def expansion(Xref, Xav, gamma, used_params, allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el):
Xexp = (1+gamma)*Xref - gamma*Xav
Xexp = check_limits(used_params, Xexp, parameter)
parameter = insert(used_params, Xexp, parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el)
Yexp = allrasd.RMS
return Xexp, Yexp
def reflection(Xmax, Xav, alpha, used_params, allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el):
Xref = (1+alpha)*Xav - alpha*Xmax
Xref = check_limits(used_params, Xref, parameter)
parameter = insert(used_params, Xref, parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el)
Yref = allrasd.RMS
return Xref, Yref
def contraction(
Xmax, Xav, beta, used_params, allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el
):
Xcon = beta * Xmax + (1 - beta) * Xav
parameter = insert(used_params, Xcon, parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el)
Ycon = allrasd.RMS
return Xcon, Ycon
def res_simplex(StatusBar, parameter,param_usage, allrasd, surface, simplex_params, use_bulk_water, use_lay_el):
alpha, beta, gamma, delta, ftol, maxiter = simplex_params
Refine_Data = allrasd.Refine_Data
StatusBar.SetStatusText('Preparing Simplex',0)
used_params = []
used_params_values = []
for i in parameter.keys():
if parameter[i][3]:
used_params.append(i)
used_params_values.append(parameter[i][0])
function_values = Num.ndarray((len(used_params)+1),float)
points = Num.ndarray((len(used_params)+1,len(used_params)),float)
for i in range(len(used_params)+1):
if i == 0:
points[i] = used_params_values
parameter = insert(used_params, points[i], parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
function_values[i] = allrasd.RMS
else:
for j in range(len(points[i])):
key = used_params[j]
points[i][j] = used_params_values[j] + random.uniform(((parameter[key][1]-used_params_values[j])*delta), ((parameter[key][2]-used_params_values[j])*delta))
parameter = insert(used_params, points[i], parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
function_values[i] = allrasd.RMS
not_converged = True
z = 0
mini, maxi = min_max(function_values)
statusstring ='iteration '+str(z)+', best chi**2 = '+str(round(function_values[mini],4))
StatusBar.SetStatusText(statusstring,0)
while not_converged:
StatusBar.SetStatusText(str(z),1)
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
old_minimum = function_values[mini]
Xav = calc_average(points)
Xref, Yref = reflection(points[maxi], Xav, alpha, used_params, allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
if Yref < function_values[mini]:
Xexp, Yexp = expansion(Xref, Xav, gamma, used_params, allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
if Yexp < function_values[mini]:
points[maxi] = Xexp
function_values[maxi] = Yexp
else:
points[maxi] = Xref
function_values[maxi] = Yref
else:
test = False
for i in range(len(points)):
if Yref < function_values[i]:
if i == maxi:
test = False
else:
test = True
if test:
points[maxi] = Xref
function_values[maxi] = Yref
else:
if Yref < function_values[maxi]:
Xcon,Ycon = contraction(Xref, Xav, beta, used_params, allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
else:
Xcon,Ycon =contraction(points[maxi], Xav, beta, used_params, allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
if Ycon < function_values[maxi]:
points[maxi] = Xcon
function_values[maxi] = Ycon
else:
points = compression(points, mini)
for i in range(len(points)):
parameter = insert(used_params, points[i], parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
function_values[i] = allrasd.RMS
mini, maxi = min_max(function_values)
act_ftol = calc_ftol(function_values)
if function_values[mini]<old_minimum:
statusstring ='iteration '+str(z)+', best chi**2 = '+str(round(function_values[mini],4))+' ftol = '+str(round(act_ftol,6))
StatusBar.SetStatusText(statusstring,0)
if act_ftol < ftol:
not_converged = False
print ' CONVERGENCE REACHED DUE TO FTOL \n\n'
if z >= maxiter:
not_converged = False
print ' NO CONVERGENCE, STOP DUE TO MAXITER \n\n'
z = z+1
print 'best fit chi**2 = '+str(round(function_values[mini],7))+'\n'
StatusBar.SetStatusText('End of Downhill Simplex, best chi**2: '+str(round(function_values[mini],4)),0)
StatusBar.SetStatusText('',1)
param_best = points[mini]
parameter = insert(used_params, param_best, parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, param_usage, use_bulk_water, Refine_Data, use_lay_el)
return allrasd, parameter
def contraction(Xmax, Xav, beta, used_params, allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el):
Xcon = beta*Xmax+(1-beta)*Xav
parameter = insert(used_params, Xcon, parameter)
allrasd = Rasd_difference(allrasd, surface, parameter, parameter_usage, use_bulk_water, Refine_Data, use_lay_el)
Ycon = allrasd.RMS
return Xcon,Ycon
def genetic(panel, dat, cell, surface, NLayers, database,\
rigid_bodies, parameter, param_usage):
km, kc, popmult, maxgen, jump, no_improve, xtol, ftol, \
random_pars = panel.genetic_params
g_inv = calc_g_inv(cell)
Rod_weight = panel.Rod_weight
use_bulk_water = panel.UBW_flag
use_BVC = panel.use_BVC
use_lay_el = panel.use_lay_el
el = panel.el
BVclusters = panel.BVclusters
RMS_flag = panel.RMS_flag
fig1 = panel.Figure1
fig3 = panel.Figure3
plot_dims = panel.plotdims
plot_bulk = panel.doplotbulk
plot_surf = panel.doplotsurf
plot_rough = panel.doplotrough
plot_water = panel.doplotwater
statusbar = panel.nb.frame.statusbar
statusbar.SetStatusText('Preparing Population', 0)
used_params = []
used_params_values = []
for i in parameter.keys():
if parameter[i][3]:
used_params.append(i)
used_params_values.append(parameter[i][0])
n = int(len(used_params) * popmult)
m = len(used_params)
if n < 15: n = 15
fitness = Num.ndarray((n), float)
individuals = Num.ndarray((n, m), float)
for i in range(n):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
if i == 0 and not random_pars:
individuals[i] = used_params_values
parameter = insert(used_params, individuals[i], parameter)
dat, fitness[i] = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
else:
for j in range(m):
key = used_params[j]
individuals[i][j] = random.uniform(parameter[key][1],
parameter[key][2])
parameter = insert(used_params, individuals[i], parameter)
dat, fitness[i] = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
z = 0
fitness, individuals = sort_population(fitness, individuals)
fig3 = parameter_plot(fig3, used_params, parameter, individuals, 0)
best = fitness[0]
best_count = 0
not_converged = True
while z < maxgen and not_converged:
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
statusstring ='generation '+str(z)+', best R = '\
+str(round(fitness[0],7))
statusbar.SetStatusText(statusstring, 0)
# mutation
for i in range(n):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
if random.uniform(0, 1) < km:
individual = individuals[i][:]
j = random.randrange(m)
key = used_params[j]
individual[j] = individuals[i][j] + random.uniform(
((parameter[key][1] - individuals[i][j]) * jump),
((parameter[key][2] - individuals[i][j]) * jump))
parameter = insert(used_params, individual, parameter)
dat, fit = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
individuals = Num.append(individuals, [individual], axis=0)
fitness = Num.append(fitness, fit)
statusbar.SetStatusText('mutation ' + str(i), 1)
#sort before crossover
fitness, individuals = sort_population(fitness, individuals)
# crossover
for i in range(int(n * kc / 2)):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
statusbar.SetStatusText('crossover ' + str(i), 1)
choice1 = random.randrange(random.randrange(5, len(fitness)))
choice2 = random.randrange(random.randrange(5, len(fitness)))
while choice2 == choice1:
choice2 = random.randrange(random.randrange(5, len(fitness)))
crosspoint1 = random.randrange(1, m - 2)
crosspoint2 = random.randrange(crosspoint1, m - 1)
