`datatypes` Package¶

TVB DataTypes, as a dictionary between multiple algorithms.

`annotations`¶

tvb.datatypes.annotations.AnnotationArray[source]¶

Holds a flatten form for the annotations for a full connectivity. Each region in the connectivity can have None, or a tree of AnnotationTerms To be stored in a compound DS in H5.

traits on this class:

dtype ()

default: [(‘id’, ‘<i4’), (‘parent_id’, ‘<i4’), (‘parent_left’, ‘<i4’), (‘parent_right’, ‘<i4’), (‘relation’, ‘S16’), (‘label’, ‘S128’), (‘definition’, ‘S1024’), (‘synonym’, ‘S2048’), (‘uri’, ‘S248’), (‘synonym_tvb_left’, ‘<i4’), (‘synonym_tvb_right’, ‘<i4’)]

class tvb.datatypes.annotations.AnnotationTerm(id, parent, parent_left, parent_right, relation, label, definition=None, synonym=None, uri=None, tvb_left=None, tvb_right=None)[source]¶

Bases: object

One single annotation node (in the tree of annotations / region)

add_child(annotation_child)[source]¶

to_json(is_right_hemisphere=False, activation_patterns=None)[source]¶

to_tuple()[source]¶

tvb.datatypes.annotations.ConnectivityAnnotations[source]¶

Ontology annotations for a Connectivity.

traits on this class:

connectivity ()

default: None

region_annotations (Region Annotations)

Flat tree of annotations for every connectivity region.

default: []

`arrays`¶

The Array datatypes. This brings together the scientific and framework methods that are associated with the Array datatypes.

tvb.datatypes.arrays.BaseArray[source]¶

Base class for array-type traits. traits on this class:

dtype ()

default: None

tvb.datatypes.arrays.BoolArray[source]¶

traited class BoolArray traits on this class:

dtype ()

default: <type ‘bool’>

tvb.datatypes.arrays.ComplexArray[source]¶

traited class ComplexArray traits on this class:

dtype ()

default: <type ‘numpy.complex128’>

tvb.datatypes.arrays.FloatArray[source]¶

traited class FloatArray traits on this class:

dtype ()

default: <type ‘numpy.float64’>

tvb.datatypes.arrays.IndexArray[source]¶

traited class IndexArray traits on this class:

dtype ()

default: None

target (Indexed array)

A link to the array that the indices index.

default: None

tvb.datatypes.arrays.IntegerArray[source]¶

traited class IntegerArray traits on this class:

dtype ()

default: <type ‘numpy.int64’>

tvb.datatypes.arrays.MappedArray[source]¶

An array stored in the database. traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

title ()

default: None

tvb.datatypes.arrays.OrientationArray[source]¶

traited class OrientationArray traits on this class:

coordinate_system_or (Coordinate system)

default: cartesian

dtype ()

default: <type ‘numpy.float64’>

tvb.datatypes.arrays.PositionArray[source]¶

traited class PositionArray traits on this class:

coordinate_space (Coordinate space)

The standard space the positions are in, eg, ‘MNI’, ‘colin27’

default: None

coordinate_system (Coordinate system)

The coordinate system used to specify the positions. Eg: ‘spherical’, ‘polar’

default: cartesian

dtype ()

default: <type ‘numpy.float64’>

tvb.datatypes.arrays.StringArray[source]¶: traited class StringArray traits on this class:

`connectivity`¶

The Connectivity datatype. This brings together the scientific and framework methods that are associated with the connectivity data.

tvb.datatypes.connectivity.Connectivity[source]¶

traited class Connectivity traits on this class:

areas (Area of regions)

Estimated area represented by the regions in the connectivity matrix. NOTE: Unknown data should be zeros.

default: None

centres (Region centres)

An array specifying the location of the centre of each region.

default: None

cortical (Cortical)

A boolean vector specifying whether or not a region is part of the cortex.

default: None

delays (Conduction delay)

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.

default: None

hemispheres (Hemispheres (True for Right and False for Left Hemisphere)

A boolean vector specifying whether or not a region is part of the right hemisphere

default: None

idelays (Conduction delay indices)

An array of time delays between regions in integration steps.

default: None

number_of_connections (Number of connections)

The number of non-zero entries represented in this Connectivity

default: None

number_of_regions (Number of regions)

The number of regions represented in this Connectivity

default: None

orientations (Average region orientation)

Unit vectors of the average orientation of the regions represented in the connectivity matrix. NOTE: Unknown data should be zeros.

default: None

parent_connectivity ()

default: None

region_labels (Region labels)

Short strings, ‘labels’, for the regions represented by the connectivity matrix.

default: None

saved_selection ()

default: None

speed (Conduction speed)

A single number or matrix of conduction speeds for the myelinated fibre tracts between regions.

default: [ 3.]

tract_lengths (Tract lengths)

The length of myelinated fibre tracts between regions. If not provided Euclidean distance between region centres is used.

default: None

undirected ()

1, when the weights matrix is square and symmetric over the main diagonal, 0 when directed graph.

default: 0

weights (Connection strengths)

Matrix of values representing the strength of connections between regions, arbitrary units.

default: None

`cortex`¶

tvb.datatypes.cortex.Cortex[source]¶

Wrapper Class over a CorticalSurface, to be used when preparing a simulation launch.

traits on this class:

bi_hemispheric ()

default: None

coupling_strength (Local coupling strength)

A factor that rescales local connectivity strengths.

default: [ 1.]

range: low = 0.0 ; high = 20.0

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

eeg_projection (EEG projection)

A 2-D array which projects the neural activity on the cortical surface to a set of EEG sensors.

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

internal_projection (Internal projection)

A 2-D array which projects the neural activity on the cortical surface to a set of embeded sensors.

default: None

local_connectivity (Local Connectivity)

Define the interaction between neighboring network nodes. This is implicitly integrated in the definition of a given surface as an excitatory mean coupling of directly adjacent neighbors to the first state variable of each population model (since these typically represent the mean-neural membrane voltage). This coupling is instantaneous (no time delays).

default: None

meg_projection (MEG projection)

A 2-D array which projects the neural activity on the cortical surface to a set of MEG sensors.

