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base

class tvb.contrib.scripts.datatypes.base.BaseModel(**kwargs)[source]

Bases: tvb.basic.neotraits._core.HasTraits

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

static copy_attributes(trg_instance, src_instance, **kwargs)[source]
classmethod from_h5_file(source_file, **kwargs)[source]
static from_instance(instance, **kwargs)[source]
classmethod from_tvb_file(filepath, return_tvb_instance=False, **kwargs)[source]
classmethod from_tvb_instance(instance, **kwargs)[source]
static labels2inds(all_labels, labels)[source]
static set_attributes(instance, **kwargs)[source]
to_tvb_instance(tvb_datatype, **kwargs)[source]

connectivity

class tvb.contrib.scripts.datatypes.connectivity.Connectivity(**kwargs)[source]

Bases: tvb.datatypes.connectivity.Connectivity, tvb.contrib.scripts.datatypes.base.BaseModel

region_labels : tvb.datatypes.connectivity.Connectivity.region_labels = NArray(label=’Region labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)
Short strings, ‘labels’, for the regions represented by the connectivity matrix.
weights : tvb.datatypes.connectivity.Connectivity.weights = NArray(label=’Connection strengths’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Matrix of values representing the strength of connections between regions, arbitrary units.
undirected : tvb.datatypes.connectivity.Connectivity.undirected = Attr(field_type=<class ‘bool’>, default=False, required=False)
1, when the weights matrix is square and symmetric over the main diagonal, 0 when directed graph.
tract_lengths : tvb.datatypes.connectivity.Connectivity.tract_lengths = NArray(label=’Tract lengths’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
The length of myelinated fibre tracts between regions. If not provided Euclidean distance between region centres is used.
speed : tvb.datatypes.connectivity.Connectivity.speed = NArray(label=’Conduction speed’, dtype=float64, default=array([3.]), dim_names=(), ndim=None, required=True)
A single number or matrix of conduction speeds for the myelinated fibre tracts between regions.
centres : tvb.datatypes.connectivity.Connectivity.centres = NArray(label=’Region centres’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying the location of the centre of each region.
cortical : tvb.datatypes.connectivity.Connectivity.cortical = NArray(label=’Cortical’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
A boolean vector specifying whether or not a region is part of the cortex.
hemispheres : tvb.datatypes.connectivity.Connectivity.hemispheres = NArray(label=’Hemispheres (True for Right and False for Left Hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
A boolean vector specifying whether or not a region is part of the right hemisphere
orientations : tvb.datatypes.connectivity.Connectivity.orientations = NArray(label=’Average region orientation’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
Unit vectors of the average orientation of the regions represented in the connectivity matrix. NOTE: Unknown data should be zeros.
areas : tvb.datatypes.connectivity.Connectivity.areas = NArray(label=’Area of regions’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
Estimated area represented by the regions in the connectivity matrix. NOTE: Unknown data should be zeros.
idelays : tvb.datatypes.connectivity.Connectivity.idelays = NArray(label=’Conduction delay indices’, dtype=int64, default=None, dim_names=(), ndim=None, required=False)
An array of time delays between regions in integration steps.
delays : tvb.datatypes.connectivity.Connectivity.delays = NArray(label=’Conduction delay’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
Matrix of time delays between regions in physical units, setting conduction speed automatically combines with tract lengths to update this matrix, i.e. don’t try and change it manually.
number_of_regions : tvb.datatypes.connectivity.Connectivity.number_of_regions = Int(field_type=<class ‘int’>, default=0, required=True)
The number of regions represented in this Connectivity
number_of_connections : tvb.datatypes.connectivity.Connectivity.number_of_connections = Int(field_type=<class ‘int’>, default=0, required=True)
The number of non-zero entries represented in this Connectivity

parent_connectivity : tvb.datatypes.connectivity.Connectivity.parent_connectivity = Attr(field_type=<class ‘str’>, default=None, required=False)

saved_selection : tvb.datatypes.connectivity.Connectivity.saved_selection = List(of=<class ‘int’>, default=(), required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

centers[source]
get_regions_inds_by_labels(labels)[source]
to_tvb_instance(**kwargs)[source]

head

class tvb.contrib.scripts.datatypes.head.Head(**kwargs)[source]

Bases: tvb.basic.neotraits._core.HasTraits

One patient virtualization. Fully configured for defining hypothesis on it.

title : tvb.contrib.scripts.datatypes.head.Head.title = Attr(field_type=<class ‘str’>, default=’Head’, required=False)

path : tvb.contrib.scripts.datatypes.head.Head.path = Attr(field_type=<class ‘str’>, default=’path’, required=False)

connectivity : tvb.contrib.scripts.datatypes.head.Head.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

cortical_surface : tvb.contrib.scripts.datatypes.head.Head.cortical_surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.Surface’>, default=None, required=False)

subcortical_surface : tvb.contrib.scripts.datatypes.head.Head.subcortical_surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.Surface’>, default=None, required=False)

cortical_region_mapping : tvb.contrib.scripts.datatypes.head.Head.cortical_region_mapping = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionMapping’>, default=None, required=False)

subcortical_region_mapping : tvb.contrib.scripts.datatypes.head.Head.subcortical_region_mapping = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionMapping’>, default=None, required=False)

region_volume_mapping : tvb.contrib.scripts.datatypes.head.Head.region_volume_mapping = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionVolumeMapping’>, default=None, required=False)

local_connectivity : tvb.contrib.scripts.datatypes.head.Head.local_connectivity = Attr(field_type=<class ‘tvb.datatypes.local_connectivity.LocalConnectivity’>, default=None, required=False)

t1 : tvb.contrib.scripts.datatypes.head.Head.t1 = Attr(field_type=<class ‘tvb.datatypes.structural.StructuralMRI’>, default=None, required=False)

t2 : tvb.contrib.scripts.datatypes.head.Head.t2 = Attr(field_type=<class ‘tvb.datatypes.structural.StructuralMRI’>, default=None, required=False)

flair : tvb.contrib.scripts.datatypes.head.Head.flair = Attr(field_type=<class ‘tvb.datatypes.structural.StructuralMRI’>, default=None, required=False)

b0 : tvb.contrib.scripts.datatypes.head.Head.b0 = Attr(field_type=<class ‘tvb.datatypes.structural.StructuralMRI’>, default=None, required=False)

eeg_sensors : tvb.contrib.scripts.datatypes.head.Head.eeg_sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.Sensors’>, default=None, required=False)

seeg_sensors : tvb.contrib.scripts.datatypes.head.Head.seeg_sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.Sensors’>, default=None, required=False)

meg_sensors : tvb.contrib.scripts.datatypes.head.Head.meg_sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.Sensors’>, default=None, required=False)

eeg_projection : tvb.contrib.scripts.datatypes.head.Head.eeg_projection = Attr(field_type=<class ‘tvb.datatypes.projections.ProjectionMatrix’>, default=None, required=False)

seeg_projection : tvb.contrib.scripts.datatypes.head.Head.seeg_projection = Attr(field_type=<class ‘tvb.datatypes.projections.ProjectionMatrix’>, default=None, required=False)

meg_projection : tvb.contrib.scripts.datatypes.head.Head.meg_projection = Attr(field_type=<class ‘tvb.datatypes.projections.ProjectionMatrix’>, default=None, required=False)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

b0

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

configure()[source]
connectivity

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

cortex(local_connectivity=None, coupling_strength=None)[source]
cortical_region_mapping

