Esempio n. 1
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def test_unaf():
    e = 2
    ea = 0
    rev_smic_chef = 1.5
    rev_smic_part = 1.5
    temps_garde ="classique" # alternee_pension_non_decl', 'alternee_pension_decl
    uc_parameters = {'alpha' : 0, 'beta' : .5, 'gamma' : 1}
    print get_unaf(e,ea,a=1)
    disabled = None
    
    opt_pension_unaf = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, 
                            criterium = "unaf2", disabled=disabled)
    print "unaf2 : ", opt_pension_unaf    
Esempio n. 2
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def optimal_pension(criterium, disable_api = False):
    e = 2
    ea = 0
    rev_smic_chef = 3.5
    rev_smic_part = 0
    temps_garde ="alternee_pension_non_decl" # 'classique', 'alternee_pension_decl
    uc_parameters = {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}

    disabled = []
    if disable_api:
        disabled += ['api']
    
    if criterium == "unaf":
        print get_unaf(e,ea)
        opt_pension_nv = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, 
                                criterium = "nivvie", disabled=disabled)
        print "nivvie : ", opt_pension_nv
          
        opt_pension_unaf = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, 
                                criterium = "unaf", disabled=disabled)
        print "unaf : ", opt_pension_unaf    
        opt_pension = max(opt_pension_nv, opt_pension_unaf)
    else: 
        opt_pension = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, 
                                criterium=criterium, disabled=disabled)
        
    asf = get_asf(e+ea, year=YEAR)
    
    print 'opt_pension :', opt_pension  
    print 'asf :', asf  
    if opt_pension >= asf:
        df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = opt_pension, disabled = disabled + ['asf'] )
    else:
        df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = 0, disabled=disabled)
    
    print df.to_string()
    return df
Esempio n. 3
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def compute_and_save_opt_pension(criterium, disable_api = False):


    if disable_api:
        csv_file = DIR  + 'opt_pension_no_api.csv'  
    else:
        csv_file = DIR  + 'opt_pension_new.csv'

    nb_enf_max = 4
    nb_enf_max_14 = 0
    rev_smic_max = 4 
    rev_smic_step = .5
    temps_garde_range = ['classique', 'alternee_pension_non_decl', 'alternee_pension_decl']
    first = True    
    disabled = None
    if disable_api:
        disabled = ['api']

    for uc_parameters in [ {'alpha' : 0, 'beta' : .5, 'gamma' : 1}, {'alpha' : 0.4, 'beta' : .7, 'gamma' : 1.4}]:  
        for nb_enf in range(1,nb_enf_max+1):
            for ea in range(0,nb_enf_max_14+1):            
                e = nb_enf - ea
                for temps_garde in temps_garde_range:
                    for rev_smic_chef in arange(0,rev_smic_max, rev_smic_step):
                        for rev_smic_part in arange(0,rev_smic_max, rev_smic_step):                 
                            opt_pension = compute_optimal_pension(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, criterium = criterium, disabled=disabled)
                            asf = get_asf(nb_enf, year=YEAR)
                            if opt_pension >= asf:
                                if disabled is not None:
                                    disabled = disabled + ['asf']
                                else:
                                    disabled = ['asf']
                                df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = opt_pension, disabled = disabled)
                            else:
                                df = get_results_df(e, ea, rev_smic_chef, rev_smic_part, temps_garde, uc_parameters = uc_parameters, pension = 0, disabled = disabled)
                            
                            print df
                            if first:
                                df_final = df  
                                first = False
                            else:
                                df_final = concat([df_final, df], axis=0)
                            df_final.to_csv(csv_file)