def setUp(self):
# get random ID as job_id
self.job_id = str(uuid.uuid4())
# load vulnerability and write to memcache
vulnerability.load_vulnerability_model(self.job_id,
os.path.join(test.DATA_DIR, TEST_FILE))
self.vulnerability_curves = \
vulnerability.load_vuln_curves_from_kvs(self.job_id)
def __init__(self, job_id, block_id):
""" Prepare the calculator for computations"""
self.job_id = job_id
self.block_id = block_id
self.vuln_curves = \
vulnerability.load_vuln_curves_from_kvs(self.job_id)
# self.vuln_curves is a dict of {string: Curve}
LOGGER.debug("ProbabilisticLossRatioCalculator init: vuln curves are")
for k, v in self.vuln_curves.items():
LOGGER.debug("%s: %s" % (k, v))
def compute_risk(self, block_id, **kwargs): #pylint: disable=W0613
"""This task computes risk for a block of sites. It requires to have
pre-initialized in memcache:
1) list of sites
2) gmfs
3) exposure portfolio (=assets)
4) vulnerability
TODO(fab): make conditional_loss_poe (set of probabilities of exceedance
for which the loss computation is done) a list of floats, and read it from
the job configuration.
"""
conditional_loss_poes = [float(x) for x in self.params.get(
'CONDITIONAL_LOSS_POE', "0.01").split()]
self.slice_gmfs(block_id)
#pylint: disable=W0201
self.vuln_curves = \
vulnerability.load_vuln_curves_from_kvs(self.job_id)
# TODO(jmc): DONT assumes that hazard and risk grid are the same
block = job.Block.from_kvs(block_id)
for point in block.grid(self.region):
key = kvs.generate_product_key(self.job_id,
kvs.tokens.GMF_KEY_TOKEN, point.column, point.row)
gmf_slice = kvs.get_value_json_decoded(key)
asset_key = kvs.tokens.asset_key(self.id, point.row, point.column)
asset_list = kvs.get_client().lrange(asset_key, 0, -1)
for asset in [json.JSONDecoder().decode(x) for x in asset_list]:
LOGGER.debug("processing asset %s" % (asset))
loss_ratio_curve = self.compute_loss_ratio_curve(
point.column, point.row, asset, gmf_slice)
if loss_ratio_curve is not None:
# compute loss curve
loss_curve = self.compute_loss_curve(
point.column, point.row,
loss_ratio_curve, asset)
for loss_poe in conditional_loss_poes:
self.compute_conditional_loss(point.column, point.row,
loss_curve, asset, loss_poe)
return True