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

cortical_surface

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

eeg_projection

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

eeg_sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

filter_regions(filter_arr)[source]
flair

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

classmethod from_file(path, **kwargs)[source]
classmethod from_folder(path=None, head=None, **kwargs)[source]
classmethod from_tvb_file(path, **kwargs)[source]
local_connectivity

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

make_cortex(local_connectivity=None, coupling_strength=None)[source]
meg_projection

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

meg_sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

number_of_regions[source]
path

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

region_volume_mapping

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

seeg_projection

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

seeg_sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

subcortical_region_mapping

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

subcortical_surface

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

surface[source]
t1

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

t2

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

local_connectivity

class tvb.contrib.scripts.datatypes.local_connectivity.LocalConnectivity(**kwargs)[source]

Bases: tvb.datatypes.local_connectivity.LocalConnectivity, tvb.contrib.scripts.datatypes.base.BaseModel

surface : tvb.datatypes.local_connectivity.LocalConnectivity.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

matrix : tvb.datatypes.local_connectivity.LocalConnectivity.matrix = Attr(field_type=<class ‘scipy.sparse.base.spmatrix’>, default=None, required=False)

equation : tvb.datatypes.local_connectivity.LocalConnectivity.equation = Attr(field_type=<class ‘tvb.datatypes.equations.FiniteSupportEquation’>, default=<tvb.datatypes.equations.Gaussian object at 0x7fc78c986450>, required=False)

cutoff : tvb.datatypes.local_connectivity.LocalConnectivity.cutoff = Float(field_type=<class ‘float’>, default=40.0, required=True)
Distance at which to truncate the evaluation in mm.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]

lookup_tables

The LookUpTable datatype.

class tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable(**kwargs)[source]

Bases: tvb.basic.neotraits._core.HasTraits

Lookup Tables for storing pre-computed functions. Specific table subclasses are implemented below.
equation : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.equation = Attr(field_type=<class ‘str’>, default=None, required=True)
A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.
xmin : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmin = NArray(label=’x-min’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Minimum value
xmax : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmax = NArray(label=’x-max’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Maximum value
data : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.data = NArray(label=’data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Tabulated values
number_of_values : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.number_of_values = Int(field_type=<class ‘int’>, default=0, required=True)
The number of values in the table
df : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.df = NArray(label=’df’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
.
dx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.dx = NArray(label=’dx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
Tabulation step
invdx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.invdx = NArray(label=’invdx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

compute_search_indices()[source]

...

configure()[source]

Invoke the compute methods for computable attributes that haven’t been set during initialization.

data

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

df

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

dx

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

equation

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

invdx

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

number_of_values

Declares an integer This is different from Attr(field_type=int). The former enforces int subtypes This allows all integer types, including numpy ones that can be safely cast to the declared type according to numpy rules

static populate_table(result, source_file)[source]
search_value(val)[source]

Search a value in this look up table

summary_info()[source]

Gather scientifically interesting summary information from an instance of this dataType, if any ...

xmax

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

xmin

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

class tvb.contrib.scripts.datatypes.lookup_tables.NerfTable(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable

Look up table containing the values of Nerf integral within the \(\phi\) function that describes how the discharge rate vary as a function of parameters defining the statistical properties of the membrane potential in presence of synaptic inputs.
equation : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.equation = Attr(field_type=<class ‘str’>, default=None, required=True)
A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.
xmin : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmin = NArray(label=’x-min’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Minimum value
xmax : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmax = NArray(label=’x-max’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Maximum value
data : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.data = NArray(label=’data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Tabulated values
number_of_values : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.number_of_values = Int(field_type=<class ‘int’>, default=0, required=True)
The number of values in the table
df : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.df = NArray(label=’df’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
.
dx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.dx = NArray(label=’dx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
Tabulation step
invdx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.invdx = NArray(label=’invdx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

static from_file(source_file='nerf_int.npz')[source]
class tvb.contrib.scripts.datatypes.lookup_tables.PsiTable(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable

Look up table containing the values of a function representing the time-averaged gating variable \(\psi(\nu)\) as a function of the presynaptic rates \(\nu\)
equation : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.equation = Attr(field_type=<class ‘str’>, default=None, required=True)
A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.
xmin : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmin = NArray(label=’x-min’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Minimum value
xmax : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.xmax = NArray(label=’x-max’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Maximum value
data : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.data = NArray(label=’data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
Tabulated values
number_of_values : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.number_of_values = Int(field_type=<class ‘int’>, default=0, required=True)
The number of values in the table
df : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.df = NArray(label=’df’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
.
dx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.dx = NArray(label=’dx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
Tabulation step
invdx : tvb.contrib.scripts.datatypes.lookup_tables.LookUpTable.invdx = NArray(label=’invdx’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=True)
.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

static from_file(source_file='psi.npz')[source]

projections

class tvb.contrib.scripts.datatypes.projections.ProjectionMatrix(**kwargs)[source]

Bases: tvb.datatypes.projections.ProjectionMatrix, tvb.contrib.scripts.datatypes.base.BaseModel

projection_type : tvb.datatypes.projections.ProjectionMatrix.projection_type = Attr(field_type=<class ‘str’>, default=None, required=True)

brain_skull : tvb.datatypes.projections.ProjectionMatrix.brain_skull = Attr(field_type=<class ‘tvb.datatypes.surfaces.BrainSkull’>, default=None, required=False)
Boundary between skull and cortex domains.
skull_skin : tvb.datatypes.projections.ProjectionMatrix.skull_skin = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkullSkin’>, default=None, required=False)
Boundary between skull and skin domains.
skin_air : tvb.datatypes.projections.ProjectionMatrix.skin_air = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkinAir’>, default=None, required=False)
Boundary between skin and air domains.
conductances : tvb.datatypes.projections.ProjectionMatrix.conductances = Attr(field_type=<class ‘dict’>, default={‘air’: 0.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘brain’: 1.0}, required=False)
A dictionary representing the conductances of ...

sources : tvb.datatypes.projections.ProjectionMatrix.sources = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

sensors : tvb.datatypes.projections.ProjectionMatrix.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.Sensors’>, default=None, required=False)
A set of sensors to compute projection matrix for them.

