def run_search(self):
print "begin PES:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
[Qmin, ymin,
ierror] = self.pesobj.search_acq(self.para['cfn'],
self.para['logsl'],
self.para['logsu'],
volper=self.para['volper'])
return [Qmin[:-1], 10**Qmin[-1], [sp.NaN]]
def run_search(self):
print "begin PESIS:"
try:
#print "aaa"
del (self.pesobj)
#print "bbb"
except:
print "ccc"
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'],
noS=True)
[xmin, ymin,
ierror] = self.pesobj.search_pes(-1, volper=self.para['volper'])
return [xmin, 0., [sp.NaN]]
def PESvsaq(optstate, persist, **para):
para = copy.deepcopy(para)
if persist == None:
persist = {'n': 0, 'd': len(para['ub'])}
n = persist['n']
d = persist['d']
if n < para['nrandinit']:
persist['n'] += 1
return randomaq(optstate, persist, **para)
logger.info('PESvsaq')
#logger.debug(sp.vstack([e[0] for e in optstate.ev]))
#raise
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
pesobj = PES.PES(x,
y,
s,
dx,
para['lb'],
para['ub'],
para['kindex'],
para['mprior'],
para['sprior'],
DH_SAMPLES=para['DH_SAMPLES'],
DM_SAMPLES=para['DM_SAMPLES'],
DM_SUPPORT=para['DM_SUPPORT'],
DM_SLICELCBPARA=para['DM_SLICELCBPARA'],
mode=para['SUPPORT_MODE'],
noS=para['noS'])
[xmin, ymin, ierror] = pesobj.search_acq(para['cfn'],
para['logsl'],
para['logsu'],
volper=para['volper'])
logger.debug([xmin, ymin, ierror])
para['ev']['s'] = 10**xmin[-1]
xout = [i for i in xmin[:-1]]
return xout, para['ev'], persist, {
'HYPdraws': [k.hyp for k in pesobj.G.kf],
'mindraws': pesobj.Z,
'DIRECTmessage': ierror,
'PESmin': ymin
}
return
def pcs(self):
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
xmin = self.reccomend()
plt.figure(1)
plt.plot(xmin[0], xmin[1], 'r.')
print xmin
plt.figure(2)
plt.subplot(4, 1, 1)
ns = 6000
sup = sp.linspace(-1, 1, ns)
for i in xrange(2):
X = sp.vstack([xmin for k in xrange(ns)])
print X.shape
for j in xrange(ns):
X[j, i] = sup[j]
[m, v] = self.pesobj.G.infer_diag_post(X, [[sp.NaN]] * ns)
s = sp.sqrt(v)
plt.subplot(4, 1, 2 * i + 1)
plt.fill_between(sup, (m - 2 * s).flatten(), (m + 2 * s).flatten(),
facecolor='lightblue',
edgecolor='lightblue')
plt.plot(sup, m.flatten())
[m, v] = self.pesobj.G.infer_diag_post(X, [[i]] * ns)
s = sp.sqrt(v)
plt.subplot(4, 1, 2 * i + 2)
plt.fill_between(sup, (m - 2 * s).flatten(), (m + 2 * s).flatten(),
facecolor='lightblue',
edgecolor='lightblue')
plt.plot(sup, m.flatten(), 'r')
p = sp.exp(-0.5 * (m**2) / v)
plt.twinx().plot(sup, p.flatten(), 'g')
return
def run_search(self):
print "begin PESFS:"
try:
del (self.pesobj)
except:
pass
self.pesobj = PES.PES(self.X,
self.Y,
self.S,
self.D,
self.lb.flatten(),
self.ub.flatten(),
self.para['kindex'],
self.para['mprior'],
self.para['sprior'],
DH_SAMPLES=self.para['DH_SAMPLES'],
DM_SAMPLES=self.para['DM_SAMPLES'],
DM_SUPPORT=self.para['DM_SUPPORT'],
DM_SLICELCBPARA=self.para['DM_SLICELCBPARA'],
mode=self.para['SUPPORT_MODE'])
[xmin, ymin,
ierror] = self.pesobj.search_pes(self.sdefault,
volper=self.para['volper'])
return [xmin, self.para['s'], [sp.NaN]]
lb = sp.array([-1.] * d)
ub = sp.array([1.] * d)
[X, Y, S, D] = ESutils.gen_dataset(nt, d, lb, ub, GPdc.SQUEXP,
sp.array([1.5, 0.35, 0.30]))
kindex = GPdc.SQUEXP
mprior = sp.array([0.] + [-1.] * d)
sprior = sp.array([1.] * (d + 1))
pesobj = PES.PES(X,
Y,
S,
D,
lb,
ub,
kindex,
mprior,
sprior,
DH_SAMPLES=8,
DM_SAMPLES=8,
DM_SUPPORT=400,
DM_SLICELCBPARA=1.,
mode=ESutils.SUPPORT_SLICEEI)
np = 150
sup = sp.linspace(-1, 1, np)
Dp = [[sp.NaN]] * np
Sp = sp.array([[1e-3] * np]).T
Xp0 = sp.vstack([sp.array([i, pesobj.Z[0, 1]]) for i in sup])
Xp1 = sp.vstack([sp.array([pesobj.Z[0, 0], i]) for i in sup])
f, a = plt.subplots(d)
# Creates stitch outlines for each shape
if args.noOutline is not True:
subshapeLineGroups = prependShapeTraces(paths, subshapeLineGroups, maxStitchDistance=maxStitchDistance)
#DEBUG lines: [[[Line(0+0j, 300+0j), Line(300+0j, 300+300j), Line(300+300j, 0+300j) , Line(0+300j, 0+0j)]]]
PECCommands = createPECStitchRoutines(subshapeLineGroups, fillColors, threadWidth, maxStitchDistance=maxStitchDistance)
left, right, bottom, top = shape.bbox()
pec = PEC(label="simple", colors=PECColors, commands=PECCommands, size=complex(right - left, top - bottom))
# Render the PEC commands
renderPEC(pec)
pes = PES(PEC=pec, shape=shape)
encodedPES = pes.encode()
with open(args.outputFile, "w") as f:
f.write(encodedPES)
print("Wrote {} to disk.".format(args.outputFile))
if args.debugRendering:
loadedPES = pyembroidery.read(args.outputFile)
if loadedPES is not None:
print("Generating debug image.")
debugImagePath = replaceFilenameAndExtensionFromPath(args.outputFile, "debugPicture" + getFilenameAndExtensionFromPath(args.outputFile)[0], "png")
pyembroidery.write_png(loadedPES, debugImagePath)
print("Image written to disk: {}".format(debugImagePath))
else:
