code: def test_correction(order):
      """Apply HFC and compare to previous computed solutions."""
      binname = (
          fil_path
          / "sub-noise_ses-001_task-noise220622_run-001_meg.bin"
      )
      raw = read_raw_fil(binname)
      raw.load_data()
      raw.info["bads"].extend([b for b in bads])
      projs = compute_proj_hfc(raw.info, order=order, accuracy="point")
      raw.add_proj(projs).apply_proj()
  
      mat = _unpack_mat(
          loadmat(fil_path / f"{fname_root}_hfc_l{order}.mat")
      )
  
      proj_list = projs[0]["data"]["col_names"]
      picks = pick_channels(raw.ch_names, proj_list, ordered=True)
      mat_list = mat["coil_label"]
      mat_inds = pick_channels(mat_list, proj_list, ordered=True)
  
      want = mat["trial"][mat_inds]
      got = (
          raw.copy().add_proj(projs).apply_proj()[picks, 0:300][0]
          * 1e15
      )
      assert_allclose(got, want, rtol=1e-7)
  
      # Now with default accuracy: not super close with tol but corr is good
      projs = compute_proj_hfc(raw.info, order=order)
      got = (
          raw.copy().add_proj(projs).apply_proj()[picks, 0:300][0]
          * 1e15
      )
      corr = np.corrcoef(got.ravel(), want.ravel())[0, 1]
      assert 0.999999 < corr <= 1.0
  
file_path: mne-python/mne/preprocessing/tests/test_hfc.py
imports: from pathlib import Path
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy.io import loadmat
  from mne._fiff.pick import pick_channels, pick_info, pick_types
  from mne.datasets import testing
  from mne.io import read_info, read_raw_fil
  from mne.preprocessing.hfc import compute_proj_hfc
label_desc: Use image node_id 3 for calling a global function with example usage: test_correction(order) without return types