capstone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Capstone: Analisando a temperatura do Brasil\n",
    "\n",
    "Nessa prática iremos juntar os conhecimentos que vimos durante toda a matéria. Vamos analisar os dados de temperatura do Brasil que vimos nas aulas de Python. Vamos utilizar uma [regressão linear](https://pt.wikipedia.org/wiki/Regress%C3%A3o_linear) para estimar a taxa de variação da temperatura ao longo dos anos. Finalmente, vamos visualizar esses valores em um mapa do Brasil.\n",
    "\n",
    "Faremos funções para cada etapa para podermos testar as partes do nosso código separadamente."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup\n",
    "\n",
    "Abaixo, vamos carregar todas as bibliotecas que precisamos:\n",
    "\n",
    "* `numpy` para fazer contas com matrizes e vetores\n",
    "* `maptlotlib.pyplot` para fazer gráficos\n",
    "* `mpl_toolkits.basemap` para fazer mapas\n",
    "* `scipy.stats` para a regressão linear\n",
    "* `glob` para descobrir os nomes de todos os arquivos em uma pasta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from scipy.stats import linregress\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert text-center\" style=\"font-size: 16pt\">\n",
    "Diferente das outras práticas, eu vou preecher o código inicial ao vivo durante a aula. \n",
    "<br><br>\n",
    "Façam junto comigo para não se perderem.\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Funções para ler informações do arquivo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def le_lat_lon (nome_arquivo):\n",
    "    \n",
    "    arquivo = open(nome_arquivo)\n",
    "    \n",
    "    for linhas in range (0, 5):\n",
    "        linha = arquivo.readline()\n",
    "        \n",
    "    partes = linha.split()\n",
    "    \n",
    "    lat = float(partes[1])\n",
    "    long = float(partes[3])\n",
    "    \n",
    "    if partes [2] == 'S,':\n",
    "        lat = -lat\n",
    "    if partes [4] == 'W':\n",
    "        long = -long\n",
    "          \n",
    "    return (lat, long)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.8 -49.02\n"
     ]
    }
   ],
   "source": [
    "lat, lon = le_lat_lon ('dados/0.80S-49.02W-TAVG-Trend.txt') \n",
    "print (lat, lon)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def le_temp_abs (nome_arquivo):\n",
    "    arquivo = open (nome_arquivo)\n",
    "    for linhas in range (48):\n",
    "        linha = arquivo.readline ()\n",
    "    partes = linha.split ()\n",
    "    temp_abs = float(partes [-3])\n",
    "    return temp_abs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26.74\n"
     ]
    }
   ],
   "source": [
    "temperatura = le_temp_abs('dados/0.80S-49.02W-TAVG-Trend.txt')\n",
    "print (temperatura)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def le_anos_anomalias(nome_arquivo):\n",
    "    \n",
    "    tabela = np.loadtxt(nome_arquivo, comments = \"%\")\n",
    "    \n",
    "    anos = tabela[0:,0]\n",
    "    meses = tabela[0:, 1]\n",
    "    anomalia = tabela[0:,4]\n",
    "    \n",
    "    anos_decimais = ((meses - 1)/12 + anos)\n",
    "\n",
    "    \n",
    "    return (anomalia, anos_decimais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anom, anos = le_anos_anomalias ('dados/0.80S-49.02W-TAVG-Trend.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f614340240>]"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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SyQph0N8wL1iwIOVjqrHClbQSwHgiGk1ENgAXAXhdvQMRDVE9PhoAmYkCwzDpx+/3o7Ky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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f6131129b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot (anos, anom, '-k' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1f6143b30b8>]"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JgjAiaGxsJCfH/RnWtKA0zWkGg1MQTEtMg5sgXHTRRVx22WUR1yoqKqKrq4ve3l7q6+sZ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\n4ziAdwiO4zhOxDsEx3EcB4DvB+6talABnEUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f61410f2e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "anom, anos = le_anos_anomalias ('dados/0.80S-49.02W-TAVG-Trend.txt')\n",
    "temp_abs = le_temp_abs ('dados/0.80S-49.02W-TAVG-Trend.txt')\n",
    "temperaturas =  temp_abs + anom \n",
    "\n",
    "plt.figure ()\n",
    "plt.plot (anos, temperaturas, '-k')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def temperatura_anos(nome_arquivo):\n",
    "    \n",
    "    anomalia, anos_decimais = le_anos_anomalias(nome_arquivo)\n",
    "    \n",
    "    temp = le_temp_abs(nome_arquivo)\n",
    "    \n",
    "    temp_abs = temp + anomalia\n",
    "    \n",
    "    return(temp_abs, anos_decimais)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "anom, anos = le_anos_anomalias ('dados/0.80S-49.02W-TAVG-Trend.txt')\n",
