loss_curves]
assets = get_assets(dstore)
fnames = []
for i, quantile in enumerate(quantiles):
losses = avg_losses[:, i]
dest = dstore.export_path('avg_losses-%s.csv' % quantile)
data = compose_arrays(assets, losses)
writers.write_csv(dest, data, fmt='%10.6E')
fnames.append(dest)
return fnames
def export_lossmaps_xml_geojson(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
unit_by_lt = {
riskmodels.cost_type_to_loss_type(ct['name']): ct['unit']
for ct in dstore['cost_types']
}
unit_by_lt['fatalities'] = 'people'
rlzs = dstore['rlzs_assoc'].realizations
avglosses = dstore[ekey[0]]
riskmodel = dstore['riskmodel']
assetcol = dstore['assetcol']
sitemesh = dstore['sitemesh']
L = len(riskmodel.loss_types)
N, R = avglosses.shape
fnames = []
export_type = ekey[1]
writercls = (risk_writers.LossMapGeoJSONWriter if export_type == 'geojson'
else risk_writers.LossMapXMLWriter)
for l, lt in enumerate(riskmodel.loss_types):
alosses = avglosses[lt]
for r in range(R):
rlz = rlzs[r]
unit = unit_by_lt[lt]
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (rlz.uid,
lt)
name = '%s%s.%s' % (ekey[0], suffix, ekey[1])
fname = dstore.export_path(name)
data = []
for ass, stat in zip(assetcol, alosses[:, r]):
loc = Location(sitemesh[ass['site_id']])
lm = LossMap(loc, ass['asset_ref'], stat['mean'],
stat['stddev'])
data.append(lm)
writer = writercls(fname,
oq.investigation_time,
poe=None,
loss_type=lt,
gsim_tree_path=None,
unit=unit,
loss_category=None)
# TODO: replace the category with the exposure category
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def test_missing_maximum_distance(self):
with self.assertRaises(ValueError):
OqParam(
calculation_mode='classical', inputs=fakeinputs,
sites='0.1 0.2').validate()
oq = OqParam(
calculation_mode='event_based', inputs=GST,
intensity_measure_types_and_levels="{'PGA': [0.1, 0.2]}",
intensity_measure_types='PGV', sites='0.1 0.2',
reference_vs30_value='200',
maximum_distance='{"wrong TRT": 200}')
oq.inputs['source_model_logic_tree'] = 'something'
oq._gsims_by_trt = {'Active Shallow Crust': []}
self.assertFalse(oq.is_valid_maximum_distance())
self.assertIn('setting the maximum_distance for wrong TRT', oq.error)
oq._gsims_by_trt = {'Active Shallow Crust': [],
'Stable Continental Crust': []}
oq.maximum_distance = {'Active Shallow Crust': 200}
self.assertFalse(oq.is_valid_maximum_distance())
self.assertEqual('missing distance for Stable Continental Crust '
'and no default', oq.error)
def _print_info(dstore, filtersources=True, weightsources=True):
assoc = dstore["rlzs_assoc"]
oqparam = OqParam.from_(dstore.attrs)
csm = dstore["composite_source_model"]
sitecol = dstore["sitecol"]
print(csm.get_info())
print("See https://github.com/gem/oq-risklib/blob/master/doc/" "effective-realizations.rst for an explanation")
print(assoc)
if filtersources or weightsources:
[info] = readinput.get_job_info(oqparam, csm, sitecol)
info["n_sources"] = csm.get_num_sources()
curve_matrix_size = info["n_sites"] * info["n_levels"] * info["n_imts"] * len(assoc) * 8
for k in info.dtype.fields:
if k == "input_weight" and not weightsources:
pass
else:
print(k, info[k])
print("curve_matrix_size", humansize(curve_matrix_size))
if "num_ruptures" in dstore:
print(datastore.view("rupture_collections", dstore))
def export_hcurves_csv(ekey, dstore):
"""
Exports the hazard curves into several .csv files
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = OqParam.from_(dstore.attrs)
rlzs_assoc = dstore['rlzs_assoc']
sitecol = dstore['sitecol']
key, fmt = ekey
fnames = []
for kind, hcurves in dstore[key].items():
fname = hazard_curve_name(dstore, ekey, kind, rlzs_assoc,
oq.number_of_logic_tree_samples)
if key == 'uhs':
export_uhs_csv(ekey, fname, sitecol, hcurves)
else:
export_hazard_curves_csv(ekey, fname, sitecol, hcurves, oq.imtls)
fnames.append(fname)
return sorted(fnames)
def get_hcurves_and_means(dstore):
"""
Extract hcurves from the datastore and compute their means.
:returns: curves_by_rlz, mean_curves
"""
oq = OqParam.from_(dstore.attrs)
hcurves = dstore['hcurves']
realizations = dstore['rlzs_assoc'].realizations
weights = [rlz.weight for rlz in realizations]
curves_by_rlz = {
rlz: hcurves['rlz-%03d' % rlz.ordinal]
for rlz in realizations
}
N = len(dstore['sitemesh'])
mean_curves = zero_curves(N, oq.imtls)
for imt in oq.imtls:
mean_curves[imt] = scientific.mean_curve(
[curves_by_rlz[rlz][imt] for rlz in sorted(curves_by_rlz)],
weights)
return curves_by_rlz, mean_curves
def export_agg_curve_stats(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
quantiles = oq.quantile_loss_curves
cost_types = dstore['cost_types']
agg_curve = dstore[ekey[0]]
fnames = []
for ct in cost_types:
loss_type = ct['name']
array = agg_curve[loss_type].value
for ins in range(oq.insured_losses + 1):
for i, sname, qvalue in _gen_idx_sname_qvalue(quantiles):
dest = dstore.export_path('agg_curve-%s-%s%s.%s' % (
sname, loss_type, '_ins' if ins else '', ekey[1]))
rec = array[i, ins]
curve = AggCurve(rec['losses'], rec['poes'], rec['avg'], None)
risk_writers.AggregateLossCurveXMLWriter(
dest, oq.investigation_time, loss_type,
statistics=sname, quantile_value=qvalue,
unit=ct['unit']).serialize(curve)
fnames.append(dest)
return sorted(fnames)
def export_disagg_xml(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
rlzs = dstore['rlzs_assoc'].realizations
group = dstore['disagg']
fnames = []
writercls = hazard_writers.DisaggXMLWriter
for key in group:
matrix = pickle.loads(group[key].value)
attrs = group[key].attrs
rlz = rlzs[attrs['rlzi']]
poe = attrs['poe']
iml = attrs['iml']
imt, sa_period, sa_damping = from_string(attrs['imt'])
fname = dstore.export_path(key + '.xml')
lon, lat = attrs['location']
# TODO: add poe=poe below
writer = writercls(
fname,
investigation_time=oq.investigation_time,
imt=imt,
smlt_path='_'.join(rlz.sm_lt_path),
gsimlt_path=rlz.gsim_rlz.uid,
lon=lon,
lat=lat,
sa_period=sa_period,
sa_damping=sa_damping,
mag_bin_edges=attrs['mag_bin_edges'],
dist_bin_edges=attrs['dist_bin_edges'],
lon_bin_edges=attrs['lon_bin_edges'],
lat_bin_edges=attrs['lat_bin_edges'],
eps_bin_edges=attrs['eps_bin_edges'],
tectonic_region_types=attrs['trts'],
)
data = [
DisaggMatrix(poe, iml, dim_labels, matrix[i])
for i, dim_labels in enumerate(disagg.pmf_map)
]
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def export_hcurves_xml_json(ekey, dstore):
export_type = ekey[1]
len_ext = len(export_type) + 1
oq = OqParam.from_(dstore.attrs)
sitemesh = dstore['sitemesh'].value
rlzs_assoc = dstore['rlzs_assoc']
hcurves = dstore[ekey[0]]
fnames = []
writercls = (hazard_writers.HazardCurveGeoJSONWriter if export_type
== 'geojson' else hazard_writers.HazardCurveXMLWriter)
for kind in hcurves:
if kind.startswith('rlz-'):
rlz = rlzs_assoc.realizations[int(kind[4:])]
smlt_path = '_'.join(rlz.sm_lt_path)
gsimlt_path = rlz.gsim_rlz.uid
else:
smlt_path = ''
gsimlt_path = ''
curves = hcurves[kind]
name = hazard_curve_name(dstore, ekey, kind, rlzs_assoc,
oq.number_of_logic_tree_samples)
for imt in oq.imtls:
imtype, sa_period, sa_damping = from_string(imt)
fname = name[:-len_ext] + '-' + imt + '.' + export_type
data = [
HazardCurve(Location(site), poes[imt])
for site, poes in zip(sitemesh, curves)
]
writer = writercls(fname,
investigation_time=oq.investigation_time,
imls=oq.imtls[imt],
imt=imtype,
sa_period=sa_period,
sa_damping=sa_damping,
smlt_path=smlt_path,
gsimlt_path=gsimlt_path)
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def export_hcurves_csv(ekey, dstore):
"""
Exports the hazard curves into several .csv files
:param ekey: export key, i.e. a pair (datastore key, fmt)
:param dstore: datastore object
"""
oq = OqParam.from_(dstore.attrs)
