def test_inputs4job_with_correct_input_type_and_path(self):
# The source inputs are returned.
inp1 = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="source",
size=self.sizes.next())
inp1.save()
models.Input2job(oq_job=self.job, input=inp1).save()
path = self.paths.next()
inp2 = models.Input(owner=self.job.owner,
path=path,
input_type="source",
size=self.sizes.next())
inp2.save()
models.Input2job(oq_job=self.job, input=inp2).save()
inp3 = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="source",
size=self.sizes.next())
inp3.save()
models.Input2job(oq_job=self.job, input=inp3).save()
self.assertEqual([inp2],
models.inputs4job(self.job.id,
input_type="source",
path=path))
def setUpClass(cls):
default_user = helpers.default_user()
cls.job = models.OqJob(owner=default_user)
cls.job.save()
# dmg dist per asset
cls.ddpa_output = models.Output(
owner=default_user, oq_job=cls.job,
display_name='Test dmg dist per asset',
output_type='dmg_dist_per_asset',
db_backed=True)
cls.ddpa_output.save()
cls.ddpa = models.DmgDistPerAsset(
output=cls.ddpa_output, dmg_states=cls.DMG_STATES)
cls.ddpa.save()
# We also need some sample exposure data records (to satisfy the dmg
# dist per asset FK).
test_input = models.Input(
owner=default_user, input_type='exposure', path='fake', size=0)
test_input.save()
i2j = models.Input2job(input=test_input, oq_job=cls.job)
i2j.save()
exp_model = models.ExposureModel(
owner=default_user, input=test_input, name='test-exp-model',
category='economic loss', stco_type='per_asset', stco_unit='CHF')
exp_model.save()
test_site = shapes.Site(3.14, 2.17)
cls.exp_data = models.ExposureData( # Asset
exposure_model=exp_model, asset_ref=helpers.random_string(),
taxonomy=helpers.random_string(), number_of_units=37,
site=test_site.point.to_wkt(), stco=1234.56)
cls.exp_data.save()
# dmg dist per taxonomy
cls.ddpt_output = models.Output(
owner=default_user, oq_job=cls.job,
display_name='Test dmg dist per taxonomy',
output_type='dmg_dist_per_taxonomy',
db_backed=True)
cls.ddpt_output.save()
cls.ddpt = models.DmgDistPerTaxonomy(
output=cls.ddpt_output, dmg_states=cls.DMG_STATES)
cls.ddpt.save()
# total dmg dist
cls.ddt_output = models.Output(
owner=default_user, oq_job=cls.job,
display_name='Test dmg dist total',
output_type='dmg_dist_total',
db_backed=True)
cls.ddt_output.save()
cls.ddt = models.DmgDistTotal(
output=cls.ddt_output, dmg_states=cls.DMG_STATES)
cls.ddt.save()
def setUp(self):
self.emdl_input = models.Input(
input_type="exposure", size=123, path="/tmp/fake-exposure-path",
owner=self.job.owner)
self.emdl_input.save()
i2j = models.Input2job(input=self.emdl_input, oq_job=self.job)
i2j.save()
def setUpClass(cls):
cls.job = cls.setup_classic_job()
cls.input = models.Input(input_type="source",
size=123,
path="/tmp/fake-source-path",
owner=cls.job.owner)
cls.input.save()
i2j = models.Input2job(input=cls.input, oq_job=cls.job)
i2j.save()
def test_inputs4job_with_wrong_path(self):
# No input is returned.
inp = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="exposure",
size=self.sizes.next())
inp.save()
models.Input2job(oq_job=self.job, input=inp).save()
self.assertEqual([], models.inputs4job(self.job.id, path="xyz"))
def test_inputs4job_with_single_input(self):
# The single input is returned.
inp = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="exposure",
size=self.sizes.next())
inp.save()
models.Input2job(oq_job=self.job, input=inp).save()
self.assertEqual([inp], models.inputs4job(self.job.id))
def insert_inputs(job, inputs):
"""Insert the input records for the given data and job."""