projection_data : tvb.datatypes.projections.ProjectionMatrix.projection_data = NArray(label=’Projection Matrix Data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

classmethod from_tvb_file(source_file, matlab_data_name=None, is_brainstorm=False, return_tvb_instance=False, **kwargs)[source]
to_tvb_instance(datatype=<class 'tvb.datatypes.projections.ProjectionMatrix'>, **kwargs)[source]
class tvb.contrib.scripts.datatypes.projections.ProjectionSurfaceEEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.projections.ProjectionMatrix, tvb.datatypes.projections.ProjectionSurfaceEEG

projection_type : tvb.datatypes.projections.ProjectionSurfaceEEG.projection_type = Attr(field_type=<class ‘str’>, default=’projEEG’, required=True)

sensors : tvb.datatypes.projections.ProjectionSurfaceEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsEEG’>, default=None, required=True)

brain_skull : tvb.datatypes.projections.ProjectionMatrix.brain_skull = Attr(field_type=<class ‘tvb.datatypes.surfaces.BrainSkull’>, default=None, required=False)
Boundary between skull and cortex domains.
skull_skin : tvb.datatypes.projections.ProjectionMatrix.skull_skin = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkullSkin’>, default=None, required=False)
Boundary between skull and skin domains.
skin_air : tvb.datatypes.projections.ProjectionMatrix.skin_air = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkinAir’>, default=None, required=False)
Boundary between skin and air domains.
conductances : tvb.datatypes.projections.ProjectionMatrix.conductances = Attr(field_type=<class ‘dict’>, default={‘air’: 0.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘brain’: 1.0}, required=False)
A dictionary representing the conductances of ...

sources : tvb.datatypes.projections.ProjectionMatrix.sources = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

projection_data : tvb.datatypes.projections.ProjectionMatrix.projection_data = NArray(label=’Projection Matrix Data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

classmethod from_tvb_file(source_file, matlab_data_name=None, is_brainstorm=False, return_tvb_instance=False, **kwargs)[source]
to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.projections.ProjectionSurfaceMEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.projections.ProjectionMatrix, tvb.datatypes.projections.ProjectionSurfaceMEG

projection_type : tvb.datatypes.projections.ProjectionSurfaceMEG.projection_type = Attr(field_type=<class ‘str’>, default=’projMEG’, required=True)

sensors : tvb.datatypes.projections.ProjectionSurfaceMEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsMEG’>, default=None, required=True)

brain_skull : tvb.datatypes.projections.ProjectionMatrix.brain_skull = Attr(field_type=<class ‘tvb.datatypes.surfaces.BrainSkull’>, default=None, required=False)
Boundary between skull and cortex domains.
skull_skin : tvb.datatypes.projections.ProjectionMatrix.skull_skin = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkullSkin’>, default=None, required=False)
Boundary between skull and skin domains.
skin_air : tvb.datatypes.projections.ProjectionMatrix.skin_air = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkinAir’>, default=None, required=False)
Boundary between skin and air domains.
conductances : tvb.datatypes.projections.ProjectionMatrix.conductances = Attr(field_type=<class ‘dict’>, default={‘air’: 0.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘brain’: 1.0}, required=False)
A dictionary representing the conductances of ...

sources : tvb.datatypes.projections.ProjectionMatrix.sources = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

projection_data : tvb.datatypes.projections.ProjectionMatrix.projection_data = NArray(label=’Projection Matrix Data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

classmethod from_tvb_file(source_file, matlab_data_name=None, is_brainstorm=False, return_tvb_instance=False, **kwargs)[source]
to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.projections.ProjectionSurfaceSEEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.projections.ProjectionMatrix, tvb.datatypes.projections.ProjectionSurfaceSEEG

projection_type : tvb.datatypes.projections.ProjectionSurfaceSEEG.projection_type = Attr(field_type=<class ‘str’>, default=’projSEEG’, required=True)

sensors : tvb.datatypes.projections.ProjectionSurfaceSEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsInternal’>, default=None, required=True)

brain_skull : tvb.datatypes.projections.ProjectionMatrix.brain_skull = Attr(field_type=<class ‘tvb.datatypes.surfaces.BrainSkull’>, default=None, required=False)
Boundary between skull and cortex domains.
skull_skin : tvb.datatypes.projections.ProjectionMatrix.skull_skin = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkullSkin’>, default=None, required=False)
Boundary between skull and skin domains.
skin_air : tvb.datatypes.projections.ProjectionMatrix.skin_air = Attr(field_type=<class ‘tvb.datatypes.surfaces.SkinAir’>, default=None, required=False)
Boundary between skin and air domains.
conductances : tvb.datatypes.projections.ProjectionMatrix.conductances = Attr(field_type=<class ‘dict’>, default={‘air’: 0.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘brain’: 1.0}, required=False)
A dictionary representing the conductances of ...

sources : tvb.datatypes.projections.ProjectionMatrix.sources = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

projection_data : tvb.datatypes.projections.ProjectionMatrix.projection_data = NArray(label=’Projection Matrix Data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

classmethod from_tvb_file(source_file, matlab_data_name=None, is_brainstorm=False, return_tvb_instance=False, **kwargs)[source]
to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.projections.TvbProjectionType[source]

Bases: enum.Enum

An enumeration.

eeg = <TvbProjectionType.eeg: <class 'tvb.datatypes.projections.ProjectionSurfaceEEG'>>
internal = <TvbProjectionType.internal: <class 'tvb.datatypes.projections.ProjectionSurfaceSEEG'>>
meg = <TvbProjectionType.meg: <class 'tvb.datatypes.projections.ProjectionSurfaceMEG'>>
tvb.contrib.scripts.datatypes.projections.get_TVB_proj_type(s_type)[source]

region_mapping

class tvb.contrib.scripts.datatypes.region_mapping.CorticalRegionMapping(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.region_mapping.RegionMapping

array_data : tvb.datatypes.region_mapping.RegionMapping.array_data = NArray(label=’‘, dtype=int64, default=None, dim_names=(), ndim=None, required=True)

connectivity : tvb.datatypes.region_mapping.RegionMapping.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

surface : tvb.datatypes.region_mapping.RegionMapping.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.Surface’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

class tvb.contrib.scripts.datatypes.region_mapping.RegionMapping(**kwargs)[source]

Bases: tvb.datatypes.region_mapping.RegionMapping, tvb.contrib.scripts.datatypes.base.BaseModel

array_data : tvb.datatypes.region_mapping.RegionMapping.array_data = NArray(label=’‘, dtype=int64, default=None, dim_names=(), ndim=None, required=True)

connectivity : tvb.datatypes.region_mapping.RegionMapping.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

surface : tvb.datatypes.region_mapping.RegionMapping.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.Surface’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(datatype=<class 'tvb.datatypes.region_mapping.RegionMapping'>, **kwargs)[source]
class tvb.contrib.scripts.datatypes.region_mapping.RegionVolumeMapping(**kwargs)[source]

