def test_hdf5():
"""Test HDF5 IO
"""
tempdir = _TempDir()
test_file = op.join(tempdir, "test.hdf5")
sp = np.eye(3) if sparse is None else sparse.eye(3, 3, format="csc")
sp[2, 2] = 2
x = dict(
a=dict(b=np.zeros(3)),
c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2.0, "hello", u"goodbyeu\u2764")), None],
f=sp,
)
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
assert_raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
assert_raises(IOError, read_hdf5, test_file + "FOO") # not found
xx = read_hdf5(test_file)
assert_true(object_diff(x, xx) == "") # no assert_equal, ugly output
# bad title
assert_raises(ValueError, read_hdf5, test_file, title="nonexist")
assert_raises(ValueError, write_hdf5, test_file, x, overwrite=True, title=1)
assert_raises(ValueError, read_hdf5, test_file, title=1)
# unsupported objects
assert_raises(TypeError, write_hdf5, test_file, {1: "foo"}, overwrite=True)
assert_raises(TypeError, write_hdf5, test_file, object, overwrite=True)
def test_path_support():
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
write_hdf5(test_file, 1, title='first')
write_hdf5(test_file, 2, title='second/third', overwrite='update')
assert_raises(ValueError, read_hdf5, test_file, title='second')
assert_equal(read_hdf5(test_file, 'first'), 1)
assert_equal(read_hdf5(test_file, 'second/third'), 2)
def test_path_support(tmpdir):
tempdir = str(tmpdir)
test_file = op.join(tempdir, 'test.hdf5')
write_hdf5(test_file, 1, title='first')
write_hdf5(test_file, 2, title='second/third', overwrite='update')
pytest.raises(ValueError, read_hdf5, test_file, title='second')
assert_equal(read_hdf5(test_file, 'first'), 1)
assert_equal(read_hdf5(test_file, 'second/third'), 2)
def read_markers(fname):
contents = h5_listdir(fname)
markers = list()
epochs = None
if 'nice/markers/order' in contents:
marker_order = read_hdf5(fname,
title='nice/markers/order',
slash='replace')
else:
marker_order = [k for k in contents if 'nice/marker/' in k]
if any('nice/data/epochs' in k for k in contents):
epochs = read_hdf5(fname, title='nice/data/epochs', slash='replace')
# MNE fix
if 'filename' in epochs['info']:
del (epochs['info']['filename'])
epochs = mne.EpochsArray(data=epochs.pop('_data'),
info=Info(epochs.pop('info')),
tmin=epochs.pop('tmin'),
event_id=epochs.pop('event_id'),
events=epochs.pop('events'),
reject=epochs.pop('reject'),
flat=epochs.pop('flat'))
# Read all PowerSpectralDensityEstimator estimators
estimators = [
k for k in contents
if 'nice/container/PowerSpectralDensityEstimator' in k
]
all_estimators = {}
for estimator_name in estimators:
estimator_comment = estimator_name.split('/')[-1]
this_estimator = read_psd_estimator(fname, comment=estimator_comment)
all_estimators[estimator_comment] = this_estimator
for content in marker_order:
_, _, my_class_name, comment = content.split('/')
my_class = _markers_classes[my_class_name]
if issubclass(my_class, BaseTimeLocked):
if not epochs:
raise RuntimeError(
'Something weird has happened. You want to read a '
'marker that depends on epochs but '
'I could not find any epochs in the file you gave me.')
markers.append(my_class._read(fname, epochs, comment=comment))
elif issubclass(my_class, BasePowerSpectralDensity):
markers.append(
my_class._read(fname,
estimators=all_estimators,
comment=comment))
elif issubclass(my_class, BaseMarker):
markers.append(my_class._read(fname, comment=comment))
else:
raise ValueError('Come on--this is not a Nice class!')
markers = Markers(markers)
return markers
def _read_my_marker(klass, fname, comment='default'):
# MANDATORY: This method should work for any marker as it is now.
data = read_hdf5(fname, _get_title(klass, comment), slash='replace')
init_params = {k: v for k, v in data.items() if not k.endswith('_')}
attrs = {k: v for k, v in data.items() if k.endswith('_')}
file_info = read_hdf5(fname, title='nice/data/ch_info', slash='replace')
if 'filename' in file_info:
del(file_info['filename'])
attrs['ch_info_'] = Info(file_info)
out = klass(**init_params)
for k, v in attrs.items():
if k.endswith('_'):
setattr(out, k, v)
return out
def test_numpy_values():
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
for cast in [np.int8, np.int16, np.int32, np.int64, np.bool_,
np.float16, np.float32, np.float64]:
value = cast(1)
write_hdf5(test_file, value, title='first', overwrite='update')
assert_equal(read_hdf5(test_file, 'first'), value)
def test_numpy_values(tmpdir):
"""Test NumPy values."""