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

region_mapping_data (region mapping)

An index vector of length equal to the number_of_vertices + the number of non-cortical regions, with values that index into an associated connectivity matrix.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Cortical Surface

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

`equations`¶

The Equation datatypes. This brings together the scientific and framework methods that are associated with the Equation datatypes.

tvb.datatypes.equations.Alpha[source]¶

An Alpha function belonging to the Exponential function family.

traits on this class:

equation (Alpha Equation)

\((\alpha * \beta) / (\beta - \alpha) * (\exp(-\alpha * (x-onset)) - \exp(-\beta * (x-onset)))\) for \((x-onset) > 0\)

default: where((var-onset) > 0, (alpha * beta) / (beta - alpha) * (exp(-alpha * (var-onset)) - exp(-beta * (var-onset))), 0.0 * var)

parameters (Alpha Parameters)

default: {‘onset’: 0.5, ‘alpha’: 13.0, ‘beta’: 42.0}

tvb.datatypes.equations.Cosine[source]¶

A Cosine equation.

traits on this class:

equation (Cosine Equation)

\(amp \cos(2.0 \pi frequency x)\)

default: amp * cos(6.283185307179586 * frequency * var)

parameters (Cosine Parameters)

default: {‘amp’: 1.0, ‘frequency’: 0.01}

tvb.datatypes.equations.DiscreteEquation[source]¶

A special case for ‘discrete’ spaces, such as the regions, where each point in the space is effectively just assigned a value.

traits on this class:

equation (Discrete Equation)

The equation defines a function of \(x\)

default: var

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

tvb.datatypes.equations.DoubleExponential[source]¶

A difference of two exponential functions to define a kernel for the bold monitor.

Parameters :

\(\tau_1\): Time constant of the second exponential function [s]
\(\tau_2\): Time constant of the first exponential function [s].
\(f_1\) : Frequency of the first sine function [Hz].
\(f_2\) : Frequency of the second sine function [Hz].
\(amp_1\): Amplitude of the first exponential function.
\(amp_2\): Amplitude of the second exponential function.
\(a\) : Amplitude factor after normalization.

Reference:

[P_2000]

Alex Polonsky, Randolph Blake, Jochen Braun and David J. Heeger (2000). Neuronal activity in human primary visual cortex correlates with perception during binocular rivalry. Nature Neuroscience 3: 1153-1159

traits on this class:

equation (Double Exponential Equation)

\(h(var) = amp_1\exp(\frac{-var}{ au_1}) \sin(2\cdot\pi f_1 \cdot var) - amp_2\cdot \exp(-\frac{var} {\tau_2})*\sin(2\pi f_2 var)\).

default: ((amp_1 * exp(-var/tau_1) * sin(2.*pi*f_1*var)) - (amp_2 * exp(-var/ tau_2) * sin(2.*pi*f_2*var)))

parameters (Double Exponential Parameters)

default: {‘a’: 0.1, ‘amp_2’: 0.1, ‘amp_1’: 0.1, ‘f_1’: 0.03, ‘f_2’: 0.12, ‘pi’: 3.141592653589793, ‘tau_2’: 7.4, ‘tau_1’: 7.22}

tvb.datatypes.equations.DoubleGaussian[source]¶

A Mexican-hat function approximated by the difference of Gaussians functions.

traits on this class:

equation (Double Gaussian Equation)

\(amp_1 \exp\left(-\left((x-midpoint_1)^2 / \left(2.0 \sigma_1^2\right)\right)\right) - amp_2 \exp\left(-\left((x-midpoint_2)^2 / \left(2.0 \sigma_2^2\right)\right)\right)\)

default: (amp_1 * exp(-((var-midpoint_1)**2 / (2.0 * sigma_1**2)))) - (amp_2 * exp(-((var-midpoint_2)**2 / (2.0 * sigma_2**2))))

parameters (Double Gaussian Parameters)

default: {‘midpoint_2’: 0.0, ‘midpoint_1’: 0.0, ‘amp_2’: 1.0, ‘amp_1’: 0.5, ‘sigma_2’: 10.0, ‘sigma_1’: 20.0}

tvb.datatypes.equations.Equation[source]¶

Base class for Equation data types. traits on this class:

equation (Equation as a string)

A latex representation of the equation, with the extra escaping needed for interpretation via sphinx.

default: None

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

tvb.datatypes.equations.FiniteSupportEquation[source]¶

Equations that decay to zero as the variable moves away from zero. It is necessary to restrict spatial equation evaluated on a surface to this class, are . The main purpose of this class is to facilitate filtering in the UI, for patters on surface (stimuli surface and localConnectivity).

traits on this class:

equation (Equation as a string)

A latex representation of the equation, with the extra escaping needed for interpretation via sphinx.

default: None

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

tvb.datatypes.equations.FirstOrderVolterra[source]¶

Integral form of the first Volterra kernel of the three used in the Ballon Windekessel model for computing the Bold signal. This function describes a damped Oscillator.

Parameters :

\(\tau_s\): Dimensionless? exponential decay parameter.
\(\tau_f\): Dimensionless? oscillatory parameter.
\(k_1\) : First Volterra kernel coefficient.
\(V_0\) : Resting blood volume fraction.

References :

[F_2000]

Friston, K., Mechelli, A., Turner, R., and Price, C., Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics, NeuroImage, 12, 466 - 477, 2000.

traits on this class:

equation (First Order Volterra Kernel)

\(G(t - t^{\prime}) = e^{\frac{1}{2} \left(\frac{t - t^{\prime}}{\tau_s} \right)} \frac{\sin\left((t - t^{\prime}) \sqrt{\frac{1}{\tau_f} - \frac{1}{4 \tau_s^2}}\right)} {\sqrt{\frac{1}{\tau_f} - \frac{1}{4 \tau_s^2}}} \; \; \; \; \; \; for \; \; \; t \geq t^{\prime} = 0 \; \; \; \; \; \; for \; \; \; t < t^{\prime}\).

default: 1/3. * exp(-0.5*(var / tau_s)) * (sin(sqrt(1./tau_f - 1./(4.*tau_s**2)) * var)) / (sqrt(1./tau_f - 1./(4.*tau_s**2)))

parameters (Mixture of Gammas Parameters)

default: {‘tau_f’: 0.4, ‘k_1’: 5.6, ‘V_0’: 0.02, ‘tau_s’: 0.8}

tvb.datatypes.equations.Gamma[source]¶

A Gamma function for the bold monitor. It belongs to the family of Exponential functions.

