def __init__(self,
jobs,
x,
y,
xspacing,
yspacing,
searchTimeout,
baseTiling=None):
# Initialize the base class first
super().__init__(jobs, x, y, xspacing, yspacing, searchTimeout)
# Track the possible permutations given the `jobs` provided
# There are (2**N)*(N!) possible permutations where N is the number of jobs.
# This is assuming all jobs are unique and each job has a rotation (i.e., is not
# square). Practically, these assumptions make no difference because the software
# currently doesn't optimize for cases of repeated jobs.
self.possiblePermutations = (2**len(jobs)) * factorial(len(jobs))
# Track the number of permutations checked so far
self.permutations = 0
# Store the tiling we've already encountered, otherwise generate
# a default tiling
if baseTiling:
self.baseTiling = baseTiling
else:
self.baseTiling = tiling.Tiling(self.x, self.y, self.xspacing,
self.yspacing)
def __init__(self, jobs, x, y, xspacing, yspacing, searchTimeout,
exhaustiveSearchJobs):
super().__init__(jobs, x, y, xspacing, yspacing, searchTimeout)
# We also store a blank tiling to start
self.baseTiling = tiling.Tiling(x, y, self.xspacing, self.yspacing)
# Record the number of jobs that should be searched exhaustively
self.RandomSearchExhaustiveJobs = exhaustiveSearchJobs
def tile_search1(Jobs, X, Y):
"""Wrapper around _tile_search1 to handle keyboard interrupt, etc."""
global _StartTime, _CkpointTime, _Placements, _TBestTiling, _TBestScore, _Permutations, _PossiblePermutations
initialize()
_StartTime = time.time()
_CkpointTime = _StartTime + 3
# There are (2**N)*(N!) possible permutations where N is the number of jobs.
# This is assuming all jobs are unique and each job has a rotation (i.e., is not
# square). Practically, these assumptions make no difference because the software
# currently doesn't optimize for cases of repeated jobs.
_PossiblePermutations = (2L**len(Jobs)) * factorial(len(Jobs))
#print "Possible permutations:", _PossiblePermutations
print '=' * 70
print "Starting placement using exhaustive search."
print "There are %ld possible permutations..." % _PossiblePermutations,
if _PossiblePermutations < 1e4:
print "this'll take no time at all."
elif _PossiblePermutations < 1e5:
print "surf the web for a few minutes."
elif _PossiblePermutations < 1e6:
print "take a long lunch."
elif _PossiblePermutations < 1e7:
print "come back tomorrow."
else:
print "don't hold your breath."
print "Press Ctrl-C to stop and use the best placement so far."
print "Estimated maximum possible utilization is %.1f%%." % (
tiling.maxUtilization(Jobs) * 100)
try:
_tile_search1(Jobs, tiling.Tiling(X, Y), 1)
printTilingStats()
print
except KeyboardInterrupt:
printTilingStats()
print
print "Interrupted."
computeTime = time.time() - _StartTime
print "Computed %ld placements in %d seconds / %.1f placements/second" % (
_Placements, computeTime, _Placements / computeTime)
print '=' * 70
return _TBestTiling
def _tile_search2(Jobs, X, Y, cfg=config.Config):
global _CkpointTime, _Placements, _TBestTiling, _TBestScore
r = random.Random()
N = len(Jobs)
# M is the number of jobs that will be placed randomly.
# N-M is the number of jobs that will be searched exhaustively.
M = N - config.RandomSearchExhaustiveJobs
M = max(M, 0)
xspacing = cfg['xspacing']
yspacing = cfg['yspacing']
# Must escape with Ctrl-C
while 1:
T = tiling.Tiling(X, Y)
joborder = r.sample(range(N), N)
minInletSize = tiling.minDimension(Jobs)
for ix in joborder[:M]:
Xdim, Ydim, job, rjob = Jobs[ix]
T.removeInlets(minInletSize)
if r.choice([0, 1]):
addpoints = T.validAddPoints(Xdim + xspacing, Ydim + yspacing)
if not addpoints:
break
pt = r.choice(addpoints)
T.addJob(pt, Xdim + xspacing, Ydim + yspacing, job)
else:
addpoints = T.validAddPoints(Ydim + xspacing, Xdim + yspacing)
if not addpoints:
break
pt = r.choice(addpoints)
T.addJob(pt, Ydim + xspacing, Xdim + yspacing, rjob)
else:
# Do exhaustive search on remaining jobs
if N - M:
remainingJobs = []
for ix in joborder[M:]:
remainingJobs.append(Jobs[ix])
tilesearch1.initialize(0)
tilesearch1._tile_search1(remainingJobs, T, 1)
T = tilesearch1.bestTiling()
if T:
score = T.area()
if score < _TBestScore:
_TBestTiling, _TBestScore = T, score
elif score == _TBestScore:
if T.corners() < _TBestTiling.corners():
_TBestTiling, _TBestScore = T, score
_Placements += 1
# If we've been at this for 3 seconds, print some status information
if time.time() > _CkpointTime:
printTilingStats()
# Check for timeout - changed to file config
if (config.Config['searchtimeout'] > 0) and (
(time.time() - _StartTime) > config.Config['searchtimeout']):
raise KeyboardInterrupt
gerbmerge.updateGUI("Performing automatic layout...")
def draw_column(self, x, y, height, img):
height = int(height)
for i in range(1, height + 1):
img = self.draw_lozenge1(x + self.xsize, y - i * self.size, img)
img = self.draw_lozenge2(x - self.xsize, y - i * self.size, img)
return self.draw_lozenge3(x, y - height * self.size - self.size / 2,
img)
def show_image(lattice):
p = Painter(lattice)
cv.imshow('lattice', p.img)
cv.waitKey(0)
def save_image(lattice, filename):
p = Painter(lattice)
cv.imwrite(filename, p.img)
tiling = tiling.Tiling(20, 100000)
tiling.metropolis(100000, thermalization=10000)
print('done')
save_image(
tiling.to_3d_lattice(
np.round(tiling.average_configuration).astype(np.int32)),
"lattice.jpg")
show_image(
tiling.to_3d_lattice(
np.round(tiling.average_configuration).astype(np.int32)))
import numpy as np
from multiprocessing import Pool
ITERATIONS = 100000
def write_results(temperatures, energies, capacities, filename):
df = pd.DataFrame({
'temperature': temperatures,
'energy': energies,
'capacity': capacities
})
df.to_csv(filename)
def call_metropolis(tiling):
tiling.metropolis(ITERATIONS)
return tiling
if __name__ == '__main__':
p = Pool(3)
n = 10
temperatures = np.linspace(0.5, 10, 50)
tilings = [tiling.Tiling(n, t) for t in temperatures]
tilings = p.map(call_metropolis, tilings)
energies = [tiling.average_energy for tiling in tilings]
capacities = [tiling.capacity() for tiling in tilings]
write_results(temperatures, energies, capacities,
'energy_' + str(n) + '.csv')