test_file = op.join(str(tmpdir), 'test.hdf5')
for cast in [
np.int8, np.int16, np.int32, np.int64, np.bool_, np.float16,
np.float32, np.float64
]:
value = cast(1)
write_hdf5(test_file, value, title='first', overwrite='update')
assert_equal(read_hdf5(test_file, 'first'), value)
def test_hdf5_use_json():
"""Test HDF5 IO
"""
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
splash_dict = {'first/second': {'one/more': 'value'}}
pytest.raises(ValueError,
write_hdf5,
test_file,
splash_dict,
overwrite=True,
slash='error',
use_json=True)
spec_dict = {'first/second': 'third'}
write_hdf5(test_file,
spec_dict,
overwrite=True,
slash='replace',
use_json=True)
assert_equal(
read_hdf5(test_file, slash='replace').keys(), spec_dict.keys())
in_keys = list(read_hdf5(test_file, slash='ignore').keys())
assert ('{FWDSLASH}' in in_keys[0])
comp_dict = {'first': [1, 2], 'second': 'str', 'third': {'a': 1}}
write_hdf5(test_file, comp_dict, overwrite=True, use_json=True)
assert_equal(sorted(read_hdf5(test_file, slash='replace').keys()),
sorted(comp_dict.keys()))
numpy_dict = {'first': np.array([1])}
write_hdf5(test_file, numpy_dict, overwrite=True, use_json=True)
assert_equal(
list(read_hdf5(test_file, slash='replace').values())[0],
list(numpy_dict.values())[0])
pytest.raises(ValueError, read_hdf5, test_file, slash='brains')
# Testing that title slashes aren't replaced
write_hdf5(test_file,
spec_dict,
title='one/two',
overwrite=True,
slash='replace',
use_json=True)
assert_equal(
read_hdf5(test_file, title='one/two', slash='replace').keys(),
spec_dict.keys())
def __getitem__(self, item):
"""
Get/ read data from the HDF5 file
Args:
item (str, slice): path to the data or key of the data object
Returns:
dict, list, float, int: data or data object
"""
if isinstance(item, slice):
if not (item.start or item.stop or item.step):
return self.values()
raise NotImplementedError("Implement if needed, e.g. for [:]")
else:
item_lst = item.split("/")
if len(item_lst) == 1 and item_lst[0] != "..":
if item in self.list_nodes():
obj = h5io.read_hdf5(self.file_name,
title=self._get_h5_path(item))
return obj
if item in self.list_groups():
with self.open(item) as hdf_item:
obj = hdf_item.copy()
return obj
raise ValueError("Unknown item: {} {} {}".format(
item, self.file_name, self.h5_path))
else:
if item_lst[
0] == "": # item starting with '/', thus we have an absoute HDF5 path
item_abs_lst = os.path.normpath(item).replace(
"\\", "/").split("/")
else: # relative HDF5 path
# The self.h5_path is an absolute path (/h5_path/in/h5/file), however, to
# reach any directory super to root, we start with a
# relative path = ./h5_path/in/h5/file and add whatever we get as item.
# The normpath finally returns a path to the item which is relative to the hdf-root.
item_abs_lst = (os.path.normpath(
os.path.join('.' + self.h5_path,
item)).replace("\\", "/").split("/"))
# print('h5_path=', self.h5_path, 'item=', item, 'item_abs_lst=', item_abs_lst)
if (item_abs_lst[0] == "." and len(item_abs_lst) == 1):
# Here, we are asked to return the root of the HDF5-file. The resulting self.path would be the
# same as the self.file_path and, thus, the path of the pyiron Project this HDF5-file belongs to:
return self.create_project_from_hdf5()
elif item_abs_lst[0] == "..":
# Here, we are asked to return a path super to the root of the HDF5-file, a.k.a. the path of it's
# pyiron Project, thus we pass the relative path to the pyiron Project to handle it:
return self.create_project_from_hdf5()["/".join(
item_abs_lst)]
else:
hdf_object = self.copy()
hdf_object.h5_path = "/".join(item_abs_lst[:-1])
return hdf_object[item_abs_lst[-1]]
def _read(self, item):
"""
Internal read function to read data from the HDF5 file
Args:
item (str): path to the data or key of the data object
Returns:
dict, list, float, int: data or data object
"""
return h5io.read_hdf5(self.file_name, title=self._get_h5_path(item))
def load(subject, event_type):
# Behavior
fname = op.join(path_data, subject, 'behavior_%s.hdf5' % event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
# MEG
fname = op.join(path_data, subject, 'epochs_%s.fif' % event_type)
epochs = mne.read_epochs(fname)
return epochs, events
def test_timezone(name, tmpdir):
"""Test datetime.timezone support."""
