class_docstr: Class for time operations on epochs/evoked-like MNE objects.
class_docstr_tok: ["Class","for","time","operations","on","epochs\/evoked-like","MNE","objects","."]
docstr: Decimate the time-series data.
  
  Parameters
  ----------
  %(decim)s
  %(offset_decim)s
  %(verbose)s
  
  Returns
  -------
  inst : MNE-object
      The decimated object.
  
  See Also
  --------
  mne.Epochs.resample
  mne.io.Raw.resample
  
  Notes
  -----
  %(decim_notes)s
  
  If ``decim`` is 1, this method does not copy the underlying data.
  
  .. versionadded:: 0.10.0
  
  References
  ----------
  .. footbibliography::
docstr_tok: ["Decimate","the","time-series","data",".","Parameters","--","--","--","--","--","%","(","decim",")","s","%","(","offset_decim",")","s","%","(","verbose",")","s","Returns","--","--","--","-","inst",":","MNE-object","The","decimated","object",".","See","Also","--","--","--","--","mne.Epochs.resample","mne.io.Raw.resample","Notes","--","--","-","%","(","decim_notes",")","s","If","``","decim","``","is","1",",","this","method","does","not","copy","the","underlying","data",".","..","versionadded",":",":","0.10.0","References","--","--","--","--","--","..","footbibliography",":",":"]
code: def decimate(self, decim, offset=0, *, verbose=None):
      """Decimate the time-series data.
  
      Parameters
      ----------
      %(decim)s
      %(offset_decim)s
      %(verbose)s
  
      Returns
      -------
      inst : MNE-object
          The decimated object.
  
      See Also
      --------
      mne.Epochs.resample
      mne.io.Raw.resample
  
      Notes
      -----
      %(decim_notes)s
  
      If ``decim`` is 1, this method does not copy the underlying data.
  
      .. versionadded:: 0.10.0
  
      References
      ----------
      .. footbibliography::
      """
      # if epochs have frequencies, they are not in time (EpochsTFR)
      # and so do not need to be checked whether they have been
      # appropriately filtered to avoid aliasing
      from ..epochs import BaseEpochs
      from ..evoked import Evoked
      from ..time_frequency import AverageTFR, EpochsTFR
  
      # This should be the list of classes that inherit
      _validate_type(
          self, (BaseEpochs, Evoked, EpochsTFR, AverageTFR), "inst"
      )
      decim, offset, new_sfreq = _check_decim(
          self.info,
          decim,
          offset,
          check_filter=not hasattr(self, "freqs"),
      )
      start_idx = int(
          round(
              -self._raw_times[0] * (self.info["sfreq"] * self._decim)
          )
      )
      self._decim *= decim
      i_start = start_idx % self._decim + offset
      decim_slice = slice(i_start, None, self._decim)
      with self.info._unlock():
          self.info["sfreq"] = new_sfreq
  
      if self.preload:
          if decim != 1:
              self._data = self._data[..., decim_slice].copy()
              self._raw_times = self._raw_times[decim_slice].copy()
          else:
              self._data = np.ascontiguousarray(self._data)
          self._decim_slice = slice(None)
          self._decim = 1
      else:
          self._decim_slice = decim_slice
      self._set_times(self._raw_times[self._decim_slice])
      self._update_first_last()
      return self
  
imports: import json
  import logging
  from collections import OrderedDict
  from copy import deepcopy
  import numpy
  from ._logging import verbose, warn
  from .check import _check_pandas_installed, _check_preload, _validate_type
  from .numerics import _time_mask, object_hash, object_size
label_desc: Use image node_id 4 for calling the ExtendedTimeMixin obj's underlying member method code with example usage: obj.decimate(decim, offset) and returns: self