Parameters:

\(\tau\) : Exponential time constant of the gamma function [seconds].
\(n\) : The phase delay of the gamma function.
math: factorial : (n-1)!. numexpr does not support factorial yet.
math: a : Amplitude factor after normalization.

Reference:

[B_1996]

Geoffrey M. Boynton, Stephen A. Engel, Gary H. Glover and David J. Heeger (1996). Linear Systems Analysis of Functional Magnetic Resonance Imaging in Human V1. J Neurosci 16: 4207-4221

Note

might be filtered from the equations used in Stimulus and Local Connectivity.

traits on this class:

equation (Gamma Equation)

\(h(var) = \frac{(\frac{var}{\tau})^{(n-1)}\exp{-(\frac{var}{\tau})}}{\tau(n-1)!}\).

default: ((var / tau) ** (n - 1) * exp(-(var / tau)) )/ (tau * factorial)

parameters (Gamma Parameters)

default: {‘factorial’: 2.0, ‘tau’: 1.08, ‘a’: 0.1, ‘n’: 3.0}

tvb.datatypes.equations.Gaussian[source]¶

A Gaussian equation. offset: parameter to extend the behaviour of this function when spatializing model parameters.

traits on this class:

equation (Gaussian Equation)

\((amp \exp\left(-\left(\left(x-midpoint\right)^2 / \left(2.0 \sigma^2\right)\right)\right)) + offset\)

default: (amp * exp(-((var-midpoint)**2 / (2.0 * sigma**2))))+offset

parameters (Gaussian Parameters)

default: {‘amp’: 1.0, ‘sigma’: 1.0, ‘midpoint’: 0.0, ‘offset’: 0.0}

tvb.datatypes.equations.GeneralizedSigmoid[source]¶

A General Sigmoid equation.

traits on this class:

equation (Generalized Sigmoid Equation)

\(low + (high - low) / (1.0 + \exp(-\pi/\sqrt(3.0) (x-midpoint)/\sigma))\)

default: low + (high - low) / (1.0 + exp(-1.8137993642342178 * (var-midpoint)/sigma))

parameters (Sigmoid Parameters)

default: {‘high’: 1.0, ‘midpoint’: 1.0, ‘sigma’: 0.3, ‘low’: 0.0}

tvb.datatypes.equations.HRFKernelEquation[source]¶

Base class for hemodynamic response functions. traits on this class:

equation (Equation as a string)

A latex representation of the equation, with the extra escaping needed for interpretation via sphinx.

default: None

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

tvb.datatypes.equations.Linear[source]¶

A linear equation.

traits on this class:

equation (Linear Equation)

\(result = a * x + b\)

default: a * var + b

parameters (Linear Parameters)

default: {‘a’: 1.0, ‘b’: 0.0}

tvb.datatypes.equations.MixtureOfGammas[source]¶

A mixture of two gamma distributions to create a kernel similar to the one used in SPM.

>> import scipy.stats as sp_stats >> import numpy >> t = numpy.linspace(1,20,100) >> a1, a2 = 6., 10. >> lambda = 1. >> c = 0.5 >> hrf = sp_stats.gamma.pdf(t, a1, lambda) - c * sp_stats.gamma.pdf(t, a2, lambda)

gamma.pdf(x, a, theta) = (lambda*x)**(a-1) * exp(-lambda*x) / gamma(a) a : shape parameter theta: 1 / lambda : scale parameter

References:

[G_1999]

(1, 2) Glover, G. Deconvolution of Impulse Response in Event-Related BOLD fMRI. NeuroImage 9, 416-429, 1999.

Parameters:

\(a_{1}\) : shape parameter first gamma pdf.
\(a_{2}\) : shape parameter second gamma pdf.
\(\lambda\) : scale parameter first gamma pdf.

Default values are based on [G_1999]: * \(a_{1} - 1 = n_{1} = 5.0\) * \(a_{2} - 1 = n_{2} = 12.0\) * \(c \equiv a_{2} = 0.4\)

Alternative values \(a_{2}=10\) and \(c=0.5\)

NOTE: gamma_a_1 and gamma_a_2 are placeholders, the true values are computed before evaluating the expression, because numexpr does not support certain functions.

NOTE: [G_1999] used a different analytical function that can be approximated by this difference of gamma pdfs

traits on this class:

equation (Mixture of Gammas)

\(\frac{\lambda \,t^{a_{1} - 1} \,\, \exp^{-\lambda \,t}}{\Gamma(a_{1})} - 0.5 \frac{\lambda \,t^{a_{2} - 1} \,\, \exp^{-\lambda \,t}}{\Gamma(a_{2})}\).

default: (l * var)**(a_1-1) * exp(-l*var) / gamma_a_1 - c * (l*var)**(a_2-1) * exp(-l*var) / gamma_a_2

parameters (Double Exponential Parameters)

default: {‘gamma_a_2’: 1.0, ‘gamma_a_1’: 1.0, ‘a_2’: 13.0, ‘a_1’: 6.0, ‘c’: 0.4, ‘l’: 1.0}

tvb.datatypes.equations.PulseTrain[source]¶

A pulse train , offset with respect to the time axis.

