code: """IIR and FIR filtering and resampling functions."""
  
  from copy import deepcopy
  from functools import partial
  
  import numpy as np
  
  from .annotations import _annotations_starts_stops
  from .io.pick import _picks_to_idx
  from .cuda import (_setup_cuda_fft_multiply_repeated, _fft_multiply_repeated,
                     _setup_cuda_fft_resample, _fft_resample, _smart_pad)
  from .fixes import irfft, ifftshift, fftfreq
  from .parallel import parallel_func, check_n_jobs
  from .time_frequency.multitaper import _mt_spectra, _compute_mt_params
  from .utils import (logger, verbose, sum_squared, check_version, warn, _pl,
                      _check_preload, _validate_type, _check_option)
  
  # These values from Ifeachor and Jervis.
  _length_factors = dict(hann=3.1, hamming=3.3, blackman=5.0)
  
  
  def is_power2(num):
      """Test if number is a power of 2.
  
      Parameters
      ----------
      num : int
          Number.
  
      Returns
      -------
      b : bool
          True if is power of 2.
  
      Examples
      --------
      >>> is_power2(2 ** 3)
      True
      >>> is_power2(5)
      False
      """
      num = int(num)
      return num != 0 and ((num & (num - 1)) == 0)
  
  
  def next_fast_len(target):
      """Find the next fast size of input data to `fft`, for zero-padding, etc.
  
      SciPy's FFTPACK has efficient functions for radix {2, 3, 4, 5}, so this
      returns the next composite of the prime factors 2, 3, and 5 which is
      greater than or equal to `target`. (These are also known as 5-smooth
      numbers, regular numbers, or Hamming numbers.)
  
      Parameters
      ----------
      target : int
          Length to start searching from.  Must be a positive integer.
  
      Returns
      -------
      out : int
          The first 5-smooth number greater than or equal to `target`.
  
      Notes
      -----
      Copied from SciPy with minor modifications.
      """
      from bisect import bisect_left
      hams = (8, 9, 10, 12, 15, 16, 18, 20, 24, 25, 27, 30, 32, 36, 40, 45, 48,
              50, 54, 60, 64, 72, 75, 80, 81, 90, 96, 100, 108, 120, 125, 128,
              135, 144, 150, 160, 162, 180, 192, 200, 216, 225, 240, 243, 250,
              256, 270, 288, 300, 320, 324, 360, 375, 384, 400, 405, 432, 450,
              480, 486, 500, 512, 540, 576, 600, 625, 640, 648, 675, 720, 729,
              750, 768, 800, 810, 864, 900, 960, 972, 1000, 1024, 1080, 1125,
              1152, 1200, 1215, 1250, 1280, 1296, 1350, 1440, 1458, 1500, 1536,
              1600, 1620, 1728, 1800, 1875, 1920, 1944, 2000, 2025, 2048, 2160,
              2187, 2250, 2304, 2400, 2430, 2500, 2560, 2592, 2700, 2880, 2916,
              3000, 3072, 3125, 3200, 3240, 3375, 3456, 3600, 3645, 3750, 3840,
              3888, 4000, 4050, 4096, 4320, 4374, 4500, 4608, 4800, 4860, 5000,
              5120, 5184, 5400, 5625, 5760, 5832, 6000, 6075, 6144, 6250, 6400,
              6480, 6561, 6750, 6912, 7200, 7290, 7500, 7680, 7776, 8000, 8100,
              8192, 8640, 8748, 9000, 9216, 9375, 9600, 9720, 10000)
  
      if target <= 6:
          return target
  
      # Quickly check if it's already a power of 2
      if not (target & (target - 1)):
          return target
  
      # Get result quickly for small sizes, since FFT itself is similarly fast.
      if target <= hams[-1]:
          return hams[bisect_left(hams, target)]
  
      match = float('inf')  # Anything found will be smaller
      p5 = 1
      while p5 < target:
          p35 = p5
          while p35 < target:
              # Ceiling integer division, avoiding conversion to float
              # (quotient = ceil(target / p35))
              quotient = -(-target // p35)
  
              p2 = 2 ** int(quotient - 1).bit_length()
  
              N = p2 * p35
              if N == target:
                  return N
              elif N < match:
                  match = N
              p35 *= 3
              if p35 == target:
                  return p35
          if p35 < match:
              match = p35
          p5 *= 5
          if p5 == target:
              return p5
      if p5 < match:
          match = p5
      return match
  
  
  def _overlap_add_filter(x, h, n_fft=None, phase='zero', picks=None,
                          n_jobs=1, copy=True, pad='reflect_limited'):
      """Filter the signal x using h with overlap-add FFTs.
  
      Parameters
      ----------
      x : array, shape (n_signals, n_times)
          Signals to filter.
      h : 1d array
          Filter impulse response (FIR filter coefficients). Must be odd length
          if phase == 'linear'.
      n_fft : int
          Length of the FFT. If None, the best size is determined automatically.
      phase : str
          If 'zero', the delay for the filter is compensated (and it must be
          an odd-length symmetric filter). If 'linear', the response is
          uncompensated. If 'zero-double', the filter is applied in the
          forward and reverse directions. If 'minimum', a minimum-phase
          filter will be used.
      picks : list | None
          See calling functions.
      n_jobs : int | str
          Number of jobs to run in parallel. Can be 'cuda' if ``cupy``
          is installed properly.
      copy : bool
          If True, a copy of x, filtered, is returned. Otherwise, it operates
          on x in place.
      pad : str
          Padding type for ``_smart_pad``.
  
      Returns
      -------
      x : array, shape (n_signals, n_times)
          x filtered.
      """
      n_jobs = check_n_jobs(n_jobs, allow_cuda=True)
      # set up array for filtering, reshape to 2D, operate on last axis
      x, orig_shape, picks = _prep_for_filtering(x, copy, picks)
      # Extend the signal by mirroring the edges to reduce transient filter
      # response
      _check_zero_phase_length(len(h), phase)
      if len(h) == 1:
          return x * h ** 2 if phase == 'zero-double' else x * h
      n_edge = max(min(len(h), x.shape[1]) - 1, 0)
      logger.debug('Smart-padding with:  %s samples on each edge' % n_edge)
      n_x = x.shape[1] + 2 * n_edge
  
      if phase == 'zero-double':
          h = np.convolve(h, h[::-1])
  
      # Determine FFT length to use
      min_fft = 2 * len(h) - 1
      if n_fft is None:
          max_fft = n_x
          if max_fft >= min_fft:
              # cost function based on number of multiplications
              N = 2 ** np.arange(np.ceil(np.log2(min_fft)),
                                 np.ceil(np.log2(max_fft)) + 1, dtype=int)
              cost = (np.ceil(n_x / (N - len(h) + 1).astype(np.float)) *
                      N * (np.log2(N) + 1))
  
              # add a heuristic term to prevent too-long FFT's which are slow
              # (not predicted by mult. cost alone, 4e-5 exp. determined)
              cost += 4e-5 * N * n_x
  
              n_fft = N[np.argmin(cost)]
          else:
              # Use only a single block
              n_fft = next_fast_len(min_fft)
      logger.debug('FFT block length:   %s' % n_fft)
      if n_fft < min_fft:
          raise ValueError('n_fft is too short, has to be at least '
                           '2 * len(h) - 1 (%s), got %s' % (min_fft, n_fft))
  
      # Figure out if we should use CUDA
      n_jobs, cuda_dict = _setup_cuda_fft_multiply_repeated(
          n_jobs, h, n_fft)
  
      # Process each row separately
      picks = _picks_to_idx(len(x), picks)
      if n_jobs == 1:
          for p in picks:
              x[p] = _1d_overlap_filter(x[p], len(h), n_edge, phase,
                                        cuda_dict, pad, n_fft)
      else:
          parallel, p_fun, _ = parallel_func(_1d_overlap_filter, n_jobs)
          data_new = parallel(p_fun(x[p], len(h), n_edge, phase,
                                    cuda_dict, pad, n_fft) for p in picks)
          for pp, p in enumerate(picks):
              x[p] = data_new[pp]
  
      x.shape = orig_shape
      return x
  
  
  def _1d_overlap_filter(x, n_h, n_edge, phase, cuda_dict, pad, n_fft):
      """Do one-dimensional overlap-add FFT FIR filtering."""
      # pad to reduce ringing
      x_ext = _smart_pad(x, (n_edge, n_edge), pad)
      n_x = len(x_ext)
      x_filtered = np.zeros_like(x_ext)
  
      n_seg = n_fft - n_h + 1
      n_segments = int(np.ceil(n_x / float(n_seg)))
      shift = ((n_h - 1) // 2 if phase.startswith('zero') else 0) + n_edge
  
      # Now the actual filtering step is identical for zero-phase (filtfilt-like)
      # or single-pass
      for seg_idx in range(n_segments):
          start = seg_idx * n_seg
          stop = (seg_idx + 1) * n_seg
          seg = x_ext[start:stop]
          seg = np.concatenate([seg, np.zeros(n_fft - len(seg))])
  
          prod = _fft_multiply_repeated(seg, cuda_dict)
  
          start_filt = max(0, start - shift)
          stop_filt = min(start - shift + n_fft, n_x)
          start_prod = max(0, shift - start)
          stop_prod = start_prod + stop_filt - start_filt
          x_filtered[start_filt:stop_filt] += prod[start_prod:stop_prod]
  
      # Remove mirrored edges that we added and cast (n_edge can be zero)
      x_filtered = x_filtered[:n_x - 2 * n_edge].astype(x.dtype)
      return x_filtered
  
  
  def _filter_attenuation(h, freq, gain):
      """Compute minimum attenuation at stop frequency."""
      from scipy.signal import freqz
      _, filt_resp = freqz(h.ravel(), worN=np.pi * freq)
      filt_resp = np.abs(filt_resp)  # use amplitude response
      filt_resp[np.where(gain == 1)] = 0
      idx = np.argmax(filt_resp)
      att_db = -20 * np.log10(np.maximum(filt_resp[idx], 1e-20))
      att_freq = freq[idx]
      return att_db, att_freq
  
  
  def _prep_for_filtering(x, copy, picks=None):
      """Set up array as 2D for filtering ease."""
      x = _check_filterable(x)
      if copy is True:
          x = x.copy()
      orig_shape = x.shape
      x = np.atleast_2d(x)
      picks = _picks_to_idx(x.shape[-2], picks)
      x.shape = (np.prod(x.shape[:-1]), x.shape[-1])
      if len(orig_shape) == 3:
          n_epochs, n_channels, n_times = orig_shape
          offset = np.repeat(np.arange(0, n_channels * n_epochs, n_channels),
                             len(picks))
          picks = np.tile(picks, n_epochs) + offset
      elif len(orig_shape) > 3:
          raise ValueError('picks argument is not supported for data with more'
                           ' than three dimensions')
      assert all(0 <= pick < x.shape[0] for pick in picks)  # guaranteed by above
  
      return x, orig_shape, picks
  
  
  def _firwin_design(N, freq, gain, window, sfreq):
      """Construct a FIR filter using firwin."""
      from scipy.signal import firwin
      assert freq[0] == 0
      assert len(freq) > 1
      assert len(freq) == len(gain)
      h = np.zeros(N)
      prev_freq = freq[-1]
      prev_gain = gain[-1]
      if gain[-1] == 1:
          h[N // 2] = 1  # start with "all up"
      assert prev_gain in (0, 1)
      for this_freq, this_gain in zip(freq[::-1][1:], gain[::-1][1:]):
          assert this_gain in (0, 1)
          if this_gain != prev_gain:
              # Get the correct N to satistify the requested transition bandwidth
              transition = (prev_freq - this_freq) / 2.
              this_N = int(round(_length_factors[window] / transition))
              this_N += (1 - this_N % 2)  # make it odd
              if this_N > N:
                  raise ValueError('The requested filter length %s is too short '
                                   'for the requested %0.2f Hz transition band, '
                                   'which requires %s samples'
                                   % (N, transition * sfreq / 2., this_N))
              # Construct a lowpass
              this_h = firwin(this_N, (prev_freq + this_freq) / 2.,
                              window=window, pass_zero=True, nyq=freq[-1])
              offset = (N - this_N) // 2
              if this_gain == 0:
                  h[offset:N - offset] -= this_h
              else:
                  h[offset:N - offset] += this_h
          prev_gain = this_gain
          prev_freq = this_freq
      return h
  
  
  def _construct_fir_filter(sfreq, freq, gain, filter_length, phase, fir_window,
                            fir_design):
      """Filter signal using gain control points in the frequency domain.
  
