Calculate an FFT on a TimeSeries DataType and return a FourierSpectrum DataType.
Bases: tvb.basic.neotraits._core.HasTraits
A class for calculating the FFT of a TimeSeries object of TVB and returning a FourierSpectrum object. A segment length and windowing function can be optionally specified. By default the time series is segmented into 1 second blocks and no windowing function is applied.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
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.
Calculate the FFT of time_series broken into segments of length segment_length and filtered by window_function.
Returns the storage size in Bytes of the extended result of the FFT. That is, it includes storage of the evaluated FourierSpectrum attributes such as power, phase, amplitude, etc.
Returns the shape of the main result (complex array) of the FFT.
Returns the storage size in Bytes of the main result (complex array) of the FFT.
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.
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.
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.
Implementation of differet BOLD signal models. Four different models are distinguished:
Classical means that the coefficients used to compute the BOLD signal are derived as described in [Obata2004] . Revised coefficients are defined in [Stephan2007]
References:
| [Stephan2007] | (1, 2) Stephan KE, Weiskopf N, Drysdale PM, Robinson PA, Friston KJ (2007) Comparing hemodynamic models with DCM. NeuroImage 38: 387-401. |
| [Obata2004] | Obata, T.; Liu, T. T.; Miller, K. L.; Luh, W. M.; Wong, E. C.; Frank, L. R. & Buxton, R. B. (2004) Discrepancies between BOLD and flow dynamics in primary and supplementary motor areas: application of the balloon model to the interpretation of BOLD transients. Neuroimage, 21:144-153 |
Bases: tvb.basic.neotraits._core.HasTraits
A class for calculating the simulated BOLD signal given a TimeSeries object of TVB and returning another TimeSeries object.
The haemodynamic model parameters based on constants for a 1.5 T scanner.
tau_o : tvb.analyzers.fmri_balloon.BalloonModel.tau_o = Float(field_type=<class ‘float’>, default=0.98, required=True)
Balloon model parameter. Haemodynamic transit time (s). The average time blood take to traverse the venous compartment. It is the ratio of resting blood volume (V0) to resting blood flow (F0).
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
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.
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.
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.
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.
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.
The Balloon model equations. See Eqs. (4-10) in [Stephan2007] .. math:
\frac{ds}{dt} &= x - \kappa\,s - \gamma \,(f-1) \\
\frac{df}{dt} &= s \\
\frac{dv}{dt} &= \frac{1}{\tau_o} \, (f - v^{1/\alpha})\\
\frac{dq}{dt} &= \frac{1}{\tau_o}(f \, \frac{1-(1-E_0)^{1/\alpha}}{E_0} - v^{&/\alpha} \frac{q}{v})\\
\kappa &= \frac{1}{\tau_s}\\
\gamma &= \frac{1}{\tau_f}
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.
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.
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.
Returns the storage size in Bytes of the extended result of the .... That is, it includes storage of the evaluated ... attributes such as ..., etc.
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.
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.
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.
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.
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.
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.
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.
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.
Useful graph analyses.
Node betweenness centrality is the fraction of all shortest paths in the network that contain a given node. Nodes with high values of betweenness centrality participate in a large number of shortest paths.
| Parameters: | A – binary (directed/undirected) connection matrix (array) |
|---|---|
| Returns: | BC: a vector representing node between centrality vector. |
Notes:
Betweenness centrality may be normalised to the range [0,1] as BC/[(N-1)(N-2)], where N is the number of nodes in the network.
Original Mika Rubinov, UNSW/U Cambridge, 2007-2012 - From BCT 2012-12-04
Reference: [1] Kintali (2008) arXiv:0809.1906v2 [cs.DS] (generalization to directed and disconnected graphs)
Author: Paula Sanz Leon
Compute the inverse shortest path lengths of G.
| Parameters: | G – binary undirected connection matrix |
|---|---|
| Returns: | D: matrix of inverse distances |
Computes global efficiency or local efficiency of a connectivity matrix. The global efficiency is the average of inverse shortest path length, and is inversely related to the characteristic path length.
