{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "607f1384",
   "metadata": {},
   "source": [
    "# Clase 1 · Slide 18 — Ejercicio guiado\n",
    "## Resolver un problema completo de principio a fin\n",
    "\n",
    "> **Datos:** $y_1 = 100$, $y_2 = 120$, $r = 5\\%$, $\\beta = 0{,}95$, $\\sigma = 2$.\n",
    "> **Se pide:** $c_1^*$, $c_2^*$ y $s_1^*$.\n",
    "\n",
    "Un hogar vive dos períodos, tiene utilidad CRRA y accede libremente al mercado de capitales:\n",
    "\n",
    "$$\\max_{c_1,c_2}\\ \\ u(c_1)+\\beta\\,u(c_2),\\qquad u(c)=\\frac{c^{1-\\sigma}}{1-\\sigma}\n",
    "\\qquad\\text{s.a.}\\qquad c_1+\\frac{c_2}{1+r}=y_1+\\frac{y_2}{1+r}\\equiv W$$\n",
    "\n",
    "Este notebook lo resuelve por **tres caminos independientes** que deben coincidir:\n",
    "\n",
    "1. **Los 6 pasos de la slide** — la receta algebraica, paso a paso.\n",
    "2. **Optimización numérica** — plantear el problema tal cual y resolverlo con `scipy.optimize`\n",
    "   (`minimize_scalar` sustituyendo la restricción, y `minimize` con SLSQP tratando la\n",
    "   restricción presupuestaria explícitamente, con su multiplicador de Lagrange).\n",
    "3. **Raíz de la ecuación de Euler** — `brentq` sobre la condición de primer orden.\n",
    "\n",
    "Después se hace estática comparativa, se revisan los tres casos límite de la slide 16 y se\n",
    "cuantifica el error frecuente del examen (olvidar el exponente $1/\\sigma$).\n",
    "\n",
    "*Fuente: `Macroeconomia_II_USM_12_clases.pptx`, slide 18 (pág. 16) — Módulo I ·\n",
    "Microfundamentos: consumo, ahorro e inversión. Marcelo Villena, PhD.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7b060b12",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:50.323281Z",
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     "shell.execute_reply": "2026-08-04T13:09:51.391474Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y₁ = 100 · y₂ = 120 · r = 5% · β = 0.95 · σ = 2\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import minimize_scalar, minimize, brentq\n",
    "\n",
    "pd.set_option(\"display.float_format\", lambda v: f\"{v:,.5f}\")\n",
    "plt.rcParams.update({\n",
    "    \"figure.figsize\": (7.2, 4.6), \"figure.dpi\": 110, \"font.size\": 10,\n",
    "    \"axes.grid\": True, \"grid.alpha\": .3, \"axes.spines.top\": False, \"axes.spines.right\": False,\n",
    "})\n",
    "\n",
    "# ---------------- datos del ejercicio (slide 18)\n",
    "y1, y2 = 100.0, 120.0\n",
    "r      = 0.05\n",
    "beta   = 0.95\n",
    "sigma  = 2.0\n",
    "\n",
    "# ---------------- valores publicados en la slide, para verificar al final\n",
    "SLIDE = {\"W\": 214.29, \"Γ\": 0.99875, \"denominador\": 1.95119,\n",
    "         \"c₁*\": 109.82, \"c₂*\": 109.69, \"s₁*\": -9.82}\n",
    "\n",
    "print(f\"y₁ = {y1:.0f} · y₂ = {y2:.0f} · r = {r:.0%} · β = {beta} · σ = {sigma:.0f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8797d22",
   "metadata": {},
   "source": [
    "---\n",
    "## Camino 1 — Los seis pasos de la slide"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b55f0d3c",
   "metadata": {},
   "source": [
    "### Paso 1 · Calcular la riqueza\n",
    "\n",
    "Traiga el ingreso futuro a valor presente. La restricción presupuestaria intertemporal se\n",
    "obtiene combinando $s_1 = y_1 - c_1$ con $c_2 = y_2 + (1+r)s_1$:\n",
    "\n",
    "$$W = y_1 + \\frac{y_2}{1+r}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "326cf8dc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.397228Z",
     "iopub.status.busy": "2026-08-04T13:09:51.396799Z",
     "iopub.status.idle": "2026-08-04T13:09:51.401481Z",
     "shell.execute_reply": "2026-08-04T13:09:51.400412Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W = 100 + 120/1.05 = 100 + 114.29 = 214.2857\n"
     ]
    }
   ],
   "source": [
    "W = y1 + y2/(1+r)\n",
    "print(f\"W = {y1:.0f} + {y2:.0f}/{1+r:.2f} = {y1:.0f} + {y2/(1+r):.2f} = {W:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b593a2dd",
   "metadata": {},
   "source": [
    "### Paso 2 · Factor de crecimiento $\\Gamma$ desde la ecuación de Euler\n",
    "\n",
    "La condición de primer orden es $u'(c_1)=\\beta(1+r)\\,u'(c_2)$. Con CRRA, $u'(c)=c^{-\\sigma}$,\n",
    "de modo que $c_1^{-\\sigma}=\\beta(1+r)c_2^{-\\sigma}$ y por lo tanto\n",
    "\n",
    "$$\\Gamma \\equiv \\frac{c_2}{c_1}=\\big[\\beta(1+r)\\big]^{1/\\sigma}$$\n",
    "\n",
    "$\\Gamma$ fija la **inclinación** del perfil de consumo, no su nivel."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a23b6e88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.403665Z",
     "iopub.status.busy": "2026-08-04T13:09:51.403437Z",
     "iopub.status.idle": "2026-08-04T13:09:51.410134Z",
     "shell.execute_reply": "2026-08-04T13:09:51.408798Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "β(1+r) = 0.95×1.05 = 0.9975\n",
      "Γ = (0.9975)^(1/2) = 0.99875\n",
      "\n",
      "Γ < 1  ->  perfil DECRECIENTE. La tasa de preferencia por el presente\n",
      "ρ = 1/β − 1 = 5.2632% supera a r = 5.00%: el hogar es levemente impaciente.\n"
     ]
    }
   ],
   "source": [
    "Gamma = (beta*(1+r))**(1/sigma)\n",
    "print(f\"β(1+r) = {beta}×{1+r:.2f} = {beta*(1+r):.4f}\")\n",
    "print(f\"Γ = ({beta*(1+r):.4f})^(1/{sigma:.0f}) = {Gamma:.5f}\")\n",
    "print(f\"\\nΓ < 1  ->  perfil DECRECIENTE. La tasa de preferencia por el presente\")\n",
    "print(f\"ρ = 1/β − 1 = {1/beta-1:.4%} supera a r = {r:.2%}: el hogar es levemente impaciente.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84546379",
   "metadata": {},
   "source": [
    "### Paso 3 · Construir el denominador de la fórmula de $c_1$\n",
    "\n",
    "Sustituyendo $c_2=\\Gamma c_1$ en la restricción: $c_1\\big[1+\\frac{\\Gamma}{1+r}\\big]=W$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7400a58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.412431Z",
     "iopub.status.busy": "2026-08-04T13:09:51.412239Z",
     "iopub.status.idle": "2026-08-04T13:09:51.417677Z",
     "shell.execute_reply": "2026-08-04T13:09:51.416340Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 + Γ/(1+r) = 1 + 0.99875/1.05 = 1 + 0.95119 = 1.95119\n"
     ]
    }
   ],
   "source": [
    "denominador = 1 + Gamma/(1+r)\n",
    "print(f\"1 + Γ/(1+r) = 1 + {Gamma:.5f}/{1+r:.2f} = 1 + {Gamma/(1+r):.5f} = {denominador:.5f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b320bae3",
   "metadata": {},
   "source": [
    "### Paso 4 · Consumo presente y futuro\n",
    "\n",
    "$$c_1^*=\\frac{W}{1+\\Gamma/(1+r)},\\qquad c_2^*=\\Gamma\\,c_1^*$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "37e76eab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.420085Z",
     "iopub.status.busy": "2026-08-04T13:09:51.419860Z",
     "iopub.status.idle": "2026-08-04T13:09:51.424139Z",
     "shell.execute_reply": "2026-08-04T13:09:51.423204Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c₁* = 214.2857 / 1.95119 = 109.8231\n",
      "c₂* = 0.99875 × 109.8231 = 109.6857\n"
     ]
    }
   ],
   "source": [
    "c1 = W/denominador\n",
    "c2 = Gamma*c1\n",
    "print(f\"c₁* = {W:.4f} / {denominador:.5f} = {c1:.4f}\")\n",
    "print(f\"c₂* = {Gamma:.5f} × {c1:.4f} = {c2:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c84580e8",
   "metadata": {},
   "source": [
    "### Paso 5 · Ahorro\n",
    "\n",
    "$$s_1^*=y_1-c_1^*$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "589bfd29",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.426307Z",
     "iopub.status.busy": "2026-08-04T13:09:51.426086Z",
     "iopub.status.idle": "2026-08-04T13:09:51.432205Z",
     "shell.execute_reply": "2026-08-04T13:09:51.430925Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s₁* = 100 − 109.8231 = -9.8231\n",
      "\n",
      "Es NEGATIVO: el hogar se endeuda en 9.82 hoy porque su ingreso futuro\n",
      "(120) es mayor que el presente (100) y desea un perfil casi plano.\n",
      "Mañana devuelve 10.31 = 9.82 de principal + 0.49 de intereses.\n"
     ]
    }
   ],
   "source": [
    "s1 = y1 - c1\n",
    "print(f\"s₁* = {y1:.0f} − {c1:.4f} = {s1:+.4f}\")\n",
    "print(f\"\\nEs NEGATIVO: el hogar se endeuda en {abs(s1):.2f} hoy porque su ingreso futuro\")\n",
    "print(f\"({y2:.0f}) es mayor que el presente ({y1:.0f}) y desea un perfil casi plano.\")\n",
    "print(f\"Mañana devuelve {abs(s1)*(1+r):.2f} = {abs(s1):.2f} de principal + {abs(s1)*r:.2f} de intereses.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c9b87edc",
   "metadata": {},
   "source": [
    "### Paso 6 · Verificación\n",
    "\n",
    "Reconstruimos $c_2$ desde la **restricción de flujo** del período 2, que es una ecuación\n",
    "distinta de la que usamos para resolver: $c_2=y_2+(1+r)s_1$. Debe coincidir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7c15a555",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.435088Z",
     "iopub.status.busy": "2026-08-04T13:09:51.434787Z",
     "iopub.status.idle": "2026-08-04T13:09:51.453415Z",
     "shell.execute_reply": "2026-08-04T13:09:51.452461Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c₂ = 120 + 1.05 × (-9.8231) = 120 − 10.3143 = 109.6857\n",
      "c₂* del paso 4                                        = 109.6857\n",
      "diferencia = 0.00e+00   ✓\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>resultado</th>\n",
       "      <th>slide 18</th>\n",
       "      <th>|dif|</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Paso 1 · W</th>\n",
       "      <td>214.28571</td>\n",
       "      <td>214.29000</td>\n",
       "      <td>0.00429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paso 2 · Γ</th>\n",
       "      <td>0.99875</td>\n",
       "      <td>0.99875</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paso 3 · denominador</th>\n",
       "      <td>1.95119</td>\n",
       "      <td>1.95119</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paso 4 · c₁*</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.82000</td>\n",
       "      <td>0.00310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paso 4 · c₂*</th>\n",
       "      <td>109.68574</td>\n",
       "      <td>109.69000</td>\n",
       "      <td>0.00426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Paso 5 · s₁*</th>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-9.82000</td>\n",
       "      <td>0.00310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      resultado  slide 18   |dif|\n",
       "Paso 1 · W            214.28571 214.29000 0.00429\n",
       "Paso 2 · Γ              0.99875   0.99875 0.00000\n",
       "Paso 3 · denominador    1.95119   1.95119 0.00000\n",
       "Paso 4 · c₁*          109.82310 109.82000 0.00310\n",
       "Paso 4 · c₂*          109.68574 109.69000 0.00426\n",
       "Paso 5 · s₁*           -9.82310  -9.82000 0.00310"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c2_verif = y2 + (1+r)*s1\n",
    "print(f\"c₂ = {y2:.0f} + {1+r:.2f} × ({s1:.4f}) = {y2:.0f} − {abs((1+r)*s1):.4f} = {c2_verif:.4f}\")\n",
    "print(f\"c₂* del paso 4                                        = {c2:.4f}\")\n",
    "print(f\"diferencia = {c2_verif-c2:.2e}   ✓\")\n",
    "\n",
    "pasos = pd.DataFrame({\n",
    "    \"resultado\": [W, Gamma, denominador, c1, c2, s1],\n",
    "    \"slide 18\":  [SLIDE[\"W\"], SLIDE[\"Γ\"], SLIDE[\"denominador\"],\n",
    "                  SLIDE[\"c₁*\"], SLIDE[\"c₂*\"], SLIDE[\"s₁*\"]],\n",
    "}, index=[\"Paso 1 · W\", \"Paso 2 · Γ\", \"Paso 3 · denominador\",\n",
    "          \"Paso 4 · c₁*\", \"Paso 4 · c₂*\", \"Paso 5 · s₁*\"])\n",
    "pasos[\"|dif|\"] = (pasos[\"resultado\"] - pasos[\"slide 18\"]).abs()\n",
    "pasos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65e5c0f9",
   "metadata": {},
   "source": [
    "---\n",
    "## Camino 2 — Optimización numérica con `scipy.optimize`\n",
    "\n",
    "No usamos ninguna fórmula cerrada: planteamos el problema de maximización tal como está en el\n",
    "enunciado y dejamos que el optimizador lo resuelva. Si la derivación algebraica es correcta,\n",
    "ambos caminos deben coincidir en todas las cifras."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10abf8d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.456095Z",
     "iopub.status.busy": "2026-08-04T13:09:51.455856Z",
     "iopub.status.idle": "2026-08-04T13:09:51.464527Z",
     "shell.execute_reply": "2026-08-04T13:09:51.463469Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2a] minimize_scalar   ->  c₁* = 109.823105   c₂* = 109.685740\n"
     ]
    }
   ],
   "source": [
    "def u(c, sigma=sigma):\n",
    "    \"Utilidad CRRA. Con σ = 1 el límite es logarítmico.\"\n",
    "    c = np.asarray(c, dtype=float)\n",
    "    return np.log(c) if np.isclose(sigma, 1.0) else (c**(1-sigma))/(1-sigma)\n",
    "\n",
    "def U(c1, c2, beta=beta, sigma=sigma):\n",
    "    return u(c1, sigma) + beta*u(c2, sigma)\n",
    "\n",
    "# --------- 2a) sustituyendo la restricción: una sola variable de elección\n",
    "def neg_U_sustituida(c1_):\n",
    "    \"c₂ se obtiene de la restricción de flujo: c₂ = y₂ + (1+r)(y₁ − c₁).\"\n",
    "    c2_ = y2 + (1+r)*(y1 - c1_)\n",
    "    if c1_ <= 0 or c2_ <= 0:\n",
    "        return np.inf\n",
    "    return -U(c1_, c2_)\n",
    "\n",