Parameters:

\(\tau\) : pulse width or pulse duration
\(T\) : pulse repetition period
\(f\) : pulse repetition frequency (1/T)
duty cycle : :math:\frac{\tau}{T} (for a square wave: 0.5)
onset time :

traits on this class:

equation (Pulse Train)

\(\frac{\tau}{T} +\sum_{n=1}^{\infty}\frac{2}{n\pi} \sin\left(\frac{\pi\,n\tau}{T}\right) \cos\left(\frac{2\pi\,n}{T} var\right)\). The starting time is halfway through the first pulse. The phase can be offset t with t - tau/2

default: where((var % T) < tau, amp, 0)

parameters (Pulse Train Parameters)

default: {‘onset’: 30.0, ‘tau’: 13.0, ‘T’: 42.0, ‘amp’: 1.0}

tvb.datatypes.equations.Sigmoid[source]¶

A Sigmoid equation. offset: parameter to extend the behaviour of this function when spatializing model parameters.

traits on this class:

equation (Sigmoid Equation)

\((amp / (1.0 + \exp(-\pi/\sqrt(3.0) (radius-x)/\sigma))) + offset\)

default: (amp / (1.0 + exp(-1.8137993642342178 * (radius-var)/sigma))) + offset

parameters (Sigmoid Parameters)

default: {‘amp’: 1.0, ‘radius’: 5.0, ‘sigma’: 1.0, ‘offset’: 0.0}

tvb.datatypes.equations.Sinusoid[source]¶

A Sinusoid equation.

traits on this class:

equation (Sinusoid Equation)

\(amp \sin(2.0 \pi frequency x)\)

default: amp * sin(6.283185307179586 * frequency * var)

parameters (Sinusoid Parameters)

default: {‘amp’: 1.0, ‘frequency’: 0.01}

tvb.datatypes.equations.SpatialApplicableEquation[source]¶

Abstract class introduced just for filtering what equations to be displayed in UI, for setting model parameters on the Surface level.

traits on this class:

equation (Equation as a string)

A latex representation of the equation, with the extra escaping needed for interpretation via sphinx.

default: None

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

tvb.datatypes.equations.TemporalApplicableEquation[source]¶

Abstract class introduced just for filtering what equations to be displayed in UI, for setting the temporal component in Stimulus on region and surface.

traits on this class:

equation (Equation as a string)

A latex representation of the equation, with the extra escaping needed for interpretation via sphinx.

default: None

parameters (Parameters in a dictionary.)

Should be a list of the parameters and their meaning, Traits should be able to take defaults and sensible ranges from any traited information that was provided.

default: {}

`fcd`¶

Adapter that uses the traits module to generate interfaces for ... Analyzer.

tvb.datatypes.fcd.Fcd[source]¶

traited class Fcd traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

labels_ordering (Dimension Names)

List of strings representing names of each data dimension

default: [‘Time’, ‘Time’, ‘State Variable’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

source (Source time-series)

Links to the time-series on which FCD is calculated.

default: None

sp (Spanning between two consecutive sliding window (ms))

Spanning= (time windows length)-(overlapping between two consecutive time window). FCD matrix is calculated in the following way: the time series is divided in time window of fixed length and with an overlapping of fixed length. The datapoints within each window, centered at time ti, are used to calculate FC(ti) as Pearson correlation. The ij element of the FCD matrix is calculated as the Pearson correlation between FC(ti) and FC(tj) arranged in a vector

default: 2000

sw (Sliding window length (ms))

Length of the time window used to divided the time series. FCD matrix is calculated in the following way: the time series is divided in time window of fixed length and with an overlapping of fixed length. The datapoints within each window, centered at time ti, are used to calculate FC(ti) as Pearson correlation. The ij element of the FCD matrix is calculated as the Pearson correlation between FC(ti) and FC(tj) arranged in a vector.

default: 120000

title ()

default: None

`graph`¶

The Graph datatypes. This brings together the scientific and framework methods that are associated with the Graph datatypes.

tvb.datatypes.graph.ConnectivityMeasure[source]¶

Measurement of based on a connectivity. traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

connectivity ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

title ()

default: None

tvb.datatypes.graph.CorrelationCoefficients[source]¶

Correlation coefficients datatype. traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

labels_ordering (Dimension Names)

List of strings representing names of each data dimension

default: [‘Node’, ‘Node’, ‘State Variable’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

source (Source time-series)

Links to the time-series on which Correlation (coefficients) is applied.

default: None

title ()

default: None

tvb.datatypes.graph.Covariance[source]¶

Covariance datatype. traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

source (Source time-series)

Links to the time-series on which NodeCovariance is applied.

default: None

title ()

default: None

`local_connectivity`¶

tvb.datatypes.local_connectivity.LocalConnectivity[source]¶

A sparse matrix for representing the local connectivity within the Cortex.

traits on this class:

cutoff (Cutoff distance (mm))

Distance at which to truncate the evaluation in mm.

default: 40.0

equation (Spatial)

default: <class ‘tvb.datatypes.equations.Gaussian’>

matrix ()

default: None

surface (Surface)

default: None

`lookup_tables`¶

The LookUpTable datatype. This brings together the scientific and framework methods that are associated with the precalculated look up tables.

tvb.datatypes.lookup_tables.LookUpTable[source]¶

Lookup Tables for storing pre-computed functions. Specific table subclasses are implemented below.

traits on this class:

data (data)

Tabulated values

default: None

df (df)

.

default: None

dx (dx)

Tabulation step

default: []

equation (String representation of the precalculated function)

A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.

default: None

invdx (invdx)

.

default: []

number_of_values (Number of values)

The number of values in the table

default: 0

xmax (x-max)

Maximum value

default: None

xmin (x-min)

Minimum value

default: None

tvb.datatypes.lookup_tables.NerfTable[source]¶

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.

traits on this class:

data (data)

Tabulated values

default: None

df (df)

.

default: None

dx (dx)

Tabulation step

default: []

equation (String representation of the precalculated function)

A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.

default: None

invdx (invdx)

.

default: []

number_of_values (Number of values)

The number of values in the table

default: 0

xmax (x-max)

Maximum value

default: None

xmin (x-min)

Minimum value

default: None

tvb.datatypes.lookup_tables.PsiTable[source]¶

Look up table containing the values of a function representing the time-averaged gating variable \(\psi(\nu)\) as a function of the presynaptic rates \(\nu\)

traits on this class:

data (data)

Tabulated values

default: None

df (df)

.

default: None

dx (dx)

Tabulation step

default: []

equation (String representation of the precalculated function)

A latex representation of the function whose values are stored in the table, with the extra escaping needed for interpretation via sphinx.

default: None

invdx (invdx)

.

default: []

number_of_values (Number of values)

The number of values in the table

default: 0

xmax (x-max)

Maximum value

default: None

xmin (x-min)

