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ddormTest_spnD1_010612.py
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import ajustador as aju
from ajustador.helpers import save_params,converge
import numpy as np
from ajustador import drawing
from waves import measurements1 as ms1
import os
# a. simplest approach is to use CAPOOL (vs CASHELL, and CASLAB for spines)
# b. no spines
# c. use ghk (and ghkkluge=0.35e-6) once that is working/implemented in moose
ghkkluge=1
modeltype='d1d2'
rootdir='/home/dandorman/ajustadorTest/SPN_opt/'
#use 1 and 3 for testing, 250 and 8 for optimization
generations=1
popsiz=3
seed=1234567
#after generations, do 25 more at a time and test for convergence
test_size=0
################## neuron /data specific specifications #############
ntype='D1'
dataname='D1_010612'
exp_to_fit = ms1.D1waves010612[[8,19,21,23]] #0, 6,
dirname=dataname+'_pas3_'+str(seed)
if not dirname in os.listdir(rootdir):
os.mkdir(rootdir+dirname)
os.chdir(rootdir+dirname)
#tmpdir='/tmp/fit'+modeltype+'-'+ntype+'-'+dirname
tmpdir=rootdir+'fit'+modeltype+'-'+ntype+'-'+dirname
if os.path.isdir(tmpdir):
import shutil
shutil.rmtree(tmpdir)
######## set up parameters and fitness
P = aju.optimize.AjuParam
params1 = aju.optimize.ParamSet(
P('junction_potential', -.013, fixed=1),
P('RA', 5.3, min=1, max=200),
P('RM', 2.78, min=0.1, max=10),
P('CM', 0.010, min=0.001, max=0.03),
P('Cond_Kir', 9.5, min=0, max=20),
P('Eleak', -0.08, min=-0.080, max=-0.030),
P('Cond_NaF_0', 219e3, min=0, max=600e3),
P('Cond_NaF_1', 1878, min=0, max=10000),
P('Cond_NaF_2', 878, min=0, max=10000),
P('Cond_KaS_0', 599, min=0, max=2000),
P('Cond_KaS_1', 372, min=0, max=2000),
P('Cond_KaS_2', 37.2, min=0, max=200),
P('Cond_KaF_0', 887, min=0, max=2000),
P('Cond_KaF_1', 641, min=0, max=2000),
P('Cond_KaF_2', 641, min=0, max=2000),
P('Cond_Krp_0', 0.05, min=0, max=600),
P('Cond_Krp_1', 0.05, min=0, max=600),
P('Cond_Krp_2', 0.05, min=0, max=600),
P('Cond_SKCa', 1.7, min=0, max=5),
P('Cond_BKCa', 5.6, min=0, max=50),
P('Cond_CaN_0', 3*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaT_1', 2*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaT_2', 2*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL12_0', 8*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL12_1', 4*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL12_2', 4*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL13_0', 12*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL13_1', 6*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaL13_2', 6*ghkkluge, min=0, max=100*ghkkluge),
P('Cond_CaR_0', 20*ghkkluge, min=0, max=1000*ghkkluge),
P('Cond_CaR_1', 45*ghkkluge, min=0, max=1000*ghkkluge),
P('Cond_CaR_2', 45*ghkkluge, min=0, max=1000*ghkkluge),
P('morph_file', 'MScelltaperspines.p', fixed=1),
P('neuron_type', ntype, fixed=1),
P('model', 'd1d2', fixed=1))
#fitness=aju.fitnesses.combined_fitness('new_combined_fitness')
fitness = aju.fitnesses.combined_fitness('empty',
response=1,
baseline_pre=1,
baseline_post=1,
rectification=0,
falling_curve_time=1,
spike_time=0,
spike_width=1,
spike_height=1,
spike_latency=1,
spike_count=1,
spike_ahp=1,
ahp_curve=4,
charging_curve_time=1,
spike_range_y_histogram=1)
########### Neuron and fit specific commands ############
fit1 = aju.optimize.Fit(tmpdir,
exp_to_fit,
modeltype, ntype,
fitness, params1,
_make_simulation=aju.optimize.MooseSimulation.make,
_result_constructor=aju.optimize.MooseSimulationResult)
fit1.load()
fit1.do_fit(generations, popsize=popsiz,seed=seed)
#mean_dict,std_dict,CV=converge.iterate_fit(fit1,test_size,popsiz)
#look at results
drawing.plot_history(fit1, fit1.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(fit1, startgood, threshold)
#to save the fit object
#save_params.persist(fit1,'.')