{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---This notebook have been runed with--- \n",
      "Openfisca-france : de25f0ff144607587e92bc977af7c205722ab269 \n",
      "\n",
      "Commit short SHA: de25\n",
      "Branch:  master\n",
      "Date (y/m/d): 2015-10-30 18:51:21  \n",
      "\n",
      "Openfisca-core : f1bd2fb7f739ee5af6fa5e87034dc8af7139153c  Date: 2015-10-30 18:51:21 \n"
     ]
    }
   ],
   "source": [
    "import git  #Requires Gitpython installed, to install do \"$ pip install Gitpython\" in your terminal\n",
    "import pkg_resources\n",
    "\n",
    "openfisca_france_location = pkg_resources.get_distribution('openfisca-france').location\n",
    "repo = git.Repo(openfisca_france_location)\n",
    "repo.git.status()\n",
    "\n",
    "sha = repo.head.object.hexsha\n",
    "\n",
    "print \"---This notebook have been runed with--- \\n\",\"Openfisca-france :\", repo.head.object.hexsha, '\\n' \n",
    "print \"Commit short SHA:\", repo.git.rev_parse(sha, short=4)\n",
    "print \"Branch: \", repo.git.rev_parse('--abbrev-ref', \"HEAD\") #equivalent to: $git rev-parse --abbrev-ref HEAD\n",
    "print \"Date (y/m/d):\", repo.git.show(\"-s\", '--format=%ci', 'HEAD^')[:-5],\"\\n\"  # equivalent to: $git show -s --format=%ci HEAD^\n",
    "\n",
    "print \"Openfisca-core :\", git.Repo(pkg_resources.get_distribution('openfisca-core').location).head.object.hexsha, ' Date:', git.Repo(pkg_resources.get_distribution('openfisca-france').location).git.show(\"-s\", '--format=%ci', 'HEAD^')[:-5]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from openfisca_core import reforms\n",
    "import openfisca_france\n",
    "from openfisca_france.model.base import *\n",
    "from numpy import maximum as max_, logical_not as not_, logical_or as or_\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "tax_benefit_system = openfisca_france.init_country()()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Reform_individualisation = reforms.make_reform(\n",
    "    key = 'individualisation',\n",
    "    name = u'Individualisation du RSA et de l\\'IRPP',\n",
    "    reference = tax_benefit_system,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class a(SimpleFormulaColumn):\n",
    "    label = u\"always returning 999\"\n",
    "    column = FloatCol\n",
    "    entity_class = Familles\n",
    "\n",
    "    def function(self, simulation, period):\n",
    "        return period, self.zeros() * 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def modify_legislation_json(reference_legislation_json_copy):\n",
    "    #reference_legislation_json_copy['children']['xxx']['values'][0]['value'] = 0\n",
    "    return reference_legislation_json_copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class nb_enfant_rsa_indiv(EntityToPersonColumn):\n",
    "    entity_class = Individus\n",
    "    label = u\"Nombre d'enfants de la famille\"\n",
    "    variable = Familles.column_by_name['nb_enfant_rsa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class rsa_eligibilite_tns_indiv(SimpleFormulaColumn):\n",
    "    # ATTENTION, on a fait le choix de se caler sur les célibataires dans le calcul du plafond,\n",
    "    # ce qui a pour effet de rendre inéligibles certains couples ou certaines familles avec enfants\n",
    "    column = BoolCol\n",
    "    entity_class = Individus\n",
    "    label = u\"Eligibilité au RSA pour un travailleur non salarié\"\n",
    "\n",
    "    def function(self, simulation, period):\n",
    "        period = period.start.offset('first-of', 'month').period('month')\n",
    "\n",
    "        #tns_benefice_exploitant_agricole_holder = simulation.compute('tns_benefice_exploitant_agricole', period)\n",
    "        #tns_benefice_exploitant_agricole = self.sum_by_entity(tns_benefice_exploitant_agricole_holder)\n",
    "        tns_benefice_exploitant_agricole = simulation.calculate('tns_benefice_exploitant_agricole', period) #new\n",
    "        #tns_employe_holder = simulation.compute('tns_employe', period)\n",
    "        #tns_employe = self.any_by_roles(tns_employe_holder)\n",
    "        tns_employe = simulation.calculate('tns_employe', period) #new\n",
    "        #tns_autres_revenus_chiffre_affaires_holder = simulation.compute('tns_autres_revenus_chiffre_affaires', period)\n",
    "        #tns_autres_revenus_chiffre_affaires = self.split_by_roles(tns_autres_revenus_chiffre_affaires_holder)\n",
    "        tns_autres_revenus_chiffre_affaires = simulation.calculate('tns_autres_revenus_chiffre_affaires', period) #new\n",
    "        #tns_autres_revenus_type_activite_holder = simulation.compute('tns_autres_revenus_type_activite', period)\n",
    "        #tns_autres_revenus_type_activite = self.split_by_roles(tns_autres_revenus_type_activite_holder)\n",
    "        tns_autres_revenus_type_activite = simulation.calculate('tns_autres_revenus_type_activite', period) #new\n",
    "        \n",
    "        #has_conjoint = simulation.calculate('nb_par', period) > 1\n",
    "        #nb_enfant_rsa = simulation.calculate('nb_enfant_rsa', period)\n",
    "        P = simulation.legislation_at(period.start)\n",
    "        P_agr = P.tns.exploitant_agricole\n",
    "        P_micro = P.ir.rpns.microentreprise\n",
    "        maj_2p = P_agr.maj_2p\n",
    "        maj_1e_2ad = P_agr.maj_1e_2ad\n",
    "        maj_e_sup = P_agr.maj_e_sup\n",
    "\n",
    "        #def eligibilite_agricole(has_conjoint, nb_enfant_rsa, tns_benefice_exploitant_agricole, P_agr):\n",
    "        def eligibilite_agricole(tns_benefice_exploitant_agricole, P_agr):\n",
    "            plafond_benefice_agricole = P_agr.plafond_rsa * P.cotsoc.gen.smic_h_b\n",
    "            #taux_avec_conjoint = 1 + maj_2p + maj_1e_2ad * (nb_enfant_rsa > 0) + maj_e_sup * max_(nb_enfant_rsa - 1, 0)\n",
    "            #taux_sans_conjoint = 1 + maj_2p * (nb_enfant_rsa > 0) + maj_e_sup * max_(nb_enfant_rsa - 1, 0)\n",
    "            #taux_majoration = has_conjoint * taux_avec_conjoint + (1 - has_conjoint) * taux_sans_conjoint\n",
    "            taux_majoration = 1 #new\n",
    "            plafond_benefice_agricole_majore = taux_majoration * plafond_benefice_agricole\n",
    "\n",
    "            return tns_benefice_exploitant_agricole < plafond_benefice_agricole_majore\n",
    "\n",
    "        def eligibilite_chiffre_affaire(ca, type_activite, P_micro):\n",
    "            plaf_vente = P_micro.vente.max\n",
    "            plaf_service = P_micro.servi.max\n",
    "\n",
    "            return ((type_activite == 0) * (ca <= plaf_vente)) + ((type_activite >= 1) * (ca <= plaf_service))\n",
    "\n",
    "        eligibilite_agricole = eligibilite_agricole(\n",
    "       #     has_conjoint, nb_enfant_rsa, tns_benefice_exploitant_agricole, P_agr\n",
    "            tns_benefice_exploitant_agricole, P_agr\n",
    "            )\n",
    "        eligibilite_chiffre_affaire = (\n",
    "            eligibilite_chiffre_affaire(\n",
    "       #         tns_autres_revenus_chiffre_affaires[CHEF], tns_autres_revenus_type_activite[CHEF], P_micro\n",
    "                tns_autres_revenus_chiffre_affaires, tns_autres_revenus_type_activite, P_micro #new\n",
    "            ) *\n",
    "            eligibilite_chiffre_affaire(\n",
    "       #         tns_autres_revenus_chiffre_affaires[PART], tns_autres_revenus_type_activite[PART], P_micro\n",
    "                tns_autres_revenus_chiffre_affaires, tns_autres_revenus_type_activite, P_micro #new\n",
    "            )\n",
    "        )\n",
    "\n",
    "        return period, eligibilite_agricole * (1 - tns_employe) * eligibilite_chiffre_affaire\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class rsa_eligibilite_indiv(SimpleFormulaColumn):\n",
    "    # On maintient un calcul en fonction du ménage : règle du RSA pour les moins de 25 ans maintenu (s'ils ont des enfants)\n",
    "    column = BoolCol\n",
    "    entity_class = Individus\n",
    "    label = u\"Eligibilité au RSA\"\n",
    "\n",
    "    def function(self, simulation, period):\n",
