def better_randfill():
g.new('')
g.select([-10, -10, GRID_SIZE[0], GRID_SIZE[1]])
g.randfill(random.randrange(50))
g.select([])
g.autoupdate(True)
cnt = 0
for x in range(0, GENS):
cnt += 1
if not g.empty():
g.run(1)
if is_static():
break
time.sleep(0.05)
else:
break
if cnt % 10 == 0:
g.fit()
# now generate the ONcell HWSSs and LWSSs
# add rest of pattern after the center area is filled in
# and p184 HWSS guns are in the same phase (3680 = 184 * 40)
ONcell = all + CellCenter[3680]
if os.access(ONcellFileName, os.W_OK) or not os.access(ONcellFileName, os.F_OK):
try:
ONcell.save (ONcellFileName, "Metapixel ON cell: Brice Due, Spring 2006")
except:
g.show("Failed to save file version of ON cell: " + ONcellFileName)
os.remove(ONcellFileName)
OFFcell += RuleBits
ONcell += RuleBits
g.autoupdate(True)
g.new(layername)
g.setalgo("QuickLife") # qlife's setcell is faster
for j in xrange(selheight):
for i in xrange(selwidth):
golly.show("Placing (" + str(i+1) + "," + str(j+1) + ") tile" +
" in a " + str(selwidth) + " by " + str(selheight) + " rectangle.")
if livecell[i][j]:
ONcell.put(2048 * i - 5, 2048 * j - 5)
else:
OFFcell.put(2048 * i - 5, 2048 * j - 5)
g.fit()
g.show("")
g.setalgo("HashLife") # no point running a metapattern without hashing
import sys
import golly
visual_execution = True #False for faster programs, True required for infinitely looping ones
file = golly.opendialog()
f = open(file).read()
golly.new("Executing " + file.split("/")[-1])
golly.setrule("B/S012345678")
golly.autoupdate(visual_execution)
commands = []
for line in f.splitlines():
important = line.split("#")[0].strip()
if not important:
continue
important = important.split()[0:5]
commands.append((important[0], int(important[1]), int(important[2]),
important[3], important[4]))
pointer = [0, 0]
instruction_pointer = "start"
while True:
command = commands[[i[0] for i in commands].index(instruction_pointer)]
pointer = [pointer[0] + command[1], pointer[1] + command[2]]
x = pointer[0]
def score_pair(g, seed1, seed2, width_factor, height_factor, \
time_factor, num_trials):
"""
Put seed1 and seed2 into the Immigration Game g and see which
one wins and which one loses. Note that this function does
not update the histories of the seeds.
"""
#
# Make copies of the original two seeds, so that the following
# manipulations do not change the originals.
#
s1 = copy.deepcopy(seed1)
s2 = copy.deepcopy(seed2)
#
# Initialize scores
#
score1 = 0.0
score2 = 0.0
#
# Run several trials with different rotations and locations.
#
for trial in range(num_trials):
#
# Randomly rotate and flip s1 and s2
#
s1 = s1.random_rotate()
s2 = s2.random_rotate()
#
# Switch cells in the second seed (s2) from state 1 (red) to state 2 (blue)
#
s2.red2blue()
#
# Rule file is "Immigration.rule"
# Set toroidal universe of height yspan and width xspan
# Base the s1ze of the universe on the s1zes of the seeds
#
# g = the Golly universe
#
[g_width, g_height, g_time] = dimensions(s1, s2, \
width_factor, height_factor, time_factor)
#
# set algorithm -- "HashLife" or "QuickLife"
#
g.setalgo("QuickLife") # use "HashLife" or "QuickLife"
g.autoupdate(False) # do not update the view unless requested
g.new("Immigration") # initialize cells to state 0
g.setrule("Immigration:T" + str(g_width) + "," +
str(g_height)) # make a toroid
#
# Find the min and max of the Golly toroid coordinates
#
[g_xmin, g_xmax, g_ymin, g_ymax] = get_minmax(g)
#
# Set magnification for Golly viewer
#
g.setmag(set_mag(g))
#
# Randomly place seed s1 somewhere in the left s1de of the toroid
#
s1.insert(g, g_xmin, -1, g_ymin, g_ymax)
#
# Randomly place seed s2 somewhere in the right s1de of the toroid
#
s2.insert(g, +1, g_xmax, g_ymin, g_ymax)
