def test_memory_usage(tmpdir, compress):
# Verify memory stays within expected bounds.
filename = tmpdir.join('test.pkl').strpath
small_array = np.ones((10, 10))
big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
small_matrix = np.matrix(small_array)
big_matrix = np.matrix(big_array)
for obj in (small_array, big_array, small_matrix, big_matrix):
size = obj.nbytes / 1e6
obj_filename = filename + str(np.random.randint(0, 1000))
mem_used = memory_used(numpy_pickle.dump,
obj,
obj_filename,
compress=compress)
# The memory used to dump the object shouldn't exceed the buffer
# size used to write array chunks (16MB).
write_buf_size = _IO_BUFFER_SIZE + 16 * 1024**2 / 1e6
assert mem_used <= write_buf_size
mem_used = memory_used(numpy_pickle.load, obj_filename)
# memory used should be less than array size + buffer size used to
# read the array chunk by chunk.
read_buf_size = 32 + _IO_BUFFER_SIZE # MiB
assert mem_used < size + read_buf_size
def test_compressed_pickle_dump_and_load(tmpdir):
expected_list = [
np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('>i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.arange(5, dtype=np.dtype('>f8')),
np.array([1, 'abc', {
'a': 1,
'b': 2
}], dtype='O'),
np.arange(256, dtype=np.uint8).tobytes(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
np.matrix([0, 1, 2], dtype=np.dtype('>i8')),
u"C'est l'\xe9t\xe9 !"
]
fname = tmpdir.join('temp.pkl.gz').strpath
dumped_filenames = numpy_pickle.dump(expected_list, fname, compress=1)
assert len(dumped_filenames) == 1
result_list = numpy_pickle.load(fname)
for result, expected in zip(result_list, expected_list):
if isinstance(expected, np.ndarray):
assert result.dtype == expected.dtype
np.testing.assert_equal(result, expected)
else:
assert result == expected
def test_compressed_pickle_dump_and_load():
expected_list = [np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('>i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.arange(5, dtype=np.dtype('>f8')),
np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
np.matrix([0, 1, 2], dtype=np.dtype('>i8')),
u"C'est l'\xe9t\xe9 !"]
with tempfile.NamedTemporaryFile(suffix='.gz', dir=env['dir']) as f:
fname = f.name
try:
dumped_filenames = numpy_pickle.dump(expected_list, fname, compress=1)
assert len(dumped_filenames) == 1
result_list = numpy_pickle.load(fname)
for result, expected in zip(result_list, expected_list):
if isinstance(expected, np.ndarray):
assert result.dtype == expected.dtype
np.testing.assert_equal(result, expected)
else:
assert result == expected
finally:
os.remove(fname)
def test_joblib_pickle_across_python_versions():
# We need to be specific about dtypes in particular endianness
# because the pickles can be generated on one architecture and
# the tests run on another one. See
# https://github.com/joblib/joblib/issues/279.
expected_list = [np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
u"C'est l'\xe9t\xe9 !"]
# Testing all the compressed and non compressed
# pickles in joblib/test/data. These pickles were generated by
# the joblib/test/data/create_numpy_pickle.py script for the
# relevant python, joblib and numpy versions.
test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
pickle_extensions = ('.pkl', '.gz', '.gzip', '.bz2', '.xz', '.lzma', 'lz4')
pickle_filenames = [os.path.join(test_data_dir, fn)
for fn in os.listdir(test_data_dir)
if any(fn.endswith(ext) for ext in pickle_extensions)]
for fname in pickle_filenames:
_check_pickle(fname, expected_list)
def test_file_handle_persistence():
objs = [np.random.random((10, 10)),
"some data",
np.matrix([0, 1, 2])]
fobjs = [bz2.BZ2File, gzip.GzipFile]
if PY3_OR_LATER:
import lzma
fobjs += [lzma.LZMAFile]
filename = env['filename'] + str(random.randint(0, 1000))
for obj in objs:
for fobj in fobjs:
with fobj(filename, 'wb') as f:
numpy_pickle.dump(obj, f)
# using the same decompressor prevents from internally
# decompress again.
with fobj(filename, 'rb') as f:
obj_reloaded = numpy_pickle.load(f)
# when needed, the correct decompressor should be used when
# passing a raw file handle.
with open(filename, 'rb') as f:
obj_reloaded_2 = numpy_pickle.load(f)
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
np.testing.assert_array_equal(obj_reloaded_2, obj)
else:
assert obj_reloaded == obj
assert obj_reloaded_2 == obj
os.remove(filename)
def test_joblib_pickle_across_python_versions():
# We need to be specific about dtypes in particular endianness
# because the pickles can be generated on one architecture and
# the tests run on another one. See
# https://github.com/joblib/joblib/issues/279.
expected_list = [np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
u"C'est l'\xe9t\xe9 !"]