    "temp_abs = le_temp_abs ('dados/0.80S-49.02W-TAVG-Trend.txt')\n",
    "temperaturas =  temp_abs + anom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    nan,     nan,  25.868, ...,     nan,     nan,     nan])"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperaturas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1845.        ,  1845.08333333,  1845.16666667, ...,  2013.5       ,\n",
       "        2013.58333333,  2013.66666667])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remover os nan's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def regressão(nome_arquivo):\n",
    "    \n",
    "    temperaturas, anos = temperatura_anos(nome_arquivo)\n",
    "    ano_sem_nan = []\n",
    "    temperatura_sem_nan = []\n",
    "\n",
    "    for temperatura, ano in zip(temperaturas, anos):\n",
    "        if not np.isnan(temperatura):\n",
    "            temperatura_sem_nan.append(temperatura)\n",
    "            ano_sem_nan.append(ano)\n",
    "            \n",
    "    return (temperatura_sem_nan, ano_sem_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "temperatura_sem_nan, ano_sem_nan = regressão('dados/0.80S-49.02W-TAVG-Trend.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       " 26.546999999999997,\n",
       " 26.446999999999999,\n",
       " 26.462999999999997,\n",
       " 26.427999999999997,\n",
       " 26.390999999999998,\n",
       " 26.369,\n",
       " 26.372999999999998,\n",
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       " 26.372999999999998,\n",
       " 26.372,\n",
       " ...]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperatura_sem_nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a,b,r_value, p_value, std_err = linregress(ano_sem_nan, temperatura_sem_nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00832160974446 °C/ano\n"
     ]
    }
   ],
   "source": [
    "print(a, '°C/ano')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.83216097444633297"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100*a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regressão"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "not_nan = ~np.isnan(temperaturas)\n",
    "temperaturas_sem_nan= temperaturas[not_nan]\n",
    "anos_sem_nan = anos[not_nan]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.00832160974446 °C/ano\n"
     ]
    }
   ],
   "source": [
    "a, b, r_value, p_value, std_err = linregress(anos_sem_nan, temperaturas_sem_nan)\n",
    "print(a, '°C/ano')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Juntar tudo para fazer um mapa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nome_arquivos = glob(\"dados/*.txt\")\n",
    "\n",
    "latitudes = []\n",
    "longitudes = []\n",
    "variação_de_temperatura = []\n",
    "\n",
    "for arquivo in nome_arquivos:\n",
    "    \n",
    "    lat, long = le_lat_lon(arquivo)\n",
    "    latitudes.append(lat)\n",
    "    longitudes.append(long)\n",
    "    \n",
    "    temp_absoluta = le_temp_abs(arquivo)\n",
    "    anomalia, anos = le_anos_anomalias(arquivo)\n",
    "    temp, anos_decimais = temperatura_anos (arquivo)\n",
    "    temp_sem_nan, anos_sem_nan = regressão(arquivo)\n",
    "    a,b,r_value, p_value, std_err = linregress(anos_sem_nan, temp_sem_nan)\n",
    "    variação_de_temperatura.append(a*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basemap http://matplotlib.org/basemap/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "bm = Basemap (projection = 'merc' , llcrnrlat = -35, llcrnrlon = -65, urcrnrlat = 5, urcrnrlon = -35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x1f614641c18>"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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rTkxMpI4dO9K0adOKFVp5AEB/m1aTCBVCWAhTU1Pq3LkzDR8+nAwNDalDhw4S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\nropbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1f614498588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "bm.scatter(longitudes , latitudes, s= 40, c= variação_de_temperatura, cmap = \"Reds\", latlon = True)\n",
    "cb = plt.colorbar(pad=0, aspect = 40)\n",
    "cb.set_label(\"ºC/ano\")\n",
    "bm.drawcoastlines()\n",
    "bm.drawcountries()\n",
    "bm.drawstates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "**Course website**: https://github.com/mat-esp/about\n",
    "\n",
    "**Note**: This notebook is part of the course \"Matemática Especial I\" of the [Universidade do Estado do Rio de Janeiro](http://www.uerj.br/). All content can be freely used and adapted under the terms of the \n",
    "[Creative Commons Attribution 4.0 International License](http://creativecommons.org/licenses/by/4.0/).\n",
    "\n",
    "![Creative Commons License](https://i.creativecommons.org/l/by/4.0/88x31.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}