def gpmaprecc(optstate, persist, **para):
if para['onlyafter'] >= optstate.n or not optstate.n % para['everyn'] == 0:
return argminrecc(optstate, persist, **para)
#return [sp.NaN for i in para['lb']],{'didnotrun':True}
logger.info('gpmap reccomender')
d = len(para['lb'])
lb = para['lb']
ub = para['ub']
maxf = para['maxf']
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
MAP = GPdc.searchMAPhyp(x, y, s, dx, para['mprior'], para['sprior'],
para['kindex'])
logger.info('MAPHYP {}'.format(MAP))
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(para['kindex'], d, MAP))
def wrap(x):
xq = copy.copy(x)
xq.resize([1, d])
a = G.infer_m_post(xq, [[sp.NaN]])
return a[0, 0]
xmin, ymin, ierror = gpbo.core.optutils.twopartopt(wrap, para['lb'],
para['ub'],
para['dpara'],
para['lpara'])
return [i for i in xmin], persist, {'MAPHYP': MAP, 'ymin': ymin}
def gphinrecc(optstate, persist, **para):
#print( [para['onlyafter'],optstate.n])
if para['onlyafter'] >= optstate.n or not optstate.n % para['everyn'] == 0:
#return [sp.NaN for i in para['lb']],{'didnotrun':True}
return argminrecc(optstate, persist, **para)
logger.info('gphin reccomender')
d = len(para['lb'])
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
# print optstate.aux
G = GPdc.GPcore(x, y, s, dx, [
GPdc.kernel(optstate.aux['kindex'], d, h)
for h in optstate.aux['HYPdraws']
])
lb = para['lb']
ub = para['ub']
def wrap(x):
xq = copy.copy(x)
xq.resize([1, d])
a = G.infer_m_post(xq, [[sp.NaN]])
return a[0, 0]
xmin, ymin, ierror = gpbo.core.optutils.twopartopt(wrap, lb, ub,
para['dpara'],
para['lpara'])
return [i for i in xmin], persist, {'ymin': ymin}
def genbiasedmat52ojf(d, lb, ub, xls, sls):
#s normalised to 0 exact, 1
from ESutils import gen_dataset
nt = 30
[X, Y, S, D] = gen_dataset(nt, d + 1, lb + [0], ub + [1], GPdc.MAT52,
sp.array([1.5] + [xls] * d + [sls]))
G = GPdc.GPcore(
X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d + 1, sp.array([1.5] + [0.30] * d + [sls])))
def ojf(x, **ev):
#print "\nojfinput: {} : {}".format(x,ev)
dx = ev['d']
s = ev['s']
if ev['s'] > 0:
noise = sp.random.normal(scale=sp.sqrt(ev['s']))
else:
noise = 0
#print 'noise in ojf {}'.format(noise)
xa = ev['xa']
x = sp.array(x)
xfull = sp.hstack([x, xa])
return G.infer_m(xfull, [dx])[0, 0] + noise, 1., dict()
def dirwrap(x, y):
z = G.infer_m(sp.hstack(sp.array(x) + [0.]), [[sp.NaN]])[0, 0]
return (z, 0)
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=40000,
logfilename='/dev/null')
#print [xmin, ymin]
def spowrap(x):
z = G.infer_m(sp.hstack(sp.array(x) + [0.]), [[sp.NaN]])[0, 0]
return z
y = spm(spowrap,
xmin,
method='l-bfgs-b',
bounds=[(-1, 1)] * d,
options={'ftol': 1e-15})
xmin = y.x
ymin = spowrap(y.x)
#print [xmin,ymin]
if sp.isnan(ymin):
logger.warning('generator got nan optimizing objective. retrying...')
