estudo_novo_sat_ref_MT.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notebook criado em 22/08/20 para desenvolvimento de uma metodologia para análise da correlação entre focos do satélite de referência (AQUA_M-T) como os S-NPP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importação de bibliotecas de referência\n",
    "import pandas as pd\n",
    "from sqlalchemy import create_engine, pool\n",
    "from scipy.stats import linregress\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.display.float_format = '{:,.2f}'.format\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Conexão com o banco de dados\n",
    "engine = create_engine('postgresql://user:senha@localhost:5432/api', poolclass=pool.NullPool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine.connect()\n",
    "sql = \"\"\"\n",
    "\n",
    "select\n",
    "\t date_trunc('month', data_hora_gmt)::date data_mes,\n",
    "\tsatelite,\n",
    "\tcount(1) as qtd\n",
    "from\n",
    "\tcollection2.focos_bdq_c2\n",
    "where\n",
    "\t--extract(month from data_hora_gmt) = 1 and \n",
    "\tdata_hora_gmt > '20120101'\n",
    "\tand satelite in ('AQUA_M-T', 'NPP-375')\n",
    "\tand id_0 = 33\n",
    "\tand id_1 = 51\n",
    "\tand data_hora_gmt::time >= '12:00:00'\n",
    "group by 1,2\n",
    "order by 1,2\n",
    "\n",
    ";\n",
    "\"\"\"\n",
    "focos = pd.read_sql(sql, engine)\n",
    "focos[\"data_mes\"] = pd.to_datetime(focos.data_mes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 202 entries, 0 to 201\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   data_mes  202 non-null    datetime64[ns]\n",
      " 1   satelite  202 non-null    object        \n",
      " 2   qtd       202 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(1), object(1)\n",
      "memory usage: 4.9+ KB\n"
     ]
    }
   ],
   "source": [
    "focos.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>satelite</th>\n",
       "      <th>qtd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>1271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>201 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  satelite   qtd\n",
       "0   2012-01-01  AQUA_M-T   179\n",
       "1   2012-01-01   NPP-375   103\n",
       "2   2012-02-01  AQUA_M-T   200\n",
       "3   2012-02-01   NPP-375   514\n",
       "4   2012-03-01  AQUA_M-T   419\n",
       "..         ...       ...   ...\n",
       "196 2020-03-01  AQUA_M-T  1271\n",
       "197 2020-03-01   NPP-375  1419\n",
       "198 2020-04-01  AQUA_M-T   992\n",
       "199 2020-04-01   NPP-375  1415\n",
       "200 2020-05-01  AQUA_M-T   438\n",
       "\n",
       "[201 rows x 3 columns]"
      ]
     },
     "execution_count": 467,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focos.head(-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 468,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum = focos.pivot_table(index='data_mes', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum = pd.DataFrame(acum.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 101 entries, 0 to 100\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype         \n",
      "---  ------    --------------  -----         \n",
      " 0   data_mes  101 non-null    datetime64[ns]\n",
      " 1   AQUA_M-T  101 non-null    int64         \n",
      " 2   NPP-375   101 non-null    int64         \n",
      "dtypes: datetime64[ns](1), int64(2)\n",
      "memory usage: 2.5 KB\n"
     ]
    }
   ],
   "source": [
    "acum.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# sns.pairplot(data, x_vars=['TV','Radio','Newspaper'], y_vars='Sales')\n",
    "#sns.pairplot(acum,x_vars=['NPP-375'], y_vars=['AQUA_M-T'],height=8.27, aspect=11.7/8.27);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.44x595.44 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot('NPP-375', 'AQUA_M-T', data=acum, kind=\"reg\",\n",
    "                  xlim=(0, 140000), ylim=(0, 15000), color=\"r\", height=8.27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.17929237226767902, intercept=328.53179428408475, rvalue=0.9942873505256595, pvalue=5.109956920504732e-98, stderr=0.0019343940943032417)"
      ]
     },
     "execution_count": 471,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "slope, intercept, r_value, p_value, std_err = linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 473,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "slope: 0.179292    intercept: 328.531794\n"
     ]
    }
   ],
   "source": [
    "print(\"slope: %f    intercept: %f\" % (slope, intercept))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.988607\n"
     ]
    }
   ],
   "source": [
