def get_classifier_window(data, start, end, prestim=0.5, sfreq=100):
	"""
	Similar to <BiModNeuroCNN.subjects.subjects.Subject.get_classifier_window> in 
	that it extracts epoched time windows within a trial period.
	
	:param data: (np.array) n_trials * n_chans * n_samples
	:param start: (float) time to begin classification window
	:param end: (float) time to end classification window
	:param prestim: (float) length of pre-stimulus period in the data
	:return: (np.array): n_trials*n_channels*len(classification_window)
	"""
	
	fcn = lambda x : x * sfreq
	
	start_samples = int(fcn(start)) + int(fcn(prestim))
	end_samples = int(fcn(end)) + int(fcn(prestim))
	classifier_data = data[:,:,start_samples:end_samples]
	epoch = classifier_data.shape[2]

	return classifier_data, epoch