Minimum value

default: None

`mapped_values`¶

tvb.datatypes.mapped_values.DatatypeMeasure[source]¶

Class to hold the metric for a previous stored DataType. E.g. Measure (single value) for any TimeSeries resulted in a group of Simulations

traits on this class:

analyzed_datatype ()

default: None

metrics ()

default: None

tvb.datatypes.mapped_values.ValueWrapper[source]¶

Class to wrap a singular value storage in DB.

traits on this class:

data_name ()

default: None

data_type ()

default: unknown

data_value ()

default: None

`mode_decompositions`¶

The Mode Decomposition datatypes. This brings together the scientific and framework methods that are associated with the Mode Decomposition datatypes.

tvb.datatypes.mode_decompositions.IndependentComponents[source]¶

Result of an Independent Component Analysis.

traits on this class:

component_time_series (Component time series. Unmixed sources.)

default: None

mixing_matrix (Mixing matrix - Spatial Maps)

The linear mixing matrix (Mixing matrix)

default: None

n_components (Number of independent components)

Observed data matrix is considered to be a linear combination of \(n\) non-Gaussian independent components

default: None

norm_source (Normalised source time series. Zero centered and whitened.)

default: None

normalised_component_time_series (Normalised component time series)

default: None

prewhitening_matrix (Pre-whitening matrix)

default: None

source (Source time-series)

Links to the time-series on which the ICA is applied.

default: None

unmixing_matrix (Unmixing matrix - Spatial maps)

The estimated unmixing matrix used to obtain the unmixed sources from the data

default: None

tvb.datatypes.mode_decompositions.PrincipalComponents[source]¶

Result of a Principal Component Analysis (PCA).

traits on this class:

component_time_series (Component time series)

default: None

fractions (Fraction explained)

A vector or collection of vectors representing the fraction of the variance explained by each principal component.

default: None

norm_source (Normalised source time series)

default: None

normalised_component_time_series (Normalised component time series)

default: None

source (Source time-series)

Links to the time-series on which the PCA is applied.

default: None

weights (Principal vectors)

The vectors of the ‘weights’ with which each time-series is represented in each component.

default: None

`noise_framework`¶

Module to handle framework specific methods related to noise sources.

tvb.datatypes.noise_framework.build_noise(parent_parameters)[source]¶: Build Noise entity from dictionary of parameters. :param parent_parameters: dictionary of parameters for the entity having Noise as attribute. The dictionary is after UI form-submit and framework pre-process. :return: Noise entity.

`patterns`¶

The Pattern datatypes. This brings together the scientific and framework methods that are associated with the pattern datatypes.

tvb.datatypes.patterns.SpatialPattern[source]¶

Equation for space variation.

traits on this class:

spatial (Spatial Equation)

default: None

tvb.datatypes.patterns.SpatialPatternVolume[source]¶

A spatio-temporal pattern defined in a volume. traits on this class:

focal_points_volume (Focal points)

default: None

spatial (Spatial Equation)

default: None

volume (Volume)

default: None

class tvb.datatypes.patterns.SpatioTemporalCall[source]¶

Bases: object

A call method to be added to all Spatio- Temporal classes

tvb.datatypes.patterns.SpatioTemporalPattern[source]¶

Combine space and time equations.

traits on this class:

spatial (Spatial Equation)

default: None

temporal (Temporal Equation)

default: None

tvb.datatypes.patterns.StimuliRegion[source]¶

A class that bundles the temporal profile of the stimulus, together with the list of scaling weights of the regions where it will applied.

traits on this class:

connectivity (Connectivity)

default: None

spatial (Spatial Equation)

default: <class ‘tvb.datatypes.equations.DiscreteEquation’>

temporal (Temporal Equation)

default: None

weight (scaling)

default: None

tvb.datatypes.patterns.StimuliSurface[source]¶

A spatio-temporal pattern defined in a Surface DataType. It includes the list of focal points.

traits on this class:

focal_points_surface (Focal points)

default: None

focal_points_triangles (Focal points triangles)

default: None

spatial (Spatial Equation)

default: None

surface (Surface)

default: None

temporal (Temporal Equation)

default: None

`projections`¶

The ProjectionMatrices DataTypes. This brings together the scientific and framework methods that are associated with the surfaces data.

tvb.datatypes.projections.ProjectionMatrix[source]¶

Base DataType for representing a ProjectionMatrix. The projection is between a source of type CorticalSurface and a set of Sensors.

traits on this class:

brain_skull (Brain Skull)

Boundary between skull and cortex domains.

default: None

conductances (Domain conductances)

A dictionary representing the conductances of ...

default: {‘brain’: 1.0, ‘air’: 0.0, ‘skull’: 0.01, ‘skin’: 1.0}

projection_data (Projection Matrix Data)

default: None

projection_type ()

default: None

sensors (Sensors)

A set of sensors to compute projection matrix for them.

default: None

skin_air (Skin Air)

Boundary between skin and air domains.

default: None

skull_skin (Skull Skin)

Boundary between skull and skin domains.

default: None

sources (surface or region)

default: None

tvb.datatypes.projections.ProjectionSurfaceEEG[source]¶

Specific projection, from a CorticalSurface to EEG sensors.

traits on this class:

brain_skull (Brain Skull)

Boundary between skull and cortex domains.

default: None

conductances (Domain conductances)

A dictionary representing the conductances of ...

default: {‘brain’: 1.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘air’: 0.0}

projection_data (Projection Matrix Data)

default: None

projection_type ()

default: projEEG

sensors ()

default: None

skin_air (Skin Air)

Boundary between skin and air domains.

default: None

skull_skin (Skull Skin)

Boundary between skull and skin domains.

default: None

sources (surface or region)

default: None

tvb.datatypes.projections.ProjectionSurfaceMEG[source]¶

Specific projection, from a CorticalSurface to MEG sensors.

traits on this class:

brain_skull (Brain Skull)

Boundary between skull and cortex domains.

default: None

conductances (Domain conductances)

A dictionary representing the conductances of ...

default: {‘brain’: 1.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘air’: 0.0}

projection_data (Projection Matrix Data)

default: None

projection_type ()

default: projMEG

sensors ()

default: None

skin_air (Skin Air)

Boundary between skin and air domains.

default: None

skull_skin (Skull Skin)

Boundary between skull and skin domains.

default: None

sources (surface or region)

default: None

tvb.datatypes.projections.ProjectionSurfaceSEEG[source]¶

Specific projection, from a CorticalSurface to SEEG sensors.

traits on this class:

brain_skull (Brain Skull)

Boundary between skull and cortex domains.

default: None

conductances (Domain conductances)

A dictionary representing the conductances of ...

default: {‘brain’: 1.0, ‘skin’: 1.0, ‘skull’: 0.01, ‘air’: 0.0}

projection_data (Projection Matrix Data)

default: None

projection_type ()

default: projSEEG

sensors ()

default: None

skin_air (Skin Air)

Boundary between skin and air domains.

default: None

skull_skin (Skull Skin)

Boundary between skull and skin domains.

default: None

sources (surface or region)

default: None

`region_mapping`¶

DataTypes for mapping some TVB DataTypes to a Connectivity (regions).