    "print(\"R-squared: %f\" % r_value**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum['ref_calc'] = acum['NPP-375']*slope + intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 476,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(210899, 210899.0)"
      ]
     },
     "execution_count": 476,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum['AQUA_M-T'].sum(), acum['ref_calc'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " O valor acima mostra que a soma de todo o período deu uma correlação perfeita. Porém na celula abaixo nota-se que os valores acumulados mensais estimados e observados em 2020, mes a mes são muito diferente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {
    "scrolled": true
   },
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "      <th>ref_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768</td>\n",
       "      <td>2344</td>\n",
       "      <td>748.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807</td>\n",
       "      <td>742</td>\n",
       "      <td>461.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1271</td>\n",
       "      <td>1419</td>\n",
       "      <td>582.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992</td>\n",
       "      <td>1415</td>\n",
       "      <td>582.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438</td>\n",
       "      <td>1587</td>\n",
       "      <td>613.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  AQUA_M-T  NPP-375  ref_calc\n",
       "96  2020-01-01       768     2344    748.79\n",
       "97  2020-02-01       807      742    461.57\n",
       "98  2020-03-01      1271     1419    582.95\n",
       "99  2020-04-01       992     1415    582.23\n",
       "100 2020-05-01       438     1587    613.07"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum [(acum.data_mes.dt.year == 2020)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 486,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4276 2988 -0.30121608980355474\n"
     ]
    }
   ],
   "source": [
    "total20_aqua = acum [(acum.data_mes.dt.year == 2020)][\"AQUA_M-T\"].sum()\n",
    "total20_calc = int(acum [(acum.data_mes.dt.year == 2020)][\"ref_calc\"].sum())\n",
    "dif = (total20_calc - total20_aqua) / total20_aqua \n",
    "print(total20_aqua, total20_calc, dif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.30121608980355474"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(total20_calc - total20_aqua) / total20_aqua"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Optei por fazer uma análise considerando a quantidade de focos.\n",
    " \n",
    " Neste sentido fiz uma análise estatística da distribuição do total de focos nos meses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAArUAAAHgCAYAAAC7C5WYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/d3fzzAAAACXBIWXMAAAsTAAALEwEAmpwYAAAdoElEQVR4nO3df7Cld10f8PenWQFhNT8a3YlJxg2dSBsSq+Q2gnScu0YkCEOYjsyECTa0ODtt0aLVsUmZKeMfmaa22NpB2u6YlLTYrDGiZGJR0ugdJh0hZvmhCSEmmm1Y+bEiELxpiyZ++sc9kcvm3r3P/Z1v9vWa2TnnfJ/nOc/3vC9c3jz3e86p7g4AAIzsr+32BAAAYLOUWgAAhqfUAgAwPKUWAIDhKbUAAAxvz25PIEnOPvvs3r9//46e8/HHH88LXvCCHT3nqGQ1nazWR17TyWo6WU0nq+lkNd12ZnXkyJHPd/c3rbTtGVFq9+/fn3vvvXdHz7mwsJD5+fkdPeeoZDWdrNZHXtPJajpZTSer6WQ13XZmVVX/e7Vtlh8AADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4e3Z7Artp/7W/vttT2DFHb3j1bk8BAGDbuFILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4a5baqrqpqo5X1X0njP9oVT1YVfdX1c8sG7+uqh6ebXvldkwaAACWm/I5te9O8s4k//Wpgao6kOTKJN/e3V+pqm+ejV+U5KokL07yLUn+Z1V9W3c/udUTBwCAp6x5pba7P5jkCycM/+MkN3T3V2b7HJ+NX5nkcHd/pbsfSfJwksu2cL4AAPA01d1r71S1P8kd3X3x7PHHkrwvyRVJ/l+Sn+zu362qdyb5UHe/Z7bfjUne3923rfCcB5McTJJ9+/Zdevjw4S15QVMtLi7mkcdOnQvIl5x7+oaPXVxczN69e7dwNs9eslofeU0nq+lkNZ2sppPVdNuZ1YEDB45099xK2zb6Nbl7kpyZ5KVJ/k6SW6vqhUlqhX1XbM3dfSjJoSSZm5vr+fn5DU5lYxYWFvKOux/f0XPupqNXz2/42IWFhez0z2dUslofeU0nq+lkNZ2sppPVdLuV1UY//eBYkvf2knuS/GWSs2fj5y/b77wkn97cFAEA4OQ2Wmp/Lcn3JklVfVuS5yT5fJLbk1xVVc+tqguSXJjkni2YJwAArGrN5QdVdUuS+SRnV9WxJG9PclOSm2Yf8/XnSa7ppcW591fVrUk+keSJJG/xyQcAAGy3NUttd79hlU1vXGX/65Ncv5lJAQDAevhGMQAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOGtWWqr6qaqOl5V962w7Serqqvq7GVj11XVw1X1YFW9cqsnDAAAJ5pypfbdSa44cbCqzk/yiiSPLhu7KMlVSV48O+ZdVXXalswUAABWsWap7e4PJvnCCpv+XZKfStLLxq5Mcri7v9LdjyR5OMllWzFRAABYTXX32jtV7U9yR3dfPHv82iSXd/dbq+pokrnu/nxVvTPJh7r7PbP9bkzy/u6+bYXnPJjkYJLs27fv0sOHD2/RS5pmcXExjzz25I6eczddcu7pGz52cXExe/fu3cLZPHvJan3kNZ2sppPVdLKaTlbTbWdWBw4cONLdcytt27PeJ6uq5yd5W5LvX2nzCmMrtubuPpTkUJLMzc31/Pz8eqeyKQsLC3nH3Y/v6Dl309Gr5zd87MLCQnb65zMqWa2PvKaT1XSymk5W08lqut3Kat2lNsnfSHJBko9XVZKcl+QjVXVZkmNJzl+273lJPr3ZSQIAwMms+yO9uvv3u/ubu3t/d+/PUpF9SXd/NsntSa6qqudW1QVJLkxyz5bOGAAATjDlI71uSfI7SV5UVceq6s2r7dvd9ye5NcknkvxGkrd096mzcBUAgF2x5vKD7n7DGtv3n/D4+iTXb25aAAAwnW8UAwBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGN6apbaqbqqq41V137Kxf1NVn6yq36uqX62qM5Ztu66qHq6qB6vqlds0bwAA+CtTrtS+O8kVJ4zdmeTi7v72JH+Q5LokqaqLklyV5MWzY95VVadt2WwBAGAFa5ba7v5gki+cMPaB7n5i9vBDSc6b3b8yyeHu/kp3P5Lk4SSXbeF8AQDgabZiTe0/TPL+2f1zk3xq2bZjszEAANg21d1r71S1P8kd3X3xCeNvSzKX5O91d1fVzyf5ne5+z2z7jUn+R3f/ygrPeTDJwSTZt2/fpYcPH97sa1mXxcXFPPLYkzt6zt10ybmnb/jYxcXF7N27dwtn8+wlq/WR13Symk5W08lqOllNt51ZHThw4Eh3z620bc9Gn7SqrknymiSX91eb8bEk5y/b7bwkn17p+O4+lORQkszNzfX8/PxGp7IhCwsLecfdj+/oOXfT0avnN3zswsJCdvrnMypZrY+8ppPVdLKaTlbTyWq63cpqQ8sPquqKJP88yWu7+/8s23R7kquq6rlVdUGSC5Pcs/lpAgDA6ta8UltVtySZT3J2VR1L8vYsfdrBc5PcWVVJ8qHu/kfdfX9V3ZrkE0meSPKW7j51/sYPAMCuWLPUdvcbVhi+8ST7X5/k+s1MCgAA1sM3igEAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhrltqquqmqjlfVfcvGzqqqO6vqodntmcu2XVdVD1fVg1X1yu2aOAAAPGXKldp3J7nihLFrk9zV3RcmuWv2OFV1UZKrkrx4dsy7quq0LZstAACsYM1S290fTPKFE4avTHLz7P7NSV63bPxwd3+lux9J8nCSy7ZmqgAAsLLq7rV3qtqf5I7uvnj2+Evdfcay7V/s7jOr6p1JPtTd75mN35jk/d192wrPeTDJwSTZt2/fpYcPH96ClzPd4uJiHnnsyR0952665NzTN3zs4uJi9u7du4WzefaS1frIazpZTSer6WQ1naym286sDhw4cKS751batmeLz1UrjK3Ymrv7UJJDSTI3N9fz8/NbPJWTW1hYyDvufnxHz7mbjl49v+FjFxYWstM/n1HJan3kNZ2sppPVdLKaTlbT7VZWG/30g89V1TlJMrs9Phs/luT8Zfudl+TTG58eAACsbaOl9vYk18zuX5PkfcvGr6qq51bVBUkuTHLP5qYIAAAnt+byg6q6Jcl8krOr6liStye5IcmtVfXmJI8meX2SdPf9VXVrkk8keSLJW7r71Fm4CgDArliz1Hb3G1bZdPkq+1+f5PrNTAoAANbDN4oBADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMb1Oltqp+vKrur6r7quqWqnpeVZ1VVXdW1UOz2zO3arIAALCSDZfaqjo3yT9NMtfdFyc5LclVSa5Ncld3X5jkrtljAADYNptdfrAnyddX1Z4kz0/y6SRXJrl5tv3mJK/b5DkAAOCkqrs3fnDVW5Ncn+T/JvlAd19dVV/q7jOW7fPF7n7aEoSqOpjkYJLs27fv0sOHD294HhuxuLiYRx57ckfPuZsuOff0DR+7uLiYvXv3buFsnr1ktT7ymk5W08lqOllNJ6vptjOrAwcOHOnuuZW27dnok87Wyl6Z5IIkX0ryy1X1xqnHd/ehJIeSZG5urufn5zc6lQ1ZWFjIO+5+fEfPuZuOXj2/4WMXFhay0z+fUclqfeQ1naymk9V0sppOVtPtVlabWX7wfUke6e4/6e6/SPLeJN+d5HNVdU6SzG6Pb36aAACwus2U2keTvLSqnl9VleTyJA8kuT3JNbN9rknyvs1NEQAATm7Dyw+6+8NVdVuSjyR5IslHs7ScYG+SW6vqzVkqvq/fiokCAMBqNlxqk6S7357k7ScMfyVLV20BAGBH+EYxAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4W2q1FbVGVV1W1V9sqoeqKqXVdVZVXVnVT00uz1zqyYLAAAr2eyV2p9L8hvd/TeT/O0kDyS5Nsld3X1hkrtmjwEAYNtsuNRW1Tcm+Z4kNyZJd/95d38pyZVJbp7tdnOS121uigAAcHLV3Rs7sOo7khxK8oksXaU9kuStSf64u89Ytt8Xu/tpSxCq6mCSg0myb9++Sw8fPryheWzU4uJiHnnsyR0952665NzTN3zs4uJi9u7du4WzefaS1frIazpZTSer6WQ1naym286sDhw4cKS751batplSO5fkQ0le3t0frqqfS/LlJD86pdQuNzc31/fee++G5rFRCwsLedNvPL6j59xNR2949YaPXVhYyPz8/NZN5llMVusjr+lkNZ2sppPVdLKabjuzqqpVS+1m1tQeS3Ksuz88e3xbkpck+VxVnTM78TlJjm/iHAAAsKYNl9ru/myST1XVi2ZDl2dpKcLtSa6ZjV2T5H2bmiEAAKxhzyaP/9Ekv1hVz0nyR0n+QZaK8q1V9eYkjyZ5/SbPAQAAJ7WpUtvdH0uy0rqGyzfzvAAAsB6+UQwAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4my61VXVaVX20qu6YPT6rqu6sqodmt2dufpoAALC6rbhS+9YkDyx7fG2Su7r7wiR3zR4DAMC22VSprarzkrw6yS8sG74yyc2z+zcned1mzgEAAGup7t74wVW3JflXSb4hyU9292uq6kvdfcayfb7Y3U9bglBVB5McTJJ9+/Zdevjw4Q3PYyMWFxfzyGNP7ug5d9Ml556+4WMXFxezd+/eLZzNs5es1kde08lqOllNJ6vpZDXddmZ14MCBI909t9K2PRt90qp6TZLj3X2kqubXe3x3H0pyKEnm5uZ6fn7dT7EpCwsLecfdj+/oOXfT0avnN3zswsJCdvrnMypZrY+8ppPVdLKaTlbTyWq63cpqw6U2ycuTvLaqfiDJ85J8Y1W9J8nnquqc7v5MVZ2T5PhWTBQAAFaz4TW13X1dd5/X3fuTXJXkt7r7jUluT3LNbLdrkrxv07MEAICT2I7Pqb0hySuq6qEkr5g9BgCAbbOZ5Qd/pbsXkizM7v9pksu34nkBAGAK3ygGAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhbbjUVtX5VfXbVfVAVd1fVW+djZ9VVXdW1UOz2zO3broAAPB0m7lS+0SSn+juv5XkpUneUlUXJbk2yV3dfWGSu2aPAQBg22y41Hb3Z7r7I7P7f5bkgSTnJrkyyc2z3W5O8rpNzhEAAE6qunvzT1K1P8kHk1yc5NHuPmPZti9299OWIFTVwSQHk2Tfvn2XHj58eNPzWI/FxcU88tiTO3rO3XTJuadv+NjFxcXs3bt3C2fz7CWr9ZHXdLKaTlbTyWo6WU23nVkdOHDgSHfPrbRtz2afvKr2JvmVJD/W3V+uqknHdfehJIeSZG5urufn5zc7lXVZWFjIO+5+fEfPuZuOXj2/4WMXFhay0z+fUclqfeQ1naymk9V0sppOVtPtVlab+vSDqvq6LBXaX+zu986GP1dV58y2n5Pk+OamCAAAJ7eZTz+oJDcmeaC7f3bZptuTXDO7f02S9218egAAsLbNLD94eZIfSvL7VfWx2di/SHJDklur6s1JHk3y+k3NEAAA1rDhUtvddydZbQHt5Rt9XrbH/mt/fcPH/sQlT+RNmzh+Nxy94dW7PQUAYAf5RjEAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMNTagEAGJ5SCwDA8JRaAACGp9QCADA8pRYAgOEptQAADE+pBQBgeEotAADDU2oBABieUgsAwPCUWgAAhqfUAgAwPKUWAIDhKbUAAAxPqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMDylFgCA4Sm1AAAMT6kFAGB4Si0AAMPbs9sTAJhq/7W/vttT2FFHb3j1bk8BYBiu1AIAMDylFgCA4Vl+wLPSbv2Z+icueSJv2uFz+xM1ALhSCwDAs4BSCwDA8JRaAACGp9QCADA8bxSDwY382a278cY6AJ6dXKkFAGB4Si0AAMOz/ADgGWr50hJLNaZ7pmfls6Vhe7hSCwDA8LbtSm1VXZHk55KcluQXuvuG7ToXAIzimfTmzp24qn0qXZl+Jv1sd8Iz7We7LVdqq+q0JD+f5FVJLkryhqq6aDvOBQAA27X84LIkD3f3H3X3nyc5nOTKbToXAACnuOrurX/Sqh9MckV3//Ds8Q8l+a7u/pFl+xxMcnD28EVJHtzyiZzc2Uk+v8PnHJWsppPV+shrOllNJ6vpZDWdrKbbzqy+tbu/aaUN27WmtlYY+5r23N2HkhzapvOvqaru7e653Tr/SGQ1nazWR17TyWo6WU0nq+lkNd1uZbVdyw+OJTl/2ePzknx6m84FAMApbrtK7e8mubCqLqiq5yS5Ksnt23QuAABOcduy/KC7n6iqH0nym1n6SK+buvv+7TjXJuza0ocByWo6Wa2PvKaT1XSymk5W08lqul3JalveKAYAADvJN4oBADA8pRYAgOGdkqW2qq6oqger6uGquna357NTquqmqjpeVfctGzurqu6sqodmt2cu23bdLKMHq+qVy8Yvrarfn237D1VVs/HnVtUvzcY/XFX7d/QFbpGqOr