Node: workflow_enumerator.funcMRIQC.ReportsWorkflow.SpikesMask
Working directory: /pasteur/projets/policy01/cinq/rto/data/abide2all/src/work/workflow_enumerator/funcMRIQC/ReportsWorkflow/_in_file_..pasteur..projets..policy01..cinq..rto..data..abide2all..data..RawData..ABIDEII-OHSU_1..sub-28947..ses-1..func..sub-28947_ses-1_task-rest_run-1_bold.nii.gz/SpikesMask

Node inputs:

function_str = def spikes_mask(in_file, in_mask=None, out_file=None):
    """
    Utility function to calculate a mask in which check
    for :abbr:`EM (electromagnetic)` spikes.
    """

    import os.path as op
    import nibabel as nb
    import numpy as np
    from nilearn.image import mean_img
    from nilearn.plotting import plot_roi
    from scipy import ndimage as nd

    if out_file is None:
        fname, ext = op.splitext(op.basename(in_file))
        if ext == '.gz':
            fname, ext2 = op.splitext(fname)
            ext = ext2 + ext
        out_file = op.abspath('{}_spmask{}'.format(fname, ext))
        out_plot = op.abspath('{}_spmask.pdf'.format(fname))

    in_4d_nii = nb.load(in_file)
    orientation = nb.aff2axcodes(in_4d_nii.affine)

    if in_mask:
        mask_data = nb.load(in_mask).get_data()
        a = np.where(mask_data != 0)
        bbox = np.max(a[0]) - np.min(a[0]), np.max(a[1]) - \
            np.min(a[1]), np.max(a[2]) - np.min(a[2])
        longest_axis = np.argmax(bbox)

        # Input here is a binarized and intersected mask data from previous section
        dil_mask = nd.binary_dilation(
            mask_data, iterations=int(mask_data.shape[longest_axis] / 9))

        rep = list(mask_data.shape)
        rep[longest_axis] = -1
        new_mask_2d = dil_mask.max(axis=longest_axis).reshape(rep)

        rep = [1, 1, 1]
        rep[longest_axis] = mask_data.shape[longest_axis]
        new_mask_3d = np.logical_not(np.tile(new_mask_2d, rep))
    else:
        new_mask_3d = np.zeros(in_4d_nii.shape[:3]) == 1

    if orientation[0] in ['L', 'R']:
        new_mask_3d[0:2, :, :] = True
        new_mask_3d[-3:-1, :, :] = True
    else:
        new_mask_3d[:, 0:2, :] = True
        new_mask_3d[:, -3:-1, :] = True

    mask_nii = nb.Nifti1Image(new_mask_3d.astype(np.uint8), in_4d_nii.affine,
                              in_4d_nii.header)
    mask_nii.to_filename(out_file)

    plot_roi(mask_nii, mean_img(in_4d_nii), output_file=out_plot)
    return out_file, out_plot

in_file = <undefined>
in_mask = <undefined>

Traceback (most recent call last):
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/plugins/linear.py", line 48, in run
    node.run(updatehash=updatehash)
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 472, in run
    result = self._run_interface(execute=True)
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 563, in _run_interface
    return self._run_command(execute)
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/pipeline/engine/nodes.py", line 643, in _run_command
    result = self._interface.run(cwd=outdir)
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/base/core.py", line 375, in run
    runtime = self._run_interface(runtime)
  File "/usr/local/miniconda/lib/python3.7/site-packages/nipype/interfaces/utility/wrappers.py", line 144, in _run_interface
    out = function_handle(**args)
TypeError: spikes_mask() missing 1 required positional argument: 'in_file'