def evaluateSphericalVariation (phi,theta,cntPhi,cntTheta):
global conf,cons
success = False
alpha0 =sp.zeros([dim['alpha']],complex)
alpha0[conf['id0']]=sp.cos(phi)*sp.sin(theta)+0j
alpha0[conf['id1']]=sp.sin(phi)*sp.sin(theta)+0j
alpha0[conf['id2']]=sp.cos(theta)+0j
# if (sp.absolute(alpha0[conf['id0']]) <= 1e-10):
# alpha0[conf['id0']]=0.0+0j
# if (sp.absolute(alpha0[conf['id1']]) <= 1e-10):
# alpha0[conf['id1']]=0.0+0j
# if (sp.absolute(alpha0[conf['id2']]) <= 1e-10):
# alpha0[conf['id2']]=0.0+0j
#
# normalize coefficients for alpha -> defines net-power
alpha0[:]=alpha0[:]/sp.linalg.norm(alpha0[:])*cons['alpha_norm']
__,res = MemoryPulseFunctional.evaluateFunctional(alpha0,1.0+0j)
myRes = sp.zeros([conf['entries']+3])
myRes[0] = alpha0[conf['id0']].real
myRes[1] = alpha0[conf['id1']].real
myRes[2] = alpha0[conf['id2']].real
myRes[3:]= res
print "### spherical map: phi/pi={0:5.3f}, theta/pi={1:5.3f}, fun={2:f}".format(phi/sp.pi,theta/sp.pi,myRes[conf['funval']])
myRes[conf['funval']] = min(conf['cutoff'],res[conf['funval']])
return myRes,cntPhi,cntTheta
def main_routine (wd="./",cfg="./python/parameter.cfg",generationType="p",\
toMinimize =2,\
cavityMatch=1,\
silent=0,\
useBeta=0,\
cutoff=10000,dimGrid=11,id0=0,id1=1,id2=2,pltId='funval',pltLim=1000,pltBins=40,myCmap="brg"):
print "#################################################################"
print "#################################################################"
print "### optimal control #############################################"
print "### memory pulse evaluation #####################################"
print "#################################################################"
print "#################################################################"
### globals for functional variation
global cons, fun, dim, conf
conf['toMinimize'] =toMinimize
conf['cavityMatch']=cavityMatch
conf['silent'] =silent
if (useBeta == 0):
conf['useBeta'] = False
else:
conf['useBeta'] = True
conf['id0'] =id0
conf['id1'] =id1
conf['id2'] =id2
conf['cutoff'] =cutoff
### globals for functional variation
### generate working environment ###
print ("### working directory: " + wd)
tmpDir = wd+"tmp/"
cmd = "mkdir -p " + tmpDir
call(cmd.split())
### generate working environment ###
### read config file ###
print ("### load config file: " + cfg)
configParser = cp.ConfigParser()
configParser.read(cfg)
print (configParser.sections())
cfg=configParser.__dict__['_sections'].copy()
conf['MEConstraints'] = cfg['MEConstraints'].copy()
conf['FITNESS']= cfg['FITNESS'].copy()
conf['FITNESS']['{mutationrate}']=sp.zeros([conf['entries']])
conf['FITNESS']['{mutationrate}'][conf['funval']] =cfg['FITNESS']['{mut_functional}']
conf['FITNESS']['{mutationrate}'][conf['fidelity_down']]=cfg['FITNESS']['{mut_fidelity_down}']
conf['FITNESS']['{mutationrate}'][conf['fidelity_up']] =cfg['FITNESS']['{mut_fidelity_up}']
conf['FITNESS']['{mutationrate}'][conf['memolap']] =cfg['FITNESS']['{mut_memolap}']
conf['FITNESS']['{mutationrate}'][conf['alpha']] =cfg['FITNESS']['{mut_alpha}']
conf['FITNESS']['{mutationrate}'][conf['beta']] =cfg['FITNESS']['{mut_beta}']
conf['FITNESS']['{mutationrate}'][conf['success']] =cfg['FITNESS']['{mut_success}']
cons['alpha_norm']=float(cfg['MEConstraints']["{storage_amplitude}"])
cons['beta_low'] =float(cfg['MEConstraints']["{limit_low_beta}"])
cons['beta_top'] =float(cfg['MEConstraints']["{limit_top_beta}"])
cons['chi2_Tol'] =float(cfg['MEConstraints']['{tol_chi2}'])
dim['alpha']=int(cfg['MEFourier']['{storage_harmonic}'])
dim['total']=dim['alpha']+3
prefix =cfg['FILES']['{prefix}']
postfix =cfg['FILES']['{postfix}']
name_optimized=cfg['FILES']['{name_optimized}']
name_spin =cfg['FILES']['{name_spin}']
name_cavity =cfg['FILES']['{name_cavity}']