+qfruqHqiq+6vqrbNxWZ2gqp5XVfdU1cdnWf30bFxWq6iq06rqo1V1x+yxrFZRVUdnr/NjVXXvbExeK6iqM6rqtqr65Ox318tk9XRV9aLZf56e+vflqvoxWa2sqn589rv9vqq6pZZ+5z9zs+ruU+pflt649odJXpjkOUk+nuSi3Z7XDr3270nykiT3LRv7mSTXzu5fm+Rfz+5fNMvmuUkumGV22mzbPUlelqXPI35/klfNxv9Jkv80u39Vkl/a7de8wZzOSfKS2f1vSPIHszxk9fSsKsne2f2vS/LhJC+V1Ukz+2dJ/nuSO2aPZbV6VkeTnH3CmLxWzurmJD88u/+cJGfIas3MTkvy2STfKqsV8zk3ySNJvn72+NYkb3omZ7Xroe3CD+llSX5z2ePrkly32/Pawde/P19bah9Mcs7s/jlJHlwplyx9ksXLZvt8ctn4G5L85+X7zO7vydK3idRuv+YtyOx9SV4hqzVzen6SjyT5LlmtmtF5Se5K8r35aqmV1ep5Hc3TS628np7TN2apfNQJ47I6eW7fn+R/yWrVfM5N8qkkZ81exx2zzJ6xWZ2Kyw+e+iE95dhs7FS1r7s/kySz22+eja+W07mz+yeOf80x3f1EkseS/PVtm/kOmP0p5DuzdAVSViuY/Tn9Y0mOJ7mzu2W1un+f5KeS/OWyMVmtrpN8oKqO1NJXqyfyWskLk/xJkv8yW9ryC1X1gshqLVcluWV2X1Yn6O4/TvJvkzya5DNJHuvuD+QZnNWpWGrX/Apfkqye08nye1ZlW1V7k/xKkh/r7i+fbNcVxk6ZrLr7ye7+jixdhbysqi4+ye6nbFZV9Zokx7v7yNRDVhg7JbJa5uXd/ZIkr0rylqr6npPseyrntSdLS8v+Y3d/Z5LHs/Rn4dWcylklSWrpi6Fem+SX19p1hbFTIqvZWtkrs7SU4FuSvKCq3niyQ1YY29GsTsVS6yt8v9bnquqcJJndHp+Nr5bTsdn9E8e/5piq2pPk9CRf2LaZb6Oq+rosFdpf7O73zoZldRLd/aUkC0muiKxW8vIkr62qo0kOJ/neqnpPZLWq7v707PZ4kl9NclnktZJjSY7N/kqSJLdlqeTKanWvSvKR7v7c7LGsnu77kjzS3X/S3X+R5L1JvjvP4KxOxVLrK3y/1u1JrpndvyZL60efGr9q9s7EC5JcmOSe2Z8a/qyqXjp79+LfP+GYp57rB5P8Vs8Wyoxk9rpuTPJAd//ssk2yOkFVfVNVnTG7//VZ+iX4ycjqabr7uu4+r7v3Z+n3zm919xsjqxVV1Quq6hueup+ltXz3RV5P092fTfKpqnrRbOjyJJ+IrE7mDfnq0oNEVit5NMlLq+r5s9d4eZIH8kzOarcXIu/GvyQ/kKV3tP9hkrft9nx28HXfkqV1MX+Rpf939OYsrV25K8lDs9uzlu3/tllGD2b2TsXZ+FyW/sflD5O8M1/9ZrrnZelPOQ9n6Z2OL9zt17zBnP5ulv788XtJPjb79wOyWjGrb0/y0VlW9yX5l7NxWZ08t/l89Y1islo5oxdm6Z3UH09y/1O/q+W1al7fkeTe2X8Xfy3JmbJaNavnJ/nTJKcvG5PVyln9dJYuVNyX5L9l6ZMNnrFZ+ZpcAACGdyouPwAA4FlGqQUAYHhKLQAAw1NqAQAYnlILAMDwlFoAAIan1AIAMLz/D9kOO2g+cE+dAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# focos.acum_ano.hist(figsize=(20,10), bins=np.arange(0,40000,25));\n",
    "focos.qtd.hist();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      202.00\n",
       "mean     5,951.06\n",
       "std     11,320.51\n",
       "min        103.00\n",
       "10%        357.60\n",
       "25%        644.50\n",
       "50%      1,468.50\n",
       "75%      5,704.00\n",
       "90%     15,174.80\n",
       "max     79,116.00\n",
       "Name: qtd, dtype: float64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focos.qtd.describe(percentiles=[0.10,0.25,0.50,0.75,0.90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "focos.boxplot(column='qtd',\n",
    "              figsize=(5,10)\n",
    ");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Pelo fato da estatística te apresentado que 90% dos meses possuem quantidade menor que 15 mil focos, optei por fazer um recorte e analisar este sub conjunto primeiro."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [],
   "source": [
    "f15 = focos[focos.qtd <= 15000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>satelite</th>\n",
       "      <th>qtd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>181 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  satelite   qtd\n",
       "0   2012-01-01  AQUA_M-T   179\n",
       "1   2012-01-01   NPP-375   103\n",
       "2   2012-02-01  AQUA_M-T   200\n",
       "3   2012-02-01   NPP-375   514\n",
       "4   2012-03-01  AQUA_M-T   419\n",
       "..         ...       ...   ...\n",
       "197 2020-03-01   NPP-375  1419\n",
       "198 2020-04-01  AQUA_M-T   992\n",
       "199 2020-04-01   NPP-375  1415\n",
       "200 2020-05-01  AQUA_M-T   438\n",
       "201 2020-05-01   NPP-375  1587\n",
       "\n",
       "[181 rows x 3 columns]"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f15.boxplot(column='qtd',\n",
    "              figsize=(5,10)\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>179.00</td>\n",
       "      <td>103.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>200.00</td>\n",
       "      <td>514.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>419.00</td>\n",
       "      <td>452.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-04-01</td>\n",
       "      <td>560.00</td>\n",
       "      <td>776.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-05-01</td>\n",
       "      <td>958.00</td>\n",
       "      <td>3,848.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768.00</td>\n",
       "      <td>2,344.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807.00</td>\n",
       "      <td>742.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1,271.00</td>\n",
       "      <td>1,419.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992.00</td>\n",
       "      <td>1,415.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438.00</td>\n",
       "      <td>1,587.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  AQUA_M-T  NPP-375\n",
