def _local_load_npy(filename, ddpr, comm):
from distarray.localapi import load_npy
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_npy(comm, filename, dim_data))
def test_load_nu(self):
dim_data_per_rank = nu_test_data
expected_indices = [dd[1]['indices'] for dd in dim_data_per_rank]
la = load_npy(comm=self.comm, filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[:, expected_indices[self.rank]])
def _local_load_npy(filename, ddpr, comm):
from distarray.localapi import load_npy
if len(ddpr):
dim_data = ddpr[comm.Get_rank()]
else:
dim_data = ()
return proxyize(load_npy(comm, filename, dim_data))
def test_load_nu(self):
dim_data_per_rank = nu_test_data
expected_indices = [dd[1]['indices'] for dd in dim_data_per_rank]
la = load_npy(comm=self.comm,
filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[:, expected_indices[self.rank]])
def test_load_nc(self):
dim_data_per_rank = nc_test_data
expected_slices = [(slice(None), slice(0, None, 2)),
(slice(None), slice(1, None, 2))]
la = load_npy(comm=self.comm, filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[expected_slices[self.rank]])
def test_load_nc(self):
dim_data_per_rank = nc_test_data
expected_slices = [(slice(None), slice(0, None, 2)),
(slice(None), slice(1, None, 2))]
la = load_npy(comm=self.comm,
filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[expected_slices[self.rank]])
def test_load_bn(self):
dim_data_per_rank = bn_test_data
la = load_npy(comm=self.comm,
filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[numpy.newaxis, self.rank])
def test_load_bn(self):
dim_data_per_rank = bn_test_data
la = load_npy(comm=self.comm, filename=self.output_path,
dim_data=dim_data_per_rank[self.rank])
assert_equal(la, self.expected[numpy.newaxis, self.rank])