fit5.load()
#Default population size is 8
fit5.do_fit(100)
for i in range(len(fit5)):
print(i, fit5.fitness_func(fit5[i], fit5.measurement, full=1), '→',
fit5.fitness_func(fit5[i], fit5.measurement, full=0))
#to print param values for all simulations
print(fit5[i].params)
#print best estimate of parameters
for i in len(fit5.param_names()):
print(fit5.param_names()[i], ':',
fit5.params.unscale(fit5.optimizer.result()[0])[i])
drawing.plot_history(fit5, fit5.measurement)
#####################################################################################
# example using combined_fitness() - which uses both hyperpolarizing and depolarizing currents
#####################################################################################
exp_to_fit = ms1.D1waves042811[[0, 6, 9]]
P = aju.optimize.AjuParam
params6 = aju.optimize.ParamSet(
P('junction_potential', 0, min=-0.015, max=+0.015),
P('RA', 12.004, min=0, max=100), P('RM', 9.427, min=0, max=10),
P('CM', 0.03604, min=0, max=0.10), P('Cond_Kir', 14.502, min=0, max=100),
P('Kir_offset', -.004, min=-0.005, max=+0.005),
P('morph_file', 'MScell-tertDendlongRE.p', fixed=1),
P('neuron_type', 'D1', fixed=1), P('Cond_NaF_0', 150e3, min=0, max=600e3),
P('Cond_KaS_0', 372, min=0, max=600), P('Cond_KaF_0', 641, min=0,
max=1000),
######## set up parameters and fitness to be used for all opts ############
params1, fitness = pfc.params_fitness(morph_file, ntype, modeltype)
# set-up and do the optimization
fit1 = fit_commands.fit_commands(dirname, exp_to_fit, modeltype, ntype,
fitness, params1, generations, popsiz, seed,
test_size)
if test_size > 0:
mean_dict, std_dict, CV = converge.iterate_fit(fit1,
test_size,
popsiz,
std_crit=0.02,
max_evals=12000)
###########look at results
drawing.plot_history(fit1, fit1.measurement)
#Save parameters of good results toward the end, and all fitness values
startgood = 0 #set to 0 to print/save all
threshold = 10 #set to high value to print/save all
save_params.save_params(fit1, startgood, threshold)
#save_params.persist(fit1,'.')
'''
#to create moose_nerp parameter file:
from ajustador.helpers.copy_param import create_npz_param
npzfile=fit1.name+'.npz'
create_npz_param.create_npz_param(npzfile,modeltype,ntype)
#to print params of centroid
for nm,val,stdev in zip(fit1.param_names(),
modeltype,
ntype,
fitness,
params4,
_make_simulation=aju.optimize.MooseSimulation.make,
_result_constructor=aju.optimize.MooseSimulationResult)
fit4.load()
fit4.do_fit(generations, popsize=popsiz, seed=seed)
if test_size > 0:
mean_dict4, std_dict4, CV4 = converge.iterate_fit(fit4,
test_size,
popsiz,
max_evals=8000)
#
drawing.plot_history(fit4, fit4.measurement)
startgood = 0 #set to 0 to print all
threshold = 10 #set to large number to print all
save_params.save_params(fit4, startgood, threshold)
'''Sag and spike times match great, but AHPs way to big.
Perhaps need another channel? Is a second KA current needed?
Could KA replace KDr? Check the "optimal" conductance
Would additional calcium currents help? What is known about them?
observation: strong Kv3 associated with nice looking AHPs, and as it decreases AHPs look worse
------>>>> evaluate Kv3 inactivation - too strong?
spike height decreases and NaS decreases along with AHP decrease. Symptom or cause?
Z gate of NaS decreases slowly across simulation - this could be causal, BUT
eliminating NaS Zgate doesn't change this.
exp_to_fit = ms1.D1waves051811[[8,20,22,23]] #0, 6 are hyperpolarizing
dirname=dataname+'_pas2_'+str(seed)
if not dirname in os.listdir(rootdir):
os.mkdir(rootdir+dirname)
os.chdir(rootdir+dirname)
tmpdir='/tmp/fit'+modeltype+'-'+ntype+'-'+dirname
fit2 = aju.optimize.Fit(tmpdir,
exp_to_fit,
modeltype, ntype,
fitness, params1,
_make_simulation=aju.optimize.MooseSimulation.make,
_result_constructor=aju.optimize.MooseSimulationResult)
fit2.load()
fit2.do_fit(generations, popsize=popsiz,seed=seed)
mean_dict,std_dict,CV=iterate_fit(fit2,test_size,popsiz)
#look at results
drawing.plot_history(fit2, fit2.measurement)
#Save parameters of good results toward the end, and all fitness values
startgood=1000 #set to 0 to print all
threshold=0.8 #set to large number to print all
save_params.save_params(fit2, startgood, threshold)
test_size = 25
################## neuron /data specific specifications #############
ntype = 'D2'
dataname = 'D2_051311'
exp_to_fit = ms1.D2waves051311[[8, 17, 19, 22]] #0, 6 are hyperpol
dirname = dataname + '_pas2_' + str(seed)
if not dirname in os.listdir(rootdir):
os.mkdir(rootdir + dirname)
os.chdir(rootdir + dirname)
######## set up parameters and fitness
params1, fitness = params_fitness.params_fitness(morph_file, ntype, modeltype)
# set-up and do optimization
fit3, mean_dict3, std_dict3, CV3 = fit_commands.fit_commands(
dirname, exp_to_fit, modeltype, ntype, fitness, params1, generations,
popsiz, seed, test_size)
#########look at results
drawing.plot_history(fit3, fit3.measurement)
#Save parameters of good results from end of optimization, and all fitness values
startgood = 1000 #set to 0 to print all
threshold = 0.8 #set to large number to print all
save_params.save_params(fit3, startgood, threshold)
#to save the fit object
#save_params.persist(fit3,'.')