return genbiasedmat52ojf(d, lb, ub, xls, sls)
logger.info(
'generated function xmin {} ymin {} globopt:{} locopt:{}'.format(
xmin, ymin, ierror, y.status))
return ojf, xmin, ymin
def traincfn1dll(x, c):
#cost modeled in a latent space c=exp(cl)
n = x.size
cl = sp.log(c)
MAP = GPdc.searchMAPhyp(x, cl, sp.array([1e-3] * n), [[sp.NaN]] * n,
sp.array([1., 0., -1.]), sp.array([2., 2., 2.]),
GPdc.MAT52CS)
print('MAPhyp in costfn {}'.format(MAP))
g = GPdc.GPcore(x, cl, sp.array([1e-3] * n), [[sp.NaN]] * n,
GPdc.kernel(GPdc.MAT52CS, 1, MAP))
if gpbo.core.debugoutput['cost1d']:
print('plotting cost1d...')
import time
from matplotlib import pyplot as plt
f, a = plt.subplots(2)
low = min(0, min(x))
high = max(1, max(x))
xaxis = sp.linspace(low, high, 100)
y, cy = g.infer_diag_post(xaxis, [[sp.NaN]] * 100)
s = 2. * sp.sqrt(cy)
u = sp.empty(100)
l = sp.empty(100)
for i in xrange(100):
s = sp.sqrt(cy[0, i])
u[i] = y[0, i] + 2. * s
l[i] = y[0, i] - 2. * s
a[0].plot(xaxis, y[0, :], 'b')
a[0].fill_between(xaxis,
l,
u,
facecolor='lightblue',
edgecolor='lightblue',
alpha=0.5)
for i in xrange(n):
a[0].plot(x[i], cl[i], 'r.')
a[0].set_ylabel('latent')
a[1].plot(xaxis, sp.exp(y[0, :]), 'b')
a[1].fill_between(xaxis,
sp.exp(l),
sp.exp(u),
facecolor='lightblue',
edgecolor='lightblue',
alpha=0.5)
for i in xrange(n):
a[1].plot(x[i], c[i], 'r.')
a[1].set_ylabel('out')
f.savefig(
os.path.join(
gpbo.core.debugoutput['path'],
'cost1d' + time.strftime('%d_%m_%y_%H:%M:%S') + '.png'))
f.clf()
plt.close(f)
del (f)
return logcfnobj(g)
def gpmap2upperrecc(optstate, persist, **para):
if para['onlyafter'] >= optstate.n:
print('{} <= {} : switch to argmin'.format(optstate.n,
para['onlyafter']))
return argminrecc(optstate, persist, **para)
#return [sp.NaN for i in para['lb']],{'didnotrun':True}
logger.info('gpmapucb2 reccomender')
d = len(para['lb'])
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
MAP = GPdc.searchMAPhyp(x, y, s, dx, para['mprior'], para['sprior'],
para['kindex'])
logger.info('MAPHYP {}'.format(MAP))
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(para['kindex'], d, MAP))
count = 0
def wrap(xq):
xq.resize([1, d])
a, v = G.infer_diag_post(xq, [[sp.NaN]])
return a[0, 0] + 2. * sp.sqrt(v[0, 0])
#print('nevals={}\n\n'.format(count))
#[xmin,ymin,ierror] = direct(directwrap,para['lb'],para['ub'],user_data=[], algmethod=1, maxf=para['maxf'], logfilename='/dev/null')
xmin, ymin, ierror = gpbo.core.optutils.twopartopt(wrap, para['lb'],
para['ub'],
para['dpara'],
para['lpara'])
logger.info('DIRECT found post. min {} at {} {}'.format(
ymin, xmin, ierror))
a, v = G.infer_diag_post(sp.array([[xmin]]), [[sp.NaN]])
print('mean {} std{} '.format(a[0, 0], sp.sqrt(v[0, 0])))
x, p, di = argminrecc(optstate, persist, **para)
a, v = G.infer_diag_post(sp.array([[x]]), [[sp.NaN]])
print('at argmin mean {} std {} wasactually {}'.format(
a[0, 0], sp.sqrt(v[0, 0]), di['yinc']))
import sys
sys.stdout.flush()
return [i for i in xmin], persist, {'MAPHYP': MAP, 'ymin': ymin}
def gphinasargminrecc(optstate, persist, **para):
