code: def patch2self(
      data,
      bvals,
      patch_radius=(0, 0, 0),
      model="ols",
      b0_threshold=50,
      out_dtype=None,
      alpha=1.0,
      verbose=False,
      b0_denoising=True,
      clip_negative_vals=False,
      shift_intensity=True,
  ):
      """Patch2Self Denoiser.
  
      Parameters
      ----------
      data : ndarray
          The 4D noisy DWI data to be denoised.
  
      bvals : 1D array
          Array of the bvals from the DWI acquisition
  
      patch_radius : int or 1D array, optional
          The radius of the local patch to be taken around each voxel (in
          voxels). Default: 0 (denoise in blocks of 1x1x1 voxels).
  
      model : string, or initialized linear model object.
              This will determine the algorithm used to solve the set of linear
              equations underlying this model. If it is a string it needs to be
              one of the following: {'ols', 'ridge', 'lasso'}. Otherwise,
              it can be an object that inherits from
              `dipy.optimize.SKLearnLinearSolver` or an object with a similar
              interface from Scikit-Learn:
              `sklearn.linear_model.LinearRegression`,
              `sklearn.linear_model.Lasso` or `sklearn.linear_model.Ridge`
              and other objects that inherit from `sklearn.base.RegressorMixin`.
              Default: 'ols'.
  
      b0_threshold : int, optional
          Threshold for considering volumes as b0.
  
      out_dtype : str or dtype, optional
          The dtype for the output array. Default: output has the same dtype as
          the input.
  
      alpha : float, optional
          Regularization parameter only for ridge regression model.
  
      verbose : bool, optional
          Show progress of Patch2Self and time taken.
  
      b0_denoising : bool, optional
          Skips denoising b0 volumes if set to False.
  
      clip_negative_vals : bool, optional
          Sets negative values after denoising to 0 using `np.clip`.
  
      shift_intensity : bool, optional
          Shifts the distribution of intensities per volume to give
          non-negative values
  
  
      Returns
      -------
      denoised array : ndarray
          This is the denoised array of the same size as that of the input data,
          clipped to non-negative values.
  
      References
      ----------
      [Fadnavis20] S. Fadnavis, J. Batson, E. Garyfallidis, Patch2Self:
                      Denoising Diffusion MRI with Self-supervised Learning,
                      Advances in Neural Information Processing Systems 33 (2020)
  
      """
      if isinstance(patch_radius, int):
          patch_radius = np.ones(3, dtype=int) * patch_radius
  
      patch_radius = np.asarray(patch_radius, dtype=int)
  
      if not data.ndim == 4:
          raise ValueError(
              "Patch2Self can only denoise on 4D arrays.", data.shape
          )
  
      if data.shape[3] < 10:
          warn(
              "The input data has less than 10 3D volumes. Patch2Self may not "
              "give denoising performance."
          )
  
      if out_dtype is None:
          out_dtype = data.dtype
  
      # We retain float64 precision, iff the input is in this precision:
      if data.dtype == np.float64:
          calc_dtype = np.float64
  
      # Otherwise, we'll calculate things in float32 (saving memory)
      else:
          calc_dtype = np.float32
  
      # Segregates volumes by b0 threshold
      b0_idx = np.argwhere(bvals <= b0_threshold)
      dwi_idx = np.argwhere(bvals > b0_threshold)
  
      data_b0s = np.squeeze(np.take(data, b0_idx, axis=3))
      data_dwi = np.squeeze(np.take(data, dwi_idx, axis=3))
  
      # create empty arrays
      denoised_b0s = np.empty(data_b0s.shape, dtype=calc_dtype)
      denoised_dwi = np.empty(data_dwi.shape, dtype=calc_dtype)
  
      denoised_arr = np.empty(data.shape, dtype=calc_dtype)
  
      if verbose is True:
          t1 = time.time()
  
      # if only 1 b0 volume, skip denoising it
      if data_b0s.ndim == 3 or not b0_denoising:
          if verbose:
              print("b0 denoising skipped...")
          denoised_b0s = data_b0s
  
      else:
          train_b0 = _extract_3d_patches(
              np.pad(
                  data_b0s,
                  (
                      (patch_radius[0], patch_radius[0]),
                      (patch_radius[1], patch_radius[1]),
                      (patch_radius[2], patch_radius[2]),
                      (0, 0),
                  ),
                  mode="constant",
              ),
              patch_radius=patch_radius,
          )
  
          for vol_idx in range(0, data_b0s.shape[3]):
              denoised_b0s[..., vol_idx] = _vol_denoise(
                  train_b0, vol_idx, model, data_b0s.shape, alpha=alpha
              )
  
              if verbose is True:
                  print("Denoised b0 Volume: ", vol_idx)
  
      # Separate denoising for DWI volumes
      train_dwi = _extract_3d_patches(
          np.pad(
              data_dwi,
              (
                  (patch_radius[0], patch_radius[0]),
                  (patch_radius[1], patch_radius[1]),
                  (patch_radius[2], patch_radius[2]),
                  (0, 0),
              ),
              mode="constant",
          ),
          patch_radius=patch_radius,
      )
  
      # Insert the separately denoised arrays into the respective empty arrays
      for vol_idx in range(0, data_dwi.shape[3]):
          denoised_dwi[..., vol_idx] = _vol_denoise(
              train_dwi, vol_idx, model, data_dwi.shape, alpha=alpha
          )
  
          if verbose is True:
              print("Denoised DWI Volume: ", vol_idx)
  
      if verbose is True:
          t2 = time.time()
          print(
              "Total time taken for Patch2Self: ", t2 - t1, " seconds"
          )
  
      if data_b0s.ndim == 3:
          denoised_arr[:, :, :, b0_idx[0][0]] = denoised_b0s
      else:
          for i, idx in enumerate(b0_idx):
              denoised_arr[:, :, :, idx[0]] = np.squeeze(
                  denoised_b0s[..., i]
              )
  
      for i, idx in enumerate(dwi_idx):
          denoised_arr[:, :, :, idx[0]] = np.squeeze(
              denoised_dwi[..., i]
          )
  
      # shift intensities per volume to handle for negative intensities
      if shift_intensity and not clip_negative_vals:
          for i in range(0, denoised_arr.shape[3]):
              shift = np.min(data[..., i]) - np.min(
                  denoised_arr[..., i]
              )
              denoised_arr[..., i] = denoised_arr[..., i] + shift
  
      # clip out the negative values from the denoised output
      elif clip_negative_vals and not shift_intensity:
          denoised_arr.clip(min=0, out=denoised_arr)
  
      elif clip_negative_vals and shift_intensity:
          msg = "Both `clip_negative_vals` and `shift_intensity` cannot be True."
          msg += " Defaulting to `clip_negative_vals`..."
          warn(msg)
          denoised_arr.clip(min=0, out=denoised_arr)
  
      return np.array(denoised_arr, dtype=out_dtype)
  
file_path: dipy/dipy/denoise/patch2self.py
imports: import numpy
  from warnings import warn
  import time
  from dipy.utils.optpkg import optional_package
  import dipy.core.optimize
label_desc: Use image node_id 4 for calling a global function with example usage: patch2self(data, bvals, patch_radius, model, b0_threshold, out_dtype, alpha, verbose, b0_denoising, clip_negative_vals, shift_intensity) and returns: np