rlzs_assoc = dstore['rlzs_assoc']
sitecol = dstore['sitecol']
key, fmt = ekey
fnames = []
for kind, hcurves in dstore[key].items():
fname = hazard_curve_name(
dstore, ekey, kind, rlzs_assoc,
oq.number_of_logic_tree_samples)
if key == 'uhs':
export_uhs_csv(ekey, fname, sitecol, hcurves)
else:
export_hazard_curves_csv(ekey, fname, sitecol, hcurves, oq.imtls)
fnames.append(fname)
return sorted(fnames)
def export_lossmaps_xml_geojson(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
unit_by_lt = {riskmodels.cost_type_to_loss_type(ct['name']): ct['unit']
for ct in dstore['cost_types']}
unit_by_lt['fatalities'] = 'people'
rlzs = dstore['rlzs_assoc'].realizations
avglosses = dstore[ekey[0]]
riskmodel = dstore['riskmodel']
assetcol = dstore['assetcol']
sitemesh = dstore['sitemesh']
L = len(riskmodel.loss_types)
N, R = avglosses.shape
fnames = []
export_type = ekey[1]
writercls = (risk_writers.LossMapGeoJSONWriter
if export_type == 'geojson' else
risk_writers.LossMapXMLWriter)
for l, lt in enumerate(riskmodel.loss_types):
alosses = avglosses[lt]
for r in range(R):
rlz = rlzs[r]
unit = unit_by_lt[lt]
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (
rlz.uid, lt)
name = '%s%s.%s' % (ekey[0], suffix, ekey[1])
fname = dstore.export_path(name)
data = []
for ass, stat in zip(assetcol, alosses[:, r]):
loc = Location(sitemesh[ass['site_id']])
lm = LossMap(loc, ass['asset_ref'],
stat['mean'], stat['stddev'])
data.append(lm)
writer = writercls(
fname, oq.investigation_time, poe=None, loss_type=lt,
gsim_tree_path=None, unit=unit, loss_category=None)
# TODO: replace the category with the exposure category
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def _print_info(dstore, filtersources=True, weightsources=True):
assoc = dstore['rlzs_assoc']
oqparam = OqParam.from_(dstore.attrs)
csm = dstore['composite_source_model']
sitecol = dstore['sitecol']
print(csm.get_info())
print('See https://github.com/gem/oq-risklib/blob/master/doc/'
'effective-realizations.rst for an explanation')
print(assoc)
if filtersources or weightsources:
[info] = readinput.get_job_info(oqparam, csm, sitecol)
info['n_sources'] = csm.get_num_sources()
curve_matrix_size = (info['n_sites'] * info['n_levels'] *
info['n_imts'] * len(assoc) * 8)
for k in info.dtype.fields:
if k == 'input_weight' and not weightsources:
pass
else:
print(k, info[k])
print('curve_matrix_size', humansize(curve_matrix_size))
if 'num_ruptures' in dstore:
print(datastore.view('rupture_collections', dstore))
def export_hmaps_xml_json(ekey, dstore):
export_type = ekey[1]
oq = OqParam.from_(dstore.attrs)
sitemesh = dstore['sitemesh'].value
rlzs_assoc = dstore['rlzs_assoc']
hmaps = dstore[ekey[0]]
fnames = []
writercls = (hazard_writers.HazardMapGeoJSONWriter if export_type
== 'geojson' else hazard_writers.HazardMapXMLWriter)
for kind in hmaps:
if kind.startswith('rlz-'):
rlz = rlzs_assoc.realizations[int(kind[4:])]
smlt_path = '_'.join(rlz.sm_lt_path)
gsimlt_path = rlz.gsim_rlz.uid
else:
smlt_path = ''
gsimlt_path = ''
maps = hmaps[kind]
for imt in oq.imtls:
for poe in oq.poes:
suffix = '-%s-%s' % (poe, imt)
fname = hazard_curve_name(dstore, ekey, kind + suffix,
rlzs_assoc,
oq.number_of_logic_tree_samples)
data = [
HazardMap(site[0], site[1], hmap['%s~%s' % (imt, poe)])
for site, hmap in zip(sitemesh, maps)
]
writer = writercls(fname,
investigation_time=oq.investigation_time,
imt=imt,
poe=poe,
smlt_path=smlt_path,
gsimlt_path=gsimlt_path)
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def export_lossmaps_xml(ekey, dstore):
oq = OqParam.from_(dstore.attrs)
unit_by_lt = {
riskmodels.cost_type_to_loss_type(ct['name']): ct['unit']
for ct in dstore['cost_types']
}
unit_by_lt['fatalities'] = 'people'