fname = op.join(str(tmpdir), 'test.hdf5')
kwargs = dict()
if name is not None:
kwargs['name'] = name
x = datetime.timezone(datetime.timedelta(hours=-7), **kwargs)
write_hdf5(fname, x)
y = read_hdf5(fname)
assert isinstance(y, datetime.timezone)
assert y == x
if name is not None:
assert y.tzname(None) == name
def load(subject, event_type):
fname = op.join(path_data, '%s/behavior_%s.hdf5') % (subject, event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
fname = op.join(path_data, '%s/epochs_%s.fif') % (subject, event_type)
epochs = mne.read_epochs(fname)
if event_type == 'Target':
epochs.crop(0, .600)
elif event_type == 'Cue':
epochs.crop(0, .900)
elif event_type == 'Probe':
epochs.crop(0, .600)
return epochs, events
def test_multi_dim_array(tmpdir):
"""Test multidimensional arrays."""
rng = np.random.RandomState(0)
traj = np.array([rng.randn(2, 1), rng.randn(3, 1)])
test_file = op.join(str(tmpdir), 'test.hdf5')
write_hdf5(test_file, traj, title='first', overwrite='update')
for traj_read, traj_sub in zip(read_hdf5(test_file, 'first'), traj):
assert (np.equal(traj_read, traj_sub).all())
traj_no_structure = np.array([rng.randn(2, 1, 1), rng.randn(3, 1, 2)])
pytest.raises(ValueError,
write_hdf5,
test_file,
traj_no_structure,
title='second',
overwrite='update')
def test_datetime(tmpdir):
"""Test datetime.datetime support."""
fname = op.join(str(tmpdir), 'test.hdf5')
# Naive
y, m, d, h, m, s, mu = range(1, 8)
dt = datetime.datetime(y, m, d, h, m, s, mu)
for key in ('year', 'month', 'day', 'hour', 'minute', 'second',
'microsecond'):
val = locals()[key[:1] if key != 'microsecond' else 'mu']
assert val == getattr(dt, key)
assert dt.year == y
assert dt.month == m
write_hdf5(fname, dt)
dt2 = read_hdf5(fname)
assert isinstance(dt2, datetime.datetime)
assert dt == dt2
assert dt2.tzinfo is None
# Aware
dt = dt.replace(tzinfo=datetime.timezone.utc)
write_hdf5(fname, dt, overwrite=True)
dt2 = read_hdf5(fname)
assert isinstance(dt2, datetime.datetime)
assert dt == dt2
assert dt2.tzinfo is datetime.timezone.utc
# Custom
dt = dt.replace(tzinfo=XT())
write_hdf5(fname, dt, overwrite=True)
dt2 = read_hdf5(fname)
assert isinstance(dt2, datetime.datetime)
assert dt == dt2
assert dt2.tzinfo is not None
assert dt2.tzinfo is not datetime.timezone.utc
for key in ('utcoffset', 'tzname', 'dst'):
v1 = getattr(dt2.tzinfo, key)(None)
v2 = getattr(dt.tzinfo, key)(None)
assert v1 == v2
def test_h5_file_object(tmpdir):
tempdir = str(tmpdir)
test_file_path = op.join(tempdir, 'test1.hdf5')
# test that wrong object type raises error
pytest.raises(ValueError, write_hdf5, fname=33, data=1)
# test that reading/writing are unaffected
with h5py.File(test_file_path, 'a') as test_file_obj:
data = {'a': 42}
write_hdf5(test_file_obj, data)
assert_equal(read_hdf5(test_file_obj), data)
# test that wrong mode raises error
with h5py.File(test_file_path, 'r') as test_file_obj:
assert test_file_obj.mode == 'r'
with pytest.raises(UnsupportedOperation):
write_hdf5(test_file_obj, data=1)
def load(subject, event_type):
# Behavior
fname = op.join(path_data, subject, 'behavior_%s.hdf5' % event_type)
events = read_hdf5(fname)
# add explicit conditions
events = complete_behavior(events)
# MEG
if target_baseline:
fname = op.join(path_data, subject,
'epochs_tf_%s.fif' % event_type) # noqa
else:
fname = op.join(path_data, subject,
'epochs_tf_%s_bsl.fif' % event_type) # noqa
epochs = mne.read_epochs(fname)
# epochs.decimate(10)
return epochs, events
def __getitem__(self, item):
"""
Get/ read data from the HDF5 file
Args:
item (str, slice): path to the data or key of the data object
Returns:
dict, list, float, int: data or data object
"""
if isinstance(item, slice):
if not (item.start or item.stop or item.step):
return self.values()
raise NotImplementedError("Implement if needed, e.g. for [:]")
else:
item_lst = item.split("/")
if len(item_lst) == 1 and item_lst[0] != "..":
if item in self.list_nodes():
obj = h5io.read_hdf5(self.file_name,
title=self._get_h5_path(item))
return obj
if item in self.list_groups():
with self.open(item) as hdf_item:
obj = hdf_item.copy()
return obj
raise ValueError("Unknown item: {}".format(item))
else:
if item_lst[0] == "": # absoute HDF5 path
item_abs_lst = os.path.normpath(item).replace(
"\\", "/").split("/")
else: # relative HDF5 path
item_abs_lst = (os.path.normpath(
os.path.join(self.h5_path,
item)).replace("\\", "/").split("/"))
# print(item, item_abs_lst)
# item_abs_lst = os.path.normpath(os.path.join(self.h5_path, item)).replace('\\', '/').split("/")
if (item_abs_lst[1] == ""
and len(item_abs_lst) == 2): # leaving the HDF5 file
return self._create_project_from_hdf5()
elif item_abs_lst[1] == "":
return self._create_project_from_hdf5()["/".join(
item_abs_lst[2:])]
else:
hdf_object = self.copy()
hdf_object.h5_path = "/".join(item_abs_lst[:-1])
return hdf_object[item_abs_lst[-1]]
def read_glm(fname):
"""
Read GLM results from disk.
Parameters
----------
fname : str
The file name, which should end with ``glm.h5``.
Returns
-------
glm : RegressionResults or ContrastResults
RegressionResults or ContrastResults class
which stores the GLM results.
"""
check_fname(fname, 'path-like', 'glm.h5')
glm = read_hdf5(fname, title='mnepython')
return _state_to_glm(glm)
def from_hdf(fname):
"""
Read an EpochsTFR from an HDF5 file.
This expects an HDF5 file that was created with
write_hdf5. It auto-populates a new EpochsTFR object.
Be careful with memory consumption!
Parameters
----------
fname : str
The path to the HDF5 file you wish to import.
Returns
-------
etfr : EpochsTFR
The EpochsTFR object contained in the HDF5 file.
"""
params = h5io.read_hdf5(fname)
etfr = EpochTFR(**params)
return etfr
def from_hdf(fname):
"""
Read an EpochsTFR from an HDF5 file.
This expects an HDF5 file that was created with
write_hdf5. It auto-populates a new EpochsTFR object.
Be careful with memory consumption!
Parameters
----------
fname : str
The path to the HDF5 file you wish to import.
Returns
-------
etfr : EpochsTFR
The EpochsTFR object contained in the HDF5 file.
"""
params = h5io.read_hdf5(fname)
etfr = EpochTFR(**params)
return etfr
def from_hdf(self):
"""
Restore input, output and the class definition from an HDF5 file - to maintain orthogonal persistence.
"""
job_dict = h5io.read_hdf5(
os.path.join(self._working_directory, "scisweeper.h5"))
if "input" in job_dict.keys():
self.input_dict = job_dict["input"]
if "settings" in job_dict.keys():
self._executable = job_dict["settings"]["executable"]
self._working_directory = job_dict["settings"]["working_directory"]
if "NotImplementedError" in inspect.getsource(self.write_input):
self._write_input_source = job_dict["settings"]["write_input"]
self.write_input = self._str_to_obj(self._write_input_source)
if "NotImplementedError" in inspect.getsource(self.collect_output):
self._collect_output_source = job_dict["settings"][
"collect_output"]
self.collect_output = self._str_to_obj(
self._collect_output_source)
if "output" in job_dict.keys():
self.output_dict = job_dict["output"]
def test_hdf5():
"""Test HDF5 IO
"""
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
sp = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csc')
df = np.eye(3) if isinstance(DataFrame, type(None)) else DataFrame(
np.eye(3))
sr = np.eye(3) if isinstance(Series, type(None)) else Series(
np.random.randn(3))
sp[2, 2] = 2
x = dict(a=dict(b=np.zeros(3)),
c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2., 'hello', u'goodbyeu\u2764')), None],
f=sp,
g=dict(dfa=df, srb=sr))
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
assert_raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
assert_raises(IOError, read_hdf5, test_file + 'FOO') # not found
xx = read_hdf5(test_file)
assert_true(object_diff(x, xx) == '') # no assert_equal, ugly output
# bad title
assert_raises(ValueError, read_hdf5, test_file, title='nonexist')
assert_raises(ValueError,
write_hdf5,
test_file,
x,
overwrite=True,
title=1)
assert_raises(ValueError, read_hdf5, test_file, title=1)
# unsupported objects
assert_raises(TypeError, write_hdf5, test_file, {1: 'foo'}, overwrite=True)
assert_raises(TypeError, write_hdf5, test_file, object, overwrite=True)
write_hdf5(test_file, 1, title='first', overwrite=True)
write_hdf5(test_file, 2, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='first'), 1)
assert_equal(read_hdf5(test_file, title='second'), 2)
assert_raises(IOError, write_hdf5, test_file, 3, title='second')
write_hdf5(test_file, 3, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='second'), 3)
write_hdf5(test_file, 5, title='second', overwrite='update', compression=5)
assert_equal(read_hdf5(test_file, title='second'), 5)
def test_hdf5():
"""Test HDF5 IO
"""
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
sp = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csc')
df = np.eye(3) if isinstance(DataFrame, type(None)) else DataFrame(
np.eye(3))
sr = np.eye(3) if isinstance(Series, type(None)) else Series(
np.random.randn(3))
sp[2, 2] = 2
x = dict(a=dict(b=np.zeros(3)), c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2., 'hello', u'goodbyeu\u2764')), None], f=sp,
g=dict(dfa=df, srb=sr))
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
assert_raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
assert_raises(IOError, read_hdf5, test_file + 'FOO') # not found
xx = read_hdf5(test_file)
assert_true(object_diff(x, xx) == '') # no assert_equal, ugly output
# bad title
assert_raises(ValueError, read_hdf5, test_file, title='nonexist')
assert_raises(ValueError, write_hdf5, test_file, x, overwrite=True,
title=1)
assert_raises(ValueError, read_hdf5, test_file, title=1)
# unsupported objects
assert_raises(TypeError, write_hdf5, test_file, {1: 'foo'},
overwrite=True)
assert_raises(TypeError, write_hdf5, test_file, object, overwrite=True)
write_hdf5(test_file, 1, title='first', overwrite=True)
write_hdf5(test_file, 2, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='first'), 1)
assert_equal(read_hdf5(test_file, title='second'), 2)
assert_raises(IOError, write_hdf5, test_file, 3, title='second')
write_hdf5(test_file, 3, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='second'), 3)
write_hdf5(test_file, 5, title='second', overwrite='update', compression=5)
assert_equal(read_hdf5(test_file, title='second'), 5)
def get_job_status_from_file(hdf5_file, job_name):
if os.path.exists(hdf5_file):
return h5io.read_hdf5(hdf5_file, job_name + "/status")
else:
return None
def get_job_status_from_file(hdf5_file, job_name):
return h5io.read_hdf5(hdf5_file, job_name + '/status')
def get_hamilton_version_from_file(hdf5_file, job_name):
return h5io.read_hdf5(hdf5_file, job_name + '/VERSION')
def test_hdf5():
"""Test HDF5 IO
"""
tempdir = _TempDir()
test_file = op.join(tempdir, 'test.hdf5')
sp = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csc')
sp_csr = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csr')
df = np.eye(3) if isinstance(DataFrame, type(None)) else DataFrame(
np.eye(3))
sr = np.eye(3) if isinstance(Series, type(None)) else Series(
np.random.randn(3))
sp[2, 2] = 2
sp_csr[2, 2] = 2
x = dict(a=dict(b=np.zeros(3)), c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2., 'hello', u'goodbyeu\u2764')), None], f=sp,
g=dict(dfa=df, srb=sr), h=sp_csr, i=sr, j='hi')
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
assert_raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
assert_raises(IOError, read_hdf5, test_file + 'FOO') # not found
xx = read_hdf5(test_file)
assert_true(object_diff(x, xx) == '') # no assert_equal, ugly output
list_file_contents(test_file) # Testing the h5 listing
assert_raises(TypeError, list_file_contents, sp) # Only string works
write_hdf5(test_file, np.bool_(True), overwrite=True)
assert_equal(read_hdf5(test_file), np.bool_(True))
# bad title
assert_raises(ValueError, read_hdf5, test_file, title='nonexist')
assert_raises(ValueError, write_hdf5, test_file, x, overwrite=True,
title=1)
assert_raises(ValueError, read_hdf5, test_file, title=1)
# unsupported objects
assert_raises(TypeError, write_hdf5, test_file, {1: 'foo'},
overwrite=True)
assert_raises(TypeError, write_hdf5, test_file, object, overwrite=True)
# special_chars
spec_dict = {'first/second': 'third'}
assert_raises(ValueError, write_hdf5, test_file, spec_dict, overwrite=True)
assert_raises(ValueError, write_hdf5, test_file, spec_dict, overwrite=True,
slash='brains')
write_hdf5(test_file, spec_dict, overwrite=True, slash='replace')
assert_equal(
read_hdf5(test_file, slash='replace').keys(), spec_dict.keys())
in_keys = list(read_hdf5(test_file, slash='ignore').keys())
assert_true('{FWDSLASH}' in in_keys[0])
assert_raises(ValueError, read_hdf5, test_file, slash='brains')
# Testing that title slashes aren't replaced
write_hdf5(
test_file, spec_dict, title='one/two', overwrite=True, slash='replace')
assert_equal(read_hdf5(test_file, title='one/two', slash='replace').keys(),
spec_dict.keys())
write_hdf5(test_file, 1, title='first', overwrite=True)
write_hdf5(test_file, 2, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='first'), 1)
assert_equal(read_hdf5(test_file, title='second'), 2)
assert_raises(IOError, write_hdf5, test_file, 3, title='second')
write_hdf5(test_file, 3, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='second'), 3)
write_hdf5(test_file, 5, title='second', overwrite='update', compression=5)
assert_equal(read_hdf5(test_file, title='second'), 5)
def __getitem__(self, item):
"""
Get/ read data from the HDF5 file
Args:
item (str, slice): path to the data or key of the data object
Returns:
dict, list, float, int: data or data object
"""
if isinstance(item, slice):
if not (item.start or item.stop or item.step):
return self.values()
raise NotImplementedError("Implement if needed, e.g. for [:]")
else:
try:
# fast path, a good amount of accesses will want to fetch a specific dataset it knows exists in the
# file, there's therefor no point in checking whether item is a group or a node or even worse recursing
# in case when item contains '/'. In most cases read_hdf5 will grab the correct data straight away and
# if not we will still check thoroughly below. Since list_nodes()/list_groups() each open the
# underlying file once, this reduces the number of file opens in the most-likely case from 2 to 1 (1 to
# check whether the data is there and 1 to read it) and increases in the worst case from 1 to 2 (1 to
# try to read it here and one more time to verify it's not a group below).
obj = h5io.read_hdf5(self.file_name,
title=self._get_h5_path(item))
if self._is_convertable_dtype_object_array(obj):
obj = self._convert_dtype_obj_array(obj.copy())
return obj
except (ValueError, OSError):
# h5io couldn't find a dataset with name item, but there still might be a group with that name, which we
# check in the rest of the method
pass
item_lst = item.split("/")
if len(item_lst) == 1 and item_lst[0] != "..":
# if item in self.list_nodes() we would have caught it in the fast path above
if item in self.list_groups():
with self.open(item) as hdf_item:
obj = hdf_item.copy()
if self._is_convertable_dtype_object_array(obj):
obj = self._convert_dtype_obj_array(obj)
return obj
raise ValueError("Unknown item: {} {} {}".format(
item, self.file_name, self.h5_path))
else:
if (
item_lst[0] == ""
): # item starting with '/', thus we have an absoute HDF5 path
item_abs_lst = os.path.normpath(item).replace(
"\\", "/").split("/")
