def test_store_load_complete_region_mapping(tmph5factory, connectivity_factory, surface_factory, region_mapping_factory):
connectivity = connectivity_factory(2)
surface = surface_factory(5)
region_mapping = region_mapping_factory(surface, connectivity)
with ConnectivityH5(tmph5factory('Connectivity_{}.h5'.format(connectivity.gid))) as conn_h5:
conn_h5.store(connectivity)
conn_stored = Connectivity()
conn_h5.load_into(conn_stored)
with SurfaceH5(tmph5factory('Surface_{}.h5'.format(surface.gid))) as surf_h5:
surf_h5.store(surface)
surf_stored = Surface()
surf_h5.load_into(surf_stored)
with RegionMappingH5(tmph5factory('RegionMapping_{}.h5'.format(region_mapping.gid))) as rm_h5:
rm_h5.store(region_mapping)
rm_stored = RegionMapping()
rm_h5.load_into(rm_stored)
# load_into will not load dependent datatypes. connectivity and surface are undefined
with pytest.raises(TraitAttributeError):
rm_stored.connectivity
with pytest.raises(TraitAttributeError):
rm_stored.surface
rm_stored.connectivity = conn_stored
rm_stored.surface = surf_stored
assert rm_stored.connectivity is not None
assert rm_stored.surface is not None
def _import(self, import_file_path, surface_gid, connectivity_gid):
"""
This method is used for importing region mappings
:param import_file_path: absolute path of the file to be imported
"""
### Retrieve Adapter instance
group = dao.find_group(
'tvb.adapters.uploaders.region_mapping_importer',
'RegionMapping_Importer')
importer = ABCAdapter.build_adapter(group)
args = {
'mapping_file': import_file_path,
'surface': surface_gid,
'connectivity': connectivity_gid,
DataTypeMetaData.KEY_SUBJECT: "test"
}
now = datetime.datetime.now()
### Launch import Operation
FlowService().fire_operation(importer, self.test_user,
self.test_project.id, **args)
# During setup we import a CFF which creates an additional RegionMapping
# So, here we have to find our mapping (just imported)
data_filter = FilterChain(
fields=[FilterChain.datatype + ".create_date"],
operations=[">"],
values=[now])
region_mapping = self._get_entity(RegionMapping(), data_filter)
return region_mapping
def setup_method(self):
self.sim = simulator.Simulator(
connectivity=connectivity.Connectivity(
load_file='connectivity_192.zip'),
monitors=(monitors.iEEG(
sensors=SensorsInternal(load_file="seeg_39.txt.bz2"),
region_mapping=RegionMapping(
load_file='regionMapping_16k_192.txt')))).configure()
def configure(self, dt=2 ** -3, model=models.Generic2dOscillator, speed=4.0,
coupling_strength=0.00042, method="HeunDeterministic",
surface_sim=False,
default_connectivity=True):
"""
Create an instance of the Simulator class, by default use the
generic plane oscillator local dynamic model and the deterministic
version of Heun's method for the numerical integration.
"""
self.method = method
if default_connectivity:
white_matter = Connectivity(load_file="connectivity_76.zip")
region_mapping = RegionMapping(load_file="regionMapping_16k_76.txt")
else:
white_matter = Connectivity(load_file="connectivity_192.zip")
region_mapping = RegionMapping(load_file="regionMapping_16k_192.txt")
white_matter_coupling = coupling.Linear(a=coupling_strength)
white_matter.speed = speed
dynamics = model()
if method[-10:] == "Stochastic":
hisss = noise.Additive(nsig=numpy.array([2 ** -11]))
integrator = eval("integrators." + method + "(dt=dt, noise=hisss)")
else:
integrator = eval("integrators." + method + "(dt=dt)")
if surface_sim:
local_coupling_strength = numpy.array([2 ** -10])
default_cortex = Cortex(region_mapping_data=region_mapping, load_file="cortex_16384.zip")
default_cortex.coupling_strength = local_coupling_strength
default_cortex.local_connectivity = LocalConnectivity(load_file="local_connectivity_16384.mat")
else:
default_cortex = None
# Order of monitors determines order of returned values.
self.sim = simulator.Simulator(model=dynamics,
connectivity=white_matter,
coupling=white_matter_coupling,
integrator=integrator,
monitors=self.monitors,
surface=default_cortex)
self.sim.configure()
def build(surface=None, connectivity=None):
if not surface:
surface = surface_factory(5)
if not connectivity:
connectivity = connectivity_factory(2)
return RegionMapping(array_data=numpy.arange(
surface.number_of_vertices),
connectivity=connectivity,
surface=surface)
def test_store_load_region_mapping(tmph5factory, region_mapping_factory):
region_mapping = region_mapping_factory()
rm_h5 = RegionMappingH5(tmph5factory())
rm_h5.store(region_mapping)
rm_h5.close()
rm_stored = RegionMapping()
with pytest.raises(TraitAttributeError):
rm_stored.array_data
rm_h5.load_into(rm_stored) # loads connectivity/surface as None inside rm_stored
assert rm_stored.array_data.shape == (5,)
def deserialize_simulator(simulator_gid, storage_path):
simulator_in_path = h5.path_for(storage_path, SimulatorH5,
simulator_gid)
simulator_in = Simulator()
with SimulatorH5(simulator_in_path) as simulator_in_h5:
simulator_in_h5.load_into(simulator_in)
connectivity_gid = simulator_in_h5.connectivity.load()
stimulus_gid = simulator_in_h5.stimulus.load()
simulation_state_gid = simulator_in_h5.simulation_state.load()
conn_index = dao.get_datatype_by_gid(connectivity_gid.hex)
conn = h5.load_from_index(conn_index)
simulator_in.connectivity = conn
if simulator_in.surface:
cortex_path = h5.path_for(storage_path, CortexH5,
simulator_in.surface.gid)
with CortexH5(cortex_path) as cortex_h5:
local_conn_gid = cortex_h5.local_connectivity.load()
region_mapping_gid = cortex_h5.region_mapping_data.load()
region_mapping_index = dao.get_datatype_by_gid(
region_mapping_gid.hex)
region_mapping_path = h5.path_for_stored_index(
region_mapping_index)
region_mapping = RegionMapping()
with RegionMappingH5(region_mapping_path) as region_mapping_h5:
region_mapping_h5.load_into(region_mapping)
region_mapping.gid = region_mapping_h5.gid.load()
surf_gid = region_mapping_h5.surface.load()
surf_index = dao.get_datatype_by_gid(surf_gid.hex)
surf_h5 = h5.h5_file_for_index(surf_index)
surf = CorticalSurface()
surf_h5.load_into(surf)
surf_h5.close()
region_mapping.surface = surf
simulator_in.surface.region_mapping_data = region_mapping
if local_conn_gid:
local_conn_index = dao.get_datatype_by_gid(local_conn_gid.hex)
local_conn = h5.load_from_index(local_conn_index)
simulator_in.surface.local_connectivity = local_conn
if stimulus_gid:
stimulus_index = dao.get_datatype_by_gid(stimulus_gid.hex)
stimulus = h5.load_from_index(stimulus_index)
simulator_in.stimulus = stimulus
return simulator_in, simulation_state_gid
def setUp(self):
"""
Sets up the environment for running the tests;
creates a test user, a test project, a connectivity and a surface;
imports a CFF data-set
"""
self.test_user = TestFactory.create_user()
self.test_project = TestFactory.import_default_project(self.test_user)
self.surface = TestFactory.get_entity(self.test_project,
CorticalSurface())
self.assertTrue(self.surface is not None)
self.region_mapping = TestFactory.get_entity(self.test_project,
RegionMapping())
self.assertTrue(self.region_mapping is not None)
def _store_related_region_mappings(self, original_conn_gid, new_connectivity_ht):
result = []
linked_region_mappings = dao.get_generic_entity(RegionMappingIndex, original_conn_gid, 'fk_connectivity_gid')
for mapping in linked_region_mappings:
original_rm = h5.load_from_index(mapping)
surface = self.load_traited_by_gid(mapping.fk_surface_gid)
new_rm = RegionMapping()
new_rm.connectivity = new_connectivity_ht
new_rm.surface = surface
new_rm.array_data = original_rm.array_data
result_rm_index = self.store_complete(new_rm)
result.append(result_rm_index)
return result
def __init__(self,
region_mapping=None,
sensors=None,
projection=None,
*args,
**kwargs):
if region_mapping is None:
region_mapping = RegionMapping()
self.region_mapping = region_mapping
if sensors is None:
sensors = SensorsEEG()
self.sensors = sensors
self.projection = projection
super(Projection, self).__init__(*args, **kwargs)
def configure_simulation(stimulate):
"""
Set up a Simulator object (a brain network model and all its individual
components + output modalities)
"""
# eeg projection matrix from regions to sensors
LOG.info("Reading sensors info")
pr = ProjectionSurfaceEEG(load_default=True)
sensors = SensorsEEG.from_file(source_file="eeg_brainstorm_65.txt")
rm = RegionMapping(load_default=True)
#Initialise a Model, Connectivity, Coupling, set speed.
