def test_1():
try:
a = np.array(['sdf', {'a': 'b'}])
dump(a, 'test.hdf5')
a2 = load('test.hdf5')
assert a[0] == a2[0]
assert a[1] == a2[1]
finally:
os.unlink('test.hdf5')
def test_masked_array_persistence():
# The special-case picker fails, because saving masked_array
# not implemented, but it just delegates to the standard pickler.
rnd = np.random.RandomState(0)
a = rnd.random_sample(10)
a = np.ma.masked_greater(a, 0.5)
filename = env['filename'] + str(random.randint(0, 1000))
p.dump(a, filename)
b = p.load(filename)
nose.tools.assert_true(isinstance(b, np.ma.masked_array))
def loads(self, buf):
f = open('hdf5test.h5', 'w')
try:
f.write(buf)
finally:
f.close()
f = tables.openFile('hdf5test.h5', 'r')
try:
return p.load(f, '/obj')
finally:
f.close()
def load(where, path=None):
"""
hdf5-unpickle an object from a given node in a HDF5 file.
"""
if isinstance(where, _tbl.File):
where = where.root
p = where._v_pathname
if path is not None:
p = '/'.join([p, path]).replace('//', '/')
return hdf5pickle.load(where._v_file, p)
def test_standard_types():
# Test pickling and saving with standard types.
filename = env['filename']
for compress in [0, 1]:
for member in typelist:
# Change the file name to avoid side effects between tests
this_filename = filename + str(random.randint(0, 1000))
p.dump(member, this_filename, compress=compress)
_member = p.load(this_filename)
# We compare the pickled instance to the reloaded one only if it
# can be compared to a copied one
if member == copy.deepcopy(member):
yield nose.tools.assert_equal, member, _member
def test_numpy_persistence():
filename = env['filename']
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
for compress in (0, 1, 9):
# We use 'a.T' to have a non C-contiguous array.
for index, obj in enumerate(((a,), (a.T,), (a, a), [a, a, a])):
# Change the file name to avoid side effects between tests
this_filename = filename + str(random.randint(0, 1000))
p.dump(obj, this_filename, compress=compress)
# Unpickle the object
obj_ = p.load(this_filename)
# Check that the items are indeed arrays
for item in obj_:
nose.tools.assert_true(isinstance(item, np.ndarray))
# And finally, check that all the values are equal.
nose.tools.assert_true(np.all(np.array(obj) ==
np.array(obj_)))
# Now test with array subclasses
for obj in (
np.matrix(np.zeros(10)),
np.core.multiarray._reconstruct(np.memmap, (), np.float)
):
this_filename = filename + str(random.randint(0, 1000))
p.dump(obj, this_filename, compress=compress)
obj_ = p.load(this_filename)
if (type(obj) is not np.memmap
and hasattr(obj, '__array_prepare__')):
# We don't reconstruct memmaps
nose.tools.assert_true(isinstance(obj_, type(obj)))
# Finally smoke test the warning in case of compress + mmap_mode
this_filename = filename + str(random.randint(0, 1000))
p.dump(a, this_filename, compress=1)
p.load(this_filename)
def loads(self, buf):
with open('hdf5test.h5', 'wb') as f:
f.write(buf)
return p.load('hdf5test.h5')
def test_numpy_subclass():
filename = env['filename']
a = SubArray((10,))
p.dump(a, filename)
c = p.load(filename)
nose.tools.assert_true(isinstance(c, SubArray))
