def get_data(filename): bload = bcontrol.Bcontrol_Loader(filename, mem_behavior = True, auto_validate = 0) bdata = bload.load() bcontrol.process_for_saving(bdata) return bdata
def load_bhv_data(filename): ''' Accepts a file name (the absolute path) to a .mat file containing recorded behavior data. Returns a dictionary with the data in a form that can be used in Python ''' bload = bcontrol.Bcontrol_Loader(filename, mem_behavior = True, auto_validate = 0) bdata = bload.load() bcontrol.process_for_saving(bdata) return bdata
loader = ns5.Loader(neural_file) loader.load_header() loader.load_file() audio = [ loader.get_analog_channel_as_array(n) for n in [7,8] ] audio = np.array(audio)*4096./2**16 onsets_obj = AudioTools.OnsetDetector(audio, verbose = True, minimum_threshhold=-20) onsets_obj.execute() n_onsets = Onsets(onsets_obj.detected_onsets) bcload = bcontrol.Bcontrol_Loader(filename = behave_file, mem_behavior= True, auto_validate = 0) bcdata = bcload.load() b_onsets = Onsets(bcdata['onsets']) syncer = DataSession.BehavingSyncer() syncer.sync(b_onsets,n_onsets, force_run = 1) with open(save_as + '.bhv','w') as f: bcontrol.process_for_saving(bcdata) pkl.dump(bcdata,f) with open(save_as + '.ons','w') as f: pkl.dump(n_onsets.audio_onsets,f) with open(save_as+ '.syn','w') as f: pkl.dump(syncer,f)