    "res_a = minimize_scalar(neg_U_sustituida, bounds=(1e-9, W-1e-9),\n",
    "                        method=\"bounded\", options={\"xatol\": 1e-13})\n",
    "c1_a = res_a.x\n",
    "c2_a = y2 + (1+r)*(y1 - c1_a)\n",
    "print(f\"[2a] minimize_scalar   ->  c₁* = {c1_a:.6f}   c₂* = {c2_a:.6f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16ade006",
   "metadata": {},
   "source": [
    "**Con la restricción explícita.** Más informativo: tratamos $(c_1,c_2)$ como dos variables y la\n",
    "restricción presupuestaria como una igualdad. El multiplicador de Lagrange asociado tiene una\n",
    "lectura económica directa: es la **utilidad marginal de la riqueza**, $\\lambda=u'(c_1^*)$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed5ba025",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.466929Z",
     "iopub.status.busy": "2026-08-04T13:09:51.466726Z",
     "iopub.status.idle": "2026-08-04T13:09:51.479219Z",
     "shell.execute_reply": "2026-08-04T13:09:51.477663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2b] SLSQP con restricción explícita\n",
      "     c₁* = 109.823105   c₂* = 109.685740   s₁* = -9.823105\n",
      "     multiplicador λ = u'(c₁*) = 0.00008291\n",
      "     comprobación de la CPO del período 2: β·u'(c₂*)·(1+r) = 0.00008291   (debe ser igual a λ)\n",
      "     restricción residual: 0.00e+00\n"
     ]
    }
   ],
   "source": [
    "# El objetivo es MUY plano cerca del óptimo (β(1+r) ≈ 1), así que las derivadas por\n",
    "# diferencias finitas pierden precisión. Se entregan los gradientes analíticos:\n",
    "#   ∂U/∂c₁ = c₁^(−σ)   ·   ∂U/∂c₂ = β c₂^(−σ)   ·   ∇(restricción) = (1, 1/(1+r))\n",
    "restriccion = {\"type\": \"eq\",\n",
    "               \"fun\": lambda x: x[0] + x[1]/(1+r) - W,\n",
    "               \"jac\": lambda x: np.array([1.0, 1/(1+r)])}\n",
    "res_b = minimize(lambda x: -U(x[0], x[1]), x0=[W/2, W/2],\n",
    "                 jac=lambda x: -np.array([x[0]**(-sigma), beta*x[1]**(-sigma)]),\n",
    "                 constraints=[restriccion], bounds=[(1e-9, None)]*2,\n",
    "                 method=\"SLSQP\", options={\"ftol\": 1e-16, \"maxiter\": 1000})\n",
    "c1_b, c2_b = res_b.x\n",
    "lam = u_prima = c1_b**(-sigma)          # λ = u'(c₁*) por la CPO\n",
    "\n",
    "print(f\"[2b] SLSQP con restricción explícita\")\n",
    "print(f\"     c₁* = {c1_b:.6f}   c₂* = {c2_b:.6f}   s₁* = {y1-c1_b:+.6f}\")\n",
    "print(f\"     multiplicador λ = u'(c₁*) = {lam:.8f}\")\n",
    "print(f\"     comprobación de la CPO del período 2: β·u'(c₂*)·(1+r) = \"\n",
    "      f\"{beta*c2_b**(-sigma)*(1+r):.8f}   (debe ser igual a λ)\")\n",
    "print(f\"     restricción residual: {c1_b + c2_b/(1+r) - W:.2e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da3ce9cb",
   "metadata": {},
   "source": [
    "**Camino 3 — raíz de la ecuación de Euler.** En vez de optimizar, buscamos directamente el\n",
    "$c_1$ que anula la condición de primer orden $c_1^{-\\sigma}-\\beta(1+r)c_2(c_1)^{-\\sigma}=0$\n",
    "con `brentq`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fe46549",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.481840Z",
     "iopub.status.busy": "2026-08-04T13:09:51.481613Z",
     "iopub.status.idle": "2026-08-04T13:09:51.494970Z",
     "shell.execute_reply": "2026-08-04T13:09:51.494028Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>c₁*</th>\n",
       "      <th>c₂*</th>\n",
       "      <th>s₁*</th>\n",
       "      <th>U</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1 · fórmula cerrada (slide)</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.68574</td>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-0.01777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2a · scipy minimize_scalar</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.68574</td>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-0.01777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2b · scipy SLSQP con restricción</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.68574</td>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-0.01777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3 · brentq sobre Euler</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.68574</td>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-0.01777</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       c₁*       c₂*      s₁*        U\n",
       "1 · fórmula cerrada (slide)      109.82310 109.68574 -9.82310 -0.01777\n",
       "2a · scipy minimize_scalar       109.82310 109.68574 -9.82310 -0.01777\n",
       "2b · scipy SLSQP con restricción 109.82310 109.68574 -9.82310 -0.01777\n",
       "3 · brentq sobre Euler           109.82310 109.68574 -9.82310 -0.01777"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def euler_residuo(c1_):\n",
    "    c2_ = y2 + (1+r)*(y1 - c1_)\n",
    "    return c1_**(-sigma) - beta*(1+r)*c2_**(-sigma)\n",
    "\n",
    "c1_c = brentq(euler_residuo, 1e-6, W-1e-6, xtol=1e-14, rtol=8.9e-16)\n",
    "c2_c = y2 + (1+r)*(y1 - c1_c)\n",
    "\n",
    "comparacion = pd.DataFrame({\n",
    "    \"c₁*\": [c1, c1_a, c1_b, c1_c],\n",
    "    \"c₂*\": [c2, c2_a, c2_b, c2_c],\n",
    "    \"s₁*\": [s1, y1-c1_a, y1-c1_b, y1-c1_c],\n",
    "    \"U\":   [U(c1, c2), U(c1_a, c2_a), U(c1_b, c2_b), U(c1_c, c2_c)],\n",
    "}, index=[\"1 · fórmula cerrada (slide)\",\n",
    "          \"2a · scipy minimize_scalar\",\n",
    "          \"2b · scipy SLSQP con restricción\",\n",
    "          \"3 · brentq sobre Euler\"])\n",
    "comparacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d0a83650",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.498305Z",
     "iopub.status.busy": "2026-08-04T13:09:51.498068Z",
     "iopub.status.idle": "2026-08-04T13:09:51.504741Z",
     "shell.execute_reply": "2026-08-04T13:09:51.503133Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Máxima discrepancia entre los cuatro métodos en c₁*: 5.19e-09\n",
      "Los cuatro caminos entregan el mismo óptimo. ✓\n"
     ]
    }
   ],
   "source": [
    "maxdif = max(abs(x - c1) for x in (c1_a, c1_b, c1_c))\n",
    "print(f\"Máxima discrepancia entre los cuatro métodos en c₁*: {maxdif:.2e}\")\n",
    "assert maxdif < 1e-5, \"los métodos deberían coincidir\"\n",
    "print(\"Los cuatro caminos entregan el mismo óptimo. ✓\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa3e8e07",
   "metadata": {},
   "source": [
    "---\n",
    "## Chequeos de consistencia (los cuatro de la slide 16)\n",
    "\n",
    "Antes de dar por buena una fórmula conviene verificarla en los extremos y contra sus propias\n",
    "implicancias. Es el mejor detector de errores algebraicos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bab77770",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.507719Z",
     "iopub.status.busy": "2026-08-04T13:09:51.507496Z",
     "iopub.status.idle": "2026-08-04T13:09:51.525418Z",
     "shell.execute_reply": "2026-08-04T13:09:51.523664Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>valor obtenido</th>\n",
       "      <th>valor esperado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chequeo</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>① Restricción: c₁* + c₂*/(1+r) − W</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>② Euler: c₁*^(−σ) − β(1+r)c₂*^(−σ)</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>③ Homogeneidad: c₁*(2y₁,2y₂)/c₁*(y₁,y₂)</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>2.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>④ Propensión c₁*/W ∈ (0,1)</th>\n",
       "      <td>0.51251</td>\n",
       "      <td>0.51251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>⑤ ∂s₁/∂β &gt; 0:&nbsp;&nbsp;s₁(β=0,99) − s₁(β=0,90)</th>\n",
       "      <td>2.55102</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>⑥ ∂c₁/∂y₂ &gt; 0:&nbsp;&nbsp;c₁*(y₂=130) − c₁*(y₂=120)</th>\n",
       "      <td>4.88103</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                           valor obtenido  valor esperado\n",
       "chequeo                                                                  \n",
       "① Restricción: c₁* + c₂*/(1+r) − W                0.00000         0.00000\n",
       "② Euler: c₁*^(−σ) − β(1+r)c₂*^(−σ)                0.00000         0.00000\n",
       "③ Homogeneidad: c₁*(2y₁,2y₂)/c₁*(y₁,y₂)           2.00000         2.00000\n",
       "④ Propensión c₁*/W ∈ (0,1)                        0.51251         0.51251\n",
       "⑤ ∂s₁/∂β > 0:  s₁(β=0,99) − s₁(β=0,90)            2.55102             NaN\n",
       "⑥ ∂c₁/∂y₂ > 0:  c₁*(y₂=130) − c₁*(y₂=120)         4.88103             NaN"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def c1_optimo(y1_, y2_, r_, beta_, sigma_):\n",
    "    \"Fórmula cerrada general, para reutilizar en toda la estática comparativa.\"\n",
    "    W_ = y1_ + y2_/(1+r_)\n",
    "    G_ = (beta_*(1+r_))**(1/sigma_)\n",
    "    return W_/(1 + G_/(1+r_))\n",
    "\n",
    "chequeos = []\n",
    "\n",
    "# ① restricción satisfecha\n",
    "chequeos.append((\"① Restricción: c₁* + c₂*/(1+r) − W\", c1 + c2/(1+r) - W, 0.0))\n",
    "# ② Euler se cumple con igualdad\n",
    "chequeos.append((\"② Euler: c₁*^(−σ) − β(1+r)c₂*^(−σ)\", c1**(-sigma) - beta*(1+r)*c2**(-sigma), 0.0))\n",
    "# ③ homogeneidad de grado 1 en la riqueza\n",
    "chequeos.append((\"③ Homogeneidad: c₁*(2y₁,2y₂)/c₁*(y₁,y₂)\",\n",
    "                 c1_optimo(2*y1, 2*y2, r, beta, sigma)/c1, 2.0))\n",
    "# ④ propensión a consumir la riqueza dentro de (0,1)\n",
    "pmc_W = c1/W\n",
    "chequeos.append((\"④ Propensión c₁*/W ∈ (0,1)\", pmc_W, 1/denominador))\n",
    "# ⑤ estática comparativa creíble: más β debe elevar el ahorro\n",
    "s_beta_bajo = y1 - c1_optimo(y1, y2, r, 0.90, sigma)\n",
    "s_beta_alto = y1 - c1_optimo(y1, y2, r, 0.99, sigma)\n",
    "chequeos.append((\"⑤ ∂s₁/∂β > 0:  s₁(β=0,99) − s₁(β=0,90)\", s_beta_alto - s_beta_bajo, np.nan))\n",
    "# ⑥ estática comparativa creíble: más y₂ debe elevar c₁\n",
    "chequeos.append((\"⑥ ∂c₁/∂y₂ > 0:  c₁*(y₂=130) − c₁*(y₂=120)\",\n",
    "                 c1_optimo(y1, 130.0, r, beta, sigma) - c1, np.nan))\n",
    "\n",
    "pd.DataFrame(chequeos, columns=[\"chequeo\", \"valor obtenido\", \"valor esperado\"]).set_index(\"chequeo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73012b66",
   "metadata": {},
   "source": [
    "El chequeo ⑤ es positivo (un hogar más paciente ahorra más, o en este caso se endeuda menos) y\n",
    "el ⑥ también (más ingreso futuro eleva la riqueza y con ella el consumo presente): la estática\n",
    "comparativa es creíble, tal como pide la slide 16."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a4f001c",
   "metadata": {},
   "source": [
    "---\n",
    "## El óptimo, gráficamente\n",
    "\n",
    "La curva de indiferencia se traza de forma **paramétrica** en $t=c_2/c_1$: para cada $t$ se\n",
    "despeja el único $c_1$ que entrega la utilidad $U^*$,\n",
    "\n",
    "$$c_1(t)=\\left[\\frac{U^*(1-\\sigma)}{1+\\beta t^{1-\\sigma}}\\right]^{1/(1-\\sigma)},\\qquad c_2=t\\,c_1$$\n",
    "\n",
    "Esta parametrización nunca se indefine, a diferencia de despejar $c_2$ desde una grilla de $c_1$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d7623886",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.528622Z",
     "iopub.status.busy": "2026-08-04T13:09:51.528375Z",
     "iopub.status.idle": "2026-08-04T13:09:51.838640Z",
     "shell.execute_reply": "2026-08-04T13:09:51.836789Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 836x682 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def curva_indiferencia(U_obj, beta_=beta, sigma_=sigma, t=None):\n",
    "    t = np.linspace(0.30, 3.0, 400) if t is None else t\n",
    "    if np.isclose(sigma_, 1.0):\n",
    "        c1_t = np.exp((U_obj - beta_*np.log(t))/(1+beta_))\n",
    "    else:\n",
    "        c1_t = (U_obj*(1-sigma_)/(1 + beta_*t**(1-sigma_)))**(1/(1-sigma_))\n",
    "    return c1_t, t*c1_t\n",
    "\n",
    "U_opt = U(c1, c2)\n",
    "ic1, ic2 = curva_indiferencia(U_opt)\n",
    "ic1_baja, ic2_baja = curva_indiferencia(U_opt*1.15 if U_opt < 0 else U_opt*0.85)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7.6, 6.2))\n",
    "ax.plot([0, W], [(1+r)*W, 0], lw=2.2, color=\"tab:blue\",\n",
    "        label=f\"Recta presupuestaria  (W = {W:.2f}, pendiente −{1+r:.2f})\")\n",
    "ax.plot(ic1, ic2, lw=2, color=\"tab:orange\", label=f\"Curva de indiferencia  U* = {U_opt:.5f}\")\n",
    "ax.plot(ic1_baja, ic2_baja, lw=1, ls=\":\", color=\"tab:orange\", alpha=.7,\n",
    "        label=\"Curva de indiferencia inferior\")\n",
    "ax.plot(y1, y2, \"D\", color=\"k\", ms=9, zorder=5)\n",
    "ax.annotate(f\"Dotación\\n({y1:.0f}, {y2:.0f})\", (y1, y2), textcoords=\"offset points\",\n",
    "            xytext=(-64, 6), fontsize=9)\n",
    "ax.plot(c1, c2, \"o\", color=\"tab:red\", ms=10, zorder=6)\n",
    "ax.annotate(f\"Óptimo\\n({c1:.2f}, {c2:.2f})\", (c1, c2), textcoords=\"offset points\",\n",
    "            xytext=(16, 12), fontsize=9, color=\"tab:red\")\n",
    "ax.annotate(\"\", xy=(c1, y2), xytext=(y1, y2),\n",
    "            arrowprops=dict(arrowstyle=\"<->\", color=\"tab:green\", lw=1.6))\n",
    "ax.text((y1+c1)/2, y2+7, f\"se endeuda\\n{abs(s1):.2f}\", ha=\"center\", fontsize=9, color=\"tab:green\")\n",
