The following conceptual live coding prototype shows what a simple session would look like if it was modeled on the Python interpreter:

$ slipmat --capture_performance my_session.txt
>>> from LiveCodeSeq import seq
>>> from MyBassLibrary import rad_rezzy
>>> from random import random
>>> p[0] = [int(random() * 12) for i in range(0, 16)]
>>> p[1] = [int(random() * 12) for i in range(0, 16)]
>>> p[0]
[5, 9, 11, 8, 7, 8, 5, 1, 10, 7, 4, 4, 6, 4, 4, 2]
>>> p[1]
[6, 6, 5, 3, 5, 7, 8, 4, 0, 0, 8, 7, 9, 7, 2, 4]
>>> r = rad_rezzy()
>>> s = seq(instr=r, pattern=p[0], base_pch=6.00, resolution=1/16, tempo=133)
>>> s.start()
>>> s.change_pattern(pattern=p[1], on_beat=0)
>>> @60 s.stop(onbeat=0)

I have a gut feeling that there are some changes that should be made. Though as a starting point, this isn’t a terrible one.

Being able to capture a live coding performance would be fantastic. Not sure how workable it would be, but perhaps such a feature would produce a file that could be played back later:

$ cat my_session.txt
@0             global.seed(7319991298)
@4.04977535403 from LiveCodeSeq import seq
@8.43528123231 from MyBassLibrary import rad_rezzy
@10.9562488312 from random import random
@15.6027957075 p[0] = [int(random() * 12) for i in range(0, 16)]
@20.7757632586 p[1] = [int(random() * 12) for i in range(0, 16)]
@26.2462371683 p[0]
@29.3961696828 p[1]
@34.0424988199 r = rad_rezzy()
@40.3211374075 s = seq(instr=r, pattern=p[0], base_pch=6.00, resolution=1/16, 
                   tempo=133)
@45.5491938514 s.start()
@47.8991166715 s.change_pattern(pattern=p[1], onbeat=0)
@52.6267958091 @60 s.stop(onbeat=0)

The @ schedules are the times in which return was originally pressed for each event. Looks like I’ll be spending some time with ChucK soon.