def read_stim_ch(self, buffer_size=100000.0):
    'Read events from data.\n\n        Parameter\n        ---------\n        buffer_size : int\n            The size of chunk to by which the data are scanned.\n\n        Returns\n        -------\n        events : array, [samples]\n           The event vector (1 x samples).\n        '
    buffer_size = int(buffer_size)
    start = int(self.first_samp)
    stop = int((self.last_samp + 1))
    pick = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[])
    stim_ch = np.empty((1, stop), dtype=np.int64)
    for b_start in range(start, stop, buffer_size):
        b_stop = (b_start + buffer_size)
        x = self[pick, b_start:b_stop][0]
        stim_ch[:, b_start:(b_start + x.shape[1])] = x
    return stim_ch
Read events from data.

Parameter
---------
buffer_size : int
    The size of chunk to by which the data are scanned.

Returns
-------
events : array, [samples]
   The event vector (1 x samples).
def read_stim_ch(self, buffer_size=100000.0):
    'Read events from data.\n\n        Parameter\n        ---------\n        buffer_size : int\n            The size of chunk to by which the data are scanned.\n\n        Returns\n        -------\n        events : array, [samples]\n           The event vector (1 x samples).\n        '
    buffer_size = int(buffer_size)
    start = int(self.first_samp)
    stop = int((self.last_samp + 1))
    pick = pick_types(self.info, meg=False, ref_meg=False, stim=True, exclude=[])
    stim_ch = np.empty((1, stop), dtype=np.int64)
    for b_start in range(start, stop, buffer_size):
        b_stop = (b_start + buffer_size)
        x = self[pick, b_start:b_stop][0]
        stim_ch[:, b_start:(b_start + x.shape[1])] = x
    return stim_ch