    "ax.set(xlabel=\"$c_1$  (consumo presente)\", ylabel=\"$c_2$  (consumo futuro)\",\n",
    "       xlim=(60, 175), ylim=(60, 175),\n",
    "       title=\"La tangencia es la ecuación de Euler: TMS = −(1+r)\")\n",
    "ax.legend(fontsize=8, loc=\"upper right\"); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a563ea39",
   "metadata": {},
   "source": [
    "El óptimo queda **a la derecha de la dotación**: $c_1^*>y_1$. Esa distancia horizontal es\n",
    "exactamente el endeudamiento del período 1. La curva de indiferencia toca la recta justo donde\n",
    "su pendiente (la tasa marginal de sustitución) iguala $-(1+r)$ — y esa igualdad *es* la\n",
    "ecuación de Euler."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddbc283a",
   "metadata": {},
   "source": [
    "---\n",
    "## Estática comparativa\n",
    "\n",
    "¿Qué pasa si movemos cada parámetro manteniendo los demás en el valor de la slide?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3f377ed2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.842131Z",
     "iopub.status.busy": "2026-08-04T13:09:51.841872Z",
     "iopub.status.idle": "2026-08-04T13:09:51.857327Z",
     "shell.execute_reply": "2026-08-04T13:09:51.855938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EIS = 1/σ</th>\n",
       "      <th>W</th>\n",
       "      <th>Γ</th>\n",
       "      <th>c₁*</th>\n",
       "      <th>c₂*</th>\n",
       "      <th>s₁*</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>σ</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.25000</th>\n",
       "      <td>4.00000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99004</td>\n",
       "      <td>110.29209</td>\n",
       "      <td>109.19330</td>\n",
       "      <td>-10.29209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.50000</th>\n",
       "      <td>2.00000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99501</td>\n",
       "      <td>110.02411</td>\n",
       "      <td>109.47468</td>\n",
       "      <td>-10.02411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.00000</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99750</td>\n",
       "      <td>109.89011</td>\n",
       "      <td>109.61538</td>\n",
       "      <td>-9.89011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2.00000</th>\n",
       "      <td>0.50000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99875</td>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.68574</td>\n",
       "      <td>-9.82310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.00000</th>\n",
       "      <td>0.20000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99950</td>\n",
       "      <td>109.78290</td>\n",
       "      <td>109.72795</td>\n",
       "      <td>-9.78290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10.00000</th>\n",
       "      <td>0.10000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99975</td>\n",
       "      <td>109.76950</td>\n",
       "      <td>109.74203</td>\n",
       "      <td>-9.76950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50.00000</th>\n",
       "      <td>0.02000</td>\n",
       "      <td>214.28571</td>\n",
       "      <td>0.99995</td>\n",
       "      <td>109.75878</td>\n",
       "      <td>109.75328</td>\n",
       "      <td>-9.75878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          EIS = 1/σ         W       Γ       c₁*       c₂*       s₁*\n",
       "σ                                                                  \n",
       "0.25000     4.00000 214.28571 0.99004 110.29209 109.19330 -10.29209\n",
       "0.50000     2.00000 214.28571 0.99501 110.02411 109.47468 -10.02411\n",
       "1.00000     1.00000 214.28571 0.99750 109.89011 109.61538  -9.89011\n",
       "2.00000     0.50000 214.28571 0.99875 109.82310 109.68574  -9.82310\n",
       "5.00000     0.20000 214.28571 0.99950 109.78290 109.72795  -9.78290\n",
       "10.00000    0.10000 214.28571 0.99975 109.76950 109.74203  -9.76950\n",
       "50.00000    0.02000 214.28571 0.99995 109.75878 109.75328  -9.75878"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def solucion(y1_=y1, y2_=y2, r_=r, beta_=beta, sigma_=sigma):\n",
    "    W_ = y1_ + y2_/(1+r_)\n",
    "    G_ = (beta_*(1+r_))**(1/sigma_)\n",
    "    c1_ = W_/(1 + G_/(1+r_))\n",
    "    return dict(W=W_, Γ=G_, c1=c1_, c2=G_*c1_, s1=y1_-c1_)\n",
    "\n",
    "# --- A) aversión al riesgo σ\n",
    "COLS = {\"c1\": \"c₁*\", \"c2\": \"c₂*\", \"s1\": \"s₁*\"}\n",
    "tabA = (pd.DataFrame([{\"σ\": s, \"EIS = 1/σ\": 1/s, **solucion(sigma_=s)}\n",
    "                      for s in (0.25, 0.5, 1.0, 2.0, 5.0, 10.0, 50.0)])\n",
    "        .rename(columns=COLS).set_index(\"σ\"))\n",
    "tabA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e35c5c37",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:51.860479Z",
     "iopub.status.busy": "2026-08-04T13:09:51.860209Z",
     "iopub.status.idle": "2026-08-04T13:09:51.873368Z",
     "shell.execute_reply": "2026-08-04T13:09:51.871630Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                W       Γ       c₁*       c₂*       s₁*  β(1+r)\n",
      "r                                                              \n",
      "0.00000 220.00000 0.97468 111.41049 108.58951 -11.41049 0.95000\n",
      "0.02000 217.64706 0.98438 110.75755 109.02730 -10.75755 0.96900\n",
      "0.04000 215.38462 0.99398 110.12881 109.46604 -10.12881 0.98800\n",
      "0.05000 214.28571 0.99875 109.82310 109.68574  -9.82310 0.99750\n",
      "0.05263 214.00000 1.00000 109.74359 109.74359  -9.74359 1.00000\n",
      "0.10000 209.09091 1.02225 108.37542 110.78704  -8.37542 1.04500\n",
      "0.20000 200.00000 1.06771 105.83374 112.99951  -5.83374 1.14000\n",
      "0.51579 179.16669 1.20000 100.00001 119.99999  -0.00001 1.44000\n",
      "\n",
      "r que aplana el consumo (β(1+r)=1):      ρ = 5.2632%\n",
      "r a la que el hogar deja de endeudarse:  r* = 51.5789%\n",
      "   fórmula cerrada  r* = (y₂/y₁)^σ/β − 1 = 51.5789%\n",
      "   comprobación: Γ(r*) = 1.20000  vs  y₂/y₁ = 1.20000\n"
     ]
    }
   ],
   "source": [
    "# --- B) tasa real r\n",
    "tabB = (pd.DataFrame([{\"r\": rr, **solucion(r_=rr), \"β(1+r)\": beta*(1+rr)}\n",
    "                      for rr in (0.0, 0.02, 0.04, 0.05, 1/beta-1, 0.10, 0.20, 0.515789)])\n",
    "        .rename(columns=COLS).set_index(\"r\"))\n",
    "print(tabB.to_string())\n",
    "\n",
    "# tasa que deja el consumo perfectamente plano:  β(1+r) = 1\n",
    "r_plano = 1/beta - 1\n",
    "# tasa a la que el hogar deja de endeudarse:  s₁ = 0  <=>  Γ = y₂/y₁\n",
    "r_cero_ahorro = brentq(lambda rr: solucion(r_=rr)[\"s1\"], -0.5, 5.0)\n",
    "print(f\"\\nr que aplana el consumo (β(1+r)=1):      ρ = {r_plano:.4%}\")\n",
    "print(f\"r a la que el hogar deja de endeudarse:  r* = {r_cero_ahorro:.4%}\")\n",
    "print(f\"   fórmula cerrada  r* = (y₂/y₁)^σ/β − 1 = {(y2/y1)**sigma/beta - 1:.4%}\")\n",
    "print(f\"   comprobación: Γ(r*) = {solucion(r_=r_cero_ahorro)['Γ']:.5f}  vs  y₂/y₁ = {y2/y1:.5f}\")"
   ]
  },
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   "id": "943bf5f9",
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    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1342x473 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rr = np.linspace(0.0, 0.60, 300)\n",
    "sol = [solucion(r_=x) for x in rr]\n",
    "\n",
    "fig, ax = plt.subplots(1, 2, figsize=(12.2, 4.3))\n",
    "ax[0].plot(rr*100, [s[\"c1\"] for s in sol], lw=2, label=\"$c_1^*$\")\n",
    "ax[0].plot(rr*100, [s[\"c2\"] for s in sol], lw=2, label=\"$c_2^*$\")\n",
    "ax[0].axvline(r*100, color=\"k\", ls=\":\", lw=1, label=f\"r del ejercicio = {r:.0%}\")\n",
    "ax[0].axvline(r_plano*100, color=\"tab:purple\", ls=\"--\", lw=1, label=f\"ρ = {r_plano:.2%}\")\n",
    "ax[0].set(xlabel=\"tasa real r (%)\", ylabel=\"consumo\", title=\"Consumo frente a la tasa\")\n",
    "ax[0].legend(fontsize=8)\n",
    "\n",
    "ax[1].plot(rr*100, [s[\"s1\"] for s in sol], lw=2, color=\"tab:red\")\n",
    "ax[1].axhline(0, color=\"k\", lw=.9)\n",
    "ax[1].axvline(r_cero_ahorro*100, color=\"tab:green\", ls=\"--\", lw=1.2,\n",
    "              label=f\"deja de endeudarse: r* = {r_cero_ahorro:.2%}\")\n",
    "ax[1].plot(r*100, s1, \"o\", color=\"k\", ms=7)\n",
    "ax[1].annotate(f\"ejercicio\\ns₁* = {s1:.2f}\", (r*100, s1), textcoords=\"offset points\",\n",
    "               xytext=(14, -22), fontsize=9)\n",
    "ax[1].fill_between(rr*100, [s[\"s1\"] for s in sol], 0,\n",
    "                   where=[s[\"s1\"] < 0 for s in sol], alpha=.12, color=\"tab:red\")\n",
    "ax[1].set(xlabel=\"tasa real r (%)\", ylabel=\"ahorro $s_1^*$\",\n",
    "          title=\"El hogar es deudor mientras Γ(r) < y₂/y₁\")\n",
    "ax[1].legend(fontsize=8)\n",
    "plt.tight_layout(); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f607fccc",
   "metadata": {},
   "source": [
    "Nótese que el ahorro sube monótonamente con $r$ pero el hogar sigue siendo **deudor** hasta una\n",
    "tasa muy alta ($r^*\\approx51{,}6\\%$). La razón es que lo que domina aquí no es el precio\n",
    "intertemporal sino el **perfil del ingreso**: con $y_2>y_1$ y un deseo de suavizar, hace falta\n",
    "adelantar recursos casi cualquiera sea $r$."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f182257e",
   "metadata": {},
   "source": [
    "---\n",
    "## Los tres casos límite de la slide 16\n",
    "\n",
    "Aplicados a este mismo ejercicio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44b9d853",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:52.205619Z",
     "iopub.status.busy": "2026-08-04T13:09:52.205396Z",
     "iopub.status.idle": "2026-08-04T13:09:52.213634Z",
     "shell.execute_reply": "2026-08-04T13:09:52.212695Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CASO 1 · β(1+r) = 1\n",
      "   β = 1/(1+r) = 0.952381   ->   Γ = 1.000000\n",
      "   c₁* = 109.7561   c₂* = 109.7561   (iguales: suavización perfecta)\n",
      "   s₁* = -9.7561\n",
      "\n",
      "CASO 2 · σ → ∞  (EIS → 0): la senda se vuelve insensible a la tasa\n",
      "   σ =      2:  Γ = 0.99874922   c₁* = 109.8231   c₂* = 109.6857\n",
      "   σ =     10:  Γ = 0.99974972   c₁* = 109.7695   c₂* = 109.7420\n",
      "   σ =    100:  Γ = 0.99997497   c₁* = 109.7574   c₂* = 109.7547\n",
      "   σ =   1000:  Γ = 0.99999750   c₁* = 109.7562   c₂* = 109.7560\n",
      "   σ =  10000:  Γ = 0.99999975   c₁* = 109.7561   c₂* = 109.7561\n",
      "   límite teórico  c₁* = c₂* = W/[1+1/(1+r)] = 109.7561\n",
      "\n",
      "CASO 3 · σ → 0  (utilidad casi lineal): aparecen soluciones de esquina\n",
      "   σ =      1.0:  Γ = 0.997500   c₁* = 109.8901   c₂* = 109.6154  \n",
      "   σ =      0.1:  Γ = 0.975279   c₁* = 111.0958   c₂* = 108.3494  \n",
      "   σ =     0.01:  Γ = 0.778557   c₁* = 123.0478   c₂* =  95.7998  \n",
      "   σ =    0.001:  Γ = 0.081828   c₁* = 198.7934   c₂* =  16.2670  \n",
      "   σ =   0.0002:  Γ = 0.000004   c₁* = 214.2850   c₂* =   0.0008  → todo hoy (esquina)\n",
      "   como β(1+r) = 0.9975 < 1, el límite es consumir toda la riqueza hoy: W = 214.29\n",
      "   si en cambio fuera β(1+r) > 1, la esquina sería la opuesta (todo mañana).\n"
     ]
    }
   ],
   "source": [
    "# ---- CASO 1 · β(1+r) = 1  ->  suavización perfecta\n",
    "beta_c1 = 1/(1+r)\n",
    "s_c1 = solucion(beta_=beta_c1)\n",
    "print(\"CASO 1 · β(1+r) = 1\")\n",
    "print(f\"   β = 1/(1+r) = {beta_c1:.6f}   ->   Γ = {s_c1['Γ']:.6f}\")\n",
    "print(f\"   c₁* = {s_c1['c1']:.4f}   c₂* = {s_c1['c2']:.4f}   (iguales: suavización perfecta)\")\n",
    "print(f\"   s₁* = {s_c1['s1']:+.4f}\\n\")\n",
    "\n",
    "# ---- CASO 2 · σ -> ∞  (EIS -> 0)\n",
    "print(\"CASO 2 · σ → ∞  (EIS → 0): la senda se vuelve insensible a la tasa\")\n",
    "for sg in (2, 10, 100, 1000, 10000):\n",
    "    s_ = solucion(sigma_=sg)\n",
    "    print(f\"   σ = {sg:>6}:  Γ = {s_['Γ']:.8f}   c₁* = {s_['c1']:.4f}   c₂* = {s_['c2']:.4f}\")\n",
    "print(f\"   límite teórico  c₁* = c₂* = W/[1+1/(1+r)] = {W/(1+1/(1+r)):.4f}\\n\")\n",
    "\n",
    "# ---- CASO 3 · σ -> 0  (utilidad casi lineal)\n",
    "print(\"CASO 3 · σ → 0  (utilidad casi lineal): aparecen soluciones de esquina\")\n",
    "for sg in (1.0, 0.1, 0.01, 0.001, 0.0002):\n",
    "    s_ = solucion(sigma_=sg)\n",
    "    esq = \"→ todo hoy (esquina)\" if s_[\"c1\"] > 0.95*W else \"\"\n",
    "    print(f\"   σ = {sg:>8}:  Γ = {s_['Γ']:.6f}   c₁* = {s_['c1']:8.4f}   c₂* = {s_['c2']:8.4f}  {esq}\")\n",
    "print(f\"   como β(1+r) = {beta*(1+r):.4f} < 1, el límite es consumir toda la riqueza hoy: W = {W:.2f}\")\n",
    "print(f\"   si en cambio fuera β(1+r) > 1, la esquina sería la opuesta (todo mañana).\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23d3b9de",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:52.217564Z",
     "iopub.status.busy": "2026-08-04T13:09:52.217352Z",
     "iopub.status.idle": "2026-08-04T13:09:52.624810Z",
     "shell.execute_reply": "2026-08-04T13:09:52.623182Z"
    }
   },
   "outputs": [
    {
     "data": {