    "        period = period.this_month\n",
    "        #age_holder = simulation.compute('age', period)\n",
    "        #age_parents = self.split_by_roles(age_holder, roles = [CHEF, PART])\n",
    "        age = simulation.calculate('age', period) #new\n",
    "        #activite_holder = simulation.compute('activite', period)\n",
    "        #activite_parents = self.split_by_roles(activite_holder, roles = [CHEF, PART])\n",
    "        activite = simulation.calculate('activite', period) #new\n",
    "        #nb_enfant_rsa = simulation.calculate('nb_enfant_rsa', period)\n",
    "        nb_enfant_rsa_indiv = simulation.calculate('nb_enfant_rsa_indiv', period)\n",
    "        rsa_eligibilite_tns_indiv = simulation.calculate('rsa_eligibilite_tns_indiv', period)\n",
    "        #rsa_condition_nationalite = simulation.compute('rsa_condition_nationalite', period)\n",
    "        #condition_nationalite = self.any_by_roles(rsa_condition_nationalite, roles = [CHEF, PART])\n",
    "        condition_nationalite = simulation.calculate('rsa_condition_nationalite', period) #new\n",
    "        rmi = simulation.legislation_at(period.start).minim.rmi\n",
    "        #age_min = (nb_enfant_rsa_indiv == 0) * rmi.age_pac\n",
    "        quifam = simulation.calculate('quifam', period)\n",
    "        condition_age = or_(age >= rmi.age_pac,  (age < rmi.age_pac) * (quifam == 0) * (nb_enfant_rsa_indiv > 1))\n",
    "        # ce qui est après le or_ a été codé pour le RSA jeune, mais je ne suis pas sûr du tout que ce soit la vraie règle\n",
    "\n",
    "        #eligib = (\n",
    "        #    (age_parents[CHEF] >= age_min) * not_(activite_parents[CHEF] == 2) +\n",
    "        #    (age_parents[PART] >= age_min) * not_(activite_parents[PART] == 2)\n",
    "        #)\n",
    "        \n",
    "        eligib = condition_age * not_(activite == 2) * (\n",
    "            condition_nationalite *\n",
    "            rsa_eligibilite_tns_indiv\n",
    "            )\n",
    "\n",
    "        return period, eligib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class nb_par_indiv(EntityToPersonColumn):\n",
    "    entity_class = Individus\n",
    "    label = u\"Nombre de parents de la famille\"\n",
    "    variable = Familles.column_by_name['nb_par']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#@Reform_individualisation.formula\n",
    "#class af_nbenf_indiv(EntityToPersonColumn):\n",
    "#    entity_class = Individus\n",
    "#    label = u\"Nombre d'enfants de la famille (au sens des allocations familiales)\"\n",
    "#    variable = Familles.column_by_name['af_nbenf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class supp_monoparental(SimpleFormulaColumn):\n",
    "    label = u\"Supplément RSA monoparental\"\n",
    "    column = FloatCol\n",
    "    entity_class = Individus\n",
    "    \n",
    "    def function(self, simulation, period):\n",
    "        quifam = simulation.calculate('quifam', period)\n",
    "        nb_enfant_rsa_indiv = simulation.calculate('nb_enfant_rsa_indiv', period)\n",
    "        nb_par_indiv = simulation.calculate('nb_par_indiv', period)\n",
    "        rmi = simulation.legislation_at(period.start).minim.rmi\n",
    "        af = simulation.legislation_at(period.start).fam.af\n",
    "        \n",
    "        return period, self.zeros() + (quifam == 0) * (nb_par_indiv == 1) * (nb_enfant_rsa_indiv > 0) * (rmi.rmi * rmi.txp2 - af.bmaf * 0.41)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class aspa_asi_ass_famille(EntityToPersonColumn):\n",
    "    # Comme on ne pouvait pas individualiser ces trois prestations, on a récupéré les prestations familialisées pour le calcul du RSA individuel\n",
    "    entity_class = Individus\n",
    "    label = u\"Somme de l'ASPA, ASI et ASS de la famille\"\n",
    "    variable = Familles.column_by_name['br_rmi_ms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "@Reform_individualisation.formula\n",
    "class rsa_indiv(SimpleFormulaColumn):\n",
    "    # Concernant les minimas sociaux qui sont déduits du RSA : - minimas sociaux individuels (aah, caah) : pas de problème\n",
    "    #   - minimas sociaux familialisés (aspa, asi, ass) : on a pris en compte l'allocation familialisée ; il faudrait idéalement les individualiser (mais c'est une autre réforme)\n",
    "    label = u\"RSA individualisé\"\n",
    "    column = FloatCol\n",
    "    entity_class = Individus\n",
    "    \n",
    "    def function(self, simulation, period):\n",
    "        period = period.start.offset('first-of', 'month').period('month')\n",
    "        eligib = simulation.calculate('rsa_eligibilite_indiv', period)\n",
    "        ra_rsa_i = simulation.calculate('ra_rsa_i', period)\n",
    "        br_rmi_i = simulation.calculate('br_rmi_i', period)\n",
    "        aah = simulation.calculate('aah', period)\n",
    "        caah = simulation.calculate('caah', period)        \n",
    "        aspa_asi_ass_famille = simulation.calculate('aspa_asi_ass_famille', period) \n",
    "        supp_monoparental = simulation.calculate('supp_monoparental', period)\n",
    "        rmi = simulation.legislation_at(period.start).minim.rmi\n",
    "        br_rmi_indiv = br_rmi_i + ra_rsa_i + aspa_asi_ass_famille + aah + caah\n",
    "        \n",
    "        return period, self.zeros() + eligib * (supp_monoparental + max_(rmi.rmi - rmi.rmi * rmi.forfait_logement.taux1 - br_rmi_indiv + rmi.pente * ra_rsa_i, 0) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "reform = Reform_individualisation()\n",
    "reform.modify_legislation_json(modifier_function = modify_legislation_json)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scenario = reform.new_scenario()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openfisca_france.scenarios.Scenario at 0x7ff547154f10>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scenario.init_single_entity(\n",
    "    period = 2012,\n",
    "    parent1 = dict(\n",
    "        age = 30,\n",
    "        salaire_de_base = 15000,\n",
    "        ),\n",
    "    enfants = [\n",
    "        dict(age = 10),\n",
    "        dict(age = 12),\n",
    "        dict(age = 18),\n",
    "        ],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reform_simulation = scenario.new_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1006.98272705], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation.calculate('rsa', \"2015-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 542.60827637,    0.        ,    0.        ,    0.        ], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation.calculate('rsa_indiv', \"2015-01\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<openfisca_france.scenarios.Scenario at 0x7ff547154f10>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scenario.init_single_entity(\n",
    "    period = 2015,\n",
    "    parent1 = dict(\n",
    "        age = 30,\n",
    "        salaire_de_base = 0,\n",
    "        ),\n",
    "    enfants = [\n",
    "        dict(birth = date(2005,1,1)),\n",
    "        dict(birth = date(2005,1,1)),\n",
    "        ],\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "reform_simulation2 = scenario.new_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 809.02276611], dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation2.calculate('rsa', \"2015-12\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 556.79467773,    0.        ,    0.        ], dtype=float32)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation2.calculate('rsa_indiv', \"2015-12\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45, 10, 10], dtype=int32)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation2.calculate('age', \"2015-03\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 128.5534668,    0.       ,    0.       ,    0.       ], dtype=float32)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reform_simulation.calculate('rsa_indiv', \"2012-12\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Graphiques"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from datetime import date  # module nécessaire pour la définition des dates, dont notamment les dates de naissances\n",