#
# Run for a fixed number of generations.
# Base the number of generations on the sizes of the seeds.
# Note that these are generations ins1de one Game of Life, not
# generations in an evolutionary sense. Generations in the
# Game of Life correspond to growth and decay of a phenotype,
# whereas generations in evolution correspond to the reproduction
# of a genotype.
#
g.run(g_time) # run the Game of Life for g_time time steps
g.update() # need to update Golly to get counts
#
# Count the populations of the two colours. State 1 = red = seed1.
# State 2 = blue = seed2.
#
[count1, count2] = count_pops(g)
#
if (count1 > count2):
score1 = score1 + 1.0
elif (count2 > count1):
score2 = score2 + 1.0
else:
score1 = score1 + 0.5
score2 = score2 + 0.5
#
#
# Normalize the scores
#
score1 = score1 / num_trials
score2 = score2 / num_trials
#
return [score1, score2]
def score_pair(g, seed1, seed2, width_factor, height_factor, \
time_factor, num_trials):
"""
Put seed1 and seed2 into the Immigration Game g and see which
one wins and which one loses. Note that this function does
not update the histories of the seeds. For updating histories,
use update_history().
"""
#
# Make copies of the original two seeds, so that the following
# manipulations do not change the originals.
#
s1 = copy.deepcopy(seed1)
s2 = copy.deepcopy(seed2)
#
# Check the number of living cells in the seeds. If the number
# is zero, it is probably a mistake. The number is initially
# set to zero and it should be updated when the seed is filled
# with living cells. We could use s1.count_ones() here, but
# we're trying to be efficient by counting only once and
# storing the count.
#
assert s1.num_living > 0
assert s2.num_living > 0
#
# Initialize scores
#
score1 = 0.0
score2 = 0.0
#
# Run several trials with different rotations and locations.
#
for trial in range(num_trials):
#
# Randomly rotate and flip s1 and s2
#
s1 = s1.random_rotate()
s2 = s2.random_rotate()
#
# Switch cells in the second seed (s2) from state 1 (red) to state 2 (blue)
#
s2.red2blue()
#
# Rule file
#
rule_name = "Immigration"
#
# Set toroidal universe of height yspan and width xspan
# Base the s1ze of the universe on the s1zes of the seeds
#
# g = the Golly universe
#
[g_width, g_height, g_time] = dimensions(s1, s2, \
width_factor, height_factor, time_factor)
#
# set algorithm -- "HashLife" or "QuickLife"
#
g.setalgo("QuickLife") # use "HashLife" or "QuickLife"
g.autoupdate(False) # do not update the view unless requested
g.new(rule_name) # initialize cells to state 0
g.setrule(rule_name + ":T" + str(g_width) + "," +
str(g_height)) # make a toroid
#
# Find the min and max of the Golly toroid coordinates
#
[g_xmin, g_xmax, g_ymin, g_ymax] = get_minmax(g)
#
# Set magnification for Golly viewer
#
g.setmag(set_mag(g))
#
# Randomly place seed s1 somewhere in the left s1de of the toroid
#
s1.insert(g, g_xmin, -1, g_ymin, g_ymax)
#
# Randomly place seed s2 somewhere in the right s1de of the toroid
#
s2.insert(g, +1, g_xmax, g_ymin, g_ymax)
#
# Run for a fixed number of generations.
# Base the number of generations on the sizes of the seeds.
# Note that these are generations ins1de one Game of Life, not
# generations in an evolutionary sense. Generations in the
# Game of Life correspond to growth and decay of a phenotype,
# whereas generations in evolution correspond to the reproduction
# of a genotype.
#
g.run(g_time) # run the Game of Life for g_time time steps
g.update() # need to update Golly to get counts
#
# Count the populations of the two colours. State 1 = red = seed1.
# State 2 = blue = seed2.
#
[count1, count2] = count_pops(g)