      The filter impulse response is constructed from a Hann window (window
      used in "firwin2" function) to avoid ripples in the frequency response
      (windowing is a smoothing in frequency domain).
  
      If x is multi-dimensional, this operates along the last dimension.
  
      Parameters
      ----------
      sfreq : float
          Sampling rate in Hz.
      freq : 1d array
          Frequency sampling points in Hz.
      gain : 1d array
          Filter gain at frequency sampling points.
          Must be all 0 and 1 for fir_design=="firwin".
      filter_length : int
          Length of the filter to use. Must be odd length if phase == "zero".
      phase : str
          If 'zero', the delay for the filter is compensated (and it must be
          an odd-length symmetric filter). If 'linear', the response is
          uncompensated. If 'zero-double', the filter is applied in the
          forward and reverse directions. If 'minimum', a minimum-phase
          filter will be used.
      fir_window : str
          The window to use in FIR design, can be "hamming" (default),
          "hann", or "blackman".
      fir_design : str
          Can be "firwin2" or "firwin".
  
      Returns
      -------
      h : array
          Filter coefficients.
      """
      assert freq[0] == 0
      if fir_design == 'firwin2':
          from scipy.signal import firwin2 as fir_design
      else:
          assert fir_design == 'firwin'
          fir_design = partial(_firwin_design, sfreq=sfreq)
      from scipy.signal import minimum_phase
  
      # issue a warning if attenuation is less than this
      min_att_db = 12 if phase == 'minimum' else 20
  
      # normalize frequencies
      freq = np.array(freq) / (sfreq / 2.)
      if freq[0] != 0 or freq[-1] != 1:
          raise ValueError('freq must start at 0 and end an Nyquist (%s), got %s'
                           % (sfreq / 2., freq))
      gain = np.array(gain)
  
      # Use overlap-add filter with a fixed length
      N = _check_zero_phase_length(filter_length, phase, gain[-1])
      # construct symmetric (linear phase) filter
      if phase == 'minimum':
          h = fir_design(N * 2 - 1, freq, gain, window=fir_window)
          h = minimum_phase(h)
      else:
          h = fir_design(N, freq, gain, window=fir_window)
      assert h.size == N
      att_db, att_freq = _filter_attenuation(h, freq, gain)
      if phase == 'zero-double':
          att_db += 6
      if att_db < min_att_db:
          att_freq *= sfreq / 2.
          warn('Attenuation at stop frequency %0.2f Hz is only %0.2f dB. '
               'Increase filter_length for higher attenuation.'
               % (att_freq, att_db))
      return h
  
  
  def _check_zero_phase_length(N, phase, gain_nyq=0):
      N = int(N)
      if N % 2 == 0:
          if phase == 'zero':
              raise RuntimeError('filter_length must be odd if phase="zero", '
                                 'got %s' % N)
          elif phase == 'zero-double' and gain_nyq == 1:
              N += 1
      return N
  
  
  def _check_coefficients(system):
      """Check for filter stability."""
      if isinstance(system, tuple):
          from scipy.signal import tf2zpk
          z, p, k = tf2zpk(*system)
      else:  # sos
          from scipy.signal import sos2zpk
          z, p, k = sos2zpk(system)
      if np.any(np.abs(p) > 1.0):
          raise RuntimeError('Filter poles outside unit circle, filter will be '
                             'unstable. Consider using different filter '
                             'coefficients.')
  
  
  def _filtfilt(x, iir_params, picks, n_jobs, copy):
      """Call filtfilt."""
      # set up array for filtering, reshape to 2D, operate on last axis
      from scipy.signal import filtfilt, sosfiltfilt
      padlen = min(iir_params['padlen'], x.shape[-1] - 1)
      n_jobs = check_n_jobs(n_jobs)
      x, orig_shape, picks = _prep_for_filtering(x, copy, picks)
      if 'sos' in iir_params:
          fun = partial(sosfiltfilt, sos=iir_params['sos'], padlen=padlen,
                        axis=-1)
          _check_coefficients(iir_params['sos'])
      else:
          fun = partial(filtfilt, b=iir_params['b'], a=iir_params['a'],
                        padlen=padlen, axis=-1)
          _check_coefficients((iir_params['b'], iir_params['a']))
      if n_jobs == 1:
          for p in picks:
              x[p] = fun(x=x[p])
      else:
          parallel, p_fun, _ = parallel_func(fun, n_jobs)
          data_new = parallel(p_fun(x=x[p]) for p in picks)
          for pp, p in enumerate(picks):
              x[p] = data_new[pp]
      x.shape = orig_shape
      return x
  
  
  def estimate_ringing_samples(system, max_try=100000):
      """Estimate filter ringing.
  
      Parameters
      ----------
      system : tuple | ndarray
          A tuple of (b, a) or ndarray of second-order sections coefficients.
      max_try : int
          Approximate maximum number of samples to try.
          This will be changed to a multiple of 1000.
  
      Returns
      -------
      n : int
          The approximate ringing.
      """
      from scipy import signal
      if isinstance(system, tuple):  # TF
          kind = 'ba'
          b, a = system
          zi = [0.] * (len(a) - 1)
      else:
          kind = 'sos'
          sos = system
          zi = [[0.] * 2] * len(sos)
      n_per_chunk = 1000
      n_chunks_max = int(np.ceil(max_try / float(n_per_chunk)))
      x = np.zeros(n_per_chunk)
      x[0] = 1
      last_good = n_per_chunk
      thresh_val = 0
      for ii in range(n_chunks_max):
          if kind == 'ba':
              h, zi = signal.lfilter(b, a, x, zi=zi)
          else:
              h, zi = signal.sosfilt(sos, x, zi=zi)
          x[0] = 0  # for subsequent iterations we want zero input
          h = np.abs(h)
          thresh_val = max(0.001 * np.max(h), thresh_val)
          idx = np.where(np.abs(h) > thresh_val)[0]
          if len(idx) > 0:
              last_good = idx[-1]
          else:  # this iteration had no sufficiently lange values
              idx = (ii - 1) * n_per_chunk + last_good
              break
      else:
          warn('Could not properly estimate ringing for the filter')
          idx = n_per_chunk * n_chunks_max
      return idx
  
  
  _ftype_dict = {
      'butter': 'Butterworth',
      'cheby1': 'Chebyshev I',
      'cheby2': 'Chebyshev II',
      'ellip': 'Cauer/elliptic',
      'bessel': 'Bessel/Thomson',
  }
  
  
  @verbose
  def construct_iir_filter(iir_params, f_pass=None, f_stop=None, sfreq=None,
                           btype=None, return_copy=True, verbose=None):
      """Use IIR parameters to get filtering coefficients.
  
      This function works like a wrapper for iirdesign and iirfilter in
      scipy.signal to make filter coefficients for IIR filtering. It also
      estimates the number of padding samples based on the filter ringing.
      It creates a new iir_params dict (or updates the one passed to the
      function) with the filter coefficients ('b' and 'a') and an estimate
      of the padding necessary ('padlen') so IIR filtering can be performed.
  
      Parameters
      ----------
      iir_params : dict
          Dictionary of parameters to use for IIR filtering.
  
              * If ``iir_params['sos']`` exists, it will be used as
                second-order sections to perform IIR filtering.
  
                .. versionadded:: 0.13
  
              * Otherwise, if ``iir_params['b']`` and ``iir_params['a']``
                exist, these will be used as coefficients to perform IIR
                filtering.
              * Otherwise, if ``iir_params['order']`` and
                ``iir_params['ftype']`` exist, these will be used with
                `scipy.signal.iirfilter` to make a filter.
                You should also supply ``iir_params['rs']`` and
                ``iir_params['rp']`` if using elliptic or Chebychev filters.
              * Otherwise, if ``iir_params['gpass']`` and
                ``iir_params['gstop']`` exist, these will be used with
                `scipy.signal.iirdesign` to design a filter.
              * ``iir_params['padlen']`` defines the number of samples to pad
                (and an estimate will be calculated if it is not given).
                See Notes for more details.
              * ``iir_params['output']`` defines the system output kind when
                designing filters, either "sos" or "ba". For 0.13 the
                default is 'ba' but will change to 'sos' in 0.14.
  
      f_pass : float or list of float
          Frequency for the pass-band. Low-pass and high-pass filters should
          be a float, band-pass should be a 2-element list of float.
      f_stop : float or list of float
          Stop-band frequency (same size as f_pass). Not used if 'order' is
          specified in iir_params.
      sfreq : float | None
          The sample rate.
      btype : str
          Type of filter. Should be 'lowpass', 'highpass', or 'bandpass'
          (or analogous string representations known to
          :func:`scipy.signal.iirfilter`).
      return_copy : bool
          If False, the 'sos', 'b', 'a', and 'padlen' entries in
          ``iir_params`` will be set inplace (if they weren't already).
          Otherwise, a new ``iir_params`` instance will be created and
          returned with these entries.
      %(verbose)s
  
      Returns
      -------
      iir_params : dict
          Updated iir_params dict, with the entries (set only if they didn't
          exist before) for 'sos' (or 'b', 'a'), and 'padlen' for
          IIR filtering.
  
      See Also
      --------
      mne.filter.filter_data
      mne.io.Raw.filter
  
      Notes
      -----
      This function triages calls to :func:`scipy.signal.iirfilter` and
      :func:`scipy.signal.iirdesign` based on the input arguments (see
      linked functions for more details).
  
      .. versionchanged:: 0.14
         Second-order sections are used in filter design by default (replacing
         ``output='ba'`` by ``output='sos'``) to help ensure filter stability
         and reduce numerical error.
  
      Examples
      --------
      iir_params can have several forms. Consider constructing a low-pass
      filter at 40 Hz with 1000 Hz sampling rate.
  