selection1 = individuals[choice1]
selection2 = individuals[choice2]
x1 = selection1[:crosspoint1]
y1 = selection2[:crosspoint1]
x2 = selection2[crosspoint1:crosspoint2]
y2 = selection1[crosspoint1:crosspoint2]
x3 = selection1[crosspoint2:]
y3 = selection2[crosspoint2:]
child1 = Num.append(x1, x2)
child1 = Num.append(child1, x3)
child2 = Num.append(y1, y2)
child2 = Num.append(child2, y3)
parameter = insert(used_params, child1, parameter)
dat, fit_child1 = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
parameter = insert(used_params, child2, parameter)
dat, fit_child2 = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
fitness = Num.append(fitness, fit_child1)
fitness = Num.append(fitness, fit_child2)
individuals = Num.append(individuals, Num.array([child1]), axis=0)
individuals = Num.append(individuals, Num.array([child2]), axis=0)
# sort population and keep only the n fittest individuals
fitness, individuals = sort_population(fitness, individuals)
fitness = fitness[:n]
individuals = individuals[:n]
#report intermediate results
fig3 = parameter_plot(fig3, used_params, parameter, individuals, 0)
parameter = insert(used_params, individuals[0], parameter)
dat, fittest = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
fig1 = plot_rods(fig1, dat, plot_dims, plot_bulk, plot_surf, plot_rough,\
plot_water, fittest)
fig1.canvas.draw()
statusstring ='generation '+str(z)+', best R = '\
+str(round(fittest,7))
statusbar.SetStatusText(statusstring, 0)
if not fittest < best:
best_count = best_count + 1
else:
best = fittest
best_count = 0
if panel.StopFit:
statusbar.SetStatusText('Fit stopped after '+str(z)+\
' generations',0)
print 'Fit aborted by user after ' + str(z) + ' generations'
not_converged = False
if calc_xdist(individuals[0], individuals[n - 1], used_params,
parameter) < xtol:
not_converged = False
print '\n CONVERGENCE REACHED DUE TO XTOL\n'
if fitness[n - 1] - fitness[0] < ftol:
not_converged = False
print '\n CONVERGENCE REACHED DUE TO FTOL\n'
if best_count > no_improve:
not_converged = False
print '\n EVOLUTION STAGNATED: NO IMPROVEMENT FOR ' + str(
no_improve) + ' GENERATIONS\n'
z = z + 1
if z == maxgen:
print '\n STOP DUE TO MAXITER\n'
print 'genetic algorithm finished after ' + str(
z) + ' generations,\nfittest individual R = ' + str(
round(fitness[0], 7))
return dat, parameter, fittest
def genetic(panel, dat, cell, surface, NLayers, database,\
rigid_bodies, parameter, param_usage):
km, kc, popmult, maxgen, jump, no_improve, xtol, ftol, \
random_pars = panel.genetic_params
g_inv = calc_g_inv(cell)
Rod_weight = panel.Rod_weight
use_bulk_water = panel.UBW_flag
use_BVC = panel.use_BVC
use_lay_el = panel.use_lay_el
el = panel.el
BVclusters = panel.BVclusters
RMS_flag = panel.RMS_flag
fig1 = panel.Figure1
fig3 = panel.Figure3
plot_dims = panel.plotdims
plot_bulk = panel.doplotbulk
plot_surf = panel.doplotsurf
plot_rough = panel.doplotrough
plot_water = panel.doplotwater
statusbar = panel.nb.frame.statusbar
statusbar.SetStatusText('Preparing Population',0)
used_params = []
used_params_values = []
for i in parameter.keys():
if parameter[i][3]:
used_params.append(i)
used_params_values.append(parameter[i][0])
n = int(len(used_params)*popmult)
m = len(used_params)
if n < 15: n = 15
fitness = Num.ndarray((n),float)
individuals = Num.ndarray((n,m), float)
for i in range(n):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
if i == 0 and not random_pars:
individuals[i] = used_params_values
parameter = insert(used_params, individuals[i], parameter)
dat, fitness[i] = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
else:
for j in range(m):
key = used_params[j]
individuals[i][j] = random.uniform(parameter[key][1],parameter[key][2])
parameter = insert(used_params, individuals[i], parameter)
dat, fitness[i] = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
z = 0
fitness, individuals = sort_population(fitness,individuals)
