text: # Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
  #         Daniel Strohmeier <daniel.strohmeier@gmail.com>
  #
  # License: Simplified BSD
  
  import numpy as np
  from scipy import linalg
  
  from ..source_estimate import SourceEstimate, _BaseSourceEstimate, _make_stc
  from ..minimum_norm.inverse import (combine_xyz, _prepare_forward,
                                      _check_reference, _log_exp_var)
  from ..forward import is_fixed_orient
  from ..io.pick import pick_channels_evoked
  from ..io.proj import deactivate_proj
  from ..utils import logger, verbose, _check_depth, _check_option, sum_squared
  from ..dipole import Dipole
  
  from .mxne_optim import (mixed_norm_solver, iterative_mixed_norm_solver, _Phi,
                           tf_mixed_norm_solver, iterative_tf_mixed_norm_solver,
                           norm_l2inf, norm_epsilon_inf)
  
  
  def _check_ori(pick_ori, forward):
      """Check pick_ori."""
      _check_option('pick_ori', pick_ori, [None, 'vector'])
      if pick_ori == 'vector' and is_fixed_orient(forward):
          raise ValueError('pick_ori="vector" cannot be combined with a fixed '
                           'orientation forward solution.')
  
  
  def _prepare_weights(forward, gain, source_weighting, weights, weights_min):
      mask = None
      if isinstance(weights, _BaseSourceEstimate):
          weights = np.max(np.abs(weights.data), axis=1)
      weights_max = np.max(weights)
      if weights_min > weights_max:
          raise ValueError('weights_min > weights_max (%s > %s)' %
                           (weights_min, weights_max))
      weights_min = weights_min / weights_max
      weights = weights / weights_max
      n_dip_per_pos = 1 if is_fixed_orient(forward) else 3
      weights = np.ravel(np.tile(weights, [n_dip_per_pos, 1]).T)
      if len(weights) != gain.shape[1]:
          raise ValueError('weights do not have the correct dimension '
                           ' (%d != %d)' % (len(weights), gain.shape[1]))
      if len(source_weighting.shape) == 1:
          source_weighting *= weights
      else:
          source_weighting *= weights[:, None]
      gain *= weights[None, :]
  
      if weights_min is not None:
          mask = (weights > weights_min)
          gain = gain[:, mask]
          n_sources = np.sum(mask) // n_dip_per_pos
          logger.info("Reducing source space to %d sources" % n_sources)
  
      return gain, source_weighting, mask
  
  
  def _prepare_gain(forward, info, noise_cov, pca, depth, loose, rank,
                    weights=None, weights_min=None):
      depth = _check_depth(depth, 'depth_sparse')
      forward, gain_info, gain, _, _, source_weighting, _, _, whitener = \
          _prepare_forward(forward, info, noise_cov, 'auto', loose, rank, pca,
                           use_cps=True, **depth)
  
      if weights is None:
          mask = None
      else:
          gain, source_weighting, mask = _prepare_weights(
              forward, gain, source_weighting, weights, weights_min)
  
      return forward, gain, gain_info, whitener, source_weighting, mask
  
  
  def _reapply_source_weighting(X, source_weighting, active_set):
      X *= source_weighting[active_set][:, None]
      return X
  
  
  def _compute_residual(forward, evoked, X, active_set, info):
      # OK, picking based on row_names is safe
      sel = [forward['sol']['row_names'].index(c) for c in info['ch_names']]
      residual = evoked.copy()
      residual = pick_channels_evoked(residual, include=info['ch_names'])
      r_tmp = residual.copy()
  
      r_tmp.data = np.dot(forward['sol']['data'][sel, :][:, active_set], X)
  