The local efficiency is the global efficiency computed on the neighborhood of the node, and is related to the clustering coefficient.
| Parameters: |
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|---|---|
| Returns: |
|
References: [1] Latora and Marchiori (2001) Phys Rev Lett 87:198701.
Note
Algorithm: algebraic path count
Note
Original: Mika Rubinov, UNSW, 2008-2010 - From BCT 2012-12-04
Note
Tested with Numpy 1.7
Warning
tested against Matlab version... needs indexing improvement
Example:
>>> import numpy.random
>>> A = np.random.rand(5, 5)
>>> E = efficiency_bin(A)
>>> E.shape == (1, )
>>> True
If you want to compute the local efficiency for every node in the network:
>>> E = efficiency_bin(A, compute_local_efficiency=True)
>>> E.shape == (5, 1)
>>> True
Author: Paula Sanz Leon
Get connected components sizes. Returns the size of the largest component of an undirected graph specified by the binary and undirected connection matrix A.
| Parameters: | A – array - binary undirected (BU) connectivity matrix. |
|---|---|
| Returns: |
|
| Raises: | Value Error - If A is not square. |
Warning
Requires NetworkX
Author: Paula Sanz Leon
A strategy to lesion a connectivity matrix.
A single node is removed at each step until the network is reduced to only 2 nodes. This method represents a structural failure analysis and it should be run several times with different random sequences.
| Parameters: |
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| Returns: |
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References: Alstott et al. (2009).
Author: Paula Sanz Leon
A strategy to lesion a connectivity matrix.
A single node is removed at each step until the network is reduced to only 2 nodes. At each step different graph metrics are computed (degree, strength and betweenness centrality). The single node with the highest degree, strength or centrality is removed.
| Parameters: |
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| Returns: |
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See also: sequential_random_deletion, localized_area_deletion
References: Alstott et al. (2009).
Author: Paula Sanz Leon
Perform Independent Component Analysis on a TimeSeries Object and returns an IndependentComponents datatype.
Bases: tvb.basic.neotraits._core.HasTraits
Takes a TimeSeries datatype (x) and returns the unmixed temporal sources (S) and the estimated mixing matrix (A).
math: x = A S ICA takes time-points as observations and nodes as variables.
It uses the FastICA algorithm implemented in the scikit-learn toolkit, and its intended usage is as a blind source separation method.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Returns the storage size in bytes of the extended result of the ICA.
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
Returns the storage size in bytes of the mixing matrix of the ICA analysis, assuming 64-bit float.
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.
Perform Fast Independent Component Analysis.
Read more in the User Guide.
The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. The derivative should be averaged along its last dimension. Example:
Estimated un-mixing matrix. The mixing matrix can be obtained by:
w = np.dot(W, K.T)
A = w.T * (w * w.T).I
The data matrix X is considered to be a linear combination of non-Gaussian (independent) components i.e. X = AS where columns of S contain the independent components and A is a linear mixing matrix. In short ICA attempts to un-mix’ the data by estimating an un-mixing matrix W where ``S = W K X.`
This implementation was originally made for data of shape [n_features, n_samples]. Now the input is transposed before the algorithm is applied. This makes it slightly faster for Fortran-ordered input.
Implemented using FastICA: A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430
Bases: builtins.object
FastICA: a fast algorithm for Independent Component Analysis.
Read more in the User Guide.
The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. Example:
Implementation based on A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430
Perform Fast Independent Component Analysis.
Read more in the User Guide.
