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)