def read_ts(path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "ts.h5"), data=None):
"""
:param path: Path towards a valid TimeSeries H5 file
:return: Timeseries in a numpy array
"""
print "Reading TimeSeries from:", path
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file)
print "Structures:", h5_file["/"].keys()
if isinstance(data, dict):
for key in data:
print "Data expected shape:", h5_file['/' + key].shape
data[key] = h5_file['/' + key][()]
print "Actual Data shape", data[key].shape
print "First Channel sv sum", numpy.sum(data[key][:, 0])
else:
print "Data expected shape:", h5_file['/data'].shape
data = h5_file['/data'][()]
print "Actual Data shape", data.shape
print "First Channel sv sum", numpy.sum(data[:, 0])
total_time = int(h5_file["/"].attrs["Simulated_period"][0])
nr_of_steps = int(h5_file["/data"].attrs["Number_of_steps"][0])
start_time = float(h5_file["/data"].attrs["Start_time"][0])
time = numpy.linspace(start_time, total_time, nr_of_steps)
h5_file.close()
return time, data
def read_hypothesis(path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "hypo_ep.h5"), output="object", hypo_name=None,
update_hypothesis=True):
"""
:param path: Path towards an hypothesis H5 file
:return: hypothesis object
"""
print "Reading Hypothesis from:", path
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file)
overwrite_fields_dict = {"seizure_indices":
numpy.array(numpy.where(h5_file["/EZ hypothesis"][()] > 0)).astype("i").squeeze()}
if isinstance(hypo_name, basestring):
overwrite_fields_dict.update({"name": hypo_name})
if output == "dict":
hyp = dict()
else:
hyp = Hypothesis(h5_file['/Connectivity'][()].shape[0], h5_file['/Connectivity'][()])
read_object_from_h5_file(hyp, h5_file, hyp_attributes_dict, add_overwrite_fields_dict=overwrite_fields_dict)
if output != "dict" and update_hypothesis:
hyp._update_parameters(hyp.seizure_indices)
h5_file.close()
return hyp
def read_simulation_settings(path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "sim_ep.h5"), output="object",
hypothesis=None):
"""
:param path: Path towards an hypothesis H5 file
:return: hypothesis object
"""
print "Reading simulation settings from:", path
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file)
if output == "dict" or not (isinstance(hypothesis, Hypothesis)):
model = dict()
if hypothesis is not None:
warnings.warn("hypothesis is not a Hypothesis object. Returning a dictionary for model.")
else:
if h5_file['/' + "model.name"][()] == "Epileptor":
model = model_build_dict[h5_file['/' + "model.name"][()]](hypothesis)
else:
model = model_build_dict[h5_file['/' + "model.name"][()]](hypothesis,
zmode=h5_file['/' + "model.zmode"][()])
if h5_file['/' + "model.name"][()] != "Epileptor":
overwrite_fields_dict = {"zmode": numpy.array(h5_file['/' + "model.zmode"][()])}
if h5_file['/' + "model.name"][()] == "EpileptorDPrealistic":
overwrite_fields_dict.update({"pmode": numpy.array(h5_file['/' + "model.pmode"][()])})
read_object_from_h5_file(model, h5_file, epileptor_model_attributes_dict[h5_file['/' + "model.name"][()]],
add_overwrite_fields_dict=overwrite_fields_dict)
if output == "dict":
sim_settings = dict()
else:
sim_settings = SimulationSettings()
# overwrite_fields_dict = {"monitor_expressions": h5_file['/' + "Monitor expressions"][()].tostring().split(","),
# "variables_names": h5_file['/' + "Variables names"][()].tostring().split(",")}
overwrite_fields_dict = {"monitor_expressions": h5_file['/' + "Monitor expressions"][()].tolist(),
"variables_names": h5_file['/' + "Variables names"][()].tolist()}
read_object_from_h5_file(sim_settings, h5_file, simulation_settings_attributes_dict,
add_overwrite_fields_dict=overwrite_fields_dict)
h5_file.close()
return model, sim_settings
def read_epileptogenicity(path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "ep.h5")):
"""
:param path: Path towards an epileptogenicity H5 file
:return: epileptogenicity in a numpy array
"""
print "Reading Epileptogenicity from:", path
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file)
print "Structures:", h5_file["/"].keys()
print "Values expected shape:", h5_file['/values'].shape
values = h5_file['/values'][()]
print "Actual values shape", values.shape
h5_file.close()
return values
def read_ts_epi(path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "ts.h5")):
"""
:param path: Path towards a valid TimeSeries H5 file
:return: Timeseries in a numpy array
"""
print "Reading TimeSeries from:", path
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file)
print "Structures:", h5_file["/"].keys()
print "Data expected shape:", h5_file['/data'].shape
data = h5_file['/data'][()]
print "Actual Data shape", data.shape
print "First Channel sv sum", numpy.sum(data[:, 0, :], axis=1)
h5_file.close()
return data
def read_epileptogenicity(
path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "ep.h5"), logger=logger):
"""
:param path: Path towards an epileptogenicity H5 file
:return: epileptogenicity in a numpy array
"""
logger.info("Reading Epileptogenicity from:\n" + path)
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file, logger)
logger.info("Structures:\n" + str(h5_file["/"].keys()))
logger.info("Values expected shape: " + str(h5_file['/values'].shape))
values = h5_file['/values'][()]
logger.info("Actual values shape: " + str(values.shape))
h5_file.close()
return values
def read_ts_epi(
path=os.path.join(PATIENT_VIRTUAL_HEAD, "ep", "ts.h5"), logger=logger):
"""
:param path: Path towards a valid TimeSeries H5 file
:return: Timeseries in a numpy array
"""
logger.info("Reading TimeSeries from:\n" + path)
h5_file = h5py.File(path, 'r', libver='latest')
print_metadata(h5_file, logger)
logger.info("Structures:\n" + str(h5_file["/"].keys()))
logger.info("Values expected shape: " + str(h5_file['/data'].shape))
h5_file['/data']
data = h5_file['/data'][()]
logger.info("Actual Data shape: " + str(data.shape))
logger.info("First Channel sv sum: " +
str(numpy.sum(data[:, 0, :], axis=1)))
h5_file.close()
return data