    "from openfisca_france.tests.base import tax_benefit_system\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "def make_couple_with_child_scenario(nombre_enfants = 0, year = None, tax_benefit_system = tax_benefit_system,\n",
    "                                    axes_variable = 'salaire_de_base',  ax_variable_max = 150000, count = 5000):\n",
    "    enfant = [dict(\n",
    "        birth = date(2005, 1, 1),\n",
    "        )]\n",
    "    enfants = enfant * nombre_enfants\n",
    "    scenario = tax_benefit_system.new_scenario().init_single_entity(\n",
    "        axes = [[\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year-2,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year-1,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year,\n",
    "                ),\n",
    "            ]],\n",
    "        period = year,\n",
    "        parent1 = dict(\n",
    "            birth = date(1980, 1, 1),\n",
    "            statmarit = 5, #pacsés\n",
    "            ),\n",
    "        parent2 = dict(\n",
    "            birth = date(1980, 1, 1),\n",
    "            statmarit = 5,\n",
    "            ),\n",
    "        enfants = enfants,\n",
    "        menage = dict(\n",
    "            loyer = 1000,\n",
    "            statut_occupation = 4,\n",
    "            ),\n",
    "        )\n",
    "    return scenario\n",
    "def make_single_with_child_scenario(nombre_enfants = 1, year = None, tax_benefit_system = tax_benefit_system,\n",
    "                                    axes_variable = 'salaire_de_base',  ax_variable_max = 150000, count = 5000):\n",
    "    enfant = [dict(\n",
    "        birth = date(2005, 1, 1),\n",
    "        )]\n",
    "    enfants = enfant * nombre_enfants\n",
    "    scenario = tax_benefit_system.new_scenario().init_single_entity(\n",
    "        axes = [[\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year-2,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year-1,\n",
    "                ),\n",
    "            dict(\n",
    "                count = count,\n",
    "                min = 0,\n",
    "                max = ax_variable_max,\n",
    "                name = axes_variable,\n",
    "                period = year,\n",
    "                ),\n",
    "            ]],\n",
    "        period = year,\n",
    "        parent1 = dict(\n",
    "            birth = date(1980, 1, 1),\n",
    "            ),\n",
    "        enfants = enfants,\n",
    "        menage = dict(\n",
    "            loyer = 1000,\n",
    "            statut_occupation = 4,\n",
    "            ),\n",
    "        )\n",
    "    return scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scenario_monoparental_actuel = make_single_with_child_scenario(1, 2014)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scenario_monoparental_reform = make_single_with_child_scenario(1, 2014, reform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simulation_monoparental_actuel = scenario_monoparental_actuel.new_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "simulation_monoparental_reform = scenario_monoparental_reform.new_simulation()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "salaire_net_actuel = simulation_monoparental_actuel.calculate_add(\"salaire_net\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "salaire_net_reform = simulation_monoparental_reform.calculate_add(\"salaire_net\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rsa_actuel = simulation_monoparental_actuel.calculate_add(\"rsa\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "rsa_reform = simulation_monoparental_reform.calculate_add(\"rsa_indiv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff53fae9cd0>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vic+VtFtSr6Q7a7srrWvePPjhD30lXDPL30iONG6jcgvXQcuBrRExC9gGdAOke4QvAWYD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\nXDTMzKxqLhpmZlY1Fw0zM6va/wdblSUey1A8BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff53fbe97d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(salaire_net_actuel[::2],\n",
    "        rsa_actuel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7ff53f6410d0>]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY0AAAEACAYAAABPiSrXAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ny81Jw8zMcnPSMDOz3Jw0zMwst/8PitQK/PzErTgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff53f7b6ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(salaire_net_reform,\n",
    "        rsa_reform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 300)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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cWZCSM5INkbPp5ib5s4x3G2+IjLiUOKqwuIIsZ5ffLkPkHAg4oBg4zz08p7uM\nDFMG/XD4B8OL3oQ/D6fP1n+mkHMi6MQrz9eqEGz1IRCAkxzHEYA1RLTeogyiLKN3JMdxUorGsgCs\nk+qEW9pMAMKs2sMs7YbjWdozQ8NATYIJALC923bDZJx/dB6AsdE+8y7NQ3hiOM4PPG+Yf2L0sdHY\n7rcdm77ZZNiucSnuv0/tPoZ9J9M8poE/zwMQU2sYsZt6u+929DvQDwBwpPcRdKraSXcZ1rt+Pyn9\nCTyHeOqeAiUhLQGN1jfCvdh7AIzLxTPv0jxMOi3ukM6bOy/8RvrpvhH1Xuw9NNnYBDEpMQCAVpVa\nwbWHq64bKIkICy4vkHd+A+LGuCXtlhiensZWhdCEiCI4jisBwJ3juLuAJRTgBV5+/6+CkVXSeA8e\nANCndh/DZDi5OCEXl8uwmO870Xcw6fQkjGo4yrB0yFNOT8HK6yuxvP1yDPxkoCEyqq2ohnux97Cs\n3TKMbTxWd35rB/XPn/2MRW0X6S7jWdozFJtbDADQqGwjXB58WfcdsnGpcSi7qCzSTGkAgJCfQnTP\nDJqYnogmG5vAL9oPgDGhqibBhKGHh8LllgsAoG6pujj7/VndK6Gtu7EOPxz9QX6/ov0KjG40WlcZ\nvlG++GrrV4hOjgYAlCxYEqf6n3JoaKpNCoGIIix/n3IcdxBAIwBRHMeVIqIojuNKA4i2nB4OwDrj\nUzlL26vaswXP8/JrJycnODk52XKpWSB537+t8a2q/rZg5j8zUb6wcdkcF19ZDACInBBpCH+mOVPe\nnenc0ZiEfLP/mY05l+ZgVstZGNNoTM4d7ESGOUPOf2RUuOKV0Cv4YuMXAICbw2/ik9Kf6C7j7/N/\ny+GDRuzINQtmdN7VGceDjgMATvc/jVYfaq+0ZY2UzBQ4bXbCtSfXABgzeCakJeCrrV/JMnrV6gWX\nLi66pmNPTE9ED9cecrK+IvmK4MqQK6heorpuMtJN6fjJ7SesvrFabpvXeh4mfjHRptBeDw8PeHh4\n6HY9tvgPCgAoZHldEMAlAG0gOpUn0audynkBVILSqewJUZlwEJ3K7V4hU7vxzYLj944banePToom\n8CCfCB9D+E1mE4EHfbXlK0P4iV74JtIy0wzhX351OYEHTT412RD+yMRI+TM8jH9oiIxBBwcReFCR\n2UUo05ypO/+DuAfyZxh0cJDu/ERECy4tkGXMujBLd/7UzFT6cuOXsoxFlxfpLiM4LpgKzSoky5jm\nMU13H9EHHTXzAAAgAElEQVSlx5cUNvtBBwfpHiziFuSmkPHZ+s8o/Hm4Zl4Y7VS2DOo+lkHeD8Bk\nS3txAKchhp26Ayhq1WeKRRG8HHbawMIRBGDpa2RqvjESpKgJo9B+W3tD+b/e8TWBhyGDEBHReLfx\nBB50M+KmIfwbvTcSeNCoo6MM4b8Wfk3+USWlJ+nO/yz1maEDnCAI1Mu1l6GhsR4PPWT+NlvbkMls\n0pU/LTONWrm0kmXM/me2rvxERBdDLioG0J1+O3XlNwtm+uPMHwoZBwIO6CojJjmG2mxto5Chd2CF\n4QrhTRx6KoQCMwsYOmAbGW4YmxJr2GyOiOjy48vyLMsI7Lm9h8CD+u7rawj/1ltbCTzonVnvGBJJ\ndCjwkKErj6thV2X+Of/M0Z0/LCFM5s89Lbfu+yMyTBnUcXtHWQZ/jteVn+jFdywdnqGeuvKHPw+n\nGs41ZP7aK2vTk+dPdOMXBIFWX1ut+Ax99vWhxPRE3WQ8TX4qTxyZQsgB4EEtXVrqxmcNrzAvAg+K\nTYk1hN9IZZaUnkTgQcXmFDOE/+jdowQe1GlHJ0P4x50YR+BB3XZ3051bEARy2uxE4EEN1jTQXdlk\nmjOp9sra8gCRkJagK3+6KZ0+XfupzO8V5qUrf6Y5k77d/a3MP+X0FF3vkSAI9PuZ32X+4nOL06P4\nR7rxE73YNyQdU89N1fUzBMUGUZVlVWT+t2e8TVdCr+jGn5aZRhNOTsiyL4EphBwAHrTm+hrd+KxR\nYl4JwwbsmxE3DY1vlx4ivc0HRC92hjfZ0ER3biKiOqvqEHjQgksLdOeWNv8ZYZYgInK942qYuYCI\naNKpSTK/3s+9yWxS7JAd7zZe10E0LTONuu3uJvM32dBEV2WZlplG/ff3Vwyieg7SmeZM+tX9VwX/\nH2f+0O03JggCrbm+RsFfeWlluhV5Sz5Hq0KwNez0/yWkkDoj6gUQEZ6mPMX0FtN15waAemvqAYAh\nRTL67BPDY++Pva97OKNnmCdabmmJGiVq4OLgi7pyZ5ozkXeGGEViRIrnNdfXYMQxMeldzC8xuual\nT0xPRJE5RUAg1CxREz4jfHStgnX47mF8s+sbAEDf2n2xtetW3RLQCSRg6OGh2OSzCYCYP2l5++W6\n8cekxKDZpmYIiBGTQw6rPwyrOq7S7dm8E30Hn234DEkZSQCAjh91xM5vd+q21+bS40totrmZXJXx\n4/c+xrE+x3TLcHvu4Tl03NFRzvUEAId6HULnap114VdAizYx6oBOK4SDAQcNm8FLO2GNmGG7+LgQ\neFDIsxDduaVt9quurdKd2yfCh8CD3l/wvu7cUjQXeFBwXLCu3CazicotKkfgQd33dNeVm4ho/qX5\nhsxIiYjuxdyTuUsvKK3rDnNBEGjEkREy/5BDQ3RNyRDwNEAx2114eaFu3IIg0FLPpQr+9TfW68b/\nPO05dd/TXcGvZ2qMezH3qO6qulnuT073H8xk9GpIy1sjIH1JekMQBAIPqrOqju7ckpO69sraunNL\nP+680/Pqzi2Zz8BDV2ccEZF/tL/MrXeSvdCEUJm7x94euppXktKTqOKSijK/ngnoBEGQfTTgQf32\n99N14nMq+JRioDsceFg37riUONn/IynJoNgg3fh3+u1UXHvH7R0pLiVOF+64lLgsSmb4keF2Rc8x\nhfAa5JqWy5BBOzUzlcCDjt07pjv30ENDCbwxewKkh0xvJ+nD+IeGcUs/wHzT8+nO/efZP+Xr1jtH\nkPQ96r2iEQSBBh8cbIgfQhAEhQ+i+57uuoY7vxxto+fenXMPzym4Rx4dSRmmDF24QxNCqeHahgp+\n9/vuunBnmDKyhLs6bXai0IRQVXxMIbzu5vCgDts76MJljdHHRhuiaKTIn59O/KQ7d7NNzQi8/imT\nrZ2weg/YE09OJPD6RypJCh28mApZT1ivZqaem6or9+abm2XucSfG6co99dxUmfvrHV/rNpiaBbNi\ntVFhcQXdwjpNZlOWSJvj947rwm0WzDTrwiwF95hjY3TboPZyOG2ZhWV0iQbTqhD+p53KgDE5jJyv\nORuSX6TOapFzcbvFuvJuvbUVF0IuYG/3vShdqLRuvDEpMSi7SMxPaP7LrGsVrUbrGuHak2uY3Wo2\nJjedrBvvxccX8eUmsUDOrRG3dPseBRLQZGMTeIZ5AgDifo1DsfzFdOG+GXET9deKKSzqlKqDq0Ov\n4u08b+vCPeufWfj97O8AgDaV2+BI7yO6pH9IyUxB191d4R7sDgBoW7kt9vfcjwJvFdDM/TjhMVq4\ntMCD+AcAxHxPR3sf1aWQk0+kD1ptaYW41DgAYmEg937uqFmypmbuy6GX0WlHJ8Snxctte77bo0uS\nR69wL/Te11szzxtfDWR3QIcVQmJ6IoEHPUt9ppnLGlLxGD3tkkRi3LLeJgCiF3ZsvVdK8anx8uxG\nT7NCpjlT99meBCnk8L157+l6zdb58Lf4bNGNNzYlVt6LAh66xuIvvLxQ5m2+qTmlZqbqwvvk+RNF\ncZ2f3X7WzRG9w3eHYlY95585uqxKUzNTFSY+8OKudD24H8Y/pMbrGiu4Z5yfoYtP5mrYVfpw6YcK\nbjCTUfbY5bfLELOO9OXqDSOc1JKDWm9eybQFXt+CQJLTGzzoXsw93XitlZeexWWSM5LpnVnvyKYQ\nve6FWTArKqPpZa8mIlpxdYXM23hdY918J9amMvD67YFIyUhROFpzTctFN57c0IVb2jwpHU03NtXF\npPo87TkNODBAwT3gwABd9lRkpwTGHh8rO561KoT/WZPRHv89hvBeDb+KQZ8M0pXzyN0jAAD/Uf66\n8lZeVhkA8GzSM90400xpKDS7EAAg+bdk3bJL+kX5ySazhMkJKJyvsC68BwIOoNsecR+Knimenb2c\nMeaEmLXV43sPNK/YXBfexVcWY7z7eADAjBYz8Huz33XhXXtjLYYfHQ5ArHvwz6B/UChvIc281vsf\nAOBU/1O67J3xifRBw7UNYSaxHvp3Nb6DSxcXzSanp8lP0WtfL5x9eFZu299jP7pW76qJ1yyYMefi\nHEUd5cZlG2PntztRqVglTdySOUgykQHAmE/HYHbr2bp8hwpo0SZGHdBhhWDEzPh08GkCr38SNfCg\nkvNL6sq56PIiAg868+CMbpwZpgz5vuq5g9R6965e5gVBEKjJhiYEXswkqZfD2zqzasftHXXjPf/o\nvMzbyqWVbiYta0d09RXVdfverDOncjxHAU8DNHMKgkBzL85VzH633dqmC6/1ykiaseuxOnq5rGWx\nOcXoYshFzbxeYV5UeWnlLE7tnMKuwVYIr0bXj7Vp/ZfRbns7ANC1OtYfZ8UZRdDYIN04A2MCMd59\nPIbWG4qWlVrqwmkWzPIu4dhfY3Wbwf9+5nfMujgLrT9sjVP9T+nCGZoQig+WiCuBvd334rsa3+nC\nO+7EOCzzWgYACBwdiGrvVdPM+STxieyYB4CoiVEoWbDka3rYhl23d8lOxkpFK+HGDzc0O7lNggkj\njo7AhpsbAAA1S9SEx0APvFfgPU28MSkx6LSjE66GXwUAVCxaEee+P4eKRStq4r0bcxfttrfDo2eP\nAACF8hbC2QFnNReZuv7kOjrv7IyIpAi5bUuXLehXp5+mwIpr4dfQZ38f3I+7L7cZthJ4FbRoE6MO\n6LRC0LNmq1R83tnLWTfOdFM6gQf1du2tG6e1U1YvWPsi9AxbbbqxKYHXN9uqs5ezfK16JR28E31H\n5px4cqIunOmmdIWz8WrYVV14rVdbZRaWoafJTzVzJqQl0OfrP5d5v9vznS77ZE7eP6mYAY93G6/Z\n2ZphypBTukvH1HNTNfOGJYQpNryBB/1+5nfN4bleYV6KJHjgQaOPjVa9ARPMqZwVUg57PXe1SlEZ\nesbaSz8yPTnf+vstAg/dokaslYFeqTSkoj/gQYcCD+nGWWp+KV0VrCAI1HpLa/la9UofPeX0FJlz\n9bXVunAeDjwsc747912KSorSzPko/hEVnVNU5v3z7J+an9VMc6a8j0c69DBrWpvcwINqraylOWV5\nckYyDT8yXMHbfU93zTuTr4Vfy6IERh0dpWm8MplNolmMmYyy4tDdQwCg6zJrgvsEFMpbSLdY+4jE\nCFwJu4LVHVfrxjnl9BRkCpm48cMN3eLUc/2dC4Bo0tLDIWtdL9h/lL8u5QhvR99G7VW1AQDnB57X\npSb0mQdn0Hqr6Bxd22kthjUYppnT2gHbu1ZvbO+2XfN3f/L+yRemzLcK4t7YeyjzThlNnJ5hnvh8\nw+fy+21dt6Fvnb6aOIPjgvHlpi9lM0uzCs1woOcBTbWPn6c/x6BDg7A/YL/ctumbTZrqdQskYInn\nEkxwnyC31S1VF3u670HVd6uq5r3x5AZ67+uNoLgXpuFRDUdh7ldzVY9TSRlJ4D14LLyyUPV1ZYGt\nmgNALgDeAA5b3heDWCntLoCTAIpYnTsFYlW0lyum1QfgC+AegCWvkaVaUxIRddjeQVeTSUJaAoEH\n/RPyj26cept1pGIrf579UzfOYnOKEXiQX5SfLnzWeYPiU+N14Zx8arLMmZKRopkvLTONSi8oTeDF\n/Qp6cEp7TKTgAT0cu1KAA3hQnr/z6FJp7eU8PZceX9LMKVXMk44lV5ZoXmVsu7VNwdl5Z2fNz5P1\nCgs8KP+M/JpXLtfDr1PV5VWzrAS0JCAMSwhT1KKQVoRSPig4ymQE4GcA26wUwlwAv1peZ1dTOQ+A\nilDWVL4K4FPL6+MA2r5CluobRiQOtrmm5dLEYQ29k+RdeHSBwOtX/SklI4XAgwrMLKALHxHJEQ7X\nwq/pwmddfUyPTTnSZwYvFmjRA9aDl1uQm2a+5IxkRcz4neg7mjmlZ0c6tJpFBEEg/hwv8xWeXZge\nxD3QxJmYnviighcPKjizoOZJRcizEPpk9SeKz641GeGtyFtZYvrXXF+jSWFlpwRGHh2pSQl4P/Gm\neqvrKTgbrGlA3k+8s5zrEIUAoByAUwCcrBRCIIBSltelAQRaXk8GMMmq7wkAjS3n+Fu19wKw6hXy\nVN88IlEh9HLtpYnjZb7mm5rryqengpH49ApVlBJ5XXh0QRc+acD5cuOXuvBZ1wjWY/USkxwj8zXf\n1Fxz6KsgCIqdr7tv79Z8jVdCrygGBK075dNN6dRzb0+Zr/G6xpp39VuXBAUvZknV4ssyC2b62+Nv\nBedPJ37S5MiNSoqS66BLx4STEzQ5ybNTAiOOjNCkBA4FHpJX6Nb+i/Dn4a/t5yiFsBfAJwCaWymE\n+JfOibP8XQ6gj1X7egDdADQA4G7V3lTiykae6htJJA6QehXIlswcOX0RtmLJlSUEHro4/YiIvj/w\nPYHXb2fvV1u+0m2GTERy8fXfz/yuC5+0Wnt/wfu6rDSsHby+kb6a+bb4bJH59EhAdy38mmJQ0Brv\nH5sSqyjfOfDgQE0TCUEQaJrHNMU1ut5x1XSN18OvU+HZhWW+DxZ/oCm9d2pmqiLBHngxgZ+WIIEb\nT25QteXVdFMCJrMpS/0G8GLyRUemv86Tg4sBHMd1BBBFRD4cxzm95lTKicsRiEmJAQC0q9JOF74u\nu7sAgGZHHSA6rH46+RNaVGyhS6y5e7A7XG65YFm7Zfjo3Y8083Xf2x2nHpzC/h770bZKW01cAgnI\n/bdY8Wpfj32aq9bFpcbh3XliBbOVHVZi5KcjNfEFxQah6grRSTiy4Uis7LhSE59PpI9c5a5WyVq4\nNuyaJsf+rchb+GTNJ/J73xG+qF2qtmq+e7H3UHNlTZgEEwBgbuu5+LXJr6r5IpMi0W5bO9yKugUA\nqP5edZzqfwplC5fNoWf2SM1Mxajjo7DZZ7PctqzdMoxpNEaV452IsOr6Kow+Plpuq/puVezrsQ+1\nStZSdY3eEd7os68P7sbeldtGNBiBeV/NU1V9LTE9EX+e+xNLry5VtDt3cMbwBsN1r2ZoC3JUCACa\nAOjMcVwHAPkBvMNx3FYAkRzHlSKiKI7jSgOItpwfDqC8Vf9ylrZXtWcLnufl105OTnBycrLhUsVU\nBQB0i7K5F3sP4z8brwvXt3u+BQC493fXzBWfGo+229qi2rvVMLbxWM18ww4Pg6u/K7Z23ap5G//z\n9OcoMqcIAMBvpJ/qH6CEvXf2ooermLU29OdQlCtcTjUXEaHr7q5yJNqT8U/w/jvvq+aLT41H+cXl\nkZyZDAB4OO6hpg1Vd6LvoNaqF/fL+wdv1Hu/nmq+cw/PoeWWF5sTtaZpOHL3CDrvelG68bemv2F6\ny+nIxeVSxfdy6gunik7Y/d1u1ROm0w9Oo8P2DsgUMuW2Y32OocNHHVTxeUd4o+/+vgiMCZTbhjcY\njnlfzVO1OTM0IRRjT4yVnz8AKFWwFDZ+s1HVNZ44dQKzts7CxccX7e6bLexZTkBpMpoHi68A2TuV\n8wKoBKVT2RNAIwAcRKdyu1fIUbHoEiHFjeuB/f77Cbw+CdziUuIIPGj6+ek6XNkLv4EeexikjTx6\nlNUMfBooX5vWjWGCIFCjdY1k/4PWz/pPyD+KaBctMAtm6ryzs8x38v5JTXzW9w289o1q62+sV/Bp\nSQiXbkqnIYeGKPi0pGeISoqS63NIh5b9KAFPA6imc00F31LPpaqfF+8n3vTxio8VfMOPDFcdHXYt\n/FqWcpiN1zWmW5G3VPH5RPjQlxu/VPDlmpZLLEDkCB8CUbYKoTiA0xDDTt0BFLU6b4pFEbwcdtoA\ngB/EkNSlr5Gj6kYRiQNl/hn5Vfd/mUsv5SLZRPWApPT08GtIDt/5l+Zr5rJOA63Vvh/yLETm2u+/\nXxNXhilD3gj09oy3NeeiWnxlsXxtWhX8/dj7ih+2llBPQRAUBWPKLCxDYQlhqvkCngYoHJtttrZR\nPSgKgiD7z6Rj8MHBqsN6Y5JjqOuurgq+UUdHqea7GXGTqq+oruD74fAPqj6vIAi0z3+fnAlXOnru\n7amqOJDJbKKVXiuz+BdaurTM4vdyqEJw1KFVIQw8OFB1fwlSCojtvts1c0nF57XOIolexIrv9Nup\nmUvaff3X2b80c828MJPAgxqta6SZy9q5pjW+3DqHvtZd0dYhny1dWmpyxkp1NaTD46GHaq6UjBR5\n7w14MTmeFqX38uCjZTe1f7Q/lVtUTuYqPLswXQ+/roor3ZSuKPEJHtR6S2vVEyM9lUCmOVNRY0I6\nJp+arCqJ3uNnj6mXa68sfFPPTX0tH1MIL98QXp/CKr+f+V23Gb1eKw2pXGUrl1aaudZcX0Pg9SnX\nKYXxaS1HmWnOpOJzixN4MRulFljXQGi4tqGmFcuT508UP0otEWKhCaFyehHwoFPBp1RzRSZGUqUl\nlWSuscfHqg6ZfZb6TJGm492571Lg00BVXBmmjCxRPX97/K3q2gRByLK57YPFH6g2gd2MuEk1nGso\n+IYdHqZKCTxLfZYlDQd4cS+DvZ9VEAQ6GHCQyiwso+CquryqXc8IUwhWiEiMIPD62PzBg8ovKq+Z\nR6qdqscGIr0Uy3bf7QQeNOjgIE08ZsFMuaflJvDaEwneirwlfz6tO8Knn58uc6mdjRKJA9tn6z+T\nua6EXlHNFZEYoTAhHLt3TDWXb6SvYtDQknDR2q8iDY5qfz9nH5xVcH2y+hPV+a8uPLqQxeSi1nSo\npxJ4FP+IOm7vqOB6f8H7qsK0n6U+y5KIDzxo6KGhdk86zIKZXHxcmEKwxvKry3UZMKOTogk8yCfC\nRxOPNIjXcK6h+ZokJ5fWxFoHAw4SeFC33d008UglSsGDbkbc1MQ18eREmUvLRiZrM4zWFcYfZ/6Q\nuVZ6rVTNE50UTSXmlZC5DgYcVM1l7aMBr36viFkwK1J+gAcduXtEFVd8arzCuQ4etPXWVlVcwXHB\n1GBNAwXX3ItzVa0sslMCQw8NVbX5zjPUM4vTusmGJnQ76rYqLmnjp3QUmlWItvhssftz+kX5Zdlk\nxxSCFaR0yloh3WStGHFkhOZBjuiFotPqg3C/707gRfu3Flg7QrWkV07OSJZ5tOZg6ruvr8ylJSur\ndVnFXq69VEeqxCTHKGznWmplv7xhSW0KjNCEUMWO2nqr61FkYqQqLuvCO9IEQ63Zpc++PgquwQcH\nq8r86RPhk2XgVqMEBEGg3bd3U/4Z+RVcfff1tft+pZvSFcWEpKPD9g50N+auXVyJ6Yn019m/snDV\nW11PzrvEFIIVwIOKzimqqu/LPF12ddHEIQ12Y46N0cRzL+aeLjNeyTRQb3U9TTzWOey1pBA48+CM\nzKNlF6pXmJfMM+vCLNU81kruvXnvqU7jEJ8ar6h0pbbil8lsUqRerra8muqdtXtu71EMINPPT1el\n6B7GP6RaK2spuM4/Om83T6Y5U5E/SZpxq1HkPhE+Wa5pyKEhdn9/GaaMLNXawIs77O2NXLofez/L\nqgk8aM4/c+xKkSEIAh29ezRLWgxp5ZTddTGFYH0zeDFKQAukAUZrDP1Hyz4i8Nr2CVjXDdCC6+HX\nCTyo4pKKmnjmX5pP4EF1V9XVxPPdnu8IvOgcVOsENZlNithuteGQyRnJigFcjRmASMyIa22i2OC9\nQRXP87Tnihj9Lru6qFphpmamZpl5e4V52c1jMpuyzEonnpyoajLwcjbVEvNKqPLL6KUE4lPjaeTR\nkVkG2w3eG+x6LgVBoB2+OxS1I8CLPhR792uEPAuhfvv7Zbmmb3d/a1N6GqYQrG8Gr73YhmTv1YLg\nuGACr93RWnBmQQIPTbVfb0fdJvBiuJ8WSDHfWvLzWCeRW3N9jWoeacMgePWJ4wRBoB8O/yDzqA3j\nTUpPUmSiVLu57/Gzxwpfw5TTU1RNJnwjfRWmjm92fqMqBPVq2FUqMLOAzPPh0g9VRR15hnoqPhd4\n0A7fHXbz3Iq8lUUJDD442O6w5OC4YGq7ta2Cp/yi8nZHe8Ukx8gmYevjx+M/2uXnSzel0zLPZZTn\n7zwKng8Wf0B77+y1+RkQBIFOBZ9iCkGC5FDUEl4oOYG1bjbSY1b/59k/Cby2HavWZhC1EARBVkxa\nCp5bzw7VbpZKTE+Ufzgfr/hYtclKivwCL4Zqqhl4UzJSFGUl1e58tjZ5gQdtvrnZbg5BEGjR5UUK\nnk03N9nNk5SelGV2utJrpd33J+RZiOzPk46p56bavW/DN9JXkYhPrRK49PhSlkR0X2780m5TpcdD\njyyO6pLzS5LrHVe77tGV0Cv0xYYvsiiTX9x/sUuZ+ET4UKcdnZQ8TCGIkBw3WrDBe4NmpSJFgqg1\nPRCJmRTBgyadmqSa4/Gzx/JDotZslZSeJHOoDd8UBEGeQbfY3EL1tVhv+lG7m1faIAhejPxSs6s1\nLTONWmxuIfPMuzhP1bW8bNdXY4uPTYlVDLxlF5al4Lhgu3n2+e9TXEvrLa3tDhZITE/Mkt6it2tv\nuwdv30hfqrOqjoJn0MFBdvFIJpx80/MpeL4/8L1dfpiUjJQs6bfBizWl7Qkjj0mOoZ9O/JSFp/mm\n5naZ8UKehdDgg4Oz8FRdXpVc77iKdd+ZQhDx6dpPNSsEPWb24MVNPWqRmplK4EG5p+VWzRGZGKlZ\nGTyMfyhzqN2IZc2hdpdwWEKY4oeo5vPEpcQpYtrV7AlJN6UrTA1qVpGCINCM8zNkjvwz8tP92Pt2\n81g75KVVjr2z74jECGqyoYmCx969EWbBnMUR22BNA7s/kx5KIN2ULu+Wf3llYo8Pxj/an9psbZOF\nZ5nnMptXpGbBTNt9t8uV96SjwMwCtOraKpt54lLiFOnZpaPonKLk7OWcrYOaKQTpRvCgUvNL2d1P\ngjQQa9kwJDnftBQakb50tWkRYlNiZQ61Kx3rAUftJiXrGb3a+2Ft41czcJoFM3XZ1UXmOBF0wm6O\nTHMmfbPzG5nj9zO/