for imt, imp in inputs:
iobj = models.Input(path=imp,
input_type=imt,
owner=job.owner,
size=random.randint(1024, 16 * 1024))
iobj.save()
i2j = models.Input2job(input=iobj, oq_job=job)
i2j.save()
def setUp(self):
emdl_input = models.Input(
input_type="exposure", size=123, path="/tmp/fake-exposure-path",
owner=self.job.owner)
emdl_input.save()
i2j = models.Input2job(input=emdl_input, oq_job=self.job)
i2j.save()
self.mdl = models.ExposureModel(
input=emdl_input, owner=self.job.owner,
name="exposure-model-testing", category="economic loss")
def setUp(self):
fmdl_input = models.Input(input_type="fragility",
size=123,
path="/tmp/fake-fragility-path",
owner=self.job.owner)
fmdl_input.save()
i2j = models.Input2job(input=fmdl_input, oq_job=self.job)
i2j.save()
self.mdl = models.FragilityModel(input=fmdl_input,
owner=self.job.owner,
format="continuous")
def setUp(self):
# md5sum digest incorrect
self.glt_i = models.Input(input_type="lt_gmpe", size=123,
path=self.GLT, owner=self.old_job.owner,
digest="0" * 32)
self.glt_i.save()
i2j = models.Input2job(input=self.glt_i, oq_job=self.old_job)
i2j.save()
# md5sum digest correct
if sys.platform == 'darwin':
digest = subprocess.check_output(["md5", self.SLT]).split()[-1]
else:
digest = subprocess.check_output(["md5sum", self.SLT]).split()[0]
self.slt_i = models.Input(input_type="lt_source", size=123,
path=self.SLT, owner=self.old_job.owner,
digest=digest)
self.slt_i.save()
i2j = models.Input2job(input=self.slt_i, oq_job=self.old_job)
i2j.save()
self.job = self.setup_classic_job()
def setUp(self):
emdl_input = models.Input(
input_type="exposure", size=123, path="/tmp/fake-exposure-path",
owner=self.job.owner)
emdl_input.save()
i2j = models.Input2job(input=emdl_input, oq_job=self.job)
i2j.save()
self.mdl = models.ExposureModel(
input=emdl_input, owner=self.job.owner, unit_type="count",
name="exposure-data-testing", category="economic loss",
coco_type="per_asset", coco_unit="USD")
self.mdl.save()
def test_inputs4job_with_correct_input_type(self):
# The exposure inputs are returned.
inp1 = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="exposure",
size=self.sizes.next())
inp1.save()
models.Input2job(oq_job=self.job, input=inp1).save()
inp2 = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="rupture",
size=self.sizes.next())
inp2.save()
models.Input2job(oq_job=self.job, input=inp2).save()
inp3 = models.Input(owner=self.job.owner,
path=self.paths.next(),
input_type="exposure",
size=self.sizes.next())
inp3.save()
models.Input2job(oq_job=self.job, input=inp3).save()
self.assertEqual([inp1, inp3],
models.inputs4job(self.job.id, input_type="exposure"))
def test_get_site_model_too_many_site_models(self):
job = engine.prepare_job()
site_model_inp1 = models.Input(
owner=job.owner, digest='fake', path='fake',
input_type='site_model', size=0
)
site_model_inp1.save()
site_model_inp2 = models.Input(
owner=job.owner, digest='fake', path='fake',
input_type='site_model', size=0
)
site_model_inp2.save()
# link both site models to the job:
models.Input2job(input=site_model_inp1, oq_job=job).save()
models.Input2job(input=site_model_inp2, oq_job=job).save()
with self.assertRaises(RuntimeError) as assert_raises:
general.get_site_model(job.id)
self.assertEqual('Only 1 site model per job is allowed, found 2.',
assert_raises.exception.message)
def test_get_site_model(self):
job = engine.prepare_job()
site_model_inp = models.Input(
owner=job.owner, digest='fake', path='fake',
input_type='site_model', size=0
)
site_model_inp.save()
# The link has not yet been made in the input2job table.
self.assertIsNone(general.get_site_model(job.id))
# Complete the link:
models.Input2job(input=site_model_inp, oq_job=job).save()
actual_site_model = general.get_site_model(job.id)
self.assertEqual(site_model_inp, actual_site_model)
def _setup_input(self, input_type, size, path, digest, jobs):
"""Create a model input and associate it with the given jobs.
Its owner will be the same as the owner of the first job.
"""
# In order for the tests in this class to work we need to disable any
# other model inputs that might still be in the database.
models.Input2job.objects.all().delete()
mdl = models.Input(input_type=input_type, size=size, path=path,
owner=jobs[0].owner, digest=digest)
mdl.save()
for job in jobs:
i2j = models.Input2job(input=mdl, oq_job=job)
i2j.save()
return mdl
def setUp(self):
fmdl_input = models.Input(input_type="fragility",
size=123,
path="/tmp/fake-fragility-path",
owner=self.job.owner)
fmdl_input.save()
i2j = models.Input2job(input=fmdl_input, oq_job=self.job)
i2j.save()
self.mdl = models.FragilityModel(input=fmdl_input,
owner=self.job.owner,
format="discrete",
lss="a b c".split(),
imls=[0.2, 0.3],
imt="mmi")
self.mdl.save()
self.continuous_mdl = models.FragilityModel(input=fmdl_input,
owner=self.job.owner,
format="continuous",
lss="d e f".split())
self.continuous_mdl.save()