# Testing all the compressed and non compressed
# pickles in joblib/test/data. These pickles were generated by
# the joblib/test/data/create_numpy_pickle.py script for the
# relevant python, joblib and numpy versions.
test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
pickle_extensions = ('.pkl', '.gz', '.gzip', '.bz2', '.xz', '.lzma')
pickle_filenames = [os.path.join(test_data_dir, fn)
for fn in os.listdir(test_data_dir)
if any(fn.endswith(ext) for ext in pickle_extensions)]
for fname in pickle_filenames:
_check_pickle(fname, expected_list)
def test_file_handle_persistence(tmpdir):
objs = [np.random.random((10, 10)), "some data", np.matrix([0, 1, 2])]
fobjs = [bz2.BZ2File, gzip.GzipFile]
if lzma is not None:
fobjs += [lzma.LZMAFile]
filename = tmpdir.join('test.pkl').strpath
for obj in objs:
for fobj in fobjs:
with fobj(filename, 'wb') as f:
numpy_pickle.dump(obj, f)
# using the same decompressor prevents from internally
# decompress again.
with fobj(filename, 'rb') as f:
obj_reloaded = numpy_pickle.load(f)
# when needed, the correct decompressor should be used when
# passing a raw file handle.
with open(filename, 'rb') as f:
obj_reloaded_2 = numpy_pickle.load(f)
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
np.testing.assert_array_equal(obj_reloaded_2, obj)
else:
assert obj_reloaded == obj
assert obj_reloaded_2 == obj
def test_joblib_decompression_format_support():
# We need to be specific about dtypes in particular endianness
# because the pickles can be generated on one architecture and
# the tests run on another one. See
# https://github.com/joblib/joblib/issues/279.
expected_list = [
np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.array([1, 'abc', {
'a': 1,
'b': 2
}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
u"C'est l'\xe9t\xe9 !"
]
test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
extensions = ('.gz', '.gzip', '.bz2', '.xz', '.lzma')
compress_filenames_list = [
glob.glob(os.path.join(test_data_dir, ext)) for ext in extensions
]
compress_filenames = sum(compress_filenames_list, [])
for fname in compress_filenames:
_check_compression_format(fname, expected_list)
def test_file_handle_persistence():
objs = [np.random.random((10, 10)), "some data", np.matrix([0, 1, 2])]
fobjs = [open]
if not PY26:
fobjs += [bz2.BZ2File, gzip.GzipFile]
if PY3_OR_LATER:
import lzma
fobjs += [lzma.LZMAFile]
filename = env['filename'] + str(random.randint(0, 1000))
for obj in objs:
for fobj in fobjs:
with fobj(filename, 'wb') as f:
numpy_pickle.dump(obj, f)
# using the same decompressor prevents from internally
# decompress again.
with fobj(filename, 'rb') as f:
obj_reloaded = numpy_pickle.load(f)
# when needed, the correct decompressor should be used when
# passing a raw file handle.
with open(filename, 'rb') as f:
obj_reloaded_2 = numpy_pickle.load(f)
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
np.testing.assert_array_equal(obj_reloaded_2, obj)
else:
nose.tools.assert_equal(obj_reloaded, obj)
nose.tools.assert_equal(obj_reloaded_2, obj)
os.remove(filename)
def test_joblib_decompression_format_support():
# We need to be specific about dtypes in particular endianness
# because the pickles can be generated on one architecture and
# the tests run on another one. See
# https://github.com/joblib/joblib/issues/279.
expected_list = [np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.array([1, 'abc', {'a': 1, 'b': 2}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
u"C'est l'\xe9t\xe9 !"]
test_data_dir = os.path.dirname(os.path.abspath(data.__file__))
extensions = ('.gz', '.gzip', '.bz2', '.xz', '.lzma')
compress_filenames_list = [glob.glob(os.path.join(test_data_dir, ext))
for ext in extensions]
compress_filenames = sum(compress_filenames_list, [])
for fname in compress_filenames:
_check_compression_format(fname, expected_list)
def test_numpy_persistence():
filename = env['filename']
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
for compress in (False, True, 0, 3):
# 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))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
# Check that only one file was created
nose.tools.assert_equal(filenames[0], this_filename)
# Check that this file does exist
nose.tools.assert_true(
os.path.exists(os.path.join(env['dir'], filenames[0])))
# Unpickle the object
obj_ = numpy_pickle.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.
