Multivariate Pattern Analysis in Python

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# mvpa2.mappers.filters.FFTResampleMapper

class `mvpa2.mappers.filters.``FFTResampleMapper`(num, window=None, chunks_attr=None, position_attr=None, attr_strategy='remove', **kwargs)

  Mapper for FFT-based resampling.

  Can do per-chunk.

  Supports positional information of samples and outputs them as sample attribute. however, only meaningful for data with equally spaced sampling points.

  Pretty much Mapper frontend for scipy.signal.resample

  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.
  - `training_time+`: Time (in seconds) it took to train the learner

  (Conditional attributes enabled by default suffixed with `+`)

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

  Parameters:

  num : int

  > Number of output samples. If operating on chunks, this is the number of samples per chunk.

  window : str or float or tuple

  > Passed to scipy.signal.resample

  chunks_attr : str or None

  > If not None, this samples attribute defines chunks that will be resampled individually.

  position_attr : str

  > A samples attribute with positional information that is passed to scipy.signal.resample. If not None, the output dataset will also contain a sample attribute of this name, with updated positional information (this is, however, only meaningful for equally spaced samples).

  attr_strategy : {‘remove’, ‘sample’, ‘resample’}

  > Strategy to process sample attributes during mapping. ‘remove’ will cause all sample attributes to be removed. ‘sample’ will pick orginal attribute values matching the new resampling frequency (e.g. every 10th), and ‘resample’ will also apply the actual data resampling procedure to the attributes as well (which might not be possible, e.g. for literal attributes).

  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

  auto_train : bool

  > Flag whether the learner will automatically train itself on the input dataset when called untrained.

  force_train : bool

  > Flag whether the learner will enforce training on the input dataset upon every call.

  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

  Methods

  `forward`(data) | Map data from input to output space.
  `forward1`(data) | Wrapper method to map single samples.
  `generate`(ds) | Yield processing results.
  `get_postproc`() | Returns the post-processing node or None.
  `get_space`() | Query the processing space name of this node.
  `reset`() |
  `reverse`(data) | Reverse-map data from output back into input space.
  `reverse1`(data) | Wrapper method to map single samples.
  `set_postproc`(node) | Assigns a post-processing node Set to `None` to disable postprocessing.
  `set_space`(name) | Set the processing space name of this node.
  `train`(ds) | The default implementation calls `_pretrain()`, `_train()`, and finally `_posttrain()`.
  `untrain`() | Reverts changes in the state of this node caused by previous training

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