realspec0[i*69:(i+1)*69] /= f
else:
realspec0[i*69:len(test_lam)] /=f
# ~ ~ ~ ~ ~ ~ Initial Conditions ~ ~ ~ ~ ~ ~ #
#startarr = np.array([2.5, 1.03, .01, -3.95*10**(-7),0.011079,816.9,.05,.05,.05,.05,.05]) #tau,norm,sig,dxm,dxb,dxc,amps (in fraction of 1, amp of central)
low = np.array([2.4,1.01,.008,-3.96*10**-7,.010,816.85,0,0,0,0,0])
high = np.array([2.6,1.04,.02,-3.94*10**-7,0.013,816.95,.1,.1,.1,.1,.1])
#chi_ev = [0] #watch evolution of minf
#~ ~ ~ ~ ~ ~ ~ ~ Run NLOPT ~ ~ ~ ~ ~ ~ ~
opt = nlopt.opt(nlopt.GN_CRS2_LM,len(startarr)) #GN_CRS2_LM, MLSL(_LDS),GN_ESCH
opt.set_lower_bounds(low)
opt.set_upper_bounds(high)
opt.set_min_objective(lambda x,grad: tapafunc(x,realspec0,spec,pixels,test_lam,nsat,chi_ev))
opt.set_stopval(.0001)
opt.set_maxeval(nit)
starttime = time.time() #time function
print 'NLOPT stuff is starting now'
x = opt.optimize(startarr)
elapsedtime = time.time() - starttime
minf = opt.last_optimum_value()
print x
print minf
print elapsedtime, ' seconds'
# ~ ~ ~ ~ Plot Results ~ ~ ~ ~ ~ ~ #
lambd = x[3] * pixels**2 + x[4] * pixels + x[5]
plt.figure()
###############################
#Import functions
import calib_fxns
reload(calib_fxns)
from calib_fxns import gaussian, sumgaus, tapafunc, binres
###########################
############################################
#~ ~ ~ ~ ~ ~ ~ ~ Run NLOPT ~ ~ ~ ~ ~ ~ ~
opt = nlopt.opt(nlopt.GN_CRS2_LM,
len(startarr)) #GN_CRS2_LM, MLSL(_LDS),GN_ESCH
opt.set_lower_bounds(lowarr)
opt.set_upper_bounds(higharr)
opt.set_min_objective(
lambda x, grad: tapafunc(x, realspec, spec, pixels, test_lam, nsat, chi_ev,
xx_smalls, lo_inds, hi_inds))
#opt.add_inequality_constraint(lambda x,grad: ampconstraint(x,2,1,nsat), 1e-8) #only for certain algorithms
opt.set_stopval(.0001)
opt.set_maxeval(nit)
starttime = time.time() #time function
#######################################
print 'NLOPT stuff is starting now'
x = opt.optimize(startarr)
elapsedtime = time.time() - starttime
minf = opt.last_optimum_value()
x
print minf
print elapsedtime, ' seconds'
####################################
#
###############################
#Import functions
import calib_fxns
reload(calib_fxns)
from calib_fxns import gaussian,sumgaus,tapafunc,binres
###########################
############################################
#~ ~ ~ ~ ~ ~ ~ ~ Run NLOPT ~ ~ ~ ~ ~ ~ ~
opt = nlopt.opt(nlopt.GN_CRS2_LM,len(startarr)) #GN_CRS2_LM, MLSL(_LDS),GN_ESCH
opt.set_lower_bounds(lowarr)
opt.set_upper_bounds(higharr)
opt.set_min_objective(lambda x,grad: tapafunc(x,realspec,spec,pixels,test_lam,
nsat,chi_ev,xx_smalls,lo_inds,hi_inds))
#opt.add_inequality_constraint(lambda x,grad: ampconstraint(x,2,1,nsat), 1e-8) #only for certain algorithms
opt.set_stopval(.0001)
opt.set_maxeval(nit)
starttime = time.time() #time function
#######################################
print 'NLOPT stuff is starting now'
x = opt.optimize(startarr)
elapsedtime = time.time() - starttime
minf = opt.last_optimum_value()
x
print minf
print elapsedtime, ' seconds'
####################################
else:
realspec0[i * 69:len(test_lam)] /= f
# ~ ~ ~ ~ ~ ~ Initial Conditions ~ ~ ~ ~ ~ ~ #
#startarr = np.array([2.5, 1.03, .01, -3.95*10**(-7),0.011079,816.9,.05,.05,.05,.05,.05]) #tau,norm,sig,dxm,dxb,dxc,amps (in fraction of 1, amp of central)
low = np.array([2.4, 1.01, .008, -3.96 * 10**-7, .010, 816.85, 0, 0, 0, 0, 0])
high = np.array(
[2.6, 1.04, .02, -3.94 * 10**-7, 0.013, 816.95, .1, .1, .1, .1, .1])
#chi_ev = [0] #watch evolution of minf
#~ ~ ~ ~ ~ ~ ~ ~ Run NLOPT ~ ~ ~ ~ ~ ~ ~
opt = nlopt.opt(nlopt.GN_CRS2_LM,
len(startarr)) #GN_CRS2_LM, MLSL(_LDS),GN_ESCH
opt.set_lower_bounds(low)
opt.set_upper_bounds(high)
opt.set_min_objective(lambda x, grad: tapafunc(x, realspec0, spec, pixels,
test_lam, nsat, chi_ev))
opt.set_stopval(.0001)
opt.set_maxeval(nit)
starttime = time.time() #time function
print 'NLOPT stuff is starting now'
x = opt.optimize(startarr)
elapsedtime = time.time() - starttime
minf = opt.last_optimum_value()
print x
print minf
print elapsedtime, ' seconds'
# ~ ~ ~ ~ Plot Results ~ ~ ~ ~ ~ ~ #
lambd = x[3] * pixels**2 + x[4] * pixels + x[5]
plt.figure()
