code: def test_clean_frequencies():
      """Using butterworth method."""
      sx1 = np.sin(np.linspace(0, 100, 2000))
      sx2 = np.sin(np.linspace(0, 100, 2000))
      sx = np.vstack((sx1, sx2)).T
      sx_orig = sx.copy()
      assert (
          clean(
              sx,
              standardize=False,
              high_pass=0.002,
              low_pass=None,
              t_r=2.5,
          ).max()
          > 0.1
      )
      assert (
          clean(
              sx,
              standardize=False,
              high_pass=0.2,
              low_pass=None,
              t_r=2.5,
          ).max()
          < 0.01
      )
      assert (
          clean(sx, standardize=False, low_pass=0.01, t_r=2.5).max()
          > 0.9
      )
      with pytest.raises(ValueError):
          clean(sx, low_pass=0.4, high_pass=0.5, t_r=2.5)
  
      # clean should not modify inputs
      _ = clean(
          sx, standardize=False, detrend=False, low_pass=0.2, t_r=2.5
      )
      assert np.array_equal(sx_orig, sx)
  
imports: import os.path
  import numpy
  import pytest
  import scipy.signal
  from pandas import read_csv
  from nilearn import signal
  from nilearn.conftest import _rng
  from nilearn.signal import clean
label_desc: Use image node_id 12 for calling a global function with example usage: test_clean_frequencies() without return types