Bases: tvb.datatypes.region_mapping.RegionVolumeMapping, tvb.contrib.scripts.datatypes.base.BaseModel

array_data : tvb.datatypes.region_mapping.RegionVolumeMapping.array_data = NArray(label=’‘, dtype=int64, default=None, dim_names=(), ndim=None, required=True)

connectivity : tvb.datatypes.region_mapping.RegionVolumeMapping.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

volume : tvb.datatypes.region_mapping.RegionVolumeMapping.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.region_mapping.SubcorticalRegionMapping(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.region_mapping.RegionMapping

array_data : tvb.datatypes.region_mapping.RegionMapping.array_data = NArray(label=’‘, dtype=int64, default=None, dim_names=(), ndim=None, required=True)

connectivity : tvb.datatypes.region_mapping.RegionMapping.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

surface : tvb.datatypes.region_mapping.RegionMapping.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.Surface’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

sensors

class tvb.contrib.scripts.datatypes.sensors.Sensors(**kwargs)[source]

Bases: tvb.datatypes.sensors.Sensors, tvb.contrib.scripts.datatypes.base.BaseModel

name : tvb.contrib.scripts.datatypes.sensors.Sensors.name = Attr(field_type=<class ‘str’>, default=’‘, required=False)
Sensors’ name

sensors_type : tvb.datatypes.sensors.Sensors.sensors_type = Attr(field_type=<class ‘str’>, default=None, required=False)

labels : tvb.datatypes.sensors.Sensors.labels = NArray(label=’Sensor labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)

locations : tvb.datatypes.sensors.Sensors.locations = NArray(label=’Sensor locations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

has_orientation : tvb.datatypes.sensors.Sensors.has_orientation = Attr(field_type=<class ‘bool’>, default=False, required=True)

orientations : tvb.datatypes.sensors.Sensors.orientations = NArray(label=’‘, dtype=float64, default=None, dim_names=(), ndim=None, required=False)

number_of_sensors : tvb.datatypes.sensors.Sensors.number_of_sensors = Int(field_type=<class ‘int’>, default=0, required=True)
The number of sensors described by these Sensors.
usable : tvb.datatypes.sensors.Sensors.usable = NArray(label=’Usable sensors’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
The sensors in set which are used for signal data.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

configure(remove_leading_zeros_from_labels=False)[source]
get_bipolar_sensors(sensors_inds=None)[source]
get_sensors_inds_by_sensors_labels(lbls)[source]
name

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

remove_leading_zeros_from_labels()[source]
sensor_label_to_index(labels)[source]
to_tvb_instance(datatype=<class 'tvb.datatypes.sensors.Sensors'>, **kwargs)[source]
class tvb.contrib.scripts.datatypes.sensors.SensorsEEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.sensors.Sensors, tvb.datatypes.sensors.SensorsEEG

name : tvb.contrib.scripts.datatypes.sensors.Sensors.name = Attr(field_type=<class ‘str’>, default=’‘, required=False)
Sensors’ name

sensors_type : tvb.datatypes.sensors.SensorsEEG.sensors_type = Attr(field_type=<class ‘str’>, default=’EEG’, required=True)

has_orientation : tvb.datatypes.sensors.SensorsEEG.has_orientation = Attr(field_type=<class ‘bool’>, default=False, required=True)

labels : tvb.datatypes.sensors.Sensors.labels = NArray(label=’Sensor labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)

locations : tvb.datatypes.sensors.Sensors.locations = NArray(label=’Sensor locations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

orientations : tvb.datatypes.sensors.Sensors.orientations = NArray(label=’‘, dtype=float64, default=None, dim_names=(), ndim=None, required=False)

number_of_sensors : tvb.datatypes.sensors.Sensors.number_of_sensors = Int(field_type=<class ‘int’>, default=0, required=True)
The number of sensors described by these Sensors.
usable : tvb.datatypes.sensors.Sensors.usable = NArray(label=’Usable sensors’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
The sensors in set which are used for signal data.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.sensors.SensorsInternal(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.sensors.Sensors, tvb.datatypes.sensors.SensorsInternal

elec_labels : tvb.contrib.scripts.datatypes.sensors.SensorsInternal.elec_labels = NArray(label=”Electrodes’ labels”, dtype=<U0, default=None, dim_names=(), ndim=None, required=False)
Labels of electrodes.
elec_inds : tvb.contrib.scripts.datatypes.sensors.SensorsInternal.elec_inds = NArray(label=”Electrodes’ indices”, dtype=int64, default=None, dim_names=(), ndim=None, required=False)
Indices of electrodes.
name : tvb.contrib.scripts.datatypes.sensors.Sensors.name = Attr(field_type=<class ‘str’>, default=’‘, required=False)
Sensors’ name

sensors_type : tvb.datatypes.sensors.SensorsInternal.sensors_type = Attr(field_type=<class ‘str’>, default=’Internal’, required=True)

labels : tvb.datatypes.sensors.Sensors.labels = NArray(label=’Sensor labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)

locations : tvb.datatypes.sensors.Sensors.locations = NArray(label=’Sensor locations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

has_orientation : tvb.datatypes.sensors.Sensors.has_orientation = Attr(field_type=<class ‘bool’>, default=False, required=True)

orientations : tvb.datatypes.sensors.Sensors.orientations = NArray(label=’‘, dtype=float64, default=None, dim_names=(), ndim=None, required=False)

number_of_sensors : tvb.datatypes.sensors.Sensors.number_of_sensors = Int(field_type=<class ‘int’>, default=0, required=True)
The number of sensors described by these Sensors.
usable : tvb.datatypes.sensors.Sensors.usable = NArray(label=’Usable sensors’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
The sensors in set which are used for signal data.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

channel_inds[source]
channel_labels[source]
configure()[source]
elec_inds

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

elec_labels

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

get_bipolar_elecs(elecs)[source]
get_elecs_inds_by_elecs_labels(lbls)[source]
get_sensors_inds_by_elec_labels(lbls)[source]
group_sensors_to_electrodes(labels=None)[source]
logger = <Logger tvb.contrib.scripts.datatypes.sensors (INFO)>
number_of_electrodes[source]
to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.sensors.SensorsMEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.sensors.Sensors, tvb.datatypes.sensors.SensorsMEG

name : tvb.contrib.scripts.datatypes.sensors.Sensors.name = Attr(field_type=<class ‘str’>, default=’‘, required=False)
Sensors’ name

sensors_type : tvb.datatypes.sensors.SensorsMEG.sensors_type = Attr(field_type=<class ‘str’>, default=’MEG’, required=True)

orientations : tvb.datatypes.sensors.SensorsMEG.orientations = NArray(label=’Sensor orientations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array representing the orientation of the MEG SQUIDs

has_orientation : tvb.datatypes.sensors.SensorsMEG.has_orientation = Attr(field_type=<class ‘bool’>, default=True, required=True)

