def test_save_netcdf(self):
# netcdf support is optional
if not ht.io.supports_netcdf():
return
# local unsplit data
local_data = ht.arange(100)
ht.save_netcdf(local_data, self.NETCDF_OUT_PATH, self.NETCDF_VARIABLE)
if local_data.comm.rank == 0:
with ht.io.nc.Dataset(self.NETCDF_OUT_PATH, "r") as handle:
comparison = torch.tensor(
handle[self.NETCDF_VARIABLE][:],
dtype=torch.int32,
device=self.device.torch_device,
)
self.assertTrue((local_data.larray == comparison).all())
# distributed data range
split_data = ht.arange(100, split=0)
ht.save_netcdf(split_data, self.NETCDF_OUT_PATH, self.NETCDF_VARIABLE)
if split_data.comm.rank == 0:
with ht.io.nc.Dataset(self.NETCDF_OUT_PATH, "r") as handle:
comparison = torch.tensor(
handle[self.NETCDF_VARIABLE][:],
dtype=torch.int32,
device=self.device.torch_device,
)
self.assertTrue((local_data.larray == comparison).all())
def test_save_netcdf_exception(self):
# netcdf support is optional
if not ht.io.supports_netcdf():
return
# dummy data
data = ht.arange(1)
with self.assertRaises(TypeError):
ht.save_netcdf(1, self.NETCDF_PATH, self.NETCDF_VARIABLE)
with self.assertRaises(TypeError):
ht.save_netcdf(data, 1, self.NETCDF_VARIABLE)
with self.assertRaises(TypeError):
ht.save_netcdf(data, self.NETCDF_PATH, 1)
with self.assertRaises(TypeError):
ht.save_netcdf(data,
self.NETCDF_PATH,
self.NETCDF_VARIABLE,
dimension_names=1)
with self.assertRaises(ValueError):
ht.save_netcdf(data,
self.NETCDF_PATH,
self.NETCDF_VARIABLE,
dimension_names=["a", "b"])
def test_save_netcdf_exception(self):
# netcdf support is optional
if not ht.io.supports_netcdf():
return
# dummy data
data = ht.arange(1, device=ht_device)
with self.assertRaises(TypeError):
ht.save_netcdf(1, self.NETCDF_PATH, self.NETCDF_VARIABLE)
with self.assertRaises(TypeError):
ht.save_netcdf(data, 1, self.NETCDF_VARIABLE)
with self.assertRaises(TypeError):
ht.save_netcdf(data, self.NETCDF_PATH, 1)