def pitch_unidir(time, correct):
"""
Unidirectional pitch-shift into a specified pitch.
Starts at most 3 semitones away from the desired pitch.
"""
trans = ratio(window(1.) + correct)
origin = ratio(correct)
return interleave([trans, origin], [0., time * 1000], [1, window(.75)])
def env_jagged(etime):
"""
Jagged envelope:
Picks at most 4, at least 2 events for each second of
the event time. Each event is categorized by a random
amplitude value and time distribution w/ a curve.
"""
dests = [0.]
times = [0.]
time_error = 0.
loctime = etime
while loctime > 0:
if loctime > 1.:
correct = 1
else:
correct = loctime
numevents = int((random() * 3. + 2.) * loctime)
dests += [random() for i in range(numevents)]
time_add = [random() / numevents for i in range(numevents)]
next_error = 1. - sum(time_add)
if len(time_add) > 0:
time_add[0] += time_error
times += time_add
time_error = next_error
loctime -= 1.
dests[len(dests) - 1] = 0.
times[len(times) - 1] += etime - sum(times)
times = [1000. * t for t in times]
curves = [0.] + [window(.75) for i in range(1, len(dests) - 1)] + [0.]
return interleave(dests, times, curves)
def pitch_peak(time, correct):
"""
A triangle-wave-like pitch shift.
Moves up / down from no-transposition and returns to normal.
Guaranteed to reach its destination within the first 75% of
the time requested.
"""
ms = lambda t: _ms(t, time)
trans = ratio(window(1.) + correct)
origin = ratio(correct)
attack_time = random() * .75 + .2
decay_time = 1. - attack_time
dest = [origin, trans, origin]
times = [0., ms(attack_time), ms(decay_time)]
curves = [1.] + window([.75, .75])
return interleave(dest, times, curves)
def amp_in_out(time):
"""
An AHD envelope.
Attack (in the first 75 percent of the time)
Hold @ sustain value (20 percent of the time)
Sustain value is in between .5 and 1.
Decay to 0. for the time remaining
"""
ms = lambda t: _ms(t, time)
attack_time = random() * .55 + .2
sustain_val = random() * .5 + .5
hold_time = random() * .2
decay_time = 1. - attack_time - hold_time
dest = [0., sustain_val, sustain_val, 0.]
times = [0., ms(attack_time), ms(hold_time), ms(decay_time)]
curves = [1., window(.75), 1., window(.75)]
return interleave(dest, times, curves)
def env_uniform(etime):
"""
Uniform cresendo or diminuendo;
"""
dests = [0., random() * .1 + .05, random() * .25 + .75, 0.]
times = [0., 30., etime * 1000. - 60., 30.]
curves = [0.] + window([.85, .85]) + [0.]
if random() < .5:
return interleave(dests, times, curves)
return interleave(dests[-1::-1], [0.] + times[-1:0:-1], curves)
def amp_in(time):
"""
A double-attack crescendo.
"""
ms = lambda t: _ms(t, time)
first_time = random() * .35 + .2
second_time = random() * .3 + .2
decay_time = 1 - first_time - second_time
first_val = random() * .4 + .4
dest = [0., first_val, 1., 0.]
times = [0., ms(first_time), ms(second_time), ms(decay_time)]
curves = [1.] + window([.75, .75, .75])
return interleave(dest, times, curves)
def pitch_cross(time, correct):
"""
Shifts up then down (or down then up), before returning to
the original pitch of the sample.
"""
ms = lambda t: _ms(t, time)
_ts = lambda: random() * .33 + .1
shifts = window([1., 1.])
if (shifts[0] * shifts[1]) > 0:
shifts[one([0, 1])] *= -1
trans = []
for s in shifts:
trans.append(s + correct)
trans = ratio(trans)
first = _ts()
second = 1 - _ts() - first
decay = 1. - (first + second)
origin = ratio(correct)
dest = [origin] + trans + [origin]
times = [0., ms(first), ms(second), ms(decay)]
curves = [1.] + window([.75, .75, .75])
return interleave(dest, times, curves)
import numpy as np
data = [int(l) for l in read_input('data.input.day9')]
# do we care about processing? at least we'll use generators...
# walk a window of preamble + 1, the last being the one we check
def check(w):
p = map(math.fsum, combinations(w[:-1], 2))
v = w[-1]
return any(map(lambda n: n == v, p))
preamble = 25
ans = next(x for x in drop(preamble, window(preamble + 1, data))
if not check(x))[-1]
# part 2
# find some set of at least 2 numbers that add to value from part 1
# i guess i still don't care about processing though...
def check_invalid(n, l):
return math.fsum(l) <= n
p = partial(check_invalid, ans)
l = map(lambda v: drop(v, data), range(0, len(data) - 2))
solns = next(x for x in map(lambda v: last(take_while_list(p, v)), l)
if math.fsum(x) == ans)
print(min(solns) + max(solns))