if para['onlyafter'] >= optstate.n or not optstate.n % para['everyn'] == 0:
# return [sp.NaN for i in para['lb']],{'didnotrun':True}
return argminrecc(optstate, persist, **para)
logger.info('gpmapas reccomender')
d = len(para['lb'])
x = sp.hstack(
[sp.vstack([e['xa'] for e in optstate.ev]),
sp.vstack(optstate.x)])
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
G = GPdc.GPcore(x, y, s, dx, [
GPdc.kernel(optstate.aux['kindex'], d + 1, h)
for h in optstate.aux['HYPdraws']
])
def wrap(xq):
xq.resize([1, d])
xe = sp.hstack([sp.array([[0.]]), xq])
# print xe
a = G.infer_m_post(xe, [[sp.NaN]])
return a[0, 0]
best = sp.Inf
incumbent = None
for i in xrange(len(optstate.x)):
thisone = wrap(sp.array(optstate.x[i]))
if thisone < best:
best = thisone
incumbent = optstate.x[i]
logger.info('reccsearchresult: x {} pred.y {}'.format(incumbent, best))
return [i for i in incumbent], persist, {'ymin': best}
def traincfn1d(x, c):
n = x.size
g = GPdc.GPcore(x, c,
sp.ones([n, 1]) * 1e-1, [[sp.NaN]] * n,
GPdc.kernel(GPdc.MAT52, 1, [1., 0.2]))
if gpbo.core.debugoutput['cost1d']:
print('plotting cost1d...')
import time
from matplotlib import pyplot as plt
f, a = plt.subplots(1)
low = min(0, min(x))
high = max(1, max(x))
xaxis = sp.linspace(low, high, 100)
y, cy = g.infer_diag_post(xaxis, [[sp.NaN]] * 100)
a.plot(xaxis, y[0, :], 'b')
s = 2. * sp.sqrt(cy)
u = sp.empty(100)
l = sp.empty(100)
for i in xrange(100):
s = sp.sqrt(cy[0, i])
u[i] = y[0, i] + 2. * s
l[i] = y[0, i] - 2. * s
a.fill_between(xaxis,
l,
u,
facecolor='lightblue',
edgecolor='lightblue',
alpha=0.5)
for i in xrange(n):
a.plot(x[i], c[i], 'r.')
f.savefig(
os.path.join(
gpbo.core.debugoutput['path'],
'cost1d' + time.strftime('%d_%m_%y_%H:%M:%S') + '.png'))
del (f)
return cfnobj(g)
def prob(G, x, tol=1e-3, dropdims=[]):
nsam = 10 * int(1. / tol) + 1
Gr, varG, H, Hvec, varHvec, M, varM = gpbo.core.optutils.gpGH(G, x)
d = G.D
vHdraws = GPdc.draw(Hvec.flatten(), varHvec, nsam)
pvecount = 0
count = sp.zeros(d)
count2 = sp.zeros(d)
for i in xrange(nsam):
Hdraw = gpbo.core.optutils.Hvec2H(vHdraws[i, :], d)
g, v = sp.linalg.eigh(Hdraw)
count += g > 0
logger.debug('eigenvector probs {}'.format((count + 1.) / (nsam + 2.)))
return
def gpfixrecc(optstate, persist, **para):
if para['onlyafter'] >= optstate.n or not optstate.n % para['everyn'] == 0:
return argminrecc(optstate, persist, **para)
#return [sp.NaN for i in para['lb']],{'didnotrun':True}
logger.info('gpmap reccomender')
d = len(para['lb'])
lb = para['lb']
ub = para['ub']
maxf = para['maxf']
x = sp.vstack(optstate.x)
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
G = GPdc.GPcore(x, y, s, dx, GPdc.kernel(para['kindex'], d, para['hyper']))
def wrap(x, null):
xq = copy.copy(x)
xq.resize([1, d])
a = G.infer_m_post(xq, [[sp.NaN]])
return a[0, 0], 0
xmin, ymin, ierror = gpbo.core.optutils.silentdirect(
wrap, para['lb'], para['ub'], **para['dpara'])
return [i for i in xmin], persist, {'ymin': ymin}
def gendecayingpositiveojf(d, lb, ub, sim):
# s normalised to 0 exact, 1
from ESutils import gen_dataset
nt = 20
cl = 2.