In FreeSurfer terms, a RegionMapping is a parcellation and a VolumeMapping is a segmentation.

tvb.datatypes.region_mapping.RegionMapping[source]¶

An array (of length Surface.vertices). Each value is representing the index in Connectivity regions to which the current vertex is mapped.

traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

connectivity ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

surface ()

default: None

title ()

default: None

tvb.datatypes.region_mapping.RegionVolumeMapping[source]¶

Each value is representing the index in Connectivity regions to which the current voxel is mapped.

traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

connectivity ()

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

title ()

default: None

volume ()

default: None

`sensors`¶

The Sensors dataType. This brings together the scientific and framework methods that are associated with the sensor dataTypes.

tvb.datatypes.sensors.Sensors[source]¶

Base Sensors class. All sensors have locations. Some will have orientations, e.g. MEG.

traits on this class:

has_orientation ()

default: False

labels (Sensor labels)

default: None

locations (Sensor locations)

default: None

number_of_sensors (Number of sensors)

The number of sensors described by these Sensors.

default: None

orientations ()

default: None

sensors_type ()

default: None

usable (Usable sensors)

The sensors in set which are used for signal data.

default: None

tvb.datatypes.sensors.SensorsEEG[source]¶

EEG sensor locations are represented as unit vectors, these need to be combined with a head(outer-skin) surface to obtain actual sensor locations

                      position
                         |
                        / \
                       /   \
file columns: labels, x, y, z

traits on this class:

has_orientation ()

default: False

labels (Sensor labels)

default: None

locations (Sensor locations)

default: None

number_of_sensors (Number of sensors)

The number of sensors described by these Sensors.

default: None

orientations ()

default: None

sensors_type ()

default: EEG

usable (Usable sensors)

The sensors in set which are used for signal data.

default: None

tvb.datatypes.sensors.SensorsInternal[source]¶

Sensors inside the brain...

traits on this class:

has_orientation ()

default: False

labels (Sensor labels)

default: None

locations (Sensor locations)

default: None

number_of_sensors (Number of sensors)

The number of sensors described by these Sensors.

default: None

orientations ()

default: None

sensors_type ()

default: Internal

usable (Usable sensors)

The sensors in set which are used for signal data.

default: None

tvb.datatypes.sensors.SensorsMEG[source]¶

These are actually just SQUIDS. Axial or planar gradiometers are achieved by calculating lead fields for two sets of sensors and then subtracting...

                      position  orientation
                         |           |
                        / \         / \
                       /   \       /   \
file columns: labels, x, y, z,   dx, dy, dz

traits on this class:

has_orientation ()

default: True

labels (Sensor labels)

default: None

locations (Sensor locations)

default: None

number_of_sensors (Number of sensors)

The number of sensors described by these Sensors.

default: None

orientations (Sensor orientations)

An array representing the orientation of the MEG SQUIDs

default: None

sensors_type ()

default: MEG

usable (Usable sensors)

The sensors in set which are used for signal data.

default: None

`sensors_bst_to_tvb`¶

Small script for converting Brainstorm sensor files for our default dataset to the simple ASCII format used by TVB (and other software).

NB: Brainstorm uses meters, TVB uses millimeters.

tvb.datatypes.sensors_bst_to_tvb.convert_brainstorm_to_tvb(tvb_data_path, chan_paths)[source]¶: Convert given set of channels from Brainstorm to TVB formats.

tvb.datatypes.sensors_bst_to_tvb.get_field_array(mat_group, n=3, dtype=<type 'numpy.float64'>)[source]¶

`simulation_state`¶

DataType for storing a simulator’s state in files and as DB reference.

tvb.datatypes.simulation_state.SimulationState[source]¶

Simulation State, prepared for H5 file storage.

traits on this class:

current_state ()

default: None

current_step ()

default: None

history ()

default: None

monitor_stock_1 ()

default: None

monitor_stock_10 ()

default: None

monitor_stock_11 ()

default: None

monitor_stock_12 ()

default: None

monitor_stock_13 ()

default: None

monitor_stock_14 ()

default: None

monitor_stock_15 ()

default: None

monitor_stock_2 ()

default: None

monitor_stock_3 ()

default: None

monitor_stock_4 ()

default: None

monitor_stock_5 ()

default: None

monitor_stock_6 ()

default: None

monitor_stock_7 ()

default: None

monitor_stock_8 ()

default: None

monitor_stock_9 ()

default: None

`spectral`¶

The Spectral datatypes. This brings together the scientific and framework methods that are associated with the Spectral datatypes.

tvb.datatypes.spectral.CoherenceSpectrum[source]¶

Result of a NodeCoherence Analysis.

traits on this class:

aggregation_functions ()

default: None

array_data ()

default: None

dimensions_labels ()

default: None

frequency (Frequency)

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nfft (Data-points per block)

NOTE: must be a power of 2

default: 256

nr_dimensions ()

default: None

source (Source time-series)

Links to the time-series on which the node_coherence is applied.

default: None

title ()

default: None

tvb.datatypes.spectral.ComplexCoherenceSpectrum[source]¶

Result of a NodeComplexCoherence Analysis.

traits on this class:

aggregation_functions ()

default: None

array_data (Complex Coherence)

The complex coherence coefficients calculated from the cross spectrum. The imaginary values of this complex ndarray represent the imaginary coherence.

default: None

cross_spectrum (The cross spectrum)

A complex ndarray that contains the nodes x nodes cross spectrum for every frequency frequency and for every segment.

default: None

dimensions_labels ()

default: None

epoch_length (Epoch length)

The timeseries was segmented into equally sized blocks (overlapping if necessary), prior to the application of the FFT. The segement length determines the frequency resolution of the resulting spectra.