oscilator = models.Generic2dOscillator(a=-0.5, b=-10., c=0.0, d=0.02)
white_matter = connectivity.Connectivity(load_default=True)
white_matter.speed = numpy.array([4.0])
white_matter_coupling = coupling.Linear(a=0.042)
#Initialise an Integrator
hiss = noise.Additive(nsig=numpy.array([0.00])) # nsigm 0.015
heunint = integrators.HeunStochastic(dt=2**-6, noise=hiss)
# Recording techniques
what_to_watch = (monitors.TemporalAverage(period=1e3 / 4096.),
monitors.EEG(projection=pr,
sensors=sensors,
region_mapping=rm,
period=1e3 / 4096.))
# Stimulation paradigm
if stimulate:
stimulus = build_stimulus(white_matter)
else:
stimulus = None
#Initialise a Simulator -- Model, Connectivity, Integrator, and Monitors.
sim = simulator.Simulator(model=oscilator,
connectivity=white_matter,
coupling=white_matter_coupling,
integrator=heunint,
monitors=what_to_watch,
stimulus=stimulus)
sim.configure()
return sim
def test_cortexdata(self):
dt = Cortex(load_file="cortex_16384.zip",
region_mapping_data=RegionMapping(
load_file="regionMapping_16k_76.txt"))
assert isinstance(dt, Cortex)
assert dt.region_mapping_data is not None
## Initialize Local Connectivity, to avoid long computation time.
dt.local_connectivity = LocalConnectivity(
load_file="local_connectivity_16384.mat")
dt.configure()
summary_info = dt._find_summary_info()
assert abs(summary_info['Region area, maximum (mm:math:`^2`)'] -
9333.39) < 0.01
assert abs(summary_info['Region area, mean (mm:math:`^2`)'] -
3038.51) < 0.01
assert abs(summary_info['Region area, minimum (mm:math:`^2`)'] -
540.90) < 0.01
assert dt.vertices.shape == (16384, 3)
assert dt.vertex_normals.shape == (16384, 3)
assert dt.triangles.shape == (32760, 3)
def _store_related_region_mappings(self, original_conn_gid,
new_connectivity_ht):
result = []
linked_region_mappings = dao.get_generic_entity(
RegionMappingIndex, original_conn_gid, 'connectivity_gid')
for mapping in linked_region_mappings:
original_rm = h5.load_from_index(mapping)
surface_idx = dao.get_generic_entity(SurfaceIndex,
mapping.surface_gid, 'gid')[0]
surface = h5.load_from_index(surface_idx)
new_rm = RegionMapping()
new_rm.connectivity = new_connectivity_ht
new_rm.surface = surface
new_rm.array_data = original_rm.array_data
result_rm_index = h5.store_complete(new_rm, self.storage_path)
result.append(result_rm_index)
return result
def from_file(cls,
sensors_fname,
projection_fname,
rm_f_name="regionMapping_16k_76.txt",
period=1e3 / 1024.0,
instance=None,
**kwds):
"""
Build Projection-based monitor from sensors and projection files, and
any extra keyword arguments are passed to the monitor class constructor.