       "0   2012-01-01    179.00   103.00\n",
       "1   2012-02-01    200.00   514.00\n",
       "2   2012-03-01    419.00   452.00\n",
       "3   2012-04-01    560.00   776.00\n",
       "4   2012-05-01    958.00 3,848.00\n",
       "..         ...       ...      ...\n",
       "96  2020-01-01    768.00 2,344.00\n",
       "97  2020-02-01    807.00   742.00\n",
       "98  2020-03-01  1,271.00 1,419.00\n",
       "99  2020-04-01    992.00 1,415.00\n",
       "100 2020-05-01    438.00 1,587.00\n",
       "\n",
       "[101 rows x 3 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum = f15.pivot_table(index='data_mes', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum = pd.DataFrame(acum.to_records())\n",
    "acum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Para ter certeza que ambos os satélites possuem registros em todos os meses fiz o teste abaixo, e verifiquei que o NPP possui 21 meses com mais de 15 focos enquanto o aqua possui menos."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "data_mes     0\n",
       "AQUA_M-T     0\n",
       "NPP-375     21\n",
       "dtype: int64"
      ]
     },
     "execution_count": 227,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2012-08-01</td>\n",
       "      <td>6,429.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2012-09-01</td>\n",
       "      <td>10,620.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2012-10-01</td>\n",
       "      <td>3,475.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2013-08-01</td>\n",
       "      <td>3,739.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2013-09-01</td>\n",
       "      <td>5,915.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>2014-08-01</td>\n",
       "      <td>6,418.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>2014-09-01</td>\n",
       "      <td>6,569.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>2014-10-01</td>\n",
       "      <td>4,657.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>4,498.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>8,933.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>5,730.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>2016-07-01</td>\n",
       "      <td>3,301.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2016-08-01</td>\n",
       "      <td>6,168.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>8,284.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>2,715.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2017-08-01</td>\n",
       "      <td>4,578.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>14,840.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2017-10-01</td>\n",
       "      <td>3,738.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2018-09-01</td>\n",
       "      <td>6,638.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>8,030.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>10,747.00</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data_mes  AQUA_M-T  NPP-375\n",
       "7  2012-08-01  6,429.00      nan\n",
       "8  2012-09-01 10,620.00      nan\n",
       "9  2012-10-01  3,475.00      nan\n",
       "19 2013-08-01  3,739.00      nan\n",
       "20 2013-09-01  5,915.00      nan\n",
       "31 2014-08-01  6,418.00      nan\n",
       "32 2014-09-01  6,569.00      nan\n",
       "33 2014-10-01  4,657.00      nan\n",
       "43 2015-08-01  4,498.00      nan\n",
       "44 2015-09-01  8,933.00      nan\n",
       "45 2015-10-01  5,730.00      nan\n",
       "54 2016-07-01  3,301.00      nan\n",
       "55 2016-08-01  6,168.00      nan\n",
       "56 2016-09-01  8,284.00      nan\n",
       "57 2016-10-01  2,715.00      nan\n",
       "67 2017-08-01  4,578.00      nan\n",
       "68 2017-09-01 14,840.00      nan\n",
       "69 2017-10-01  3,738.00      nan\n",
       "80 2018-09-01  6,638.00      nan\n",
       "91 2019-08-01  8,030.00      nan\n",
       "92 2019-09-01 10,747.00      nan"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum[acum[\"NPP-375\"].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum = acum[~(acum[\"NPP-375\"].isnull() )]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum = acum[~(acum.data_mes.dt.year == 2020)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.44x595.44 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot('NPP-375', 'AQUA_M-T', data=acum, kind=\"reg\",\n",
    "                  color=\"r\", height=8.27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.16461862017795845, intercept=374.4089184335206, rvalue=0.9328491117829526, pvalue=4.282870670954499e-34, stderr=0.007440994465769859)"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 233,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8702074653542436"