The functional form of the G function used in the approximation to neg-entropy. Could be either ‘logcosh’, ‘exp’, or ‘cube’. You can also provide your own function. It should return a tuple containing the value of the function, and of its derivative, in the point. The derivative should be averaged along its last dimension. Example:
Estimated un-mixing matrix. The mixing matrix can be obtained by:
w = np.dot(W, K.T)
A = w.T * (w * w.T).I
The data matrix X is considered to be a linear combination of non-Gaussian (independent) components i.e. X = AS where columns of S contain the independent components and A is a linear mixing matrix. In short ICA attempts to un-mix’ the data by estimating an un-mixing matrix W where ``S = W K X.`
This implementation was originally made for data of shape [n_features, n_samples]. Now the input is transposed before the algorithm is applied. This makes it slightly faster for Fortran-ordered input.
Implemented using FastICA: A. Hyvarinen and E. Oja, Independent Component Analysis: Algorithms and Applications, Neural Networks, 13(4-5), 2000, pp. 411-430
Filler analyzer: Takes a TimeSeries object and returns a Float.
Bases: tvb.analyzers.metrics_base.BaseTimeseriesMetricAlgorithm
Return the Kuramoto synchronization index.
Useful metric for a parameter analysis when the collective brain dynamics represent coupled oscillatory processes.
The order parameters are \(r\) and \(Psi\).
\[r e^{i * \psi} = \frac{1}{N}\,\sum_{k=1}^N(e^{i*\theta_k})\]The first is the phase coherence of the population of oscillators (KSI) and the second is the average phase.
When \(r=0\) means 0 coherence among oscillators.
Input: TimeSeries DataType
Output: Float
This is a crude indicator of synchronization among nodes over the entire network.
#NOTE: For the time being it is meant to be another global metric. However, it should be consider to have a sort of TimeSeriesDatatype for this analyzer.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Filler analyzer: Takes a TimeSeries object and returns two Floats.
These metrics are described and used in:
Hellyer et al. The Control of Global Brain Dynamics: Opposing Actions of Frontoparietal Control and Default Mode Networks on Attention. The Journal of Neuroscience, January 8, 2014, 34(2):451– 461
Proxy of spatial coherence (V):
Proxy metastability (M): the variability in spatial coherence of the signal globally or locally (within a network) over time.
Proxy synchrony (S) : the reciprocal of mean spatial variance across time.
Bases: tvb.analyzers.metrics_base.BaseTimeseriesMetricAlgorithm
Subtract the mean time-series and compute.
Input: TimeSeries DataType
Output: Float, Float
The two metrics given by this analyzers are a proxy for metastability and synchrony. The underlying dynamical model used in the article was the Kuramoto model.
\[\begin{split}V(t) &= \frac{1}{N} \sum_{i=1}^{N} |S_i(t) - <S(t)>| \\ M(t) &= \sqrt{E[V(t)^{2}]-(E[V(t)])^{2}} \\ S(t) &= \frac{1}{\bar{V(t)}}\end{split}\]
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Filler analyzer: Takes a TimeSeries object and returns a Float.
Bases: tvb.analyzers.metrics_base.BaseTimeseriesMetricAlgorithm
Zero-centres all the time-series and then calculates the variance over all data points.
Input: TimeSeries DataType
Output: Float
This is a crude indicator of “excitability” or oscillation amplitude of the models over the entire network.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Filler analyzer: Takes a TimeSeries object and returns a Float.
Bases: tvb.analyzers.metrics_base.BaseTimeseriesMetricAlgorithm
Zero-centres all the time-series, calculates the variance for each node time-series and returns the variance of the node variances.
Input: TimeSeries DataType
Output: Float
This is a crude indicator of how different the “excitability” of the model is from node to node.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Bases: tvb.basic.neotraits._core.HasTraits
This is a base class for all metrics on timeSeries dataTypes. Metric means an algorithm computing a single value for an entire TimeSeries.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
This method needs to be implemented in each subclass. Will describe current algorithm.
| Returns: | single numeric value or a dictionary (displayLabel: numeric value) to be persisted. |
|---|
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
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.
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.
Compute cross coherence between all nodes in a time series.
Bases: tvb.basic.neotraits._core.HasTraits
Adapter for cross-coherence algorithm(s)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Returns the storage size in Bytes of the extended result of the FFT. That is, it includes storage of the evaluated FourierSpectrum attributes such as power, phase, amplitude, etc.