262UMkwZirDYBmsa2D1zNplNWWaZ9oYhC4KQJb3CsMPD7HZaHwg4oOB4Z9Y7\ndqfeyE4JDDw40C6TSVxKHA07PCzLgLnp5iabvyOzYKaN3hvp7RlvKzg+W/+ZXdUC/aP9FVXipGPw\nwcE2m0hTM1NpyZUlCt+NdPx19i+bfkdMIUg3ghcdOmohbUFXiwxTBoEXqxqphfRwqy2C8jztufwA\nqbWvSwnIPl7xsar+meZMeTY++OBgVRzWpp0/zvyhisM6dp8/x9vd32Q2KSqKTTw50W5FEJcSp3A4\n99/f3+7v5WH8Q/pg8Qcyx+frP6eY5Bi7OG5H3VakRCg+t7jdmwlvRtykCosrKAapDd4b7LonflF+\nWbJ+2qsEgmKDFCk2wIMqLalE5x6es5kjIjGCBh4cmGXQ/dX9V5sL3CSlJyl2xEtHrZW1bFbUZsFM\nO3x3UMUlFbPwjDgywmZFcjPiJnXb3U3syxSCCPDaUvKCB9VZVUd1f8mWqzaMUpqVL7q8SFV/6xrD\najfCSQPgyKMjVfW/GXFTvobLjy/b3d8smBVmDDWhv9apGJw2O9m90jILZhpwYIDCHGOvIrgfe18R\n6TPrwiy7Oawd3+DFbLT2cKSb0mnMsTEKjlkXZtn1fD55/kSuoCcdk05NsmvVqIcSuPDogpytVjpa\nbG5hV9STW5BblvrJHyz+gI7ePWoXx8sb38CDZpyfYXMk4NkHZxXpUKSj666uNpeDvRZ+LdvVSJmF\nZZhCIBJnDVoGYylCSW2dWimHktqcMs9SnxF49fsE0k3p8kOhZoenIAj07tx3CTxoo/dGVddgbc5Q\nU5/2+L3jcn81kTbSdyAd9u4oNQtmhfnhh8M/2P08nX90XnENe+/stat/ckbyi5keD3rr77fsnsmf\nCj6luIaGaxtSaEKozf1TMlKyJGzruqurXasSvyg/+mT1JwqO7w98b7MSEASBtt7aKufJsja/2Ors\nTkxPpN9O/5Zl0Oy3v5/N2VHDEsKyXUl03tnZ5ggl30jfbDepfbHhC5tXNZcfX6Z229pl4ai4pCJt\n9N6oWHVqVQh5bCuj8+/G7tu7AUB11aaerj0BAFWKV1HV//2FYsWtUZ+OUtW/6NyiAIAHPz6wu69J\nMO9J5BQAACAASURBVCHfjHwAgPhJ8SiUt5Bd/VMzU1FgVgEAgOcQTzQu19iu/skZySg0W5Q5zWka\n/mr+l93y31/4PhLSE1C+cHkEjQ1Cvjz5bO6fac6Ek4sTLodeBgBcHnwZn5f/3Ob+RIQfT/yIFddW\nAAC+r/s9Nn6z0a5nycXHBQMPDZTfew31wqdlP7W5/40nN9BwXUP5fc+aPbHpm03I/1Z+m/rHp8aj\n34F+OB50XG7b+e1O9KrVy6b+RITlXssxzm2c3FazRE249nDFx+99bBPHneg76HegH3wifeS2AXUH\nYEnbJSiWv1iO/dNN6Zh/eT7+PPenon16i+n45YtfbHomfCJ9MPbEWEX1sDy58mBlh5UYXG9wjiUp\nTYIJ626swwT3CUg1pcrtpQuVxqI2i9CzVs8cn4vQhFBMvzAd67zXKdqrFK+CWS1n4dsa3+bIcSHk\nAqadn4azD88q2j8q/hGmNp+KnrV6Ik8ug4ZuLdrEqAN2rhAk55RagFef+fNiyEXVJhIikvPY2zOL\nk2AWzPJsQU1aA+siNGoKd1jPRtVsWrLOu3/2wVm7+0t7NcCDVlxdYVffl4vJ9Nzb0y4nvCAIigRx\nJeeXpMfPHtvV/+WoGHs3x71cFa3H3h52FXm3XpWBB+X5O49djurbUbcVPhLwYooVW019MckxNOjg\nIEX/3NNy09ZbW20yj2WaM2nF1RVZZs4tXVrabH7xCvPKUr0NvJgW3pZVUXxqvCIRonQUmlWIlnku\ny3G1LAgCnQ4+ne011HCuQbtv787xuYxPjaeZF2aKvjujTUYA8gG4CrEsph+AqZb2YhArpd0FcBJA\nEas+UyBWRXu5Ylp9AL4A7gFY8hqZOX4R1pDsgWpwOvg0gYfqgiLSl6cGrndcCTxoi88Wu/tap8NW\ns9HLutiLGhOPFHlTeWllu00rUUlRsux229rZbV+3zvrZY28Pu9MMSLUuwIupIezxM6Rmpiqijppv\nam6XmS4qKUqR0bPKsip25fAJjgtWlHfM83ceu3xnd6LvZNmk5ezlbPN3cCf6jiYlcDfmrmIfB3jQ\nR8s+oguPLtjUP+RZCPXY2yPL4Mmf422y48elxCmy60pHkw1NbJrUpWWm0TLPZVRoVqEsHH+c+SPH\n6DFBEOhE0Ils/Qh1V9Wl/f77c3yeg2KDFBF4isNohUDiAF3A8jc3XtRFngvgV0t7djWV8wCoCGVN\n5asAPrW8Pg6g7Svk5fjFWAO8+uIs0s5XNZCykKrJGCntFfhy45eqZEsPgJqNSM5ezgRejM6wdzCW\nUoODB62/sd5u2T+7/Sz3t3enqJQSBLwYKWNvOKv1JqP229rb5RyNSoqS81OBF6NA7FlRvDwb/8X9\nF5v7m8ymLPbwyacm26zIniY/zeKEHHdinM3BB3ei71D9NfUV/fvv72+zEvB46KEo5AMe9NWWr2zy\n2QmCQPv992eJ6f94xcc2pakRBIH23N6jyDwLXvTPLPNcluMzYBbMtPv27ixObfBiyG5OylwQBDoc\neDhLymvwon/nyN0jr/0NCoJA5x+dz9aHkHtabvr9zO+KfUIOUQj0YqAuAOA6gE8BBAIoZWkvDSDQ\n8noygElWfU4AaGw5x9+qvReAVa+Q89qb/DLAq9tJK5lc7DU3WPdttqmZ3X21FruRIljUZAmVImjU\nhIRaR77Ya2Lyj/aX+453G29X3+SMZMVgbKs5QIL15qkWm1vYFYUl5YKSjqWeS23um2HKUGQsBW9f\nqczLjy9T3ul55b5Vl1e1OfAhLTNNYRKTVmO2Tl6yUwL99vezSQkIgkCbbm7KdgC1pX98any2O3t/\nOPyDTabRezH3FM55ayX2KP5Rjv09Hnpkm1qi887OdCvy1mv7mgUz7fPfl2WPBXjRkewW5PZaBZBh\nyqCtt7ZmSZEhTeDWXF/z2udXq0LIY4ufgeO4XABuAKgMwJmIrnEcV4qIoiyjdyTHcSUtp5cFcMWq\ne7ilzQQgzKo9zNKuCXei7wAA6r9f3+6+SzyXAFDnDJYc0WcGnLG7b7019QAAMb/E2N233KJySDWl\n4ubwm6heorrN/YgI5RaXw5PEJ1jTaQ1+aPCDXX3rrK6D29G30frD1jjV/5Rdfdttbwf3YHcAQNTE\nKJQsWDKHXi/6jjo2CqtvrAYA7Oi2A71r97ZZ9lLPpfjp5E8AgC/Kf4HT/U/b7Kh1u++G9tvby++P\n9TmGDh91sKlvUGwQmm5qiujkaABAy0otsa/HPhR9u2iOfZMykjDsyDDsur1LblvbaS2G1h8KjuNe\n25eIsN57PX44+uK7rVS0Eg70PIC6pevmKNv/qT/6H+gP7whvua1fnX5Y2m4piucv/tq+aaY0zLk4\nB9POT1O0z241G+M/H4+8ufO+tr9nmCfGHB+DGxE35LbC+QpjZYeV6FO7z2s/e2pmKpZdXYbJZyYr\n2j9+72MsbLMQ7au0f23/29G38ee5P3Ew8KCi/bNyn2Fmy5loWanlK/uaBTNc/V0x7fw0BMQEKP7X\nrEIzTG0+FS0qtnil/Gdpz+Ds5YzZF2cjOTNZ8b/mFZpjctPJaFu57Sv7B8UGYbHnYqy6vuqV12gP\nbFIIRCQAqMdxXGEABziOqwmAXj5NlyuygOd5+bWTkxOcnJyyPW/PnT0AkOOPJTtMcJ+AQnkL2d03\nIS0Brv6umNp8qt3e/jXX1+BW1C0c63MM7xZ4166+dVbVQXhiOC4PvoxPSn9ic780UxryzxQHwouD\nLqLJB01s7vsg/gEqL6sMADja+yg6Vu1oc99zD8+h5Rbxx7Sq4yqMaDjC5r7bfbej34F+AIDRn47G\n8vbLbf6eVl9fjZHHRgIQJwoXBl5AwbwFberr7OWMMSfGyO99R/iidqnaNvVd770ew44Mk98vb78c\nYxqNeU2PF9h7Zy96uPaQ37et3Bbbu2236Rk59/AcOu7oqIiMOdTrEDpX65xj34CnAeh/oL9iILZV\nCcSkxGCi+0S43HKR2/LlzofNXTajZ82er/2+MswZWHZ1GX459YuivcNHHbC47WJUfbfqa2WffXgW\nE9wnKKKaAGBq86mY+MXE10bbhT8Px4wLM+SJhoRKRSthZsuZr40mMgkm7Lq9C9POT8P9uPuK/7X+\nsDX+bPYnmlVo9krZ9+PuY96leVmikACgf53++LXJr6hVsla2fTPNmXD1d8Uiz0W4/uS62PgQwCPx\nZaOyjeAFr1fKtgV2jWZE9JzjOA8A7QBESasEjuNKA4i2nBYOoLxVt3KWtle1ZwtrhfA67L6z29bL\nV+B5+nMAgFtfN7v7VlpaCQDAO/F29XsQ/wAjjo1Ar1q9bJ5tSmi+uTn8ov1wZsAZu8Iqw5+Ho9zi\ncgCA0J9DUa5wOZv7zrs0D5NOTwIAJExOQOF8hW3ql25KR+VllRGeGI5ibxdD+Phwm2fmflF+qLO6\nDgBxhuf9g7fNfTfe3Ighh4cAEMMmLw+5bNM1CyTgxxM/wvmaMwCgcrHKuDT4EkoVKpVj38T0RPR0\n7YkT908AEGe1V4ZcQY0SNXLs+yTxCbrt7oar4VflNre+bmhbpW2OfYNig9B9b3fcirolty1ssxA/\nffZTjmGNAU8DMODggBeDCoC+tftiabulOSqggKcBGH50OP55/I/cVu3datjQeUOOE43guGCMdx+P\nw3cPK9rntp6LcY3HvTa0NDIpElPPTcVa77WK9vZV2mPeV/NeOYgC4gRu0ZVF+PvC34r2Am8VwKyW\nszC84XC8neftbPtmmjOxzXcbpp2fhpCEkCyy/2r+Fz4r91m2fYkIF0IuYPbF2TgZfFLxv9xcbkxu\nOhk/Nv7xlSvmkGchWHp1KRZ7Ls7yv3ql62HC5xPQvWZ3xeqL22H/xDjLRb/uAPAeLBFEAPIDuACg\nA0Sn8iR6tVM5L4BKUDqVJYc0B9Gp3O4VMl9rp1PYzCy2VXvR27W3Khu+VODc3rw4WordSBtb7K17\ne+nxJVmmPXbzDFOG7KcYfmS4XTKtbcf23KP41HhFgZEHcQ9s7mvt26iyrIrNeYKS0pMUES+ddnSy\nOQPq5ceXFfbdgQcH2hStJQhCloRwI4+OtKlvXEocdd/TPYtd3ZYIOf9o/yyOzT77+tgUWnk6+DSV\nX1Q+iz8ip4AGLYVjTGYTrbuxjgrPLqzo+96898jFx+W1kTjppnRy9nLOIhc86LfTv712g1xaZhqt\nvrZakerD2ofwuvxGGaYM2uKzhaqvqG63/d9kNv0fe+cZFkW2te27ATGgOJhzQlEQFRExoSgq5ogB\nAwqKAQUTJjCBASOMgooBFSOjjtkxB1AcFROGUVFHMUcUBZXY9f3o6bLLqgY8b/jOeWfWdfGDqr27\nq6ur99rrWc96lrDnzh6ZbLju96wvZ6hWq4UziWc0z8b/Qg6hLLDxrzyCAbBdEIRDKpXqArBDpVIN\nAR4Dff5ayW+rVKodwG0gExglCIIWThoNRAIFgEOCIPz49lzB+lj1yX3QdxZ1KwrHyo4/PE+7e21f\nvf0PzSu1RLMLSPVL/aF5g/YMYn/Cfn5x+YXOFp3zPE8LX5QtXJbnE57nGW7RLZK66HkR+/L2eZr3\n/ut7ii/S7C6bV2pOtHt0noq71IKa3jt7s/vObgAO9T9EhxodcpmlMV2YpaJpReJHxucKc4AmarJb\na8er1FcATGo6iYVtFuZ6j9SCmoDoAOacmSMe29N3D91rdc/1PW+8vkHbzW3FvEIpk1IcdztO3dJ1\nc5yXmZ3J7JjZzD07VzzmWNmRLT235BrtKUUC/ev0J7R9aI6RgFpQs+HaBjwPeEqOe9l5EdQ6KMdc\nyLsv75hxaoYMjhljP4aAlgE5FqnFv4pn0vFJnHh4QnLcu6E3Mx1nUtKkpOI8QRDYfWc3/qf8uZd0\nT3JuiM0QZjrOpPJPlRXnpmWlse7qOgJjAnn75a3knIulCzNazNCbf/nw9QMrLq1gQeyCH8b/X6a8\nZHnccoLPB5OenS45V6tELXyb+DKw7kDFyOXpx6esvLSS0LhQvmR+Uby2f9n+K97kf+qPPEYIWrbO\nj7JOtIyXvJawa23bjW0/vHsVBEEUwfpRHX+tFs2P0ju1Egxuu93+pfczDDT8IUqmLq8/NxaGrmlp\nuwRoZIrzanvv7BXnlVxUMs/y3No+E9q/vLa5fPHphUTDxnqldZ6enbTMNFlXrUWxi/JE9f1ey6js\nkrJ50s6/8/aOKAX/I5HAl4wvku9R+7cwdmGugnynH53+lxrHJH9NVpR3tl9rn2tdwpnEM4q76c7b\nOuco9/E547MQ8nuIYvTg+qtrjn1M7r27p6iuqv2t6VuH1Gq1cOT+EZkulPZv0J5Beq/5S8YXIeJK\nhCJriQBNQWXs41jxPvNfjBD+vy/+iheVR4eg/YH/KJde27T6R0zrfH4UntIqd/4o1VLLO//RTlza\n/sA/QqVNSU8RH7C5MXPzPE+rIfWj0JK2upsADW03rwqgugVppvNNhZcpL/M073u55rxqyHw/b+ap\nmXkqgjt8/7BkXuOIxnlyIOeenBPMFphJ5ualReidt3cE+7X2knn9fu2Xq+7P69TXElluAhBM5pkI\nO27tyPE3lVPjmJxondqagu9rEgjQiDrmBJvdfnNb7MWt+9dwTUPh2INjeq83JT1FWBi7UFFS2m23\nm94Ke7VaLZx+dFrWblO7Yfqe/69rbz+/FWZHz1Z0OlWWVhFCL4QqFjOq1Woh+lG0ImWWAI1seuS1\nSEXYKflrsmaD9V90CHmBjP5tTath9KMsoXtJ95jQeMIPzfE57ANA/Ij4XEZ+s/SsdGxWa9hAwe2C\n8zxvYexCgmKDmNNqDmMbj819wl/vVWCeJryMHhyNY5W8wWFHHxyl/VYN/HXP+x41itfIdY4gCPTa\n2UuEeZ5PeE65IuVynfcq9ZWo+wTw0vclZQqXyXXeiYcnaLu5LaBhsTwY8yBPyXHdpLixoTE3vW7m\nyl5Jz0pnxMEREuZMXvSRkr4k0X93f5FeC7Cj1w561+6d47zE5ERcf3WVJJbntprLVIepOWrvJLxL\nYNDeQcQ9/8YqcbV2JaxDGCUKldA77483fzD84HBR+wk0Cfh1XdflqGN1++1txh4ZK4NzQtuH4tXQ\nSy/b7uGHh/id9BPZgLrXGuQURFWzqorzXqa8ZO6Zuay8vFJyvFLRSgQ5BeFq7ap4fz6lfyLsYhiz\nz8wmIztDcm6IzRD8mvspapZlZmcSdSuKBbELZPTRqj9Vxc/Bj0H1BskS34KgSRwHnw/mwL0Dstft\nbdWbCU0mKCaeHyc/FqGftKw0ybmfCvyEj70PIxqMoLyplJ3/JfMLG65tIPxyOJdeXJK97n/F/qMd\nwo7bO3If9J3tubMHgPlt5ud5ztfMr6y4tAIvO688M14AcYHOmJ6Ry8hvtiJuBVNPTmVy08lMbzE9\nT3N0F9rEsYl68dLvrfO2zvx2/zdqFq/J7dG384T5n3tyDocNDgCEOIcwvsn4XOdkZmfSelNrkZly\nbsg5mlZsmuu86MRoWm1sJf6fl8+Wpc7Cc7+nuKDXK12PU4NP5ZpbuP32No0jGpOSkQJApxqdiHKJ\nokj+InrnCILAmitrGPnbNzpt/zr9Wd15dY60x5T0FLwPe7Pp+ibxmFtdN5Z3XJ4jK+pfdQLH/jyG\n+153Xqa+FI91qtGJ5R2XU+WnKopztHmEUYdGSRbWphWbEto+lAblGijOS89KZ3ncciYenyg5Xs2s\nGiHOIXSt2VVxA/cp/RM/n/+ZgJgAyfECRgWY5zRP728vOS2ZpReWyuofAEY2