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ZsyeqV6+uVS6Xqy6DYWFhOHHiBJo2bYratWtrLPPNN9/AxsZGfc5lNnToUI16sbGxQVBQkMa5kP772b17N6Kjo3N1HO8il8vx1VdfacyrX78+AFWfufS/JwBwd3dHQECARmz79+/H69evMWzYMLi6uqrnm5qaYtKkSQCQ5XG/y4QJEzSmq1SpgubNm+PYsWMIDw8HAKxfvx5CCHz++eda16pPPvkESqUSBw8e1NiOs7NzljczvktOrjmRkZHYvXs36tevj44dO2ptK/0c+ZDc/M0HBQUhKChIY169evUghMCIESM0rm3pv9fMv7/c1GV2r2Hvs2XLFiQlJWHAgAGIiIjQ2G+bNm0AQF2/H2JnZ4dhw4ZpzOvQoQNKlSqF7du3Q6lUvnf99OuGEAKxsbEIDw9Xn+tZ1Xl2jz/9vJ82bZrGsiVLlkSPHj3w33//4fr16++NTV+u4QDw119/oX379vj8889Rr149/PPPPzh//jyGDh2KjRs3onfv3mjWrBlWrlz5zn626ceR3Wu6lEylDoByLzY2VuPN6/bt20hMTISnp+c713n58mWu9nXt2jVMnjwZhw8fRlxcnEZZdsYiTR/x4csvv8SXX3753ti8vb0xZcoUTJ8+HUWKFEG5cuVQp04dtG/fXn0RAqDun1W6dGmtbZUpUyZ7B5YLvr6+WvPS/+jfvrv0r7/+wrx583Dx4kWtod0iIyOzvU+RRT/I0NBQAEDZsmW1ysqXLw8A+O+//9Tzli9fjps3b2Z7n8nJydi3bx+aN2+uftNOTk7OctmkpCT1MlnFnt3xavN6nmUl/Q2rcuXK713uwYMHAIBy5cpplVlbW8PPzy/LPoHvOh8ynwv16tVD//798csvv2DDhg2oXLky6tSpg06dOmklNzlVpEgRWFpaasxzcnJ6Z2xOTk54/Pixevp9x50+L7t9IR0dHVGkSBGt+aVLl8aePXtw//59uLq6qq8HFSpUeOe23r5W+fn5wcTEJFtxADm75ty7dw9CiA+eI9mR07/5d/2OsipLn5/53MpNXebkGvYu6ftNT2azs9938fPzy/LDc+nSpXH79m2EhYXB3d39nesfP34c3333HU6fPo3ExESNsvR+xJll9/gfPHgAFxcXFC5cWGv5zH8b76v7dFJfwwHgyZMnGvvy8PDAoEGDsGnTJjRp0kQ9v3Tp0ggJCXnvcRjCGORMcA3UgwcPEBcXh1q1aqnnKZVKODg4YMuWLe9cLzeJX2hoKOrUqQNbW1uMHz8egYGBsLGxgVwux+zZs3H48OEPbiP9E/jMmTOzbEUDMi7egGpIlX79+mHPnj04ceIEtm7diuXLl6Ndu3bYvn17rv643rfO2+O4vs/73mQzX8R27NiBDh06oGrVqli0aBGKFSsGKysrAEC3bt0+2CoBqC66crk8yzed9Diyupkj/XhSU1PV87K64SIrK1euxJdffokWLVqob+rz8vICkHFBfltoaKh6mbdFRUWhYsWKH9xvfpxnUshu0rV69WqMHj0ae/bswcmTJ/HLL79g0aJFGDZsGJYsWQIgd+fo+/b/rrKs3mw/pvRzf9euXe/8VuDtJORdH6A+tI/sXnPyQ27+5vP6+8tNXWb3GvY+6ftdtWoVvL29s1wmqw87+e3ixYto3LgxfH19MXPmTPj6+sLa2hoymQwjRoxAfHy81jr5cfzZpS/XcEB1Hc/c4pySkoKdO3fiyZMn2LRpE7p27QpA9eHlXdfz9OMoVKhQtmKREhNcA/XTTz8BANq2baueV7JkSQQHB6NSpUpZfh2SW9u2bUNcXBz++usvNGrUSKNs4sSJ2dpGyZIlAai+ys78SfF9vL29MXjwYAwePBhpaWno27cv1q9fj2PHjqFBgwbw8/MDANy6dQvVqlXTWDerT7npwwhFRkZqDCmUmJiI58+fw9/fP1txZddvv/0GS0tLHDt2TOPNOT4+HlFRUdnahlwuR6lSpTQ+xacLCAgAAFy/fl3jPAAyLoQ5PaalS5di+PDhaNu2LbZs2aIeJs3d3R3FihXDlStXkJiYqH7TBoDHjx/j+fPnaNWqldb2Hjx4gLS0tCxbB9+WH+dZVtLPvcuXL793ufQkIKtzJzExEQ8ePMjzORIYGIjAwECMHDkSiYmJaNWqFZYuXYpRo0bBx8dH4xx9W3pLa35L/zu6efOmVmtc+nmUvsyHREdH49mzZ1qJza1btzS2U7JkSezduxeFCxfOl1bTrOTkmuPv7w+ZTPbBcwR4/4eQ/Pibzyld1uX7jjW9fp2cnLJ9TX+X+/fvIzk5WStBv3XrFuzt7eHm5vbOddevX4+0tDTs2bNHK5GPiIjQ+nYjJ/z8/BAcHIyXL19qtSBn929DX67hANCuXTt8++23qFSpEkqXLo2ZM2fCyckJixcvxqBBgyCTyeDt7Y2hQ4di5MiRWW7/v//+g1wuz/KbU33DPrgGaM2aNVi4cCGKFSuGIUOGqOf37t0bADBmzJgsP4XmtntC+qfMt7e5Z88enD9/PlvbaN68Odzd3TF//vws+9WmpaWp39RjYmI0PrUCqv6A6V8DpX+CTO8rN3v2bI3YHj58iPXr12vtI/1i8nZ/tIULF2arNTWnTExMIJPJtLY9ffr0HO2vQYMGePDggdbvr0mTJnBycsKyZcs0EqK0tDT103I+/fTTHMX88uVLdOzYEVu3btUaA7hXr15ISEjAihUrNOYvXLhQXf629D7fDRs2/OC+8+M8y0qFChVQtmxZ/P7771kO7ZP+u3Bzc0PdunWxb98+rf0tXLgQr1+/znF9pouMjNT6nVtZWanfJNLP6fSk4e1z9MSJE+8cHiqvmjZtCltbW/z4448aSZhCocjVefT2UH4XL17Evn37UK9ePXUf3/RzZfz48Vp/64DqGvCu7jDZlZNrjrOzM1q1aoWjR49ix44dWstm/t3Z2toCyPpDSH79zeeELuvyfcfapUsXWFpaYurUqXj9+rVWeWJiolY3o3eJi4vD0qVLNeZt374dt2/fRvv27d/bB/pd142VK1fm+j0vXfp7zNtP77p37x42bNiAEiVKqLsSvI++XMNdXFywadMmLFq0CA0aNEBSUhL+/PNPDB06FF9//TUGDBiAVq1aoUWLFu+8j+XMmTOoVKmSRp9hfcUWXD129epV/P777wBULQAPHz7Enj17cO3aNZQqVQpbt26FnZ2devlPP/0UAwcOxM8//4yrV6+iffv28PDwwLNnz3Dx4kXs3r07ywvgh7Rs2RI2Njbo1asXhgwZAldXV1y6dAnr169HuXLlPtjJHlB9vbhu3Tq0a9cOpUqVQr9+/RAYGIi4uDjcv38f27Ztw5w5c9C3b18cOXIEAwcORIcOHRAQEABHR0fcunULK1euhKenp7q1oHHjxujUqRO2bNmCJk2aoF27doiMjMSKFStQunRpXLx4USOG7t27Y+LEiRg4cCBu3rwJd3d3HDt2DBcvXtS4uSa/dO7cGVu2bEH9+vXRt29fCCGwb98+3Lp1K0f769q1K5YtW4Zdu3bh888/V8+3sbHBsmXL0LNnT1StWhX9+/eHra0tNm/ejNOnT2PgwIEaXViyY8aMGVAqlVm+oYwZMwZbtmzBmDFj8OjRI1SoUAHHjh3DunXr0KtXL/UNMJnt2rULzs7O2WrhyY/zLCsymQxr1qxBo0aNULt2bfTv3x8VKlRAfHw8zp49C19fX8ydOxcAsGTJEtSrVw+NGjXCF198AV9fX5w8eRIbNmxAhQoVMGrUqFzFsHbtWixatAjt27eHn58frK2tcfHiRaxatQoVKlRQd+EICAhA8+bNsXLlSigUClSpUgW3b9/GmjVrUL58eVy9ejVX+38fBwcHLF68GAMHDlSfRzY2NtiyZQtOnTqFgQMHok6dOtnalqurK3bt2oWnT5+iadOm6rG6LS0tNR6fW7VqVcyYMQOTJk1C2bJl0b17d3h5eeHVq1e4fv06duzYgVu3bmmNuZoTObnmAMCyZctw+fJldOzYET169ECNGjWgUChw+fJlpKWlqa/F1apVg1wux8yZMxEVFQUbGxsUL14cNWrUyLe/+ZzQZV26uLjA398fGzduhJ+fH9zd3WFjY4O2bdvC09MTP/30E/r374+AgAD06dMHvr6+iIyMRHBwMLZt24a//vpLayzxrPj5+WHWrFm4efMmatSogdu3b2PFihVwc3PDjBkz3rtux44dsWjRIrRs2RKDBg2CtbU1Tp48iX379sHPzy9H3c/e1rt3b/z+++9YtmwZnjx5gubNm+PFixdYsWIFhBD46aefstVdTl+u4QBQp04dXLlyRWv+5MmT8e233773eO7cuYN79+6pr5d672MO2UDZkz5MWPqPTCYTdnZ2okSJEqJz585i/fr1WsPeZLZhwwbRoEED4eDgIMzNzUXRokVFy5Yt8zQO7smTJ0W9evWEvb29sLOzE40aNRInT57M0VikQghx+/Zt0adPH+Hl5SXMzMyEq6urqFKlihg/frx48uSJEEI1zNfgwYNF6dKlhb29vbCyshL+/v5i2LBhIiQkRGN7ycnJYtKkSaJo0aLC3NxcBAQEiGXLlr1zKJ9///1X1KtXT1haWgonJyfRrVs38fTp0xwNE/b2PCHePXzN6tWrRdmyZYWlpaVwc3MTPXr0ECEhIe/czruUK1dO1K1bN8uy/fv3i4YNGwpbW1thYWEhypcvL3788UetoXvyw6tXr8SgQYOEh4eHMDc3FyVKlBCzZ8/WGE80XUxMjLC0tBRff/11trefk/Msp+fef//9J/r06SMKFy6sHmOzefPm4uDBgxrL3bp1S3Tp0kW4uroKMzMz4e3tLUaNGqUx3JYQORvS6/Lly6Jv376iRIkSwtbWVtjY2IjAwEAxceJEERkZqbHuy5cvRbdu3YSDg4OwtrYW9erVE6dPn37nMGHe3t5a+0+/hvz6669aZe9aZ/fu3aJ+/foa59HSpUuzfR6lb/fhw4eiY8eO6vgbNWok/v333yzX2bt3r2jVqpVwcXERZmZmokiRIqJhw4Zi4cKFIjExUb3ch/5e3leenWtOuufPn4uhQ4cKb29v9bL16tUTf/75p8Zya9asEaVKlRJmZmZa18uc/M1nda0VQogpU6YIAOLhw4daZe9aJ691+a5r2Llz50StWrWEtbW1AKB17pw9e1Z06tRJuLu7q/+ugoKCxPTp07M11nd6PFeuXBFNmzYVtra2ws7OTrRr1078999/2Yrx77//FlWrVhXW1tbCyclJtG3bVty8eTPLcz2nx5+YmCimTJkiSpYsKczNzYWjo6No06aNOH/+/AePLTN9uYbnxYQJE4SFhYXGcIL6TCaExHcbEOnAmjVr0K9fPxw5ciRbLQj6bvv27ejYsSPOnz+v1d9YX82ZMwczZ87Ef//9Bw8PD6nDIR1r0KABHj16hEePHkkdChkQHx8f+Pj4ZPn0PmNiiNfwzOLi4uDr64u+ffti/vz5UoeTLeyDS2QAOnTogEaNGqnHJdV3MTExmDdvHiZMmMDklogKPEO7hr9t8eLFkMlk+Pbbb6UOJdvYB5fIQBw6dEjqELLNwcEhR+P8EhEZO0O6hr/t22+/NajkFmALLhEREREZGfbBJSIiIiKjwhZcIiIiIjIqTHCJiIiIyKgwwSUiIiIio8IE9z3S0tIQGhqapyehEBEREdHHxQT3PV68eIGiRYtm+RxzY6ZQKPDixQsoFAqpQzFKrF/dYd3qzseq28OHD+Pw4cM63Ye+4XmrO6xb3dLn+uU4uEREpDcaNWokdQhEZATYgktERHojJCQEISEhUodBRAaOLbhERKQ3unfvDgA4efKkxJEQkSFjgktERHpj3LhxUodAREaACS4REemNNm3aSB0CERkB9sElIiK9ER4ejvDwcKnDICIDxxZcIiLSG+3btwfAPrhElDdMcImISG+MGDFC6hCIyAgwwSUiIr3RuXNnqUMgIiPAPrhERKQ3EhISkJCQIHUYRGTg2IJLRER6o1mzZgDYB5eI8oYJLhER6Y2BAwdKHQIRGQEmuEREpDf69OkjdQhEZATYB5eIiIiIjAoTXD0RnZACpVJIHQYRkaTq1KmDOnXqSB0GERk4dlHIhpChw5Bmba2etgkKgtuwoaqyL4dAER2tsbxlqVLw+HYSAODZhIlIefRIo9zMwwOeixYCAF7Om4/EK1dw81kMklKVsDCVw8TKCheGToOPizXKHvsLDlfPQy6TaWyj2C+rIbe0RNSmPxGzY4dWzJ6LFsLMwwOx+/cjcs1vWuUeUybDMiAA8efPI2zxDxplQgiY9voMaN4cSXfv4sXUaVrrO/fuBfsWLZD66hWefjVSq9zhk7Zw6tYNIiUFj/v20yq3rVcXroMHAwCeDBwEZXy8RrlVuXJwH696Jv3T0WOQ+vSpRrl50aIoMncOAODFrFlIunFTo9zEzg5Ff1oJAAhbtgzxp05rxeC99jfITE0RuX49Yv/ZrVXutXQJTF1cEPPPP4hav0GrvPD072Dh54fXp04hfNlyrfJCX4+CdZUqSLp1Cy9mzFTPF0IgNTUVVv83CA5NmyL12TM8/Wa01vqOn3aE46efQpmQgCcDtPsl2jVqCJcBAwAAj/v1g0hO0Si3rlwJhb75BgAQOnIk0l6+0ig39y2OIjNmAACeT52K5Lv/aZSbODuh6I8/AgBe/fADEs6d1yiXyeXw/n0dACDyt98Qu2+/VoxFVyyHiYMDYnbsQNSmP7XKi8yeBXNvb7w+dgzhP/1Pq9x97BhYVaiAxKtX8XLuPK1y1/8bBNv69ZHy+DGejZ+grttEMzPIZDI4de0Ch3btoIiJQcgXX2qtb9+8GZzffCX++LNeEEqlRrl1jeoo9GZc1pChQ6GIjNIotyhZAoWnTgUAPJs0CSkPHmqUm7oXgtf33wMAXi1YgIRLlzXKZRbm8P71VwBAxKpViDt8RCvGYqt+htzaGtFbtyJ66zatcs8F82FWpAjiDh1CxOpftMo9Jk2EZenSSLh4Ea8WLtIqdx3yJWxr10by/ft4/u1krXKnnj3g0Lo10iIiEDt0mLpu09m3bgXnnj0h0tLwuLd29wKb2rXgNmQIACDk/wZDERenUW5Ztgw8JkwAADwbOw4t35zHj3r0BACYeXrCc77qd/9y9hwkXr+usb7cxgbFfladO+ErV+L18RNaMXiv+RUyc3NEbdyImL93apV7Lv4eZoUKIXbvXkSuXadV7jF1CixLlkT82bMIW7JUq9ztqxGwqV4dSXfu4MW077TKnfv2gX2zZkh98QJPR32tVW7Xti3QsAGUSUkIGThIq9y2QQO4DlJdA570/xzKpCSNcquKFeE+RnUNeTrqa6S+eKFRbu7jgyKzVNegF9NnIOn2bY1yE0dHFF2+DAAQtvRHxJ85oxWDz4b1AIDIdb8jds8erXKvZT/C1MkJMTt3IeqPP7TKC8+YAQvf4nh94iTCV6zQKi/0zTewrlwJidev4+XsOVrlLgMGwK5RQ6SEhuLZmLFa5Y6dO8OxQ3soXr9GyKD/U89PvyaYtGwBt88/BwA87t0HIi1NY33rqlVRaJTqvSx0+AikvfU0PQt/fxT+TvVe+HzyFCTfu6dRburqCq8lqvfSV4u+R8KFCxrlMlNTeK9VvRdH/PIr4g4e1DqGov/7CSa2toje/heiN2/WKi8yby7MvbwQd/gIIlat0ip3Hz8OVuXKIeHSZbxasECr3PWLL2Bbtw6SHzzE80mTtMqduneHQ9s2SIuKQuiQoVrl9i1bwrnXZwAy/j4zX3Nta9XK99woM7mlJYr9slorrndhgqsnktOUEEIgKVWBZGUKVp9UvVF2vhuCGi+iYGVuAntLMzhYmcHWkr82IjJOPUuUkDoEIjICMiEEvxd/h9DQUBQtWhQhISHw8vLS2X4i41NQc9YhpCiUH14YgLW5CVqU9UDnKkVRo7gz5HLZh1fKAYVCgbCwMLi5ucHExCRft02sX11i3eoO61Z3WLe6w7rVLX2uXzYF6gFnG3MET2+BF7FJeBQRj8cRCXgUEY+HYfG48TQGz2I0v45KSFFg26Wn2HbpKbxdrDGoni86VfGChal+nVxERDnVtGlTAMCBAwckjoSIDBkTXD0hl8tQxNEKRRytUMtPs+xVbBLOP4rE0TthOHonDOGvk9VljyMSMHH7Dfx4+B6+aOCHHtWLwdSE9w4SkWFq2bKl1CEQkRFggmsACtlbok35ImhTvgiUSoGzDyLw54UQ7LnxAslpqm4Nz2OSMHnHTfxxPgQzO5RF5WJOEkdNRJRzo0aNkjoEIjICbOozMHK5DLX8XbG4WyWcHd8Ywxr5w84i43PK7eex+HTFaUz66zoSUxQSRkpEREQkDSa4BszJxhxfNwvAybGN8H/1fWH65mYzIYDfzz5Bh+WncD/stcRREhFlX7t27dCuXTupwyAiA8cE1wg4WJthfMtS2D2iLqr5ZHRNCH4Rh0+WnsSua88kjI6IKPuqV6+O6tWrSx0GERk49sE1IiXd7bBpUBD+d+IB5u+7A4VSID5FgaEbLuNFTBIG1PWVOkQioveaOHGi1CEQkRFgC66RkctlGFzfDxsH1YS7vYV6/ox/bmPW7tt8HDAREREZPSa4RqqajzN2Dq2DUoXt1fP+d/wBxm+7ziSXiPRWt27d0K1bN6nDICIDxwTXiBWyt8Sm/6uJmr7O6nmbLoTgu123wAfYEZE+8vf3h7+/v9RhEJGBYx9cI2dvaYbf+lfH0A2XceDWSwDAmtOPYG9pilHNAiSOjohI04wZM6QOgYiMAFtwCwALUxP82KMS6pZwVc9bcvge1px6KGFURERERLrBBLeAsDA1wU+9qqCqd8YwYt/tuoXjd8MkjIqISFP//v3Rv39/qcMgIgPHBLcAsTY3xS/9qqGkuy0AQCmAoRsu4QEfBkFEesLR0RGOjo5Sh0FEBo4JbgFjb2mGVb2rwdHaDAAQm5SGAb9dQExiqsSREREBixYtwqJFi6QOg4gMHBPcAqiYizWW96ysfrTvg/B4TNh2nSMrEBERkVHQiwR38+bNaNeuHby8vGBjY4OKFSvil19+0Uq4Vq9ejZIlS8LS0hIVKlTArl27tLYVExODzz//HM7OzrCzs0OnTp3w/Pnzj3UoBqOWnyumtC2tnv7n+nNs/DdEwoiIiIAhQ4ZgyJAhUodBRAZOLxLcRYsWwdraGgsXLsTOnTvRsmVLDBw4EN999516mY0bN2LgwIHo2rUr9uzZg6CgIHTo0AFnz57V2FbXrl2xf/9+rFy5EuvXr8edO3fQsmVLpKWlfezD0nuf1fRG6/KF1dPTdt7Efy/jJIyIiAo6pVIJpVIpdRhEZOBkQg++lw4PD4erq6vGvEGDBmHTpk2IioqCXC5HQEAAqlSpgg0bNqiXqVWrFhwdHbF7924AwJkzZ1CrVi3s27cPzZo1AwDcuXMHpUqVwsaNG9GlS5ccxRUaGoqiRYsiJCQEXl5eeTxK/RSblIpWP5xAaFQiACDA3Q7bv6iJ2OhIuLm5wcTEROIIjY9CoUBYWBjrVwdYt7rDutUd1q3usG51S5/rVy9acN9ObgGgUqVKiI2NRXx8PB48eIC7d+9qJajdunXDoUOHkJycDADYs2cPHB0d0bRpU/UyAQEBqFixojoJJk32lmZY0r2Suj/unZdx+OHwPYmjIiIiIso9vUhws3Ly5El4enrCzs4OwcHBAIDAwECNZUqVKoWUlBQ8fKh6YEFwcDACAgIgk8m0lkvfBmmrXMwJI5uWVE//fOIhbr2IlzAiIiqoRo8ejdGjR0sdBhEZOL18VO/JkyexceNGLFy4EAAQFRUFAFpjIzo5qR5aEBkZqV4uq/ETnZyc1Mu8T2xsLGJjY9XT6TenKRQKKBSKHB+HIRlQ2xt7bzzH9aexUApg+v5HqF7SE1YWUkdmfBQKBZRKpdGfU1Jg3erOx6rbly9fqvdXUPC81R3WrW597PrNSTcIvUtwQ0ND0bVrVzRs2BDDhw//qPtetGgRpk2bpjU/IiICFhbGn+mNa+iFPhtuI00p8DAyCfP33MAXtY2z77GUlEolYmJiAAByud5+iWKQWLe687Hqdt68eQCAsLCC85RFnre6w7rVrY9dvx4eHtleVq8S3OjoaLRs2RIuLi7YunWrurLSW2pjYmI0Di69ZdfZ2Vm9XEiI9lBXUVFR6mXeZ9SoURgwYIB6+vnz56hevTpcXFzg5uaW+wMzEG5uwLBGqfj+4H8AgPUXX6FrkD8C3O0kjsy4pH/SdXV11btO+YaOdas7rFvdYd3qDutWt/S5fvUmwU1MTESbNm0QExODM2fOwMHBQV2W3vc2vY9tuuDgYJibm8PX11e93MGDByGE0OiHGxwcjHLlyn0wBnt7e9jb22vNNzEx0btfnK582dAf+26+wK3ncUhTCkz9+zY2/V9NrX7NlDdyubxAnVcfE+tWdz5G3U6ePBkANIaJLAh43uoO61a39LV+9aK9Pi0tDV26dMHt27exd+9eeHp6apT7+vqiZMmS2Lx5s8b8TZs2oXHjxjA3NwcAtGzZElFRUTh06JB6mbt37+Ly5cto1aqV7g/ECJiZyPHdJ2XU0+cfRWLbpacSRkREBUlwcDBvCiaiPNOLFtwvv/wSu3btwsKFCxEbG6vx8IZKlSrBwsICU6dORc+ePeHn54eGDRti06ZNOHfuHI4fP65eNigoCM2bN0f//v2xcOFCWFpaYuLEiShfvjw6duwoxaEZpErFHNGurCt23AgHAMzafRtNSrnDwdpM4siIyNj9+eefUodAREZALxLc/fv3AwC+/vprrbKHDx/Cx8cH3bt3R0JCAubMmYM5c+YgICAA27dvR1BQkMbymzZtwqhRozBo0CCkpaWhWbNmWLp0KUxN9eJQDcaXtT1x/EEMohJSERGfgvn7gzGj/Ye7eRARERFJTS+eZKavCsKTzLKS/mSSI4+TMX77DQCAXAbs/aoeSvKGszzT5ye/GDrWre58rLqdM2cOAGDcuHE624e+4XmrO6xb3dLn+tWLPriknzpV9kQFL9XNfkqh6qpARKRLJ0+exMmTJ6UOg4gMHBNceie5XIaJrUurp4/eCcPxuwVnbEoi+vh27dqFXbt2SR0GERk4Jrj0XtWLO6NFmYyxh2ftvg2Fkr1aiIiISH8xwaUPGtcyEGYmqnFwg1/EYfMF7YdpEBHlhx9++AE//PCD1GEQkYFjgksf5ONqg95BPurphQfu4nVymnQBEZHR2rFjB3bs2CF1GERk4JjgUrYMb1QCjm/GwQ2LS8ZPx+5LHBERGaPDhw/j8OHDUodBRAaOCS5li4O1GYY3KqGe/vnEA7yKTZIwIiIiIqKsMcGlbPuspjd8XKwBAEmpSvx45J7EERGRsfnf//6H//3vf1KHQUQGjgkuZZu5qRwjm5ZUT/9x/glCIhMkjIiIjM3atWuxdu1aqcMgIgPHBJdypG35Igj0UD3NLFUhsPjgfxJHRETG5OjRozh69KjUYRCRgWOCSzkil8vwTbMA9fT2y6G49ypOwoiIyJiYmprC1NRU6jCIyMAxwaUca1yqECoVcwSgeoTvogN3pQ2IiIzG77//jt9//13qMIjIwDHBpRyTyWQYnakVd/f1F7geGiNhRERkLFauXImVK1dKHQYRGTgmuJQrtfxdUdvfRT29YP8dCaMhImOxZ88e7NmzR+owiMjAMcGlXMvcF/fY3TBceBQpYTREZAzs7OxgZ2cndRhEZOCY4FKuVSrmhCal3NXTPxziiApElDfbtm3Dtm3bpA6DiAwcE1zKk6+aZDzd7MR/4bj4mK24RJR7ixYtwqJFi6QOg4gMHBNcypOyng4arbgcF5eI8oItuESUH5jgUp6NaKzZinvpSZSE0RCRIStUqBAKFSokdRhEZOCY4FKelfNyQOPAjDekH9iKS0S5tHfvXuzdu1fqMIjIwDHBpXwxIlNf3GN3w3CZrbhElAszZszAjBkzpA6DiAwcE1zKF+W9HNEocysuR1Qgolzgk8yIKD8wwaV8k7kv7tE7YbgSEi1dMERkkHx8fODj4yN1GERk4JjgUr6pUNQRDQPc1NNL2IpLRDl07NgxHDt2TOowiMjAMcGlfDWiSUn168PBr3CVrbhElAMTJ07ExIkTpQ6DiAwcE1zKVxWLOqJ+SbbiElHu/Pzzz/j555+lDoOIDBwTXMp3mUdUOBT8CtdDYySMhogMSalSpVCqVCmpwyAiA8cEl/Jd5WJOqJepFZcjKhBRdv3777/4999/pQ6DiAwcE1zSicwjKhy8/RI3nrIVl4g+bOTIkRg5cqTUYRCRgWOCSzpRxdsJdUu4qqfZiktE2fHDDz/ghx9+kDoMIjJwTHBJZzK34h649RI3n7EVl4jer0qVKqhSpYrUYRCRgWOCSzpT1ccZtf1d1NMcUYGIPuTGjRu4ceOG1GEQkYFjgks6NaJxxri4+26+xO3nsRJGQ0T6bvDgwRg8eLDUYRCRgWOCSzpVvbgzavmxFZeIsmfOnDmYM2eO1GEQkYFjgks6l7kv7p4bLxD8gq24RJS1OnXqoE6dOlKHQUQGjgku6VwNXxfU9HVWTy89dE/CaIhInz148AAPHjyQOgwiMnBMcOmjyNwXd/eN57jzIk7CaIhIX/Xu3Ru9e/eWOgwiMnCmUgdABUOQnwuqF3fG+YeREAJYcvg/LOtRWeqwiEjPTJ48WeoQiMgIsAWXPpqvMvXF3X39Of57yVZcItLUrFkzNGvWTOowiMjAMcGljybIzwXVfJwA4E0rLvviEpGm58+f4/nz51KHQUQGjgkufTQymUyjL+6ua89w7xVbcYkoQ+fOndG5c2epwyAiA8c+uPRR1fZ3QRVvJ1x8HAUhgKWH7+GHbpWkDouI9MTo0aOlDoGIjABbcOmjUrXiZvTF3Xn1Ge6HvZYwIiLSJ+3atUO7du2kDoOIDBwTXPro6pZwRaVijgAApQB+ZF9cInojOjoa0dHRUodBRAaOCS59dG+34u648hQP2IpLRADatGmDNm3aSB0GERk4Jrgkifol3VCxqCMAVSvuUrbiEhGAIUOGYMiQIVKHQUQGjgkuSUImk2FEk4xW3L+uPMVdjotLVOB1794d3bt3lzoMIjJwTHBJMg1KuqGqd8a4uAv335E4IiKSWkpKClJSUqQOg4gMHBNckoxMJsM3zQPU0/tuvsS10GjpAiIiyTVq1AiNGjWSOgwiMnBMcElSNX1dULeEq3p6wf67EkZDRFLr378/+vfvL3UYRGTg+KAHktzXzQJw4r9wAMDxu2E49yACNXxdJI6KiKTA5JaI8gNbcElyFYs6ollpd/X0gv13IISQMCIiIiIyZExwSS983SwAMpnq9b+PonDsbpi0ARGRJOrXr4/