else: # relative HDF5 path
# The self.h5_path is an absolute path (/h5_path/in/h5/file), however, to
# reach any directory super to root, we start with a
# relative path = ./h5_path/in/h5/file and add whatever we get as item.
# The normpath finally returns a path to the item which is relative to the hdf-root.
item_abs_lst = (os.path.normpath(
os.path.join("." + self.h5_path,
item)).replace("\\", "/").split("/"))
# print('h5_path=', self.h5_path, 'item=', item, 'item_abs_lst=', item_abs_lst)
if item_abs_lst[0] == "." and len(item_abs_lst) == 1:
# Here, we are asked to return the root of the HDF5-file. The resulting self.path would be the
# same as the self.file_path and, thus, the path of the pyiron Project this HDF5-file belongs to:
return self.create_project_from_hdf5()
elif item_abs_lst[0] == "..":
# Here, we are asked to return a path super to the root of the HDF5-file, a.k.a. the path of it's
# pyiron Project, thus we pass the relative path to the pyiron Project to handle it:
return self.create_project_from_hdf5()["/".join(
item_abs_lst)]
else:
hdf_object = self.copy()
hdf_object.h5_path = "/".join(item_abs_lst[:-1])
return hdf_object[item_abs_lst[-1]]
epochs = mne.read_epochs(fname2)
epochs.info['dev_head_t'] = dev_head_t
if subject == 'sub01_YFAALKWR_JA': # miss button 2 recordings on
# 1st session for s01
epochs.drop_channels(['UADC007-2104'])
epochs_list.append(epochs)
epochs = concatenate_epochs(epochs_list)
fname = op.join(path_data, subject,
'epochs_%s%s.fif' % (event_type, suffix))
epochs.save(fname)
# delete epoch-1 and epoch-2 to keep only the concatenate one
os.remove(fname1)
os.remove(fname2)
# concatenate behavior files
fname1 = op.join(path_data, subject, 'behavior_%s_1.hdf5' % event_type)
events1 = read_hdf5(fname1)
fname2 = op.join(path_data, subject_2,
'behavior_%s_2.hdf5' % event_type)
events2 = read_hdf5(fname2)
frames = [events1, events2]
events = pd.concat(frames, axis=0)
fname = op.join(path_data, subject, 'behavior_%s.hdf5' % event_type)
write_hdf5(fname, events, overwrite=True)
# if only one session has been acquired (one subject)
else:
fname1 = op.join(path_data, subject,
'epochs_%s%s_1.fif' % (event_type, suffix))
fname = op.join(path_data, subject,
'epochs_%s%s.fif' % (event_type, suffix))
shutil.copy(fname1, fname)
os.remove(fname1)
def test_hdf5(tmpdir):
"""Test HDF5 IO."""
tempdir = str(tmpdir)
test_file = op.join(tempdir, 'test.hdf5')
sp = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csc')
sp_csr = np.eye(3) if sparse is None else sparse.eye(3, 3, format='csr')
df = np.eye(3) if isinstance(DataFrame, type(None)) else DataFrame(
np.eye(3))
sr = np.eye(3) if isinstance(Series, type(None)) else Series(
np.random.randn(3))
sp[2, 2] = 2
sp_csr[2, 2] = 2
x = dict(a=dict(b=np.zeros(3)),
c=np.zeros(2, np.complex128),
d=[dict(e=(1, -2., 'hello', u'goodbyeu\u2764')), None],
f=sp,
g=dict(dfa=df, srb=sr),
h=sp_csr,
i=sr,
j='hi')
write_hdf5(test_file, 1)
assert_equal(read_hdf5(test_file), 1)
pytest.raises(IOError, write_hdf5, test_file, x) # file exists
write_hdf5(test_file, x, overwrite=True)
pytest.raises(IOError, read_hdf5, test_file + 'FOO') # not found
xx = read_hdf5(test_file)
assert (object_diff(x, xx) == '') # no assert_equal, ugly output
list_file_contents(test_file) # Testing the h5 listing
pytest.raises(TypeError, list_file_contents, sp) # Only string works
write_hdf5(test_file, np.bool_(True), overwrite=True)
assert_equal(read_hdf5(test_file), np.bool_(True))
# bad title
pytest.raises(ValueError, read_hdf5, test_file, title='nonexist')
pytest.raises(ValueError,
write_hdf5,
test_file,
x,
overwrite=True,
title=1)
pytest.raises(ValueError, read_hdf5, test_file, title=1)
