code: def test_ssd():
      """Test Common Spatial Patterns algorithm on raw data."""
      X, A, S = simulate_data()
      sf = 250
      n_channels = X.shape[0]
      info = create_info(ch_names=n_channels, sfreq=sf, ch_types="eeg")
      n_components_true = 5
  
      # Init
      filt_params_signal = dict(
          l_freq=freqs_sig[0],
          h_freq=freqs_sig[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      filt_params_noise = dict(
          l_freq=freqs_noise[0],
          h_freq=freqs_noise[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      ssd = SSD(info, filt_params_signal, filt_params_noise)
      # freq no int
      freq = "foo"
      filt_params_signal = dict(
          l_freq=freq,
          h_freq=freqs_sig[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      filt_params_noise = dict(
          l_freq=freqs_noise[0],
          h_freq=freqs_noise[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      with pytest.raises(TypeError, match="must be an instance "):
          ssd = SSD(info, filt_params_signal, filt_params_noise)
  
      # Wrongly specified noise band
      freq = 2
      filt_params_signal = dict(
          l_freq=freq,
          h_freq=freqs_sig[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      filt_params_noise = dict(
          l_freq=freqs_noise[0],
          h_freq=freqs_noise[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      with pytest.raises(ValueError, match="Wrongly specified "):
          ssd = SSD(info, filt_params_signal, filt_params_noise)
  
      # filt param no dict
      filt_params_signal = freqs_sig
      filt_params_noise = freqs_noise
      with pytest.raises(ValueError, match="must be defined"):
          ssd = SSD(info, filt_params_signal, filt_params_noise)
  
      # Data type
      filt_params_signal = dict(
          l_freq=freqs_sig[0],
          h_freq=freqs_sig[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      filt_params_noise = dict(
          l_freq=freqs_noise[0],
          h_freq=freqs_noise[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      ssd = SSD(info, filt_params_signal, filt_params_noise)
      raw = io.RawArray(X, info)
  
      pytest.raises(TypeError, ssd.fit, raw)
  
      # check non-boolean return_filtered
      with pytest.raises(ValueError, match="return_filtered"):
          ssd = SSD(
              info,
              filt_params_signal,
              filt_params_noise,
              return_filtered=0,
          )
  
      # check non-boolean sort_by_spectral_ratio
      with pytest.raises(ValueError, match="sort_by_spectral_ratio"):
          ssd = SSD(
              info,
              filt_params_signal,
              filt_params_noise,
              sort_by_spectral_ratio=0,
          )
  
      # More than 1 channel type
      ch_types = np.reshape([["mag"] * 10, ["eeg"] * 10], n_channels)
      info_2 = create_info(
          ch_names=n_channels, sfreq=sf, ch_types=ch_types
      )
  
      with pytest.raises(ValueError, match="At this point SSD"):
          ssd = SSD(info_2, filt_params_signal, filt_params_noise)
  
      # Number of channels
      info_3 = create_info(
          ch_names=n_channels + 1, sfreq=sf, ch_types="eeg"
      )
      ssd = SSD(info_3, filt_params_signal, filt_params_noise)
      pytest.raises(ValueError, ssd.fit, X)
  
      # Fit
      n_components = 10
      ssd = SSD(
          info,
          filt_params_signal,
          filt_params_noise,
          n_components=n_components,
      )
  
      # Call transform before fit
      pytest.raises(AttributeError, ssd.transform, X)
  
      # Check outputs
      ssd.fit(X)
  
      assert ssd.filters_.shape == (n_channels, n_channels)
      assert ssd.patterns_.shape == (n_channels, n_channels)
  
      # Transform
      X_ssd = ssd.fit_transform(X)
      assert X_ssd.shape[0] == n_components
      # back and forward
      ssd = SSD(
          info,
          filt_params_signal,
          filt_params_noise,
          n_components=None,
          sort_by_spectral_ratio=False,
      )
      ssd.fit(X)
      X_denoised = ssd.apply(X)
      assert_array_almost_equal(X_denoised, X)
      # denoised by low-rank-factorization
      ssd = SSD(
          info,
          filt_params_signal,
          filt_params_noise,
          n_components=n_components,
          sort_by_spectral_ratio=True,
      )
      ssd.fit(X)
      X_denoised = ssd.apply(X)
      assert np.linalg.matrix_rank(X_denoised) == n_components
  
      # Power ratio ordering
      ssd = SSD(
          info,
          filt_params_signal,
          filt_params_noise,
          n_components=None,
          sort_by_spectral_ratio=False,
      )
      ssd.fit(X)
      spec_ratio, sorter_spec = ssd.get_spectral_ratio(ssd.transform(X))
      # since we now that the number of true components is 5, the relative
      # difference should be low for the first 5 components and then increases
      index_diff = np.argmax(-np.diff(spec_ratio))
      assert index_diff == n_components_true - 1
      # Check detected peaks
      # fit ssd
      n_components = n_components_true
      filt_params_signal = dict(
          l_freq=freqs_sig[0],
          h_freq=freqs_sig[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      filt_params_noise = dict(
          l_freq=freqs_noise[0],
          h_freq=freqs_noise[1],
          l_trans_bandwidth=1,
          h_trans_bandwidth=1,
      )
      ssd = SSD(
          info,
          filt_params_signal,
          filt_params_noise,
          n_components=n_components,
          sort_by_spectral_ratio=False,
      )
      ssd.fit(X)
  
      out = ssd.transform(X)
      psd_out, _ = psd_array_welch(out[0], sfreq=250, n_fft=250)
      psd_S, _ = psd_array_welch(S[0], sfreq=250, n_fft=250)
      corr = np.abs(np.corrcoef((psd_out, psd_S))[0, 1])
      assert np.abs(corr) > 0.95
      # Check pattern estimation
      # Since there is no exact ordering of the recovered patterns
      # a pair-wise greedy search will be done
      error = list()
      for ii in range(n_channels):
          corr = np.abs(
              np.corrcoef(ssd.patterns_[ii, :].T, A[:, 0])[0, 1]
          )
          error.append(1 - corr)
          min_err = np.min(error)
      assert min_err < 0.3
  
file_path: mne-python/mne/decoding/tests/test_ssd.py
imports: import numpy
  import pytest
  from numpy.testing import assert_array_almost_equal, assert_array_equal
  from mne import create_info, io
  from mne.decoding import CSP
  from mne.decoding.ssd import SSD
  from mne.filter import filter_data
  from mne.time_frequency import psd_array_welch
label_desc: Use image node_id 2 for calling a global function with example usage: test_ssd() without return types