if __name__ == '__main__':

    from mpi4py import MPI
    from mpi4py.futures import MPICommExecutor

    with MPICommExecutor(MPI.COMM_WORLD, root=0) as executor:
        if executor is not None:

            def map_func(f,arglist, callback_function= None):
                result = [executor.submit(f,args) for args in arglist]
                # def res_callback(result, callback_function):
                #     for r in result:
                #         result.wait()
                #         callback_function()
                # result[-1].add_done_callback(result,callback_function)
                return result

            import ajustador as aju
            from ajustador.helpers import save_params,converge
            #from ajustador import drawing
            import PlotkinD1PatchMatrix as waves
            import os
            import fit_commands
            import param_fitness_chan_patch_sample_2 as params_fitness
            ########### Optimization of GP neurons ##############3
            #proto 079, 154

            ntype='D1'
            modeltype='d1patchsample2'
            morph_file='D1_long_matrix.p'
            rootdir = os.getcwd() + '/output'
            #use 1 and 3 for testing, 200 and 8 for optimization
            generations= 500 #200#200
            popsiz = 24
            seed = 93239 #1753 #62938
            #after generations, do 25 more at a time and test for convergence
            test_size = 10#25

            ################## neuron /data specific specifications #############
            dataname='D1_Matrix_Sample_3'
            exp_to_fit = waves.data[dataname][[0,1,2,3]]
            #datasuffix = '_real_morph_full_charging_curve_'
            datasuffix = '_NSG_full_'
            dirname=dataname+datasuffix+'tmp_'+str(seed)
            if not dirname in os.listdir(rootdir):
                os.mkdir(rootdir+dirname)
            os.chdir(rootdir+dirname)

            ######## set up parameters and fitness to be used for all opts  ############
            params1,fitness=params_fitness.params_fitness(morph_file,ntype,modeltype)

            ######## set-up and do the optimization
            fit1,mean_dict1,std_dict1,CV1=fit_commands.fit_commands(dirname,exp_to_fit,modeltype,ntype,fitness,params1,generations,popsiz, seed, test_size, map_func = map_func)

            ###########look at results
            #drawing.plot_history(fit1, fit1.measurement)

            #Save parameters of good results toward the end, and all fitness values
            #startgood=0#1500  #set to 0 to print/save all
            #threshold=100#0.40  #set to high value to print/save all
            #save_params.save_params(fit1, startgood, threshold)
            save_params.persist(fit1,'.')

            ################## Next neuron #############
            '''dataname='proto154'
            exp_to_fit = gpe.data[dataname+'-2s'][[0,2,4]]
            dirname=dataname+'F_'+str(seed)
            if not dirname in os.listdir(rootdir):
                os.mkdir(rootdir+dirname)
            os.chdir(rootdir+dirname)
            fit2,mean_dict2,std_dict2,CV2=fit_commands(dirname,exp_to_fit,modeltype,ntype,fitness,params1,generations,popsiz, seed, test_size)
            #look at results
            drawing.plot_history(fit2, fit2.measurement)
            threshold=0.34
            save_params.save_params(fit2, startgood, threshold)
            #save_params.persist(fit2,'.')
            '''