docstr: Compute the signal-to-noise ratio in the corpus callosum.
  
  Parameters
  ----------
  data_files : string
      Path to the dwi.nii.gz file. This path may contain wildcards to
      process multiple inputs at once.
  bvals_files : string
      Path of bvals.
  bvecs_files : string
      Path of bvecs.
  mask_file : string
      Path of a brain mask file.
  bbox_threshold : variable float, optional
      Threshold for bounding box, values separated with commas for ex.
      [0.6,1,0,0.1,0,0.1].
  out_dir : string, optional
      Where the resulting file will be saved. (default current directory)
  out_file : string, optional
      Name of the result file to be saved.
  out_mask_cc : string, optional
      Name of the CC mask volume to be saved.
  out_mask_noise : string, optional
      Name of the mask noise volume to be saved.
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code: def run(
      self,
      data_files,
      bvals_files,
      bvecs_files,
      mask_file,
      bbox_threshold=(0.6, 1, 0, 0.1, 0, 0.1),
      out_dir="",
      out_file="product.json",
      out_mask_cc="cc.nii.gz",
      out_mask_noise="mask_noise.nii.gz",
  ):
      """Compute the signal-to-noise ratio in the corpus callosum.
  
      Parameters
      ----------
      data_files : string
          Path to the dwi.nii.gz file. This path may contain wildcards to
          process multiple inputs at once.
      bvals_files : string
          Path of bvals.
      bvecs_files : string
          Path of bvecs.
      mask_file : string
          Path of a brain mask file.
      bbox_threshold : variable float, optional
          Threshold for bounding box, values separated with commas for ex.
          [0.6,1,0,0.1,0,0.1].
      out_dir : string, optional
          Where the resulting file will be saved. (default current directory)
      out_file : string, optional
          Name of the result file to be saved.
      out_mask_cc : string, optional
          Name of the CC mask volume to be saved.
      out_mask_noise : string, optional
          Name of the mask noise volume to be saved.
  
      """
      io_it = self.get_io_iterator()
  
      for (
          dwi_path,
          bvals_path,
          bvecs_path,
          mask_path,
          out_path,
          cc_mask_path,
          mask_noise_path,
      ) in io_it:
          data, affine = load_nifti(dwi_path)
          bvals, bvecs = read_bvals_bvecs(bvals_path, bvecs_path)
          gtab = gradient_table(bvals=bvals, bvecs=bvecs)
  
          mask, affine = load_nifti(mask_path)
  
          logging.info("Computing tensors...")
          tenmodel = TensorModel(gtab)
          tensorfit = tenmodel.fit(data, mask=mask)
  
          logging.info(
              "Computing worst-case/best-case SNR using the CC..."
          )
  
          if np.ndim(data) == 4:
              CC_box = np.zeros_like(data[..., 0])
          elif np.ndim(data) == 3:
              CC_box = np.zeros_like(data)
          else:
              raise OSError("DWI data has invalid dimensions")
  
          mins, maxs = bounding_box(mask)
          mins = np.array(mins)
          maxs = np.array(maxs)
          diff = (maxs - mins) // 4
          bounds_min = mins + diff
          bounds_max = maxs - diff
  
          CC_box[
              bounds_min[0] : bounds_max[0],
              bounds_min[1] : bounds_max[1],
              bounds_min[2] : bounds_max[2],
          ] = 1
  
          if len(bbox_threshold) != 6:
              raise OSError("bbox_threshold should have 6 float values")
  
          mask_cc_part, cfa = segment_from_cfa(
              tensorfit, CC_box, bbox_threshold, return_cfa=True
          )
  
          if not np.count_nonzero(mask_cc_part.astype(np.uint8)):
              logging.warning(
                  "Empty mask: corpus callosum not found."
                  " Update your data or your threshold"
              )
  
          save_nifti(
              cc_mask_path, mask_cc_part.astype(np.uint8), affine
          )
          logging.info("CC mask saved as {0}".format(cc_mask_path))
  
          masked_data = data[mask_cc_part]
          mean_signal = 0
          if masked_data.size:
              mean_signal = np.mean(masked_data, axis=0)
          mask_noise = binary_dilation(mask, iterations=10)
          mask_noise[..., : mask_noise.shape[-1] // 2] = 1
          mask_noise = ~mask_noise
  
          save_nifti(
              mask_noise_path, mask_noise.astype(np.uint8), affine
          )
          logging.info(
              "Mask noise saved as {0}".format(mask_noise_path)
          )
  
          noise_std = 0
          if np.count_nonzero(mask_noise.astype(np.uint8)):
              noise_std = np.std(data[mask_noise, :])
  
          logging.info(
              "Noise standard deviation sigma= " + str(noise_std)
          )
  
          idx = np.sum(gtab.bvecs, axis=-1) == 0
          gtab.bvecs[idx] = np.inf
          axis_X = np.argmin(
              np.sum((gtab.bvecs - np.array([1, 0, 0])) ** 2, axis=-1)
          )
          axis_Y = np.argmin(
              np.sum((gtab.bvecs - np.array([0, 1, 0])) ** 2, axis=-1)
          )
          axis_Z = np.argmin(
              np.sum((gtab.bvecs - np.array([0, 0, 1])) ** 2, axis=-1)
          )
  
          SNR_output = []
          SNR_directions = []
          for direction in ["b0", axis_X, axis_Y, axis_Z]:
              if direction == "b0":
                  SNR = mean_signal[0] / noise_std if noise_std else 0
                  logging.info("SNR for the b=0 image is :" + str(SNR))
              else:
                  logging.info(
                      "SNR for direction "
                      + str(direction)
                      + " "
                      + str(gtab.bvecs[direction])
                      + "is :"
                      + str(SNR)
                  )
                  SNR_directions.append(direction)
                  SNR = (
                      mean_signal[direction] / noise_std
                      if noise_std
                      else 0
                  )
              SNR_output.append(SNR)
  
          data = [
              {
                  "data": str(SNR_output[0])
                  + " "
                  + str(SNR_output[1])
                  + " "
                  + str(SNR_output[2])
                  + " "
                  + str(SNR_output[3]),
                  "directions": "b0"
                  + " "
                  + str(SNR_directions[0])
                  + " "
                  + str(SNR_directions[1])
                  + " "
                  + str(SNR_directions[2]),
              }
          ]
  
          with open(os.path.join(out_dir, out_path), "w") as myfile:
              json.dump(data, myfile)
  
imports: import logging
  import numpy
  import os
  import json
  import warnings
  from time import time
  from scipy.ndimage import binary_dilation
  from dipy.utils.optpkg import optional_package
  from dipy.io import read_bvals_bvecs
  from dipy.io.image import load_nifti, save_nifti
  from dipy.core.gradients import gradient_table
  from dipy.reconst.dti import TensorModel
  from dipy.io.peaks import load_peaks
  from dipy.io.streamline import load_tractogram
  from dipy.segment.mask import segment_from_cfa
  from dipy.segment.mask import bounding_box
  from dipy.tracking.streamline import transform_streamlines
  from glob import glob
  from dipy.workflows.workflow import Workflow
  from dipy.segment.bundles import bundle_shape_similarity
  from dipy.stats.analysis import assignment_map
  from dipy.stats.analysis import anatomical_measures
  from dipy.stats.analysis import peak_values
label_desc: Use image node_id 2 for calling the SNRinCCFlow obj's underlying member method code with example usage: obj.run(data_files, bvals_files, bvecs_files, mask_file, bbox_threshold, out_dir, out_file, out_mask_cc, out_mask_noise) without return types