      ]
     },
     "execution_count": 233,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0.9328491117829526**2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  Como pode ser notado o R2 desta analise não foi satisfatório e portanto vou tentar a abordagem de fazer por trimeste.\n",
    "  \n",
    "# Analise por trimestre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 513,
   "metadata": {
    "scrolled": false
   },
   "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>data_mes</th>\n",
       "      <th>satelite</th>\n",
       "      <th>qtd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  satelite   qtd\n",
       "0   2012-01-01  AQUA_M-T   179\n",
       "1   2012-01-01   NPP-375   103\n",
       "2   2012-02-01  AQUA_M-T   200\n",
       "3   2012-02-01   NPP-375   514\n",
       "4   2012-03-01  AQUA_M-T   419\n",
       "..         ...       ...   ...\n",
       "197 2020-03-01   NPP-375  1419\n",
       "198 2020-04-01  AQUA_M-T   992\n",
       "199 2020-04-01   NPP-375  1415\n",
       "200 2020-05-01  AQUA_M-T   438\n",
       "201 2020-05-01   NPP-375  1587\n",
       "\n",
       "[202 rows x 3 columns]"
      ]
     },
     "execution_count": 513,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [],
   "source": [
    "focos_mes = focos[focos.data_mes.dt.month.isin([5,6,7,11,12])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 456,
   "metadata": {},
   "outputs": [],
   "source": [
    "focos_mes = focos[focos.data_mes.dt.month.isin([1,2,3,4,5])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 514,
   "metadata": {},
   "outputs": [],
   "source": [
    "focos_mes = focos[focos.data_mes.dt.month.isin([8,9,10])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 525,
   "metadata": {},
   "outputs": [],
   "source": [
    "focos_mes = focos[focos.data_mes.dt.month.isin([5,6,7,11,12,8,9,10])] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 526,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768</td>\n",
       "      <td>2344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807</td>\n",
       "      <td>742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1271</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438</td>\n",
       "      <td>1587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data_mes  AQUA_M-T  NPP-375\n",
       "0 2020-01-01       768     2344\n",
       "1 2020-02-01       807      742\n",
       "2 2020-03-01      1271     1419\n",
       "3 2020-04-01       992     1415\n",
       "4 2020-05-01       438     1587"
      ]
     },
     "execution_count": 526,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focos20 = focos[(focos.data_mes.dt.year == 2020)]\n",
    "acum20 = focos20.pivot_table(index='data_mes', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum20 = pd.DataFrame(acum20.to_records())\n",
    "acum20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 527,
   "metadata": {},
   "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>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>64.00</td>\n",
       "      <td>64.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2,958.08</td>\n",
       "      <td>15,018.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3,036.36</td>\n",
       "      <td>16,563.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>249.00</td>\n",
       "      <td>887.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>928.75</td>\n",
       "      <td>3,780.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1,712.00</td>\n",
       "      <td>8,105.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3,928.75</td>\n",
       "      <td>22,226.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>14,840.00</td>\n",
       "      <td>79,116.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AQUA_M-T   NPP-375\n",
       "count     64.00     64.00\n",
       "mean   2,958.08 15,018.33\n",
       "std    3,036.36 16,563.30\n",
       "min      249.00    887.00\n",
       "25%      928.75  3,780.25\n",
       "50%    1,712.00  8,105.00\n",
       "75%    3,928.75 22,226.00\n",
       "max   14,840.00 79,116.00"
      ]
     },
     "execution_count": 527,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum = focos_mes.pivot_table(index='data_mes', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum = pd.DataFrame(acum.to_records())\n",
    "\n",
    "acum = acum[~(acum.data_mes.dt.year == 2020)]\n",
    "\n",
    "acum.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum['mes']=acum.data_mes.dt.month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.44x595.44 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# acum.plot.scatter('NPP-375', 'AQUA_M-T', c='mes',colormap='viridis');\n",