      # Take care of proj
      active_projs = list()
      non_active_projs = list()
      for p in evoked.info['projs']:
          if p['active']:
              active_projs.append(p)
          else:
              non_active_projs.append(p)
  
      if len(active_projs) > 0:
          r_tmp.info['projs'] = deactivate_proj(active_projs, copy=True,
                                                verbose=False)
          r_tmp.apply_proj(verbose=False)
          r_tmp.add_proj(non_active_projs, remove_existing=False, verbose=False)
  
      residual.data -= r_tmp.data
  
      return residual
  
  
  @verbose
  def _make_sparse_stc(X, active_set, forward, tmin, tstep,
                       active_is_idx=False, pick_ori=None, verbose=None):
      source_nn = forward['source_nn']
      vector = False
      if not is_fixed_orient(forward):
          if pick_ori != 'vector':
              logger.info('combining the current components...')
              X = combine_xyz(X)
          else:
              vector = True
              source_nn = np.reshape(source_nn, (-1, 3, 3))
  
      if not active_is_idx:
          active_idx = np.where(active_set)[0]
      else:
          active_idx = active_set
  
      n_dip_per_pos = 1 if is_fixed_orient(forward) else 3
      if n_dip_per_pos > 1:
          active_idx = np.unique(active_idx // n_dip_per_pos)
  
      src = forward['src']
      vertices = []
      n_points_so_far = 0
      for this_src in src:
          this_n_points_so_far = n_points_so_far + len(this_src['vertno'])
          this_active_idx = active_idx[(n_points_so_far <= active_idx) &
                                       (active_idx < this_n_points_so_far)]
          this_active_idx -= n_points_so_far
          this_vertno = this_src['vertno'][this_active_idx]
          n_points_so_far = this_n_points_so_far
          vertices.append(this_vertno)
      source_nn = source_nn[active_idx]
      return _make_stc(
          X, vertices, src.kind, tmin, tstep, src[0]['subject_his_id'],
          vector=vector, source_nn=source_nn)
  
  
  def _split_gof(M, X, gain):
      # parse out the variance explained using an orthogonal basis
      # assuming x is estimated using elements of gain, with residual res
      # along the first axis
      assert M.ndim == X.ndim == gain.ndim == 2, (M.ndim, X.ndim, gain.ndim)
      assert gain.shape == (M.shape[0], X.shape[0])
      assert M.shape[1] == X.shape[1]
      norm = (M * M.conj()).real.sum(0, keepdims=True)
      norm[norm == 0] = np.inf
      M_est = gain @ X
      assert M.shape == M_est.shape
      res = M - M_est
      assert gain.shape[0] == M.shape[0], (gain.shape, M.shape)
      # find an orthonormal basis for our matrices that spans the actual data
      U, s, _ = np.linalg.svd(gain, full_matrices=False)
      U = U[:, s >= s[0] * 1e-6]
      # the part that gets explained
      fit_orth = U.T @ M
      # the part that got over-explained (landed in residual)
      res_orth = U.T @ res
      # determine the weights by projecting each one onto this basis
      w = (U.T @ gain)[:, :, np.newaxis] * X
      w_norm = np.linalg.norm(w, axis=1, keepdims=True)
      w_norm[w_norm == 0] = 1.
      w /= w_norm
      # our weights are now unit-norm positive (will presrve power)
      fit_back = np.linalg.norm(fit_orth[:, np.newaxis] * w, axis=0) ** 2
      res_back = np.linalg.norm(res_orth[:, np.newaxis] * w, axis=0) ** 2
      # and the resulting goodness of fits
      gof_back = 100 * (fit_back - res_back) / norm
      assert gof_back.shape == X.shape, (gof_back.shape, X.shape)
      return gof_back
  
  
  @verbose
  def _make_dipoles_sparse(X, active_set, forward, tmin, tstep, M,
                           gain_active, active_is_idx=False,
                           verbose=None):
      times = tmin + tstep * np.arange(X.shape[1])
  
      if not active_is_idx:
          active_idx = np.where(active_set)[0]
      else:
          active_idx = active_set
  