#
# We need to make an adjustment to these counts. We don't want to
# use the total count of living cells; instead we want to use
# the increase in the number of living cells over the course of
# the contest between the two organisms. The idea here is that
# we want to reward seeds according to their growth during the
# contest, not according to their initial states. This should
# avoid an evolutionary bias towards larger seeds simply due
# to size rather than due to functional properties. It should
# also encourage efficient use of living cells, as opposed to
# simply ignoring useless living cells.
#
# s1.num_living = initial number of living cells in s1
# s2.num_living = initial number of living cells in s2
#
if (s1.num_living < count1):
count1 = count1 - s1.num_living
else:
count1 = 0
#
if (s2.num_living < count2):
count2 = count2 - s2.num_living
else:
count2 = 0
#
# Now we are ready to determine the winner.
#
if (count1 > count2):
score1 = score1 + 1.0
elif (count2 > count1):
score2 = score2 + 1.0
else:
score1 = score1 + 0.5
score2 = score2 + 0.5
#
#
# Normalize the scores
#
score1 = score1 / num_trials
score2 = score2 / num_trials
#
return [score1, score2]
#
current_cpu_id = cparams.current_cpu_id
#
# read the rule list and extract rules that match the current
# run's CPU id
#
rule_list = cfuncs.tsv_BS_rule_cpu(rule_file_name, current_cpu_id)
#
# Golly screen magnification
#
screen_mag = cparams.screen_mag
#
# initialize Golly
#
g.setalgo("QuickLife") # select the algorithm for Golly
g.autoupdate(False) # do not update the view unless requested
g.new("Classification") # create an empty universe
g.setmag(screen_mag) # screen magnification
#
# show parameter settings in the log file
#
parameter_settings = cfuncs.show_parameters()
log_handle.write("\nParameter Settings\n\n")
for setting in parameter_settings:
log_handle.write(setting + "\n")
log_handle.write("\n")
#
# write a header line for the list of results
#
columns = ["rule", \
"prob pop incr", \
import os
import golly as g
#g.select(g.getrect())
oldrule=g.getrule()
i=0
g.autoupdate(0)
while i==0:
g.run(1)
g.save('temp','rle')
g.setrule('S_input')
g.run(1)
cellindex=g.getlayer()
execfile("histogram_2002_r75.py")
histindex=g.getlayer()
g.setlayer(cellindex)
g.open('temp')
g.setlayer(histindex)
g.fit()
g.update()
g.setlayer(cellindex)
i=+1
# and p184 HWSS guns are in the same phase (3680 = 184 * 40)
ONcell = all + CellCenter[3680]
if os.access(ONcellFileName,
os.W_OK) or not os.access(ONcellFileName, os.F_OK):
try:
ONcell.save(ONcellFileName,
"Metapixel ON cell: Brice Due, Spring 2006")
except:
g.show("Failed to save file version of ON cell: " + ONcellFileName)
os.remove(ONcellFileName)
OFFcell += RuleBits
ONcell += RuleBits
g.autoupdate(True)
g.new(layername)
g.setalgo("QuickLife") # qlife's setcell is faster
for j in xrange(selheight):
for i in xrange(selwidth):
golly.show("Placing (" + str(i + 1) + "," + str(j + 1) + ") tile" +
" in a " + str(selwidth) + " by " + str(selheight) +
" rectangle.")
if livecell[i][j]:
ONcell.put(2048 * i - 5, 2048 * j - 5)
else:
OFFcell.put(2048 * i - 5, 2048 * j - 5)
g.fit()
g.show("")
d = 0
else:
d = float(g.getpop()) / float(bbox.wd*bbox.ht)
return d
grid_size = int(g.getstring("Tamanho do tabuleiro:", "3"))
max_iter = grid_size * 500
number_of_tests = 2 ** (grid_size**2)
name = "%dx%d" % (grid_size, grid_size)
out_filename = "output/results_%s.csv" % (name)
patterns_filename = "patterns/patterns_%s.csv" % (name)
rlepatterns_filename = "patterns/patterns_" + name + "-%d.rle"
g.autoupdate(False)
f = open(out_filename, 'w')
f.write("id,number_of_iterations,initial_pop,final_pop,initial_density,final_density,end_status,elapsed_time\n")
#f2 = open(patterns_filename, 'w')
#f2.write("id, initial_cell_list\n")
trial_number = 1
for test_list in create_cell_list(grid_size):
iterations = 0
status = ''
o.clear()
g.new(name)
g.reset()
g.putcells(test_list)
initial_pop = g.getpop()