      In the most basic (2-parameter) form of iir_params, the order of the
      filter 'N' and the type of filtering 'ftype' are specified. To get
      coefficients for a 4th-order Butterworth filter, this would be:
  
      >>> iir_params = dict(order=4, ftype='butter', output='sos')  # doctest:+SKIP
      >>> iir_params = construct_iir_filter(iir_params, 40, None, 1000, 'low', return_copy=False)  # doctest:+SKIP
      >>> print((2 * len(iir_params['sos']), iir_params['padlen']))  # doctest:+SKIP
      (4, 82)
  
      Filters can also be constructed using filter design methods. To get a
      40 Hz Chebyshev type 1 lowpass with specific gain characteristics in the
      pass and stop bands (assuming the desired stop band is at 45 Hz), this
      would be a filter with much longer ringing:
  
      >>> iir_params = dict(ftype='cheby1', gpass=3, gstop=20, output='sos')  # doctest:+SKIP
      >>> iir_params = construct_iir_filter(iir_params, 40, 50, 1000, 'low')  # doctest:+SKIP
      >>> print((2 * len(iir_params['sos']), iir_params['padlen']))  # doctest:+SKIP
      (6, 439)
  
      Padding and/or filter coefficients can also be manually specified. For
      a 10-sample moving window with no padding during filtering, for example,
      one can just do:
  
      >>> iir_params = dict(b=np.ones((10)), a=[1, 0], padlen=0)
      >>> iir_params = construct_iir_filter(iir_params, return_copy=False)
      >>> print((iir_params['b'], iir_params['a'], iir_params['padlen']))  # doctest:+SKIP
      (array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]), [1, 0], 0)
  
      For more information, see the tutorials
      :ref:`disc-filtering` and :ref:`tut-filter-resample`.
      """  # noqa: E501
      from scipy.signal import iirfilter, iirdesign, freqz, sosfreqz
      known_filters = ('bessel', 'butter', 'butterworth', 'cauer', 'cheby1',
                       'cheby2', 'chebyshev1', 'chebyshev2', 'chebyshevi',
                       'chebyshevii', 'ellip', 'elliptic')
      if not isinstance(iir_params, dict):
          raise TypeError('iir_params must be a dict, got %s' % type(iir_params))
      # if the filter has been designed, we're good to go
      Wp = None
      if 'sos' in iir_params:
          system = iir_params['sos']
          output = 'sos'
      elif 'a' in iir_params and 'b' in iir_params:
          system = (iir_params['b'], iir_params['a'])
          output = 'ba'
      else:
          output = iir_params.get('output', 'sos')
          _check_option('output', output, ('ba', 'sos'))
          # ensure we have a valid ftype
          if 'ftype' not in iir_params:
              raise RuntimeError('ftype must be an entry in iir_params if ''b'' '
                                 'and ''a'' are not specified')
          ftype = iir_params['ftype']
          if ftype not in known_filters:
              raise RuntimeError('ftype must be in filter_dict from '
                                 'scipy.signal (e.g., butter, cheby1, etc.) not '
                                 '%s' % ftype)
  
          # use order-based design
          f_pass = np.atleast_1d(f_pass)
          if f_pass.ndim > 1:
              raise ValueError('frequencies must be 1D, got %dD' % f_pass.ndim)
          edge_freqs = ', '.join('%0.2f' % (f,) for f in f_pass)
          Wp = f_pass / (float(sfreq) / 2)
          # IT will de designed
          ftype_nice = _ftype_dict.get(ftype, ftype)
          logger.info('')
          logger.info('IIR filter parameters')
          logger.info('---------------------')
          logger.info('%s %s zero-phase (two-pass forward and reverse) '
                      'non-causal filter:' % (ftype_nice, btype))
          # SciPy designs for -3dB but we do forward-backward, so this is -6dB
          if 'order' in iir_params:
              kwargs = dict(N=iir_params['order'], Wn=Wp, btype=btype,
                            ftype=ftype, output=output)
              for key in ('rp', 'rs'):
                  if key in iir_params:
                      kwargs[key] = iir_params[key]
              system = iirfilter(**kwargs)
              logger.info('- Filter order %d (effective, after forward-backward)'
                          % (2 * iir_params['order'] * len(Wp),))
          else:
              # use gpass / gstop design
              Ws = np.asanyarray(f_stop) / (float(sfreq) / 2)
              if 'gpass' not in iir_params or 'gstop' not in iir_params:
                  raise ValueError('iir_params must have at least ''gstop'' and'
                                   ' ''gpass'' (or ''N'') entries')
              system = iirdesign(Wp, Ws, iir_params['gpass'],
                                 iir_params['gstop'], ftype=ftype, output=output)
  
      if system is None:
          raise RuntimeError('coefficients could not be created from iir_params')
      # do some sanity checks
      _check_coefficients(system)
  
      # get the gains at the cutoff frequencies
      if Wp is not None:
          if output == 'sos':
              cutoffs = sosfreqz(system, worN=Wp * np.pi)[1]
          else:
              cutoffs = freqz(system[0], system[1], worN=Wp * np.pi)[1]
          # 2 * 20 here because we do forward-backward filtering
          cutoffs = 40 * np.log10(np.abs(cutoffs))
          cutoffs = ', '.join(['%0.2f' % (c,) for c in cutoffs])
          logger.info('- Cutoff%s at %s Hz: %s dB'
                      % (_pl(f_pass), edge_freqs, cutoffs))
      # now deal with padding
      if 'padlen' not in iir_params:
          padlen = estimate_ringing_samples(system)
      else:
          padlen = iir_params['padlen']
  
      if return_copy:
          iir_params = deepcopy(iir_params)
  
      iir_params.update(dict(padlen=padlen))
      if output == 'sos':
          iir_params.update(sos=system)
      else:
          iir_params.update(b=system[0], a=system[1])
      logger.info('')
      return iir_params
  
  
  def _check_method(method, iir_params, extra_types=()):
      """Parse method arguments."""
      allowed_types = ['iir', 'fir', 'fft'] + list(extra_types)
      _validate_type(method, 'str', 'method')
      _check_option('method', method, allowed_types)
      if method == 'fft':
          method = 'fir'  # use the better name
      if method == 'iir':
          if iir_params is None:
              iir_params = dict()
          if len(iir_params) == 0 or (len(iir_params) == 1 and
                                      'output' in iir_params):
              iir_params = dict(order=4, ftype='butter',
                                output=iir_params.get('output', 'sos'))
      elif iir_params is not None:
          raise ValueError('iir_params must be None if method != "iir"')
      return iir_params, method
  
  
  @verbose
  def filter_data(data, sfreq, l_freq, h_freq, picks=None, filter_length='auto',
                  l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1,
                  method='fir', iir_params=None, copy=True, phase='zero',
                  fir_window='hamming', fir_design='firwin',
                  pad='reflect_limited', verbose=None):
      """Filter a subset of channels.
  
      Parameters
      ----------
      data : ndarray, shape (..., n_times)
          The data to filter.
      sfreq : float
          The sample frequency in Hz.
      %(l_freq)s
      %(h_freq)s
      %(picks_nostr)s
          Currently this is only supported for 2D (n_channels, n_times) and
          3D (n_epochs, n_channels, n_times) arrays.
      %(filter_length)s
      %(l_trans_bandwidth)s
      %(h_trans_bandwidth)s
      %(n_jobs-fir)s
      %(method-fir)s
      %(iir_params)s
      copy : bool
          If True, a copy of x, filtered, is returned. Otherwise, it operates
          on x in place.
      %(phase)s
      %(fir_window)s
      %(fir_design)s
      %(pad-fir)s
          The default is ``'reflect_limited'``.
  
          .. versionadded:: 0.15
      %(verbose)s
  
      Returns
      -------
      data : ndarray, shape (..., n_times)
          The filtered data.
  
      See Also
      --------
      construct_iir_filter
      create_filter
      mne.io.Raw.filter
      notch_filter
      resample
  
      Notes
      -----
      Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
      filter to the channels selected by ``picks``.
  
      ``l_freq`` and ``h_freq`` are the frequencies below which and above
      which, respectively, to filter out of the data. Thus the uses are:
  
          * ``l_freq < h_freq``: band-pass filter
          * ``l_freq > h_freq``: band-stop filter
          * ``l_freq is not None and h_freq is None``: high-pass filter
          * ``l_freq is None and h_freq is not None``: low-pass filter
  
      .. note:: If n_jobs > 1, more memory is required as
                ``len(picks) * n_times`` additional time points need to
                be temporaily stored in memory.
  
      For more information, see the tutorials
      :ref:`disc-filtering` and :ref:`tut-filter-resample` and
      :func:`mne.filter.create_filter`.
      """
      data = _check_filterable(data)
      iir_params, method = _check_method(method, iir_params)
      filt = create_filter(
          data, sfreq, l_freq, h_freq, filter_length, l_trans_bandwidth,
          h_trans_bandwidth, method, iir_params, phase, fir_window, fir_design)
      if method in ('fir', 'fft'):
          data = _overlap_add_filter(data, filt, None, phase, picks, n_jobs,
                                     copy, pad)
      else:
          data = _filtfilt(data, filt, picks, n_jobs, copy)
      return data
  
  
  @verbose
  def create_filter(data, sfreq, l_freq, h_freq, filter_length='auto',
                    l_trans_bandwidth='auto', h_trans_bandwidth='auto',
                    method='fir', iir_params=None, phase='zero',
                    fir_window='hamming', fir_design='firwin', verbose=None):
      r"""Create a FIR or IIR filter.
  
      ``l_freq`` and ``h_freq`` are the frequencies below which and above
      which, respectively, to filter out of the data. Thus the uses are:
  
          * ``l_freq < h_freq``: band-pass filter
          * ``l_freq > h_freq``: band-stop filter
          * ``l_freq is not None and h_freq is None``: high-pass filter
          * ``l_freq is None and h_freq is not None``: low-pass filter
  
      Parameters
      ----------
      data : ndarray, shape (..., n_times) | None
          The data that will be filtered. This is used for sanity checking
          only. If None, no sanity checking related to the length of the signal
          relative to the filter order will be performed.
      sfreq : float
          The sample frequency in Hz.
      %(l_freq)s
      %(h_freq)s
      %(filter_length)s
      %(l_trans_bandwidth)s
      %(h_trans_bandwidth)s
      %(method-fir)s
      %(iir_params)s
      %(phase)s
      %(fir_window)s
      %(fir_design)s
      %(verbose)s
  
      Returns
      -------
      filt : array or dict
          Will be an array of FIR coefficients for method='fir', and dict
          with IIR parameters for method='iir'.
  
      See Also
      --------
      filter_data
  
      Notes
      -----
      .. note:: For FIR filters, the *cutoff frequency*, i.e. the -6 dB point,
                is in the middle of the transition band (when using phase='zero'
                and fir_design='firwin'). For IIR filters, the cutoff frequency
                is given by ``l_freq`` or ``h_freq`` directly, and
                ``l_trans_bandwidth`` and ``h_trans_bandwidth`` are ignored.
  