"""
if instance is None:
result = cls(period=period, **kwds)
else:
result = instance
result.sensors = type(cls.sensors)(load_file=sensors_fname)
result.projection = ProjectionMatrix(load_file=projection_fname)
result.region_mapping = RegionMapping(load_file=rm_f_name)
return result
def setup_method(self):
oscillator = models.Generic2dOscillator()
white_matter = connectivity.Connectivity(load_file='connectivity_' +
str(self.n_regions) + '.zip')
white_matter.speed = numpy.array([self.speed])
white_matter_coupling = coupling.Difference(a=self.coupling_a)
heunint = integrators.HeunStochastic(
dt=2**-4, noise=noise.Additive(nsig=numpy.array([
2**-10,
])))
mons = (
monitors.EEG(projection=ProjectionMatrix(
load_file='projection_eeg_65_surface_16k.npy'),
sensors=SensorsEEG(load_file="eeg_brainstorm_65.txt"),
period=self.period),
monitors.MEG(
projection=ProjectionMatrix(
load_file='projection_meg_276_surface_16k.npy'),
sensors=SensorsMEG(load_file='meg_brainstorm_276.txt'),
period=self.period),
monitors.iEEG(projection=ProjectionMatrix(
load_file='projection_seeg_588_surface_16k.npy'),
sensors=SensorsInternal(load_file='seeg_588.txt'),
period=self.period),
)
local_coupling_strength = numpy.array([2**-10])
region_mapping = RegionMapping(load_file='regionMapping_16k_' +
str(self.n_regions) + '.txt')
default_cortex = Cortex(
region_mapping_data=region_mapping, load_file="cortex_16384.zip"
) #region_mapping_file="regionMapping_16k_192.txt")
default_cortex.coupling_strength = local_coupling_strength
self.sim = simulator.Simulator(model=oscillator,
connectivity=white_matter,
coupling=white_matter_coupling,
integrator=heunint,
monitors=mons,
surface=default_cortex)
self.sim.configure()
def test_regionmapping(self):
dt = RegionMapping(load_file="regionMapping_16k_76.txt")
assert isinstance(dt, RegionMapping)
assert dt.mapping.shape == (16384, )
triangle_normals=numpy.zeros((3, 3)),
number_of_vertices=5,
number_of_triangles=3,
edge_mean_length=1.0,
edge_min_length=0.0,
edge_max_length=2.0,
zero_based_triangles=False,
split_triangles=numpy.arange(0),
number_of_split_slices=1,
split_slices=dict(),
bi_hemispheric=False,
surface_type="surface",
valid_for_simulations=True)
region_mapping = RegionMapping(array_data=numpy.arange(5),
connectivity=connectivity,
surface=surface)
cortical_surface = CorticalSurface(
vertices=numpy.zeros((5, 3)),
triangles=numpy.zeros((3, 3), dtype=int),
vertex_normals=numpy.zeros((5, 3)),
triangle_normals=numpy.zeros((3, 3)),
number_of_vertices=5,
number_of_triangles=3,
edge_mean_length=1.0,
edge_min_length=0.0,
edge_max_length=2.0,
zero_based_triangles=False,
split_triangles=numpy.arange(0),
number_of_split_slices=1,
def test_regionmapping(self):
dt = RegionMapping(load_default=True)
assert isinstance(dt, RegionMapping)
assert dt.shape == (16384, )
class Projection(Monitor):
"Base class monitor providing lead field suppport."
_ui_name = "Projection matrix"
region_mapping = RegionMapping(
required=False,
label="region mapping", order=3,
doc="A region mapping specifies how vertices of a surface correspond to given regions in the"
" connectivity. For iEEG/EEG/MEG monitors, this must be specified when performing a region"
" simulation but is optional for a surface simulation.")
@staticmethod
def oriented_gain(gain, orient):
"Apply orientations to gain matrix."
return (gain.reshape((gain.shape[0], -1, 3)) * orient).sum(axis=-1)
@classmethod
def _projection_class(cls):
if hasattr(cls, 'projection'):
return type(cls.projection)
else:
return ProjectionMatrix
@classmethod
def from_file(cls, sensors_fname, projection_fname, rm_f_name="regionMapping_16k_76.txt",
period=1e3/1024.0, instance=None, **kwds):
"""
Build Projection-based monitor from sensors and projection files, and
any extra keyword arguments are passed to the monitor class constructor.
"""
if instance is None:
result = cls(**kwds)
else:
result = instance
result.sensors = type(cls.sensors).from_file(sensors_fname)
result.projection = cls._projection_class().from_file(projection_fname)
result.region_mapping = RegionMapping.from_file(rm_f_name)
return result
def analytic(self, loc, ori):
"Construct analytic or default set of spatial filters for simulation."
# this will not be implemented but kept for API uniformity
raise NotImplementedError(
"No general purpose analytic formula available for spatial "
"projection matrices. Please select an appropriate projection "
"matrix."