labels : tvb.datatypes.sensors.Sensors.labels = NArray(label=’Sensor labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)

locations : tvb.datatypes.sensors.Sensors.locations = NArray(label=’Sensor locations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

number_of_sensors : tvb.datatypes.sensors.Sensors.number_of_sensors = Int(field_type=<class ‘int’>, default=0, required=True)
The number of sensors described by these Sensors.
usable : tvb.datatypes.sensors.Sensors.usable = NArray(label=’Usable sensors’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
The sensors in set which are used for signal data.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.sensors.SensorsSEEG(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.sensors.SensorsInternal

sensors_type : tvb.contrib.scripts.datatypes.sensors.SensorsSEEG.sensors_type = Attr(field_type=<class ‘str’>, default=’Internal’, required=False)

elec_labels : tvb.contrib.scripts.datatypes.sensors.SensorsInternal.elec_labels = NArray(label=”Electrodes’ labels”, dtype=<U0, default=None, dim_names=(), ndim=None, required=False)
Labels of electrodes.
elec_inds : tvb.contrib.scripts.datatypes.sensors.SensorsInternal.elec_inds = NArray(label=”Electrodes’ indices”, dtype=int64, default=None, dim_names=(), ndim=None, required=False)
Indices of electrodes.
name : tvb.contrib.scripts.datatypes.sensors.Sensors.name = Attr(field_type=<class ‘str’>, default=’‘, required=False)
Sensors’ name

labels : tvb.datatypes.sensors.Sensors.labels = NArray(label=’Sensor labels’, dtype=<U128, default=None, dim_names=(), ndim=None, required=True)

locations : tvb.datatypes.sensors.Sensors.locations = NArray(label=’Sensor locations’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

has_orientation : tvb.datatypes.sensors.Sensors.has_orientation = Attr(field_type=<class ‘bool’>, default=False, required=True)

orientations : tvb.datatypes.sensors.Sensors.orientations = NArray(label=’‘, dtype=float64, default=None, dim_names=(), ndim=None, required=False)

number_of_sensors : tvb.datatypes.sensors.Sensors.number_of_sensors = Int(field_type=<class ‘int’>, default=0, required=True)
The number of sensors described by these Sensors.
usable : tvb.datatypes.sensors.Sensors.usable = NArray(label=’Usable sensors’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)
The sensors in set which are used for signal data.

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

sensors_type

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]

structural

class tvb.contrib.scripts.datatypes.structural.B0(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.structural.StructuralMRI

weighting : tvb.contrib.scripts.datatypes.structural.B0.weighting = Attr(field_type=<class ‘str’>, default=’B0’, required=True)

array_data : tvb.datatypes.structural.StructuralMRI.array_data = NArray(label=’contrast’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

volume : tvb.datatypes.structural.StructuralMRI.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

weighting

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

class tvb.contrib.scripts.datatypes.structural.Flair(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.structural.StructuralMRI

weighting : tvb.contrib.scripts.datatypes.structural.Flair.weighting = Attr(field_type=<class ‘str’>, default=’Flair’, required=True)

array_data : tvb.datatypes.structural.StructuralMRI.array_data = NArray(label=’contrast’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

volume : tvb.datatypes.structural.StructuralMRI.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

weighting

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

class tvb.contrib.scripts.datatypes.structural.StructuralMRI(**kwargs)[source]

Bases: tvb.datatypes.structural.StructuralMRI, tvb.contrib.scripts.datatypes.base.BaseModel

array_data : tvb.datatypes.structural.StructuralMRI.array_data = NArray(label=’contrast’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

weighting : tvb.datatypes.structural.StructuralMRI.weighting = Attr(field_type=<class ‘str’>, default=None, required=True)

volume : tvb.datatypes.structural.StructuralMRI.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(datatype=<class 'tvb.datatypes.structural.StructuralMRI'>, **kwargs)[source]
class tvb.contrib.scripts.datatypes.structural.T1(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.structural.StructuralMRI

weighting : tvb.contrib.scripts.datatypes.structural.T1.weighting = Attr(field_type=<class ‘str’>, default=’T1’, required=True)

array_data : tvb.datatypes.structural.StructuralMRI.array_data = NArray(label=’contrast’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

volume : tvb.datatypes.structural.StructuralMRI.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

weighting

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

class tvb.contrib.scripts.datatypes.structural.T2(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.structural.StructuralMRI

weighting : tvb.contrib.scripts.datatypes.structural.T2.weighting = Attr(field_type=<class ‘str’>, default=’T2’, required=True)

array_data : tvb.datatypes.structural.StructuralMRI.array_data = NArray(label=’contrast’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)

volume : tvb.datatypes.structural.StructuralMRI.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

weighting

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

surface

class tvb.contrib.scripts.datatypes.surface.BrainSkullSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.BrainSkull

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.BrainSkull.surface_type = Final(field_type=<class ‘str’>, default=’Brain Skull’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.CorticalSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.CorticalSurface

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.CorticalSurface.surface_type = Attr(field_type=<class ‘str’>, default=’Cortical Surface’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.EEGCapSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.EEGCap

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.EEGCap.surface_type = Final(field_type=<class ‘str’>, default=’EEG Cap’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.FaceSurfaceSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.FaceSurface

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.FaceSurface.surface_type = Final(field_type=<class ‘str’>, default=’Face’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.SkinAirSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.SkinAir

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.SkinAir.surface_type = Final(field_type=<class ‘str’>, default=’Skin Air’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.SkullSkinSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.SkullSkin

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.SkullSkin.surface_type = Final(field_type=<class ‘str’>, default=’Skull Skin’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.SubcorticalSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.CorticalSurface

surface_type : tvb.contrib.scripts.datatypes.surface.SubcorticalSurface.surface_type = Attr(field_type=<class ‘str’>, default=’Subcortical Surface’, required=True)

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.
vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

surface_type

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.surface.Surface(**kwargs)[source]

Bases: tvb.datatypes.surfaces.Surface, tvb.contrib.scripts.datatypes.base.BaseModel

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.
vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

surface_type : tvb.datatypes.surfaces.Surface.surface_type = Attr(field_type=<class ‘str’>, default=None, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

add_vertices_and_triangles(new_vertices, new_triangles, new_vertex_normals=array([], dtype=float64), new_triangle_normals=array([], dtype=float64))[source]
compute_surface_area()[source]

This function computes the surface area :param: surface: input surface object :return: (sub)surface area, float

configure()[source]
get_triangle_normals()[source]
get_vertex_areas()[source]
get_vertex_normals()[source]
to_tvb_instance(datatype=<class 'tvb.datatypes.surfaces.Surface'>, **kwargs)[source]
vox2ras

