print "Energy scale of Lennard Jones Potential in kT units:", solver.getEpsilonInkTUnits()
#Initialize HMSA with some (random) start alpha
solver.doHMSAclosure(1.0)
else:
#Yukawa possible with RY: With negative K (attractive besides HS) convergence may be reached for exp shift only (real HS-Yukawa)
#positive (repulsive as HS) K converges for fully shifted potential (1/r included)
solver.setPotentialByName('Yukawa', 1.0, -1.0, True) #lamda in SASfit is different; K = 0.1 is default in SASfit
print "Energy scale of Yukawa HS Potential in kT units:", solver.getInteractionStrengthInKTunits()
print "Length scale of Yukawa HS Potential in sigma units:", solver.getShieldingConstantInSigmaUnits()
#Initialize RY with some (random) start alpha
solver.doRYclosure(1.0)
solver.setVolumeDensity(0.3)
print "Volume density of liquid:", solver.getVolumeDensity()
print "start solving for optimal closure alpha...."
#Should we minimize cost functional or find a root?
doUseMinimizer = False
doSearchIndependentPlot = False
#Save iterated points in alpha space as dict to plot later
finderFunctionGraph = {}
if doUseMinimizer:
#Ein Minimum macht noch keine Nullstelle, minimierungs algorithmen
#kommen aber (auch) mit doppelten (mehrfachen) Nullstellen klar. *Falls* eine Nullstelle vorliegt,
#ist sie in jedem Fall auch ein Minimum der quadrierten funktion (bei mehreren Nullstellen kein globales Minimum)
alpha_opt = optimize.minimize(finderFunctionForAlpha, alpha_initial, args=(solver, finderFunctionGraph, doUseMinimizer,))
print "optimal alpha according minimizer:", alpha_opt.x[0]
else:
#Yukawa possible with RY: With negative K (attractive besides HS) convergence may be reached for exp shift only (real HS-Yukawa)
#positive (repulsive as HS) K converges for fully shifted potential (1/r included)
solver.setPotentialByName(
'Yukawa', 1.0, -1.0,
True) #lamda in SASfit is different; K = 0.1 is default in SASfit
print "Energy scale of Yukawa HS Potential in kT units:", solver.getInteractionStrengthInKTunits(
)
print "Length scale of Yukawa HS Potential in sigma units:", solver.getShieldingConstantInSigmaUnits(
)
#Initialize RY with some (random) start alpha
solver.doRYclosure(1.0)
solver.setVolumeDensity(0.3)
print "Volume density of liquid:", solver.getVolumeDensity()
print "start solving for optimal closure alpha...."
#Should we minimize cost functional or find a root?
doUseMinimizer = False
doSearchIndependentPlot = False
#Save iterated points in alpha space as dict to plot later
finderFunctionGraph = {}
if doUseMinimizer:
#Ein Minimum macht noch keine Nullstelle, minimierungs algorithmen
#kommen aber (auch) mit doppelten (mehrfachen) Nullstellen klar. *Falls* eine Nullstelle vorliegt,
#ist sie in jedem Fall auch ein Minimum der quadrierten funktion (bei mehreren Nullstellen kein globales Minimum)
alpha_opt = optimize.minimize(finderFunctionForAlpha,
alpha_initial,
