mvpa2.base.node.ChainNode¶
- class mvpa2.base.node.ChainNode(nodes, **kwargs)¶
Chain of nodes.
This class allows to concatenate a list of nodes into a processing chain. When called with a dataset, it is sequentially fed through a nodes in the chain. A ChainNode may also be used as a generator. In this case, all nodes in the chain are treated as generators too, and the ChainNode behaves as a single big generator that recursively calls all embedded generators and yield the results.
A ChainNode behaves similar to a list container: Nodes can be appended, and the chain can be sliced like a list, etc ...
Notes
Available conditional attributes:
- calling_time+: Time (in seconds) it took to call the node
- raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
(Conditional attributes enabled by default suffixed with +)
Parameters : nodes: list :
Node instances.
enable_ca : None or list of str
Names of the conditional attributes which should be enabled in addition to the default ones
disable_ca : None or list of str
Names of the conditional attributes which should be disabled
space: str, optional :
Name of the ‘processing space’. The actual meaning of this argument heavily depends on the sub-class implementation. In general, this is a trigger that tells the node to compute and store information about the input data that is “interesting” in the context of the corresponding processing in the output dataset.
postproc : Node instance, optional
Node to perform post-processing of results. This node is applied in __call__() to perform a final processing step on the to be result dataset. If None, nothing is done.
descr : str
Description of the instance
- append(node)¶
Append a node to the chain.
- generate(ds, startnode=0)¶
Parameters : ds: Dataset :
To be processed dataset
startnode: int :
First node in the chain that shall be considered. This argument is mostly useful for internal optimization.
- nodes¶
