def test_directaccess():
f = tempfile.NamedTemporaryFile()
h5save(f.name, "test")
assert_equal(h5load(f.name), "test")
f.close()
f = tempfile.NamedTemporaryFile()
h5save(f.name, datasets["uni4medium"])
assert_array_equal(h5load(f.name).samples, datasets["uni4medium"].samples)
def test_directaccess():
f = tempfile.NamedTemporaryFile()
h5save(f.name, 'test')
assert_equal(h5load(f.name), 'test')
f.close()
f = tempfile.NamedTemporaryFile()
h5save(f.name, datasets['uni4medium'])
assert_array_equal(h5load(f.name).samples, datasets['uni4medium'].samples)
def test_recursion():
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
f = tempfile.NamedTemporaryFile()
h5save(f.name, obj)
lobj = h5load(f.name)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_recursion():
obj = range(2)
obj.append(HDFDemo())
obj.append(obj)
f = tempfile.NamedTemporaryFile()
h5save(f.name, obj)
lobj = h5load(f.name)
assert_equal(obj[:2], lobj[:2])
assert_equal(type(obj[2]), type(lobj[2]))
ok_(obj[3] is obj)
ok_(lobj[3] is lobj)
def test_function_ptrs():
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
f = tempfile.NamedTemporaryFile()
h5save(f.name, ds)
ds_loaded = h5load(f.name)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh),
ds.samples)
def test_function_ptrs():
if not externals.exists('nifti') and not externals.exists('nibabel'):
raise SkipTest
ds = load_example_fmri_dataset()
# add a mapper with a function ptr inside
ds = ds.get_mapped(mean_sample())
f = tempfile.NamedTemporaryFile()
h5save(f.name, ds)
ds_loaded = h5load(f.name)
fresh = load_example_fmri_dataset().O
# check that the reconstruction function pointer in the FxMapper points
# to the right one
assert_array_equal(ds_loaded.a.mapper.forward(fresh), ds.samples)
def test_h5py_clfs(lrn):
# lets simply clone it so we could make its all states on
lrn = lrn.clone()
# Lets enable all the states
lrn.ca.enable('all')
f = tempfile.NamedTemporaryFile()
# Store/reload untrained learner
try:
h5save(f.name, lrn)
except Exception, e:
raise AssertionError, \
"Failed to store due to %r" % (e,)
def test_h5py_clfs(lrn):
# lets simply clone it so we could make its all states on
lrn = lrn.clone()
# Lets enable all the states
lrn.ca.enable('all')
f = tempfile.NamedTemporaryFile()
# Store/reload untrained learner
try:
h5save(f.name, lrn)
except Exception, e:
raise AssertionError, \
"Failed to store due to %r" % (e,)
def _test_h5py_clfs():
# YOH: For now just to see which ones work (could be stored/loaded)
# Later on to become a proper valid test
from mvpa.clfs.warehouse import clfswh, regrswh
for lrn in clfswh[:] + regrswh[:]:
print lrn
f = tempfile.NamedTemporaryFile()
try:
h5save(f.name, lrn)
lrn_ = h5load(f.name)
print "ok: %s" % lrn_
except Exception, e:
#raise AssertionError,
print "Failed to store %s due to %r" % (lrn, e)
def test_h5py_dataset_typecheck():
ds = datasets['uni2small']
_, fpath = tempfile.mkstemp('mvpa', 'test')
h5save(fpath, [[1, 2, 3]])
assert_raises(ValueError, AttrDataset.from_hdf5, fpath)
# this one just catches if there is such a group
assert_raises(ValueError, AttrDataset.from_hdf5, fpath, name='bogus')
hdf = h5py.File(fpath, 'w')
ds = AttrDataset([1, 2, 3])
obj2hdf(hdf, ds, name='non-bogus')
obj2hdf(hdf, [1, 2, 3], name='bogus')
hdf.close()
assert_raises(ValueError, AttrDataset.from_hdf5, fpath, name='bogus')
ds_loaded = AttrDataset.from_hdf5(fpath, name='non-bogus')
assert_array_equal(ds, ds_loaded) # just to do smth useful with ds ;)
# cleanup and ignore stupidity
os.remove(fpath)
def test_h5py_dataset_typecheck():
ds = datasets['uni2small']
_, fpath = tempfile.mkstemp('mvpa', 'test')
h5save(fpath, [[1, 2, 3]])
assert_raises(ValueError, AttrDataset.from_hdf5, fpath)
# this one just catches if there is such a group
assert_raises(ValueError, AttrDataset.from_hdf5, fpath, name='bogus')
hdf = h5py.File(fpath, 'w')
ds = AttrDataset([1, 2, 3])
obj2hdf(hdf, ds, name='non-bogus')
obj2hdf(hdf, [1, 2, 3], name='bogus')
hdf.close()
assert_raises(ValueError, AttrDataset.from_hdf5, fpath, name='bogus')
ds_loaded = AttrDataset.from_hdf5(fpath, name='non-bogus')
assert_array_equal(ds, ds_loaded) # just to do smth useful with ds ;)
# cleanup and ignore stupidity
os.remove(fpath)
te = TransferError(lrn, errorfx)
te_ = TransferError(lrn_, errorfx)
error = te(ds_test, ds_train)
error_ = te_(ds_test, ds_train)
ok_(error == error_)
if len(set(['swig', 'rpy2']).intersection(lrn.__tags__)):
raise SkipTest("Trained swigged and R-interfaced classifiers can't "
"be stored/reloaded yet")
# now lets store/reload the trained one
try:
h5save(f.name, lrn_)
except Exception, e:
raise AssertionError, \
"Failed to store trained lrn due to %r" % (e,)
# This lrn__ is doubly stored/loaded ;-)
try:
lrn__ = h5load(f.name)
except Exception, e:
raise AssertionError, \
"Failed to load trained lrn due to %r" % (e,)
# Verify that we have the same ca enabled
# TODO
#ok_(set(lrn.ca.enabled) == set(lrn__.ca.enabled))
# and having the same values?
postproc=BinaryFxNode(errorfx, 'targets')
te = TransferMeasure(lrn, splitter, postproc=postproc)
te_ = TransferMeasure(lrn_, splitter, postproc=postproc)
error = te(ds)
error_ = te_(ds)
assert_array_equal(error, error_)
if len(set(['swig', 'rpy2']).intersection(lrn.__tags__)):
raise SkipTest("Trained swigged and R-interfaced classifiers can't "
"be stored/reloaded yet")
# now lets store/reload the trained one
try:
h5save(f.name, lrn_)
except Exception, e:
raise AssertionError, \
"Failed to store trained lrn due to %r" % (e,)
# This lrn__ is doubly stored/loaded ;-)
try:
lrn__ = h5load(f.name)
except Exception, e:
raise AssertionError, \
"Failed to load trained lrn due to %r" % (e,)
# Verify that we have the same ca enabled
# TODO
#ok_(set(lrn.ca.enabled) == set(lrn__.ca.enabled))
# and having the same values?
def test_0d_object_ndarray():
f = tempfile.NamedTemporaryFile()
a = np.array(0, dtype=object)
h5save(f.name, a)
a_ = h5load(f.name)
ok_(a == a_)
def test_0d_object_ndarray():
f = tempfile.NamedTemporaryFile()
a = np.array(0, dtype=object)
h5save(f.name, a)
a_ = h5load(f.name)
ok_(a == a_)