      **Band-pass filter**
  
      The frequency response is (approximately) given by::
  
         1-|               ----------
           |             /|         | \
       |H| |            / |         |  \
           |           /  |         |   \
           |          /   |         |    \
         0-|----------    |         |     --------------
           |         |    |         |     |            |
           0        Fs1  Fp1       Fp2   Fs2          Nyq
  
      Where:
  
          * Fs1 = Fp1 - l_trans_bandwidth in Hz
          * Fs2 = Fp2 + h_trans_bandwidth in Hz
  
      **Band-stop filter**
  
      The frequency response is (approximately) given by::
  
          1-|---------                   ----------
            |         \                 /
        |H| |          \               /
            |           \             /
            |            \           /
          0-|             -----------
            |        |    |         |    |        |
            0       Fp1  Fs1       Fs2  Fp2      Nyq
  
      Where ``Fs1 = Fp1 + l_trans_bandwidth`` and
      ``Fs2 = Fp2 - h_trans_bandwidth``.
  
      Multiple stop bands can be specified using arrays.
  
      **Low-pass filter**
  
      The frequency response is (approximately) given by::
  
          1-|------------------------
            |                        \
        |H| |                         \
            |                          \
            |                           \
          0-|                            ----------------
            |                       |    |              |
            0                      Fp  Fstop           Nyq
  
      Where ``Fstop = Fp + trans_bandwidth``.
  
      **High-pass filter**
  
      The frequency response is (approximately) given by::
  
          1-|             -----------------------
            |            /
        |H| |           /
            |          /
            |         /
          0-|---------
            |        |    |                     |
            0      Fstop  Fp                   Nyq
  
      Where ``Fstop = Fp - trans_bandwidth``.
  
      .. versionadded:: 0.14
      """
      sfreq = float(sfreq)
      if sfreq < 0:
          raise ValueError('sfreq must be positive')
      # If no data specified, sanity checking will be skipped
      if data is None:
          logger.info('No data specified. Sanity checks related to the length of'
                      ' the signal relative to the filter order will be'
                      ' skipped.')
      if h_freq is not None:
          h_freq = np.array(h_freq, float).ravel()
          if (h_freq > (sfreq / 2.)).any():
              raise ValueError('h_freq (%s) must be less than the Nyquist '
                               'frequency %s' % (h_freq, sfreq / 2.))
      if l_freq is not None:
          l_freq = np.array(l_freq, float).ravel()
          if (l_freq == 0).all():
              l_freq = None
      iir_params, method = _check_method(method, iir_params)
      if l_freq is None and h_freq is None:
          data, sfreq, _, _, _, _, filter_length, phase, fir_window, \
              fir_design = _triage_filter_params(
                  data, sfreq, None, None, None, None,
                  filter_length, method, phase, fir_window, fir_design)
          if method == 'iir':
              out = dict() if iir_params is None else deepcopy(iir_params)
              out.update(b=np.array([1.]), a=np.array([1.]))
          else:
              freq = [0, sfreq / 2.]
              gain = [1., 1.]
      if l_freq is None and h_freq is not None:
          logger.info('Setting up low-pass filter at %0.2g Hz' % (h_freq,))
          data, sfreq, _, f_p, _, f_s, filter_length, phase, fir_window, \
              fir_design = _triage_filter_params(
                  data, sfreq, None, h_freq, None, h_trans_bandwidth,
                  filter_length, method, phase, fir_window, fir_design)
          if method == 'iir':
              out = construct_iir_filter(iir_params, f_p, f_s, sfreq, 'lowpass')
          else:  # 'fir'
              freq = [0, f_p, f_s]
              gain = [1, 1, 0]
              if f_s != sfreq / 2.:
                  freq += [sfreq / 2.]
                  gain += [0]
      elif l_freq is not None and h_freq is None:
          logger.info('Setting up high-pass filter at %0.2g Hz' % (l_freq,))
          data, sfreq, pass_, _, stop, _, filter_length, phase, fir_window, \
              fir_design = _triage_filter_params(
                  data, sfreq, l_freq, None, l_trans_bandwidth, None,
                  filter_length, method, phase, fir_window, fir_design)
          if method == 'iir':
              out = construct_iir_filter(iir_params, pass_, stop, sfreq,
                                         'highpass')
          else:  # 'fir'
              freq = [stop, pass_, sfreq / 2.]
              gain = [0, 1, 1]
              if stop != 0:
                  freq = [0] + freq
                  gain = [0] + gain
      elif l_freq is not None and h_freq is not None:
          if (l_freq < h_freq).any():
              logger.info('Setting up band-pass filter from %0.2g - %0.2g Hz'
                          % (l_freq, h_freq))
              data, sfreq, f_p1, f_p2, f_s1, f_s2, filter_length, phase, \
                  fir_window, fir_design = _triage_filter_params(
                      data, sfreq, l_freq, h_freq, l_trans_bandwidth,
                      h_trans_bandwidth, filter_length, method, phase,
                      fir_window, fir_design)
              if method == 'iir':
                  out = construct_iir_filter(iir_params, [f_p1, f_p2],
                                             [f_s1, f_s2], sfreq, 'bandpass')
              else:  # 'fir'
                  freq = [f_s1, f_p1, f_p2, f_s2]
                  gain = [0, 1, 1, 0]
                  if f_s2 != sfreq / 2.:
                      freq += [sfreq / 2.]
                      gain += [0]
                  if f_s1 != 0:
                      freq = [0] + freq
                      gain = [0] + gain
          else:
              # This could possibly be removed after 0.14 release, but might
              # as well leave it in to sanity check notch_filter
              if len(l_freq) != len(h_freq):
                  raise ValueError('l_freq and h_freq must be the same length')
              msg = 'Setting up band-stop filter'
              if len(l_freq) == 1:
                  msg += ' from %0.2g - %0.2g Hz' % (h_freq, l_freq)
              logger.info(msg)
              # Note: order of outputs is intentionally switched here!
              data, sfreq, f_s1, f_s2, f_p1, f_p2, filter_length, phase, \
                  fir_window, fir_design = _triage_filter_params(
                      data, sfreq, h_freq, l_freq, h_trans_bandwidth,
                      l_trans_bandwidth, filter_length, method, phase,
                      fir_window, fir_design, bands='arr', reverse=True)
              if method == 'iir':
                  if len(f_p1) != 1:
                      raise ValueError('Multiple stop-bands can only be used '
                                       'with FIR filtering')
                  out = construct_iir_filter(iir_params, [f_p1[0], f_p2[0]],
                                             [f_s1[0], f_s2[0]], sfreq,
                                             'bandstop')
              else:  # 'fir'
                  freq = np.r_[f_p1, f_s1, f_s2, f_p2]
                  gain = np.r_[np.ones_like(f_p1), np.zeros_like(f_s1),
                               np.zeros_like(f_s2), np.ones_like(f_p2)]
                  order = np.argsort(freq)
                  freq = freq[order]
                  gain = gain[order]
                  if freq[0] != 0:
                      freq = np.r_[[0.], freq]
                      gain = np.r_[[1.], gain]
                  if freq[-1] != sfreq / 2.:
                      freq = np.r_[freq, [sfreq / 2.]]
                      gain = np.r_[gain, [1.]]
                  if np.any(np.abs(np.diff(gain, 2)) > 1):
                      raise ValueError('Stop bands are not sufficiently '
                                       'separated.')
      if method == 'fir':
          out = _construct_fir_filter(sfreq, freq, gain, filter_length, phase,
                                      fir_window, fir_design)
      return out
  
  
  @verbose
  def notch_filter(x, Fs, freqs, filter_length='auto', notch_widths=None,
                   trans_bandwidth=1, method='fir', iir_params=None,
                   mt_bandwidth=None, p_value=0.05, picks=None, n_jobs=1,
                   copy=True, phase='zero', fir_window='hamming',
                   fir_design='firwin', pad='reflect_limited', verbose=None):
      r"""Notch filter for the signal x.
  
      Applies a zero-phase notch filter to the signal x, operating on the last
      dimension.
  
      Parameters
      ----------
      x : array
          Signal to filter.
      Fs : float
          Sampling rate in Hz.
      freqs : float | array of float | None
          Frequencies to notch filter in Hz, e.g. np.arange(60, 241, 60).
          None can only be used with the mode 'spectrum_fit', where an F
          test is used to find sinusoidal components.
      %(filter_length)s
      notch_widths : float | array of float | None
          Width of the stop band (centred at each freq in freqs) in Hz.
          If None, freqs / 200 is used.
      trans_bandwidth : float
          Width of the transition band in Hz.
          Only used for ``method='fir'``.
      %(method-fir)s
          'spectrum_fit' will use multi-taper estimation of sinusoidal
          components. If freqs=None and method='spectrum_fit', significant
          sinusoidal components are detected using an F test, and noted by
          logging.
      %(iir_params)s
      mt_bandwidth : float | None
          The bandwidth of the multitaper windowing function in Hz.
          Only used in 'spectrum_fit' mode.
      p_value : float
          P-value to use in F-test thresholding to determine significant
          sinusoidal components to remove when method='spectrum_fit' and
          freqs=None. Note that this will be Bonferroni corrected for the
          number of frequencies, so large p-values may be justified.
      %(picks_nostr)s
          Only supported for 2D (n_channels, n_times) and 3D
          (n_epochs, n_channels, n_times) data.
      %(n_jobs-fir)s
      copy : bool
          If True, a copy of x, filtered, is returned. Otherwise, it operates
          on x in place.
      %(phase)s
      %(fir_window)s
      %(fir_design)s
      %(pad-fir)s
      %(verbose)s
  
      Returns
      -------
      xf : array
          The x array filtered.
  
      See Also
      --------
      filter_data
      resample
  
      Notes
      -----
      The frequency response is (approximately) given by::
  
          1-|----------         -----------
            |          \       /
        |H| |           \     /
            |            \   /
            |             \ /
          0-|              -
            |         |    |    |         |
            0        Fp1 freq  Fp2       Nyq
  
      For each freq in freqs, where ``Fp1 = freq - trans_bandwidth / 2`` and
      ``Fs2 = freq + trans_bandwidth / 2``.
  