Declares a numpy array. dtype enforces the dtype. The default dtype is float32. An optional symbolic shape can be given, as a tuple of Dim attributes from the owning class. The shape will be enforced, but no broadcasting will be done. domain declares what values are allowed in this array. It can be any object that can be checked for membership Defaults are checked if they are in the declared domain. For performance reasons this does not happen on every attribute set.

class tvb.contrib.scripts.datatypes.surface.WhiteMatterSurface(**kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.surface.Surface, tvb.datatypes.surfaces.WhiteMatterSurface

vox2ras : tvb.contrib.scripts.datatypes.surface.Surface.vox2ras = NArray(label=’vox2ras’, dtype=float64, default=array([], dtype=float64), dim_names=(), ndim=None, required=False)
Voxel to RAS coordinates transformation array.

surface_type : tvb.datatypes.surfaces.WhiteMatterSurface.surface_type = Final(field_type=<class ‘str’>, default=’White Matter’, required=True)

vertices : tvb.datatypes.surfaces.Surface.vertices = NArray(label=’Vertex positions’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array specifying coordinates for the surface vertices.
triangles : tvb.datatypes.surfaces.Surface.triangles = NArray(label=’Triangles’, dtype=int64, default=None, dim_names=(), ndim=None, required=True)
Array of indices into the vertices, specifying the triangles which define the surface.
vertex_normals : tvb.datatypes.surfaces.Surface.vertex_normals = NArray(label=’Vertex normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces vertices.
triangle_normals : tvb.datatypes.surfaces.Surface.triangle_normals = NArray(label=’Triangle normal vectors’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of unit normal vectors for the surfaces triangles.
geodesic_distance_matrix : tvb.datatypes.surfaces.Surface.geodesic_distance_matrix = Attr(field_type=<class ‘scipy.sparse.csc.csc_matrix’>, default=None, required=False)
A sparse matrix of truncated geodesic distances
number_of_vertices : tvb.datatypes.surfaces.Surface.number_of_vertices = Int(field_type=<class ‘int’>, default=0, required=True)
The number of vertices making up this surface.
number_of_triangles : tvb.datatypes.surfaces.Surface.number_of_triangles = Int(field_type=<class ‘int’>, default=0, required=True)
The number of triangles making up this surface.

edge_mean_length : tvb.datatypes.surfaces.Surface.edge_mean_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_min_length : tvb.datatypes.surfaces.Surface.edge_min_length = Float(field_type=<class ‘float’>, default=0, required=True)

edge_max_length : tvb.datatypes.surfaces.Surface.edge_max_length = Float(field_type=<class ‘float’>, default=0, required=True)

hemisphere_mask : tvb.datatypes.surfaces.Surface.hemisphere_mask = NArray(label=’An array specifying if a vertex belongs to the right hemisphere’, dtype=bool, default=None, dim_names=(), ndim=None, required=False)

zero_based_triangles : tvb.datatypes.surfaces.Surface.zero_based_triangles = Attr(field_type=<class ‘bool’>, default=None, required=True)

bi_hemispheric : tvb.datatypes.surfaces.Surface.bi_hemispheric = Attr(field_type=<class ‘bool’>, default=False, required=True)

valid_for_simulations : tvb.datatypes.surfaces.Surface.valid_for_simulations = Attr(field_type=<class ‘bool’>, default=True, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

to_tvb_instance(**kwargs)[source]

time_series

class tvb.contrib.scripts.datatypes.time_series.PossibleVariables[source]

Bases: enum.Enum

An enumeration.

EEG = <PossibleVariables.EEG: 'eeg'>
LFP = <PossibleVariables.LFP: 'lfp'>
MEEG = <PossibleVariables.MEEG: 'meeg'>
SEEG = <PossibleVariables.SEEG: 'seeg'>
SENSORS = <PossibleVariables.SENSORS: 'sensors'>
SOURCE = <PossibleVariables.SOURCE: 'source'>
X = <PossibleVariables.X: 'x'>
Y = <PossibleVariables.Y: 'y'>
Z = <PossibleVariables.Z: 'z'>
class tvb.contrib.scripts.datatypes.time_series.TimeSeries(data=None, **kwargs)[source]

Bases: tvb.datatypes.time_series.TimeSeries, tvb.contrib.scripts.datatypes.base.BaseModel

title : tvb.datatypes.time_series.TimeSeries.title = Attr(field_type=<class ‘str’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_ordering : tvb.datatypes.time_series.TimeSeries.labels_ordering = List(of=<class ‘object’>, default=(‘Time’, ‘State Variable’, ‘Space’, ‘Mode’), required=True)
List of strings representing names of each data dimension
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

configure()[source]
decimate_time(new_sample_period, **kwargs)[source]
duplicate(**kwargs)[source]
duration[source]
end_time[source]
from_xarray_DataArray(xrdtarr, **kwargs)[source]
get_data_from_slice(slice_tuple, **kwargs)[source]
get_dimension_index(dim_name_or_index)[source]
get_dimension_labels(dimension_label_or_index)[source]
get_dimension_name(dim_index)[source]
get_indices_for_labels(region_labels)[source]
get_indices_for_state_variables(sv_labels)[source]
get_modes(modes_inputs, **kwargs)[source]
get_modes_by_index(list_of_index, **kwargs)[source]
get_modes_by_label(list_of_labels, **kwargs)[source]
get_modes_by_slice(slice_arg, **kwargs)[source]
get_sample_window(index_start, index_end, **kwargs)[source]
get_state_variables(sv_inputs, **kwargs)[source]
get_state_variables_by_index(sv_indices, **kwargs)[source]
get_state_variables_by_label(sv_labels, **kwargs)[source]
get_state_variables_by_slice(slice_arg, **kwargs)[source]
get_subspace(subspace_inputs, **kwargs)[source]
get_subspace_by_index(list_of_index, **kwargs)[source]
get_subspace_by_label(list_of_labels, **kwargs)[source]
get_subspace_by_slice(slice_arg, **kwargs)[source]
get_time_window(index_start, index_end, **kwargs)[source]
get_time_window_by_units(unit_start, unit_end, **kwargs)[source]
get_times(list_of_times, **kwargs)[source]
get_times_by_index(list_of_times_indices, **kwargs)[source]
logger = <Logger tvb.contrib.scripts.datatypes.time_series (INFO)>
number_of_dimensions[source]
number_of_labels[source]
number_of_samples[source]
number_of_variables[source]
sample_rate[source]
shape[source]
size[source]
slice_data_across_dimension(inputs, dimension, **kwargs)[source]
slice_data_across_dimension_by_index(indices, dimension, **kwargs)[source]
slice_data_across_dimension_by_label(labels, dimension, **kwargs)[source]
slice_data_across_dimension_by_slice(slice_arg, dimension, **kwargs)[source]
space_labels[source]
squeezed[source]
swapaxes(ax1, ax2)[source]
time_length[source]
time_unit[source]
to_tvb_instance(datatype=<class 'tvb.datatypes.time_series.TimeSeries'>, **kwargs)[source]
update_dimension_names(dim_names, dim_indices=None)[source]
variables_labels[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesBrain(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeries

title : tvb.datatypes.time_series.TimeSeries.title = Attr(field_type=<class ‘str’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_ordering : tvb.datatypes.time_series.TimeSeries.labels_ordering = List(of=<class ‘object’>, default=(‘Time’, ‘State Variable’, ‘Space’, ‘Mode’), required=True)
List of strings representing names of each data dimension
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

brain_labels[source]
get_source()[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesDimensions[source]

Bases: enum.Enum

An enumeration.