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

segment_length (Segment length)

The timeseries was segmented into equally sized blocks (overlapping if necessary), prior to the application of the FFT. The segement length determines the frequency resolution of the resulting spectra.

default: None

source (Source time-series)

Links to the time-series on which the node_coherence is applied.

default: None

title ()

default: None

windowing_function (Windowing function)

The windowing function applied to each time segment prior to application of the FFT.

default: None

tvb.datatypes.spectral.FourierSpectrum[source]¶

Result of a Fourier Analysis.

traits on this class:

aggregation_functions ()

default: None

amplitude (Amplitude)

default: None

array_data ()

default: None

average_power (Average Power)

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

normalised_average_power (Normalised Power)

default: None

nr_dimensions ()

default: None

phase (Phase)

default: None

power (Power)

default: None

segment_length (Segment length)

The timeseries was segmented into equally sized blocks (overlapping if necessary), prior to the application of the FFT. The segement length determines the frequency resolution of the resulting spectra.

default: None

source (Source time-series)

Links to the time-series on which the FFT is applied.

default: None

title ()

default: None

windowing_function (Windowing function)

The windowing function applied to each time segment prior to application of the FFT.

default: None

tvb.datatypes.spectral.WaveletCoefficients[source]¶

This class bundles all the elements of a Wavelet Analysis into a single object, including the input TimeSeries datatype and the output results as arrays (FloatArray)

traits on this class:

aggregation_functions ()

default: None

amplitude (Amplitude)

default: None

array_data ()

default: None

dimensions_labels ()

default: None

frequencies (Frequencies)

A vector that maps scales to frequencies.

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

mother (Mother wavelet)

A string specifying the type of mother wavelet to use, default is ‘morlet’.

default: morlet

normalisation (Normalisation type)

default: None

nr_dimensions ()

default: None

phase (Phase)

default: None

power (Power)

default: None

q_ratio (Q-ratio)

default: 5.0

sample_period (Sample period)

default: None

source (Source time-series)

default: None

title ()

default: None

`structural`¶

The Volume datatypes. This brings together the scientific and framework methods that are associated with the volume datatypes.

tvb.datatypes.structural.StructuralMRI[source]¶

Quantitative volumetric data recorded by means of Magnetic Resonance Imaging.

traits on this class:

aggregation_functions ()

default: None

array_data (contrast)

default: None

dimensions_labels ()

default: None

label_x ()

default: None

label_y ()

default: None

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions ()

default: None

title ()

default: None

volume ()

default: None

weighting (MRI weighting)

default: None

class tvb.datatypes.structural.VolumetricDataMixin[source]¶

Bases: object

Provides subclasses with useful methods for volumes.

get_min_max_values()[source]¶: Retrieve the minimum and maximum values from the metadata. :returns: (minimum_value, maximum_value)

get_volume_slice(x_plane, y_plane, z_plane)[source]¶

get_volume_view(x_plane, y_plane, z_plane, **kwargs)[source]¶

write_data_slice(data)[source]¶: We are using here the same signature as in TS, just to allow easier parsing code. This is not a chunked write.

`surfaces`¶

Surface relates DataTypes. This brings together the scientific and framework methods that are associated with the surfaces data.

tvb.datatypes.surfaces.BrainSkull[source]¶

Brain - inner skull interface surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Brain Skull

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.CorticalSurface[source]¶

Cortical or pial surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Cortical Surface

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.EEGCap[source]¶

EEG cap surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: EEG Cap

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.FaceSurface[source]¶

Face surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Face

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.OpenSurface[source]¶

Base class for open surfaces. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: None

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.SkinAir[source]¶

Skin - air interface surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Skin Air

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.SkullSkin[source]¶

Outer-skull - scalp interface surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: Skull Skin

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.Surface[source]¶

A base class for other surfaces. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: None

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

class tvb.datatypes.surfaces.ValidationResult[source]¶

Bases: object

Used by surface validate methods to report non-fatal failed validations

add_warning(msg, data)[source]¶

merge(other)[source]¶

summary()[source]¶

tvb.datatypes.surfaces.WhiteMatterSurface[source]¶

White matter - gray matter interface surface. traits on this class:

bi_hemispheric ()

default: None

edge_max_length ()

default: None

edge_mean_length ()

default: None

edge_min_length ()

default: None

geodesic_distance_matrix (Geodesic distance matrix)

A sparse matrix of truncated geodesic distances

default: None

hemisphere_mask (An array specifying if a vertex belongs to the right hemisphere)

default: None

number_of_split_slices ()

default: None

number_of_triangles (Number of triangles)

The number of triangles making up this surface.

default: None

number_of_vertices (Number of vertices)

The number of vertices making up this surface.

default: None

split_slices ()

default: None

split_triangles ()

default: None

surface_type ()

default: White Matter

triangle_normals (Triangle normal vectors)

An array of unit normal vectors for the surfaces triangles.

default: None

triangles (Triangles)

Array of indices into the vertices, specifying the triangles which define the surface.

default: None

valid_for_simulations ()

default: None

vertex_normals (Vertex normal vectors)

An array of unit normal vectors for the surfaces vertices.

default: None

vertices (Vertex positions)

An array specifying coordinates for the surface vertices.

default: None

zero_based_triangles ()

default: None

tvb.datatypes.surfaces.center_vertices(vertices)[source]¶: Centres the vertices using means along axes. :param vertices: a numpy array of shape (n, 3) :returns: the centered array

tvb.datatypes.surfaces.make_surface(surface_type)[source]¶: Build a Surface instance, based on an input type :param surface_type: one of the supported surface types :return: Instance of the corresponding surface lass, or None

tvb.datatypes.surfaces.paths2url(datatype_entity, attribute_name, flatten=False, parameter=None, datatype_kwargs=None)[source]¶: Prepare a File System Path for passing into an URL.