      References
      ----------
      Multi-taper removal is inspired by code from the Chronux toolbox, see
      www.chronux.org and the book "Observed Brain Dynamics" by Partha Mitra
      & Hemant Bokil, Oxford University Press, New York, 2008. Please
      cite this in publications if method 'spectrum_fit' is used.
      """
      iir_params, method = _check_method(method, iir_params, ['spectrum_fit'])
  
      if freqs is not None:
          freqs = np.atleast_1d(freqs)
      elif method != 'spectrum_fit':
          raise ValueError('freqs=None can only be used with method '
                           'spectrum_fit')
  
      # Only have to deal with notch_widths for non-autodetect
      if freqs is not None:
          if notch_widths is None:
              notch_widths = freqs / 200.0
          elif np.any(notch_widths < 0):
              raise ValueError('notch_widths must be >= 0')
          else:
              notch_widths = np.atleast_1d(notch_widths)
              if len(notch_widths) == 1:
                  notch_widths = notch_widths[0] * np.ones_like(freqs)
              elif len(notch_widths) != len(freqs):
                  raise ValueError('notch_widths must be None, scalar, or the '
                                   'same length as freqs')
  
      if method in ('fir', 'iir'):
          # Speed this up by computing the fourier coefficients once
          tb_2 = trans_bandwidth / 2.0
          lows = [freq - nw / 2.0 - tb_2
                  for freq, nw in zip(freqs, notch_widths)]
          highs = [freq + nw / 2.0 + tb_2
                   for freq, nw in zip(freqs, notch_widths)]
          xf = filter_data(x, Fs, highs, lows, picks, filter_length, tb_2, tb_2,
                           n_jobs, method, iir_params, copy, phase, fir_window,
                           fir_design, pad=pad)
      elif method == 'spectrum_fit':
          xf = _mt_spectrum_proc(x, Fs, freqs, notch_widths, mt_bandwidth,
                                 p_value, picks, n_jobs, copy)
  
      return xf
  
  
  def _mt_spectrum_proc(x, sfreq, line_freqs, notch_widths, mt_bandwidth,
                        p_value, picks, n_jobs, copy):
      """Call _mt_spectrum_remove."""
      from scipy import stats
      # set up array for filtering, reshape to 2D, operate on last axis
      n_jobs = check_n_jobs(n_jobs)
      x, orig_shape, picks = _prep_for_filtering(x, copy, picks)
  
      # XXX need to implement the moving window version for raw files
      n_times = x.shape[1]
  
      # max taper size chosen because it has an max error < 1e-3:
      # >>> np.max(np.diff(dpss_windows(953, 4, 100)[0]))
      # 0.00099972447657578449
      # so we use 1000 because it's the first "nice" number bigger than 953.
      # but if we have a new enough scipy,
      # it's only ~0.175 sec for 8 tapers even with 100000 samples
      dpss_n_times_max = 100000 if check_version('scipy', '1.1') else 1000
  
      # figure out what tapers to use
      window_fun, eigvals, _ = _compute_mt_params(
          n_times, sfreq, mt_bandwidth, False, False,
          interp_from=min(n_times, dpss_n_times_max), verbose=False)
  
      # F-stat of 1-p point
      threshold = stats.f.ppf(1 - p_value / n_times, 2, 2 * len(window_fun) - 2)
  
      if n_jobs == 1:
          freq_list = list()
          for ii, x_ in enumerate(x):
              if ii in picks:
                  x[ii], f = _mt_spectrum_remove(x_, sfreq, line_freqs,
                                                 notch_widths, window_fun,
                                                 threshold)
                  freq_list.append(f)
      else:
          parallel, p_fun, _ = parallel_func(_mt_spectrum_remove, n_jobs)
          data_new = parallel(p_fun(x_, sfreq, line_freqs, notch_widths,
                                    window_fun, threshold)
                              for xi, x_ in enumerate(x)
                              if xi in picks)
          freq_list = [d[1] for d in data_new]
          data_new = np.array([d[0] for d in data_new])
          x[picks, :] = data_new
  
      # report found frequencies
      for rm_freqs in freq_list:
          if line_freqs is None:
              if len(rm_freqs) > 0:
                  logger.info('Detected notch frequencies:\n%s'
                              % ', '.join([str(rm_f) for rm_f in rm_freqs]))
              else:
                  logger.info('Detected notch frequecies:\nNone')
  
      x.shape = orig_shape
      return x
  
  
  def _mt_spectrum_remove(x, sfreq, line_freqs, notch_widths,
                          window_fun, threshold):
      """Use MT-spectrum to remove line frequencies.
  
      Based on Chronux. If line_freqs is specified, all freqs within notch_width
      of each line_freq is set to zero.
      """
      # drop the even tapers
      n_tapers = len(window_fun)
      tapers_odd = np.arange(0, n_tapers, 2)
      tapers_even = np.arange(1, n_tapers, 2)
      tapers_use = window_fun[tapers_odd]
  
      # sum tapers for (used) odd prolates across time (n_tapers, 1)
      H0 = np.sum(tapers_use, axis=1)
  
      # sum of squares across tapers (1, )
      H0_sq = sum_squared(H0)
  
      # make "time" vector
      rads = 2 * np.pi * (np.arange(x.size) / float(sfreq))
  
      # compute mt_spectrum (returning n_ch, n_tapers, n_freq)
      x_p, freqs = _mt_spectra(x[np.newaxis, :], window_fun, sfreq)
  
      # sum of the product of x_p and H0 across tapers (1, n_freqs)
      x_p_H0 = np.sum(x_p[:, tapers_odd, :] *
                      H0[np.newaxis, :, np.newaxis], axis=1)
  
      # resulting calculated amplitudes for all freqs
      A = x_p_H0 / H0_sq
  
      if line_freqs is None:
          # figure out which freqs to remove using F stat
  
          # estimated coefficient
          x_hat = A * H0[:, np.newaxis]
  
          # numerator for F-statistic
          num = (n_tapers - 1) * (A * A.conj()).real * H0_sq
          # denominator for F-statistic
          den = (np.sum(np.abs(x_p[:, tapers_odd, :] - x_hat) ** 2, 1) +
                 np.sum(np.abs(x_p[:, tapers_even, :]) ** 2, 1))
          den[den == 0] = np.inf
          f_stat = num / den
  
          # find frequencies to remove
          indices = np.where(f_stat > threshold)[1]
          rm_freqs = freqs[indices]
      else:
          # specify frequencies
          indices_1 = np.unique([np.argmin(np.abs(freqs - lf))
                                 for lf in line_freqs])
          notch_widths /= 2.0
          indices_2 = [np.logical_and(freqs > lf - nw, freqs < lf + nw)
                       for lf, nw in zip(line_freqs, notch_widths)]
          indices_2 = np.where(np.any(np.array(indices_2), axis=0))[0]
          indices = np.unique(np.r_[indices_1, indices_2])
          rm_freqs = freqs[indices]
  
      fits = list()
      for ind in indices:
          c = 2 * A[0, ind]
          fit = np.abs(c) * np.cos(freqs[ind] * rads + np.angle(c))
          fits.append(fit)
  
      if len(fits) == 0:
          datafit = 0.0
      else:
          # fitted sinusoids are summed, and subtracted from data
          datafit = np.sum(np.atleast_2d(fits), axis=0)
  
      return x - datafit, rm_freqs
  
  
  def _check_filterable(x, kind='filtered'):
      x = np.asanyarray(x)
      if x.dtype != np.float64:
          raise ValueError('Data to be %s must be real floating, got %s'
                           % (kind, x.dtype,))
      return x
  
  
  @verbose
  def resample(x, up=1., down=1., npad=100, axis=-1, window='boxcar', n_jobs=1,
               pad='reflect_limited', verbose=None):
      """Resample an array.
  
      Operates along the last dimension of the array.
  
      Parameters
      ----------
      x : ndarray
          Signal to resample.
      up : float
          Factor to upsample by.
      down : float
          Factor to downsample by.
      %(npad)s
      axis : int
          Axis along which to resample (default is the last axis).
      %(window-resample)s
      %(n_jobs-cuda)s
      %(pad-fir)s
          The default is ``'reflect_limited'``.
  
          .. versionadded:: 0.15
      %(verbose)s
  
      Returns
      -------
      y : array
          The x array resampled.
  
      Notes
      -----
      This uses (hopefully) intelligent edge padding and frequency-domain
      windowing improve scipy.signal.resample's resampling method, which
      we have adapted for our use here. Choices of npad and window have
      important consequences, and the default choices should work well
      for most natural signals.
  
      Resampling arguments are broken into "up" and "down" components for future
      compatibility in case we decide to use an upfirdn implementation. The
      current implementation is functionally equivalent to passing
      up=up/down and down=1.
      """
      from scipy.signal import get_window
      # check explicitly for backwards compatibility
      if not isinstance(axis, int):
          err = ("The axis parameter needs to be an integer (got %s). "
                 "The axis parameter was missing from this function for a "
                 "period of time, you might be intending to specify the "
                 "subsequent window parameter." % repr(axis))
          raise TypeError(err)
  
      # make sure our arithmetic will work
      x = _check_filterable(x, 'resampled')
      ratio = float(up) / down
      if axis < 0:
          axis = x.ndim + axis
      orig_last_axis = x.ndim - 1
      if axis != orig_last_axis:
          x = x.swapaxes(axis, orig_last_axis)
      orig_shape = x.shape
      x_len = orig_shape[-1]
      if x_len == 0:
          warn('x has zero length along last axis, returning a copy of x')
          return x.copy()
      bad_msg = 'npad must be "auto" or an integer'
      if isinstance(npad, str):
          if npad != 'auto':
              raise ValueError(bad_msg)
          # Figure out reasonable pad that gets us to a power of 2
          min_add = min(x_len // 8, 100) * 2
          npad = 2 ** int(np.ceil(np.log2(x_len + min_add))) - x_len
          npad, extra = divmod(npad, 2)
          npads = np.array([npad, npad + extra], int)
      else:
          if npad != int(npad):
              raise ValueError(bad_msg)
          npads = np.array([npad, npad], int)
      del npad
  
      # prep for resampling now
      x_flat = x.reshape((-1, x_len))
      orig_len = x_len + npads.sum()  # length after padding
      new_len = int(round(ratio * orig_len))  # length after resampling
      final_len = int(round(ratio * x_len))
      to_removes = [int(round(ratio * npads[0]))]
      to_removes.append(new_len - final_len - to_removes[0])
      to_removes = np.array(to_removes)
      # This should hold:
      # assert np.abs(to_removes[1] - to_removes[0]) <= int(np.ceil(ratio))
  
      # figure out windowing function
      if window is not None:
          if callable(window):
              W = window(fftfreq(orig_len))
          elif isinstance(window, np.ndarray) and \
                  window.shape == (orig_len,):
              W = window
          else:
              W = ifftshift(get_window(window, orig_len))
      else:
          W = np.ones(orig_len)
      W *= (float(new_len) / float(orig_len))
  
      # figure out if we should use CUDA
      n_jobs, cuda_dict = _setup_cuda_fft_resample(n_jobs, W, new_len)
  
      # do the resampling using an adaptation of scipy's FFT-based resample()
      # use of the 'flat' window is recommended for minimal ringing
      if n_jobs == 1:
          y = np.zeros((len(x_flat), new_len - to_removes.sum()), dtype=x.dtype)
          for xi, x_ in enumerate(x_flat):
              y[xi] = _fft_resample(x_, new_len, npads, to_removes,
                                    cuda_dict, pad)
      else:
          parallel, p_fun, _ = parallel_func(_fft_resample, n_jobs)
          y = parallel(p_fun(x_, new_len, npads, to_removes, cuda_dict, pad)
                       for x_ in x_flat)
          y = np.array(y)
  
      # Restore the original array shape (modified for resampling)
      y.shape = orig_shape[:-1] + (y.shape[1],)
      if axis != orig_last_axis:
          y = y.swapaxes(axis, orig_last_axis)
  
      return y
  
  
  def _resample_stim_channels(stim_data, up, down):
      """Resample stim channels, carefully.
  
      Parameters
      ----------
      stim_data : array, shape (n_samples,) or (n_stim_channels, n_samples)
          Stim channels to resample.
      up : float
          Factor to upsample by.
      down : float
          Factor to downsample by.
  
      Returns
      -------
      stim_resampled : array, shape (n_stim_channels, n_samples_resampled)
          The resampled stim channels.
  