      # Compute the GOF split amongst the dipoles
      assert M.shape == (gain_active.shape[0], len(times))
      assert gain_active.shape[1] == len(active_idx) == X.shape[0]
      gof_split = _split_gof(M, X, gain_active)
      assert gof_split.shape == (len(active_idx), len(times))
      assert X.shape[0] in (len(active_idx), 3 * len(active_idx))
  
      n_dip_per_pos = 1 if is_fixed_orient(forward) else 3
      if n_dip_per_pos > 1:
          active_idx = active_idx // n_dip_per_pos
          _, keep = np.unique(active_idx, return_index=True)
          keep.sort()  # maintain old order
          active_idx = active_idx[keep]
          gof_split.shape = (len(active_idx), n_dip_per_pos, len(times))
          gof_split = gof_split.sum(1)
          assert (gof_split < 100).all()
      assert gof_split.shape == (len(active_idx), len(times))
  
      dipoles = []
      for k, i_dip in enumerate(active_idx):
          i_pos = forward['source_rr'][i_dip][np.newaxis, :]
          i_pos = i_pos.repeat(len(times), axis=0)
          X_ = X[k * n_dip_per_pos: (k + 1) * n_dip_per_pos]
          if n_dip_per_pos == 1:
              amplitude = X_[0]
              i_ori = forward['source_nn'][i_dip][np.newaxis, :]
              i_ori = i_ori.repeat(len(times), axis=0)
          else:
              if forward['surf_ori']:
                  X_ = np.dot(forward['source_nn'][
                      i_dip * n_dip_per_pos:(i_dip + 1) * n_dip_per_pos].T, X_)
              amplitude = np.linalg.norm(X_, axis=0)
              i_ori = np.zeros((len(times), 3))
              i_ori[amplitude > 0.] = (X_[:, amplitude > 0.] /
                                       amplitude[amplitude > 0.]).T
  
          dipoles.append(Dipole(times, i_pos, amplitude, i_ori, gof_split[k]))
  
      return dipoles
  
  
  @verbose
  def make_stc_from_dipoles(dipoles, src, verbose=None):
      """Convert a list of spatio-temporal dipoles into a SourceEstimate.
  
      Parameters
      ----------
      dipoles : Dipole | list of instances of Dipole
          The dipoles to convert.
      src : instance of SourceSpaces
          The source space used to generate the forward operator.
      %(verbose)s
  
      Returns
      -------
      stc : SourceEstimate
          The source estimate.
      """
      logger.info('Converting dipoles into a SourceEstimate.')
      if isinstance(dipoles, Dipole):
          dipoles = [dipoles]
      if not isinstance(dipoles, list):
          raise ValueError('Dipoles must be an instance of Dipole or '
                           'a list of instances of Dipole. '
                           'Got %s!' % type(dipoles))
      tmin = dipoles[0].times[0]
      tstep = dipoles[0].times[1] - tmin
      X = np.zeros((len(dipoles), len(dipoles[0].times)))
      source_rr = np.concatenate([_src['rr'][_src['vertno'], :] for _src in src],
                                 axis=0)
      n_lh_points = len(src[0]['vertno'])
      lh_vertno = list()
      rh_vertno = list()
      for i in range(len(dipoles)):
          if not np.all(dipoles[i].pos == dipoles[i].pos[0]):
              raise ValueError('Only dipoles with fixed position over time '
                               'are supported!')
          X[i] = dipoles[i].amplitude
          idx = np.all(source_rr == dipoles[i].pos[0], axis=1)
          idx = np.where(idx)[0][0]
          if idx < n_lh_points:
              lh_vertno.append(src[0]['vertno'][idx])
          else:
              rh_vertno.append(src[1]['vertno'][idx - n_lh_points])
      vertices = [np.array(lh_vertno).astype(int),
                  np.array(rh_vertno).astype(int)]
      stc = SourceEstimate(X, vertices=vertices, tmin=tmin, tstep=tstep,
                           subject=src._subject)
      logger.info('[done]')
      return stc
  
  
  @verbose
  def mixed_norm(evoked, forward, noise_cov, alpha, loose='auto', depth=0.8,
                 maxit=3000, tol=1e-4, active_set_size=10,
                 debias=True, time_pca=True, weights=None, weights_min=0.,
                 solver='auto', n_mxne_iter=1, return_residual=False,
                 return_as_dipoles=False, dgap_freq=10, rank=None, pick_ori=None,
                 verbose=None):
      """Mixed-norm estimate (MxNE) and iterative reweighted MxNE (irMxNE).
  