    "g = sns.jointplot('NPP-375', 'AQUA_M-T', data=acum, kind=\"reg\",\n",
    "                  color=\"r\", height=8.27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.18257060654420001, intercept=216.1728499389319, rvalue=0.9959196555943176, pvalue=1.7448145770381907e-66, stderr=0.002101022233771913)"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9873938914366083"
      ]
     },
     "execution_count": 531,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.rvalue**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 532,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 533,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum20['ref_calc'] = acum20['NPP-375']*a.slope + a.intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 534,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "      <th>ref_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768</td>\n",
       "      <td>2344</td>\n",
       "      <td>644.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807</td>\n",
       "      <td>742</td>\n",
       "      <td>351.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1271</td>\n",
       "      <td>1419</td>\n",
       "      <td>475.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992</td>\n",
       "      <td>1415</td>\n",
       "      <td>474.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438</td>\n",
       "      <td>1587</td>\n",
       "      <td>505.91</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data_mes  AQUA_M-T  NPP-375  ref_calc\n",
       "0 2020-01-01       768     2344    644.12\n",
       "1 2020-02-01       807      742    351.64\n",
       "2 2020-03-01      1271     1419    475.24\n",
       "3 2020-04-01       992     1415    474.51\n",
       "4 2020-05-01       438     1587    505.91"
      ]
     },
     "execution_count": 534,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4276, 2451)"
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum20['AQUA_M-T'].sum(),int(acum20['ref_calc'].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 535,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mes1_5 4276 2451 -0.426800748362956\n"
     ]
    }
   ],
   "source": [
    "total20_aqua = acum20 [(acum20.data_mes.dt.year == 2020)][\"AQUA_M-T\"].sum()\n",
    "total20_calc = int(acum20 [(acum20.data_mes.dt.year == 2020)][\"ref_calc\"].sum())\n",
    "dif = (total20_calc - total20_aqua) / total20_aqua \n",
    "print('mes1_5' , total20_aqua, total20_calc, dif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão  \n",
    "  Conforme foi testado todos os valores considerando os meses mais criticos e mesnos criticos, com melhor r2 todos não conseguem fazer uma estimativa mensal que seja interessante.\n",
    "  \n",
    "  Existem ainda duas abordagem uma de separar apenas o ano anterior como referencia e outra considenrando as variações de dia.\n",
    "  \n",
    "  # Análise considerando apenas o ano anterior"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>satelite</th>\n",
       "      <th>qtd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-02-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2012-03-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>AQUA_M-T</td>\n",
       "      <td>438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>NPP-375</td>\n",
       "      <td>1587</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>202 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      data_mes  satelite   qtd\n",
       "0   2012-01-01  AQUA_M-T   179\n",
       "1   2012-01-01   NPP-375   103\n",
       "2   2012-02-01  AQUA_M-T   200\n",
       "3   2012-02-01   NPP-375   514\n",
       "4   2012-03-01  AQUA_M-T   419\n",
       "..         ...       ...   ...\n",
       "197 2020-03-01   NPP-375  1419\n",
       "198 2020-04-01  AQUA_M-T   992\n",
       "199 2020-04-01   NPP-375  1415\n",
       "200 2020-05-01  AQUA_M-T   438\n",
       "201 2020-05-01   NPP-375  1587\n",
       "\n",
       "[202 rows x 3 columns]"
      ]
     },
     "execution_count": 536,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "focos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "metadata": {},
   "outputs": [],
   "source": [
    "focos19 = focos[focos.data_mes.dt.year == 2019]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "metadata": {},
   "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>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.00</td>\n",
       "      <td>12.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2,597.42</td>\n",
       "      <td>12,336.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3,275.64</td>\n",