69etLHQYRGTgmuKQXAjzs8EmFIupptuISFUydO3dG586dpQ6DiAwc++CS3hjZpCR2XXsOhVLgxtNY7L3xAi3LFZY6LCL6iIYOHSp1CERkBNiCS3rDx9UGXap6qacXHrgLhZKtuERERJQzTHBJrwxrVALmJqrT8t6r19h6MVTiiIjoY2rZsiVatmwpdRhEZOCY4JJeKeJohV5B3urphQfuICElTcKIiOhjatiwIRo2bCh1GERk4Jjgkt4Z1sgf9paq7uEvY5Ox6sRDiSMioo9lzJgxGDNmjNRhEJGBY4JLesfR2hxDG/mrp386dh9hcckSRkRERESGhAku6aXeQT7wdLQCAMSnKLD4IB/hS1QQdOzYER07dpQ6DCIycExwSS9ZmplgTIsA9fTGf0Nw71WchBER0cdQoUIFVKhQQeowiMjAMcElvdW2fBGU83QAACiUAnP2BEscERHp2pQpUzBlyhSpwyAiA8cEl/SWXC7DhFal1NMHb7/C6fvhEkZEREREhoAJLum1ID8XNClVSD393c5bSFMoJYyIiHTps88+w2effSZ1GERk4Jjgkt4b36oUzExkAIDgF3HYcP6JxBERka54enrC09NT6jCIyMCZSh0A0Yf4udmif+3i+On4AwDAwv130aZ8ETjbmEscGRHlt7lz50odAhEZAbbgkkEY2sgfbnYWAICYxFQs2H9H4oiIiIhIXzHBJYNgZ2mGcS0C1dN/nH+CG09jJIyIiHRh0KBBGDRokNRhEJGBY4JLBqNDJU9ULuYIABACmPr3TQghpA2KiPKVpaUlLC0tpQ6DiAyc3iS49+7dw+DBg1GxYkWYmpqibNmyWss0aNAAMplM6yc4WHN81JiYGHz++edwdnaGnZ0dOnXqhOfPn3+sQyEdkctlmPpJGchU95vhwuMo/HXlqbRBEVG+WrJkCZYsWSJ1GERk4PTmJrObN2/in3/+QY0aNaBUKqFUZj0UVO3atbFgwQKNeT4+PhrTXbt2xc2bN7Fy5UpYWlpi4sSJaNmyJS5cuABTU705ZMqF8l6O6Fq1KDb+GwIAmLHrNhoGFIKjNW84IyIiIhW9yfbatm2Ldu3aAQD69u2LCxcuZLmco6Mjatas+c7tnDlzBvv27cO+ffvQrFkzAEBAQABKlSqFbdu2oUuXLvkfPH1Uo5sHYO/NF4hOSEVEfApm7w7G3E7lpQ6LiPLBV199BQBYvHixpHEQkWHTmy4Kcnn+hLJnzx44OjqiadOm6nkBAQGoWLEidu/enS/7IGm52FpoPOFs04UQnHsQIWFERJRfXr9+jdevX0sdBhEZOL1JcLPr2LFjsLGxgaWlJerXr4/jx49rlAcHByMgIACy9I6ab5QqVUqrry4Zrs5VvFDT11k9PX77dSSnKSSMiIjyw6pVq7Bq1SqpwyAiA6c3XRSyo379+ujduzdKlCiBZ8+eYcGCBWjSpAmOHTuGoKAgAEBUVBQcHR211nVyckJkZOR7tx8bG4vY2Fj1dPqNaQqFAgpFwUmeFAoFlEql3h/zjHZl0GrpKaSkKfEgLB7LD9/D8Mb+Uof1QYZSv4aIdas7rFvdYd3qDutWtz52/ZqYmGR7WYNKcKdNm6Yx3aZNG5QpUwbTp0/Pl+4HixYt0toHAERERMDCwiLP2zcUSqUSMTGqMWbzq+uILtgC6FvNA/878wwAsPzYfQR5WcDHWb+HGDKU+jVErFvd+Vh1O3PmTADAxIkTdbYPfcPzVndYt7r1sevXw8Mj28saVIL7NhsbG7Ru3RpbtmxRz3NyckJISIjWslFRUXB2dtaan9moUaMwYMAA9fTz589RvXp1uLi4wM3NLf8C13Ppn8RcXV1z9GlJCiNbuODwvRjcC4tHqkJg/rGn+GNADZjIZR9eWSKGVL+GhnWrOx+rbiMiVP3pec2l/MC61S19rl+DTnCzEhgYiIMHD0IIodEPNzg4GOXKlXvvuvb29rC3t9eab2Jione/OF2Ty+UGcdxWJiaY/Wl5dF55BgBw8XE0fjvzBAPr+Uoc2fsZSv0aItat7nyMut2wYYPOtq3PeN7qDutWt/S1fg26vT4+Ph67du1CtWrV1PNatmyJqKgoHDp0SD3v7t27uHz5Mlq1aiVFmKRj1Xyc0beWj3p6/v47+O9lnHQBERERkaT0pgU3ISFB3Y/28ePHiI2NVXc9qF+/PoKDgzF//nx06NABPj4+ePbsGRYuXIgXL15g8+bN6u0EBQWhefPm6N+/PxYuXKh+0EP58uXRsWNHSY6NdG9si0AcuxuGh+HxSElT4uvNV7H1i1owMzHoz3BEBc53330HAJg8ebLEkRCRIdObBPfVq1fo3Lmzxrz06SNHjsDLywspKSmYMGECIiIiYGNjg1q1amHlypWoXr26xnqbNm3CqFGjMGjQIKSlpaFZs2ZYunQpn2JmxKzMTbCgcwV0XnkaSgFcC43B8iP3MaJJCalDI6IcuHz5stQhEJERkAkhhNRB6KvQ0FAULVoUISEh8PLykjqcj0ahUCAsLAxubm5616fmQ+buDcaKo/cBAKZyGf4aUhtlPR0kjkqTIdevvmPd6g7rVndYt7rDutUtfa5ffn9LRuWrJiUQ6GEHAEhTCozcdAWJKRz/kIiIqCBhgktGxcLUBAu7VIDpm2HC/nv1Gt/tuilxVESUXQsWLMCCBQukDoOIDBwTXDI6ZYo44JvmAerpP86H4O+rzySMiIiy6+DBgzh48KDUYRCRgeNdV2SUBtX1xen7ETh+NwwAMGHbdZT3dICPq43EkRHR++zdu1fqEIjICLAFl4ySXC7Doi4VUMhO9Yjl18lpGPbHZSSnsT8uERGRsWOCS0bL1dYCi7tVRPoD7a4/jcGcPcHSBkVE77Vs2TIsW7ZM6jCIyMAxwSWjVsvPFcMaZYyF++upR+yPS6TH/vzzT/z5559Sh0FEBo59cMnojWhcAuceRODcw0gAwJgtV+HraqN34+MSEXDs2DGpQyAiI8AWXDJ6JnIZfuxRGYUdLAEASalK/N+6i4h4nSxxZERERKQLTHCpQHCzs8BPvarAwlR1yj+NTsSX6y8hVaGUODIiyuzXX3/Fr7/+KnUYRGTgmOBSgVHeyxFzPi2nnj73MBIzdt2SMCIietvq1auxevVqqcMgIgPHPrhUoHSo5IWbT2Ox6uRDAMBvZx7Dv5AtegX5SBsYEQEADh06JHUIRGQE2IJLBc64loGoW8JVPT3575vYe+OFhBERUToLCwtYWFhIHQYRGTgmuFTgmJrI8WP3yvAvZAsAEAIYsfEyLj6OlDgyItq4cSM2btwodRhEZOCY4FKB5GBtht/6V4e7vaqlKDlNic9/u4D7Ya8ljoyoYPvxxx/x448/Sh0GERk4JrhUYHk6WuHXvtVha6Hqih6dkIreq8/jVWySxJERFVw7d+7Ezp07pQ6DiAwcE1wq0EoXscdPvarAzET1PN+n0YnoseocwjlGLpEknJyc4OTkJHUYRGTgmOBSgVfb3xXzO1VQT9979RqfrTqHyPgUCaMiKpj+/vtv/P3331KHQUQGjgkuEYD2lTwxo31Z9XTwizh8tuocohOY5BJ9TPPmzcO8efOkDoOIDBzHwSV647Oa3khTKDF1p+rhD7eex6LX6vP4fUANOFiZSRwdUcGwadMmqUMgIiPAFlyiTPrWLo5JrUupp68/jUGPn8+yTy7RR+Lp6QlPT0+pwyAiA8cEl+gtA+r6YkyLAPX0zWex6LLyDJ5GJ0oYFVHBcODAARw4cEDqMIjIwDHBJcrClw38Ma5loHr6QXg8Oq04jXuvOE4ukS5NmzYN06ZNkzoMIjJw7INL9A6D6/vB0coME7Zfh1IAz2OS0OWnM1jTrxrKezlKHR6RUVqzZo3UIRCREWALLtF7dKteDD/2qKweJzcyPgVdfzqLvTeeSxwZkXHy9/eHv7+/1GEQkYFjgkv0Aa3KFcYvfavB2twEAJCYqsDg3y9h2ZF7EEJIHB2RcTl16hROnToldRhEZOCY4BJlQ90Sbtg4qCYK2Vmo583fdwej/ryKpFSFhJERGZexY8di7NixUodBRAaOfXCJsqm8lyN2DK2NAb9dwM1nsQCA7Zef4lFEPJb1qIwijlYSR0hk+JYvXy51CERkBNiCS5QDhR2ssHlwEFqU8VDPu/wkGq2XnMCRO68kjIzIOJQvXx7ly5eXOgwiMnBMcIlyyNrcFMt7VsbQhhk3wkQlpKLfr/9i7t5gpCmUEkZHZNguX76My5cvSx0GERk4JrhEuSCXy/BN8wCs7lNV4zG+K47eR4+fzyEkMkHC6IgM17BhwzBs2DCpwyAiA8cElygPGpdyx+4RdVGpmKN63vlHkWj5wwls+vcJR1kgyqGFCxdi4cKFUodBRAYuzzeZ3bx5EydPnkRkZCScnZ1Rp04dlClTJj9iIzIIno5W2DQoCHP3BmP1yYcAgNfJaRi79Tr23niBOZ+Wh7u9pcRREhmGGjVqSB0CERmBXCe4ycnJ6NWrF7Zu3QohBCwsLJCcnAyZTIZOnTph3bp1MDc3z89YifSWuakc37Ypjfol3TBmyzW8iE0CABy5E4Zm3x/H+JaB6FK1KORymcSREum34OBgAEBgYOAHliQierdcd1GYMGEC/vnnH6xcuRLR0dFITExEdHQ0Vq5ciX/++QcTJkzIzziJDEK9km7YN7IeOlb2VM+LSUzFuG3X0Wnladx+HithdET6b8CAARgwYIDUYRCRgct1C+7GjRsxe/ZsDBw4UD3P3t4eAwcOREJCAubNm4cFCxbkS5BEhsTBygyLulREizIemLD9BsJfJwMALj2JRpulJ9E3yBs9yjvCTeI4ifTR9OnTpQ6BiIxArhPcyMjId36FFBgYiMjIyFwHRWQMmpXxQA1fFyzYdwe/n3sMIQCFUmD1qUfYdskUI5oko2dNH5iZ8F5PonQNGzaUOgQiMgK5fmcNDAzEunXrsiz7/fff2X+KCKrW3Onty+KvL2ujrKe9en5UYhqm7ryNZt8fx94bzznaAtEbT548wZMnT6QOg4gMXK5bcL/99lt07twZjx49wqeffgp3d3e8evUKW7ZswZkzZ7B58+b8jJPIoFUo6ogdQ+pgw7nH+P7AXUQmpAIAHobHY/Dvl1ChqCNGNPZHw4BCkMl4IxoVXD169AAAnDx5UuJIiMiQ5TrB7dixI7Zv345p06bh66+/hhACMpkMFStWxPbt29G2bdv8jJPI4JnIZegV5IO25T3w/d6b2Hj5FZLTVE89uxoSjf5rLqCcpwOGNy6BJqWY6FLBxBuUiSg/yEQ+fDcaHx+P6OhoODo6wsbGJj/i0guhoaEoWrQoQkJC4OXlJXU4H41CoUBYWBjc3NxgYmIidThGJ71+08zt8MOhe9h6KRTKt/4KAz3s0L9OcbSrWAQWpvwdZBfPXd1h3eoO61Z3WLe6pc/1my93t9jY2MDT09OoklsiXSvsYIn5nSvg0NcN0KmKF0wyjZEb/CIOY7ZcQ+05R7Dk0H+IeDMSA5GxCwsLQ1hYmNRhEJGBy9OTzM6ePYvNmzcjJCQESUlJGmUymQw7duzIU3BEBUFxVxss6FwBwxr5Y9mRe9h26SnS3jTphr9OxqIDd/Hj4XtoUdYD3aoXRZCvC7svkNHq0KEDAPbBJaK8yXWC+8MPP2DkyJEoVKgQ/Pz8+NQyojzydrHBvE4VMKppANaeeYT1554gJlF1M1qKQom/rz7D31efobirDbpWK4pPKhRBEUcriaMmyl8jR46UOgQiMgK57oNbtGhRdOjQAYsXL4ZcbpzjeLIPrv71qTEG2a3fhJQ0bL30FL+eeogHYfFZLlO9uDPaVSyCVmULw8mGHzJ57uoO61Z3WLe6w7rVLX2u31y34MbHx6Ndu3ZGm9wSSc3a3BS9anrjsxrFcP5hJP44/wS7b7xAypuRFwDg/MNInH8YiSk7bqJeSTe0KlcYjQILwZnJLhmo+HjVhzne00FEeZHrBLdr167Ys2cPGjdunJ/xENFbZDIZavi6oIavC6YmpGD75af468ozXA2JVi+TphQ4HPwKh4NfQS4Dqng7oUkpdzQu5Q4/Nxv22SWD0bx5cwDsg0tEeZPrBHfx4sUYMGAAevTogSZNmsDR0VFrmY4dO+YlNiJ6i6O1OfrVLo5+tYvjUXg8dl59hr+uPMX9TF0YlAL491EU/n0Uhdl7guHjYo06JVxR288VQX4ucLRm6y7pr//7v/+TOgQiMgK5TnCDg4Nx6tQpPHr0CBs3btQql8lkUCgUeQqOiN7Nx9UGwxqXwNBG/rj1PBb/XHuOQ7df4c7LOI3lHkUk4FHEE/x+9glkMqBMEXvU9nNFTV8XVCrmyISX9EqvXr2kDoGIjECuE9z+/fvD1tYWO3fuRMmSJTmKApFEZDIZyhRxQJkiDhjTIhBPIhJw8PZLHLz9EucfRqqHHAMAIYAbT2Nx42ksfjr+AADgX8gWVYo5oYq3Eyp7O8LX1RZyObs0kDSUSlUfc97fQUR5kesE9/bt29i2bRtatGiRn/EQUR4Vc7FG/zrF0b9OccQkpuLM/QicuR+OU/cjcO/Va63l7716jXuvXmPThRAAgI25CUoVtkeZIvaqxNnTHiUK2cHclAkH6V69evUAsA8uEeVNrhPcihUr4uXLl/kZCxHlMwcrM7Qo64EWZT0AAC9jk3D6fjhO34vAxSdRWQ4/Fp+iwIXHUbjwOEo9z8xEhpLudihRyBb+mX68XWxgZsLEl/JP7969pQ6BiIxArhPc5cuXo2/fvihcuDAaNWoEU9M8PRSNiD4Cd3tLdKjkhQ6VVOM6R8an4PKTKFx8rPq5FhqDxFTtvvOpCoGbz2Jx81msxnxTuQzeLtbwc7OFt4s1ijq/+XGyhpeTFSzN9GtcRNJ/gwYNkjoEIjICuc5K69ati9TUVLRs2RJyuRxWVppPVJLJZIiJiclzgESkO8425mj8ZjgxAFAoBR6Gx+PmsxjcepPQ3ngWg+iE1CzXT1MK3A+L1xjFITN3ewsUdbJGMWdrFHG0gru9BdztLeHhYAl3e0u42lrAhP19iYgon+U6wf366685tiaRkTGRy9TdD9pV9AQACCHwLCYJwc9j1f1174Wp/o1LSnvv9l7GJuNlbLJGd4e39+dmawF3ewsUsreEi405nN/8uNiaw8XGQv3a2cYcFqZsETZ2TZo0AQAcPHhQ4kiIyJDlOsGdOnVqPoZBRPpKJpPB09EKno5W6pZeQJX4hsUl417YazwIi0dIVAJCIxPxJDIBIVEJ72z1zUyhFHgRm4QXsUkAPvyNj62FKRytzWBvaQZ7K9M3/5rBwUo1z9ZCDllqEjwLCTham8PW0hQ25qawNjeBtYUprMxM2GKs59q0aSN1CERkBNhxlohyRSaToZC9JQrZW6KWn6tWeWxSKkIiExASmYjQqAQ8j1Elsi9jkvAyLgkvY5KRolBmseV3e52chtfJaQASP7Dko3eWWJrJYZ2e9JqbwMrcFDZvXqfPtzI3gbmpHBamJrAwlWf6MYGFmRzmJnJYmKmmzTOVmWda1sxUDlO5DKZyOcxMZPzGK5u++uorqUMgIiOQ6wS3UaNGH1zm8OHDud08ERk4e0sz9fi8WRFCICohFS9ikvAyNglhccmIiE9BZHz6v6qfiNeqf7O6+S03klKVSEpNQWTW3YZ1Ri4DTE3kMJPLYCKXwcxEDlMTVQKs+jfT6yyXU03LZTLI3/xrIkOmaVWXD5lMBhOZalq9nFwGmQxv5mdaPtO2NNdXrSuTySADIJMBQinw+nUc7O2SYGIihwwyvPlPYzmZDJBBtb90GuUCkMkEZBCQQ/UBRwZADqVqmTfz07cDmcjYh0jffsa/gFDvT4aMMZ9lQrzZd+bfQqZ5ItOy6etlzIJMlkU5ZJnWy3pbqng0NyYTItMHnMzzVf8qlQrExsYgIuz5W98wvNkHZJrrIXuy+kwly2LtLD97ZbmufpBp1Pf7KYQCsdGxeB1pDxNZfnZxElnX28eWg7rQBYVSgdjYWDg61IKJlbWksbwt1wmuvb29VotEVFQULl26BEdHR1StWjXPwemLPSuuwcUxY0g0r0AnVG/rCwD4Z/k1JMdrfhXrWtQO9bqVBAAcWnsbMS8TNMptnSzQbEBZAMCprffw8oHmV7Om5nJ8MqISAODi3kd4fD1CK6ZPRlSEqbkJbp54ijtnX2iVNxtQBrZOlrh/+RWuHgzRKq/XPQCuXrZ4ejcK53Y80CgTQqBEHSe4ubkh4ulrHNtwR2v98o2Kwr9KIcTHJGPf/25olZes4YGy9TyhSFNix/eXtcqLlXFB1VY+AICdS68gNUkzeSnkY486nUsAAA78chNxEUka5fZuVmjStzQA4MSfdxH2WPPpXebWpmgzpAIA4N9/HiLkVqRWDO1HVYLcRI7rR0Px37/aQ961+L9ysLY3x3//vsT1o6Fa5Q0+C4RzYRuE3IrEv/881Cqv2cEPRfwdEfYkDic23VXPF0IgNTUVVVvK4F/ZHXGRSTiw+qbW+oG1CqN07SJITVZg55IrWuU+5V1Rubk3AGDH4stQpGq2hnr4OaBWR38AwL6fbyA+Olmj3NHDGo16lQIAHN1wB5FPNcfItbQ1Q6svygMAzv39AE/vaPajlcll6PB1ZQDA1UMhuH/plVaMrb4sD0sbM9w5+xw3TzzTKm/UpxRKF7GHdUQKoo6+gjuAjE4QJqjVqRw8ijvgxcMYnNj8H9IUAmlKJRRKAYVSwLy8E5JdzREbngT5+QikKZVITVNCQNX9IdgGuG2hQFpiGlrHaF/u7popcNFSde51izPXegN/YqrEKStVP+P28eaweqvBOdJEgdPWcbBAKuol2MJBYaJO0mQQSJWn4K7dfZgjDcUSvGCXZqcuk0FAJlMgxv4UzJAG68QSMEsp/KYsYxtWDjthKktBalIgUpJKq5Ot9OUK2e+AhUk0EpL9EZUQpFkuA7xtd8DeLBSvU7zxOL5VpqRQ9W8Jm39QyPw2Xqe543pcL7ydSPlZH4K35TkkK21xOnrYm5KMZXwszyDQej+EkGFf1Lfq9dKX8zK/iqq2myGHwO7oCUhW2mjUoZvZPdS1/wUAcDB6OMbu+AsAMKHNYACAnckrNHVcDAA4GdsPL1NLaKxvJktEW+fpAIALrzvhSXJlvK2d82SYyNJwI6E57ibW1ypv7jgfNiZRuJdUC9fitbtI1LdfCRezJwhNLofzr7trldewWw9P85sIT/XG8VjtRw1XsPkbfpZn8Vrhgv3RX2uVB1gdQZD1AaQJc/wdOVWr3NviAqrYbgMA/B05BWnCQqPc3ewOatv/BgDYHz0KrxWa36g4mD5DY4cfAQDHYwcgPNVXo9xCHofWTrMBAOfjuiI0pYJWDB1dJgAArsW3wr2kOlrlLZ1mw0oeh7uJdXEjoaVWeUOHH+Fk+gxPkiviwusuWuVBdr+hsPkdvEz1x6nY/lrllWy2objlBcSmFcLBmK+0yktbH0Cg1RGkKK2w6815mFlxy7OoZPM3AOCvyO+gFJrXg8LmNxFktx4AsDdqNBKUThrlTqZP0NBhJQDgSMxgRKUV0yi3lkehhdN8AMCZuJ54nlJGo1wuS0N758kAgMvxn+BhUk2tGNs4TYe5PBHBiQ1xK6GpVnkTh8WwN32Fh0lVcTm+o1Z5bftf4G52D89TAnAmro9WeVXbP1HM4gqi0orgSMxQrfKy1ntQ0uoEEpV22BM1Xqvc3/IkytvsBgBsi5ilUXbnkCV8KxT5aLlRduQ6wf3rr7+ynB8eHo5PPvkE3bp1y+2miYi0mMhkMDGVwQIZ4+5W9neFTzlXRL9KwOF7ieoPD2ZmZpDJZOgS5IKAClZIionBP7+9gFKRBqFUQKlUQCgVaOYeAR/Ph1Amx+HcpWoQSiUgVD8yKNHQ8iHmOu6BiTIZ55J7I0Vho0ochRIyCLiYPMZPlj8BAA4nf4lo4aURs41JBKaZLwQAnE7uhReKUm8dUwramakSk0vJ7fFIpv1EyLYmh2EmT8YtORAs89Qqb2hyBXYm4Xggl+OKrLxWeRn5Y7jJH+KZ3AzhSNYqLyyLQlH5S0TKzXAX2v2mHRGPQrJoJEDADNqt6JZIgZ0sEUrIYQLt1iQTmYCpLPtdUQI9imd7WSKid5EJkf/t23/99RfGjBmDu3fvfnhhPRYaGoqiRYsiJCQEXl5eH17BSCgUCoSFhcHNzQ0mJrxrPb+xfnNIqQASIoH4MNVPYiSQGA0kRQNJMRmvE6MhEqOhiI+ASWocZEkxqmSVjJJSo5046++KxVvt1arXmWlvI6tykY1lNWN4f3lW8b6rXGMbmb81lfab6ffS49A0vOu80T/6G6d483/x+SG4F/WTOBpNOrnJTKFQ4MUL7a/NiYjUkmKB2GdA7FPVv69fAPHhGYlsfDjw+hWQEIHsvmXKINGdsyYWgJml6l8TM9WP3AwwMQdMTFX/ys3eep15ufQfc0D+ZhkTM0BmAsjlb/41eevffJqv7jgrV9XgO14rhEBkZBScXVxgYmL6puPmm/IPvpZpz//A/t7qxKtV5cb0/Dx+6NUd1q1uZa5ffZPr94JLly5pzUtJScHt27cxbdo0VK9ePU+BEZEBE0KVpEY+BKIeqv6NDX2T0L75SY798Hbyg6klYG7z5scu02sbwCLztK3qXzMrwNQKMLV489ri/dMmFqrk0dgpFFAowwBnN0CHiULXrl0BAJs2bdLZPojI+OU6wa1atarWTWbpvR1q1KiBn3/+OW+REZH+iw8HwoKB8LtA5IM3Ce0j1b+p+ThMgdwUsHEDbFwBK2fAyhGwdMz419IBsHKEwtweUUkCToWLw8TGRZXAmpjlXxykcyVLlpQ6BCIyArlOcI8cOaI1z9LSEl5eXvD01L4RgogMWFIM8OIGEHYbeBWsSmpf3QYSwvO2XWtXwL4IYO8J2HkAtoXeJLKZf1xVSWx2WkkVCqSF6b6VkXRn+vTpUodAREYg1wlu/fraQ60QkRFIigWeXwWeXwGeXQaeXQEi7+d8OzITwLEY4FwccCoOOBYF7L3eJLRFALvCqn6rRERE+SzXCe7jx48RGxuLcuXKAQCSk5OxYMEC3L59G02aNEHfvn3zK0Yi0hUhgIj7wJPTwOMzQOi/QMR/2V9fJlclr26BgIuv6nV6QutQVHVTFVEOpL93rFmzRtI4iMiw5frdZ+DAgahYsSLmzZsHABg7diyWL1+O8uXL488//0RCQgK+/PLLfAuUiPKBEKr+svePAI9PAU/OAvHaD2jIkmMxwL2sKpktVEr1r2sJ1Y1WRPnExcVF6hCIyAjkOsG9cuUKhg1TPdUmLS0Nv/32G+bOnYuRI0di1qxZWLFiBRNcIn2QEAk8OArcP6xKbGO1n8qmxbEYULgiUKQSUKSi6rW1s27jJAKwcOFCqUMgIiOQ6wQ3Li4ODg6qZ8yfO3cOsbGx6qeX1alTBzNnzsyfCIko5yIfALd3AcG7gJDzeO84sibmgGcVoFiQ6sezCmDDVjQiIjJcuR680cvLC2fPngUAbNu2DaVLl0bhwoUBAFFRUbC2ts7R9u7du4fBgwejYsWKMDU1RdmyZbNcbvXq1ShZsiQsLS1RoUIF7Nq1S2uZmJgYfP7553B2doadnR06deqE58+f5/AIiQyIEMCL68CR2cDyWsCSSsCBb4GQc9BKbk3MgeL1gUbfAv32AuNCgP57gSZTgJLNmNySpL744gt88cUXUodBRAYu1y24n3/+OSZNmoTNmzfj8uXL+P7779VlZ8+eRalSpd6ztrabN2/in3/+QY0aNaBUKqFUaj9ic+PGjRg4cCAmTpyIRo0aYdOmTejQoQNOnDiBmjVrqpfr2rUrbt68iZUrV8LS0hITJ05Ey5YtceHCBZia8qYXMiIxT4HrfwJXN6qG7noX1wDArxHg3xjwrqV6oAEREZGRkon0pzPkwtq1a/Hvv/+icuXK6Nu3r/rBD4MHD0atWrXQu3fvbG9LqVRC/macy759++LChQu4ceOGxjIBAQGoUqUKNmzYoJ5Xq1YtODo6Yvfu3QCAM2fOoFatWti3bx+aNWsGALhz5w5KlSqFjRs3okuXLtmOKTQ0FEWLFkVISAi8vLyyvZ6h46MNdSvP9ZsSr+p+cPUPVd/arLofyOSq7gaBbYDA1oCTd17DNgg8d3WHdas7rFvdYd3qlj7Xb56aM3v37p1lErty5cocb0v+gUHcHzx4gLt372Lu3Lka87t164bRo0cjOTkZFhYW2LNnDxwdHdG0aVP1MgEBAahYsSJ2796dowSXSK+E3QH+XQVc+QNIidMul5sCvg2BUm2BgFaArf49G5yIiOhjyPP39c+ePUNoaCiSkpK0yurVq5fXzasFB6u+fg0MDNSYX6pUKaSkpODhw4cIDAxEcHAwAgICtB4jXKpUKfU2iAyGUgEE/wP8+zPw8HjWyxSpBFToDpTtxP6zZPC+/vprABxNgYjyJtcJ7oMHD9CrVy/1jWZv93SQyWRQKBR5iy6TqKgoAICjo6PGfCcnJwBAZGSkerm3l0lfLn2Zd4mNjUVsbKx6Ov3GNIVCka/Hou8UCgWUSmWBOuaPKVv1m5YE2dWNkJ39EbLIB1rFwq4wRLkuEOW7qsajzdi4DiI2HDx3dedj1W14eLh6fwUFz1vdYd3q1seu35x0g8jTgx5CQ0Pxyy+/oHTp0jA3N8/tpvTGokWLMG3aNK35ERERsLCwkCAiaSiVSsTExAD4cNcRyrn31a8s5TWsb6yH9fW1kCeGa62b7FkTCWV6ItmnkapLAgCEhek8ZkPBc1d3PlbdpndDCytA5zXPW91h3erWx65fDw+PbC+b6wT3/Pnz+O2339CxY8fcbiJH0ltqY2JiNA4wvWXX2dlZvVxISIjW+lFRUepl3mXUqFEYMGCAevr58+eoXr06XFxc4OZWcPozpn8Sc3V11btO48Ygy/pNTYDs31WQnf4BssQojeWFqRVExR4QVT+HqVsg7D92wAaE567usG51h3WrO6xb3dLn+s11guvp6flRDya97216H9t0wcHBMDc3h6+vr3q5gwcPQgih0Q83ODgY5cqVe+8+7O3tYW+vnT6YmJjo3S9O1+RyeYE87o9FXb8iDbj4G3BiAfD6peZCVk5A9UGQVR8EmY2rNIEaIJ67uvMx6vbbb78FAEyfPl1n+9BHPG91h3WrW/pav7luT545cybmzJnzwX6t+cXX1xclS5bE5s2bNeZv2rQJjRs3VneRaNmyJaKionDo0CH1Mnfv3sXly5fRqlWrjxIr0QcJobp5bFl1YM9ozeTWphDQfBbw1Q2g4QSAyS0VIHfv3sXdu3elDoOIDFyuW3DXrFmD0NBQ+Pj4oGLFilo3dslkMuzYsSPb20tISFCPZfv48WPExsZiy5YtAID69evDzc0NU6dORc+ePeHn54eGDRti06ZNOHfuHI4fz7i7PCgoCM2bN0f//v2xcOFC9YMeypcv/9G6UxC916tbcNo1GiZPz2jOt3QE6nwFVB/EBzFQgbVp0yapQyAiI5DrBPf169fw9/dXT8fFZTEuZw68evUKnTt31piXPn3kyBE0aNAA3bt3R0JCAubMmYM5c+YgICAA27dvR1BQkMZ6mzZtwqhRozBo0CCkpaWhWbNmWLp0KZ9iRtJKigUOz4D8359hITI9qc/MGggaCtQaClg6SBcfERGRkcjTk8yMHZ9kpn9PJjFYd/YC/4wCYp9qzi/XBWgyFXDwlCQsY8NzV3c+Vt3Onj0bADB+/Hid7UPf8LzVHdatbulz/bJJk0iXXocBe8cCN7ZqzE51Kwt56/kw8aklUWBE+un06dNSh0BERiBPg5ZdvnwZnTt3RuHChWFhYYHChQujS5cuuHz5cn7FR2S4bv0NLKummdya20HZcgEiOm4GitaQLjYiPbVz507s3LlT6jCIyMDlugX3xIkTaNq0KTw8PNC9e3e4u7vj5cuX2L59O2rVqoUDBw6gTp06+RkrkWFIiQf2jgMurdWcX7IF0HohhG1hPpyBiIhIh3Kd4I4bNw4NGjTArl27NG7emj9/Plq3bo1x48bh5MmT+RIkkcF4dhnYOgCIuJcxz9oFaDUfKNMRkMkK/ON0id5n8eLFAICvvvpK0jiIyLDluovC5cuXMXz4cK2RCUxMTDB8+HBcunQpz8ERGQwhgHM/Aauaaia3fo2AL04DZT9VJbdE9F67du3Crl27pA6DiAxcrltwbWxs8OrVqyzLXr58CRsbjuNJBURqIrDzK+Daxox5JuZAk2lAjcEAn39OlG0HDx6UOgQiMgK5TnDbtm2LsWPHwsvLC02aNFHPP3jwIMaPH49PPvkkXwIk0mtRj4FNnwEvrmXMcykBdP4V8Hj/o6GJiIhIN3Kd4C5cuBA3b95E8+bNYW9vj0KFCuHVq1eIjY1FtWrVsGDBgvyMk0j/PDwO/NkHSMz0uOrANkD7FYClvXRxERmw//3vfwCAQYMGSRwJERmyXCe4Tk5OOHPmDHbt2oWTJ08iKioKzs7OqFOnDlq3bg05v5YlY3Z1I7BjKKBMfTNDBjSaBNQZxS4JRHmwdq1q9BEmuESUF7lOcA8dOoQnT56gX79+Wt0R1qxZA29vbzRs2DDPARLpFSGAEwuBw9Mz5lk6AJ+uBko0lS4uIiNx/PhxqUMgIiOQ66amSZMm4eXLl1mWhYWFYdKkSbkOikgvKdKAXV9pJreO3sCAQ0xuifKJXC7nN4BElGe5vorcvHkTVatWzbKscuXKuHnzZq6DItI7qUmqm8kursmYV6QSMOAg4FpCsrCIjM26deuwbt06qcMgIgOX6y4KMpkMMTExWZZFRUVBwcHsyVikJACbegL3D2fMK9kC6PQLYM7h8Ijy008//QQA6NWrl8SREJEhy3ULbo0aNbBs2TIIITTmCyGwfPly1KhRI8/BEUku+TWwoYtmclu5D9B1PZNbIh3Yt28f9u3bJ3UYRGTgct2CO23aNDRs2BDly5dH3759UbhwYTx79gxr167F3bt3cfTo0XwMk0gCSbHA+s5AyNmMeTUGAy3m8KlkRDrChwQRUX7IdYIbFBSEQ4cOYcyYMRg7diyUSiXkcrl6fs2aNfMzTqKPKzkOWNcBeHohY16t4UDT75jcEunQ1q1bAQCffvqpxJEQkSHLdYILALVr18apU6eQmJiIqKgoODo6wtraOr9iI5JGahLwR3fN5LbeaKDhRCa3RDr2/fffA2CCS0R5k6cEN52VlRWsrKzyY1NE0lKkAVv6AY9OZMxrMB5oME66mIgKkO3bt0sdAhEZgXxJcImMglIJ/D0UuLM7Y16tYUD9sdLFRFTAuLm5SR0CERkBjqZNBKieULZvPHD1j4x5lXoBTaezWwLRR7R7927s3r37wwsSEb0HW3CJAOD0EuDcyozp0u2Atj8wuSX6yGbNmgUAaNWqlcSREJEhY4JLdHsXcGBKxrRfI6Djz4DcRLqYiAqoDRs2SB0CERkBJrhUsD2/CmwbCODNA0s8ygFd1gGmFpKGRVRQFStWTOoQiMgIsA8uFVyxz4AN3YDUBNW0rQfQfRNgYSttXEQF2JEjR3DkyBGpwyAiA8cWXCqYUuKBP7oBcc9U06ZWQPc/AAdPaeMiKuC+/fZbAMDJkycljoSIDBkTXCp4hAD+Hq7qnpCuw0rAs7J0MRERAGDVqlVSh0BERoAJLhU8/64CbmzJmG70LVCmvWThEFGGwMBAqUMgIiPAPrhUsIReAPaOz5gu3Q6o+7V08RCRhnPnzuHcuXNSh0FEBo4tuFRwxEcAf/YBlKmqaRd/4JMfOdYtkR75+mvVB072wSWivGCCSwWDUgFsGwDEhqqmzaxVw4FZ2ksbFxFpWLp0qdQhEJERYIJLBcPxBcD9wxnTbRYD7qUlC4eIslapUiWpQyAiI8A+uGT8Qs4Dx+ZkTFf9HKjQVbp4iOidrl27hmvXrkkdBhEZOLbgknFLjlM9qUwoVdMe5YEWs6WNiYje6csvvwTAPrhElDdMcMm47RkHRD1SvTa1BD5dxcfwEumxuXPnSh0CERkBJrhkvG7tAK78njHdbAbgFiBdPET0QbVr15Y6BCIyAuyDS8Yp9hmwc0TGtH9ToNoA6eIhomy5d+8e7t27J3UYRGTg2IJLxkcI4K8vgcQo1bS1C9BuGce7JTIAffv2BcA+uESUN0xwyfhc/h14cCRj+pMfATt36eIhomybMmWK1CEQkRFggkvGJfY5sG9ixnTFz4DAVtLFQ0Q50rRpU6lDICIjwD64ZFx2fwMkx6he27oDzWdIGw8R5cjTp0/x9OlTqcMgIgPHFlwyHrd2AMG7MqZbLwKsnKSLh4hyrGtX1UNY2AeXiPKCCS4Zh4RI4J9vMqZLtwdKtZEsHCLKnTFjxkgdAhEZASa4ZBz2TwLiX6leWzoCreZLGg4R5c4nn3widQhEZATYB5cM38MTwJX1GdMt5gC2haSLh4hyLSoqClFRUVKHQUQGji24ZNgUqaoby9L5NQIqdJMuHiLKk7Zt2wJgH1wiyhsmuGTYzq0EwoJVr03MgVYL+EAHIgM2dOhQqUMgIiPABJcMV+xz4OicjOnaIwAXP+niIaI869aN38AQUd6xDy4Zrv2TgJTXqtcOxYA6o6SNh4jyLDk5GcnJyVKHQUQGji24ZJgeHgdubMmYbjEbMLeWLh4iyheNGzcGwD64RJQ3THDJ8ChSgd2jM6b9mwCBraWLh4jyzeeffy51CERkBJjgkuH5d5XmjWUt5/HGMiIj0a9fP6lDICIjwD64ZFgSo4BjczOmg4byxjIiIiLSwASXDMvxBaokFwBsCgF1eWMZkTGpX78+6tevL3UYRGTg2EWBDEfkA+DcTxnTjSYCFnbSxUNE+a5Lly5Sh0BERoAJLhmOg1MBZarqdaHSQKVekoZDRPlvyJAhUodAREaACS4ZhidngVs7MqabTQfkJtLFQ0R6JS0tDeHh4UhJSYFSqZQ6nBwRQiApKQmJiYmQ8YbZfMW61a38rl8TExM4ODjA3t4+z9tigkv6Twhg38SMab/GqqHBiMjotGjRAgCwd+/ebK8THx+Pp0+fQqFQwMLCAnK5Yd1eIpPJYGFhwQRMB1i3upXf9ZuUlITXr1UPcMprkssEl/Tfja3A0wuq1zI50GyGtPEQkc40aZLzD69RUaobT4sXLw5LS8v8DknnhBBIS0uDqakpE7F8xrrVrfyuX4VCgQcPHiAmJoYJLhk5RSpwOFNCW7k34F5auniISKe++eabHK+TlpYGc3Nzg0xuiSiDiYkJzMzMoFAo8rwtw/oehwqey78DUQ9Vr02tgAbjpY2HiIiI9B4TXNJfqYmaD3WoORiw85AuHiLSuQ4dOqBDhw5Sh0FEBo5dFEh//bsKiHuuem3hANQeIW08RKRzlSpVkjoEyURFRSE2NhZHjx5Fu3bt4OTkJHVIRAaLCS7pp6RY4MSijOnawwErXuyJjN3kyZOlDkEy586dw4EDB/DkyRMkJibiiy++kDokIoPFLgqkn84sAxIjVa9t3IAag6WNh4hIx9LvQhdC8I5/ojxiCy7pn/hw4MyPGdP1RgMWttLFQ0QfTc+ePQEA69evlziSj6969eoIDAxUd1Egotxjgkv65+T3QIpqoGc4FAOq9JU0HCL6eIoVKyZ1CJJxcnKCs7Mz+vbtK3UoRAaPXRRIv8S9VN1clq7BOMDUQrp4iOijmj17NmbPni11GDq1fft2VKxYETY2NihatCg2btwodUhERoctuKRfTi8B0pJUr539gPJdpY2HiCgfrVixAgsWLMC6detQs2ZNPHz4EAkJCVKHRWR0mOCS/nj9Cvh3dcZ0vdGACU9RooJkwIABAIBVq1Z9YMl3E0IgRaHMr5CyxdxE/sEbw6KiojB27Fjs3bsXtWrVAgD4+fmpH3dKRPmH2QPpj9NLgLRE1WtnX6BcZ2njIaKPztY27zeUpiiUCJi0Nx+iyb47M1rAwtTkvcscOnQIfn5+6uSWiHTHoPrgrlmzBjKZTOtn3LhxGsutXr0aJUuWhKWlJSpUqIBdu3ZJFDFl2+swzdbbut+w9ZaoAFq8eDEWL14sdRg6ERERkeXDGyIjI1GrVi3Y2dnhypUrHz8wIiNkUAluur179+LMmTPqnyFDhqjLNm7ciIEDB6Jr167Ys2cPgoKC0KFDB5w9e1bCiOmDziwFUt/0Q3PyYd9bIjI6lSpVwvnz53H+/HkAwKNHj3Dp0iXY2dnh77//RqdOnSSOkMh4GGQTWZUqVeDq6ppl2ZQpU9CtWzdMnz4dANCwYUNcu3YN3333HXbv3v0xw6Tsio8Azmfqb8fWW6ICa/jw4QCAJUuW5Hob5iZy3JnRIr9CyvY+P6R69eqYNWsWOnfujPDwcHh6emLZsmUwMzN753saEeWOUWURDx48wN27dzF37lyN+d26dcPo0aORnJwMCwsOOaV3ziwFUuNVrx29gQrdpI2HiCSTlJSU523IZLIP9oeVyvDhw9VJfDohhETREBkvg0xwy5Qpg/DwcHh7e2PgwIEYM2YMTExMEBwcDAAIDAzUWL5UqVJISUnBw4cPtcoyi42NRWxsrHr6+fPnAACFQgGFQqGDI9FPCoUCSqXy4xxzQiTk539G+r3HyjojISAHjLi+P2r9FjCsW935WHW7YsUK9f6yK/3RtoaaKAohtH4of7BOdUtX9SuEyPIaYGKS/Q+uBpXgFi5cGNOmTUONGjUgk8nw999/Y9KkSXj69Cl+/PFHREVFAQAcHR011kvv1B8ZGfne7S9atAjTpk3Tmh8REVGgWn6VSiViYmIAAHK5brtp2577HrZvnlqmsPVEWOHGQFiYTvcptY9ZvwUN61Z39Lluk5KSYGFhYbBDbaW/macPF2aox6GPMidKHxrGjXJOF/WrVCqRnJyMsCxyAQ8Pj2xvx6AS3ObNm6N58+bq6WbNmsHKygrff/89Jk6cmOftjxo1Sj0GI6Bqwa1evTpcXFzg5uaW5+0bivST1dXVNUeflnIsMQrym7+rJ2X1v4abh6fu9qcnPlr9FkCsW935WHWbPirOnDlzsr1OYmIiZDIZTE0N6i1NTQiBFi1aIDg4GHfv3kW/fv0waNAgqcMyCukti6ampkxwdUAX9SuXy2FpaZnnvMswrwaZdOnSBQsWLMCVK1fULbUxMTEaWX56y66zs/N7t2Vvbw97e3ut+SYmJgXuzVIul+v+uP/9GXjTeguHopBX6gUUkHr+KPVbQLFudedj1G1617Cc7CP9jdWQE5h9+/YxCdORzMOKUv7TRf3KZLI8X2cMPsHNLL1/bXBwMAICAtTzg4ODYW5uDl9fX6lCo7clvwbOrcyYrj0CMDWXLh4i0gu///77hxciIvoA/epIlQsbN26EiYkJKlWqBF9fX5QsWRKbN2/WWGbTpk1o3LgxzM2ZQOmNi2uApGjVa5tCQKVeUkZDRERERsSgWnCbN2+ORo0aoVy5cgCAv//+G//73/8wYsQIdZeEqVOnomfPnvDz80PDhg2xadMmnDt3DsePH5cydMosLRk482PGdNCXgJmldPEQkd5Iv9F3ypQpEkdCRIbMoBLcwMBArF69GqGhoVAqlShZsiQWL16MYcOGqZfp3r07EhISMGfOHMyZMwcBAQHYvn07goKCJIycNFzdCMSp+tnBwgGo+rm08RCR3rh69arUIRCRETCoBPeHH37ADz/88MHlPv/8c3z+OZMmvaRUAKcy/Q6rDwAstW/sI6KCadu2bVKHQERGwOD74JKBubUDiLyvem1qCdT4Qtp4iIiIyOgwwaWPRwjg5KKM6cq9AduCM74wEX3YvHnzMG/ePKnDICIDxwSXPp57h4AX11Wv5aZArWHvX56ICpwjR47gyJEjUodBRAbOoPrgkoHL3HpbrjPgWEy6WIhIL+3Zs0fqEIjICLAFlz6OJ+eAx6cypmuPkC4WIiI9FBUVhUePHmHNmjXqJ3ASUe6wBZc+jsyttwGtgUKlpIuFiPTWjz+qxsgeOnSoxJF8fOfOncOBAwfw5MkTJCYm4osveBMuUW6xBZd07+VN4O7ejOm6o6SLhYj02ubNm7WeRllQyGQyAIAQQv2aiHKHLbikeye/z3jtUxfwqipdLESk144dOyZ1CJKpXr06AgMDcfToUbRr107qcIgMGhNc0q3Ih8CNrRnTbL0lIsqSk5MTnJ2d0bdvX6lDITJ47KJAunV6KSCUqteFKwK+DSUNh4j02y+//IJffvlF6jB0avv27ahYsSJsbGxQtGhRbNy4UeqQiIwOW3BJd+JeApd/z5iuOwpgvzIieo/05LZ///4SR6IbK1aswIIFC7Bu3TrUrFkTDx8+REJCgtRhERkdJrikO2eXA4pk1WuXEkBgW2njISK9d/jwYalD0JmoqCiMHTsWe/fuRa1atQAAfn5+EEIgLS1N4uiIjAsTXNKNxGjg39UZ03W+AuTsEUNE72dubp73jQgBKFLyvp2cMDH/4DdUhw4dgp+fnzq5JSLdYYJLuvHvz0BKnOq1vSdQrou08RCRQfjjjz8AAN27d8/9RhQpwIxC+RRRNk16BZhavHeRiIgIODk5ac0/duwYxo4dCwsLC3h6emLt2rUwMzPTVaREBQKb1Cj/pSQAZ1dkTNcaDpjmQ6sMERm9ZcuWYdmyZVKHoROVKlXC+fPncf78eQDAo0ePcOnSJfj7++PgwYM4duwYfHx8sHXr1g9siYg+hC24lP8urwMSIlSvrV2Ayr2ljYeIDMauXbukDkFnqlevjlmzZqFz584IDw+Hp6cnli1bhkqVKqn74FpYWEDO7lxEecYEl/JXWgpwaknGdI0vAHNr6eIhIoPi6OiY942YmKu6DHxMJtn7lmr48OEYPny4xjwhBADg4cOH2LdvHyZMmJDv4REVNExwKX9d3wzEhqpem9sC1QdIGw8RGZQdO3YAQN6e5CWTfbA/rL6JiopCr169sGbNmvy50Y6ogGOCS/lHqQROLc6YrvY5YKV9QwUR0bvMnz8fQB4TXAOTnJyMHj16YObMmQgICJA6HCKjwASX8k/wLiD8ruq1iQVQc4i08RCRwdm8ebPUIXx0v/zyC65evYqpU6cCAAYNGoQePXpIGxSRgWOCS/lDCODEwozpSj0BO3fp4iEig1S4cGGpQ/jovvjiCwwcOBCmpqaQ8WmPRPmCt2pS/nhwBHh+RfVaJlcNDUZElEP79+/H/v37pQ6DiAwcW3Apf5xYlPG6bCfAubh0sRCRwfruu+8AAM2aNZM4EiIyZExwKe9C/gUenciYrjNSuliIyKCtXbtW6hCIyAgwwaW8O5mp9bZkS8C9tHSxEJFB8/X1lToEIjIC7INLefPyFnBnd8Z03VHSxUJEBu/kyZM4efKk1GEQkYFjCy7lzcnvM1771AWKVpcuFiIyeOPGjQMAJrlElCdMcCn3wu8BN7ZkTLPvLRHl0cqVK6UOgYiMABNcyr3j8wGhVL32rAL4NZI2HiIyeGXLlpU6BCIyAuyDS7kTcR+4/mfGdIPxque/ExHlwcWLF3Hx4kWpwyAiA8cWXMqdzK23RSoD/k2kjYeIjMKIESMAsA8uEeUNW3Ap5yLuA9cyt96OY+stEeWL77//Ht9///2HF6QC6d69eyhfvjwUCoVkMZw4cQJ+fn6S7T8nkpKSIJPJ8OjRoxyve/ToUXh4eOR/UADatGmj8ycWMsGlnDuxEBBvLi5FKgEl+MQhIsof1apVQ7Vq1aQOI9/NmTMHDRs21JjXoEEDlChRQmPeZ599hsGDB+fbfpOTkzFgwAAUL14cdnZ2KFOmDDZs2KCxzKBBgxAQEAC5XP7em/ymTp0KmUyGvXv3vnOZ0aNHo2TJkrCzs4O/v7/Wh5V///0XtWrVgp2dHQICArBz584cHc/kyZMxcuRImJiY5Gi9/FS3bl3cv3//o+yrQYMGRnnj5YQJEzBhwgSd7oMJLuVM5APg6saM6fpsvSWi/HP79m3cvn1b6jDyXf369XH27FkkJycDUCWe165dQ2pqKp49e6Ze7tixY2jQoEG+7TctLQ1FihTBoUOHEBsbi59++glffPEFzpw5o16mQoUKWL58OapXf/cwj3fv3sWWLVtQuHDh9+7P0tIS27dvR0xMDHbs2IGFCxdi06ZNAIDo6Gi0bt0a//d//4fo6Gj88MMP6Nq1K+7du5etY3n16hX++ecfdOrUKVvLk/6qVasWoqOjceHCBZ3tgwku5czxTK23hSsCJZtLGg4RGZeBAwdi4MCBUoeR76pVqwYTExOcP38eAHDu3DlUrFgRDRs2xLFjxwAADx48QGhoaL4muDY2Nvjuu+/g6+sLmUyGOnXqoHbt2jh9+rR6mSFDhqBx48awtLR853YGDx6MhQsXwtzc/L37mz59OsqUKQO5XI4yZcrgk08+UfenPnXqFBwdHdGnTx+YmJigRYsWqFGjBtatW5etY9m/fz8qVKgAOzs79bzY2FgMHjwYXl5e8PDwwNChQ5GUlAQAiIiIQPv27VGoUCE4OzujRo0aCA8PBwA8efIEjRs3hp2dHWrXro3x48er6/3Ro0eQyWTq7QBAt27dMHXqVADaX937+Phg4cKFqFKlCuzt7dGqVStERUWpy7t3747ChQvDwcEBdevWxfXr17N1vGPHjsWJEyfw1VdfwdbWFr169QKg+rDRtGlTODk5oWTJkli9erV6HaVSifHjx8PNzQ3FihXD+vXrNbYZGxuLzz//HB4eHvDy8sKoUaPUH7reZenSpShcuDAKFSqE+fPnq+enpKRg9OjRKF68ODw8PNC3b1/ExMQAAIYNG4Yvv/xSYztffPEFhg4dqp5u2LBhjlvwc4IJLmVf+H/A1T8yptn3lojy2cyZMzFz5sx82dajHj21fsKW/qguD/lyiFb5i+kz1OXPJkzUKn866mt1+ct585FdpqamqFWrljqZPXbsGOrVq4d69eppzAsICHhnv8c2bdrA0dExy5/y5ctnK474+HhcuHAhR8OxrV27Fi4uLmjePGcNGkqlEidOnFDvSwgBIYTGMkIIXLt2LVvbu3btGgIDAzXm9evXD0lJSbh16xaCg4Px33//Yfr06QCABQsWQKlU4tGjRwgLC8Py5cvVSXz37t0REBCAsLAwLFmyRCNJzI3ff/8d27dvx7NnzxAdHa3RNaNZs2a4c+cOXr16herVq6N79+7qsg0bNrzzdzd37lzUrVsXixcvxuvXr7Fu3TqkpqaiTZs2CAoKwsuXL7FhwwaMHTsWBw8eBACsXr0amzdvxrlz53Dz5k2tBHL48OF4+vQpgoODceHCBZw6dQrffffdO48rPDwcISEhePz4MXbt2oWJEyeqW9xnzZqFw4cP49SpU7hz5w4iIyPVSW2/fv2wadMmpKSkAFAlw5s2bULfvn3V2y5VqhSuXLmS/UrOISa4lH2Hp7/VettC0nCIyPjUr18f9evXlzoMnWjQoEGWCe7x48cBAMePH39v6+2uXbsQHR2d5U92kkSlUom+ffuiWrVqaNYse/dOREZGYurUqVi8eHG2ls9s9OjRMDMzUyc1QUFBCA8Px+rVq5Gamopdu3bh1KlTSEhIyNb2oqKiYG9vr55+9eoV/v77byxduhT29vZwdHTEpEmT8McfqoYYc3NzRERE4N69ezAxMUGVKlVga2uLJ0+e4MyZM5g9ezYsLS1RpUoV9OzZM8fHl9mIESNQrFgx2NraolOnTrh06ZK6rF+/frC3t4eFhQUmT56MmzdvIiIiAgDQo0ePbCf4gKrlPyoqClOmTIG5uTmqVq2KAQMG4LfffgMA/PHHHxgxYgR8fX1hZ2eHadOmqddVKBT4448/MHfuXDg6OsLDwwPTpk3D2rVr37k/uVyOGTNmwNzcHNWrV0dgYKA6Kf39998xefJkFClSBA4ODpg3b546qa1cuTK8vLywa9cuAMDOnTtRuHBhVK1aVb1tOzs7jZbu/MZhwih7nl4Ebu3ImG4yla23RJTv0u/29vHxyfO2fDasf2950eXL3lteZNb7W5Ldx4zOUTz169fHzJkzER8fj4sXLyIoKAhWVlaIi4vDy5cvcezYMcyePTtH28wuIQQGDx6MZ8+eYd++fZBl8/o9ZswYfPnll/D09MzR/mbMmIFdu3bh2LFjsLCwAAC4uLhg586d+OabbzB69GgEBQWha9eu6vIPcXJyQnR0tHr60aNHUCgUKFq0qHqeEEI9wsLo0aORkJCAbt26IT4+Hp999hlmzpyJZ8+ewcHBAQ4ODur1vL29cfny5RwdY2aZW92tra3x+vVrAKqkcuLEidi8eTPCwsIgl6vaFcPDw+Hi4pLj/Tx9+hReXl4aN9n5+Piou748e/YMxYoVU5d5e3urX4eHhyMlJUXjb8vHxwfPnz+HECLLc8LZ2VmjW0rmY3v69KnWthQKBV68eIFixYqhX79+WLt2LTp27Ii1a9eiT58+GtuOi4uDk5NTjusgu9iCS9lzMONTIHwbAH4N37koEVFuffbZZ/jss8+kDkMn0m/iWrFiBUqVKgUrKysAqrvyN2zYgEePHr23Bbdly5awtbXN8qdMmTLvXE8IgSFDhuDKlSvYs2cPbG1tsx3zwYMHMW/ePHh4eMDDwwMhISHo0aMHJk+e/M515syZgzVr1uDw4cNa3S3q1KmDs2fPIjIyEv/88w/u37+PmjVrZiuW8uXLIzg4WD1drFgxmJqa4tWrV+qW7JiYGHUCZmtri3nz5uHWrVs4fvw4/vrrL6xduxZFihRBTEwMYmNj1dt68uSJ+nV6/WRuWX7x4kW2Ynzbhg0bsG3bNhw4cAAxMTF4/PgxAGh11XiXt5NOT09PhIaGagyT9ujRI/UHkCJFimgcS+bXrq6uMDc31xgy7NGjRyhcuHC2P/C8Hcvb25LL5erfec+ePXHw4EEEBwdj//79Wn/Xt2/fRsWKFXO83+xigksfdv8w8PBYxnTjKdLFQkRGbdKkSZg0aZLUYeiEmZkZgoKCsGDBAtSrV089v27duli0aNF7+98CwJ49e/D69essf27evPnO9YYOHYqzZ89i3759Gl/xp0tJSUFSUhKUSiXS0tKQlJSkTqD+/fdfXLt2DVeuXMGVK1dQpEgRLFu2DKNHZ916PW/ePKxcuRKHDh3KstX30qVLSElJQXx8PGbPno2wsDCtlr13adq0Ka5evapOYD08PNC6dWuMGDECUVFREEIgJCREPYzZrl27cPfuXSiVStjb28PMzAwmJiYoVqwYatasiQkTJiA5ORmXL1/WuBnL1dUVXl5e+O2336BQKLBjxw6NUSdyIi4uDhYWFnBxcUFiYmKOz213d3eNIclq1KgBR0dHTJ8+HSkpKbh06RJWr16tvgGta9euWLJkCR4+fIi4uDj1jXEAYGJigm7dumH8+PGIjo7Gy5cvMW3aNPW6OdWzZ0/MmDEDz58/R2xsLMaNG4euXbuqW3zd3NzQrFkzdO3aFQ0aNECRIkU01j927Bhat26dq31nBxNcej+lEjg4NWO6dHvAs7JU0RCRkWvRogVatDDe/v0NGjTAy5cvNRLcevXq4eXLlzrpe/z48WMsX74ct27dQtGiRdUtvrNmzVIv06xZM1hZWeHEiRMYNmwYrKys1CMbuLm5qVtvPTw8YGJiAicnJ/VIBrNmzULLli3V2xo7diyeP3+OMmXKqPeVeVzf77//Xr3NM2fO4MCBAx8cmSGdu7s7WrVqhS1btqjn/fbbbzAzM0PFihXh4OCA5s2b4+7duwBUD4Vo2bIlnJ2dUaFCBTRr1kydzG3YsAE3b96Eq6srhg4div79+2vsa/Xq1fjxxx/h7OyMf/75B23bts1Jtav17t0bvr6+8PT0RKlSpbTGeF6/fv17W99HjBiBv//+G05OTujTpw/MzMywc+dOnDhxAoUKFULXrl0xc+ZMdZ/qAQMGoEOHDqhevTpKly6tlUAuWbIE7u7uCAgIQKVKlVC9evX3tsa/z4QJE1CvXj0EBQWhRIkSsLe3x/LlyzWW6devH65du6b1Iebs2bOwtbV979B0eSUT2W0nL4BCQ0NRtGhRhISEwMvLS+pwPhqFQoGwsDC4ubnB5PYOYEs/VYHMBBhyHnD1lzZAA6dRvxIOVm6MWLe687Hq9tWrVwCAQoUKZXud/Oy3KwUhBNLS0mBqapqrr4oLkv/++w+ffvopLl++nK3zMLt1u3LlSmzcuBFHjx7Nx2iN34fq99q1a6hXrx5evHihMQxd27ZtMXTo0CxH5sivv2feZEbvpkgFDmcMmYPKvZjcEpFOdezYEQDUY6cSZVaiRIkcjTpA0klLS8P333+PPn36aI2xrMvxb9MxwaV3kl1YBUS+6ftjaqV6ahkRkQ6NGjVK6hCIKI/+++8/VKxYEQEBAdi3b58kMTDBpSzJEiMhOzY3Y0bQEMD+/Y9oJCLKq/QWXKKPafDgwRp9hSlvSpQogfj4eElj4E1mlCW784shS34zhIpdYaDOSGkDIqICIS4uDnFxcVKHQUQGji24pO3FdVjd/jNjusk0wCL74yYSEeVW+h357INLRHnBBJc0CQH5vnGQ4c3gGl7VgHKdpY2JiAoMfk1MRPmBCS5puvw