Home · Quickstart · Documentation · About · Nipy

# interfaces.fsl.fix

## Classifier

Link to code

Wraps command None

Classify ICA components using a specific training dataset (<thresh> is in the range 0-100, typically 5-20).

Inputs:

    [Mandatory]
    thresh: (an integer (int or long))
            Threshold for cleanup.
            flag: %d, position: -1
    trained_wts_file: (an existing file name)
            trained-weights file
            flag: %s, position: 2

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    artifacts_list_file: (a file name)
            Text file listing which ICs are artifacts; can be the output from
            classification or can be created manually
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mel_ica: (an existing directory name)
            Melodic output directory or directories
            flag: %s, position: 1
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    artifacts_list_file: (a file name)
            Text file listing which ICs are artifacts; can be the output from
            classification or can be created manually

## Cleaner

Link to code

Wraps command None

Extract features (for later training and/or classifying)

Inputs:

    [Mandatory]
    artifacts_list_file: (an existing file name)
            Text file listing which ICs are artifacts; can be the output from
            classification or can be created manually
            flag: %s, position: 1

    [Optional]
    aggressive: (a boolean)
            Apply aggressive (full variance) cleanup, instead of the default
            less-aggressive (unique variance) cleanup.
            flag: -A, position: 3
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    cleanup_motion: (a boolean)
            cleanup motion confounds, looks for design.fsf for highpass filter
            cut-off
            flag: -m, position: 2
    confound_file: (a file name)
            Include additional confound file.
            flag: -x %s, position: 4
    confound_file_1: (a file name)
            Include additional confound file.
            flag: -x %s, position: 5
    confound_file_2: (a file name)
            Include additional confound file.
            flag: -x %s, position: 6
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    highpass: (a float, nipype default value: 100)
            cleanup motion confounds
            flag: -m -h %f, position: 2
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    cleaned_functional_file: (an existing file name)
            Cleaned session data

## FeatureExtractor

Link to code

Wraps command None

Extract features (for later training and/or classifying)

Inputs:

    [Mandatory]

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mel_ica: (an existing directory name)
            Melodic output directory or directories
            flag: %s, position: -1
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored

Outputs:

    mel_ica: (an existing directory name)
            Melodic output directory or directories
            flag: %s, position: -1

## Training

Link to code

Wraps command None

Train the classifier based on your own FEAT/MELODIC output directory.

Inputs:

    [Mandatory]

    [Optional]
    args: (a unicode string)
            Additional parameters to the command
            flag: %s
    environ: (a dictionary with keys which are a newbytes or None or a
             newstr or None and with values which are a newbytes or None or a
             newstr or None, nipype default value: {})
            Environment variables
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    loo: (a boolean)
            full leave-one-out test with classifier training
            flag: -l, position: 2
    mel_icas: (a list of items which are an existing directory name)
            Melodic output directories
            flag: %s, position: -1
    terminal_output: (u'stream' or u'allatonce' or u'file' or u'none')
            Control terminal output: `stream` - displays to terminal immediately
            (default), `allatonce` - waits till command is finished to display
            output, `file` - writes output to file, `none` - output is ignored
    trained_wts_filestem: (a unicode string)
            trained-weights filestem, used for trained_wts_file and output
            directories
            flag: %s, position: 1

Outputs:

    trained_wts_file: (an existing file name)
            Trained-weights file

## TrainingSetCreator

Link to code

Goes through set of provided melodic output directories, to find all the ones that have a hand_labels_noise.txt file in them.

This is outsourced as a separate class, so that the pipeline is rerun everytime a handlabeled file has been changed, or a new one created.

Inputs:

    [Mandatory]

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the
            interface fails to run
    mel_icas_in: (a list of items which are an existing directory name)
            Melodic output directories
            flag: %s, position: -1

Outputs:

    mel_icas_out: (a list of items which are an existing directory name)
            Hand labels for noise vs signal
            flag: %s, position: -1