rlzs = dstore['rlzs_assoc'].realizations
avglosses = dstore['avglosses']
riskmodel = dstore['riskmodel']
assetcol = dstore['assetcol']
sitemesh = dstore['sitemesh']
N, L, R = avglosses.shape
fnames = []
for l, r in itertools.product(range(L), range(R)):
rlz = rlzs[r]
lt = riskmodel.loss_types[l]
unit = unit_by_lt[lt]
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (rlz.uid, lt)
fname = os.path.join(dstore.export_dir,
'%s%s.%s' % (ekey[0], suffix, ekey[1]))
data = []
for ass, stat in zip(assetcol, avglosses[:, l, r]):
loc = Location(sitemesh[ass['site_id']])
lm = LossMap(loc, ass['asset_ref'], stat['mean'], stat['stddev'])
data.append(lm)
writer = risk_writers.LossMapXMLWriter(fname,
oq.investigation_time,
poe=None,
loss_type=lt,
gsim_tree_path=None,
unit=unit,
loss_category=None)
# TODO: replace the category with the exposure category
writer.serialize(data)
fnames.append(fname)
return sorted(fnames)
def _gen_writers(dstore, writercls, root):
# build XMLWriter instances
oq = OqParam.from_(dstore.attrs)
rlzs = dstore['rlzs_assoc'].realizations
cost_types = dstore['cost_types']
L, R = len(cost_types), len(rlzs)
for l, ct in enumerate(cost_types):
loss_type = riskmodels.cost_type_to_loss_type(ct['name'])
for ins in range(oq.insured_losses + 1):
if root.endswith('-rlzs'):
for rlz in rlzs:
suffix = '' if L == 1 and R == 1 else '-gsimltp_%s_%s' % (
rlz.uid, loss_type)
dest = dstore.export_path(
'%s%s%s.xml' %
(root[:-5], suffix, '_ins' if ins else ''))
yield writercls(dest,
oq.investigation_time,
loss_type,
unit=ct['unit'],
**get_paths(rlz)), (loss_type, rlz.ordinal,
ins)
elif root.endswith('-stats'):
pairs = [('mean', None)] + [('quantile-%s' % q, q)
for q in oq.quantile_loss_curves]
for ordinal, (statname, statvalue) in enumerate(pairs):
dest = dstore.export_path('%s-%s-%s%s.xml' %
(root[:-6], statname, loss_type,
'_ins' if ins else ''))
yield writercls(
dest,
oq.investigation_time,
loss_type,
statistics='mean' if ordinal == 0 else 'quantile',
quantile_value=statvalue,
unit=ct['unit']), (loss_type, ordinal, ins)
def main(what, report=False):
"""
Give information about the passed keyword or filename
"""
if os.environ.get('OQ_DISTRIBUTE') not in ('no', 'processpool'):
os.environ['OQ_DISTRIBUTE'] = 'processpool'
if what == 'calculators':
for calc in sorted(base.calculators):
print(calc)
elif what == 'gsims':
for gs in gsim.get_available_gsims():
print(gs)
elif what == 'portable_gsims':
for gs in gsim.get_portable_gsims():
print(gs)
elif what == 'imts':
for im in vars(imt).values():
if inspect.isfunction(im) and is_upper(im):
print(im.__name__)
elif what == 'views':
for name in sorted(view):
print(name)
elif what == 'exports':
dic = groupby(export, operator.itemgetter(0),
lambda group: [r[1] for r in group])
items = [(DISPLAY_NAME.get(exporter, '?'), exporter, formats)
for exporter, formats in dic.items()]
n = 0
for dispname, exporter, formats in sorted(items):
print(dispname, '"%s"' % exporter, formats)
n += len(formats)
print('There are %d exporters defined.' % n)
elif what == 'extracts':
for key in extract:
func = extract[key]
if hasattr(func, '__wrapped__'):
fm = FunctionMaker(func.__wrapped__)
elif hasattr(func, 'func'): # for partial objects
fm = FunctionMaker(func.func)
else:
fm = FunctionMaker(func)
print('%s(%s)%s' % (fm.name, fm.signature, fm.doc))
elif what == 'parameters':
docs = OqParam.docs()
names = set()
for val in vars(OqParam).values():
if hasattr(val, 'name'):
names.add(val.name)
params = sorted(names)
for param in params:
print(param)
print(docs[param])
elif what == 'mfds':
for cls in gen_subclasses(BaseMFD):
print(cls.__name__)
elif what == 'venv':
print(sys.prefix)
elif what == 'sources':
for cls in gen_subclasses(BaseSeismicSource):