      Compute L1/L2 mixed-norm solution [1]_ or L0.5/L2 [2]_ mixed-norm
      solution on evoked data.
  
      Parameters
      ----------
      evoked : instance of Evoked or list of instances of Evoked
          Evoked data to invert.
      forward : dict
          Forward operator.
      noise_cov : instance of Covariance
          Noise covariance to compute whitener.
      alpha : float in range [0, 100)
          Regularization parameter. 0 means no regularization, 100 would give 0
          active dipole.
      %(loose)s
      %(depth)s
      maxit : int
          Maximum number of iterations.
      tol : float
          Tolerance parameter.
      active_set_size : int | None
          Size of active set increment. If None, no active set strategy is used.
      debias : bool
          Remove coefficient amplitude bias due to L1 penalty.
      time_pca : bool or int
          If True the rank of the concatenated epochs is reduced to
          its true dimension. If is 'int' the rank is limited to this value.
      weights : None | array | SourceEstimate
          Weight for penalty in mixed_norm. Can be None, a
          1d array with shape (n_sources,), or a SourceEstimate (e.g. obtained
          with wMNE, dSPM, or fMRI).
      weights_min : float
          Do not consider in the estimation sources for which weights
          is less than weights_min.
      solver : 'prox' | 'cd' | 'bcd' | 'auto'
          The algorithm to use for the optimization. 'prox' stands for
          proximal iterations using the FISTA algorithm, 'cd' uses
          coordinate descent, and 'bcd' applies block coordinate descent.
          'cd' is only available for fixed orientation.
      n_mxne_iter : int
          The number of MxNE iterations. If > 1, iterative reweighting
          is applied.
      return_residual : bool
          If True, the residual is returned as an Evoked instance.
      return_as_dipoles : bool
          If True, the sources are returned as a list of Dipole instances.
      dgap_freq : int or np.inf
          The duality gap is evaluated every dgap_freq iterations. Ignored if
          solver is 'cd'.
      %(rank_None)s
  
          .. versionadded:: 0.18
      %(pick_ori)s
      %(verbose)s
  
      Returns
      -------
      stc : SourceEstimate | list of SourceEstimate
          Source time courses for each evoked data passed as input.
      residual : instance of Evoked
          The residual a.k.a. data not explained by the sources.
          Only returned if return_residual is True.
  
      See Also
      --------
      tf_mixed_norm
  
      References
      ----------
      .. [1] A. Gramfort, M. Kowalski, M. Hämäläinen,
         "Mixed-norm estimates for the M/EEG inverse problem using accelerated
         gradient methods", Physics in Medicine and Biology, 2012.
         https://doi.org/10.1088/0031-9155/57/7/1937
  
      .. [2] D. Strohmeier, Y. Bekhti, J. Haueisen, A. Gramfort,
         "The Iterative Reweighted Mixed-Norm Estimate for Spatio-Temporal
         MEG/EEG Source Reconstruction", IEEE Transactions of Medical Imaging,
         Volume 35 (10), pp. 2218-2228, 2016.
      """
      if not (0. <= alpha < 100.):
          raise ValueError('alpha must be in [0, 100). '
                           'Got alpha = %s' % alpha)
      if n_mxne_iter < 1:
          raise ValueError('MxNE has to be computed at least 1 time. '
                           'Requires n_mxne_iter >= 1, got %d' % n_mxne_iter)
      if dgap_freq <= 0.:
          raise ValueError('dgap_freq must be a positive integer.'
                           ' Got dgap_freq = %s' % dgap_freq)
  
      pca = True
      if not isinstance(evoked, list):
          evoked = [evoked]
  
      _check_reference(evoked[0])
  
      all_ch_names = evoked[0].ch_names
      if not all(all_ch_names == evoked[i].ch_names
                 for i in range(1, len(evoked))):
          raise Exception('All the datasets must have the same good channels.')
  
