probably need to make numpy int scalers into python native types, this is annoying. Maybe implement in cleanup_slice()
for start_ix in ca_trial_range_ixs[:, 0]:
start_ix = int(start_ix)
plot_temporal["cue"].colors[start_ix:(start_ix + cue_frame_calcium)] = np.repeat(np.array([Color("w")]), 30, axis=0)ca_trial_range_ixs is an array like this:
array([[ 0, 290],
[ 290, 580],
[ 580, 870],
[ 870, 1160],
[1160, 1450],
[1450, 1740],
[1740, 2030],
[2030, 2320],
[2320, 2610],
[2610, 2900],
[2900, 3190],
[3190, 3480],
[3480, 3770],
[3770, 4060],
[4060, 4350],
[4350, 4640],
[4640, 4930],
[4930, 5220],
[5220, 5510],
[5510, 5800],
[5800, 6090],
[6090, 6380],
[6380, 6670],
[6670, 6960],
[6960, 7250],
[7250, 7540],
[7540, 7830],
[7830, 8120],
[8120, 8410]])
probably need to make numpy int scalers into python native types, this is annoying. Maybe implement in
cleanup_slice()ca_trial_range_ixs is an array like this: