This is the module where all TVB Analyzers are hooked into the framework.
Define in __all__ attribute, modules to be introspected for finding adapters.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
Interface between Brain Connectivity Toolbox of Olaf Sporns and TVB Framework. This adapter requires BCT deployed locally, and Matlab or Octave installed separately of TVB.
Bases: tvb.core.neotraits.view_model.ViewModel
connectivity : tvb.adapters.analyzers.bct_adapters.BaseBCTModel.connectivity = DataTypeGidAttr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Keep a GID but also link the type of DataType it should point to
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeBinary
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeWeighted
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeBinary
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeBinary
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeBinary
Bases: tvb.adapters.analyzers.bct_centrality_adapters.ParticipationCoefficient
Bases: tvb.adapters.analyzers.bct_centrality_adapters.CentralityNodeBinary
Bases: tvb.adapters.analyzers.bct_clustering_adapters.ClusteringCoefficient
Bases: tvb.adapters.analyzers.bct_clustering_adapters.TransitivityBinaryDirected
Bases: tvb.adapters.analyzers.bct_clustering_adapters.TransitivityBinaryUnDirected
Bases: tvb.adapters.analyzers.bct_degree_adapters.DensityDirected
Adapter that uses the traits module to generate interfaces for ... Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the CrossCorrelate algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Returns the required memory to be able to run the adapter.
Launch algorithm and build results. Compute the node-pairwise cross-correlation of the source 4D TimeSeries represented by the index given as input.
Return a CrossCorrelationIndex. Create a CrossCorrelationH5 that contains the cross-correlation sequences for all possible combinations of the nodes.
See: http://www.scipy.org/doc/api_docs/SciPy.signal.signaltools.html#correlate
Parameters: | view_model – the ViewModel keeping the algorithm inputs |
---|---|
Returns: | the cross correlation index for the given time series |
Return type: | CrossCorrelationIndex |
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Keep a GID but also link the type of DataType it should point to
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the Pearson correlation coefficients algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Returns the required memory to be able to run this adapter.
Launch algorithm and build results. Compute the node-pairwise pearson correlation coefficient of the given input 4D TimeSeries datatype.
The result will contain values between -1 and 1, inclusive.
Parameters: | view_model – the ViewModel keeping the algorithm inputs |
---|---|
Returns: | the correlation coefficient for the given time series |
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits model to generate interfaces for FCD Analyzer.
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the Pearson CrossCorrelation algorithm.
The present class will do the following actions:
the time series is divided in time window of fixed length and with an overlapping of fixed length. The data-points 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) -in a vector
(length of time during which FC matrix are high correlated).
The algorithm can produce 2 kind of results:
– fcs calculated over the epochs of stability (excluded the first one = artifact, due to initial conditions) – 3 eigenvectors, associated to the 3 largest eigenvalues, of the fcs are extracted
– fc over the all time series is calculated – 3 first eigenvectors, associated to the 3 largest eigenvalues, of the fcs are extracted
fcd matrix whose values are between -1 and 1, inclusive.
(Value=1.1 for time not belonging to epochs of stability identified with spectral embedding algorithm) in case 2: fcd matrix segmented identical to the fcd matrix not segmented
dictionary containing the eigenvectors.
dictionary containing the eigenvalues
connectivity associated to the TimeSeriesRegions
Adapter that uses the traits module to generate interfaces for BalloonModel Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the BalloonModel algorithm.
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
Bases: tvb.core.neotraits.view_model.ViewModel
Parameters have the following meaning: - time_series: the input time series to which the fft is to be applied - segment_length: the block size which determines the frequency resolution of the resulting power spectra - window_function: windowing functions can be applied before the FFT is performed - detrend: None; specify if detrend is performed on the time series
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to
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.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the FFT algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Returns the required memory to be able to run the adapter.
Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the fourier spectrum for the specified time series
Adapter that uses the traits module to generate interfaces for ICA Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the ICA algorithm.
Store the input shape to be later used to estimate memory usage. Also create the algorithm instance.
Returns the required disk size to be able to run the adapter (in kB).
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
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
Keep a GID but also link the type of DataType it should point to
This module implements a class for executing arbitray MATLAB code
Conversion between Python types and MATLAB types is handled and dependent on scipy.io’s loadmat and savemat function.
Bases: builtins.object
MatlabAnalyzer is an helper class for calling arbitrary MATLAB code with arbitrary parameters.
Specific analyzers should derive from this class and implement the interface and launch methods inherited from Asynchronous Adapter.
Add a path to the list of paths that will be added to the path in the MATLAB session
method matlab takes as arguments:
code: MATLAB code in a string data: a dict of data that scipy.io.savemat knows how to deal with work_dir: working directory to be used by MATLAB cleanup: set to False to keep files
and returns a tuple:
[0] string of code exec’d by MATLAB [1] string of log produced by MATLAB [2] dict of data from MATLAB’s workspace
Adapter that uses the traits module to generate interfaces for group of Analyzer used to calculate a single measure for TimeSeries.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for exposing as a group the measure algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
The attribute is a list of values. Choices and type are reinterpreted as applying not to the list but to the elements of it
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.
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the NodeCoherence algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the node coherence for the specified time series
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
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
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the NodeComplexCoherence algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to
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
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the NodeCovariance algorithm.
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits module to generate interfaces for FFT Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the PCA algorithm.
Returns the required disk size to be able to run the adapter (in kB).
Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the PrincipalComponentsIndex object built with the given timeseries as source
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
gid : tvb.basic.neotraits._core.HasTraits.gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=True)
Keep a GID but also link the type of DataType it should point to
Adapter that uses the traits module to generate interfaces for ContinuousWaveletTransform Analyzer.
Bases: tvb.core.adapters.abcadapter.ABCAdapter
TVB adapter for calling the ContinuousWaveletTransform algorithm.
Returns the required disk size to be able to run the adapter.(in kB)
Launch algorithm and build results. :param view_model: the ViewModel keeping the algorithm inputs :return: the wavelet coefficients for the specified time series
Bases: tvb.core.neotraits.view_model.ViewModel
operation_group_gid : tvb.core.neotraits.view_model.ViewModel.operation_group_gid = Attr(field_type=<class ‘uuid.UUID’>, default=None, required=False)
ranges : tvb.core.neotraits.view_model.ViewModel.ranges = Attr(field_type=<class ‘str’>, default=None, required=False)
range_values : tvb.core.neotraits.view_model.ViewModel.range_values = Attr(field_type=<class ‘str’>, default=None, required=False)
is_metric_operation : tvb.core.neotraits.view_model.ViewModel.is_metric_operation = Attr(field_type=<class ‘bool’>, default=False, required=True)
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.
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.
Keep a GID but also link the type of DataType it should point to