      forward, gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
          forward, evoked[0].info, noise_cov, pca, depth, loose, rank,
          weights, weights_min)
      _check_ori(pick_ori, forward)
  
      sel = [all_ch_names.index(name) for name in gain_info['ch_names']]
      M = np.concatenate([e.data[sel] for e in evoked], axis=1)
  
      # Whiten data
      logger.info('Whitening data matrix.')
      M = np.dot(whitener, M)
  
      if time_pca:
          U, s, Vh = linalg.svd(M, full_matrices=False)
          if not isinstance(time_pca, bool) and isinstance(time_pca, int):
              U = U[:, :time_pca]
              s = s[:time_pca]
              Vh = Vh[:time_pca]
          M = U * s
  
      # Scaling to make setting of tol and alpha easy
      tol *= sum_squared(M)
      n_dip_per_pos = 1 if is_fixed_orient(forward) else 3
      alpha_max = norm_l2inf(np.dot(gain.T, M), n_dip_per_pos, copy=False)
      alpha_max *= 0.01
      gain /= alpha_max
      source_weighting /= alpha_max
  
      if n_mxne_iter == 1:
          X, active_set, E = mixed_norm_solver(
              M, gain, alpha, maxit=maxit, tol=tol,
              active_set_size=active_set_size, n_orient=n_dip_per_pos,
              debias=debias, solver=solver, dgap_freq=dgap_freq, verbose=verbose)
      else:
          X, active_set, E = iterative_mixed_norm_solver(
              M, gain, alpha, n_mxne_iter, maxit=maxit, tol=tol,
              n_orient=n_dip_per_pos, active_set_size=active_set_size,
              debias=debias, solver=solver, dgap_freq=dgap_freq, verbose=verbose)
  
      if time_pca:
          X = np.dot(X, Vh)
          M = np.dot(M, Vh)
  
      gain_active = gain[:, active_set]
      if mask is not None:
          active_set_tmp = np.zeros(len(mask), dtype=bool)
          active_set_tmp[mask] = active_set
          active_set = active_set_tmp
          del active_set_tmp
  
      if active_set.sum() == 0:
          raise Exception("No active dipoles found. alpha is too big.")
  
      # Reapply weights to have correct unit
      X = _reapply_source_weighting(X, source_weighting, active_set)
      source_weighting[source_weighting == 0] = 1  # zeros
      gain_active /= source_weighting[active_set]
      del source_weighting
      M_estimate = np.dot(gain_active, X)
  
      outs = list()
      residual = list()
      cnt = 0
      for e in evoked:
          tmin = e.times[0]
          tstep = 1.0 / e.info['sfreq']
          Xe = X[:, cnt:(cnt + len(e.times))]
          if return_as_dipoles:
              out = _make_dipoles_sparse(
                  Xe, active_set, forward, tmin, tstep,
                  M[:, cnt:(cnt + len(e.times))],
                  gain_active)
          else:
              out = _make_sparse_stc(
                  Xe, active_set, forward, tmin, tstep, pick_ori=pick_ori)
          outs.append(out)
          cnt += len(e.times)
  
          if return_residual:
              residual.append(_compute_residual(forward, e, Xe, active_set,
                                                gain_info))
  
      _log_exp_var(M, M_estimate, prefix='')
      logger.info('[done]')
  
      if len(outs) == 1:
          out = outs[0]
          if return_residual:
              residual = residual[0]
      else:
          out = outs
  
      if return_residual:
          out = out, residual
  
      return out
  
  
  def _window_evoked(evoked, size):
      """Window evoked (size in seconds)."""
      if isinstance(size, (float, int)):
          lsize = rsize = float(size)
      else:
          lsize, rsize = size
      evoked = evoked.copy()
      sfreq = float(evoked.info['sfreq'])
      lsize = int(lsize * sfreq)
      rsize = int(rsize * sfreq)
      lhann = np.hanning(lsize * 2)[:lsize]
      rhann = np.hanning(rsize * 2)[-rsize:]
      window = np.r_[lhann, np.ones(len(evoked.times) - lsize - rsize), rhann]
      evoked.data *= window[None, :]
      return evoked
  
