Esempio n. 1
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def test_Segment_inputs():
    input_map = dict(ignore_exception=dict(nohash=True,
    usedefault=True,
    ),
    paths=dict(),
    clean_masks=dict(field='output.cleanup',
    ),
    bias_fwhm=dict(field='opts.biasfwhm',
    ),
    mask_image=dict(field='opts.msk',
    ),
    bias_regularization=dict(field='opts.biasreg',
    ),
    warp_frequency_cutoff=dict(field='opts.warpco',
    ),
    affine_regularization=dict(field='opts.regtype',
    ),
    use_v8struct=dict(min_ver='8',
    usedefault=True,
    ),
    warping_regularization=dict(field='opts.warpreg',
    ),
    use_mcr=dict(),
    gm_output_type=dict(field='output.GM',
    ),
    tissue_prob_maps=dict(field='opts.tpm',
    ),
    sampling_distance=dict(field='opts.samp',
    ),
    matlab_cmd=dict(),
    gaussians_per_class=dict(field='opts.ngaus',
    ),
    mfile=dict(usedefault=True,
    ),
    save_bias_corrected=dict(field='output.biascor',
    ),
    data=dict(copyfile=False,
    mandatory=True,
    field='data',
    ),
    wm_output_type=dict(field='output.WM',
    ),
    csf_output_type=dict(field='output.CSF',
    ),
    )
    inputs = Segment.input_spec()

    for key, metadata in input_map.items():
        for metakey, value in metadata.items():
            yield assert_equal, getattr(inputs.traits()[key], metakey), value
Esempio n. 2
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def test_Segment_inputs():
    input_map = dict(
        affine_regularization=dict(field='opts.regtype', ),
        bias_fwhm=dict(field='opts.biasfwhm', ),
        bias_regularization=dict(field='opts.biasreg', ),
        clean_masks=dict(field='output.cleanup', ),
        csf_output_type=dict(field='output.CSF', ),
        data=dict(
            copyfile=False,
            field='data',
            mandatory=True,
        ),
        gaussians_per_class=dict(field='opts.ngaus', ),
        gm_output_type=dict(field='output.GM', ),
        ignore_exception=dict(
            nohash=True,
            usedefault=True,
        ),
        mask_image=dict(field='opts.msk', ),
        matlab_cmd=dict(),
        mfile=dict(usedefault=True, ),
        paths=dict(),
        sampling_distance=dict(field='opts.samp', ),
        save_bias_corrected=dict(field='output.biascor', ),
        tissue_prob_maps=dict(field='opts.tpm', ),
        use_mcr=dict(),
        use_v8struct=dict(
            min_ver='8',
            usedefault=True,
        ),
        warp_frequency_cutoff=dict(field='opts.warpco', ),
        warping_regularization=dict(field='opts.warpreg', ),
        wm_output_type=dict(field='output.WM', ),
    )
    inputs = Segment.input_spec()

    for key, metadata in input_map.items():
        for metakey, value in metadata.items():
            yield assert_equal, getattr(inputs.traits()[key], metakey), value
Esempio n. 3
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def test_Segment_outputs():
    output_map = dict(bias_corrected_image=dict(),
    native_csf_image=dict(),
    normalized_wm_image=dict(),
    modulated_wm_image=dict(),
    modulated_input_image=dict(new_name='bias_corrected_image',
    deprecated='0.10',
    ),
    native_wm_image=dict(),
    inverse_transformation_mat=dict(),
    transformation_mat=dict(),
    normalized_csf_image=dict(),
    modulated_gm_image=dict(),
    modulated_csf_image=dict(),
    native_gm_image=dict(),
    normalized_gm_image=dict(),
    )
    outputs = Segment.output_spec()

    for key, metadata in output_map.items():
        for metakey, value in metadata.items():
            yield assert_equal, getattr(outputs.traits()[key], metakey), value
Esempio n. 4
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def test_Segment_outputs():
    output_map = dict(
        bias_corrected_image=dict(),
        inverse_transformation_mat=dict(),
        modulated_csf_image=dict(),
        modulated_gm_image=dict(),
        modulated_input_image=dict(
            deprecated='0.10',
            new_name='bias_corrected_image',
        ),
        modulated_wm_image=dict(),
        native_csf_image=dict(),
        native_gm_image=dict(),
        native_wm_image=dict(),
        normalized_csf_image=dict(),
        normalized_gm_image=dict(),
        normalized_wm_image=dict(),
        transformation_mat=dict(),
    )
    outputs = Segment.output_spec()

    for key, metadata in output_map.items():
        for metakey, value in metadata.items():
            yield assert_equal, getattr(outputs.traits()[key], metakey), value