cfg['dim_grid'] =dimGrid
name_readwrite=IOHelper.getNameReadWrite(**cfg)
name_storage =IOHelper.getNameStorage (**cfg)
name_varinit =IOHelper.getNameInitialVariation (**cfg)
myTime =IOHelper.functionaltimes_readwrite(**cfg) # reads time and updates cfg:
# cfg['METime']['{fidelity_ti}'] = myTime['idx_ti']
# cfg['METime']['{fidelity_tf}'] = myTime['idx_tf']
gridPhi =sp.linspace(0.0, 2.0*sp.pi, num=2*dimGrid-1)
gridTheta =sp.linspace(0.0, sp.pi, num=dimGrid)
minfun =sp.zeros([len(gridPhi),len(gridTheta),conf['entries']+3])
if (generationType == "p"):
print "## read from file: " + name_varinit
raw = sp.loadtxt(name_varinit)
minfun = raw.reshape(len(gridPhi),len(gridTheta),conf['entries']+3)
for i in sp.arange(0,len(gridPhi),1):
for j in sp.arange(0,len(gridTheta),1):
minfun[i,j,conf['fitness']] = MemoryPulseFunctional.fitnessFunction(minfun[i,j,:])
else:
### prepare and complie fortran routines ###
print ("### prepare fortran routines")
replace_in_file('./python/py.parNvCenter.F95' , tmpDir +'parNvCenter.F95' , **cfg['NVSETUP'])
replace_in_file('./python/py.parMemoryPulse.F95', tmpDir +'parMemoryPulse.F95', **cfg['MEFourier'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['OCFourier'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['MESpin'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['MEConstraints'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['OCConstraints'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['METime'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['OCTime'])
replace_in_file(tmpDir +'parMemoryPulse.F95' , tmpDir +'parMemoryPulse.F95', **cfg['FILES'])
#write config file
with open(prefix+"parameter.cfg", 'wb') as configfile:
configParser.write(configfile)
### read config file ###
print ("### compile fortran routines")
cmd = "mv "+tmpDir+"parMemoryPulse.F95 "+wd+"srcOptCntrl/parMemoryPulse.F95"
call(cmd.split())
cmd = "mv "+tmpDir+"parNvCenter.F95 "+wd+"srcNv/parNvCenter.F95"
call(cmd.split())
cmd = "./scripts/ifort-memoryHarmonics.sh " + wd
call(cmd.split())
print ("### invoke fortran routines")
print ("### generation Type: " + generationType)
cmd = wd+"memoryHarmonics" # location of executable fortran program
generateHarmonics = Popen(cmd.split(), stdin=PIPE) # run fortran program with piped standard input
cmd = "echo " + generationType # communication with fortran-routine: chose action -> read or generate
generateInput = Popen(cmd.split(), stdout=generateHarmonics.stdin) # send action to fortran program
output = generateHarmonics.communicate()[0]
generateInput.wait()
### prepare and complie fortran routines ###
### read data for functional variation ###
cons['cavityT2_down'], \
cons['cavityT2_up'] = IOHelper.read_CavityMemory (**cfg['FILES'])
fun ['mtrxBeta_up'], \
cons['vecT2_up'] , \
cons['fidelity_up'], \
cons['mtrxMemOlap'] = IOHelper.read_MtrxMemory("up", **cfg['FILES'])
fun ['mtrxBeta_down'], \
cons['vecT2_down'] , \
cons['fidelity_down'], \
__ = IOHelper.read_MtrxMemory("down", **cfg['FILES'])
### read data for functional variation ###
### functional variation ###
print ("\n### start minimization: ")
print ("### on initial "+ str(sp.shape(minfun)[0])+"x"+str(sp.shape(minfun)[1]) + "-grid (phi,theta) on sphere")
mincnt=0
cntPhi =0
t0=0
tcnt=len(gridTheta)
for phi in gridPhi[0:len(gridPhi)-1]:
theta_i=0
if(cntPhi > 0):
t0=1
tcnt=len(gridTheta)-1
theta_i=1
### parallel evaluation with <cpu_count()> cores ###################################
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count())
results = [ pool.apply_async(evaluateSphericalVariation, args=(phi,theta,cntPhi,cntTheta))
for theta,cntTheta in zip(gridTheta[t0:tcnt],sp.arange(t0,tcnt,1)) ]
for p in results:
myResult,i,j =p.get()
minfun[i,j,:]=myResult
#kill multiprocessing pool !!!