MODES = <TimeSeriesDimensions.MODES: 'Mode'>
REGIONS = <TimeSeriesDimensions.REGIONS: 'Region'>
SAMPLES = <TimeSeriesDimensions.SAMPLES: 'Sample'>
SENSORS = <TimeSeriesDimensions.SENSORS: 'Sensor'>
SPACE = <TimeSeriesDimensions.SPACE: 'Space'>
TIME = <TimeSeriesDimensions.TIME: 'Time'>
VARIABLES = <TimeSeriesDimensions.VARIABLES: 'State Variable'>
VERTEXES = <TimeSeriesDimensions.VERTEXES: 'Vertex'>
X = <TimeSeriesDimensions.X: 'x'>
Y = <TimeSeriesDimensions.Y: 'y'>
Z = <TimeSeriesDimensions.Z: 'z'>
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesEEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors, tvb.datatypes.time_series.TimeSeriesEEG

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesEEG.title = Attr(field_type=<class ‘str’>, default=’EEG Time Series’, required=True)

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Sensor’, ‘Sample’), required=True)

sensors : tvb.datatypes.time_series.TimeSeriesEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsEEG’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

EEGsensor_labels[source]
configure()[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesMEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors, tvb.datatypes.time_series.TimeSeriesMEG

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesMEG.title = Attr(field_type=<class ‘str’>, default=’MEG Time Series’, required=True)

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Sensor’, ‘Sample’), required=True)

sensors : tvb.datatypes.time_series.TimeSeriesMEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsMEG’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

MEGsensor_labels[source]
configure()[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesRegion(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeriesBrain, tvb.datatypes.time_series.TimeSeriesRegion

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesRegion.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Region’, ‘Sample’), required=True)

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesRegion.title = Attr(field_type=<class ‘str’>, default=’Region Time Series’, required=True)

connectivity : tvb.datatypes.time_series.TimeSeriesRegion.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

region_mapping_volume : tvb.datatypes.time_series.TimeSeriesRegion.region_mapping_volume = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionVolumeMapping’>, default=None, required=False)

region_mapping : tvb.datatypes.time_series.TimeSeriesRegion.region_mapping = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionMapping’>, default=None, required=False)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

labels_ordering

The attribute is a list of values. Choices and type are reinterpreted as applying not to the list but to the elements of it

region_labels[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesSEEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors, tvb.datatypes.time_series.TimeSeriesSEEG

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSEEG.title = Attr(field_type=<class ‘str’>, default=’SEEG Time Series’, required=True)

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Sensor’, ‘Sample’), required=True)

sensors : tvb.datatypes.time_series.TimeSeriesSEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsInternal’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

SEEGsensor_labels[source]
configure()[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeries

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Sensor’, ‘Sample’), required=True)

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors.title = Attr(field_type=<class ‘str’>, default=’Sensor Time Series’, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

get_bipolar(**kwargs)[source]
labels_ordering

The attribute is a list of values. Choices and type are reinterpreted as applying not to the list but to the elements of it

sensor_labels[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

class tvb.contrib.scripts.datatypes.time_series.TimeSeriesSurface(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeriesBrain, tvb.datatypes.time_series.TimeSeriesSurface

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSurface.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Vertex’, ‘Sample’), required=True)

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesSurface.title = Attr(field_type=<class ‘str’>, default=’Surface Time Series’, required=True)

surface : tvb.datatypes.time_series.TimeSeriesSurface.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

labels_ordering

The attribute is a list of values. Choices and type are reinterpreted as applying not to the list but to the elements of it

surface_labels[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series.TimeSeriesVolume(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series.TimeSeries, tvb.datatypes.time_series.TimeSeriesVolume

labels_ordering : tvb.contrib.scripts.datatypes.time_series.TimeSeriesVolume.labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘x’, ‘y’, ‘z’), required=True)

title : tvb.contrib.scripts.datatypes.time_series.TimeSeriesVolume.title = Attr(field_type=<class ‘str’>, default=’Volume Time Series’, required=True)

volume : tvb.datatypes.time_series.TimeSeriesVolume.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

data : tvb.datatypes.time_series.TimeSeries.data = NArray(label=’Time-series data’, dtype=float64, default=None, dim_names=(), ndim=None, required=True)
An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions
labels_dimensions : tvb.datatypes.time_series.TimeSeries.labels_dimensions = Attr(field_type=<class ‘dict’>, default={}, required=True)
A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }
time : tvb.datatypes.time_series.TimeSeries.time = NArray(label=’Time-series time’, dtype=float64, default=None, dim_names=(), ndim=None, required=False)
An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

start_time : tvb.datatypes.time_series.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.datatypes.time_series.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.datatypes.time_series.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

labels_ordering

The attribute is a list of values. Choices and type are reinterpreted as applying not to the list but to the elements of it

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
volume_labels[source]
tvb.contrib.scripts.datatypes.time_series.prepare_4d(data, logger)[source]

time_series_xarray

class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries(data=None, **kwargs)[source]

Bases: tvb.basic.neotraits._core.HasTraits

Base time-series dataType.
_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries._default_labels_ordering = List(of=<class ‘object’>, default=(‘Time’, ‘State Variable’, ‘Space’, ‘Mode’), required=True)
List of strings representing names of each data dimension