print(cls.__name__)
elif what == 'consequences':
known = scientific.KNOWN_CONSEQUENCES
print('The following %d consequences are implemented:' % len(known))
for cons in known:
print(cons)
elif os.path.isdir(what) and report:
with Monitor('info', measuremem=True) as mon:
with mock.patch.object(logging.root, 'info'): # reduce logging
do_build_reports(what)
print(mon)
elif what.endswith('.xml'):
node = nrml.read(what)
if node[0].tag.endswith('sourceModel'):
print(source_model_info([node]))
elif node[0].tag.endswith('logicTree'):
bset = node[0][0]
if bset.tag.endswith("logicTreeBranchingLevel"):
bset = bset[0]
if bset.attrib['uncertaintyType'] == 'sourceModel':
sm_nodes = []
for smpath in logictree.collect_info(what).smpaths:
sm_nodes.append(nrml.read(smpath))
print(source_model_info(sm_nodes))
elif bset.attrib['uncertaintyType'] == 'gmpeModel':
print(logictree.GsimLogicTree(what))
else:
print(node.to_str())
elif what.endswith(('.ini', '.zip')):
with Monitor('info', measuremem=True) as mon:
if report:
print('Generated', reportwriter.build_report(what))
else:
print(readinput.get_oqparam(what).json())
if mon.duration > 1:
print(mon)
elif what:
print("No info for '%s'" % what)
def __init__(self, dstore):
description = OqParam.from_(dstore.attrs).description
self.dstore = dstore
self.text = description + '\n' + '=' * len(description)
def parse_config(source, hazard_calculation_id=None, hazard_output_id=None):
"""
Parse a dictionary of parameters from an INI-style config file.
:param source:
File-like object containing the config parameters.
:param hazard_job_id:
The ID of a previous calculation (or None)
:param hazard_ouput_id:
The output of a previous job (or None)
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
cp = ConfigParser.ConfigParser()
cp.readfp(source)
base_path = os.path.dirname(
os.path.join(os.path.abspath('.'), source.name))
params = dict(base_path=base_path, inputs={},
hazard_calculation_id=hazard_calculation_id,
hazard_output_id=hazard_output_id)
# Directory containing the config file we're parsing.
base_path = os.path.dirname(os.path.abspath(source.name))
for sect in cp.sections():
for key, value in cp.items(sect):
if key == 'sites_csv' or key.endswith('_file'):
input_type = key[:-5]
path = value if os.path.isabs(value) else os.path.join(
base_path, value)
params['inputs'][input_type] = path
else:
params[key] = value
# load source inputs (the paths are the source_model_logic_tree)
smlt = params['inputs'].get('source_model_logic_tree')
if smlt:
params['inputs']['source'] = [
os.path.join(base_path, src_path)
for src_path in _collect_source_model_paths(smlt)]
# check for obsolete calculation_mode
is_risk = hazard_calculation_id or hazard_output_id
cmode = params['calculation_mode']
if is_risk and cmode in ('classical', 'event_based', 'scenario'):
raise ValueError('Please change calculation_mode=%s into %s_risk '
'in the .ini file' % (cmode, cmode))
oqparam = OqParam(**params)
# define the parameter `intensity measure types and levels` always
oqparam.intensity_measure_types_and_levels = get_imtls(oqparam)
# remove the redundant parameter `intensity_measure_types`
if hasattr(oqparam, 'intensity_measure_types'):
delattr(oqparam, 'intensity_measure_types')
return oqparam
def __init__(self, dstore):
self.dstore = dstore
self.oq = oq = OqParam.from_(dstore.attrs)
self.text = oq.description + '\n' + '=' * len(oq.description)
def __init__(self, dstore):
self.dstore = dstore
self.oq = oq = OqParam.from_(dstore.attrs)
self.text = oq.description + '\n' + '=' * len(oq.description)
self.text += '\n\nnum_sites = %d' % len(dstore['sitemesh'])
def show(calc_id, key=None, rlzs=None):
"""
Show the content of a datastore.
