def test_load_netcdf(self):
# netcdf support is optional
if not ht.io.supports_netcdf():
return
# default parameters
iris = ht.load_netcdf(self.NETCDF_PATH,
self.NETCDF_VARIABLE,
device=ht_device)
self.assertIsInstance(iris, ht.DNDarray)
self.assertEqual(iris.shape, self.IRIS.shape)
self.assertEqual(iris.dtype, ht.float32)
self.assertEqual(iris._DNDarray__array.dtype, torch.float32)
self.assertTrue((self.IRIS == iris._DNDarray__array).all())
# positive split axis
iris = ht.load_netcdf(self.NETCDF_PATH,
self.NETCDF_VARIABLE,
split=0,
device=ht_device)
self.assertIsInstance(iris, ht.DNDarray)
self.assertEqual(iris.shape, self.IRIS.shape)
self.assertEqual(iris.dtype, ht.float32)
lshape = iris.lshape
self.assertLessEqual(lshape[0], self.IRIS.shape[0])
self.assertEqual(lshape[1], self.IRIS.shape[1])
# negative split axis
iris = ht.load_netcdf(self.NETCDF_PATH, self.NETCDF_VARIABLE, split=-1)
self.assertIsInstance(iris, ht.DNDarray)
self.assertEqual(iris.shape, self.IRIS.shape)
self.assertEqual(iris.dtype, ht.float32)
lshape = iris.lshape
self.assertEqual(lshape[0], self.IRIS.shape[0])
self.assertLessEqual(lshape[1], self.IRIS.shape[1])
# different data type
iris = ht.load_netcdf(self.NETCDF_PATH,
self.NETCDF_VARIABLE,
dtype=ht.int8,
device=ht_device)
self.assertIsInstance(iris, ht.DNDarray)
self.assertEqual(iris.shape, self.IRIS.shape)
self.assertEqual(iris.dtype, ht.int8)
self.assertEqual(iris._DNDarray__array.dtype, torch.int8)
def test_load_netcdf_exception(self):
# netcdf support is optional
if not ht.io.supports_netcdf():
return
# improper argument types
with self.assertRaises(TypeError):
ht.load_netcdf(1, "data")
with self.assertRaises(TypeError):
ht.load_netcdf("iris.nc", variable=1)
with self.assertRaises(TypeError):
ht.load_netcdf("iris.nc", variable="data", split=1.0)
# file or variable does not exist
with self.assertRaises(IOError):
ht.load_netcdf("foo.nc", variable="data")
with self.assertRaises(IOError):
ht.load_netcdf("iris.nc", variable="foo")