`temporal_correlations`¶

The Temporal Correlation datatypes. This brings together the scientific and framework methods that are associated with the Temporal Correlation datatypes.

tvb.datatypes.temporal_correlations.CrossCorrelation[source]¶

Result of a CrossCorrelation Analysis.

traits on this class:

array_data ()

default: None

labels_ordering (Dimension Names)

List of strings representing names of each data dimension

default: [‘Offsets’, ‘Node’, ‘Node’, ‘State Variable’, ‘Mode’]

source (Source time-series)

Links to the time-series on which the cross_correlation is applied.

default: None

time (Temporal Offsets)

default: None

`time_series`¶

The TimeSeries datatypes. This brings together the scientific and framework methods that are associated with the time-series data.

tvb.datatypes.time_series.SensorsTSBase[source]¶

Add framework related functionality for TS Sensor classes

traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering (Dimension Names)

List of strings representing names of each data dimension

default: [‘Time’, ‘State Variable’, ‘Space’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeries[source]¶

Base time-series dataType.

traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering (Dimension Names)

List of strings representing names of each data dimension

default: [‘Time’, ‘State Variable’, ‘Space’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesEEG[source]¶

A time series associated with a set of EEG sensors. traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘1’, ‘EEG Sensor’, ‘1’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

sensors ()

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesMEG[source]¶

A time series associated with a set of MEG sensors. traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘1’, ‘MEG Sensor’, ‘1’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

sensors ()

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesRegion[source]¶

A time-series associated with the regions of a connectivity. traits on this class:

connectivity ()

default: None

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘State Variable’, ‘Region’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

region_mapping ()

default: None

region_mapping_volume ()

default: None

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesSEEG[source]¶

A time series associated with a set of Internal sensors. traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘1’, ‘sEEG Sensor’, ‘1’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

sensors ()

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesSurface[source]¶

A time-series associated with a Surface. traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘State Variable’, ‘Vertex’, ‘Mode’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

start_time (Start Time:)

default: None

surface ()

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

tvb.datatypes.time_series.TimeSeriesVolume[source]¶

A time-series associated with a Volume. traits on this class:

data (Time-series data)

An array of time-series data, with a shape of [tpts, :], where ‘:’ represents 1 or more dimensions

default: None

has_surface_mapping ()

default: True

has_volume_mapping ()

default: False

labels_dimensions (Specific labels for each dimension for the data stored in this timeseries.)

A dictionary containing mappings of the form {‘dimension_name’ : [labels for this dimension] }

default: {}

labels_ordering ()

default: [‘Time’, ‘X’, ‘Y’, ‘Z’]

length_1d ()

default: None

length_2d ()

default: None

length_3d ()

default: None

length_4d ()

default: None

nr_dimensions (Number of dimension in timeseries)

default: 4

sample_period (Sample period)

default: 1.0

sample_period_unit (Sample Period Measure Unit)

default: ms

sample_rate (Sample rate)

The sample rate of the timeseries

default: None

start_time (Start Time:)

default: None

time (Time-series time)

An array of time values for the time-series, with a shape of [tpts,]. This is ‘time’ as returned by the simulator’s monitors.

default: None

title ()

default: None

volume ()

default: None

tvb.datatypes.time_series.prepare_time_slice(total_time_length, max_length=10000)[source]¶

Limit the time dimension when retrieving from TS. If total time length is greater than MAX, then retrieve only the last part of the TS

Parameters:	total_time_length – TS time dimension max_length – limiting number of TS steps
Returns:	python slice

`tracts`¶

module docstring .. moduleauthor:: Mihai Andrei <mihai.andrei@codemart.ro>

tvb.datatypes.tracts.Tracts[source]¶

Datatype for results of diffusion imaging tractography. traits on this class:

region_volume_map (Region volume Mapping used to create the tract_region index)

default: None

tract_region (Tract region index)

An index used to find quickly all tract emerging from a region tract_region[i] is the region of the i’th tract. -1 represents the background

default: None

tract_start_idx (Tract starting indices)

Where is the first vertex of a tract in the vertex array

default: None

vertices (Vertex positions)

An array specifying coordinates for the tracts vertices.

default: None

`volumes`¶

The Volume datatypes. This brings together the scientific and framework methods that are associated with the volume datatypes.

tvb.datatypes.volumes.Volume[source]¶

Data defined on a regular grid in three dimensions.

traits on this class:

origin (Volume origin coordinates)

default: None

voxel_size (Voxel size)

default: None

voxel_unit (Voxel Measure Unit)

default: mm

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`datatypes` Package¶

`annotations`¶

`arrays`¶

`connectivity`¶

`cortex`¶

`equations`¶

`fcd`¶

`graph`¶

`local_connectivity`¶

`lookup_tables`¶

`mapped_values`¶

`mode_decompositions`¶

`noise_framework`¶

`patterns`¶

`projections`¶

`region_mapping`¶

`sensors`¶

`sensors_bst_to_tvb`¶

`simulation_state`¶

`spectral`¶

`structural`¶

`surfaces`¶

`temporal_correlations`¶

`time_series`¶

`tracts`¶

`volumes`¶

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datatypes Package¶

annotations¶

arrays¶

connectivity¶

cortex¶

equations¶

fcd¶

graph¶

local_connectivity¶

lookup_tables¶

mapped_values¶

mode_decompositions¶

noise_framework¶

patterns¶

projections¶

region_mapping¶

sensors¶

sensors_bst_to_tvb¶

simulation_state¶

spectral¶

structural¶

surfaces¶

temporal_correlations¶

time_series¶

tracts¶

volumes¶

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`datatypes` Package¶

`annotations`¶

`arrays`¶

`connectivity`¶

`cortex`¶

`equations`¶

`fcd`¶

`graph`¶

`local_connectivity`¶

`lookup_tables`¶

`mapped_values`¶

`mode_decompositions`¶

`noise_framework`¶

`patterns`¶

`projections`¶

`region_mapping`¶

`sensors`¶

`sensors_bst_to_tvb`¶

`simulation_state`¶

`spectral`¶

`structural`¶

`surfaces`¶

`temporal_correlations`¶

`time_series`¶

`tracts`¶

`volumes`¶