  
  @verbose
  def tf_mixed_norm(evoked, forward, noise_cov,
                    loose='auto', depth=0.8, maxit=3000,
                    tol=1e-4, weights=None, weights_min=0., pca=True,
                    debias=True, wsize=64, tstep=4, window=0.02,
                    return_residual=False, return_as_dipoles=False, alpha=None,
                    l1_ratio=None, dgap_freq=10, rank=None, pick_ori=None,
                    n_tfmxne_iter=1, verbose=None):
      """Time-Frequency Mixed-norm estimate (TF-MxNE).
  
      Compute L1/L2 + L1 mixed-norm solution on time-frequency
      dictionary. Works with evoked data
      :footcite:`GramfortEtAl2013,GramfortEtAl2011`.
  
      Parameters
      ----------
      evoked : instance of Evoked
          Evoked data to invert.
      forward : dict
          Forward operator.
      noise_cov : instance of Covariance
          Noise covariance to compute whitener.
      %(loose)s
      %(depth)s
      maxit : int
          Maximum number of iterations.
      tol : float
          Tolerance parameter.
      weights : None | array | SourceEstimate
          Weight for penalty in mixed_norm. Can be None or
          1d array of length n_sources or a SourceEstimate e.g. obtained
          with wMNE or dSPM or fMRI.
      weights_min : float
          Do not consider in the estimation sources for which weights
          is less than weights_min.
      pca : bool
          If True the rank of the data is reduced to true dimension.
      debias : bool
          Remove coefficient amplitude bias due to L1 penalty.
      wsize : int or array-like
          Length of the STFT window in samples (must be a multiple of 4).
          If an array is passed, multiple TF dictionaries are used (each having
          its own wsize and tstep) and each entry of wsize must be a multiple
          of 4. See :footcite:`BekhtiEtAl2016`.
      tstep : int or array-like
          Step between successive windows in samples (must be a multiple of 2,
          a divider of wsize and smaller than wsize/2) (default: wsize/2).
          If an array is passed, multiple TF dictionaries are used (each having
          its own wsize and tstep), and each entry of tstep must be a multiple
          of 2 and divide the corresponding entry of wsize. See
          :footcite:`BekhtiEtAl2016`.
      window : float or (float, float)
          Length of time window used to take care of edge artifacts in seconds.
          It can be one float or float if the values are different for left
          and right window length.
      return_residual : bool
          If True, the residual is returned as an Evoked instance.
      return_as_dipoles : bool
          If True, the sources are returned as a list of Dipole instances.
      alpha : float in [0, 100) or None
          Overall regularization parameter.
          If alpha and l1_ratio are not None, alpha_space and alpha_time are
          overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max
          * l1_ratio. 0 means no regularization, 100 would give 0 active dipole.
      l1_ratio : float in [0, 1] or None
          Proportion of temporal regularization.
          If l1_ratio and alpha are not None, alpha_space and alpha_time are
          overridden by alpha * alpha_max * (1. - l1_ratio) and alpha * alpha_max
          * l1_ratio. 0 means no time regularization a.k.a. MxNE.
      dgap_freq : int or np.inf
          The duality gap is evaluated every dgap_freq iterations.
      %(rank_None)s
  
          .. versionadded:: 0.18
      %(pick_ori)s
      n_tfmxne_iter : int
          Number of TF-MxNE iterations. If > 1, iterative reweighting is applied.
      %(verbose)s
  
      Returns
      -------
      stc : instance of SourceEstimate
          Source time courses.
      residual : instance of Evoked
          The residual a.k.a. data not explained by the sources.
          Only returned if return_residual is True.
  