# unsupported objects
pytest.raises(TypeError, write_hdf5, test_file, {1: 'foo'}, overwrite=True)
pytest.raises(TypeError, write_hdf5, test_file, object, overwrite=True)
# special_chars
spec_dict = {'first/second': 'third'}
pytest.raises(ValueError, write_hdf5, test_file, spec_dict, overwrite=True)
pytest.raises(ValueError,
write_hdf5,
test_file,
spec_dict,
overwrite=True,
slash='brains')
write_hdf5(test_file, spec_dict, overwrite=True, slash='replace')
assert_equal(
read_hdf5(test_file, slash='replace').keys(), spec_dict.keys())
in_keys = list(read_hdf5(test_file, slash='ignore').keys())
assert ('{FWDSLASH}' in in_keys[0])
pytest.raises(ValueError, read_hdf5, test_file, slash='brains')
# Testing that title slashes aren't replaced
write_hdf5(test_file,
spec_dict,
title='one/two',
overwrite=True,
slash='replace')
assert_equal(
read_hdf5(test_file, title='one/two', slash='replace').keys(),
spec_dict.keys())
write_hdf5(test_file, 1, title='first', overwrite=True)
write_hdf5(test_file, 2, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='first'), 1)
assert_equal(read_hdf5(test_file, title='second'), 2)
pytest.raises(IOError, write_hdf5, test_file, 3, title='second')
write_hdf5(test_file, 3, title='second', overwrite='update')
assert_equal(read_hdf5(test_file, title='second'), 3)
write_hdf5(test_file, 5, title='second', overwrite='update', compression=5)
assert_equal(read_hdf5(test_file, title='second'), 5)
def get_hamilton_from_file(hdf5_file, job_name):
return h5io.read_hdf5(hdf5_file, job_name + '/TYPE').split(".")[-1].split("'")[0]
freqs = ['DC', 'delta', 'theta', 'alpha', 'beta', 'gamma']
labels = mne.read_labels_from_annot('fsaverage', 'aparc_sub')
labels = [label for label in labels if 'unknown' not in label.name]
###############################################################################
# Load data
# ---------
X, y = [list() for _ in range(len(ages))], list()
for ai, age in enumerate(ages):
shape = None
for mi, measure in enumerate(measures):
fast_fname = 'genz_%s_%s_fast.h5' % (age, measure)
if not op.isfile(fast_fname):
print('Converting %s measure %s' % (age, measure))
data = read_hdf5('genz_%s_%s.h5' % (age, measure))
data = data['data_vars'][measure]['data']
data = np.array(data)
assert data.dtype == np.float
write_hdf5(fast_fname, data)
data = read_hdf5(fast_fname)
if shape is None:
shape = data.shape
assert shape[-1] == 2
assert data.shape == shape
assert data.ndim == 4
#data = data[freq_idx] # only use these freqs
# deal with reordering (undo it to restore original order)
order = np.argsort(data[:, :, :, 0], axis=-1)
data = data[..., 1]
for ii in range(data.shape[0]):
plt.rcParams['font.sans-serif'] = ['Arial']
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['xtick.labelsize'] = 16
plt.rcParams['ytick.labelsize'] = 16
all_correct = list()
cueangle_correct = list()
cuesfreq_correct = list()
cueleft_correct = list()
cueright_correct = list()
for subject in subjects:
# Read behav file (hdf5)
print '**********' + subject + '************'
fname = op.join(path_data, subject, 'behavior_target.hdf5')
events = read_hdf5(fname)
events = complete_behavior(events)
# Select behav perf on all trials
isfixed = np.where(events['is_eye_fixed'] == 1)
iscorrect = np.array(events['is_correct'])
iscorrect_fixed = iscorrect[isfixed]
if len(iscorrect_fixed) != 800:
warnings.warn("Total isfixed trial is not 800")
print 'total is:' + str(len(iscorrect_fixed))
perc = sum(iscorrect_fixed) / len(iscorrect_fixed)
all_correct.append(perc)
# behav perf only cue angle
cue_angle = np.where(events['cue_type'] == 'angle')
if len(cue_angle[0]) != 400:
warnings.warn("Total trial with cue angle is not 400")
