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Optimize Conway to improve framerate #410

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Feb 8, 2025
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Optimize Conway to improve framerate #410

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83 changes: 45 additions & 38 deletions software/contrib/conway.py
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Original file line number Diff line number Diff line change
Expand Up @@ -99,10 +99,17 @@ def __init__(self):
# Set to True if we want to clear the field & respawn
self.reset_requested = False

# statically allocated array of the indices of the cells around the one we're considering
# this is just an optimization to avoid re-allocating this array
self.neighbourhood = [0, 0, 0, 0, 0, 0, 0, 0]

# keep the last few changes in population in a list to check if it's oscillating predictably
self.population_deltas = []
self.MAX_DELTAS = 12

# statically allocated array we use to store the sums of cells when checking for statis
self.statis_sums = [0] * self.MAX_DELTAS

@b1.handler
def on_b1():
self.reset_requested = True
Expand Down Expand Up @@ -130,14 +137,12 @@ def get_neigbour_indices(self, index):
def rowcol2index(r, c):
return (r % OLED_HEIGHT) * OLED_WIDTH + (c % OLED_WIDTH)

neighbours = []

n = 0
for i in range(-1, 2):
for j in range(-1, 2):
if i != 0 or j != 0:
neighbours.append(rowcol2index(row+i, col+j))

return neighbours
self.neighbourhood[n] = rowcol2index(row+i, col+j)
n += 1

def calculate_spawn_level(self):
"""Calculate what percentage of the field should contain new cells
Expand Down Expand Up @@ -211,34 +216,36 @@ def tick(self):
self.reset()

for bit_index in range(NUM_PIXELS):
if get_bit(self.changed_spaces, bit_index):
neighbourhood = self.get_neigbour_indices(bit_index)
num_neighbours = sum(1 for n in neighbourhood if get_bit(self.field, n))

if get_bit(self.field, bit_index):
if num_neighbours == 2 or num_neighbours == 3: # happy cell, stays alive
set_bit(self.next_field, bit_index, True)
else: # sad cell, dies
set_bit(self.next_field, bit_index, False)
self.num_died += 1
self.num_alive -= 1

self.num_changes += 1
set_bit(self.next_changed_spaces, bit_index, 1)
for n in neighbourhood:
set_bit(self.next_changed_spaces, n, 1)
else:
if num_neighbours == 3: # baby cell is born!
set_bit(self.next_field, bit_index, True)
self.num_alive += 1
self.num_born += 1

self.num_changes += 1
set_bit(self.next_changed_spaces, bit_index, 1)
for n in neighbourhood:
set_bit(self.next_changed_spaces, n, 1)
else: # empty space remains empty
set_bit(self.next_field, bit_index, False)
if not get_bit(self.changed_spaces, bit_index):
continue

self.get_neigbour_indices(bit_index)
num_neighbours = sum(1 for n in self.neighbourhood if get_bit(self.field, n))

if get_bit(self.field, bit_index):
if num_neighbours == 2 or num_neighbours == 3: # happy cell, stays alive
set_bit(self.next_field, bit_index, True)
else: # sad cell, dies
set_bit(self.next_field, bit_index, False)
self.num_died += 1
self.num_alive -= 1

self.num_changes += 1
set_bit(self.next_changed_spaces, bit_index, 1)
for n in self.neighbourhood:
set_bit(self.next_changed_spaces, n, 1)
else:
if num_neighbours == 3: # baby cell is born!
set_bit(self.next_field, bit_index, True)
self.num_alive += 1
self.num_born += 1

self.num_changes += 1
set_bit(self.next_changed_spaces, bit_index, 1)
for n in self.neighbourhood:
set_bit(self.next_changed_spaces, n, 1)
else: # empty space remains empty
set_bit(self.next_field, bit_index, False)

# swap field & next_field so we don't need to copy between arrays
tmp = self.next_field
Expand Down Expand Up @@ -268,13 +275,13 @@ def check_for_stasis(self):
# if the population is oscillating up and down predicatbly, we've probably reached stasis
# check for 2, 3, and 4 step repetitions
for pattern_length in range(2, 5):
sums = []
for i in range(self.MAX_DELTAS // pattern_length):
sums.append(sum(self.population_deltas[i*pattern_length:i*pattern_length+pattern_length]))
count = self.MAX_DELTAS // pattern_length
for i in range(count):
self.statis_sums[i] = sum(self.population_deltas[i*pattern_length:i*pattern_length+pattern_length])

# check the standard deviation
deviation = stdev(sums)
mean = sum(sums)/len(sums)
deviation = stdev(self.statis_sums[0:count])
mean = sum(self.statis_sums[0:count])/count
if deviation <= 1 and abs(mean) <= 1:
return True

Expand Down
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