      See Also
      --------
      mixed_norm
  
      References
      ----------
      .. footbibliography::
      """
      _check_reference(evoked)
  
      all_ch_names = evoked.ch_names
      info = evoked.info
  
      if not (0. <= alpha < 100.):
          raise ValueError('alpha must be in [0, 100). '
                           'Got alpha = %s' % alpha)
  
      if not (0. <= l1_ratio <= 1.):
          raise ValueError('l1_ratio must be in range [0, 1].'
                           ' Got l1_ratio = %s' % l1_ratio)
      alpha_space = alpha * (1. - l1_ratio)
      alpha_time = alpha * l1_ratio
  
      if n_tfmxne_iter < 1:
          raise ValueError('TF-MxNE has to be computed at least 1 time. '
                           'Requires n_tfmxne_iter >= 1, got %s' % n_tfmxne_iter)
  
      if dgap_freq <= 0.:
          raise ValueError('dgap_freq must be a positive integer.'
                           ' Got dgap_freq = %s' % dgap_freq)
  
      tstep = np.atleast_1d(tstep)
      wsize = np.atleast_1d(wsize)
      if len(tstep) != len(wsize):
          raise ValueError('The same number of window sizes and steps must be '
                           'passed. Got tstep = %s and wsize = %s' %
                           (tstep, wsize))
  
      forward, gain, gain_info, whitener, source_weighting, mask = _prepare_gain(
          forward, evoked.info, noise_cov, pca, depth, loose, rank,
          weights, weights_min)
      _check_ori(pick_ori, forward)
  
      n_dip_per_pos = 1 if is_fixed_orient(forward) else 3
  
      if window is not None:
          evoked = _window_evoked(evoked, window)
  
      sel = [all_ch_names.index(name) for name in gain_info["ch_names"]]
      M = evoked.data[sel]
  
      # Whiten data
      logger.info('Whitening data matrix.')
      M = np.dot(whitener, M)
  
      n_steps = np.ceil(M.shape[1] / tstep.astype(float)).astype(int)
      n_freqs = wsize // 2 + 1
      n_coefs = n_steps * n_freqs
      phi = _Phi(wsize, tstep, n_coefs)
  
      # Scaling to make setting of tol and alpha easy
      tol *= sum_squared(M)
      alpha_max = norm_epsilon_inf(gain, M, phi, l1_ratio, n_dip_per_pos)
      alpha_max *= 0.01
      gain /= alpha_max
      source_weighting /= alpha_max
  
      if n_tfmxne_iter == 1:
          X, active_set, E = tf_mixed_norm_solver(
              M, gain, alpha_space, alpha_time, wsize=wsize, tstep=tstep,
              maxit=maxit, tol=tol, verbose=verbose, n_orient=n_dip_per_pos,
              dgap_freq=dgap_freq, debias=debias)
      else:
          X, active_set, E = iterative_tf_mixed_norm_solver(
              M, gain, alpha_space, alpha_time, wsize=wsize, tstep=tstep,
              n_tfmxne_iter=n_tfmxne_iter, maxit=maxit, tol=tol, verbose=verbose,
              n_orient=n_dip_per_pos, dgap_freq=dgap_freq, debias=debias)
  
      if active_set.sum() == 0:
          raise Exception("No active dipoles found. "
                          "alpha_space/alpha_time are too big.")
  
      # Compute estimated whitened sensor data for each dipole (dip, ch, time)
      gain_active = gain[:, active_set]
  
      if mask is not None:
          active_set_tmp = np.zeros(len(mask), dtype=bool)
          active_set_tmp[mask] = active_set
          active_set = active_set_tmp
          del active_set_tmp
  
      X = _reapply_source_weighting(X, source_weighting, active_set)
      gain_active /= source_weighting[active_set]
  
      if return_residual:
          residual = _compute_residual(
              forward, evoked, X, active_set, gain_info)
  
      if return_as_dipoles:
          out = _make_dipoles_sparse(
              X, active_set, forward, evoked.times[0], 1.0 / info['sfreq'],
              M, gain_active)
      else:
          out = _make_sparse_stc(
              X, active_set, forward, evoked.times[0], 1.0 / info['sfreq'],
              pick_ori=pick_ori)
  
      logger.info('[done]')
  
      if return_residual:
          out = out, residual
  
      return out
  