[X, Y, S, D] = gen_dataset(nt, d, lb, ub, GPdc.MAT52,
sp.array([1] + [0.30] * d))
G0 = GPdc.GPcore(X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d, sp.array([1] + [0.30] * d)))
[X, Y, S, D] = gen_dataset(nt, d, lb, ub, GPdc.MAT52,
sp.array([1] + [0.30] * d))
G1 = GPdc.GPcore(X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d, sp.array([1] + [0.30] * d)))
[X, Y, S, D] = gen_dataset(nt, d, lb, ub, GPdc.MAT52,
sp.array([1] + [0.30] * d))
G2 = GPdc.GPcore(X, Y, S, D,
GPdc.kernel(GPdc.MAT52, d, sp.array([1] + [0.30] * d)))
def p0(x):
v = G0.infer_m(x, [[sp.NaN]])[0, 0]
y = v + 1 if v > 0 else sp.exp(v)
return y
def p1(x):
v = G1.infer_m(x, [[sp.NaN]])[0, 0]
y = v + 1 if v > 0 else sp.exp(v)
return y
def p2(x):
v = G2.infer_m(x, [[sp.NaN]])[0, 0]
y = v + 1 if v > 0 else sp.exp(v)
return y
def ojf(x, **ev):
#print "ex: {} {}".format(x,ev['xa'])
# print "\nojfinput: {} : {}".format(x,ev)
dx = ev['d']
s = ev['s']
if ev['s'] > 0:
noise = sp.random.normal(scale=sp.sqrt(ev['s']))
else:
noise = 0
# print 'noise in ojf {}'.format(noise)
xa = ev['xa']
x = sp.array(x)
y0 = p0(x)
y1 = sim * p1(x) + y0
l = p2(x)
A = (y1 - y0) / (sp.exp(l) - 1)
B = y0 - A
y = A * sp.exp(l * xa) + B
c = sp.exp(-cl * xa)
return y + noise, c, dict()
def dirwrap(x, y):
z = ojf(x, **{'d': [sp.NaN], 's': 0, 'xa': 0})[0]
return (z, 0)
[xmin, ymin, ierror] = DIRECT.solve(dirwrap,
lb,
ub,
user_data=[],
algmethod=1,
maxf=20000,
logfilename='/dev/null')
# print [xmin, ymin]
def spowrap(x):
z = ojf(x, **{'d': [sp.NaN], 's': 0, 'xa': 0})[0]
return z
y = spm(spowrap, xmin, method='nelder-mead', options={'fatol': 1e-12})
xmin = y.x
ymin = spowrap(y.x)
# print [xmin,ymin]
logger.info(
'generated function xmin {} ymin {} yminisnan{} globopt:{} locopt:{}'.
format(xmin, ymin, sp.isnan(ymin), ierror, y.status))
if sp.isnan(ymin):
logger.warning('generator got nan optimizing objective. retrying...')
return gendecayingpositiveojf(d, lb, ub)
return ojf, xmin, ymin
def genmat52ojf(d, lb, ub, A=1., ls=0.3, fixs=-1, ki=GPdc.MAT52):
from ESutils import gen_dataset
if isinstance(ls, float):
ls = [ls] * d
#nt=sp.maximum(250,sp.minimum(int(d*100./sp.product(ls)),3000))
nt = sp.maximum(150, sp.minimum(int(d * 20. / sp.product(ls)), 2500))
#print(nt)
#nt=500
s = 1e-9
for s in sp.logspace(-9, 0, 20):
try:
[X, Y, S, D] = gen_dataset(nt,
d,
lb,
ub,
ki,
sp.array([A] + ls),
s=1e-9)
break
except:
raise
# from matplotlib import pyplot as plt
# plt.figure()
# plt.plot(sp.array(X),sp.array(Y),'b.')
# plt.show(block=True)
print('training GP')
G = GPdc.GPcore(X, Y, S, D, GPdc.kernel(ki, d, sp.array([A] + ls)))
def wrap(x):
xq = sp.copy(x)
xq.resize([1, d])
a = G.infer_m_post(xq, [[sp.NaN]])
return a[0, 0]
dpara = {
'user_data': [],
'algmethod': 1,
'maxf': 40000,
'logfilename': '/dev/null'
}
lpara = {'ftol': 1e-20, 'maxfun': 1200}
xmin, ymin, ierror = gpbo.core.optutils.twopartopt(wrap, lb, ub, dpara,
lpara)
print('init {} {}'.format(xmin, ymin))
for i in xrange(250):
p = sp.random.normal(size=d) * 1e-2
res = spm(wrap,
xmin + p,
method='L-BFGS-B',
bounds=tuple([(lb[j], ub[j]) for j in xrange(d)]),
options={'gtol': 1e-30})
# print(res)
#print(xmin,res.x,wrap(xmin),wrap(res.x)<wrap(xmin))
if wrap(res.x) < wrap(xmin):
xmin = res.x
# ymin = ymin+res.fun
print('change: {} {}'.format(xmin, wrap(res.x) - ymin))
ymin = wrap(xmin)
def ojf(x, **ev):
dx = ev['d']
s = ev['s']
if fixs < 0:
if ev['s'] > 0 and not 'cheattrue' in list(ev.keys()):
noise = sp.random.normal(scale=sp.sqrt(ev['s']))
else:
noise = 0
else:
if not 'cheattrue' in list(ev.keys()):
noise = sp.random.normal(scale=sp.sqrt(fixs))
else:
noise = 0.
y = wrap(x) + noise #G.infer_m(sp.array(x),[dx])[0,0]+noise
if not 'silent' in list(ev.keys()):
print('ojf at {} {} returned {} noise {}'.format([i for i in x],
ev, y, noise))
return y - ymin, 1., dict()
logger.info(
'generated function xmin {} ymin {}(shifted to 0.) opt:{}'.format(
xmin, ymin, ierror))
return ojf, xmin, 0.
def gphinasrecc(optstate, persist, **para):
if para['onlyafter'] >= optstate.n or not optstate.n % para['everyn'] == 0:
#return [sp.NaN for i in para['lb']],{'didnotrun':True}
return argminrecc(optstate, persist, **para)
logger.info('gpmapas reccomender')
d = len(para['lb'])
x = sp.hstack(
[sp.vstack([e['xa'] for e in optstate.ev]),
sp.vstack(optstate.x)])
y = sp.vstack(optstate.y)
s = sp.vstack([e['s'] + 10**optstate.condition for e in optstate.ev])
dx = [e['d'] for e in optstate.ev]
G = GPdc.GPcore(x, y, s, dx, [
GPdc.kernel(optstate.aux['kindex'], d + 1, h)
for h in optstate.aux['HYPdraws']
])
# def directwrap(xq,y):
# xq.resize([1,d])
# xe = sp.hstack([sp.array([[0.]]),xq])
# #print xe
# a = G.infer_m_post(xe,[[sp.NaN]])
# return (a[0,0],0)
# [xmin,ymin,ierror] = direct(directwrap,para['lb'],para['ub'],user_data=[], algmethod=1, maxf=para['maxf'], logfilename='/dev/null')
def wrap(x):
xq = copy.copy(x)
xq.resize([1, d])
xe = sp.hstack([sp.array([[0.]]), xq])
a = G.infer_m_post(xe, [[sp.NaN]])
return a[0, 0]
xmin, ymin, ierror = gpbo.core.optutils.twopartopt(wrap, para['lb'],
para['ub'],
para['dpara'],
para['lpara'])
logger.info('reccsearchresult: {}'.format([xmin, ymin, ierror]))
from gpbo.core import debugoutput
if debugoutput['datavis']:
if not os.path.exists(debugoutput['path']):
os.mkdir(debugoutput['path'])
l = sp.mean([h[3] for h in optstate.aux['HYPdraws']])
from matplotlib import pyplot as plt
fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(10, 30))
n = 200
x_ = sp.linspace(-1, 1, n)
y_ = sp.linspace(-1, 1, n)
z_ = sp.empty([n, n])
s_ = sp.empty([n, n])
for i in xrange(n):
for j in xrange(n):
m_, v_ = G.infer_diag_post(sp.array([0., y_[j], x_[i]]),
[[sp.NaN]])
z_[i, j] = m_[0, 0]
s_[i, j] = sp.sqrt(v_[0, 0])
CS = ax[1].contour(x_, y_, z_, 20)
ax[1].clabel(CS, inline=1, fontsize=10)
CS = ax[2].contour(x_, y_, s_, 20)
ax[2].clabel(CS, inline=1, fontsize=10)
for i in xrange(x.shape[0] - 1):
ax[0].plot(x[i, 1], x[i, 2], 'b.')