      Note
      ----
      The approach taken here is equivalent to the approach in the C-code.
      See the decimate_stimch function in MNE/mne_browse_raw/save.c
      """
      stim_data = np.atleast_2d(stim_data)
      n_stim_channels, n_samples = stim_data.shape
  
      ratio = float(up) / down
      resampled_n_samples = int(round(n_samples * ratio))
  
      stim_resampled = np.zeros((n_stim_channels, resampled_n_samples))
  
      # Figure out which points in old data to subsample protect against
      # out-of-bounds, which can happen (having one sample more than
      # expected) due to padding
      sample_picks = np.minimum(
          (np.arange(resampled_n_samples) / ratio).astype(int),
          n_samples - 1
      )
  
      # Create windows starting from sample_picks[i], ending at sample_picks[i+1]
      windows = zip(sample_picks, np.r_[sample_picks[1:], n_samples])
  
      # Use the first non-zero value in each window
      for window_i, window in enumerate(windows):
          for stim_num, stim in enumerate(stim_data):
              nonzero = stim[window[0]:window[1]].nonzero()[0]
              if len(nonzero) > 0:
                  val = stim[window[0] + nonzero[0]]
              else:
                  val = stim[window[0]]
              stim_resampled[stim_num, window_i] = val
  
      return stim_resampled
  
  
  def detrend(x, order=1, axis=-1):
      """Detrend the array x.
  
      Parameters
      ----------
      x : n-d array
          Signal to detrend.
      order : int
          Fit order. Currently must be '0' or '1'.
      axis : int
          Axis of the array to operate on.
  
      Returns
      -------
      y : array
          The x array detrended.
  
      Examples
      --------
      As in :func:`scipy.signal.detrend`::
  
          >>> randgen = np.random.RandomState(9)
          >>> npoints = int(1e3)
          >>> noise = randgen.randn(npoints)
          >>> x = 3 + 2*np.linspace(0, 1, npoints) + noise
          >>> (detrend(x) - noise).max() < 0.01
          True
      """
      from scipy.signal import detrend
      if axis > len(x.shape):
          raise ValueError('x does not have %d axes' % axis)
      if order == 0:
          fit = 'constant'
      elif order == 1:
          fit = 'linear'
      else:
          raise ValueError('order must be 0 or 1')
  
      y = detrend(x, axis=axis, type=fit)
  
      return y
  
  
  # Taken from Ifeachor and Jervis p. 356.
  # Note that here the passband ripple and stopband attenuation are
  # rendundant. The scalar passband ripple δp is expressed in dB as
  # 20 * log10(1+δp), but the scalar stopband ripple δs is expressed in dB as
  # -20 * log10(δs). So if we know that our stopband attenuation is 53 dB
  # (Hamming) then δs = 10 ** (53 / -20.), which means that the passband
  # deviation should be 20 * np.log10(1 + 10 ** (53 / -20.)) == 0.0194.
  _fir_window_dict = {
      'hann': dict(name='Hann', ripple=0.0546, attenuation=44),
      'hamming': dict(name='Hamming', ripple=0.0194, attenuation=53),
      'blackman': dict(name='Blackman', ripple=0.0017, attenuation=74),
  }
  _known_fir_windows = tuple(sorted(_fir_window_dict.keys()))
  _known_phases = ('linear', 'zero', 'zero-double', 'minimum')
  _known_fir_designs = ('firwin', 'firwin2')
  _fir_design_dict = {
      'firwin': 'Windowed time-domain',
      'firwin2': 'Windowed frequency-domain',
  }
  
  
  def _triage_filter_params(x, sfreq, l_freq, h_freq,
                            l_trans_bandwidth, h_trans_bandwidth,
                            filter_length, method, phase, fir_window,
                            fir_design, bands='scalar', reverse=False):
      """Validate and automate filter parameter selection."""
      _validate_type(phase, 'str', 'phase')
      _check_option('phase', phase, _known_phases)
      _validate_type(fir_window, 'str', 'fir_window')
      _check_option('fir_window', fir_window, _known_fir_windows)
      _validate_type(fir_design, 'str', 'fir_design')
      _check_option('fir_design', fir_design, _known_fir_designs)
  
      # Helpers for reporting
      report_phase = 'non-linear phase' if phase == 'minimum' else 'zero-phase'
      causality = 'causal' if phase == 'minimum' else 'non-causal'
      if phase == 'zero-double':
          report_pass = 'two-pass forward and reverse'
      else:
          report_pass = 'one-pass'
      if l_freq is not None:
          if h_freq is not None:
              kind = 'bandstop' if reverse else 'bandpass'
          else:
              kind = 'highpass'
              assert not reverse
      elif h_freq is not None:
          kind = 'lowpass'
          assert not reverse
      else:
          kind = 'allpass'
  
      def float_array(c):
          return np.array(c, float).ravel()
  
      if bands == 'arr':
          cast = float_array
      else:
          cast = float
      sfreq = float(sfreq)
      if l_freq is not None:
          l_freq = cast(l_freq)
          if np.any(l_freq <= 0):
              raise ValueError('highpass frequency %s must be greater than zero'
                               % (l_freq,))
      if h_freq is not None:
          h_freq = cast(h_freq)
          if np.any(h_freq >= sfreq / 2.):
              raise ValueError('lowpass frequency %s must be less than Nyquist '
                               '(%s)' % (h_freq, sfreq / 2.))
  
      dB_cutoff = False  # meaning, don't try to compute or report
      if bands == 'scalar' or (len(h_freq) == 1 and len(l_freq) == 1):
          if phase == 'zero':
              dB_cutoff = '-6 dB'
          elif phase == 'zero-double':
              dB_cutoff = '-12 dB'
  
      if method == 'iir':
          # Ignore these parameters, effectively
          l_stop, h_stop = l_freq, h_freq
      else:  # method == 'fir'
          l_stop = h_stop = None
          logger.info('')
          logger.info('FIR filter parameters')
          logger.info('---------------------')
          logger.info('Designing a %s, %s, %s %s filter:'
                      % (report_pass, report_phase, causality, kind))
          logger.info('- %s design (%s) method'
                      % (_fir_design_dict[fir_design], fir_design))
          this_dict = _fir_window_dict[fir_window]
          if fir_design == 'firwin':
              logger.info('- {name:s} window with {ripple:0.4f} passband ripple '
                          'and {attenuation:d} dB stopband attenuation'
                          .format(**this_dict))
          else:
              logger.info('- {name:s} window'.format(**this_dict))
  
          if l_freq is not None:  # high-pass component
              if isinstance(l_trans_bandwidth, str):
                  if l_trans_bandwidth != 'auto':
                      raise ValueError('l_trans_bandwidth must be "auto" if '
                                       'string, got "%s"' % l_trans_bandwidth)
                  l_trans_bandwidth = np.minimum(np.maximum(0.25 * l_freq, 2.),
                                                 l_freq)
              msg = ('- Lower transition bandwidth: %0.2f Hz'
                     % (l_trans_bandwidth))
              if dB_cutoff:
                  logger.info('- Lower passband edge: %0.2f' % (l_freq,))
                  msg += ' (%s cutoff frequency: %0.2f Hz)' % (
                      dB_cutoff, l_freq - l_trans_bandwidth / 2.)
              logger.info(msg)
              l_trans_bandwidth = cast(l_trans_bandwidth)
              if np.any(l_trans_bandwidth <= 0):
                  raise ValueError('l_trans_bandwidth must be positive, got %s'
                                   % (l_trans_bandwidth,))
              l_stop = l_freq - l_trans_bandwidth
              if reverse:  # band-stop style
                  l_stop += l_trans_bandwidth
                  l_freq += l_trans_bandwidth
              if np.any(l_stop < 0):
                  raise ValueError('Filter specification invalid: Lower stop '
                                   'frequency negative (%0.2f Hz). Increase pass'
                                   ' frequency or reduce the transition '
                                   'bandwidth (l_trans_bandwidth)' % l_stop)
          if h_freq is not None:  # low-pass component
              if isinstance(h_trans_bandwidth, str):
                  if h_trans_bandwidth != 'auto':
                      raise ValueError('h_trans_bandwidth must be "auto" if '
                                       'string, got "%s"' % h_trans_bandwidth)
                  h_trans_bandwidth = np.minimum(np.maximum(0.25 * h_freq, 2.),
                                                 sfreq / 2. - h_freq)
              msg = ('- Upper transition bandwidth: %0.2f Hz'
                     % (h_trans_bandwidth))
              if dB_cutoff:
                  logger.info('- Upper passband edge: %0.2f Hz' % (h_freq,))
                  msg += ' (%s cutoff frequency: %0.2f Hz)' % (
                      dB_cutoff, h_freq + h_trans_bandwidth / 2.)
              logger.info(msg)
              h_trans_bandwidth = cast(h_trans_bandwidth)
              if np.any(h_trans_bandwidth <= 0):
                  raise ValueError('h_trans_bandwidth must be positive, got %s'
                                   % (h_trans_bandwidth,))
              h_stop = h_freq + h_trans_bandwidth
              if reverse:  # band-stop style
                  h_stop -= h_trans_bandwidth
                  h_freq -= h_trans_bandwidth
              if np.any(h_stop > sfreq / 2):
                  raise ValueError('Effective band-stop frequency (%s) is too '
                                   'high (maximum based on Nyquist is %s)'
                                   % (h_stop, sfreq / 2.))
          if isinstance(filter_length, str):
              filter_length = filter_length.lower()
              if filter_length == 'auto':
                  h_check = h_trans_bandwidth if h_freq is not None else np.inf
                  l_check = l_trans_bandwidth if l_freq is not None else np.inf
                  mult_fact = 2. if fir_design == 'firwin2' else 1.
                  filter_length = max(int(round(
                      _length_factors[fir_window] * sfreq * mult_fact /
                      float(min(h_check, l_check)))), 1)
              else:
                  err_msg = ('filter_length, if a string, must be a '
                             'human-readable time, e.g. "10s", or "auto", not '
                             '"%s"' % filter_length)
                  if filter_length.lower().endswith('ms'):
                      mult_fact = 1e-3
                      filter_length = filter_length[:-2]
                  elif filter_length[-1].lower() == 's':
                      mult_fact = 1
                      filter_length = filter_length[:-1]
                  else:
                      raise ValueError(err_msg)
                  # now get the number
                  try:
                      filter_length = float(filter_length)
                  except ValueError:
                      raise ValueError(err_msg)
                  if phase == 'zero-double':  # old mode
                      filter_length = 2 ** int(np.ceil(np.log2(
                          filter_length * mult_fact * sfreq)))
                  else:
                      filter_length = max(int(np.ceil(filter_length * mult_fact *
                                                      sfreq)), 1)
              if fir_design == 'firwin':
                  filter_length += (filter_length - 1) % 2
          elif not isinstance(filter_length, int):
              raise ValueError('filter_length must be a str, int, or None, got '
                               '%s' % (type(filter_length),))
          logger.info('- Filter length: %s samples (%0.3f sec)'
                      % (filter_length, filter_length / sfreq))
          logger.info('')
  
      if filter_length != 'auto':
          if phase == 'zero' and method == 'fir':
              filter_length += (filter_length % 2 == 0)
          if filter_length <= 0:
              raise ValueError('filter_length must be positive, got %s'
                               % (filter_length,))
  
      # If we have data supplied, do a sanity check
      if x is not None:
          x = _check_filterable(x)
          len_x = x.shape[-1]
          if method != 'fir':
              filter_length = len_x
          if filter_length > len_x and not (l_freq is None and h_freq is None):
              warn('filter_length (%s) is longer than the signal (%s), '
                   'distortion is likely. Reduce filter length or filter a '
                   'longer signal.' % (filter_length, len_x))
  
      logger.debug('Using filter length: %s' % filter_length)
      return (x, sfreq, l_freq, h_freq, l_stop, h_stop, filter_length, phase,
              fir_window, fir_design)
  
  
  class FilterMixin(object):
      """Object for Epoch/Evoked filtering."""
  