)
def config_for_sim(self, simulator):
"Configure projection matrix monitor for given simulation."
super(Projection, self).config_for_sim(simulator)
self._sim = simulator
if hasattr(self, 'sensors'):
self.sensors.configure()
# handle region vs simulation, analytic vs numerical proj, cortical vs subcortical.
# setup convenient locals
surf = simulator.surface
conn = simulator.connectivity
using_cortical_surface = surf is not None
if using_cortical_surface:
non_cortical_indices, = numpy.where(numpy.bincount(surf.region_mapping) == 1)
self.rmap = surf.region_mapping
else:
# assume all cortical if no info
if conn.cortical.size == 0:
conn.cortical = numpy.array([True] * conn.weights.shape[0])
non_cortical_indices, = numpy.where(~conn.cortical)
if self.region_mapping is None:
raise Exception("Please specify a region mapping on the EEG/MEG/iEEG monitor when "
"performing a region simulation.")
else:
self.rmap = self.region_mapping
LOG.debug('Projection used in region sim has %d non-cortical regions', non_cortical_indices.size)
have_subcortical = len(non_cortical_indices) > 0
# determine source space
if using_cortical_surface:
sources = {'loc': surf.vertices, 'ori': surf.vertex_normals}
else:
sources = {'loc': conn.centres[conn.cortical], 'ori': conn.orientations[conn.cortical]}
# compute analytic if not provided
if self.projection is None:
LOG.debug('Precomputed projection not unavailable using analytic approximation.')
self.gain = self.analytic(**sources)
# reduce to region lead field if region sim
if not using_cortical_surface and self.gain.shape[1] == self.rmap.size:
gain = numpy.zeros((self.gain.shape[0], conn.number_of_regions))
numpy_add_at(gain.T, self.rmap, self.gain.T)
LOG.debug('Region mapping gain shape %s to %s', self.gain.shape, gain.shape)
self.gain = gain
# append analytic sub-cortical to lead field
if have_subcortical:
# need matrix of shape (proj.shape[0], len(sc_ind))
src = conn.centres[non_cortical_indices], conn.orientations[non_cortical_indices]
self.gain = numpy.hstack((self.gain, self.analytic(*src)))
LOG.debug('Added subcortical analytic gain, for final shape %s', self.gain.shape)
if self.sensors.usable is not None and not self.sensors.usable.all():
mask_unusable = ~self.sensors.usable
self.gain[mask_unusable] = 0.0
LOG.debug('Zeroed gain coefficients for %d unusable sensors', mask_unusable.sum())
# unconditionally zero NaN elements; framework not prepared for NaNs.
nan_mask = numpy.isfinite(self.gain).all(axis=1)
self.gain[~nan_mask] = 0.0
LOG.debug('Zeroed %d NaN gain coefficients', nan_mask.sum())
# attrs used for recording
self._state = numpy.zeros((self.gain.shape[0], len(self.voi)))
self._period_in_steps = int(self.period / self.dt)
LOG.debug('State shape %s, period in steps %s', self._state.shape, self._period_in_steps)
LOG.info('Projection configured gain shape %s', self.gain.shape)
def sample(self, step, state):
"Record state, returning sample at sampling frequency / period."
self._state += self.gain.dot(state[self.voi].sum(axis=-1).T)
if step % self._period_in_steps == 0:
time = (step - self._period_in_steps / 2.0) * self.dt
sample = self._state.copy() / self._period_in_steps
self._state[:] = 0.0
return time, sample.T[..., numpy.newaxis] # for compatibility
_gain = None
def _get_gain(self):
if self._gain is None:
self._gain = self.projection.projection_data
return self._gain
def _set_gain(self, new_gain):
self._gain = new_gain
gain = property(_get_gain, _set_gain)
_rmap = None
def _reg_map_data(self, reg_map):
return getattr(reg_map, 'array_data', reg_map)
def _get_rmap(self):
"Normalize obtaining reg map vector over various possibilities."
if self._rmap is None:
self._rmap = self._reg_map_data(self.region_mapping)
return self._rmap
def _set_rmap(self, reg_map):
if self._rmap is not None:
self._rmap = self._reg_map_data(self.region_mapping)
rmap = property(_get_rmap, _set_rmap)
def test_regionmapping(self):
dt = RegionMapping(load_default=True)
self.assertTrue(isinstance(dt, RegionMapping))
self.assertEqual(dt.shape, (16384, ))
def launch(self, view_model):
# type: (RegionMappingImporterModel) -> [RegionMappingIndex]
"""
Creates region mapping from uploaded data.