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.title = Attr(field_type=<class ‘str’>, default=’Time Series’, required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

configure()[source]
coords[source]
data[source]
decimate_time(new_sample_period, **kwargs)[source]
dims[source]
duplicate(**kwargs)[source]
duration[source]
end_time[source]
flattened[source]
from_TVB_time_series(ts, **kwargs)[source]
from_numpy(data, **kwargs)[source]
from_xarray_DataArray(xarr, **kwargs)[source]
get_dimension_index(dim_name_or_index)[source]
get_dimension_labels(dimension_label_or_index)[source]
get_dimension_name(dim_index)[source]
get_indices_for_labels(region_labels)[source]
get_indices_for_state_variables(sv_labels)[source]
get_modes(modes_inputs, **kwargs)[source]
get_modes_by_index(list_of_index, **kwargs)[source]
get_modes_by_label(list_of_labels, **kwargs)[source]
get_modes_by_slice(slice_arg, **kwargs)[source]
get_sample_window(index_start, index_end, **kwargs)[source]
get_state_variables(sv_inputs, **kwargs)[source]
get_state_variables_by_index(sv_indices, **kwargs)[source]
get_state_variables_by_label(sv_labels, **kwargs)[source]
get_state_variables_by_slice(slice_arg, **kwargs)[source]
get_subspace(subspace_inputs, **kwargs)[source]
get_subspace_by_index(list_of_index, **kwargs)[source]
get_subspace_by_label(list_of_labels, **kwargs)[source]
get_subspace_by_slice(slice_arg, **kwargs)[source]
get_time_window(index_start, index_end, **kwargs)[source]
get_time_window_by_units(unit_start, unit_end, **kwargs)[source]
get_times(list_of_times, **kwargs)[source]
get_times_by_index(list_of_times_indices, **kwargs)[source]
labels_dimensions[source]
labels_ordering[source]
name[source]
nr_dimensions[source]
number_of_dimensions[source]
number_of_labels[source]
number_of_samples[source]
number_of_variables[source]
plot(time=None, data=None, y=None, hue=None, col=None, row=None, figname=None, plotter_config=None, **kwargs)[source]
plot_line(**kwargs)[source]
plot_map(**kwargs)[source]
plot_raster(**kwargs)[source]
plot_timeseries(**kwargs)[source]
sample_period

Declares a float. This is different from Attr(field_type=float). The former enforces float subtypes. This allows any type that can be safely cast to the declared float type according to numpy rules.

Reading and writing this attribute is slower than a plain python attribute. In performance sensitive code you might want to use plain python attributes or even better local variables.

sample_period_unit

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

sample_rate[source]
shape[source]
size[source]
slice_data_across_dimension(inputs, dimension, **kwargs)[source]
slice_data_across_dimension_by_index(indices, dimension, **kwargs)[source]
slice_data_across_dimension_by_label(labels, dimension, **kwargs)[source]
slice_data_across_dimension_by_slice(slice_arg, dimension, **kwargs)[source]
space_labels[source]
squeeze()[source]
squeezed[source]
start_time

Declares a float. This is different from Attr(field_type=float). The former enforces float subtypes. This allows any type that can be safely cast to the declared float type according to numpy rules.

Reading and writing this attribute is slower than a plain python attribute. In performance sensitive code you might want to use plain python attributes or even better local variables.

summary_info()[source]

Gather scientifically interesting summary information from an instance of this datatype.

swapaxes(ax1, ax2)[source]
time[source]
time_length[source]
time_unit[source]
title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(datatype=<class 'tvb.contrib.scripts.datatypes.time_series.TimeSeries'>, **kwargs)[source]
update_dimension_names(dim_names, dim_indices=None)[source]
variables_labels[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesEEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSensors

A time series associated with a set of EEG sensors.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesEEG.title = Attr(field_type=<class ‘str’>, default=’EEG Time Series’, required=True)

sensors : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsEEG’>, default=None, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesEEG._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘1’, ‘EEG Sensor’, ‘1’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesMEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSensors

A time series associated with a set of MEG sensors.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesMEG.title = Attr(field_type=<class ‘str’>, default=’MEG Time Series’, required=True)

sensors : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesMEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsMEG’>, default=None, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesMEG._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘1’, ‘MEG Sensor’, ‘1’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries

A time-series associated with the regions of a connectivity.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion.title = Attr(field_type=<class ‘str’>, default=’Region Time Series’, required=True)

connectivity : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion.connectivity = Attr(field_type=<class ‘tvb.datatypes.connectivity.Connectivity’>, default=None, required=True)

region_mapping_volume : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion.region_mapping_volume = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionVolumeMapping’>, default=None, required=False)

region_mapping : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion.region_mapping = Attr(field_type=<class ‘tvb.datatypes.region_mapping.RegionMapping’>, default=None, required=False)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesRegion._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Region’, ‘Mode’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

configure()[source]
connectivity

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

region_mapping

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

region_mapping_volume

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

summary_info()[source]

Gather scientifically interesting summary information from an instance of this datatype.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSEEG(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSensors

A time series associated with a set of Internal sensors.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSEEG.title = Attr(field_type=<class ‘str’>, default=’SEEG Time Series’, required=True)

sensors : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSEEG.sensors = Attr(field_type=<class ‘tvb.datatypes.sensors.SensorsInternal’>, default=None, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSEEG._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘1’, ‘sEEG Sensor’, ‘1’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

sensors

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSensors(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSensors.title = Attr(field_type=<class ‘str’>, default=’Sensor Time Series’, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries._default_labels_ordering = List(of=<class ‘object’>, default=(‘Time’, ‘State Variable’, ‘Space’, ‘Mode’), required=True)
List of strings representing names of each data dimension

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

configure()[source]
summary_info()[source]

Gather scientifically interesting summary information from an instance of this datatype.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(datatype=<class 'tvb.contrib.scripts.datatypes.time_series.TimeSeriesSensors'>, **kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSurface(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries

A time-series associated with a Surface.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSurface.title = Attr(field_type=<class ‘str’>, default=’Surface Time Series’, required=True)

surface : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSurface.surface = Attr(field_type=<class ‘tvb.datatypes.surfaces.CorticalSurface’>, default=None, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesSurface._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘State Variable’, ‘Vertex’, ‘Mode’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

summary_info()[source]

Gather scientifically interesting summary information from an instance of this datatype.

surface

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
class tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesVolume(data=None, **kwargs)[source]

Bases: tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries

A time-series associated with a Volume.

title : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesVolume.title = Attr(field_type=<class ‘str’>, default=’Volume Time Series’, required=True)

volume : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesVolume.volume = Attr(field_type=<class ‘tvb.datatypes.volumes.Volume’>, default=None, required=True)

_default_labels_ordering : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeriesVolume._default_labels_ordering = List(of=<class ‘str’>, default=(‘Time’, ‘X’, ‘Y’, ‘Z’), required=True)

start_time : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.start_time = Float(field_type=<class ‘float’>, default=0, required=True)

sample_period : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period = Float(field_type=<class ‘float’>, default=1.0, required=True)

sample_period_unit : tvb.contrib.scripts.datatypes.time_series_xarray.TimeSeries.sample_period_unit = Attr(field_type=<class ‘str’>, default=’ms’, required=True)

gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)

summary_info()[source]

Gather scientifically interesting summary information from an instance of this datatype.

title

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

to_tvb_instance(**kwargs)[source]
volume

An Attr declares the following about the attribute it describes: * the type * a default value shared by all instances * if the value might be missing * documentation It will resolve to attributes on the instance.

tvb.contrib.scripts.datatypes.time_series_xarray.coords_to_dict(coords)[source]
tvb.contrib.scripts.datatypes.time_series_xarray.save_show_figure(plotter_config, figure_name=None, fig=None)[source]