np.testing.assert_array_equal(np.array(obj), np.array(obj_))
# Now test with array subclasses
for obj in (np.matrix(np.zeros(10)),
np.memmap(filename + str(random.randint(0, 1000)) + 'mmap',
mode='w+', shape=4, dtype=np.float)):
this_filename = filename + str(random.randint(0, 1000))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
obj_ = numpy_pickle.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)))
np.testing.assert_array_equal(obj_, obj)
# Test with an object containing multiple numpy arrays
obj = ComplexTestObject()
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
obj_loaded = numpy_pickle.load(this_filename)
nose.tools.assert_true(isinstance(obj_loaded, type(obj)))
np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)
def test_in_memory_persistence():
objs = [np.random.random((10, 10)), "some data", np.matrix([0, 1, 2])]
for obj in objs:
f = io.BytesIO()
numpy_pickle.dump(obj, f)
obj_reloaded = numpy_pickle.load(f)
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
else:
assert obj_reloaded == obj
def test_in_memory_persistence():
objs = [np.random.random((10, 10)),
"some data",
np.matrix([0, 1, 2])]
for obj in objs:
f = io.BytesIO()
numpy_pickle.dump(obj, f)
obj_reloaded = numpy_pickle.load(f)
if isinstance(obj, np.ndarray):
np.testing.assert_array_equal(obj_reloaded, obj)
else:
assert obj_reloaded == obj
def test_numpy_persistence(tmpdir, compress):
filename = tmpdir.join('test.pkl').strpath
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
# We use 'a.T' to have a non C-contiguous array.
for index, obj in enumerate(((a, ), (a.T, ), (a, a), [a, a, a])):
filenames = numpy_pickle.dump(obj, filename, compress=compress)
# All is cached in one file
assert len(filenames) == 1
# Check that only one file was created
assert filenames[0] == filename
# Check that this file does exist
assert os.path.exists(filenames[0])
# Unpickle the object
obj_ = numpy_pickle.load(filename)
# Check that the items are indeed arrays
for item in obj_:
assert isinstance(item, np.ndarray)
# And finally, check that all the values are equal.
np.testing.assert_array_equal(np.array(obj), np.array(obj_))
# Now test with array subclasses
for obj in (np.matrix(np.zeros(10)),
np.memmap(filename + 'mmap',
mode='w+',
shape=4,
dtype=np.float)):
filenames = numpy_pickle.dump(obj, filename, compress=compress)
# All is cached in one file
assert len(filenames) == 1
obj_ = numpy_pickle.load(filename)
if (type(obj) is not np.memmap and hasattr(obj, '__array_prepare__')):
# We don't reconstruct memmaps
assert isinstance(obj_, type(obj))
np.testing.assert_array_equal(obj_, obj)
# Test with an object containing multiple numpy arrays
obj = ComplexTestObject()
filenames = numpy_pickle.dump(obj, filename, compress=compress)
# All is cached in one file
assert len(filenames) == 1
obj_loaded = numpy_pickle.load(filename)
assert isinstance(obj_loaded, type(obj))
np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)
def test_compressed_pickle_dump_and_load():
expected_list = [
np.arange(5, dtype=np.dtype('<i8')),
np.arange(5, dtype=np.dtype('>i8')),
np.arange(5, dtype=np.dtype('<f8')),
np.arange(5, dtype=np.dtype('>f8')),
np.array([1, 'abc', {
'a': 1,
'b': 2
}], dtype='O'),
# .tostring actually returns bytes and is a
# compatibility alias for .tobytes which was
# added in 1.9.0
np.arange(256, dtype=np.uint8).tostring(),
# np.matrix is a subclass of np.ndarray, here we want
# to verify this type of object is correctly unpickled
# among versions.
np.matrix([0, 1, 2], dtype=np.dtype('<i8')),
np.matrix([0, 1, 2], dtype=np.dtype('>i8')),
u"C'est l'\xe9t\xe9 !"