GMlUh6mKz0xu+L+f\ngx/O5s6ya/2Y9pG1V9cSfD5YzEFprbRJaXyb+DKswTBZnuVzxme23txKWFwYt97ckl1P/zr98bH3\noVH5RpL3zMzOZOcfOwm/HM7pxNOyeTWL18TLzotB9QZhVtAMVcB/jWX0H+0QEpMTqVMqbxxxrfXc\n0RMg10IZXbNdoyl6W9FxRZ7njP5tNAA3vW6SzzBfnuZsjN+I92FvRjQYwcK2C/M0J+55HI0iNDu7\nz/6fKZSvUK5zXqe+pkywZmce2S2SwTaDc52TmZ2Jdbg195LuYWxoTNLkpDwpqwZEB4g/1rzy8n9/\n+jvN1n+jMD7weYB5MfMc53xM+0jbzW3FHZOrtSsbu2/M9XteHrdcjP4A1nZZi6etZ45zHrx/QIet\nHUQeegGjApwefFov/RA0BUwLzy1k2qlp4rFG5RsR5RKld5cMGicweO9gSQSRmxNQC2oirkYw4uAI\nyXEfex/mOs3V63Reprxk6smpEkcFMLXZVPyb++t1jmcen8H3mK8keQ3g7+DPFIcpiu+XmZ3J+mvr\n8T/lz/uv7yXnJjedzORmkxUT30lfkgg5H0JQbJDsnHdDb6Y4TFGMHu8n3Wfx74v18v+nNJtC7VK1\nZecuPrtIyIUQWYQD0NmiM75NfHGs7ChZxAVB4OTDk4TFhbEvYZ9snn15e3zsfeht1VsScWSrszn8\n4DCrLq9SjDYqmFbAy86LIfWHSCLrbHU2R/88SsTVCNmcH7X/aIcA0Kd23hlGWeosQFPxmld79OER\nd9/dZWvPrXmGps48PsPKyytZ2GZhjvxoXfv19q+473PH1dqVVZ1X5T4BiIyPxGOfB2YFzEianJSn\n69OVan7l+ypPXPvtt7bjuksjpZxXVs3h+4fpuE1Ta+Fi6cKO3jtyjUAuPb+EfcQ3VtPd0XepWaJm\njnMefnhIvVX1SM3QsLcCWwYyo8WMHO9FcloyPbf3FHdcpUxKEesRmyNclqXOYtrJaSz6fZF4bFrz\naQS2DMwR2vn19q/03vkNNjIrYMaBfgdy5OzfS7rHoD2DJE6gb+2+hHUI08u0+ZL5hdkxs1l4TrqR\nCHYOZkyjMXohncP3DzPq0CgSkxPFY1V+qsKKjiv01sq8+fyGwOhAGZzTtlpbFrVdpFg0KQgCe+/u\nxf+UP3ff3ZWcc7dxZ2aLmYqO8c3nNyz5fQmLf18sOzeh8QQmNp1I2SJlJccFQSDmcQwLYhco8v/9\nHPwY02iM7F5+zvhMZHwkweeDeZT8SHLONL8pvk188bLzks17+OEhKy+tJCwuTAZTFStYjDH2Yxje\nYLjkOgVB4OyTs6y6vIqoW1Gyz/ZTgZ/wsvNimO0wyX3JyM5g3919rL26luMP864YkFfT1gf8W5lK\npRJyuy61oMZwtiEJ3gm54sJam35qOvPOzkOYlffPrArULCx5nZOSnoLpAlPKFi7LC98XeZqjXTw7\nVO/AoQGHcp+AJgJZeXklva16s6N37tCZWlBjtcKKhKSEPL/Px7SPYq+G+mXqc2nYpVw15R99eES1\n0GqA5qF+NPZRrpIN115eE6MwgFtetxR3a7qmC10B/OLyC32t++Y458zjMzhGfsutjGwwktAOoTlG\ncLFPYnGMdEQtqAGwLGHJwf4HqWZWTe+cyy8u0+2XbrxI+fb9b+y+Ebe6bnod1b2kewzeO5gLzy6I\nx/rU7sPyDsv1OoFXqa8Yd2ScpDiziHERIrtH0qNWD8X3Ss1IJehsEPNjpZDpoHqDmN96vmIuKFud\nzZYbW5hwbIJkN180f1GCnYNxt3FXfC5+f/o7005NIzoxWnK8Y42OzGk1R1Fu5mXKSxadW8TSi0tl\n5yY3nYxvU19ZIVdO+H81s2r4OfjhVtdNhv9ff3WdkAshsogINFXHExpPoF31dpKNTGpGKpuvbyYs\nLkz2XqCp8vax95HRta++vEr4pXAirsl38UYGRnjZeTGiwQjJc/8l8ws7/9jJ2qtrOff0nGxeyUIl\n8bT1ZEj9IWJeRKVSIQjCv4wb/cdGCBefaXZPeXUGAPPOzqOiacXcB/5lRx5oyiRujLyR5zmmCzTh\n8bMJz/I0Pjoxmo7bOtK4QuM8OwOrFVbceXcnzxj+/aT7WCzX3Ke8VsIGnQ0S4Y28NHz5mvkVm9U2\nIg88L5IPulXJoEnY56a5s+XGFtz2uIn/59bUJ1udzdQTU1lyfol4LLdah5T0FIbsH8Kvt38Vj0V0\niWCo7VC9c55/es7APQMli9+05tOY5ThLr8P5V5zAzdc3GXZgmCR6qFe6HhFdI7ArZ6c459rLa/gc\n9pEsKvkM8hHeKVzvYn7z9U0mn5gs/ga0NqLBCAJaBiiSARLeJTAzeqYMXrEta0uQU5AiJv/s0zMW\nxC4Qq8R1bXrz6YxrPE4GHWnx//mx82U8/JZVWuLn4Efbam0l76WtNwi5EMLtt7clc4wNjZnQeALe\n9t6SBK4gCJx4eILQuFAO3jsou77GFRozxn4MLlYuEmgy4V0C44+MJ/xyuKzGAMDDxgMvOy/Jbyo5\nLZltN7fhtseNa6+uyeZULloZT1tP3G3cRUhMLaiJToxmdsxsNt/YLJvzr9h/rEP4UcmKt581RScH\n+smxOX3WYWsHiuYvmmctm57bNfmJxLGJeUrQxj2Po9XGVtQsXpPzQ8/nOj4zOxPjuZoH74TbCVpX\na53rnPln5+N/yh+AFL+UXHH/Jx+fUHmpJgk3sO5ANvfI+UETBAHvQ94ifLC151b61+mf45y77+5i\nueKbMN+lYZf0Lmba95hxegbzzs4DNGH41eFXc0wwP/34lFYbW/Hnhz8BaFiuIb/1/03vQgvwy61f\n6Lfrm3hepxqd2Nxjs95iqi+ZX5hwdAKrr6wWj/W26s3qzqv1zrmfdJ/Bewdz/tm37zs3J3D4/mHc\n97mLRW0A3Wp2I7RDKJWKVpKNz1JnsfryaokmE0Drqq1Z1n6ZYvSVmpHK4nOLZfIO9cvUJ9g5mFZV\nW8nmvEp9xbwz88Ruc1qrYFqBIKcg+tfpL3M2icmJzD87XyZBYaAyYJbjLMY0GiOLKO8n3WfRuUWK\nu+vB9QYzqekk2WdKeJfAzxd+lnw3WmtWsRm+TXzpWrOr5PoevH/A+CPjCY0LFSNCrZUoVEKEfnRh\n1qcfnzInZg7hl8NJ+poke6/eVr3xsvOiZZWWooN68/kNm65vwm2PGwlJCbI5liUs8bT1xK2um/hM\npGak8uvtXxmwewBnHp+RzQHoZ92PKOTw04/Y38YhDN6rSZzmRfURIDBakwhNHJeYp/H77u5jz909\nrOu6Lk8snxuvb9AoohGlTEpx1/turuPffH5D6SWaB/HhmIc5JiJBgzWaBJmQpc5idMPRLO+4PMfx\noKmK1u408sLo2XZzGwN2DwA0VawrOq7IEbt/8P4BNcK+4fTnh57PMRGbnpVO/939RXprs4rNODTg\nUI5MHN18B0CQUxBTHabqva5nn57R/ZfuEnG3427HaVOtjeJ4taBm6YWl+B7zFY/VK12PHb136I1W\nlZxAb6verOi4QtEJZKuzWX1lNaMPjZYcH9toLHNazVFM7j75+IRJxyfJducBjgFMajZJRjYQBIHf\n7v+G7zFfWXXvwjYL8bH3kbF6UjNSWXZhGdNPS9lv+QzyMc9pHqPtR8ve58H7B8w7O4/I+EjJ8fyG\n+ZnlOIvR9qMl36cgCEQnRjM/dr6EwgsaaMXPwQ8fex/JfcvMzmTbzW0Enw+WKLVqbVyjcYxpNEby\nm0lJT2HV5VWExoXKPj9oIDQfex/JRuXN5zesv7aeVZdXyXSNQKNt5GXnRccaHUVH8/TjUzbEb2Dw\n3sE8/fRUNqdB2QZ42nrSz7ofRQsUBTROc2P8Rhqubaj4PmUKl8HDxoPB9QaLObZHHx6x9eZW2dgf\ntf9Yh/Aq9VWOu8rv7fCDw3lKhoJmhxUQE0BPy555kix++/kt3bd3x768PUPqD8l1/L2ke9RbVQ9D\nlSGvJ77OdfzVl1dpsEZD80v1S81VuVOXeZTb7hs0mHfDtZrwdW6ruUxrMS3H8bpQj0VxC66NuJYj\nu0k3rwBwxv0MzSs31zv+3Zd3tNjQQsRoh9kOI7xTuN78xdfMr3js8xA3CSpUXBp2SS81Ui2oWRi7\nUIycQMNQCW4XrJeVdCDhAF1/+aYeWsCoAAf7HdQbpd1Puo/7PncJ37+XVS9WdFyhKGb2OeMzKdaP\n9wAAIABJREFUAdEBEmgLYFn7ZYxuOFr22bVJ2lGHRknoj7VK1GJFxxWKks2Pkx8z7dQ02cLhYunC\n/NbzZUn1zOxMIuMj8T/lz7svUqn2iU0mMsVhiozpdPfdXeadnceWG1skxwsbF2aW4yy87Lwkz29m\ndiabrm9ifux8WbLZ3MycqQ5TZfh/YnIiQUeCFPMMtmVtNaJvVr1FqE4tqDn+53G8D3tL+k5rrVnF\nZvjY+9DTsqc452PaRzbf2Iz7Xnf+ePuHbE6Lyi3wsvOiR60e4rXdT7rPumvr8NjnoRgtNK/UHE9b\nT3pZ9aJQvkJiYjkyPpJRv41CUBCMblS+Ee427vSt3RezgmakZ6Xz2/3f2HJjC7VW5K3f9Y/Yf6xD\ngLxrGF16rqEiruu6Lk/jtUVQO3vvzHWsIAiiTtGFoRdyGa35UdZcrvHqmTMycx2vlYE2yWdCil9K\nrkyikQdHsvrKagoaFeTj1I85Jkyz1dnYR9iLu6rkKcniLkXJPqZ9pOqyqnxI+wDAn2P+zDG5+vTj\nU6ouq0q2kA1oekfkpC3/PZQU7BzMhCb6Cwivv7pOw7UNyVRr7qOLpQubemzS65ziX8XTelNrMTFa\nrkg5jg08pjeBffP1TXps7yHCTgCrO69mmO0wxe/hR53Ai5QXjD0yVpKrMCtgxoZuG+hWq5tsfHJa\nMoHRgbKFcLjtcOY6zZVFGxnZGYRfCsf3mK/4HYCmuCvYORgXSxcZXXJ/wn6mnZomWwTd6roxy3GW\njP77x5s/mHNmjixiNytgxizHWQxrMEzyfXz4+oGQmBDmx86XyHUDtKrSiqkOUyX4f7Y6m/0J+wk+\nH6yYWB3RYATjGo+jVolvi+P9pPtMPDaR0LhQ2fjSJqXxsfdhWINh4nfyNfMr2//YjsMGB0mNh9Zs\ny9riZeeFq7WrCLneeH2DiKsRDNk3RPY5QCNf7mnrSdeaXTE2NOZL5hd239lNZHykiFZ8b72teuNu\n446zuTNGBkbcS7rHlhtbWHhuoSjnrmsqVLjVc8OtrhutqrRCLagxDsg7nV7J/iMdQrZa83DnlXLa\naZtGwz8vwmevU18TnRhNaPvQPOUBtDvxNxPf5LpYv0x5SZVlVQBQz1TnOn78kfEsvbiUbjW7sdd1\nb45jP6V/ougCzWK+sM1CJjebnOP4fXf30X27JmLKrfGMWlDT99e+4sKVW7OYFykvqB5aXfyhHB5w\nOEcxQN1KZID9rvvpUrOL4lhBEFjy+xImn/j2+Tb32MzAugMVx6dlpeFzyEeCP4c4hzCu8TjF+//m\n8xvc97qLctYAvk18mec0T1Ge+UedQPyreDz3e0ogKtuytqztslaRdXPh2QVGHxotgUJyahxz/ul5\nfI/5SuAp0DB0/Jr7ySLeC88uMO3UNE49OiU57mzuzNxWc2VkgvhX8cw5M0eE8bRWyqQUsxxnMaT+\nEIkg272keyw6t4h11+SbscH1BjO52WSJVPiLlBf4n/Qn5EKIjMJpVdKKCY0nMKDuAPE9PqV/IjI+\nki5RXWT9CUCTwPW29xbvbWZ2JvsS9tFvVz/ZZwZNxKst9CpWsBiCIHDx+UVZrwtd61GrB8Nsh+Fs\n7oyhgSHPPj1j0/VN+J/0l1COtVa8YHHcbdwZXG8wdUrX4WvmV/Yn7Gfzjc3iWvW9NSjbgIF1B+Jq\n7Uppk9JcfXmVqFtRRN2KUmRJ/av2H0k71TZeyQsVVBAEDGYbMKfVnDxV/v4IzVSrv5+XZiTvvryj\n5GLNDi57Znauzqb+6vrEv4pnUZtFTGo2Kcexh+4fEh+k3Iq4UjNSKb6oOBnZGVgUt+CW160cowjd\nhjEzWsxgdqvZese+Tn2N5QpLMYLIaWEHTbMg3SrfnFhGSV+S6BLVRVzoKhetTLR7tN5K29/u/Ubn\nqG/qsA6VHNjZe6ciOyYtK42pJ6ay7OIy8Vhni86s67pOcVF/8P4B7nvdJTtWF0sXVnRcoVjXcSDh\ngAxGcLF0YVn7ZTJZgozsDJZdWCZxeNrrCXEOkcE6SV+SmHNmjuTaQcO2Wdx2sQwuvJ90n5nRMyUd\n2QBsytgQ5BRE++rtJU7m0vNLzD4zW8ayqWBagZktZjLYZrAIs+ni/99z5PMZ5GOqw1QJ/i8IAkce\nHCHkQohMEgM0OP6ExhPEZ0ItqDny4AhhcWEy9hNoYBwfex+61exGPsN8qAU1x/48RvjlcFkPBoDy\nRcoz0m4kQ+sPpWyRsiJrJ+JqhGJtAGj6RgyzHSY2wbn4/CKR8ZFExkcqMorql6mPu407/ev0p0Sh\nEtx6c4stN7aw+cZmCS1Za/kN8+NW1w23em44VHLg2adn/HLrF6JuRcmaAWmtYbmG9LPuR5/afahQ\ntMJ/iXb6H+kQvA56serKqh9atLNmZOXKob/w7AJN1jXJU1exx8mPqbKsCj0te7Krz64cx+ry+TNn\nZObYZS1LnUW+OZoFOi8U0XZb2nHsz2NYl7LmxsgbOUYdSy8sZfxRDU01t894/ul5mq7XyEs4VHLg\n1KBTeh3Huy/vqBteV5RG+LX3r7hYuSiOVQtqJhydIC5glYtW5vzQ87LiIq0d//M4zlucxf/HNRrH\nEuclit/l289vcd3lKtn57e6zmx6WPWRjBUEg/HK4JHFrUdyCXX12KRYTPnj/AI99HsQ+iRWP9bTs\nycqOK2VOIFudzYpLKxh7RKpD5dvEl4CWATKm14P3Dxh/dLxs0V3UZhFjGo2RRCZqQU3UzSh8j/ny\n+vO3/FOhfIUIcQ5hqO1QyfP15vMb5p+dL4OZyhYuyzynebjVc5OMP//0PIExgbKCrqo/VWWW4yz6\n1+kvPgfaZO782PkytowS/v/281vCL4cTfD5YbFCl+/q+TXwZbDNYvD93391ledxyRUpquSLlGGM/\nhqG2QylRqASCIBD7JJbwy+GKi3nR/EUZaTeS4Q2GU82sGlnqLI48OELE1QjFamIVKjxtPRlmOwy7\ncnakZ6ezP2E/kfGRkuhR17rV7IaHjQcda3QkPTudPXf2sPnGZr0FZE0qNGFg3YH0qd0HY0Nj9tzZ\nQ9StKNm911rlopXpZ92PfnX6UbloZfYn7GfH7R3S5yaAv59DKL6oOO+/vs+TQ/iRHX9ex2qL4vIy\n9nPGZwrP1zzgadPScuwKpRtF3Pe5ryjCpTVd/aKcIBPQQFXlQjQFRz1q9WBXn116HYeurAXkLED3\n4esHbNfYilWuOUFPXzK/0GN7D5E50s68Hbv77lbE+7PUWYw/Ml5CZ9SXfxAEgZWXVkooloPqDSK8\nU7jia594eIKOWzuKeQfQD4H9iBNIzUhl5umZsu5WyzssZ6TdSIkDEwSBbTe3MerQKMnCaFvWluUd\nlsuE9O68vcOUE1NkcgZDbIYwu9VsSZTxOeMzoRdDJQlz0NA6g5yC8Lb3liR1zzw+Q2BMoAw+sShu\nwcwWM+lr3Vd0GO+/vmd53HIWxC7IFf/XVgsHnw9W5PD3qd2HCY0niDUkyWnJbLi2gbC4MFmVMIBn\nfU+87b3FaOHay2uEXw5XlKIwVBky0m4kIxqMoE7pOqRnpbMvYR8RVyMUF2eTfCZ42noytP5Q6pSu\nw6vUV2y+vpkN8RsUi8+KGBfBw8YDj/oe1Ctdj2uvrrHlxha23Ngi66mgHT+w7kDc6rphV86Ok49O\nEnUrim03t4nqCbpWwKiAZuG37kfD8g05fP8wO27vkPV8/v5+9rbqTe/avf9+DkEVqKJZxWbEDonV\nOwa+9RLOS4P08EvhjDo0Kk8KnOWCy/Ey9SWfpn7KUfxMV4E0N52h66+ui8qoub2ubqvINxPf5Mit\n10ZTkLOTyVJn0XpTa5HjnFME8THtI40iGom7wg3dNuBu46449mXKSxpFNBIpd+Mbj2eJ8xJFyOzh\nh4c039BcDKWbV2rOXte9irmfhHcJtN/aXnRGJvlMODX4lGJDn4R3CbjscJEkSpe2W4pPIx/Zdfz5\n/k/c97nnyQk8+/QMn8M+kh9qyUIl2dBtg6z39Lsv75h2cpqMfz+u0ThmtZwlwfY/Z3wm5HwIM6Nn\nSsZal7Im2DkYZ/NvEVOWOouN8RuZdmqaJGIAjbTDVIepEojm1KNTBMYESlpggkbxdJbjLFysXMR7\n8iP4f3JaMmuvaETfvr+OMoXL4NvEF09bT34q8BPZ6mwO3T9EaFyoIlTUskpLxtiPoUvNLmJyddXl\nVay6vEoxgetu464p9CrXUFPde3snEVcj9Fb3DrMdxpD6Q6hmVo2rL6+yIX4DkfGRMpE7gDql6oiQ\nT0Gjguy6s4vNNzbLqq+11qJyC9zquuFi6cKj5EdE3dTg/M9TlLsFd6rRiX7W/WhdrTUxiTHsuL1D\nlp/RtZ6WPelj1QfLkpYcfXCU3Xd3Swob/8cjBJVKVQHYBJQG1MBaQRBCVSqVGbAdqIymzXMfQRA+\n/jXHDxgCZAFjBUE49tdxW6Qd08bpec9cHUJo+1B8GvnoHQPgfcibFZdW5HnH37RiU84NkT9EurYw\ndiFTT07NlTqpC/3k1o94xx876PtrX1SoyJqZpTe/oBbU1AirwcMPD3NNNN94fYN6qzS7KX8Hf+a1\nnqd37OyY2cyKngVoaI5jGo1RHJeakUqz9c248VpTub2682qGNxiuODb+VTz1V9cX/89prK6+EsDP\n7X5mbKOxsigmMzuTqSemEnIhRDw2y3EWM1rMkEFISV+SGHZgGHvu7hGPjbIbxWLnxTLH/PDDQ9z3\nuksWyR61erCy00rZ5uDKiysM3T9U0su4YbmGrO2yVpb/iE6MZtRvoyS7zDKFy7Cy40q61+ou+XxH\nHxxlwrEJsiraua3mMr7JePGaBUHg0P1D+J/yF78HrfWv058AxwAxzyAIAkf/PEpgTKB00UCTM5jl\nOIuuNbtioDJAEAROJ55mQeyCPOH/F55dIPh8sIQhpbWuNbsyofEEWlRugUql4vbb24RdDJN1UQNN\nLkIL/RQrWIynH5+y9upawi+Hy6iuoEnYe9l50apKKz6mf2TbzW1EXI3QW907zHYY7jbulDQpycF7\nB4mMj1QUjgPN4uxu405ni85cf3WdzTc2s+XGFj6mf5SNLV6wOAPrDmRg3YGUNinN9j+2E3UrSrEO\nAsCunB39rPvRxaILN17fYMftHYqCeVrrXqs7va16U8G0Asf/PM6uO7sUoxXttbhYuuBYxZEBdQf8\njzuEMkAZQRDiVSpVYeAK0A3wAJIEQVikUqmmAGaCIExVqVRWwFagIVABOAHUEARBUKlUFwFvQRAu\nqVSqQ8AyQRBkgFlODkFbrZsXDX5VoIq6petyfeT1HMe5/urK9j+2kzE9I8cE6603t6gTXifXQi9d\nSOndpHc5tiucemIqC88tpJ15O44M1N9RNOFdgsg7PjbwGG3N2yqOUwtqWka2FBe3nN7/yIMjdNiq\nkXDoadmTnb13KjqjL5lfaLWxlUjJy0m1dH/Cfrr98o0yqa+i+nPGZ/rv7i8m+woaFeSi50XFqvCY\nxBhabmwp/l+nVB3299svSyhnZGcw6/QsFpxbIB5rU60NG7tvlD0rDz88xGOfh6Tqs3ut7oR3Cpc5\ngb139+K+112yMPSp3Yef2/0sed2vmV9Z8vsS2c6+T+0+LG67WFJV/OzTM2acniEr2OpWsxsL2iyQ\n0Cjjnscx7dQ02W66TbU2zG01V4RdBEHg4L2DBMYESlhMoHFasxxn0bFGR1QqFRnZGWy7uY0FsQty\nxf9TM1LZcG0DIRdCJCJ4oNGrmtB4AiPtRlLSpCQfvn5g/bX1hMaF8uTjE7634bbD8bb3pk7pOrz9\n/JYN8Rpd/+9fFzRtarWFXu+/vmfT9U2svbpWsZDMqqQVw2yHMbDuQARBYOvNrUTGR0oct9YKGhXE\n3cYddxt3qherzo4/drD5xmYJU0zXWldtjVtdN9pUayPCPUpJbdBQevtZ96N7re68SHnBzts72fnH\nTgntV9c6W3TGxdIFswJmnHh4gt13dysmm0Gjy9SzVk8silvwPOU5+xP2yx1hwP9yDkGlUu0Flv/1\n5ygIwuu/nEa0IAi1VCrVVDRdexb+Nf6w5jJ5DJwSBMHqr+Ouf82XEWxzcgjahi657fq1Sd/csHht\nwjc3Bk1Gdgb552rw/5zeW8tqAngx4YXeZClAk3VNuPDsQq7FYHNi5oiLTE6FabrNbtZ3XY9HfQ/F\ncYnJiVRdpqnaNM1vSuLYREWphbSsNJw3O4vOZUnbJfg29ZWNAwj+PVjUwFeh4vbo25JFTWvfK5oO\nqDOAiK4Rst6xn9I/4b7XXbLDV4KmBEEgMj6SIfu/FQRWNK3IXte9MhpnXp1AljqLsIthTDgmrYGY\n0mwKM1rMkNx/fY1jlndYzgi7ESL+npmdScTVCCYcmyBphlK2cFmCnYPpa91XdMYP3j8gIDpAVkBW\np1Qd5jnNo7NFZ1QqFWpBzZ47e5h9ZrYsWmhasSmzHGeJmH5u+L+fgx9tqrVBpVJx/dV1gs8HK+rj\ntK3WFt8mvjibO6MW1By8d5DQuFBFCqdTVSfG2I+hs0VnPmd+ZsuNLYRfDlfsB+BQyQEvOy9cLF3E\niuC1V9cqQi125ewYZjsMV2tXEpMTRZaPlt2mazWL18Tdxp0BdQbw+ONjNl/fzOYbmxWhpzKFy+BW\n1w1Xa1fefn4r0jq/p7+Chg3Ur04/etbqSZY6i913d7Pjjx2KY0FTxdzZojMFjQoS/TiaXbd3KUJU\noGEmdajegSL5i5CQlMDeu3tJTktWHFu2cFnaVW+HqbEpKRkpbOi+4X/PIahUqipANGANPBUEwUzn\n3HtBEIqpVKow4LwgCNv+Oh4BHELjEOYLguD813EHYLIgCDK+Zk4OYci+IWyI35CrQ2gc0ZiLzy/m\nOq7k4pK8+/Iu13HahHP69HS91ay6ziAn6YdsdTZGczQLxcF+B2V4s9Z0cxBjG41laXt5ZSZodqYV\nfq7A+6/vKVekHA/HPFRMXn/N/IrtGluxIvT6yOuKTd7Ts9LpEtVFhA7mOc3Dv7m/bFyWOouRB0eK\nGHPtkrWJdo+WVa8KgsDcM3MlO2d93cS+F6/rWrMrG7tvlPHnzz4+S6dtncSGNgC7+uyip2VPybi8\nOoFP6Z+Yfmo6YXFhkvnhncIZ3mC4uFjraxzTrGIzwjqEUb/sN5js0vNLTDw+UaY9M77xeKY1nyZG\nbm8/v2VB7AIJFAYabn+QUxCDbQZjZGBEtjqbnbd3EhgTKKvqdazsyCzHWaJmTl7x/7SsNLbc2ELw\n+WDZa+Y3zI9vE19G24+mXJFy3Hpzi7CLYbI8CGjgGR97H7EOYccfOwi/HC4R4dNa/TL1GWk3kn7W\n/XiV+oqIqxGsvbpWcUF3rOyIp60n3Wp2I+ZxDBviN+jF2NtWa4u7jTvNKjZjX8I+ttzYorejWIfq\nHRhYdyCVi1ZmX8I+om5F8eyTsiBlxxodcbF0oVC+Qhx5cISdt3fqbW7vbO5M22ptMVQZEv04WpHu\nqrWWVVpiV9YOlUrFpReX9OYlQNNDoXqx6hioDLifdF/xvgL/exHCX3BRNDBHEIR9Wgegcz5JEITi\n/10OYdasWeL/LVu2pGXLlgCYBJnwJfNLnhZwDxsP1ndbr3fMH2/+wDrcOsdFGb4ViOWmxmk42xC1\noM5RkvvD1w8UW6S5bTnp/WspsABXh1+VLDS6tubKGrERSk5MHJ/DPiJ9Tx8rKTM7kx7be/Db/d8A\nmNliJoGt5J2oPqV/ov2W9mJNQC+rXmzpsUXmhF6nvqb91vYif7pm8ZqcGHRC1sDkyccndI3qKgnv\nTw06JRNUe/jhIX129pHAIQtaL2Bi04mSHMKjD4/w2OdBzOMY8Vi3mt0I7xQuidiefHyC9yFvCaZc\npnAZNnTbICmky2vjmA9fPxB0NkgmP9GsYjOWOC8RdZu+ZH4h7GIY005Nk0EJQU5B+DTyobBxYbLU\nWUTdjGL2mdmyoqs21dows8VMmlduniv+7+fgh7e9NyVNSnL33V1+Pv+z4qLuUMkB3ya+dLHoQnJa\nMuuurSMsLkxxoRzZYCTe9t5YFLdgf8J+wi+Hc/LRSdm4GsVqMNJuJIPrDebZp2esvbqWiKsRipz9\nDtU74GnrSbOKzdh1Zxcb4jfImu6AhkXkYePBoHqDyMjOYMvNLWy+vlkRlqloWlFTyVu1FddfXSfq\nVpQMTtNag7IN6GXVixKFSnD+6Xl23N4h9tn43pyqOtG0goaWffbJWcmz9r11tuhMGZMyZAlZxCTG\nKLKoQOOAm1dujrGhMZnZmZx5fEbxPoGGuWRezJzP9z7zMeEjb1L/Ej6M+V9wCCqVygg4CBwWBGHZ\nX8fuAC11IKPTgiBYKkBGR4BZaBzCaUEQLP86/i9BRqpAFU5VnTg5SP7wae3kw5O02dwmV92fvNBM\ntd27coN1tJFGTrLPWgcEOctEeO73ZN21dRQxLsL7Ke8V6xbefn4rSmY4mztzZMARRSqproLnyAYj\nWdlppWxcljoL119d2XVHU08xpdkU5reeLxv3OPkxNqttxPB1RosZBLYMlI07eO8gXaK+FaT5Ofgx\n12muJD+hFtTMOzNPEjWMazSORW0XSfI4H9M+MurQKLbd/NbUyMPGg9AOoRI+f16dQNzzOIbuHyqB\nLZpUaMLqzqsl31teGscIgsDO2zvxPeYrWTTzGeQj2DmYEXYjMDY0FvsJ+J/yl+HDY+zH4N/cn9KF\nS5OZncmWG1sIjAmUiZp1qN6BmY4zaVyhca74v5+DHwPrDsRAZcCOP3YQfD5YMek6vvF4xjQaQwXT\nCuy7u4+wuDDFha1ttbb42PvQoUYHTjw8warLqxS5+2ULl8XLzouhtkN5nPyYtVfXsiF+g2wcaArz\nPG09qWBagU3XNxEZH6lI2axmVg0PGw9aV23Nuafn2HJji2JeADTfd7ea3chSZ7Hn7h699QIVTCvQ\nx6oP5YqUIyEpgZ23d+qFZFpWaUmdUnU0dQ5PY/UWh5nkM6FllZYUMCrA58zPHH1wVFGbSPuZShYq\niaGBIa9SX/Hww0PFcaDJSRiqDDFQGUgkVJSsgFEB0qan/a84hE3AO0EQJugcWwi8FwRhoZ6kciOg\nPHCcb0nlC8AY4BLwGxAqCIIsO5ObQ8iJsQKQb04+stRZOS70WlXMnCp7tTUExQsW591kOeNBaxZh\nFtx/f5+LnhcVaY+g6eWsbd+pr1JZt4AtJ7x+4rGJBJ8PBuD2qNtYlrSUjdF1PtWLVef6yOsydk22\nOpvBeweLWPW4RuMIaRciW+B1oxWALT22MKDuAMmYjOwMRv02SgJRnPU4i0MlB8m4Ky+u4LTJSeTf\nVypaiSMDjkg+Q5Y6i6CzQSLzCTS77G0u2ySJ2UcfHjFk/xBJqN21ZldWdVolOgFBENh1Zxfue90l\nmG0/636EtAsRYaPUjFTmnZknSUiDpqZhQesF4uvdS7rH1BNTJbkN0Gj9zHWaS6WilcQKXP9T/rIF\nxNXalcCWgVgUtyA9K53I+Ehmn5ktcxRda3ZlZouZNCjXQMT/58fOlzVjd6rqhJ+DH62rtiYxOZGl\nF5YqavjYlbNjQuMJ9LLqxe23twm9GMr6eHn0XM2sGj72PrjbuHPrzS1WXV6lqKJZxLiIpqNXg2Ek\nJicScTVCrwKxW103PGw8SMtKY+P1jXrHtazSEre6bpjmN2XP3T2STYCumZuZ08+6H2WLlOXSi0tE\n3YxS3EkbGxrTp3Yfyhcpz7sv79hzd4+sXafWmlZsKpIUzj89r3cXX7ZwWSyKW2BoYMjbz2+5+eam\n4jjQREfayPV7OE7XTPKZYGRghKGBIZ/SPynWJuTZAv7naafNgDPATUD4688fiAN2ABXR7P77CIKQ\n/NccP2AokImUdtoAKe1UWsr57T0VHYK2ruDtpLc59pM1nG3I8g7LGW0/WnEMaByLuZk5D8bI9U90\nxwA5VjlrcxXRg6NxrOKoOGbGqRnMPTuXVlVacWqwPPkGUjVNffLWuuJv4xqN4+f2P8vGfEz7iHmo\nuSiToOTw1IKaYfuHiQuCPunq7/sDnBtyjqYVm0rGJLxLoOn6puIPrW21tuzsvVMS/XzN/MqoQ6Mk\njJrQ9qF423tL3vP79ytZqCT7++2XSGTnxQlkqbNYemEpk45LJT/8HfyZ3mK6KOt89eVVfA77SNgl\nxobGrOy4Eo/6HhioDPia+ZVlF5fhd9JP8lq1StQi2DmYDtU7oFKpuPLiCtNPT5exT5yqOjG31Vya\nVGxCWlYaEVcjCIwJlFEqe1n1Ynrz6dQrU4+Edwks/n2xIv7vbuPO5KaTsShuwd67ewm5EKLIjhnZ\nYKTYXGbtlbWExoXKmsIDjG44Gm97b9Ky0lh1eZVi/wAVKrzsvPC09eTpp6dEXI1QpG4aqgzxtPWk\nt1VvHrx/wMbrG2WaSlobXG8wTlWdePrxKVtvbtVLqexl1Yu6peryIe0Du+7sUmQugSZKrmRaiQx1\nBkceHJH0jtA1u3J2lChUAhUqLj6/qNdBVPmpCqb5TTFUGfIo+ZHeCKJQvkKY5DPB0MCQlPQUvYli\nralQ6Y0c/lss4G9UqawVZMtp5x9yPgTfY745iseNPTyW0LjQHIvFtFTUnBQ9229pz9E/j+Yo3tYy\nsiUxj2P04vGCINB6U2tOJ56mfpn6XBl+RXbdgiDQaVsnMQRW6oUsCAKuu1xFbvOBfgfobNFZNmb0\nodGEXw4HNNBLRNcISbQiCAKzY2YTEBMAaHaC10delzmo73WIVnZciVdDKfr3PQW1ZZWWbO+1XaIP\ndPHZRbpEdZHABd832UlMTmTIviFiD2SALhZdWNV5lUj7/Jj2Ef+T/rI+v2u7rGVI/SEYqAzIUmcR\nfimcMUekdRZtqrVhabulourpyYcn8T3mK4MmAhwD8G3qS2Hjwjz68IiAmABZXsGqpBVBTkF0rdmV\nr1lfWX15NYExgTIuez/rfkxvMR3LEpacTjzN/Nj5MqaSsaGxiP+nZ6UTFhdG8Plg2Q4USwF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jIqmFbg4YeHippAtUvWxtjQmISkBEUVTNP8phgZGOmtiv3H/rH/Ngv4GzmEKJcoXK1dJce1EYBS\nYZeWnqmUhNUmoZUK1LSCdkrNdXSbymTOyJTs/HX1ir6Xv975x076/NoHkDd+1+oqgSaPsLrzajGZ\nu/vOblx2aArfyhQuw02vm2LNwqH7h+i07ZtK65EBR2hXvR0gTwJ7N/Tm5/Y/Y2RgRNzzOFptbCUu\nXtXMqnGo/yFqlqjJ7be36bm9p2TnuqLjCrzsvHj66SnDDgyT7HrbV2/Pms5rMDE2YfLxyZI6AwOV\nAZHdInGxcmHxucVi9bPW+tTuw8I2C4lOjMb3mK9kwSyavyjBzsFULFqRGadniM15tNbTsicD6gxg\n953dMq2dIsZF8Lb3JjM7k7C4MBn+37xSc6qaVSX+VbysjwBoxO4ARUaPWQEzChgVICUjRa8S5j/2\nj/1/s4C/gUPQFnql+KVIdtQA009NZ97ZeYrRgb7IQdt1TenctZfXsF1jS0XTijwZL92Fattn2pa1\n5crwb1RFQRBwjHTk7JOzNCzXkIueF8UFXRdaqle6HpeHXxadyO23t6m9UiOXUM2sGjdG3hBxfl3F\nULMCZtwZfUekwoZeDJXAN9rit2x1NpOOT5I0ej868CjO5s58zvjMyN9GSvDvlR1XMtJuJElfkxiy\nb4gkeTzGfgwL2y7k7ee3eB7wVHQCaVlpjPxtpIQ+am5mzvpu6ylesDhjjoyR8f6Xd1hO04pN8Tvp\nJ6tSHdFgBD0texIWFyZLJDtUcqCfdT9iHsfIksjFChajb+2+fEj7wC+3fuF7c6zsiLGhsWKD9XJF\nylEoXyGef3quKONgoDJAhUqv9ME/9o/9W1nA38AhaCmh+hZ9pcU77nkcjSIaKXL1tRXPX6d9lXTq\n0i7QJvlMSPWX7v66RnXlwL0DssT1s0/PqPhzRQC299pOn9p9xHMLYheIXHpd9s6n9E9UD60uSgno\ndnXTZRUVNCrIfZ/7lDctT7Y6m9GHRou0zJrFa3LW4ywlTUryOPkxjpGOIsukcYXGHOh3gBKFSkgi\nDNAkmH/p9QtFjIsw7dQ0kYEEmkrWyO6RZGZnypxAO/N2rO2ylkfJGplpXcne1lVbs6LjCs48PsOo\nQ6MkCU+HSg4EOQVx/OFxkXarNduytoxtNJaYxBhZIZdFcQt6W/Xm1ptbMgZOKZNSOFRy4HXqa1kj\ndSMDI2oWr0lGdoZiMVahfIUwUBn8s7v/x/5vWsDfwCF0ierCwXsHZQ5B2xNAibevLzqYcnwKi35f\nJGs6o0v/1J0jCAIF5xUkPTtdJrq28tJKRh/SKKomTU4SMX0tjRU0ukFbemxBpVIhCAL9d/cXd7G6\nlb+nHp2i9SZN72EDlQGPxj6iUtFKpKSn0Dmqs6gN1L1Wd6JcoihgVICtN7YycM+3RjeL2ixiYtOJ\nvP78GpcdLhIO+sF+B+lYoyMRVyMYfvCbPlPVn6qyp+8eihUsxrADwyS79nbm7VjTZQ3RidG473WX\nsFeG1h/KuMbjWHhuoYx14+fgR51SdZgVPUu2KPs5+JGelS7rDmaa35QetXrwIuWFbCdfyqQU5mbm\nvEh5IaNWatUmlXR6/rF/7G9nAX8Dh6AKVKFChXqWtJS949aOHH5wWLboa5U4vy9Su/jsIo3XNZYp\nj+ru8nUlK1IzUikyX0Pl1HUg2epsKvxcgVepr+hbuy+/9PoGU3js8xCTmo/GPhI11lddXoXXbxph\nOn8Hf+a1ngdoWkG2iGwhztdKXz/9+JQGaxqIC52fgx/znObxNesrg/YMEguf8hnkI25YHPVK1+Pn\nCz+LxWmgyUeEdgjl/NPzdNrWSQKJ7HPdh21ZWzz3e0qcgLO5M8s7LGfbzW0yzH+e0zysSlox7sg4\nycJczawak5tO5syTMzIN+84WnalStArr49fLEq5tq7UlJSNFVhVrmt+UYgWL8Tj58T8Uyn/sH/sR\nC/ibOARXa1eiXKT8blWgiu61urOn77dmJcJffY0blW/EBc9vC82XzC+YBJnIitN0dYF0mUi6EcOb\niW8oaVISkDae0e2BcOXFFezW2gEa8bMZjjOAb04INFBOjHsMxobGsqYz97zvUaN4DVkTeq1Ux9WX\nV7FbYycukH1q9yGyWyR/fvgT583Oogxw8YLFOTHoBCb5TOi9s7dEwGxJ2yX0rt2bEQdHSHT7nc2d\nmd96PqEXQ9l4faN4PJ9BPsI6hPHww0MW/b5Icu/71+lPJdNKLPp9kURzpoJpBRpXaMyVF1dkAmu1\nStRCLagVVSnzG+bX2y7wH/vH/rE8WsDfxCF8r7mjXTh1oRr4JpH9fU2CUrMbXe0g3eNayilAxvQM\nURRu2slpoq7NF/8vFMxXkGx1No3XNRZ58lppDd0Wl4AYreg6DvjW8UyXhQQanSKHSg4sPLdQoumz\nrec2XKxcmHB0gqQ4bH7r+QxvMByv37wkSddh/6+9c4+rqsz6+HcBXsELXgBTxFujhhhO6lSSzoui\nXaa0acjMvOY7ryPqm1am1YzYmOVkWmlNjRrZRS0qtd4pBUMnnUlwSkXFwmsoGl7wlnfkef/Y+2zO\nERgrkXM4re/ncz7s87CBZ/Gcc9bez1rrt3753zx808M8tOKhUk7gkZse4ek1T3u0TfxFw18wusto\nFm1Z5JFlUzOoJgNjBrL54OZSGT9drunCyfMnS3WFCqkeQlBAULnNRRRFqWCSr37HtPnAb4ACY0xH\neywUeBeIAvZgdUs7bn9vEjAcKMKzW9ov8eyW5pkH6vk3HYfgkqO+NAAc9lwYh04f8tguOnnuJHWf\nrcukuElM61kiSDZ4yWDeyn7LI3jrvh3k7jxctQnRjaPZMsrqu3vmwhlqT7PkINzlKdzF7FzxhaLi\nIvq83cfJrnEVd236bhOxr5UUmG0auYmYsBimrZnm5OvXrlab7JHZ1K1RlzsW3sH6/esBa0tm1ZBV\nfHP4G3q/3dv5HZ2v6UxqYirzv5rP1DUl+ko9onowvdd0pvxjikevgIRWCQyMGcifVv/JI4+/Z8ue\ndAjrUEotNDYilka1G5XS1GlYqyE1g2qWK1WsKIqXSL76DiEO+B54080hTAeOGGP+Uk4/5S5AM2Al\nJf2UM4HRxpj1IvIJ8KIxpszuGO4OwSXRcGmgN+CpgFKN75s834Tvvv/O41xX1o57S033D3jXlT6U\nyGMkdUlizu1zAPj828/p8Ya1LbT5D5vpENaBU+dP0fi5xpwpOkPr0NZsS9pGtcBqjkQGwMzeMxl3\n0ziP1FKwROViwmIYtmyYkz9/Q5MbWDl4Jev2rXNqHAAevflRJnSbwJClQzwE2Rbds4ii4iIGLSmR\n8G4S0oRX7niFv335Nw8n0LNlT7pFdvMoPgMrdfTCxQulqnTbNmzL/pP7S+nyK4pSBUiuhC0jEYkC\nPnZzCF8DPYwxBSISAaw2xrQTkYmAMcZMt8/71Joi3wIZxpjr7PH77J8v3f4LT4eQ8FYCK3et9PiQ\nf33D6zz40YMe2zyuQjP3zB3XllD7Ru3JSbKkIM5fPE+NqZYc9ImJJ6hTow7GGOpPr8+Jcyc85LVd\n20/hweHkj88nMCDQowbA1UQ+fWe6c+V+V9u7WNJ/CTsKd9B2TltnzpkjMrm2wbX0fLOnU+06qOMg\nXvvNazyc9rDT1hKs2MSOwh0e6qT3Rt/L8NjhDPhgAEfPHnXGZ/WZRfqudA+HEdc8jlahrUpV3Xa5\npgs7Cnd4/DxYWzuahqkofkCydxxCoTGmgdv3C40xDURkNvCFMWahPT4P+ATLITxjjOltj8cBE4wx\nd5Xz9xyHIFOEWkG1OP1ESYZKWSml/2nMlTnk3tbSJXTnCjZDSa1A4ZlCGv7FkqNw3VkcOHmAa2Za\nGUt92/ZlSf8l5B3PcwTogqsFkzcuj2NnjznBaLCcRkRIBDF/jXH0dqbFTyMxOpG41+MoOFUAWAJv\nMxJmMPDDgU6VcFBAEAv6LWB21myPTJyRN4xk97HdHtlBHcM7Uq9GvVIdriJCIspsrq4oih+SfGUO\nIejyp/wgKjwynZycbB2shu69StIyXR+qf7+/pNuUSzE0d3RJ9oorp3//+P2ICMWm2HEGBY8UEFor\n1ENR9MDDB4gIieD9nPdJTLVUS/MeyiOyXiRJf09ytHVyR+cSWS+Sjq92ZMtBK8aw4X82EFoz1HEi\nABmDMwgMCOSWlJKiuNTEVI6fPc6Ij0c4TWxm9ZnFwVMHeWbtM06wOalLEkfPHmXh5oUM/HAgAPEt\n4zl/8Txr89by6pevAtY2UWBAIPtO7CtTggFQZ6Ao/sxurChuBfFTHUKBiIS7bRm5hNbzgUi385rZ\nY+WNl4vLIUyZMoWx/cc644+kWdLH7m0lE1MTaVG/Bdc2vBaARZsXkbE7g8X3LKZJnSYYYxyZ6r3j\n9hIWHObRvP7ck+eoFlCNG+fdSGZ+JjdH3szaYWvZemgrzadYBWYTu03kmV7PMG75OEc//42+b9Cr\nVS9av9TaSZlc8cAKDpw84KEjlDE4g+e/eN5xNHWq12FWn1mM+mSUoz7aOrQ13aO6k7Ixxckeiqwb\nSZ0adcg5lFNms3VXqqmiKD9TWtoPF/8o78Qfxg91CGI/XHwEDAWmA0OAZW7j74jILKAp0AbIsoPK\nx0WkK7AeGAy8xGVwXd32atXLGXt5/cseUtPjV4wHYMsfrKv1/Sf3c/+H95PQKoH+HfoDEPCUVVuw\nY8wOmtVtxpysOYz5dAxtGrRh+5jt5B3PI+oFS4jug3s/oF+7fsS/Ge+0qjz86GEydmc4W1APdnqQ\nqfFTaTunraPb//GAj/ln3j/p87YlLhcWHMbLt79MYmqi4xzuaX8PxaaYJV8vceIDCa0SSN+Vzs6j\nO9l5dCdgtYI8dvYYe0/svdy/SFEUpcK4rEMQkYXAr4GGIpIHTAaeBVJFZDhWfOBeAGNMjoi8B+QA\nF4BRbgUFSXimnS7nMnyQY1XjulJCvz1mVcd+cK81frboLLPWzWJ47HCCqwdjjKHpTKvJTNogS4fH\nlVq6ddRWWjdo7QSKXSqnszNnM3a5dQdSOKGQf+//t3M3Me/OedwceTONnrPURVvUb8GqIavoOrer\no+q56J5FLN6y2BGi6x7Vnc5NOjNz3UznjmBAhwEs2rLIqS5uWKsh35//nnMXz5UpuKZ5+4qieAOf\nLkzrntKdNXlrnECxa0vH9fz6V68nuyDbCRq72mi6isOiXogi73geX/7+SzpFdCLi+QgOnjrIvDvn\nMTR2KOEzwjly5ggPdHyAuXfOJXJWJIdPHyYiJILskdl0+GsHp+1g5ohMfvfe75yr9tm3zealzJcc\nrZ5+7fqReyTXaWwTWjOUkOohepWvKErlkXxlQeWAy5/iPdbkraFejXrO88z8TIbHDgcg73ge2QXZ\npPRNQUSYnTmb3CO5pD2QRv2a9Yl9NZa843msHbaW9o3aE/CU1VP2X8P/RdemXQn6cxBHzhzh86Gf\n0yOqB7WersXh04dJH5ROfMt4wmaEcfDUQRb0W0CbBm341bxfsffEXh7rZjV5H/PpGLYXbie+pbUd\ntPTrpeQcyqFGoJXSevTsUXUGiqJUKSoqy+iq0T/aigN8tssqoHrpNiv04NrzHxo7lNwjuYxdPpah\nsUNJaJ1A/IJ4NhVsIn1QOs3rNXeK0PLH5/PiuhcdXZ694/Y6onY9W/Yk8bpER7IiqUsSq/esZshS\nqyYh8bpEUnNSnX694cHhFJwqKBXsVT0eRVGqKr7vEOzAsKtHQHD1YMc5fPX7rygqLnIKwFL6pnD3\nu3ezas8qlt23jJpBNR0Z6sIJhY5u0RO3PMH5i+cdZ5DSN4Vhy4bx2e7PiG4cjYg4mT7RjaPZemgr\nqTmpHvNy1Q8oiqL4Cz7rEFxaO92julNsiikqLmLObZacRK+3elErqBadmnRyispOP36aYcuGsfTr\npbzz23c4cPIAfRf3JbJuJAv6LXCcwZL+S7j7Xat95YAOA1i8ZTHDllmN5qPqRXk0mQdKPVcURfFX\nfNYhuIrNggKCmPmF1UxlVJdRPLvWEpbbN34fT2Y8yekLp8kakcWkzybxxsY3mBFeftIAAAXXSURB\nVHvnXFbuWknKxhSGXD+E0xdOE/9mPE3rNCUmPMZxBjFhMSzaYslpB0gAxaa4VPMVRVGUnxM+m2XU\ndW5XsvKzMJMNMkUIqR7CsceOEfTnIO649g6m/HoKned2ZlLcJAIlkKlrpjKz90xeyHyBvON5TP2v\nqY7QnCvtE6Bpnaaq0qkoin+S7BvSFRVOVn4W4cHhTuHX8oHLuX2hVZ38XuJ7BE8LpnpgdUJrhjJh\n5QQej3uc8WlWkdroLqMdZ1AtoJrjDAB1BoqiKOXgsw4BLIXPkf83EoC2jdqStjONGQkznLjBjIQZ\njF0+loExA53GNY1rN2bO+jnO77hQfKHyJ64oilIF8WmH0D+6P3EpcfSI6kHEjAgAth3eBliZQmOX\nj6VjeEenrwCgzdYVRVF+Ij5dmBZaKxSA8TeN56K5yB+7/5H5G+ZzS/NbeHqN1aS+PJVPRVEU5cfh\ns0Flkq3+vmU1ZFcURVHKINmPpStyj+RSt0Zdb09DURTlZ4FPOwTAyTJSFEVRri4+7xAURVGUykEd\ngqIoigJ4wSGIyK0i8rWI5IrIY5X99xVFUZSyqVSHICIBwBygDxANDBCRdpU5B59gt7cncBXxZ9tA\n7avq+Lt9V0hl3yF0BbYbY741xlwAFgN9K3kO3mePtydwFdnj7QlcZfZ4ewJXmT3ensBVZo+3J+Db\nVLZDaAq4txHbZ48piqIoXkaDyoqiKApQyZXKInIjkGyMudV+PhEwxpjpl5zne+XTiqIoVYArqVSu\nbIcQCHwD9AQOAFnAAGPMtkqbhKIoilImlap2aoy5KCKjgTSs7ar56gwURVF8A58Ut1MURVEqH58K\nKvtj0ZqI7BGRTSKyQUSy7LFQEUkTkW9EZIWI1PP2PH8oIjJfRApEJNttrFx7RGSSiGwXkW0i0ts7\ns/7hlGPfZBHZJyJf2Y9b3b5XZewTkWYikiEiW0Vks4iMtcf9Yv3KsG+MPe4v61dDRDLtz5LNIjLZ\nHq+49TPG+MQDyzntAKKAasBGoJ2351UBdu0CQi8Zmw5MsI8fA5719jx/hD1xQCyQfTl7gOuADVhb\nky3s9RVv2/AT7JsMjC/j3PZVyT4gAoi1j0Ow4nnt/GX9/oN9frF+9pxr218DgXVYtV0Vtn6+dIfg\nr0VrQuk7sb7AAvt4AdCvUmd0BRhj1gJHLxkuz567gMXGmCJjzB5gO9Y6+yzl2AfWOl5KX6qQfcaY\n74wxG+3j74FtQDP8ZP3Ksc9V51Tl1w/AGHPaPqyB9UFvqMD18yWH4K9FawZYISLrRWSEPRZujCkA\n60UMhHltdhVDWDn2XLqm+VTdNU0SkY0iMs/tlrzK2iciLbDuhNZR/uvRH+zLtIf8Yv1EJEBENgDf\nAenGmPVU4Pr5kkPwV7oZYzoDt2O9KG/BchLu+Ftk39/seQVobYyJxXojPu/l+VwRIhICvA/8r30l\n7VevxzLs85v1M8YUG2M6Yd3ZdRWRaCpw/XzJIeQDzd2eN7PHqjTGmAP210PAUqxbtgIRCQcQkQjg\noPdmWCGUZ08+EOl2XpVcU2PMIWNvygJzKbntrnL2iUgQ1oflW8aYZfaw36xfWfb50/q5MMacAFYD\nt1KB6+dLDmE90EZEokSkOnAf8JGX53RFiEht+2oFEQkGegObsewaap82BFhW5i/wXQTPPdny7PkI\nuE9EqotIS6ANVjGir+Nhn/0mc/FbYIt9XBXtex3IMca86DbmT+tXyj5/WT8RaeTa7hKRWkACVpyk\n4tbP21HzSyLot2JlBmwHJnp7PhVgT0usbKkNWI5goj3eAFhp25oG1Pf2XH+ETQuB/cA5IA8YBoSW\nZw8wCSu7YRvQ29vz/4n2vQlk22u5FGvPtsrZB3QDLrq9Jr+y33Plvh79xD5/Wb8Y26aNtj1P2OMV\ntn5amKYoiqIAvrVlpCiKongRdQiKoigKoA5BURRFsVGHoCiKogDqEBRFURQbdQiKoigKoA5BURRF\nsVGHoCiKogDw/+sRKxPuRLzkAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ff53fadc510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(salaire_net_actuel[::2],\n",
    "        rsa_actuel, salaire_net_reform,\n",
    "        rsa_reform)\n",
    "plt.xlim(0,300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000,)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}