class_docstr: Raw object from NeuroElectrics nedf file.
class_docstr_tok: ["Raw","object","from","NeuroElectrics","nedf","file","."]
code: def __init__(self, filename, preload=False, verbose=None):
      filename = str(_check_fname(filename, "read", True, "filename"))
      with open(filename, mode="rb") as fid:
          header = fid.read(_HDRLEN)
      header, dt, dt_last, n_samp, n_full = _parse_nedf_header(header)
      ch_names = header["ch_names"] + ["STI 014"]
      ch_types = ["eeg"] * len(ch_names)
      ch_types[-1] = "stim"
      info = create_info(ch_names, header["sfreq"], ch_types)
      # scaling factor ADC-values -> volts
      # taken from the NEDF EEGLAB plugin
      # (https://www.neuroelectrics.com/resources/software/):
      for ch in info["chs"][:-1]:
          ch["cal"] = 2.4 / (6.0 * 8388607)
      with info._unlock():
          info["meas_date"] = header["meas_date"]
      raw_extra = dict(dt=dt, dt_last=dt_last, n_full=n_full)
      super().__init__(
          info,
          preload=preload,
          filenames=[filename],
          verbose=verbose,
          raw_extras=[raw_extra],
          last_samps=[n_samp - 1],
      )
  
file_path: mne-python/mne/io/nedf/nedf.py
imports: from copy import deepcopy
  from datetime import datetime, timezone
  import numpy
  from ..._fiff.meas_info import create_info
  from ..._fiff.utils import _mult_cal_one
  from ...utils import _check_fname, _soft_import, verbose, warn
  from ..base import BaseRaw
label_desc: Use image node_id 1 to create a new RawNedf object from inherited base classes: BaseRaw with example: obj = RawNedf(filename, preload, verbose)