]
with tempfile.NamedTemporaryFile(suffix='.gz', dir=env['dir']) as f:
fname = f.name
try:
dumped_filenames = numpy_pickle.dump(expected_list, fname, compress=1)
nose.tools.assert_equal(len(dumped_filenames), 1)
result_list = numpy_pickle.load(fname)
for result, expected in zip(result_list, expected_list):
if isinstance(expected, np.ndarray):
nose.tools.assert_equal(result.dtype, expected.dtype)
np.testing.assert_equal(result, expected)
else:
nose.tools.assert_equal(result, expected)
finally:
os.remove(fname)
def test_memory_usage():
# Verify memory stays within expected bounds.
filename = env['filename']
small_array = np.ones((10, 10))
big_array = np.ones(shape=100 * int(1e6), dtype=np.uint8)
small_matrix = np.matrix(small_array)
big_matrix = np.matrix(big_array)
for compress in (True, False):
for obj in (small_array, big_array, small_matrix, big_matrix):
size = obj.nbytes / 1e6
obj_filename = filename + str(np.random.randint(0, 1000))
mem_used = memory_used(numpy_pickle.dump,
obj, obj_filename, compress=compress)
# The memory used to dump the object shouldn't exceed the buffer
# size used to write array chunks (16MB).
write_buf_size = _IO_BUFFER_SIZE + 16 * 1024 ** 2 / 1e6
assert mem_used <= write_buf_size
mem_used = memory_used(numpy_pickle.load, obj_filename)
# memory used should be less than array size + buffer size used to
# read the array chunk by chunk.
read_buf_size = 32 + _IO_BUFFER_SIZE # MiB
assert mem_used < size + read_buf_size
def test_numpy_persistence():
filename = env['filename']
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
for compress, cache_size in ((0, 0), (1, 0), (1, 10)):
# 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))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress,
cache_size=cache_size)
# Check that one file was created per array
if not compress:
nose.tools.assert_equal(len(filenames), len(obj) + 1)
# Check that these files do exist
for file in filenames:
nose.tools.assert_true(
os.path.exists(os.path.join(env['dir'], file)))
# Unpickle the object
obj_ = numpy_pickle.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))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress,
cache_size=cache_size)
obj_ = numpy_pickle.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))
numpy_pickle.dump(a, this_filename, compress=1)
numpy_pickle.load(this_filename, mmap_mode='r')
def test_numpy_persistence():
filename = env['filename']
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
for compress, cache_size in ((0, 0), (1, 0), (1, 10)):
# 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))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress,
cache_size=cache_size)
# Check that one file was created per array
if not compress:
nose.tools.assert_equal(len(filenames), len(obj) + 1)
# Check that these files do exist
for file in filenames:
nose.tools.assert_true(
os.path.exists(os.path.join(env['dir'], file)))
# Unpickle the object
obj_ = numpy_pickle.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))
filenames = numpy_pickle.dump(obj, this_filename,
compress=compress,
cache_size=cache_size)
obj_ = numpy_pickle.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))
numpy_pickle.dump(a, this_filename, compress=1)
numpy_pickle.load(this_filename, mmap_mode='r')
def test_numpy_persistence():
filename = env['filename']
rnd = np.random.RandomState(0)
a = rnd.random_sample((10, 2))
for compress in (False, True, 0, 3):
# 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))
filenames = numpy_pickle.dump(obj,
this_filename,
compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
# Check that only one file was created
nose.tools.assert_equal(filenames[0], this_filename)
# Check that this file does exist
nose.tools.assert_true(
os.path.exists(os.path.join(env['dir'], filenames[0])))
# Unpickle the object
obj_ = numpy_pickle.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.
np.testing.assert_array_equal(np.array(obj), np.array(obj_))
# Now test with array subclasses
for obj in (np.matrix(np.zeros(10)),
np.memmap(filename + str(random.randint(0, 1000)) + 'mmap',
mode='w+',
shape=4,
dtype=np.float)):
this_filename = filename + str(random.randint(0, 1000))
filenames = numpy_pickle.dump(obj,
this_filename,
compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
obj_ = numpy_pickle.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)))
np.testing.assert_array_equal(obj_, obj)
# Test with an object containing multiple numpy arrays
obj = ComplexTestObject()
filenames = numpy_pickle.dump(obj, this_filename, compress=compress)
# All is cached in one file
nose.tools.assert_equal(len(filenames), 1)
obj_loaded = numpy_pickle.load(this_filename)
nose.tools.assert_true(isinstance(obj_loaded, type(obj)))
np.testing.assert_array_equal(obj_loaded.array_float, obj.array_float)
np.testing.assert_array_equal(obj_loaded.array_int, obj.array_int)
np.testing.assert_array_equal(obj_loaded.array_obj, obj.array_obj)