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
Returns the storage size in Bytes of the main result of NodeCoherence.
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.
Calculate the cross spectrum and complex coherence on a TimeSeries datatype and return a ComplexCoherence datatype.
Bases: tvb.basic.neotraits._core.HasTraits
A class for calculating the FFT of a TimeSeries and returning a ComplexCoherenceSpectrum datatype.
- This algorithm is based on the matlab function data2cs_event.m written by Guido Nolte:
[Freyer_2012] Freyer, F.; Reinacher, M.; Nolte, G.; Dinse, H. R. and Ritter, P. Repetitive tactile stimulation changes resting-state functional connectivity-implications for treatment of sensorimotor decline. Front Hum Neurosci, Bernstein Focus State Dependencies of Learning and Bernstein Center for Computational Neuroscience Berlin, Germany., 2012, 6, 144 Input: originally the input could be 2D (tpts x nodes/channels), and it was possible to give a 3D array (e.g., tpspt x nodes/cahnnels x trials) via the segment_length attribute. Current TVB implementation can handle 4D or 2D TimeSeries datatypes. Be warned: the 4D TimeSeries will be averaged and squeezed.
Output: (main arrays) - the cross-spectrum - the complex coherence, from which the imaginary part can be extracted
By default the time series is segmented into 1 second epoch blocks and 0.5 second 50% overlapping segments to which a Hanning function is applied.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
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.
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.
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.
Calculate the FFT, Cross Coherence and Complex Coherence of time_series broken into (possibly) epochs and segments of length epoch_length and segment_length respectively, filtered by window_function.
Returns the storage size in Bytes of the extended result of the ComplexCoherence. That is, it includes storage of the evaluated ComplexCoherence attributes such as ...
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.
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.
Returns the shape of the main result and the average over epochs
Returns the storage size in Bytes of the main result (complex array) of the ComplexCoherence
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.
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.
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.
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.
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.
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
Perform Principal Component Analysis (PCA) on a TimeSeries datatype and return a PrincipalComponents datatype.
Bases: tvb.basic.neotraits._core.HasTraits
Return principal component weights and the fraction of the variance that they explain.
PCA takes time-points as observations and nodes as variables.
- NOTE: The TimeSeries must be longer(more time-points) than the number of
- nodes – Mostly a problem for TimeSeriesSurface datatypes, which, if sampled at 1024Hz, would need to be greater than 16 seconds long.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Returns the storage size in Bytes of the extended result of the PCA. That is, it includes storage of the evaluated PrincipleComponents attributes such as norm_source, component_time_series, etc.
Returns the shape of the main result of the PCA analysis – compnnent weights matrix and a vector of fractions.
Returns the storage size in Bytes of the results of the PCA analysis.
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.
Calculate a wavelet transform on a TimeSeries datatype and return a WaveletSpectrum datatype.
Bases: tvb.basic.neotraits._core.HasTraits
A class for calculating the wavelet transform of a TimeSeries object of TVB and returning a WaveletSpectrum object. The sampling period and frequency range of the result can be specified. The mother wavelet can also be specified... (So far, only Morlet.)
- References:
[TBetal_1996] C. Tallon-Baudry et al, Stimulus Specificity of Phase-Locked and Non-Phase-Locked 40 Hz Visual Responses in Human., J Neurosci 16(13):4240-4249, 1996.
[Mallat_1999] S. Mallat, A wavelet tour of signal processing., book, Academic Press, 1999.
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Returns the storage size in Bytes of the extended result of the continuous wavelet transform. That is, it includes storage of the evaluated WaveletCoefficients attributes such as power, phase, amplitude, etc.
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.
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.
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.
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.
Returns the shape of the main result (complex array) of the continuous wavelet transform.
Returns the storage size in Bytes of the main result (complex array) of the continuous wavelet transform.
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.
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.