circle = plt.Circle([x[i, 1], x[i, 2]],
radius=0.5 * x[i, 0] * l,
edgecolor="none",
color='lightblue',
alpha=0.8 - 0.6 * x[i, 2])
ax[0].add_patch(circle)
ax[0].plot(x[i + 1, 1], x[i + 1, 2], 'r.')
circle = plt.Circle([x[i + 1, 1], x[i + 1, 2]],
radius=0.5 * x[i, 0] * l,
edgecolor="none",
color='lightblue',
alpha=0.8 - 0.6 * x[i, 2])
ax[0].add_patch(circle)
ax[0].axis([-1., 1., -1., 1.])
ax[1].plot(xmin[0], xmin[1], 'ro')
fig.savefig(
os.path.join(
debugoutput['path'],
'datavis' + time.strftime('%d_%m_%y_%H:%M:%S') + '.png'))
fig.clf()
plt.close(fig)
del (fig)
return [i for i in xmin], persist, {'ymin': ymin}
def traincfnfull(x, c):
#cost modeled in a latent space c=exp(cl)
n, d = x.shape
c_ = sp.log(c)
off = sp.mean(c_)
cl = c_ - off
MAP = GPdc.searchMAPhyp(x, cl, sp.array([1e-6] * n), [[sp.NaN]] * n,
sp.array([1.] + [-0.] * d),
sp.array([2.] * (d + 1)), GPdc.MAT52)
print('MAPhyp in costfn {}'.format(MAP))
g = GPdc.GPcore(x, cl, sp.array([1e-3] * n), [[sp.NaN]] * n,
GPdc.kernel(GPdc.MAT52, 1, MAP))
if gpbo.core.debugoutput['cost1d']:
print('plotting cost...')
import time
from matplotlib import pyplot as plt
f, a = plt.subplots(d, 2)
n = 60
x = sp.linspace(-1, 1, n)
xa = sp.linspace(0, 1, n)
z = sp.empty([n, n])
vz = sp.empty([n, n])
for D in xrange(d - 1):
for i in xrange(n):
for j in xrange(n):
q = sp.zeros([1, d])
q[0, 0] = xa[j]
q[0, D + 1] = x[i]
m, v = g.infer_diag(q, [[sp.NaN]])
z[i, j] = m[0, 0]
vz[i, j] = v[0, 0]
try:
CS = a[D, 0].contour(xa, x, z, 30)
a[D, 0].clabel(CS, inline=1, fontsize=8)
except ValueError:
pass
try:
CS = a[D, 1].contour(xa, x, vz, 30)
a[D, 1].clabel(CS, inline=1, fontsize=8)
except ValueError:
pass
for i in xrange(n):
for j in xrange(n):
q = sp.zeros([1, d])
q[0, 0] = 0.
q[0, 1] = x[j]
q[0, 2] = x[i]
m, v = g.infer_diag(q, [[sp.NaN]])
z[i, j] = m[0, 0]
vz[i, j] = v[0, 0]
try:
CS = a[d - 1, 0].contour(x, x, z, 30)
a[d - 1, 0].clabel(CS, inline=1, fontsize=8)
except ValueError:
pass
try:
CS = a[d - 1, 1].contour(x, x, vz, 30)
a[d - 1, 1].clabel(CS, inline=1, fontsize=8)
except ValueError:
pass
f.savefig(
os.path.join(
debugoutput['path'],
'cost1d' + time.strftime('%d_%m_%y_%H:%M:%S') + '.png'))
f.clf()
plt.close(f)
del (f)
return logcfnobjfull(g, offset=off)