      @verbose
      def savgol_filter(self, h_freq, verbose=None):
          """Filter the data using Savitzky-Golay polynomial method.
  
          Parameters
          ----------
          h_freq : float
              Approximate high cut-off frequency in Hz. Note that this
              is not an exact cutoff, since Savitzky-Golay filtering
              :footcite:`SavitzkyGolay1964` is done using polynomial fits
              instead of FIR/IIR filtering. This parameter is thus used to
              determine the length of the window over which a 5th-order
              polynomial smoothing is used.
          %(verbose_meth)s
  
          Returns
          -------
          inst : instance of Epochs or Evoked
              The object with the filtering applied.
  
          See Also
          --------
          mne.io.Raw.filter
  
          Notes
          -----
          For Savitzky-Golay low-pass approximation, see:
  
              https://gist.github.com/larsoner/bbac101d50176611136b
  
          .. versionadded:: 0.9.0
  
          References
          ----------
          .. footbibliography::
  
          Examples
          --------
          >>> import mne
          >>> from os import path as op
          >>> evoked_fname = op.join(mne.datasets.sample.data_path(), 'MEG', 'sample', 'sample_audvis-ave.fif')  # doctest:+SKIP
          >>> evoked = mne.read_evokeds(evoked_fname, baseline=(None, 0))[0]  # doctest:+SKIP
          >>> evoked.savgol_filter(10.)  # low-pass at around 10 Hz # doctest:+SKIP
          >>> evoked.plot()  # doctest:+SKIP
          """  # noqa: E501
          from scipy.signal import savgol_filter
          _check_preload(self, 'inst.savgol_filter')
          h_freq = float(h_freq)
          if h_freq >= self.info['sfreq'] / 2.:
              raise ValueError('h_freq must be less than half the sample rate')
  
          # savitzky-golay filtering
          window_length = (int(np.round(self.info['sfreq'] /
                                        h_freq)) // 2) * 2 + 1
          logger.info('Using savgol length %d' % window_length)
          self._data[:] = savgol_filter(self._data, axis=-1, polyorder=5,
                                        window_length=window_length)
          return self
  
      @verbose
      def filter(self, l_freq, h_freq, picks=None, filter_length='auto',
                 l_trans_bandwidth='auto', h_trans_bandwidth='auto', n_jobs=1,
                 method='fir', iir_params=None, phase='zero',
                 fir_window='hamming', fir_design='firwin',
                 skip_by_annotation=('edge', 'bad_acq_skip'), pad='edge',
                 verbose=None):
          """Filter a subset of channels.
  
          Parameters
          ----------
          %(l_freq)s
          %(h_freq)s
          %(picks_all_data)s
          %(filter_length)s
          %(l_trans_bandwidth)s
          %(h_trans_bandwidth)s
          %(n_jobs-fir)s
          %(method-fir)s
          %(iir_params)s
          %(phase)s
          %(fir_window)s
          %(fir_design)s
          skip_by_annotation : str | list of str
              If a string (or list of str), any annotation segment that begins
              with the given string will not be included in filtering, and
              segments on either side of the given excluded annotated segment
              will be filtered separately (i.e., as independent signals).
              The default (``('edge', 'bad_acq_skip')`` will separately filter
              any segments that were concatenated by :func:`mne.concatenate_raws`
              or :meth:`mne.io.Raw.append`, or separated during acquisition.
              To disable, provide an empty list. Only used if ``inst`` is raw.
  
              .. versionadded:: 0.16.
          %(pad-fir)s
          %(verbose_meth)s
  
          Returns
          -------
          inst : instance of Epochs, Evoked, or Raw
              The filtered data.
  
          See Also
          --------
          mne.filter.create_filter
          mne.Evoked.savgol_filter
          mne.io.Raw.notch_filter
          mne.io.Raw.resample
          mne.filter.create_filter
          mne.filter.filter_data
          mne.filter.construct_iir_filter
  
          Notes
          -----
          Applies a zero-phase low-pass, high-pass, band-pass, or band-stop
          filter to the channels selected by ``picks``.
          The data are modified inplace.
  
          The object has to have the data loaded e.g. with ``preload=True``
          or ``self.load_data()``.
  
          ``l_freq`` and ``h_freq`` are the frequencies below which and above
          which, respectively, to filter out of the data. Thus the uses are:
  
              * ``l_freq < h_freq``: band-pass filter
              * ``l_freq > h_freq``: band-stop filter
              * ``l_freq is not None and h_freq is None``: high-pass filter
              * ``l_freq is None and h_freq is not None``: low-pass filter
  
          ``self.info['lowpass']`` and ``self.info['highpass']`` are only
          updated with picks=None.
  
          .. note:: If n_jobs > 1, more memory is required as
                    ``len(picks) * n_times`` additional time points need to
                    be temporaily stored in memory.
  
          For more information, see the tutorials
          :ref:`disc-filtering` and :ref:`tut-filter-resample` and
          :func:`mne.filter.create_filter`.
  
          .. versionadded:: 0.15
          """
          from .io.base import BaseRaw
          _check_preload(self, 'inst.filter')
          if pad is None and method != 'iir':
              pad = 'edge'
          update_info, picks = _filt_check_picks(self.info, picks,
                                                 l_freq, h_freq)
          if isinstance(self, BaseRaw):
              # Deal with annotations
              onsets, ends = _annotations_starts_stops(
                  self, skip_by_annotation, invert=True)
              logger.info('Filtering raw data in %d contiguous segment%s'
                          % (len(onsets), _pl(onsets)))
          else:
              onsets, ends = np.array([0]), np.array([self._data.shape[1]])
          max_idx = (ends - onsets).argmax()
          for si, (start, stop) in enumerate(zip(onsets, ends)):
              # Only output filter params once (for info level), and only warn
              # once about the length criterion (longest segment is too short)
              use_verbose = verbose if si == max_idx else 'error'
              filter_data(
                  self._data[:, start:stop], self.info['sfreq'], l_freq, h_freq,
                  picks, filter_length, l_trans_bandwidth, h_trans_bandwidth,
                  n_jobs, method, iir_params, copy=False, phase=phase,
                  fir_window=fir_window, fir_design=fir_design, pad=pad,
                  verbose=use_verbose)
          # update info if filter is applied to all data channels,
          # and it's not a band-stop filter
          _filt_update_info(self.info, update_info, l_freq, h_freq)
          return self
  
      @verbose
      def resample(self, sfreq, npad='auto', window='boxcar', n_jobs=1,
                   pad='edge', verbose=None):  # lgtm
          """Resample data.
  
          .. note:: Data must be loaded.
  
          Parameters
          ----------
          sfreq : float
              New sample rate to use.
          %(npad)s
          %(window-resample)s
          %(n_jobs-cuda)s
          %(pad-fir)s
              The default is ``'edge'``, which pads with the edge values of each
              vector.
  
              .. versionadded:: 0.15
          %(verbose_meth)s
  
          Returns
          -------
          inst : instance of Epochs or Evoked
              The resampled object.
  
          See Also
          --------
          mne.io.Raw.resample
  
          Notes
          -----
          For some data, it may be more accurate to use npad=0 to reduce
          artifacts. This is dataset dependent -- check your data!
          """
          from .epochs import BaseEpochs
          from .evoked import Evoked
          # Should be guaranteed by our inheritance, and the fact that
          # mne.io.base.BaseRaw overrides this method
          assert isinstance(self, (BaseEpochs, Evoked))
  
          _check_preload(self, 'inst.resample')
  
          sfreq = float(sfreq)
          o_sfreq = self.info['sfreq']
          self._data = resample(self._data, sfreq, o_sfreq, npad, window=window,
                                n_jobs=n_jobs, pad=pad)
          self.info['sfreq'] = float(sfreq)
          lowpass = self.info.get('lowpass')
          lowpass = np.inf if lowpass is None else lowpass
          self.info['lowpass'] = min(lowpass, sfreq / 2.)
          new_times = (np.arange(self._data.shape[-1], dtype=np.float) /
                       sfreq + self.times[0])
          # adjust indirectly affected variables
          if isinstance(self, BaseEpochs):
              self._set_times(new_times)
              self._raw_times = self.times
          else:  # isinstance(self, Evoked)
              self.times = new_times
              self._update_first_last()
          return self
  
      @verbose
      def apply_hilbert(self, picks=None, envelope=False, n_jobs=1, n_fft='auto',
                        verbose=None):
          """Compute analytic signal or envelope for a subset of channels.
  
          Parameters
          ----------
          %(picks_all_data_noref)s
          envelope : bool
              Compute the envelope signal of each channel. Default False.
              See Notes.
          %(n_jobs)s
          n_fft : int | None | str
              Points to use in the FFT for Hilbert transformation. The signal
              will be padded with zeros before computing Hilbert, then cut back
              to original length. If None, n == self.n_times. If 'auto',
              the next highest fast FFT length will be use.
          %(verbose_meth)s
  
          Returns
          -------
          self : instance of Raw, Epochs, or Evoked
              The raw object with transformed data.
  
          Notes
          -----
          **Parameters**
  
          If ``envelope=False``, the analytic signal for the channels defined in
          ``picks`` is computed and the data of the Raw object is converted to
          a complex representation (the analytic signal is complex valued).
  
          If ``envelope=True``, the absolute value of the analytic signal for the
          channels defined in ``picks`` is computed, resulting in the envelope
          signal.
  
          .. warning: Do not use ``envelope=True`` if you intend to compute
                      an inverse solution from the raw data. If you want to
                      compute the envelope in source space, use
                      ``envelope=False`` and compute the envelope after the
                      inverse solution has been obtained.
  
          If envelope=False, more memory is required since the original raw data
          as well as the analytic signal have temporarily to be stored in memory.
          If n_jobs > 1, more memory is required as ``len(picks) * n_times``
          additional time points need to be temporaily stored in memory.
  
          Also note that the ``n_fft`` parameter will allow you to pad the signal
          with zeros before performing the Hilbert transform. This padding
          is cut off, but it may result in a slightly different result
          (particularly around the edges). Use at your own risk.
  
