{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelo Modelo Neo-Kaleckiano Canônico\n",
    "\n",
    "**Referência:** Lavoie (Capítulo 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Equações:\n",
    "\n",
    "- Lucro PC: $r = \\frac{P\\cdot u}{v}$\n",
    "- Lucro ED: $r^{ED} = (\\alpha - \\beta u_n + \\beta u)/s_p$\n",
    "- Participação dos lucros: $P = \\frac{\\theta}{1+\\theta}$\n",
    "- Poupança: $g^s = s_pr$\n",
    "    - Investimento: $g^i = \\alpha + \\beta(u-u_n)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Construindo modelo em python\n",
    "\n",
    "Carregando pacotes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This line configures matplotlib to show figures embedded in the notebook, \n",
    "# If you are using an old version of IPython, try using '%pylab inline' instead.\n",
    "%matplotlib inline\n",
    "\n",
    "from pysolve3.model import Model\n",
    "from pysolve3.utils import is_close,round_solution\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando modelo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = Model()\n",
    "model.set_var_default(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo variáveis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.var('r', desc= 'Lucro')\n",
    "model.var('red', desc= ' Lucro demanda efetiva')\n",
    "model.var('P', desc= 'Participação dos Lucros')\n",
    "model.var('u', desc= 'Grau de utilização da capacidade')\n",
    "model.var('gs', desc= 'Taxa de poupança')\n",
    "model.var('gi', desc= 'Taxa de investimento');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo parâmetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.param('v', desc='Relação K/Y', default=0.5)\n",
    "model.param('sp', desc = 'Propensão a poupar a partir dos lucros', default= 0.7)\n",
    "model.param('theta', desc = 'Markup', default= 0.3)\n",
    "model.param('un', desc = 'Grau de utilitação normal', default=0.7)\n",
    "model.param('alpha', desc= 'Animal Spirits', default= 1)\n",
    "model.param('beta', desc= 'Sensibilidade do investimento ao grau de utilização', default=2);\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Definindo equações:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.add('P = theta/(1+theta)')\n",
    "model.add('r = (P*u)/v')\n",
    "model.add('red = (alpha - beta*un + beta*u)/sp')\n",
    "model.add('gs = sp*r')\n",
    "model.add('gi = alpha + beta*(u-un)')\n",
    "model.add('u = (alpha - beta*un)/(sp*P/v - beta)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resolvendo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P         : 0.2\n",
      "r         : 0.1\n",
      "red       : 0.1\n",
      "u         : 0.2\n",
      "gs        : 0.1\n",
      "gi        : 0.1\n"
     ]
    }
   ],
   "source": [
    "model.solve(iterations=100, threshold=1e-5)\n",
    "\n",
    "prev = round_solution(model.solutions[-2], decimals=1)\n",
    "solution = round_solution(model.solutions[-1], decimals=1)\n",
    "print(\"P         : \" + str(solution['P']))\n",
    "print(\"r         : \" + str(solution['r']))\n",
    "print(\"red       : \" + str(solution['red']))\n",
    "print(\"u         : \" + str(solution['u']))\n",
    "print(\"gs        : \" + str(solution['gs']))\n",
    "print(\"gi        : \" + str(solution['gi']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Criando tabela:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Variável  Valores\n",
      "0        P      0.2\n",
      "1        r      0.1\n",
      "2      red      0.1\n",
      "3        u      0.2\n",
      "4       gs      0.1\n",
      "5       gi      0.1\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "import numpy\n",
    "import pandas as pd\n",
    "from pysolve3.utils import generate_html_table\n",
    "data = {'Variável': ['P', 'r', 'red', 'u', 'gs', 'gi'],\n",
    "        'Valores' : [solution['P'], solution['r'], solution['red'], solution['u'], solution['gs'], solution['gi']]}\n",
    "tabela = pd.DataFrame(data=data)\n",
    "print(tabela)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Steady State\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P         : 0.2\n",
      "r         : 0.1\n",
      "red       : 0.1\n",
      "u         : 0.2\n",
      "gs        : 0.1\n",
      "gi        : 0.1\n"
     ]
    }
   ],
   "source": [
    "steady_state = Model()\n",
    "\n",
    "for _ in range(100):\n",
    "    steady_state.solve(iterations=100, threshold=1e-5)\n",
    "\n",
    "    prev_soln = steady_state.solutions[-2]\n",
    "    soln = steady_state.solutions[-1]\n",
    "    if is_close(prev_soln, soln, atol=1e-4):\n",
    "        break\n",
    "\n",
    "prev = round_solution(model.solutions[-2], decimals=1)\n",
    "solution = round_solution(model.solutions[-1], decimals=1)\n",
    "print(\"P         : \" + str(solution['P']))\n",
    "print(\"r         : \" + str(solution['r']))\n",
    "print(\"red       : \" + str(solution['red']))\n",
    "print(\"u         : \" + str(solution['u']))\n",
    "print(\"gs        : \" + str(solution['gs']))\n",
    "print(\"gi        : \" + str(solution['gi']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graficamente:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "alphagi = 1\n",
    "betagi = 2\n",
    "ungi = 0.7\n",
    "index_gi = list(range(0, 11, 1))\n",
    "linhagi = [alphagi + betagi*(index_gi[i]/10 - ungi) for i in index_gi]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "Spgs = 0.7\n",
    "index_gs = list(range(0, 11, 1))\n",
    "linhags = [Spgs*index_gs[i]/10 for i in index_gs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import transforms\n",
    "\n",
    "base = plt.gca().transData\n",
    "rot = transforms.Affine2D().rotate_deg(90)\n",
    "\n",
    "caption = '''\n",
    "    Modelo Neo-Kaleckiano Canônico'''\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "axes = fig.add_axes([0.1, 0.1, 1.0, 1.0])\n",
    "axes.tick_params(top=False)\n",
    "axes.set_ylim(0, 1)\n",
    "axes.plot(linhags, linestyle='-', color='r', transform= rot + base)\n",
    "axes.plot(linhagi, 'b')\n",
    "plt.grid(True)\n",
    "\n",
    "fig.text(0.1, -0.05, caption)\n",
    "axes.set_ylabel(\"Grau de utilização\")\n",
    "axes.set_xlabel(\"Investimento; Poupança\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}