def save_exposure_atts(self, filename, use_parallel=True):
"""
Save the exposure attributes, including latitude and longitude.
The file type saved is based on the filename extension.
Options
'.npz': Save the arrays into a single file in uncompressed .npz
format.
:param use_parallel: Set to True for parallel behaviour
Which is only node 0 writing to file.
:param filename: The file to be written.
:return write_dict: The whole dictionary, returned for testing.
"""
write_dict = self.exposure_att.copy()
write_dict[EX_LAT] = self.exposure_lat
write_dict[EX_LONG] = self.exposure_long
if use_parallel:
assert misc.INTID in write_dict
write_dict = parallel.gather_dict(write_dict,
write_dict[misc.INTID])
if parallel.STATE.rank == 0 or not use_parallel:
if filename[-4:] == '.csv':
save_csv(write_dict, filename)
else:
numpy.savez(filename, **write_dict)
# The write_dict is returned for testing
# When running in paralled this is a way of getting all
# of the context info
return write_dict
def save_exposure_atts(self, filename, use_parallel=True):
"""
Save the exposure attributes, including latitude and longitude.
The file type saved is based on the filename extension.
Options
'.npz': Save the arrays into a single file in uncompressed .npz
format.
:param use_parallel: Set to True for parallel behaviour
Which is only node 0 writing to file.
:param filename: The file to be written.
:return write_dict: The whole dictionary, returned for testing.
"""
[filename, bucket_name, bucket_key] = \
misc.create_temp_file_path_for_s3(filename)
s1 = self.prov.entity(
":HazImp output file", {
"prov:label": "Full HazImp output file",
"prov:type": "void:Dataset",
"prov:atLocation": os.path.basename(filename)
})
a1 = self.prov.activity(":SaveImpactData",
datetime.now().strftime(DATEFMT), None)
self.prov.wasGeneratedBy(s1, a1)
self.prov.wasInformedBy(a1, self.provlabel)
write_dict = self.exposure_att.copy()
write_dict[EX_LAT] = self.exposure_lat
write_dict[EX_LONG] = self.exposure_long
if use_parallel:
assert misc.INTID in write_dict
write_dict = parallel.gather_dict(write_dict,
write_dict[misc.INTID])
if parallel.STATE.rank == 0 or not use_parallel:
if filename[-4:] == '.csv':
save_csv(write_dict, filename)
else:
numpy.savez(filename, **write_dict)
misc.upload_to_s3_if_applicable(filename, bucket_name, bucket_key)
# The write_dict is returned for testing
# When running in paralled this is a way of getting all
# of the context info
return write_dict
def test_gather_dict2D(self):
# This test can be run with
# mpirun -n 2 python test_parallel.py
# if there is a path problem, try adding -x PYTHONPATH
try:
import pypar # pylint: disable=W0612, W0404
except ImportError:
# can't do this test
return
# In a real run the subsets will not be the same
subset = {"foo": numpy.array([0, 2]),
"woo": numpy.array([[1, 3, 4], [2, 4, 5]])}
if STATE.size == 1:
all_indexes = [[0, 1]]
elif STATE.size == 2:
all_indexes = [[0, 2], [1, 3]]
elif STATE.size == 3:
all_indexes = [[0, 3], [1, 4], [2, 5]]
if STATE.size < 4:
whole = gather_dict(subset, all_indexes[STATE.rank])
if STATE.size == 1:
self.assertDictEqual(whole, subset)
elif STATE.size == 2 and STATE.rank == 0:
act = {"foo": numpy.array([0, 0, 2, 2]),
"woo": numpy.array([[1, 3, 4], [1, 3, 4],
[2, 4, 5], [2, 4, 5]])}
for key in list(act.keys()):
self.assertTrue(numpy.allclose(act[key],
whole[key]))
elif STATE.size == 3 and STATE.rank == 0:
act = {"foo": numpy.array([0, 0, 0, 2, 2, 2]),
"woo": numpy.array([[1, 3, 4], [1, 3, 4], [1, 3, 4],
[2, 4, 5], [2, 4, 5], [2, 4, 5]])}
for key in list(act.keys()):
self.assertTrue(numpy.allclose(act[key],
whole[key]))
else:
pass