7ZE8yDWjdci4gZ08WIvo4jPUpZkT0cTFzoQyvw4D9GU9ZUVbqDXhWkTAgIipo0tLSkJaWJnUYRGTgmOBShn3jgaRoAIDCyhWi8VRJwyGigqdBgwZo0KCB1GHQB9y7dw/ly5dXP9KX9NfRo0ff+wjo/PL06VOUKlUKycnJOt9XdjDBJZV7h4Drm9WTcbUnAlaO0sVDRAVS79690bt3b6nD0Ikff/wRVatWhYWFBbp166ZVfuPGDdSsWRPW1tYoXbo0Dh8+rFG+YMEC+Pj4wM7ODm3btsWLFy/eua/Mj8q1tbWFmZkZypcvr7HM8uXL4e/vDxsbG5QoUQKnT5/O9rFMnjwZI0eO5BMDSc3T0xN169bFzz//LHUoAJjgEgAkRgN/D1dPCv8mSPJr+e7liYh0ZNCgQRg0aJDUYehEkSJFMGnSJAwcOFCrLDU1FZ988gk++eQTREVFYcqUKejQoYP60cXr16/H8uXLcejQIYSFhaFQoULo0aPHO/d18+ZNvH79Wv1TuXJldOnSRV2+Zs0aLF++HNu3b8fr169x+PDhbD8a9dWrV/jnn3/QqVOnnFUAGb3evXtj5cqVUocBgAkuAcDu0UBsqOq1mQ2ULRcAfB46EVG+6tixI9q3bw9XV1etsmPHjiEhIQHjxo2DhYUFunbtirJly2LzZtU3a3/99RcGDBgAPz8/WFpaYvLkyThy5AgePHjwwf3evHkTFy5cQJ8+fQAASqUSkydPxvfff49y5cpBJpOhaNGiKFKkSLaOY//+/ahQoQLs7OzU8+7cuYNGjRrB0dERMplM/TN16tT3bksIka195lRycjLGjx8PX19fODs7o1q1aggJCQEA3L17F02bNoWTkxNKliyJ1atXq9dbs2YNatasiYkTJ8LFxQWenp5Yv369unzv3r0oV64c7Ozs4OHhgdGjRwPIuhtAzZo1sWbNGo3tjhs3Ds7OzvDy8sKuXbuwb98+BAQEwNHREd98841GvcybNw++vr5wcXFB+/bt8ezZs3cer0wmw9KlS+Hv7w9nZ2cMHDjwnV0F5s+fD39/f9jZ2aFUqVLYunVrto8/NjYWn3/+OTw8PODl5YVRo0Zp7KdmzZp48OABHj58+M5YPxYmuAXdja3A9Uxj3raYDTgWky4eIirQGjVqhEaNGuXLtrbNv6j1c35nRkL4z/JrWuXHN95Vlx9ae1urfP+qG+ryU1vv5UucgCoJLVeuHOSZRq2pWLEibtxQ7U8IoZEMpr++fv36B7f966+/okmTJihatCgAVV/JkJAQ3L59G97e3ihWrBi+/vrrbPedvHbtGgIDAzVi6dGjB0qUKIHnz5/j4cOHKFmyJEaOHPneYd92796Ndu3aaew3OTkZAwYMQI0aNfDXX38hJCQErVu3RpMmTXDt2jX1cidPnoSjo+M7tz127FgcP34cR44cQXh4OFatWgVra2ukpqaiTZs2CAoKwsuXL7FhwwaMHTsWBw8eVK978eJFeHh44OXLl1i6dCn+7//+D7Gxqgcf9evXD2PGjEFcXBzu3buXo1bsixcvwtvbG69evcLYsWPRr18//Prrrzh//jwuXbqE//3vf+puIr/99huWL1+O3bt3IzQ0FIULF9Zogc/Kxo0bcfr0aQQHB+PKlSuYOXNmlssVL14cx44dQ0xMDKZPn45evXohNDQ0W8c/fPhwPH36FMHBwbhw4QJOnz6NGTNmqNc1NTWFv78/rly5ku160RUmuAVZ7DNgV6bnvge0AiobZ983IjIM7dq1Q7t27aQO46N7/fq1VsLm6Oiofqpb69at8fPPP+Pu3btISEjA1KlTIZPJkJCQ8N7tpqWl4ffff0f//v3V89JbMv/55x9cvnwZZ8+exdGjRzFnzpxsxRoVFQV7e3v19JMnT3D58mXMmzcPVlZW8PHxwciRI3Hu3Ln33tzUrFkzWFpaokOHDkhKSgKgSsabN2+OPXv2YN68eejRowcmTJiAjRs3aiRsderUQXR0dJbbVSqV+N///ocffvgB3t7ekMvlqFChAlxcXHDu3Dl1FxBzc3NUrVoVAwYMwG+//aZe39PTE8OGDYOpqSk6duwIuVyOu3dVH3zMzc1x7949hIeHw9bWFjVq1MhWnaVv94svvoCpqSl69uyJ8PBwfPXVV3BwcICvry9q1vz/9u48rolr/R/4J+w7hH1fXHDfFapiVRRtAdGqpWpbQaBW69KCu+jFW0VbEa/rFb1f1IraVvpTqUVb1OKttYp6XWprqWJZIyIgi8qa5Pz+sIzGBAiSkBCe9+vF6+WcnDnzzJNxeJicmbyGa9euAQAOHjyIyMhIdO/eHYaGhoiLi0NGRgbu3bvX6PjLly+Hra0tbG1tsWrVKhw+fFhmv6lTp8LJyQlaWlqYOnUqunXrhoyMjGb3XyQS4csvv8Tnn38OCwsL2NvbY82aNRJXeAHA1NQUZWVlcudFWeg5uB2VsA5IDuWemgAja2DCNpqaQAhRqY8//lhhY01e0vRjDgM+6tvk62Nm9mjy9eFTurQ4psaYmJigoqJCoq2iooKbBhAaGoqCggK88cYbqKqqwqJFi2BiYgJnZ+cmx01NTUVdXR0mTZrEtRkZGQEAli5dCktLSwBAVFQUtmzZgpiYmGZj5fP5EsXlgwcPYG5uDnNzc67N3d2d+0j9s88+w4oVK5occ9euXYiMjMTt27exceNGGBgYYPHixdi3bx+GDx8OABJFdVNKSkpQXV2NLl2k3x+BQABnZ2eJm+Pc3d1x+fJlbvnlotzIyAhPnjwBABw7dgzr1q1D165d0aVLF8TExCAwMFCuuF4ct+E9eLmtYTsCgUBiTrSJiQmsrKwgEAjQuXNnmeO7uj7/9NXNzQ0CgUBmvwMHDmDz5s3IyckB8OyPq5KSEplxvhhXSUkJ6urqJOJyd3dHYWEhGGPg/V0/PH78GHw+v7E0tBm6gttRpa0C8p//xYaJOwATG9XFQwghHVivXr1w69YtiMViru3GjRvo3bs3gGdzLFevXo2//voLDx48wIQJEyAWi7nXG7N3717MmDED+vr6XJunpyf09fW5gqSl+vbti8zMTG7Z1dUVFRUVePToEdeWnZ3NFd/Lly/npli8+FNfX4/g4GC88cYbmDt3Ljf2kSNHUFxcjN27d6OsrAwnTpxAYWEhV/w1x9raGoaGhsjKkp5C4uTkhIKCAonHm+Xk5MDJyUmusQcOHIijR4+ipKQEkZGRmDp1Kp4+fQoTExOpq+lNPeWiOU5OTlwBCjwrQktLS5uMMy8vT+Lfsvrm5uYiIiIC27ZtQ2lpKcrLy9GnTx+55kJbW1tDT09PIq6cnBw4ODhwx5JQKERWVhb69+/f/E4qGRW4HdHNr4HLu58vD/8E6EZPTSCEqF5gYKDcV8TaG6FQiJqaGgiFQojFYtTU1KC+vh4AMHLkSBgaGmLjxo2ora1FcnIybt26hbfffvZV6WVlZbh79y4YY8jOzsYHH3yAqKioJq+UFRUV4eTJkxLTE4BnV+RmzJiBuLg4VFRU4OHDh9i6dSuCgoLk2g8/Pz/cvHmTKzgdHBzg5+eHxYsXo6qqCllZWYiPj8f777/f5DhpaWmorq7G8ePHYWBgAODZlepbt24hODgY8+fPR0pKCg4dOoTw8HCsXr1arvi0tLQQERGByMhI5OXlgTGGmzdvorS0FN7e3rCwsMDatWtRV1eHa9euITExsdlYAaCurg5JSUkoKyuDtrY2d0OdtrY2PD09IRKJcPToUQiFQuzcubPRK6jyePfdd7FlyxbcuXMHNTU1WLZsGby8vBq9egsAGzduRHFxMYqLixEbG4vp06dL9Xn69CkAwMbm2QWtAwcOcPO8m6OtrY1p06ZhxYoVKC8vR1FRET799FOJp3lkZGTA3d0dHh4eLdldpaACt6MRXANOvPARoMfrgK98Jw1CCFE2Hx8f+Pj4qDoMpVi3bh0MDQ0RGxuL5ORkGBoaco8M09XVRUpKCo4dOwYLCwv84x//wNGjR2FrawsAePToESZMmABjY2P4+PjA19dX4gkF69evx5tvSl6oOHjwIHr16oWBAwdKxbJ161ZYWVnBxcUF/fv3x4gRI7Bs2TK59sPOzg7+/v745ptvuLakpCQ8fvwYrq6uGDt2LEJDQxEREdHkOP7+/khJSZG4uqyjo4O4uDikp6djwoQJsLKywldffYWTJ0+iZ8+eXL/z58/DxMSk0bE3btwIb29vjBgxAtbW1vjggw9QXV0NXV1dnDhxAufPn4etrS3eeecdxMbGYty4cXLt++HDh9G5c2eYmppi2bJlOHLkCAwMDGBmZoaEhAQsWLAAdnZ2ePDgAQYNevVvAg0JCcHs2bMxfvx4ODo6Ij8/H19//XWT6wQHB2Po0KHw9PRE7969ER0dLdWnZ8+eWLJkCYYPHw47OzvcuHEDw4YNkzuubdu2wc7ODt26dcOAAQMwZMgQrFr1/BtQk5KSuKvxqsZjynpGhwYoKCiAi4sL8vPzm53n1C6U3gMS/YCq0mfLZs7Ah/8FjCUfWSMSiVBcXAwbGxt6iLcSUH6Vh3KrPOqc24aPTOV9jqu6YYxBKBRCR0fnlacNtLW7d+9iypQpuH79utodDy9qj7l9FTweD3/88YfE0y3awov5vX//PsaOHYsbN25I/NHSUor6/0w3mXUUj4uApLeeF7e6xsC0g1LFLSGEENKcrl27Sjy2ixAnJyf88ccfqg6DQ1MUOoKnpcDBKUB57rNlLR0g+ADgOEC1cRFCyEuCg4Obfd4nIYQ0h67garrKQiBpElD8/I5XTNwJdB2rspAIIaQxbf0RKyGagGabSqMCV5OV5QIHgoCynOdt42KBftNUFhIhhDTl008/VXUIhBANQAWupsr5+dkXOTwtft7mvwnw+kBlIRFCCCGEtAUqcDUNY0BGAvBDNMD+fpA1TwuY+G+gv/Qz8QghRJ2EhIQAgMRXpxJCSEtRgatJKu8DqYuAP08+b9M3AybvoS9yIIS0Cw3PfSWEkNagAlcTiEXAtQPA6X8AtZXP2627AdMOAdZdVRcbIYS0QFxcnKpDIIRoAHpMWHvGGHD7W2DXcOC7TySL295TgQ/OUnFLCCEawt7eHufOnZOrL4/HQ2ZmZvMdX3L+/Pkmvw62Nd58800kJiY222/OnDmIiYlRSgyk49DIAjczMxN+fn4wNjaGvb09li5dirq6OlWHpTg1lcCVRCDBBzjyPlD8woOVTR2AaV8CUxMBfVPVxUgIIa9g7ty5avNVnx3RiBEjcO/ePaWMferUKYSHhzfbLyEhAf/85z+VEoMstbW1iIiIgIeHB0xNTdGrVy8cPny4zbZPlEPjpiiUlZXB19cXXbt2xdGjRyEQCBAVFYWqqirs2LFD1eG9upoKIOss8OcpIDMVqH8q+bq2HjA4DBi9EjAwV02MhBDSSlpaGnndpUNo+MrW9kYoFMLR0RFnz56Fh4cHLly4gICAAHh4eGDo0KGqDo+8Io07kyQkJKCyshLHjh3D+PHjERYWho0bNyIhIQH3799XdXjyqX0CFN4Ebn0DfL8C+I8vsLET8M0s4NYRyeKWpw0MDAEWXgfe/JyKW0JIu7Zz507s3LlT1WEoxaVLl+Dl5QVTU1PweDzuZ//+/TL7/+tf/4KTkxNsbW0RHx8v8RpjDJs3b4anpycsLS3h7++PgoICueKorKzEnDlz4OzsDHt7e8yfPx81NTUAgHPnzsHe3p7r++DBA7zzzjuws7ODi4sL1qxZA7FYDADYv38/XnvtNSxbtgy2traYN28exGIx4uPj4enpyV0NvXbtGgBg1KhRSEhI4MY+cOAAevfuDVNTU3Tp0gXff/89ACA0NBTLly/n+n3xxRfo3r07LCws4Ovr+0pTL5pibGyMTz/9FJ06dQKPx4OPjw+GDx+OX375RaHbIW1L4wrcU6dOYezYsbC0tOTagoODIRaLkZaWpsLImlBdDny7ANjnD2zqBmxwAna/Dvy/cODSvwHB/wCxUHIdcxdgdDQQ+RsQtA0wd1ZJ6IQQoq58fHwkCio/Pz9s3ryZW544cSJiY2O55WnTpmHVqlXcclhYGKKiorjlefPmSUyfWLJkidyxVFdXY+rUqZgwYQJKS0tx48YN2NjYIC4uDkFBQVL9T58+jXXr1uHEiRPIy8vD3bt3UVJSwr2+Y8cOHDp0CGlpaSgqKsLAgQMxbZp8X+Iza9Ys1NTU4Pbt28jMzMTdu3exdu1aqX5isRhBQUHo0qULcnNzkZGRgZSUFIl5tFevXoW1tTUEAgG2bNmCbdu24T//+Q+OHj2KyspKHD9+HFZWVlJjHz9+HEuXLsWePXtQWVmJ9PR0uLm5SfU7d+4cPvnkE+zfvx8PHz7EmDFjEBAQgNraWpn7FhgYCAsLC+6Hz+fDxsYGfD4fffv2lSs/T58+xdWrV9G7d2+5+hP11P4+S2hGZmYmwsLCJNosLCzg4ODQ7F99lZWVqKx8fqNWYWEhAEAkEkEkEik+2Aba+tC6fhA8Jm6yG+N7gHm+Aeb5BuA6DNDSxt8BKjQckUgEsVis3H3uwCi/ykO5VZ62yu2iRYsAQOqKZVMYY+DxeDK/rpQxJtHekuWGfze3LI+MjAzU1tZixYoV0NbWRt++fTFr1ixcv34dUVFREAqFEmMdPnwYISEhGDBgAABgw4YN2LNnD7fNXbt2YdOmTVxRGBMTg7i4OOTm5sLV1bXR+B4+fIhvv/0WpaWlMDV9dp9GdHQ0QkNDsW7dOol9u3LlCvLz87F27VrweDw4ODggMjIS+/fvR0REBBhjsLW1xaJFi8Dj8aCjo4OEhASsXbsWvXr1AgB06dKFG+/FmBISErBo0SJuCoCzs7PMfklJSQgJCYG3tzcAYPny5di5cyfOnz+PMWPGSOX5xIkTEsuMMW7qRGPHyIvEYjFCQ0MxZMgQ+Pn50VfgNuPl/xOKHFfWuUZbW1vuMTSuwC0rK4OFhYVUO5/Px6NHj5pcd/PmzTIntpeWlkJfX19RIcpkbeoEncp8bllkZAORuRvqLbuh3n4A6u36Q2TqDPB4fwfV9L60hlgsRkVFBQCaD6cMlF/lodwqT1vltqioCABQXFzcTM/nampqoK+vD6FQ8pOu9PR0AODaT548KbH8zTffSCwnJSVJLO/Zs0dieevWrRLLGzZskNpmYwQCAZycnLiCCwBcXV2RkZEBoVDI/TLn/X2Ov3//Pvr06cP1NTExgZmZGUQiEYRCIXJycjBt2jSJ90JLSwu5ublwdHTk4nw5vnv37kEkEnFFMPC8mHgxDqFQiHv37qG4uFjiE1GxWAxnZ2cIhULu3y8WInl5eXB3d5eZF8YYxGIxhEIhcnNz4eHhIbOfWCzm+gkEAowbN06in4uLC/Ly8uTK/YuFUkNum+r70UcfQSAQIDU1lf5QlkNL8isvsViM2tpameeAF6fPNEfjCtzWiIqKQkREBLdcWFgILy8vWFlZwcbGRqnb5vmuhlhLC8yyC2DpAeibQhuANgADpW5ZWsPBam1t3aK/loh8KL/KQ7lVnrbK7cGDB1u8TnV1NXcFUV15eHhwc2Qb4szNzYWLiwu33HCVEQAcHR1RUFDAvVZeXo7Kykpoa2tDR0cHrq6u2LVrF0aNGtXoNnV0dKRy4uHhAR0dHRQVFUFPT09qnYb3VkdHBx4eHnB2dkZ2drbM8bW0tKClpSWxDVdXV+Tk5GDw4MFS/Xk8Htffzc0N2dnZMt+zF8d1cnJCXl4e108sFqOgoACurq4y1/X398f58+dlxuvm5obffvtN5muMMcybNw+//vorTp8+DTMzM5n9iKSGK7cvHrutpaWlBQMDg1bXXep7NnhFfD6fu8rworKyMom/QmUxMzOTeVBra2sr/5dl/3eUO34LaWlptc1+d1CUX+Wh3CqPuua24Reron7BKoO3tzdsbW2xdu1arFq1Cr/++iv27t2Lw4cPS9xw1rAP06ZNw7vvvouZM2eie/fuiI6OhpaWFtdn7ty5WLVqFQ4cOIDOnTujrKwMp0+fRnBwMLfNF8dr4ODggICAAHzyySdYv349LCwsUFBQgN9//x1vvPGGRC69vLxgY2ODdevWISoqCoaGhrh37x7u37+PkSNHysz77NmzERMTg169eqFHjx7IysqCnp4eN5WiIabZs2dj3rx5GDFiBLy9vSEQCPDkyRN0795dot97772HyZMnY8aMGRgwYAA2bdoEPT09jBgxQub7ferUKYnll6coNGb+/PnIyMjA2bNnYW5ON2u3xMvHrqLGbO15RuM+w+vevbvUXNuKigoUFhZy/3EIIYSop1WrVknc6KUptLW1ceLECVy6dAl2dnaYPn06NmzYgHHjxsnsP378eKxYsQIBAQFwdXVF586dYW1tzb2+YMECTJs2DYGBgTAzM0O/fv3www8/yBXLF198AV1dXfTv3x/m5uYYP3487ty502jMd+/eRdeuXcHn8xEcHMzdnyLLxx9/jJkzZ2LChAkwNTXF5MmTZU4PnDx5MtatW4ewsDCYmZlh9OjRyM3Nleo3evRoxMfH4/3334eNjQ3S0tKQmpqq0GmDubm5+Pe//43bt2/DxcUFJiYmMDExwfr16xW2DdL2eEzDZlBv2LAB69evR35+PjcX9//+7/8wZ84c5OXlcXOT5FFQUAAXFxfk5+dzE+A7ApFIhOLiYtjY2KjdlRpNQPlVHsqt8rRVbhueBPDVV1/JvU5OTg4AwN3dXQkRKZ+8VxnbwtmzZzFr1izk5eWpNA5FUafcaiJl5FdR/581borCnDlzsH37dkyaNAkrV66EQCDAkiVLMGfOnBYVt4QQQtpeSwpboni3bt2Ch4eHqsMgpNU0rsDl8/k4e/YsFixYgEmTJsHU1BQRERESzzokhBBCiKQPP/wQaWlp2Ldvn6pDIaTVNK7ABYAePXrgzJkzqg6DEEJICzVcjIiOjlZxJB3P7t27VR0CIQqjkQUuIYSQ9uny5cuqDoEQogGowCWEEKI2UlJSWryOjo4OqqqqUFNTAwODtn5yOCFEUUQiEerr6xXy/5gKXEIIIe0an89HVVUVsrOzoa+v3y6/xU4sFrfLuNsDyq1yKTK/9fX1EAqFCnkWMb3jhBBC1MbmzZuxefPmFq1jbGyMTp06gc/nq/W3mTWGMYba2lpo2FM71QLlVrkUnV8DAwM4OTkp5Jvk2t+ZgBBCiMZq+CaqqKioFq2no6PTou+pVyf0/GblodwqlzrnlwpcQgghauP06dOqDoEQogFoigIhhBBCCNEoVOASQghRGwkJCUhISFB1GISQdo6mKDRBKBQCAAoLC1UcSdsSiUQoLS1FbW2t2s2p0QSUX+Wh3CpPW+U2MTERABAYGKi0bagbOm6Vh3KrXKrIr729vVw3k1KB24Ti4mIAgJeXl4ojIYSQjsXFxUXVIRBC1FB+fj6cnZ2b7cdj9OyMRtXU1ODWrVuwsbHBoEGDcPPmzVcap1+/fnKv21zfpl5v7DVZ7S+3vbhcWFgILy8vXL58GQ4ODnLF3RotyU9r1lVVbl9ua8v8qktum+pDuW19X007L7Qmty1dv6Odcym3yqUu5wVN/n1GV3AVwMDAAEOGDAEAaGtry/UXgywtWbe5vk293thrstpfbpPVx8HB4ZX3uSU0PbeNtbVFftUlt031ody2vq+mnRdak9uWrt/RzrmUW+VSl/MC/T6jm8zktmTJkjZZt7m+Tb3e2Guy2l9ua83+tZam51aebSuLuuS2qT6U29b31bTzQmu32x7OC5Rbzctta7fdHnIrz7aVpaXbpSkKREpBQQFcXFzknudCWobyqzyUW+Wh3CoP5VZ5KLfKpc75pSu4RIqZmRliYmIU8lV5RBrlV3kot8pDuVUeyq3yUG6VS53zS1dwCSGEEEKIRqEruIQQQgghRKNQgUsIIYQQQjQKFbiEEEIIIUSjUIFLCCGEEEI0ChW4hBBCCCFEo1CBSxRi3Lhx6NevH/r164fx48cjPz9f1SFpjPz8fIwdOxbdunVDnz59EB4ejtraWlWHpTFWrlwJDw8P8Hg8ZGVlqTqcdiszMxNeXl7w9PTEqFGjcP/+fVWHpDHoGFUeOr8qlyprAypwiUIkJyfj5s2buHnzJvz9/bFs2TJVh6QxdHR0EBsbiz///BM3b95EVVUVtm7dquqwNEZgYCB++uknuLm5qTqUdu3DDz/EsmXLcOfOHbz11lsq/TYpTUPHqPLQ+VW5VFkbUIGrobKysjBnzhz0798fOjo66N27t8x+mZmZ8PPzg7GxMezt7bF06VLU1dW1eHvm5ubcvysrK1857vaiLfPr4OAAb29vAICWlhYGDx6MvLy8Vu+DumrrY3fYsGFwcXFpbdjtkqJyXVRUhD/++AOTJ08GAISHhyMlJQUd+THrijyOO/Ix2hhF5bejnV/lochjV5W1gU6bbo20md9//x2pqanw9vaGWCyGWCyW6lNWVgZfX1907doVR48ehUAgQFRUFKqqqrBjx44Wb/Odd97BTz/9BGtra6SlpSliN9SWKvILANXV1di7dy/i4+NbuwtqS1W57YgUleuGr+vk8XgAABMTExgZGeHhw4ews7Nr031SF3QcK5cy8tsRzq/yUHRuVVYbMKKRRCIR9++QkBDWq1cvqT7r169nxsbGrLS0lGvbvXs309bWZgKBgGsbMmQIs7KykvoZMGCA1JhisZjFxcWxuXPnKniP1Isq8isUCtmkSZPYwoULlbBH6kNVx66bmxu7e/eugvdGvSkq11evXmUDBw6UWM/GxoY9ePBASZGrP0Uexw064jHaGEXnt6OcX+WhjGNXFbUBTVHQUFpazb+1p06dwtixY2Fpacm1BQcHQywWS/yVdfnyZZSUlEj9XLt2TWpMHo+HiIgIHDhwQDE7oqbaOr+MMcyaNQumpqbYsmWLQvdF3ajq2O2IFJVrZ2dn5Ofnc1MSnjx5gqqqKtjY2Cgn8HZAkccxkabI/Hak86s8lHHsqqI2oAK3A8vMzET37t0l2iwsLODg4IDMzEy5x6msrIRAIOCWk5OTG52z05EoKr8A8NFHH+Hp06fYt28f9zFwR6bI3JKmyZNrOzs7dO/eHUePHgUAJCYmIigoSK5flB0ZHcfKJW9+6fzacvLkVtW1Ac3B7cDKyspgYWEh1c7n8/Ho0SO5x6moqMCUKVNQXV0NHo8HV1dXHDx4UIGRtk+Kyu+FCxeQkJCAnj17YtCgQQCA0aNH41//+peiQm13FJVbAFi6dCkOHz6MBw8eYMSIEXB3d8fFixcVFGn7J2+uExISEBISguXLl8PR0RGHDh1qwyjbJ3lzS8foq5Env3R+fTXy5FbVtQEVuKTVXFxccPnyZVWHobGGDx/eoe9GV7aNGzdi48aNqg6j3evZsyeuXLmi6jA0Eh2jykPnV+VRdW1Anx91YHw+HxUVFVLtZWVlEvNqyKuh/CoP5bbtUK6Vh3KrXJRf5WkPuaUCtwPr3r271DyviooKFBYWSs2tIS1H+VUeym3boVwrD+VWuSi/ytMecksFbgf25ptv4syZMygvL+fakpOToaWlhXHjxqkuMA1B+VUeym3boVwrD+VWuSi/ytMucttmDyQjberp06csOTmZJScns1GjRjEXFxdu+eHDh4wxxh49esQcHBzYyJEj2Q8//MD27t3LLCws2Lx581Qcvfqj/CoP5bbtUK6Vh3KrXJRf5dGU3FKBq6Gys7MZAJk/6enpXL/bt2+zMWPGMENDQ2Zra8sWL17MamtrVRd4O0H5VR7KbduhXCsP5Va5KL/Koym55TFGtw8SQgghhBDNQXNwCSGEEEKIRqEClxBCCCGEaBQqcAkhhBBCiEahApcQQgghhGgUKnAJIYQQQohGoQKXEEIIIYRoFCpwCSGEEEKIRqEClxBCCCGEaBQqcAkhhBBCiEahApcQQkibqa+vh5eXF8LCwlQdCiFEg1GBSwjpEFJTU+Hs7Iy6ujpVhyKTu7s75s+f3+px9u/fDx6Ph5KSEgVEpXirV6+Grq4uEhISAABr1qzBL7/8orDxc3JywOPx8M033yhsTAB4/PgxLC0tceHCBYWOSwhRDipwCSEajzGG6OhoREZGQk9PT9XhdFg//vgjvvrqKxw7dox7H/75z38qtMB1cHDAxYsX4evrq7AxAcDU1BQLFizAypUrFTouIUQ5qMAlhGi8c+fO4bfffsPMmTNVHUqHxBhDbW0tfH19kZOTA1tbW6VtS19fH6+99hosLS0VPnZYWBh++ukn3Lx5U+FjE0IUiwpcQoha2rRpE7p27QpdXV3weDyJn3PnzrVorC+++AIjR46EjY2NRHt5eTk++ugjODg4QF9fH4MGDUJaWppEnwsXLuD111+Hubk5TE1N0adPH3zxxRcSfVJTUzF8+HAYGRmBz+dj1KhRuH79OgDg6dOnmD9/Prp16wYjIyO4u7tjzpw5qKioaDLmixcvIigoCI6OjjA2Nkb//v2RlJTUov1u8OjRI4SFhcHa2hqGhoYYNmwYfvrpJ4k+jDF8+umnsLe3h4mJCd5++22cOXNGKt/x8fEYMmQIzM3NYWtri8DAQNy5c0dirNDQUPTu3RsnT55Ev379oK+vjxMnTuDcuXPg8Xi4evUqAIDH4wEAlixZIvXe8ng8fP7554iOjoatrS0sLCywdOlSMMZw9uxZ9O/fHyYmJhgzZgzy8/O5bTc2ReHAgQMYMGAADAwMYG1tDX9/f+Tm5gIACgsLERYWhk6dOsHQ0BBdu3bFypUrUVtbKzGGm5sbvLy8sH///ld6HwghbUdH1QEQQsjLdu7ciSVLliA0NBQ7duxAVlYWVqxYARMTE4SFhcHNza1F4505c0bqpqa6ujr4+fmhqKgIsbGxcHJywsGDBxEQEIBr166hT58+qKysREBAAHx8fPDll19CX18ft2/fRnl5OTfO119/jenTp2PixIk4fPgw9PT0cOHCBQgEAgwYMABVVVUQiUSIjY2FjY0N8vPzERsbi0mTJiE9Pb3RmHNzczF8+HDMmTMHBgYGuHDhAsLDwyEWixESEiL3votEIrz55pv466+/8Pnnn8POzg7btm2Dn58ffvnlFwwaNAgAsH37dqxZswZLly6Fr68vfvzxR0REREiNV1BQgPnz58PNzQ2VlZVISEjAsGHDcOfOHYmrpvfv38fChQuxatUquLq6wtXVFQUFBRJjXbx4EUOHDsWCBQswY8YMAEDPnj2513fs2IFRo0YhKSkJGRkZiImJgUgkwunTpxEdHQ09PT0sXLgQ4eHhUn+YvCguLg5Lly5FeHg4YmNjUV9fjx9//BHFxcVwc3NDSUkJLC0tsXnzZvD5fNy5cwdr1qxBYWEh9u3bJzHWsGHDcPr0abnzTwhREUYIIWqmc+fO7PXXX5doS0xMZFpaWiwrK6tFY92/f58BYMnJyRLte/fuZTo6Ouz333+XaPf29mZvv/02Y4yxK1euMADs119/lTm2WCxmzs7ObPz48XLHU19fz37++WcGgP35559cu5ubG5s3b16j26mvr2ezZ89mQ4cObXL8ffv2MQCsuLiYMcZYSkoKA8C+//57rk9dXR1zdXVlkydPZowxJhQKmYODAwsLC5MYKzw8nAFg6enpMrclFApZVVUVMzExYbt37+baQ0JCGAB26dIlif7p6ekMALty5QrXBoDFxcVJjQ2AeXl5SbQNGjSI8Xg8dvv2ba5t+/btDAArKytjjDGWnZ0t8X6Xl5czIyMjNnv2bJn7IEt9fT07dOgQ09HRYU+fPpV4bd++fYzH47HKykq5xyOEtD2aokAIUSsVFRW4d+8e3nrrLYn2qVOnQiwW4/z58y0ar7CwEACkpiekpaWhT58+8PT0hFAo5H78/Pxw5coVAEDnzp1hZmaGuXPn4siRIyguLpYY488//0RBQUGzj7xKSkrCgAEDYGJiAl1dXfj4+ACA1Ef7LyorK8PChQvh5uYGXV1d6OrqYs+ePU2uI8v58+dhZmaG8ePHc226urqYPHkyfv75ZwDPrsoWFhYiKChIYt2JEydKjXfp0iX4+fnBysoKOjo6MDIywpMnT6TisrKygre3d4tifZmfn5/EsqenJxwdHdGjRw+JtoZ9kOXixYuoqqpCeHh4o9thjGHLli3o2bMnDA0Noauri3fffRdCoRB//fWXRF9ra2swxlBUVPSqu0UIaQNU4BJC1Mrjx48BSBekZmZmMDAw4ApWedXU1AB4dvPRi0pKSnD9+nWueGz4WbduHTenk8/n4/Tp0zA1NcX7778Pe3t7jBo1Crdu3QIAlJaWAgAcHR0b3f6xY8cwc+ZMeHl54ciRI7h06RKOHTsmEZssoaGh+PLLL7F48WKkpaXhypUrCAsLa3IdWcrKymTe1GVnZ4dHjx4BaPyPgJfXy8vLw7hx4yASibB7925cuHABV65cga2trVRcdnZ2LYpTFgsLC4llPT09mW1A47mU5z3asmULFi1ahIkTJyIlJQWXL1/Gzp07ZY7bcBxVV1fLvR+EkLZHc3AJIWrF0tISWlpaePDggUR7eXk5ampqwOfzWzxew/ovt/ft2xeJiYlNru/l5YVTp06huroa6enpWLx4MSZNmoR79+7BysoKwLP5po1JTk5G//79sXv3bq7tv//9b5PbrKmpwXfffYfNmzdjwYIFXLtYLG5yPVksLS3x8OFDqfaioiIuNw4ODgAgdYX65fW+//57PHnyBEePHuUKTaFQyBXKL2q4gUzVXnyPnJ2dZfZJTk5GUFAQNmzYwLXdvn1bZt+G46hhXEKIeqIruIQQtWJkZIQhQ4YgOTlZov2rr74CAIwYMaJF47m7u0NPTw/Z2dkS7WPHjsVff/0FR0dHDB48WOrnZYaGhvD398fcuXORnZ2NmpoadOvWDc7OzlI3Ir2ourpa6tm7hw4dajLm2tpaiMViifUeP36Mb7/9Vp5dluDj44PKykqJm7CEQiGOHTvGTZVwdnaGvb09UlJSJNY9fvy41L7weDzo6upybUeOHIFQKGxxXA10dXVbfFW6JYYOHQojIyOFvUc5OTkwNzeHvb29QuMkhCgWXcElhKidNWvWICAgADNmzMDMmTORmZmJ6OhoBAcHo1evXi0ay8DAAIMGDcL//vc/ifaZM2di9+7dGDVqFBYvXgxPT0+Ul5fj+vXrqKurw4YNG5CamorExES89dZbcHV1xYMHD7B9+3YMHz4cBgYGAJ49zmz69OmYMmUKZs6cCX19fVy8eBFDhgxBYGAg/Pz8MG/ePKxduxZDhw7FyZMncfbs2SZjNjc3x5AhQ/DZZ5/BxsYGOjo6+Oyzz2Bubi7zamxTAgIC4OXlhffeew+fffYZ7OzssH37dhQWFnJfWqCtrY0VK1bgk08+gZ2dHUaPHo309HScOXMGAKCl9exaSMOXJ8yaNQsffvghfv/9d8THx0tNG2iJHj16ICUlBSNGjICxsTG6desGU1PTVx7vZebm5oiJicGyZcsgFosxceJEiMVipKenY/r06Rg8eDD8/PywdetW7NixA56enjh48CCysrJkjnf16lUMGzaMywkhRE2p+i43QgiR5fjx46xfv35MT0+P2dvbs6ioKFZTU/NKY8XHxzNnZ2cmFosl2isqKlhkZCRzdXVlurq6zMHBgfn7+7PvvvuOMcZYZmYmmzJlCnNxcWH6+vrM0dGRhYaGssLCQolxvv32W+bt7c0MDAyYhYUF8/X1ZdevX2eMPXvSwKJFi5iNjQ0zNTVlU6dOZZcuXZJ6ssPLT1G4e/cu8/X1ZUZGRszFxYXFxcWxmJgYZmxs3OS+vvwUBcYYKykpYaGhoczS0pLp6+uzoUOHsnPnzkmsJxaL2Zo1a5itrS0zMjJiQUFB7Ouvv2YA2I0bN7h+Bw4cYJ06dWIGBgbstddeY5cvX5aKPSQkhPXq1UsqNllPUTh//jwbOHAgMzQ0lHhiA2Q8XUHWuC+P+fJTFBrs3buX9enTh+np6TErKysWGBjIcnNzGWOMPX78mIWGhjI+n8/4fD774IMP2IkTJ6RiraurY5aWliwxMbHxN4AQohZ4jDGmsuqaEELaQHFxMVxcXJCWlobXX39d1eG0G6tXr0Z8fDxKS0thaGio6nBULjU1FTNmzIBAIICJiYmqwyGENIGmKBBCNJ6NjQ3mzp2LLVu2UIHbiD/++AMHDx7EsGHDoKenh3PnzmHTpk2YO3cuFbd/i4+Px6JFi6i4JaQdoAKXENIhrFy5Ert27UJdXZ3UDUXk2c19Fy9exK5du/D48WM4OTlhyZIlWLNmjapDUwtPnjzByJEjERkZqepQCCFyoCkKhBBCCCFEo9BtoIQQQgghRKNQgUsIIYQQQjQKFbiEEEIIIUSjUIFLCCGEEEI0ChW4hBBCCCFEo1CBSwghhBBCNAoVuIQQQgghRKNQgUsIIYQQQjQKFbiEEEIIIUSjUIFLCCGEEEI0yv8Hz6mBtzF+MTAAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 792x506 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg = np.logspace(-3.4, 3, 400)\n",
    "c1_sg = np.array([solucion(sigma_=x)[\"c1\"] for x in sg])\n",
    "c2_sg = np.array([solucion(sigma_=x)[\"c2\"] for x in sg])\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.semilogx(sg, c1_sg, lw=2, label=\"$c_1^*$\")\n",
    "ax.semilogx(sg, c2_sg, lw=2, label=\"$c_2^*$\")\n",
    "ax.axhline(W, color=\"tab:red\", ls=\"--\", lw=1, label=f\"W = {W:.2f}  (esquina: todo hoy)\")\n",
    "ax.axhline(W/(1+1/(1+r)), color=\"tab:purple\", ls=\"--\", lw=1,\n",
    "           label=f\"{W/(1+1/(1+r)):.2f}  (σ→∞: consumo plano)\")\n",
    "ax.axvline(sigma, color=\"k\", ls=\":\", lw=1, label=f\"σ del ejercicio = {sigma:.0f}\")\n",
    "ax.set(xlabel=\"σ  (escala logarítmica)\", ylabel=\"consumo\",\n",
    "       title=\"De la esquina (σ→0) al consumo perfectamente plano (σ→∞)\")\n",
    "ax.legend(fontsize=8); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "223389b9",
   "metadata": {},
   "source": [
    "---\n",
    "## El error frecuente en el examen\n",
    "\n",
    "> Escribir $c_2/c_1=\\beta(1+r)$ **olvidando el exponente $1/\\sigma$**. Solo es válido con\n",
    "> utilidad logarítmica ($\\sigma=1$).\n",
    "\n",
    "¿Cuánto cuesta ese error? Depende de qué tan lejos esté $r$ de $\\rho$: aquí es pequeño porque\n",
    "$\\beta(1+r)=0{,}9975$ está pegado a 1, pero crece rápido en cuanto se separan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "5dd91cab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:52.627459Z",
     "iopub.status.busy": "2026-08-04T13:09:52.627249Z",
     "iopub.status.idle": "2026-08-04T13:09:52.637818Z",
     "shell.execute_reply": "2026-08-04T13:09:52.636501Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Con los datos del ejercicio (r = 5%):\n",
      "   correcta: Γ = 0.99875   c₁* = 109.8231\n",
      "   errónea : Γ = 0.99750   c₁* = 109.8901   ->  error de +0.0670\n",
      "\n",
      "         c₁* correcto  c₁* con el error  error absoluto  error %\n",
      "r                                                               \n",
      "0.00000     111.41049         112.82051         1.41002  1.26561\n",
      "0.05000     109.82310         109.89011         0.06701  0.06101\n",
      "0.10000     108.37542         107.22611        -1.14931 -1.06049\n",
      "0.20000     105.83374         102.56410        -3.26964 -3.08941\n",
      "0.30000     103.67828          98.61933        -5.05895 -4.87947\n",
      "0.50000     100.23263          92.30769        -7.92494 -7.90654\n"
     ]
    }
   ],
   "source": [
    "def solucion_erronea(y1_=y1, y2_=y2, r_=r, beta_=beta):\n",
    "    \"Versión SIN el exponente 1/σ.\"\n",
    "    W_ = y1_ + y2_/(1+r_)\n",
    "    G_ = beta_*(1+r_)                      # <-- el error\n",
    "    c1_ = W_/(1 + G_/(1+r_))\n",
    "    return dict(Γ=G_, c1=c1_, c2=G_*c1_, s1=y1_-c1_)\n",
    "\n",
    "ok, mal = solucion(), solucion_erronea()\n",
    "print(f\"Con los datos del ejercicio (r = {r:.0%}):\")\n",
    "print(f\"   correcta: Γ = {ok['Γ']:.5f}   c₁* = {ok['c1']:.4f}\")\n",
    "print(f\"   errónea : Γ = {mal['Γ']:.5f}   c₁* = {mal['c1']:.4f}   ->  error de {mal['c1']-ok['c1']:+.4f}\")\n",
    "\n",
    "filas = []\n",
    "for rv in (0.00, 0.05, 0.10, 0.20, 0.30, 0.50):\n",
    "    a, b = solucion(r_=rv), solucion_erronea(r_=rv)\n",
    "    filas.append({\"r\": rv, \"c₁* correcto\": a[\"c1\"], \"c₁* con el error\": b[\"c1\"],\n",
    "                  \"error absoluto\": b[\"c1\"]-a[\"c1\"], \"error %\": (b[\"c1\"]/a[\"c1\"]-1)*100})\n",
    "tab_err = pd.DataFrame(filas).set_index(\"r\")\n",
    "print()\n",
    "print(tab_err.to_string())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "2c9dafb1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:52.640018Z",
     "iopub.status.busy": "2026-08-04T13:09:52.639792Z",
     "iopub.status.idle": "2026-08-04T13:09:52.769582Z",
     "shell.execute_reply": "2026-08-04T13:09:52.767992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 792x506 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El error se anula exactamente en r = ρ, donde β(1+r) = 1 y elevar a 1/σ no cambia nada.\n",
      "A medida que r se aleja de ρ el error crece — y crece más cuanto mayor sea σ.\n"
     ]
    }
   ],
   "source": [
    "rv = np.linspace(0.0, 0.8, 300)\n",
    "err = np.array([(solucion_erronea(r_=x)[\"c1\"]/solucion(r_=x)[\"c1\"] - 1)*100 for x in rv])\n",
    "fig, ax = plt.subplots()\n",
    "ax.plot(rv*100, err, lw=2, color=\"tab:red\")\n",
    "ax.axhline(0, color=\"k\", lw=.9)\n",
    "ax.axvline(r*100, color=\"k\", ls=\":\", lw=1, label=f\"r del ejercicio = {r:.0%}  (error {err[np.argmin(abs(rv-r))]:.3f}%)\")\n",
    "ax.axvline((1/beta-1)*100, color=\"tab:purple\", ls=\"--\", lw=1,\n",
    "           label=f\"ρ = {1/beta-1:.2%}  (aquí el error se anula)\")\n",
    "ax.set(xlabel=\"tasa real r (%)\", ylabel=\"error en $c_1^*$  (%)\",\n",
    "       title=\"Costo de olvidar el exponente 1/σ (con σ = 2)\")\n",
    "ax.legend(fontsize=8); plt.show()\n",
    "print(\"El error se anula exactamente en r = ρ, donde β(1+r) = 1 y elevar a 1/σ no cambia nada.\")\n",
    "print(\"A medida que r se aleja de ρ el error crece — y crece más cuanto mayor sea σ.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d36d4db2",
   "metadata": {},
   "source": [
    "---\n",
    "## Verificación final contra la slide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0c670ac",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-08-04T13:09:52.772320Z",
     "iopub.status.busy": "2026-08-04T13:09:52.772027Z",
     "iopub.status.idle": "2026-08-04T13:09:52.786363Z",
     "shell.execute_reply": "2026-08-04T13:09:52.784979Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 de 6 magnitudes coinciden con los valores publicados en la slide.\n"
     ]
    },
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calculado</th>\n",
       "      <th>slide 18</th>\n",
       "      <th>|diferencia|</th>\n",
       "      <th>coincide</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>W</th>\n",
       "      <td>214.28571</td>\n",
       "      <td>214.29000</td>\n",
       "      <td>0.00429</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Γ</th>\n",
       "      <td>0.99875</td>\n",
       "      <td>0.99875</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>denominador</th>\n",
       "      <td>1.95119</td>\n",
       "      <td>1.95119</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c₁*</th>\n",
       "      <td>109.82310</td>\n",
       "      <td>109.82000</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c₂*</th>\n",
       "      <td>109.68574</td>\n",
       "      <td>109.69000</td>\n",
       "      <td>0.00426</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s₁*</th>\n",
       "      <td>-9.82310</td>\n",
       "      <td>-9.82000</td>\n",
       "      <td>0.00310</td>\n",
       "      <td>✓</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             calculado  slide 18  |diferencia| coincide\n",
       "W            214.28571 214.29000       0.00429        ✓\n",
       "Γ              0.99875   0.99875       0.00000        ✓\n",
       "denominador    1.95119   1.95119       0.00000        ✓\n",
       "c₁*          109.82310 109.82000       0.00310        ✓\n",
       "c₂*          109.68574 109.69000       0.00426        ✓\n",
       "s₁*           -9.82310  -9.82000       0.00310        ✓"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = pd.DataFrame({\n",
    "    \"calculado\": [W, Gamma, denominador, c1, c2, s1],\n",
    "    \"slide 18\":  [SLIDE[\"W\"], SLIDE[\"Γ\"], SLIDE[\"denominador\"],\n",
    "                  SLIDE[\"c₁*\"], SLIDE[\"c₂*\"], SLIDE[\"s₁*\"]],\n",
    "}, index=[\"W\", \"Γ\", \"denominador\", \"c₁*\", \"c₂*\", \"s₁*\"])\n",
    "final[\"|diferencia|\"] = (final[\"calculado\"] - final[\"slide 18\"]).abs()\n",
    "final[\"coincide\"] = np.where(final[\"|diferencia|\"] <= 5e-3, \"✓\", \"✗\")\n",
    "\n",
    "n_ok = (final[\"coincide\"] == \"✓\").sum()\n",
    "print(f\"{n_ok} de {len(final)} magnitudes coinciden con los valores publicados en la slide.\")\n",
    "assert n_ok == len(final), final\n",
    "final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49bfd36f",
   "metadata": {},
   "source": [
    "---\n",
    "### Resumen\n",
    "\n",
    "| | valor | lectura |\n",
    "|---|---|---|\n",
    "| $W$ | 214,29 | toda la riqueza del hogar, en pesos de hoy |\n",
    "| $\\Gamma$ | 0,99875 | perfil apenas decreciente: $\\rho = 5{,}26\\% > r = 5\\%$ |\n",
    "| $c_1^*$ | 109,82 | consume por sobre su ingreso corriente de 100 |\n",
    "| $c_2^*$ | 109,69 | casi lo mismo que hoy: suavización |\n",
    "| $s_1^*$ | −9,82 | **deudor neto**: se endeuda contra un ingreso futuro mayor |\n",
    "\n",
    "**Interpretación.** Como $\\beta(1+r)<1$ el hogar es levemente impaciente y prefiere un perfil de\n",
    "consumo apenas decreciente… pero casi plano. Al mismo tiempo su ingreso *crece* ($y_2>y_1$), así\n",
    "que para lograr ese perfil plano necesita adelantar recursos: se endeuda en 9,82 hoy y devuelve\n",
    "10,31 mañana (9,82 de principal más 0,49 de intereses).\n",
    "\n",
    "Dos fuerzas separadas y fáciles de confundir: **el nivel** del consumo lo fija la riqueza $W$;\n",
    "**la pendiente** la fija $\\Gamma=[\\beta(1+r)]^{1/\\sigma}$. Separarlas es la manera limpia de\n",
    "resolver cualquier modelo intertemporal de este curso.\n",
    "\n",
    "**Métodos de `scipy.optimize` usados**\n",
    "\n",
    "| Método | Para qué |\n",
    "|---|---|\n",
    "| `minimize_scalar` (`bounded`) | maximizar la utilidad sustituyendo la restricción de flujo |\n",
    "| `minimize` (SLSQP) con restricción de igualdad | resolver en $(c_1,c_2)$ y recuperar el multiplicador $\\lambda=u'(c_1^*)$ |\n",
    "| `brentq` | raíz de la ecuación de Euler; también la tasa $r^*$ que anula el ahorro |"
   ]
  }
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