class_docstr: Calculate a simple Activation Count Maps
  
  Adapted from: https://github.com/poldracklab/CNP_task_analysis/    blob/61c27f5992db9d8800884f8ffceb73e6957db8af/CNP_2nd_level_ACM.py
code: def _run_interface(self, runtime):
      allmaps = nb.concat_images(self.inputs.in_files).dataobj
      acm_pos = np.mean(
          allmaps > self.inputs.threshold, axis=3, dtype=np.float32
      )
      acm_neg = np.mean(
          allmaps < -1.0 * self.inputs.threshold,
          axis=3,
          dtype=np.float32,
      )
      acm_diff = acm_pos - acm_neg
  
      template_fname = self.inputs.in_files[0]
      ext = split_filename(template_fname)[2]
      fname_fmt = os.path.join(runtime.cwd, "acm_{}" + ext).format
  
      self._results["out_file"] = fname_fmt("diff")
      self._results["acm_pos"] = fname_fmt("pos")
      self._results["acm_neg"] = fname_fmt("neg")
  
      img = nb.load(template_fname)
      img.__class__(acm_diff, img.affine, img.header).to_filename(
          self._results["out_file"]
      )
      img.__class__(acm_pos, img.affine, img.header).to_filename(
          self._results["acm_pos"]
      )
      img.__class__(acm_neg, img.affine, img.header).to_filename(
          self._results["acm_neg"]
      )
  
      return runtime
  
file_path: nipype/nipype/algorithms/stats.py
imports: import os
  import nibabel
  import numpy
  from ..interfaces.base import BaseInterfaceInputSpec, TraitedSpec, SimpleInterface, traits, InputMultiPath, File
  from ..utils.filemanip import split_filename
label_desc: Use image node_id 1 for calling the ActivationCount obj's underlying member method code with example usage: obj._run_interface(runtime) and returns: runtime