def save(where, name, obj, overwrite=True):
"""
hdf5pickle an object to given node in a HDF5 file.
:Parameters:
where : tables.Group : The parent node where to save
name : str : Sub-path where to save
obj : anything : The object to save
overwrite : bool: Whether to overwrite the object, if it already is there
:raises: tables.NodeError, if overwrite==False and node exists
"""
if isinstance(where, _tbl.File):
where = where.root
file = where._v_file
path = '/'.join([where._v_pathname, name]).replace('//', '/')
create_space(where, name, overwrite=overwrite)
if isinstance(obj, _n.ndarray):
obj = _typeconvert(obj)
hdf5pickle.dump(obj, file, path, type_map=_type_map)
def dumps(self, arg, proto=0, fast=0):
try:
os.unlink('hdf5test.h5')
except IOError: pass
except OSError: pass
p.dump(arg, 'hdf5test.h5')
with open('hdf5test.h5', 'rb') as f:
return f.read()
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 save_and_redo(self):
newfn = self.entry1.get()
newclus = self.entry2.get()
savefn = (self.savedir + os.sep + newfn + '_' + 'cluster_' + newclus +
'.h5')
hdf5pickle.dump(self.data, savefn, '/data')
fid = open('curr_clus.dat', 'w')
fid.write('%s' % newclus)
fid.close()
self.redo = True
self.frame.quit()
self.frame.destroy()
del self
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 dumps(self, arg, proto=0, fast=0):
try: os.unlink('hdf5test.h5')
except IOError: pass
except OSError: pass
f = tables.openFile('hdf5test.h5', 'w')
try:
p.dump(arg, f, '/obj')
finally:
f.close()
f = open('hdf5test.h5', 'r')
try:
return f.read()
finally:
f.close()
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 test_numpy_subclass():
filename = env['filename']
a = SubArray((10,))
p.dump(a, filename)
c = p.load(filename)
nose.tools.assert_true(isinstance(c, SubArray))