:param calc_id: numeric calculation ID; if 0, show all calculations
:param key: key of the datastore
:param rlzs: flag; if given, print out the realizations in order
"""
if calc_id == 0: # show all
if not os.path.exists(datastore.DATADIR):
return
rows = []
for calc_id in datastore.get_calc_ids(datastore.DATADIR):
try:
ds = datastore.DataStore(calc_id, mode='r')
oq = OqParam.from_(ds.attrs)
cmode, descr = oq.calculation_mode, oq.description
except:
# invalid datastore file, or missing calculation_mode
# and description attributes, perhaps due to a manual kill
logging.warn('Removed invalid calculation %d', calc_id)
os.remove(
os.path.join(datastore.DATADIR, 'calc_%s.hdf5' % calc_id))
continue
else:
rows.append((calc_id, cmode, descr))
ds.close()
for row in sorted(rows, key=lambda row: row[0]): # by calc_id
print('#%d %s: %s' % row)
return
ds = datastore.DataStore(calc_id, mode='r')
if key:
if key in datastore.view:
print(datastore.view(key, ds))
return
obj = ds[key]
if hasattr(obj, 'value'): # an array
print(write_csv(io.StringIO(), obj.value))
else:
print(obj)
return
oq = OqParam.from_(ds.attrs)
# this part is experimental
if rlzs and 'hcurves' in ds:
min_value = 0.01 # used in rmsep
curves_by_rlz, mean_curves = get_hcurves_and_means(ds)
dists = []
for rlz, curves in curves_by_rlz.items():
dist = sum(
rmsep(mean_curves[imt], curves[imt], min_value)
for imt in mean_curves.dtype.fields)
dists.append((dist, rlz))
print('Realizations in order of distance from the mean curves')
for dist, rlz in sorted(dists):
print('%s: rmsep=%s' % (rlz, dist))
else:
# print all keys
print(
oq.calculation_mode, 'calculation (%r) saved in %s contains:' %
(oq.description, ds.hdf5path))
for key in ds:
print(key, humansize(ds.getsize(key)))
def parse_config(source, hazard_calculation_id=None, hazard_output_id=None):
"""
Parse a dictionary of parameters from an INI-style config file.
:param source:
File-like object containing the config parameters.
:param hazard_job_id:
The ID of a previous calculation (or None)
:param hazard_ouput_id:
The output of a previous job (or None)
:returns:
An :class:`openquake.commonlib.oqvalidation.OqParam` instance
containing the validate and casted parameters/values parsed from
the job.ini file as well as a subdictionary 'inputs' containing
absolute paths to all of the files referenced in the job.ini, keyed by
the parameter name.
"""
cp = ConfigParser.ConfigParser()
cp.readfp(source)
base_path = os.path.dirname(os.path.join(os.path.abspath('.'),
source.name))
params = dict(base_path=base_path,
inputs={},
hazard_calculation_id=hazard_calculation_id,
hazard_output_id=hazard_output_id)
# Directory containing the config file we're parsing.
base_path = os.path.dirname(os.path.abspath(source.name))
for sect in cp.sections():
for key, value in cp.items(sect):
if key == 'sites_csv':
# Parse site coordinates from the csv file,
# return a string 'lon1 lat1, lon2 lat2, ... , lonN latN'
path = value if os.path.isabs(value) else os.path.join(
base_path, value)
sites = open(path, 'U').read().strip().replace(',', ' ')
params['sites'] = sites.replace('\n', ',')
elif key.endswith('_file'):
input_type = key[:-5]
path = value if os.path.isabs(value) else os.path.join(
base_path, value)
params['inputs'][input_type] = path
else:
params[key] = value
# load source inputs (the paths are the source_model_logic_tree)
smlt = params['inputs'].get('source_model_logic_tree')
if smlt:
params['inputs']['source'] = [
os.path.join(base_path, src_path)
for src_path in _collect_source_model_paths(smlt)
]
is_risk = hazard_calculation_id or hazard_output_id
cmode = params['calculation_mode']
if is_risk and cmode in ('classical', 'event_based', 'scenario'):
raise ValueError('Please change calculation_mode=%s into %s_risk '
'in the .ini file' % (cmode, cmode))
return OqParam(**params)