fig3 = parameter_plot(fig3,used_params,parameter,individuals,0)
best = fitness[0]
best_count = 0
not_converged = True
while z < maxgen and not_converged:
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
statusstring ='generation '+str(z)+', best R = '\
+str(round(fitness[0],7))
statusbar.SetStatusText(statusstring,0)
# mutation
for i in range(n):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
if random.uniform(0,1) < km:
individual = individuals[i][:]
j = random.randrange(m)
key = used_params[j]
individual[j] = individuals[i][j]+ random.uniform(((parameter[key][1]-individuals[i][j])*jump),((parameter[key][2]-individuals[i][j])*jump))
parameter = insert(used_params, individual, parameter)
dat, fit = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
individuals = Num.append(individuals,[individual], axis =0)
fitness = Num.append(fitness, fit)
statusbar.SetStatusText('mutation '+str(i),1)
#sort before crossover
fitness, individuals = sort_population(fitness, individuals)
# crossover
for i in range(int(n*kc/2)):
while wx.GetApp().Pending():
wx.GetApp().Dispatch()
wx.GetApp().Yield(True)
statusbar.SetStatusText('crossover '+str(i),1)
choice1 = random.randrange(random.randrange(5,len(fitness)))
choice2 = random.randrange(random.randrange(5,len(fitness)))
while choice2 == choice1:
choice2 = random.randrange(random.randrange(5,len(fitness)))
crosspoint1 = random.randrange(1,m-2)
crosspoint2 = random.randrange(crosspoint1,m-1)
selection1 = individuals[choice1]
selection2 = individuals[choice2]
x1 = selection1[:crosspoint1]
y1 = selection2[:crosspoint1]
x2 = selection2[crosspoint1:crosspoint2]
y2 = selection1[crosspoint1:crosspoint2]
x3 = selection1[crosspoint2:]
y3 = selection2[crosspoint2:]
child1 = Num.append(x1,x2)
child1 = Num.append(child1,x3)
child2 = Num.append(y1,y2)
child2 = Num.append(child2,y3)
parameter = insert(used_params, child1, parameter)
dat, fit_child1 = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
parameter = insert(used_params, child2, parameter)
dat, fit_child2 = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
fitness = Num.append(fitness, fit_child1)
fitness = Num.append(fitness, fit_child2)
individuals = Num.append(individuals, Num.array([child1]), axis = 0)
individuals = Num.append(individuals, Num.array([child2]), axis = 0)
# sort population and keep only the n fittest individuals
fitness, individuals = sort_population(fitness, individuals)
fitness = fitness[:n]
individuals = individuals[:n]
#report intermediate results
fig3 = parameter_plot(fig3,used_params,parameter,individuals,0)
parameter = insert(used_params, individuals[0], parameter)
dat, fittest = calc_CTRs(parameter,param_usage,\
dat, cell,surface,\
NLayers, database, g_inv,\
Rod_weight, rigid_bodies,\
use_bulk_water, use_BVC,\
BVclusters, RMS_flag,\
use_lay_el, el)
fig1 = plot_rods(fig1, dat, plot_dims, plot_bulk, plot_surf, plot_rough,\
plot_water, fittest)
fig1.canvas.draw()
statusstring ='generation '+str(z)+', best R = '\
+str(round(fittest,7))
statusbar.SetStatusText(statusstring,0)
if not fittest < best:
best_count = best_count+1
else:
best = fittest
best_count = 0
if panel.StopFit:
statusbar.SetStatusText('Fit stopped after '+str(z)+\
' generations',0)
print 'Fit aborted by user after '+str(z)+' generations'
not_converged = False
if calc_xdist(individuals[0],individuals[n-1],used_params,parameter) < xtol:
not_converged = False
print '\n CONVERGENCE REACHED DUE TO XTOL\n'
if fitness[n-1]-fitness[0] < ftol:
not_converged = False
print '\n CONVERGENCE REACHED DUE TO FTOL\n'
if best_count > no_improve:
not_converged = False
print '\n EVOLUTION STAGNATED: NO IMPROVEMENT FOR '+str(no_improve)+' GENERATIONS\n'
z = z+1
if z == maxgen:
print '\n STOP DUE TO MAXITER\n'
print 'genetic algorithm finished after '+str(z)+' generations,\nfittest individual R = '+str(round(fitness[0],7))
return dat, parameter, fittest