:raises LaunchException: when
* a parameter is None or missing
* archive has more than one file
* uploaded files are empty
* number of vertices in imported file is different to the number of surface vertices
* imported file has negative values
* imported file has regions which are not in connectivity
"""
if view_model.mapping_file is None:
raise LaunchException(
"Please select mappings file which contains data to import")
if view_model.surface is None:
raise LaunchException(
"No surface selected. Please initiate upload again and select a brain surface."
)
if view_model.connectivity is None:
raise LaunchException(
"No connectivity selected. Please initiate upload again and select one."
)
self.logger.debug("Reading mappings from uploaded file")
if zipfile.is_zipfile(view_model.mapping_file):
tmp_folder = tempfile.mkdtemp(
prefix='region_mapping_zip_',
dir=TvbProfile.current.TVB_TEMP_FOLDER)
try:
files = FilesHelper().unpack_zip(view_model.mapping_file,
tmp_folder)
if len(files) > 1:
raise LaunchException(
"Please upload a ZIP file containing only one file.")
array_data = self.read_list_data(files[0], dtype=numpy.int32)
finally:
if os.path.exists(tmp_folder):
shutil.rmtree(tmp_folder)
else:
array_data = self.read_list_data(view_model.mapping_file,
dtype=numpy.int32)
# Now we do some checks before building final RegionMapping
if array_data is None or len(array_data) == 0:
raise LaunchException(
"Uploaded file does not contains any data. Please initiate upload with another file."
)
# Check if we have a mapping for each surface vertex.
surface_index = self.load_entity_by_gid(view_model.surface)
if len(array_data) != surface_index.number_of_vertices:
msg = "Imported file contains a different number of values than the number of surface vertices. " \
"Imported: %d values while surface has: %d vertices." % (
len(array_data), surface_index.number_of_vertices)
raise LaunchException(msg)
# Now check if the values from imported file correspond to connectivity regions
if array_data.min() < 0:
raise LaunchException(
"Imported file contains negative values. Please fix problem and re-import file"
)
connectivity_index = self.load_entity_by_gid(view_model.connectivity)
if array_data.max() >= connectivity_index.number_of_regions:
msg = "Imported file contains invalid regions. Found region: %d while selected connectivity has: %d " \
"regions defined (0 based)." % (array_data.max(), connectivity_index.number_of_regions)
raise LaunchException(msg)
self.logger.debug("Creating RegionMapping instance")
connectivity_ht = h5.load_from_index(connectivity_index)
surface_ht = h5.load_from_index(surface_index)
region_mapping = RegionMapping(surface=surface_ht,
connectivity=connectivity_ht,
array_data=array_data)
return h5.store_complete(region_mapping, self.storage_path)
# zipped directory that contains connectivity/tractography info
conn_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/connectivity/DH_20120806_Connectivity/DHconn.zip"
conn = Connectivity(load_file=conn_fname)
conn.configure()
print("connectivity loaded")
# print(conn.summary_info)
plot_connectivity(connectivity=conn)
# pyplot.show()
# triangulated cortical surface mesh
ctx_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/cortex/DH_20120806_Surface_Cortex.zip"
# maps nodes from cortex mesh to brain regions
region_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_RegionMapping.txt"
rm = RegionMapping(load_file=region_fname)
# matlab matrix that describes how waves propagate to the surface
eeg_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_ProjectionMatrix.mat"
ctx = Cortex(load_file=ctx_fname, region_mapping_data=rm)
ctx.configure()
print("cortex loaded")
pyplot.figure()
ax = pyplot.subplot(111, projection='3d')
x, y, z = ctx.vertices.T
ax.plot_trisurf(x, y, z, triangles=ctx.triangles, alpha=0.1, edgecolor='k')
# pyplot.show()
print("cortex plot ready")
# unit vectors that describe the location of eeg sensors
sensoreeg_fname = "/home/annajo/git/tvb/tvb-data/tvb_data/berlinSubjects/DH_20120806/DH_20120806_EEGLocations.txt"