          **Analytic signal**
  
          The analytic signal "x_a(t)" of "x(t)" is::
  
              x_a = F^{-1}(F(x) 2U) = x + i y
  
          where "F" is the Fourier transform, "U" the unit step function,
          and "y" the Hilbert transform of "x". One usage of the analytic
          signal is the computation of the envelope signal, which is given by
          "e(t) = abs(x_a(t))". Due to the linearity of Hilbert transform and the
          MNE inverse solution, the enevlope in source space can be obtained
          by computing the analytic signal in sensor space, applying the MNE
          inverse, and computing the envelope in source space.
          """
          _check_preload(self, 'inst.apply_hilbert')
          if n_fft is None:
              n_fft = len(self.times)
          elif isinstance(n_fft, str):
              if n_fft != 'auto':
                  raise ValueError('n_fft must be an integer, string, or None, '
                                   'got %s' % (type(n_fft),))
              n_fft = next_fast_len(len(self.times))
          n_fft = int(n_fft)
          if n_fft < len(self.times):
              raise ValueError("n_fft (%d) must be at least the number of time "
                               "points (%d)" % (n_fft, len(self.times)))
          dtype = None if envelope else np.complex128
          picks = _picks_to_idx(self.info, picks, exclude=(), with_ref_meg=False)
          args, kwargs = (), dict(n_fft=n_fft, envelope=envelope)
  
          data_in = self._data
          if dtype is not None and dtype != self._data.dtype:
              self._data = self._data.astype(dtype)
  
          if n_jobs == 1:
              # modify data inplace to save memory
              for idx in picks:
                  self._data[..., idx, :] = _check_fun(
                      _my_hilbert, data_in[..., idx, :], *args, **kwargs)
          else:
              # use parallel function
              parallel, p_fun, _ = parallel_func(_check_fun, n_jobs)
              data_picks_new = parallel(
                  p_fun(_my_hilbert, data_in[..., p, :], *args, **kwargs)
                  for p in picks)
              for pp, p in enumerate(picks):
                  self._data[..., p, :] = data_picks_new[pp]
          return self
  
  
  def _check_fun(fun, d, *args, **kwargs):
      """Check shapes."""
      want_shape = d.shape
      d = fun(d, *args, **kwargs)
      if not isinstance(d, np.ndarray):
          raise TypeError('Return value must be an ndarray')
      if d.shape != want_shape:
          raise ValueError('Return data must have shape %s not %s'
                           % (want_shape, d.shape))
      return d
  
  
  def _my_hilbert(x, n_fft=None, envelope=False):
      """Compute Hilbert transform of signals w/ zero padding.
  
      Parameters
      ----------
      x : array, shape (n_times)
          The signal to convert
      n_fft : int
          Size of the FFT to perform, must be at least ``len(x)``.
          The signal will be cut back to original length.
      envelope : bool
          Whether to compute amplitude of the hilbert transform in order
          to return the signal envelope.
  
      Returns
      -------
      out : array, shape (n_times)
          The hilbert transform of the signal, or the envelope.
      """
      from scipy.signal import hilbert
      n_x = x.shape[-1]
      out = hilbert(x, N=n_fft, axis=-1)[..., :n_x]
      if envelope:
          out = np.abs(out)
      return out
  
  
  @verbose
  def design_mne_c_filter(sfreq, l_freq=None, h_freq=40.,
                          l_trans_bandwidth=None, h_trans_bandwidth=5.,
                          verbose=None):
      """Create a FIR filter like that used by MNE-C.
  
      Parameters
      ----------
      sfreq : float
          The sample frequency.
      l_freq : float | None
          The low filter frequency in Hz, default None.
          Can be None to avoid high-passing.
      h_freq : float
          The high filter frequency in Hz, default 40.
          Can be None to avoid low-passing.
      l_trans_bandwidth : float | None
          Low transition bandwidthin Hz. Can be None (default) to use 3 samples.
      h_trans_bandwidth : float
          High transition bandwidth in Hz.
      %(verbose)s
  
      Returns
      -------
      h : ndarray, shape (8193,)
          The linear-phase (symmetric) FIR filter coefficients.
  
      Notes
      -----
      This function is provided mostly for reference purposes.
  
      MNE-C uses a frequency-domain filter design technique by creating a
      linear-phase filter of length 8193. In the frequency domain, the
      4197 frequencies are directly constructed, with zeroes in the stop-band
      and ones in the passband, with squared cosine ramps in between.
      """
      n_freqs = (4096 + 2 * 2048) // 2 + 1
      freq_resp = np.ones(n_freqs)
      l_freq = 0 if l_freq is None else float(l_freq)
      if l_trans_bandwidth is None:
          l_width = 3
      else:
          l_width = (int(((n_freqs - 1) * l_trans_bandwidth) /
                         (0.5 * sfreq)) + 1) // 2
      l_start = int(((n_freqs - 1) * l_freq) / (0.5 * sfreq))
      h_freq = sfreq / 2. if h_freq is None else float(h_freq)
      h_width = (int(((n_freqs - 1) * h_trans_bandwidth) /
                     (0.5 * sfreq)) + 1) // 2
      h_start = int(((n_freqs - 1) * h_freq) / (0.5 * sfreq))
      logger.info('filter : %7.3f ... %6.1f Hz   bins : %d ... %d of %d '
                  'hpw : %d lpw : %d' % (l_freq, h_freq, l_start, h_start,
                                         n_freqs, l_width, h_width))
      if l_freq > 0:
          start = l_start - l_width + 1
          stop = start + 2 * l_width - 1
          if start < 0 or stop >= n_freqs:
              raise RuntimeError('l_freq too low or l_trans_bandwidth too large')
          freq_resp[:start] = 0.
          k = np.arange(-l_width + 1, l_width) / float(l_width) + 3.
          freq_resp[start:stop] = np.cos(np.pi / 4. * k) ** 2
  
      if h_freq < sfreq / 2.:
          start = h_start - h_width + 1
          stop = start + 2 * h_width - 1
          if start < 0 or stop >= n_freqs:
              raise RuntimeError('h_freq too high or h_trans_bandwidth too '
                                 'large')
          k = np.arange(-h_width + 1, h_width) / float(h_width) + 1.
          freq_resp[start:stop] *= np.cos(np.pi / 4. * k) ** 2
          freq_resp[stop:] = 0.0
      # Get the time-domain version of this signal
      h = irfft(freq_resp, n=2 * len(freq_resp) - 1)
      h = np.roll(h, n_freqs - 1)  # center the impulse like a linear-phase filt
      return h
  
  
  def _filt_check_picks(info, picks, h_freq, l_freq):
      from .io.pick import _picks_to_idx
      update_info = False
      # This will pick *all* data channels
      picks = _picks_to_idx(info, picks, 'data_or_ica', exclude=())
      if h_freq is not None or l_freq is not None:
          data_picks = _picks_to_idx(info, None, 'data_or_ica', exclude=(),
                                     allow_empty=True)
          if len(data_picks) == 0:
              logger.info('No data channels found. The highpass and '
                          'lowpass values in the measurement info will not '
                          'be updated.')
          elif np.in1d(data_picks, picks).all():
              update_info = True
          else:
              logger.info('Filtering a subset of channels. The highpass and '
                          'lowpass values in the measurement info will not '
                          'be updated.')
      return update_info, picks
  
  
  def _filt_update_info(info, update_info, l_freq, h_freq):
      if update_info:
          if h_freq is not None and (l_freq is None or l_freq < h_freq) and \
                  (info["lowpass"] is None or h_freq < info['lowpass']):
              info['lowpass'] = float(h_freq)
          if l_freq is not None and (h_freq is None or l_freq < h_freq) and \
                  (info["highpass"] is None or l_freq > info['highpass']):
              info['highpass'] = float(l_freq)
  
  
  ###############################################################################
  # Class for interpolation between adjacent points
  
  class _Interp2(object):
      r"""Interpolate between two points.
  
      Parameters
      ----------
      interp : str
          Can be 'zero', 'linear', 'hann', or 'cos2'.
  
      Notes
      -----
      This will process data using overlapping windows of potentially
      different sizes to achieve a constant output value using different
      2-point interpolation schemes. For example, for linear interpolation,
      and window sizes of 6 and 17, this would look like::
  
          1 _     _
            |\   / '-.           .-'
            | \ /     '-.     .-'
            |  x         |-.-|
            | / \     .-'     '-.
            |/   \_.-'           '-.
          0 +----|----|----|----|---
            0    5   10   15   20   25
  
      """
  
      def __init__(self, interp='hann'):
          # set up interpolation
          self._last = dict()
          self._current = dict()
          self._count = dict()
          self._n_samp = None
          self.interp = interp
  
      def __setitem__(self, key, value):
          """Update an item."""
          if value is None:
              assert key not in self._current
              return
          if key in self._current:
              self._last[key] = self._current[key].copy()
          self._current[key] = value.copy()
          self._count[key] = self._count.get(key, 0) + 1
  
      @property
      def n_samp(self):
          return self._n_samp
  
      @n_samp.setter
      def n_samp(self, n_samp):
          # all up to date
          assert len(set(self._count.values())) == 1
          self._n_samp = n_samp
          self.interp = self.interp
  
      @property
      def interp(self):
          return self._interp
  
      @interp.setter
      def interp(self, interp):
          known_types = ('cos2', 'linear', 'zero', 'hann')
          if interp not in known_types:
              raise ValueError('interp must be one of %s, got "%s"'
                               % (known_types, interp))
          self._interp = interp
          if self.n_samp is not None:
              if self._interp == 'zero' or np.isinf(self.n_samp):  # ZOH
                  self._interpolators = None
              else:
                  if self._interp == 'linear':
                      interp = np.linspace(1, 0, self.n_samp, endpoint=False)
                  elif self._interp == 'cos2':
                      interp = np.cos(0.5 * np.pi * np.arange(self.n_samp)) ** 2
                  else:  # interp == 'hann'
                      interp = np.hanning(self.n_samp * 2 + 1)[self.n_samp:-1]
                  self._interpolators = np.array([interp, 1 - interp])
  
      def interpolate(self, key, data, out, picks=None, interp_sl=None):
          """Interpolate."""
          picks = slice(None) if picks is None else picks
          interp_sl = slice(None) if interp_sl is None else interp_sl
          # Process data in large chunks to save on memory
          this_data = np.dot(self._last[key], data)
          if self._interpolators is not None:
              this_data *= self._interpolators[0][interp_sl]
          out[picks, ] += this_data
          if self._interpolators is not None:
              this_data = np.dot(self._current[key], data)
              this_data *= self._interpolators[1][interp_sl]
              out[picks, :] += this_data
  
apis: numpy.sum numpy.ones scipy.signal.minimum_phase numpy.diff numpy.any numpy.argsort numpy.ones_like scipy.signal.sos2zpk scipy.signal.tf2zpk scipy.signal.get_window scipy.signal.sosfreqz numpy.logical_and numpy.argmin numpy.abs numpy.cos numpy.in1d scipy.signal.hilbert numpy.where numpy.linspace numpy.unique scipy.signal.sosfilt numpy.round scipy.signal.detrend numpy.tile numpy.atleast_2d numpy.ceil numpy.zeros numpy.dot scipy.signal.iirdesign numpy.argmax numpy.asanyarray numpy.arange scipy.signal.freqz numpy.max numpy.prod numpy.maximum numpy.hanning scipy.signal.savgol_filter numpy.zeros_like numpy.roll numpy.log2 scipy.signal.firwin scipy.signal.iirfilter numpy.isinf numpy.atleast_1d scipy.signal.lfilter numpy.angle numpy.array numpy.convolve