pool.terminate()
### parallel evaluation with <cpu_count()> cores ###################################
### sequentiell evaluateion 1 core #################################################
# for theta in gridTheta[t0:tcnt]:
# minfun[cntPhi,theta_i,:],__,__=MemoryPulseFunctional.evaluateSphericalVariation (phi,theta,cntPhi,theta_i)
# theta_i+=1
### sequentiell evaluateion 1 core #################################################
cntPhi=cntPhi+1
# end for phi
minfun[ :, 0,:]=minfun[0, 0,:] # theta=0 : same vector on unit-sphere
minfun[ :,-1,:]=minfun[0,-1,:] # theta=pi : same vector on unit-sphere
minfun[-1, :,:]=minfun[0, :,:] # periodic
print "\n### write to file: " + name_varinit
sp.savetxt(name_varinit,minfun.reshape((len(gridPhi)*len(gridTheta),conf['entries']+3)),\
header=' alpha0['+str(id0)+']; alpha0['+str(id1)+']; alpha0['+str(id2)+'];'\
+' fitness; success; norm[alpha]; abs[beta]; memolap; fidelity_up; fidelity_down; minfun')
print "#################################################################"
print "#################################################################"
font = {
'fontsize' : 26,
}
mytitle=MemoryPulseFunctional.getName(pltId)
### prepare surface_plot ###############################################################
x = outer(cos(gridPhi), sin(gridTheta))
y = outer(sin(gridPhi), sin(gridTheta))
z = outer(ones(size(gridPhi)), cos(gridTheta))
myDensity=sp.zeros([len(gridPhi),len(gridTheta)])
for i in sp.arange(0,len(gridPhi),1):
for j in sp.arange(0,len(gridTheta),1):
if (minfun[i,j,conf[pltId]] >= pltLim):
myDensity[i,j] = pltLim+1
else:
myDensity[i,j] = minfun[i,j,conf[pltId]]
# "afmhot", "hot", "terrain", "brg"
cm = plt.cm.get_cmap(myCmap)
myColors = cm(myDensity/pltLim)
### prepare surface_plot ###############################################################
### prepare colormap ###################################################################
fig0_colors=sp.linspace(0,pltLim,pltBins)
### prepare colormap ###################################################################
### prepare histogram ##################################################################
yHist,xHist = sp.histogram(myDensity.flatten(),pltBins)
# xSpan = pltxHist.max()-xHist.min()
# Color = [cm(((ix-xHist.min())/xSpan)) for ix in xHist]
Color = [cm(((ix)/pltLim)) for ix in xHist]
### prepare histogram ##################################################################
### calculate fittest solutions ########################################################
cntFit = 0
for i in sp.arange(0,len(gridPhi),1):
for j in sp.arange(0,len(gridTheta),1):
if minfun[i,j,conf['fitness']] != 0.0 :
cntFit+=1
if (minfun[i,j,conf[pltId]] >= pltLim):
myDensity[i,j] = pltLim
else:
myDensity[i,j] = minfun[i,j,conf[pltId]]
if (cntFit > 0):
fitAlpha =sp.zeros([cntFit,int(cfg['MEFourier']['{storage_harmonic}'])])
cntFit = 0
for i in sp.arange(0,len(gridPhi),1):
for j in sp.arange(0,len(gridTheta),1):
if minfun[i,j,conf['fitness']] != 0.0 :
print minfun[i,j,3:]
fitAlpha[cntFit,conf['id0']]=minfun[i,j,0]
fitAlpha[cntFit,conf['id1']]=minfun[i,j,1]
fitAlpha[cntFit,conf['id2']]=minfun[i,j,2]
cntFit+=1
name_fittest=IOHelper.getNameInitialFittest(**cfg)
print "\n### write fittest to file: " + name_fittest
sp.savetxt(name_fittest,fitAlpha,\
header='# alpha0[0] ... alpha0[n]')
print "### fitness counter: {0:} of {1:}".format(cntFit,sp.shape(minfun)[0]*sp.shape(minfun)[1])
### calculate fittest solutions ########################################################
fig = plt.figure()
### plot surface_plot ##################################################################
fig3D = fig.add_subplot(131, projection='3d')
surf = fig3D.plot_surface(x, y, z, rstride=1, cstride=1,facecolors=myColors)
fig3D.plot(cos(gridPhi), sin(gridPhi), zs=0, zdir='z',lw=0.5, color="black")
fig3D.plot(cos(gridPhi), sin(gridPhi), zs=0, zdir='x',lw=0.5, color="black")
fig3D.set_xlabel("$\\alpha_{:}$".format(id0), **font)
fig3D.set_ylabel("$\\alpha_{:}$".format(id1), **font)
fig3D.set_zlabel("$\\alpha_{:}$".format(id2), **font)
fig3D.set_title("a) 4d-plot of "+mytitle, fontsize = 20)
### plot surface_plot ##################################################################
### plot colormap ######################################################################
fig0 = fig.add_subplot(132)
fig0.invert_yaxis()
cp0=fig0.contourf(gridPhi/sp.pi,gridTheta/sp.pi,minfun[:,:,conf[pltId]].T,fig0_colors,cmap=cm)
plt.colorbar(cp0)
fig0.set_xlabel("$\phi/\pi$", **font)
fig0.set_ylabel("$\\theta/\pi$", **font)
fig0.set_title("b) map of "+mytitle, fontsize = 20)
### plot colormap ######################################################################
### plot histogram #####################################################################
figBar=fig.add_subplot(133)
figBar.bar(xHist[:-1],yHist,color=Color,width=xHist[1]-xHist[0])
figBar.set_xlim([0,pltLim])
figBar.set_ylim([0,max(yHist[0:len(yHist)-1])])
figBar.set_xlabel(mytitle, fontsize=20)
figBar.set_ylabel("count", fontsize=20)
figBar.set_title("c) histogram", fontsize = 20)
### plot histogram #####################################################################
plt.show()
