def GunArea(cells, curGunPeriod): maxBox = [] minpop = -100000 for i in xrange(0, curGunPeriod, 4): g.new(str(i)) g.putcells(g.evolve(cells, i)) g.setbase(8) g.setstep(3) g.step() g.step() edgeGlider = EdgeGlider() while PerformDelete(edgeGlider, curGunPeriod): edgeGlider = DevolveGlider(edgeGlider, curGunPeriod) for j in xrange(0, 4): if g.getpop() > minpop: maxpop = g.getpop() maxpopgun = g.getcells(g.getrect()) maxBox = AppendBox(maxBox, g.getrect()) g.run(1) return [BoxValue(maxBox), maxpopgun, maxBox]
def main():
g.setstep(0)
g.setalgo('QuickLife')
velocity = g.getstring('Please specify velocity', '(2,1)c/6')
a, b, p = parse_velocity(velocity)
params = partial_derivatives(a, b, p)
dvdx = params['dvdx']
dudx = params['dudx']
dvdy = params['dvdy']
dudy = params['dudy']
dvdt = params['dvdt']
dudt = params['dudt']
cells = []
for t in xrange(p):
things = g.getcells(g.getrect())
things = zip(things[::2], things[1::2])
cells += [(dudx * x + dudy * y + dudt * t,
dvdx * x + dvdy * y + dvdt * t) for (x, y) in things]
g.step()
g.setlayer(g.addlayer())
g.putcells([x for y in cells for x in y])
def gofast(newgen, delay):
''' Fast goto '''
#Save current settings
oldbase = g.getbase()
# oldhash = g.setoption("hashing", True)
g.show('gofast running, hit escape to abort')
oldsecs = time()
#Advance by binary powers, to maximize advantage of hashing
g.setbase(2)
for i, b in enumerate(intbits(newgen)):
if b:
g.setstep(i)
g.step()
g.dokey(g.getkey())
newsecs = time()
if newsecs - oldsecs >= delay: # do an update every sec
oldsecs = newsecs
g.update()
if g.empty():
break
g.show('')
#Restore settings
# g.setoption("hashing", oldhash)
g.setbase(oldbase)
def goto(newgen, delay):
g.show("goto running, hit escape to abort...")
oldsecs = time()
# before stepping we advance by 1 generation, for two reasons:
# 1. if we're at the starting gen then the *current* step size
# will be saved (and restored upon Reset/Undo) rather than a
# possibly very large step size
# 2. it increases the chances the user will see updates and so
# get some idea of how long the script will take to finish
# (otherwise if the base is 10 and a gen like 1,000,000,000
# is given then only a single step() of 10^9 would be done)
if delay <= 1.0:
g.run(1)
newgen -= 1
# use fast stepping (thanks to PM 2Ring)
for i, d in enumerate(intbase(newgen, g.getbase())):
if d > 0:
g.setstep(i)
for j in range(d):
if g.empty():
g.show("Pattern is empty.")
return
g.step()
newsecs = time()
if newsecs - oldsecs >= delay: # time to do an update?
oldsecs = newsecs
g.update()
g.show("")
def goto(newgen, delay):
g.show("goto running, hit escape to abort...")
oldsecs = time()
# before stepping we advance by 1 generation, for two reasons:
# 1. if we're at the starting gen then the *current* step size
# will be saved (and restored upon Reset/Undo) rather than a
# possibly very large step size
# 2. it increases the chances the user will see updates and so
# get some idea of how long the script will take to finish
# (otherwise if the base is 10 and a gen like 1,000,000,000
# is given then only a single step() of 10^9 would be done)
if delay <= 1.0:
g.run(1)
newgen -= 1
# use fast stepping (thanks to PM 2Ring)
for i, d in enumerate(intbase(newgen, g.getbase())):
if d > 0:
g.setstep(i)
for j in xrange(d):
if g.empty():
g.show("Pattern is empty.")
return
g.step()
newsecs = time()
if newsecs - oldsecs >= delay: # time to do an update?
oldsecs = newsecs
g.update()
g.show("")
def gofast(newgen, delay):
''' Fast goto '''
#Save current settings
oldbase = g.getbase()
# oldhash = g.setoption("hashing", True)
g.show('gofast running, hit escape to abort')
oldsecs = time()
#Advance by binary powers, to maximize advantage of hashing
g.setbase(2)
for i, b in enumerate(intbits(newgen)):
if b:
g.setstep(i)
g.step()
g.dokey(g.getkey())
newsecs = time()
if newsecs - oldsecs >= delay: # do an update every sec
oldsecs = newsecs
g.update()
if g.empty():
break
g.show('')
#Restore settings
# g.setoption("hashing", oldhash)
g.setbase(oldbase)
def __init__(self):
logging.info('script started')
lg_envmt = lifegenes_environment()
lg_envmt.drawColor()
logging.info('setup complete; beginning evolution cycles')
try:
while(True): #until stopped by golly
g.show('cells moving')
for i in range(5): # rounds of cell movement
#logging.debug('movement round '+str(i))
lg_envmt.cellMotions()
#logging.debug('cells moved')
lg_envmt.drawColor()
#logging.debug('cells recolored')
g.update()
#logging.debug('golly updated')
#time.sleep(1)
g.show('cells evolving')
# one round of evolution:
#logging.debug('evolution round started')
g.step()
#g.update()
#logging.debug('golly evolution complete')
lg_envmt.update()
#logging.debug('cellList updated')
lg_envmt.drawColor()
#logging.debug('cells recolored')
g.update()
#logging.debug('golly updated')
#time.sleep(1)
finally:
logging.info('cycling halted from external source (probably golly)')
g.show('closing gracefully, hold on just a sec...')
lg_envmt.teardown()
def GotoLimited(gen, power): g.setbase(8) g.setstep(power) while gen > int(g.getgen()) + g.getbase()**power: g.step() g.update() goto(gen)
def GotoLimited(gen, power):
g.setbase(8)
g.setstep(power)
while gen > int(g.getgen()) + g.getbase()**power:
g.step()
g.update()
goto(gen)
def EvolveRecipe(recipe):
g.new("")
g.setstep(3)
g.putcells(blck)
for r in recipe:
g.putcells(gld, 40, 40 + r)
g.step()
g.step()
def goto(newgen):
currgen = int(g.getgen())
if newgen < currgen:
# try to go back to starting gen (not necessarily 0) and
# then forwards to newgen; note that reset() also restores
# algorithm and/or rule, so too bad if user changed those
# after the starting info was saved;
# first save current location and scale
midx, midy = g.getpos()
mag = g.getmag()
g.reset()
# restore location and scale
g.setpos(midx, midy)
g.setmag(mag)
# current gen might be > 0 if user loaded a pattern file
# that set the gen count
currgen = int(g.getgen())
if newgen < currgen:
g.error("Can't go back any further; pattern was saved " +
"at generation " + str(currgen) + ".")
return
if newgen == currgen: return
oldsecs = time()
# before stepping we advance by 1 generation, for two reasons:
# 1. if we're at the starting gen then the *current* step size
# will be saved (and restored upon Reset/Undo) rather than a
# possibly very large step size
# 2. it increases the chances the user will see updates and so
# get some idea of how long the script will take to finish
# (otherwise if the base is 10 and a gen like 1,000,000,000
# is given then only a single step() of 10^9 would be done)
g.run(1)
currgen += 1
# use fast stepping (thanks to PM 2Ring)
oldstep = g.getstep()
for i, d in enumerate(intbase(newgen - currgen, g.getbase())):
if d > 0:
g.setstep(i)
for j in xrange(d):
if g.empty():
g.show("Pattern is empty.")
return
g.step()
newsecs = time()
if newsecs - oldsecs >= 1.0: # do an update every sec
oldsecs = newsecs
g.update()
g.setstep(oldstep)
def goto(newgen):
currgen = int(g.getgen())
if newgen < currgen:
# try to go back to starting gen (not necessarily 0) and
# then forwards to newgen; note that reset() also restores
# algorithm and/or rule, so too bad if user changed those
# after the starting info was saved;
# first save current location and scale
midx, midy = g.getpos()
mag = g.getmag()
g.reset()
# restore location and scale
g.setpos(midx, midy)
g.setmag(mag)
# current gen might be > 0 if user loaded a pattern file
# that set the gen count
currgen = int(g.getgen())
if newgen < currgen:
g.error("Can't go back any further; pattern was saved " +
"at generation " + str(currgen) + ".")
return
if newgen == currgen: return
oldsecs = time()
# before stepping we advance by 1 generation, for two reasons:
# 1. if we're at the starting gen then the *current* step size
# will be saved (and restored upon Reset/Undo) rather than a
# possibly very large step size
# 2. it increases the chances the user will see updates and so
# get some idea of how long the script will take to finish
# (otherwise if the base is 10 and a gen like 1,000,000,000
# is given then only a single step() of 10^9 would be done)
g.run(1)
currgen += 1
# use fast stepping (thanks to PM 2Ring)
oldstep = g.getstep()
for i, d in enumerate(intbase(newgen - currgen, g.getbase())):
if d > 0:
g.setstep(i)
for j in xrange(d):
if g.empty():
g.show("Pattern is empty.")
return
g.step()
newsecs = time()
if newsecs - oldsecs >= 1.0: # do an update every sec
oldsecs = newsecs
g.update()
g.setstep(oldstep)
def GunArea(cells, curGunPeriod): maxBox = [10000, 10000, -1000, -1000] for i in xrange(0, curGunPeriod, 4): g.new(str(i)) g.putcells(g.evolve(cells, i)) g.setbase(8) g.setstep(3) g.step() g.step() g.step() g.step() edgeGlider = EdgeGlider() while PerformDelete(edgeGlider, curGunPeriod): edgeGlider = DevolveGlider(edgeGlider, curGunPeriod) for j in xrange(0, 4): maxBox = AppendBox(maxBox, g.getrect()) g.run(1) if i == 0: somegun = g.getcells(g.getrect()) return [BoxValue(maxBox), somegun, maxBox]
def AdaptiveGoto(hwssRecipe, enablePrinting=True):
#g.setrule("LifeHistory")
fense50 = g.parse(
"F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F!"
)
helixD = CalcHelix(hwssRecipe)
curgen = -helixD * 2
curgen += 2 * distForward
goto(curgen)
g.setbase(8)
g.setstep(3)
delta = 10
lastmaxi = delta
idx = 0
for i in hwssRecipe:
if enablePrinting and (100 * (idx + 1)) / len(hwssRecipe) != (
100 * idx) / len(hwssRecipe):
percent = (100 * (idx + 1)) / len(hwssRecipe)
g.update()
g.show("Iterating forward progress " + str(percent) + "%")
curgen += 2 * distForward
idx += 1
while int(g.getgen()) < curgen:
g.step()
if i == 'SKIP':
continue
if i > lastmaxi:
g.select([fenseX, helixD + fenseY + lastmaxi - delta, 1, delta])
g.clear(0)
lastmaxi += delta
#g.update()
if i < lastmaxi - delta:
g.putcells(fense50, fenseX, helixD + fenseY + lastmaxi - 2 * delta)
lastmaxi -= delta
def LeftMost(recipe):
g.new("")
g.setstep(3)
g.putcells(block_cells)
for r in recipe:
#g.putcells(glider_cells, 80, 80 + r )
g.putcells(glider_cells, 80, 80 + 2 - r )
g.step()
g.step()
rect = g.getrect()
return rect[0]
def Validate(recipe):
g.new("")
g.setstep(3)
g.putcells(block_cells)
for r in recipe:
g.putcells(glider_cells, 120, 120 + r)
#g.putcells(glider_cells, 80, 80 + 2 - r )
g.step()
g.step()
rect = g.getrect()
return rect
def __init__(self):
logging.info('script started')
lg_envmt = lifegenes_environment()
lg_envmt.drawColor()
logging.info('setup complete; beginning evolution cycles')
try:
while(True): #until stopped by golly
g.step()
#g.update()
lg_envmt.update()
lg_envmt.drawColor()
g.update()
finally:
logging.info('cycling halted from external source (probably golly)')
g.show('closing gracefully, hold on just a sec...')
lg_envmt.teardown()
def LeftMost(recipe):
g.new("")
g.setstep(3)
g.putcells(block_cells)
for r in recipe:
#g.putcells(glider_cells, 80, 80 + r )
g.putcells(glider_cells, 80, 80 + 2 - r)
g.step()
g.step()
rect = g.getrect()
return rect[0]
def Validate(recipe):
g.new("")
g.setstep(3)
g.putcells(block_cells)
for r in recipe:
g.putcells(glider_cells, 120, 120 + r)
#g.putcells(glider_cells, 80, 80 + 2 - r )
g.step()
g.step()
rect = g.getrect()
return rect
def EvolveRecipe(recipe):
g.new("")
g.setstep(3)
g.putcells(blck)
minx = g.getrect()[0]
for r in recipe:
g.putcells(gld, 40, 40 + r)
g.step()
g.step()
if minx > g.getrect()[0]:
minx > g.getrect()[0]
return minx
def AdaptiveGoto(hwssRecipe, enablePrinting = True):
#g.setrule("LifeHistory")
fense50 = g.parse("F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F!")
helixD = CalcHelix(hwssRecipe)
curgen = -helixD * 2
curgen += 2 * distForward
goto(curgen)
g.setbase(8)
g.setstep(3)
delta = 10
lastmaxi = delta
idx = 0
for i in hwssRecipe:
if enablePrinting and (100 * (idx + 1)) / len(hwssRecipe) != (100 * idx) / len(hwssRecipe):
percent = (100 * (idx + 1)) / len(hwssRecipe)
g.update()
g.show("Iterating forward progress " + str(percent) + "%")
curgen += 2 * distForward
idx += 1
while int(g.getgen()) < curgen:
g.step()
if i == 'SKIP':
continue
if i > lastmaxi:
g.select([fenseX, helixD + fenseY + lastmaxi - delta, 1, delta])
g.clear(0)
lastmaxi += delta
#g.update()
if i < lastmaxi - delta:
g.putcells(fense50, fenseX, helixD + fenseY + lastmaxi - 2 * delta)
lastmaxi -= delta
def find_all_glider_idx(mask):
idxs = extract_indexes(mask)
idxs.sort(key=lambda idx: (1.01 * gliders_in[idx][0] + gliders_in[idx][1]))
copy_idxs = idxs[:]
for edge_i in enum_shooters(mask):
reaction_cells = shoot_defs[edge_i][4]
stable_cells = shoot_defs[edge_i][5]
for g_i in list(copy_idxs):
g.new("")
for g_j in idxs:
x, y, idx = gliders_in[g_j]
if g_j == g_i:
g.putcells(g.evolve(reaction_cells, idx), x, y)
else:
g.putcells(g.evolve(gld, idx), x - 128, y + 128)
g.setbase(8)
g.setstep(3)
g.step()
x, y, _ = gliders_in[g_i]
# test if the pattern consists of the stable cells plus the
# necessary gliders and nothing else
g.putcells(stable_cells, x, y, 1, 0, 0, 1, "xor")
if int(g.getpop()) != 5 * len(idxs):
continue
for g_j in idxs:
x, y, idx = gliders_in[g_j]
g.putcells(g.evolve(gld, idx), x, y, 1, 0, 0, 1, "xor")
if g.empty():
copy_idxs.remove(g_i)
yield g_i,edge_i
def PeriodCalculator(): g.setbase(8) g.setstep(3) g.step() g.step() cells = g.getrect() rect = g.getrect() cells = g.getcells(rect) for i in xrange(0, 10000): g.run(1) if str(g.getcells(rect)) == str(cells): #g.show(str(i + 1)) #g.reset return i + 1 return -1
def golly_lhistory():
ngens = int(g.getstring('How many generations to run the pattern?', '1'))
d = threes_and_fours(0)
g.setstep(0)
for i in range(ngens):
g.step()
d.update(threes_and_fours(i + 1))
exes = [k[0] for k in d]
whys = [k[1] for k in d]
zeds = [k[2] for k in d]
minx = min(exes)
maxx = max(exes)
miny = min(whys)
maxy = max(whys)
width = (maxx - minx) + 10
height = maxy - miny
g.addlayer()
g.setrule('Grills')
for (k, v) in d.iteritems():
x = (k[0] - minx) + (k[2] * width)
y = k[1] - miny
c = {3: LIVE_VARIABLE_STATE, 4: DEAD_VARIABLE_STATE}[v]
g.setcell(x, y, c)
for i in range(1, max(zeds) + 1):
for j in range(height + 1):
g.setcell(i * width - 5, j, VERTICAL_LINE_STATE)
for i in range(max(zeds) + 1):
g.setcell(i * width + 3, -3, ORIGIN_STATE)
g.setcell(i * width + 3, -4, ZEROTH_GENERATION_STATE + i)
def FinadOptimalRecipe(slv, idx):
moveTable = slv.SLsMoveTable[idx]
bests = [-1, -1]
slCount = [1000, 1000]
g.setrule("B3/S23")
g.setalgo("HashLife")
for i in xrange(0, len(moveTable)):
g.new("")
slv.ApplyRecipeSmart(i, idx)
slv.PlaceRecipe()
g.setstep(5)
g.step()
numSL = len(CountSL())
laneID = (moveTable[i][1] + 10000) % 2
if slCount[laneID] > numSL:
slCount[laneID] = numSL
bests[laneID] = i
slv.Reset()
g.setrule("LifeHistory")
for i in xrange(0, len(bests)):
slv.ApplyRecipeSmart(bests[i], idx)
slv.PlaceRecipe(i * 100, 0, i == 0)
slv.Reset()
g.show(str(bests))
#11 - [32, 249]
#12 - [138, 123]
#13 - [29, 27]
#14 - [89, 15]
return bests
def AdaptiveGoto(hwssRecipe):
#g.setrule("LifeHistory")
fense50 = g.parse("F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F!")
helixD = -step * len(hwssRecipe) - 1000
while helixD % 7500 != 0:
helixD -= 1
curgen = -helixD * 2
curgen += 2 * distForward
goto(curgen)
g.setstep(3)
delta = 10
lastmaxi = delta
for i in hwssRecipe:
curgen += 2 * distForward
while int(g.getgen()) < curgen:
g.step()
if i == 'SKIP':
continue
if i > lastmaxi:
g.select([240, helixD + fenseY + lastmaxi - delta, 1, delta])
g.clear(0)
lastmaxi += delta
#g.update()
if i < lastmaxi - delta:
g.putcells(fense50, 240, helixD + fenseY + lastmaxi - 2 * delta)
lastmaxi -= delta
def AdaptiveGoto(hwssRecipe):
#g.setrule("LifeHistory")
fense50 = g.parse("F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F$F!")
helixD = -step * len(hwssRecipe) - 1000
while helixD % 7500 != 0:
helixD -= 1
curgen = -helixD * 2
curgen += 2 * distForward
goto(curgen)
g.setstep(3)
delta = 10
lastmaxi = delta
for i in hwssRecipe:
curgen += 2 * distForward
while int(g.getgen()) < curgen:
g.step()
if i == 'SKIP':
continue
if i > lastmaxi:
g.select([240, helixD + fenseY + lastmaxi - delta, 1, delta])
g.clear(0)
lastmaxi += delta
#g.update()
if i < lastmaxi - delta:
g.putcells(fense50, 240, helixD + fenseY + lastmaxi - 2 * delta)
lastmaxi -= delta
def get_pop(numsteps=44100, x=256, y=256):
if numsteps < 1:
g.exit("numsteps must be greater than 0.")
randfill(x, y)
poplist = [int(g.getpop())]
extinction = sys.maxint
rule = g.getrule()
oldsecs = time.time()
for i in xrange(numsteps - 1):
if g.empty():
extinction = int(g.getgen())
break
g.step()
poplist.append(int(g.getpop()))
newsecs = time.time()
if newsecs - oldsecs >= 1.0:
oldsecs = newsecs
g.show("Rule {}, Step {} of {}".format(rule, i + 1, numsteps))
return (poplist, extinction)
def CanApplyRecipe(self, recipe, expected):
g.new("")
g.setstep(3)
g.putcells(blck)
g.putcells(self.init)
for r in self.recipe:
g.putcells(gld, 80, 80 + r)
g.step()
g.select([self.block0[0], self.block0[1], 2, 2])
g.clear(0)
cells = g.getcells(g.getrect())
g.putcells(blck, self.block0[0], self.block0[1])
delta = self.block0[1] - self.block0[0]
for r in recipe:
g.putcells(gld, 80, 80 + r + delta)
g.step()
for i in xrange(0, len(cells), 2):
x = cells[i]
y = cells[i + 1]
if g.getcell(x, y) == 0:
return False
for i in xrange(0, len(expected), 2):
x = expected[i] + self.block0[0]
y = expected[i + 1] + self.block0[1]
if g.getcell(x, y) == 0:
return False
return True
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
g.setoption("hyperspeed", False) # avoid going too fast
g.setbase(8)
g.setstep(4)
g.step() # save start and populate hash tables
# g.run(35328) # run one full cycle (can lock up Golly if construction has failed)
#
# Note that the first cycle is abnormal, since it does not advance the metapattern by
# one metageneration: the first set of communication signals to adjacent cells is
# generated during this first cycle. Thus at the end of 35328 ticks, the pattern
# is ready to start its first "normal" metageneration (where cell states may change).
#
# It should be possible to define a version of ONcell that is not fully populated
# with LWSSs and HWSSs until the end of the first full cycle. This would be much
# quicker to generate from a script definition, and so it wouldn't be necessary to
# save it to a file. The only disadvantage is that ONcells would be visually
# indistinguishable from OFFcells until after the initial construction phase.
def FindAllHs(cells, minx):
g.new("")
g.putcells(cells)
rect = g.getrect()
min = 10000
max = -10000
for i in xrange(1, len(cells), 2):
cx = cells[i - 1]
cy = cells[i]
dx = 40 - cx
cy += dx
if cy < min:
min = cy
if cy > max:
max = cy
answer = [[],[]]
if (min - 9) % 2 != 0:
min += 1
for i in xrange(min - 9 - 40, max + 6 - 40, 2):
g.new("")
g.putcells(cells)
g.putcells(gld, 40, 40 + i)
g.setstep(3)
g.step()
g.step()
if int(g.getpop()) > 60:
continue
if int(g.getpop()) == 0:
continue
if g.getrect()[0] < -120:
continue
edgeType = HasEdgeShooter(minx)
if edgeType != False:
answer[0].append([i, 0, 0, [edgeType]])
rect = g.getrect()
if rect[2] > 12 or rect[3] > 12:
continue
s = str(g.getcells(g.getrect()))
g.run(2)
if s == str(g.getcells(g.getrect())):
key = str(rect) + ":" + str(g.getpop())
if not (key in existingKeys):
existingDic[key] = []
existingKeys.append(key)
if s in existingDic[key]:
continue
else:
existingDic[key].append(s)
answer[1].append(i)
return answer
import golly
s = golly.getstring('rules split by ";"', golly.getclipstr())
rs = [x for x in s.split(';') if x]
# golly.setrule(rs[0]);
L = len(rs)
for i in range(100):
# golly.run(1)
golly.step()
golly.setrule(rs[i % L])
def __init__(self):
try:
g.show('setting up test')
testSize = 100 #this is the size of one side of the square testing area
g.select([-testSize/2,-testSize/2,testSize,testSize])
#clear the canvas
g.clear(1)
g.clear(0)
g.randfill(50)
g.update()
g.show('stressTest started')
startTime = time()
lg_envmt = lifegenes_environment()
endTime = time()
logging.debug('\tsetup t\t=\t\t'+str(endTime-startTime))
g.update()
startTime = time()
lg_envmt.drawColor()
endTime = time()
logging.debug('\tcolorDraw t\t=\t' + str( endTime - startTime))
g.update()
startTime = time()
lg_envmt.cellMotions()
endTime = time()
logging.debug('\tmovement t\t=\t' + str( endTime - startTime))
g.update()
startTime = time()
lg_envmt.drawColor()
endTime = time()
logging.debug('\tcolor update t \t= ' + str( endTime - startTime))
g.update()
g.step()
startTime = time()
lg_envmt.update()
endTime = time()
logging.debug('\tevolve update t\t= ' + str( endTime - startTime))
startTime = time()
lg_envmt.cellMotions()
endTime = time()
logging.debug('\tmovement t\t=\t' + str( endTime - startTime))
g.update()
startTime = time()
lg_envmt.drawColor()
endTime = time()
logging.debug('\tcolor update t\t= ' + str( endTime - startTime))
g.update()
g.update()
g.show('test complete')
finally:
logging.info('cycling halted from external source (probably golly)')
#!/usr/bin/env python2.6
# Q: http://www.hacker.org/challenge/chal.php?id=117
# A: http://www.hacker.org/challenge/chal.php?answer=821%2C319&id=117&go=Submit
# Use Golly, a Game of Life simulator. It can be shown that after 1500 generations, the populations are always 116. So only need to search the first 1500 generations to find the max population.
import golly as g
GENERATION_NUM = 1500
max_population = -1
best_generation = -1
for generation in range(GENERATION_NUM):
population = int(g.getpop())
if population > max_population:
max_population = population
best_generation = generation
g.step()
g.note('%d,%d' % (best_generation, max_population))
def FindAllHs(cells):
g.new("")
g.putcells(cells)
rect = g.getrect()
min = 10000
max = -10000
for i in xrange(1, len(cells), 2):
cx = cells[i - 1]
cy = cells[i]
dx = 40 - cx
cy += dx
if cy < min:
min = cy
if cy > max:
max = cy
answer = [[],[]]
if (min - 9) % 2 != 0:
min += 1
for i in xrange(min - 9 - 40, max + 6 - 40, 2):
g.new("")
g.putcells(cells)
g.putcells(gld, 40, 40 + i)
g.setstep(3)
g.step()
g.step()
if int(g.getpop()) > 80 or int(g.getpop()) == 0:
continue
if g.getrect()[0] < -120:
continue
rect = g.getrect()
if rect[2] > 25 or rect[3] > 25:
continue
s = str(g.getcells(g.getrect()))
g.run(1)
if s == str(g.getcells(g.getrect())):
key = str(rect) + ":" + str(g.getpop())
SLs = []
if rect[2] < 8 and rect[3] < 8:
SLs = CountSL()
if len(SLs) == 1:
SLs[0].sort(key=lambda r: 100000 * r[1] + r[0])
x1 = SLs[0][0][0]
y1 = SLs[0][0][1]
for o in xrange(0, len(SLs[0])):
SLs[0][o][0] -= x1
SLs[0][o][1] -= y1
answer[0].append([i, x1, y1, SLs[0]])
if not (key in existingKeys):
existingDic[key] = []
existingKeys.append(key)
if s in existingDic[key]:
continue
else:
existingDic[key].append(s)
answer[1].append(i)
return answer
left = -int(tilewd / 2)
top = -int(tilewd / 2)
boxsel = [-int(tilewd / 2), -int(tilewd / 2), tilewd, tilewd]
tileit(i, tilewd)
wd = tilewd * pbox.wd
ht = tilewd * pbox.ht
boxsel = [-int(wd / 2), -int(wd / 2), wd, ht]
randfill_mash(boxsel, density)
poplist = [int(yfunc())]
genlist = [int(xfunc())]
xlimlist = [int(g.getrect()[1])]
oldsecs = time()
for i in xrange(numsteps):
g.step()
poplist.append(int(yfunc()))
genlist.append(int(xfunc()))
newsecs = time()
if newsecs - oldsecs >= 1.0: # show pattern every second
oldsecs = newsecs
fit_if_not_visible()
g.update()
g.show("Step %i of %i, Topo %i" % (i + 1, numsteps, posi))
event = g.getevent()
if event.startswith("key"):
evt, ch, mods = event.split()
if ch == "q":
out = 1
numsteps = i
# MAIN
logging.info('script started')
nruns = 10
print 'testing environment...'
from LifeGenes.lifegenes_core.environment import environment as lifegenes_environment
lg_envmt = lifegenes_environment()
testNames = list()
testTimes = list()
g.getcellsconfig = 'const growing'
g.generation = 0
print 'testing drawColor for constantly growing cell list'
testNames.append('drawColor')
testTimes.append( timedRuns(lg_envmt.drawColor,nruns,lambda:[g.step(),lg_envmt.update()]) )
#wait()
g.getcellsconfig = 'const growing'
g.generation = 0
print 'testing update() for constantly growing cell list'
testNames.append('update()')
testTimes.append( timedRuns(lg_envmt.update,nruns,g.step) )
#wait()
g.getcellsconfig = 'const growing'
g.generation = 0
print 'testing cellMotions() for constantly growing cell list'
testNames.append('cellMotions()')
testTimes.append( timedRuns(lg_envmt.cellMotions,nruns,lambda:[g.step(),lg_envmt.update()]) )
#wait()
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
g.setoption("hyperspeed", False) # avoid going too fast
g.setbase(8)
g.setstep(4)
g.step() # save start and populate hash tables
# g.run(35328) # run one full cycle (can lock up Golly if construction has failed)
#
# Note that the first cycle is abnormal, since it does not advance the metapattern by
# one metageneration: the first set of communication signals to adjacent cells is
# generated during this first cycle. Thus at the end of 35328 ticks, the pattern
# is ready to start its first "normal" metageneration (where cell states may change).
#
# It should be possible to define a version of ONcell that is not fully populated
# with LWSSs and HWSSs until the end of the first full cycle. This would be much
# quicker to generate from a script definition, and so it wouldn't be necessary to
# save it to a file. The only disadvantage is that ONcells would be visually
# indistinguishable from OFFcells until after the initial construction phase.
