code: def write_wonambi(data, filename, subj_id='', dtype='float64'):
      
      filename = Path(filename)
  
      json_file = filename.with_suffix('.won')
      memmap_file = filename.with_suffix('.dat')
  
      start_time = data.start_time + timedelta(seconds=data.axis['time'][0][0])
  
      start_time_str = start_time.strftime('%Y-%m-%d %H:%M:%S.%f')
      dataset = {'subj_id': subj_id,
                 'start_time': start_time_str,
                 's_freq': data.s_freq,
                 'chan_name': list(data.axis['chan'][0]),
                 'n_samples': int(data.number_of('time')[0]),
                 'dtype': dtype,
                 }
  
      with json_file.open('w') as f:
          dump(dataset, f, sort_keys=True, indent=4)
  
      memshape = (len(dataset['chan_name']),
                  dataset['n_samples'])
  
      mem = memmap(str(memmap_file), dtype, mode='w+', shape=memshape, order='F')
      mem[:, :] = data.data[0]
      mem.flush()
docs: Write file in simple Wonambi format.
  
      Parameters
      ----------
      data : instance of ChanTime
          data with only one trial
      filename : path to file
          file to export to (the extensions .won and .dat will be added)
      subj_id : str
          subject id
      dtype : str
          numpy dtype in which you want to save the data
  
      Notes
      -----
      Wonambi format creates two files, one .won with the dataset info as json
      file and one .dat with the memmap recordings.
  
      It will happily overwrite any existing file with the same name.
  
      Memory-mapped matrices are column-major, Fortran-style, to be compatible
      with Matlab.