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: Crop data to a given time interval.
  
  Parameters
  ----------
  tmin : float | None
      Start time of selection in seconds.
  tmax : float | None
      End time of selection in seconds.
  %(include_tmax)s
  %(verbose)s
  
  Returns
  -------
  inst : instance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate
      The cropped time-series object, modified in-place.
  
  Notes
  -----
  %(notes_tmax_included_by_default)s
docstr_tok: ["Crop","data","to","a","given","time","interval",".","Parameters","--","--","--","--","--","tmin",":","float","|","None","Start","time","of","selection","in","seconds",".","tmax",":","float","|","None","End","time","of","selection","in","seconds",".","%","(","include_tmax",")","s","%","(","verbose",")","s","Returns","--","--","--","-","inst",":","instance","of","Raw",",","Epochs",",","Evoked",",","AverageTFR",",","or","SourceEstimate","The","cropped","time-series","object",",","modified","in-place",".","Notes","--","--","-","%","(","notes_tmax_included_by_default",")","s"]
code: def crop(self, tmin=None, tmax=None, include_tmax=True, verbose=None):
      """Crop data to a given time interval.
  
      Parameters
      ----------
      tmin : float | None
          Start time of selection in seconds.
      tmax : float | None
          End time of selection in seconds.
      %(include_tmax)s
      %(verbose)s
  
      Returns
      -------
      inst : instance of Raw, Epochs, Evoked, AverageTFR, or SourceEstimate
          The cropped time-series object, modified in-place.
  
      Notes
      -----
      %(notes_tmax_included_by_default)s
      """
      t_vars = dict(tmin=tmin, tmax=tmax)
      for name, t_var in t_vars.items():
          _validate_type(
              t_var,
              types=("numeric", None),
              item_name=name,
          )
  
      if tmin is None:
          tmin = self.tmin
      elif tmin < self.tmin:
          warn(
              f"tmin is not in time interval. tmin is set to "
              f"{type(self)}.tmin ({self.tmin:g} s)"
          )
          tmin = self.tmin
  
      if tmax is None:
          tmax = self.tmax
      elif tmax > self.tmax:
          warn(
              f"tmax is not in time interval. tmax is set to "
              f"{type(self)}.tmax ({self.tmax:g} s)"
          )
          tmax = self.tmax
          include_tmax = True
  
      mask = _time_mask(
          self.times,
          tmin,
          tmax,
          sfreq=self.info["sfreq"],
          include_tmax=include_tmax,
      )
      self._set_times(self.times[mask])
      self._raw_times = self._raw_times[mask]
      self._update_first_last()
      self._data = self._data[..., mask]
  
      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 3 for calling the ExtendedTimeMixin obj's underlying member method code with example usage: obj.crop(tmin, tmax, include_tmax, verbose) and returns: self