       "      <td>18,621.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>576.00</td>\n",
       "      <td>481.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>708.75</td>\n",
       "      <td>1,282.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1,182.00</td>\n",
       "      <td>4,745.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2,194.75</td>\n",
       "      <td>11,403.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10,747.00</td>\n",
       "      <td>59,482.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       AQUA_M-T   NPP-375\n",
       "count     12.00     12.00\n",
       "mean   2,597.42 12,336.00\n",
       "std    3,275.64 18,621.00\n",
       "min      576.00    481.00\n",
       "25%      708.75  1,282.50\n",
       "50%    1,182.00  4,745.00\n",
       "75%    2,194.75 11,403.50\n",
       "max   10,747.00 59,482.00"
      ]
     },
     "execution_count": 539,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum = focos19.pivot_table(index='data_mes', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum = pd.DataFrame(acum.to_records())\n",
    "\n",
    "acum.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 540,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.44x595.44 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot('NPP-375', 'AQUA_M-T', data=acum, kind=\"reg\",\n",
    "                  color=\"r\", height=8.27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-01</td>\n",
       "      <td>576</td>\n",
       "      <td>1408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-02-01</td>\n",
       "      <td>709</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2019-03-01</td>\n",
       "      <td>1188</td>\n",
       "      <td>488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2019-04-01</td>\n",
       "      <td>708</td>\n",
       "      <td>481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019-05-01</td>\n",
       "      <td>1118</td>\n",
       "      <td>3930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2019-06-01</td>\n",
       "      <td>2151</td>\n",
       "      <td>9382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019-07-01</td>\n",
       "      <td>2326</td>\n",
       "      <td>11591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2019-08-01</td>\n",
       "      <td>8030</td>\n",
       "      <td>41187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2019-09-01</td>\n",
       "      <td>10747</td>\n",
       "      <td>59482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2019-10-01</td>\n",
       "      <td>1774</td>\n",
       "      <td>11341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2019-11-01</td>\n",
       "      <td>1176</td>\n",
       "      <td>5560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2019-12-01</td>\n",
       "      <td>666</td>\n",
       "      <td>2276</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     data_mes  AQUA_M-T  NPP-375\n",
       "0  2019-01-01       576     1408\n",
       "1  2019-02-01       709      906\n",
       "2  2019-03-01      1188      488\n",
       "3  2019-04-01       708      481\n",
       "4  2019-05-01      1118     3930\n",
       "5  2019-06-01      2151     9382\n",
       "6  2019-07-01      2326    11591\n",
       "7  2019-08-01      8030    41187\n",
       "8  2019-09-01     10747    59482\n",
       "9  2019-10-01      1774    11341\n",
       "10 2019-11-01      1176     5560\n",
       "11 2019-12-01       666     2276"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 541,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.1750231684454076, intercept=438.33086072411834, rvalue=0.9949537104325284, pvalue=2.5553786379776348e-11, stderr=0.00558142587036569)"
      ]
     },
     "execution_count": 541,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 542,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9918559603991042"
      ]
     },
     "execution_count": 542,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.rvalue**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 545,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum20['ref_calc'] = acum20['NPP-375']*a.slope + a.intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "      <th>ref_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768</td>\n",
       "      <td>2344</td>\n",
       "      <td>848.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807</td>\n",
       "      <td>742</td>\n",
       "      <td>568.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1271</td>\n",
       "      <td>1419</td>\n",
       "      <td>686.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992</td>\n",
       "      <td>1415</td>\n",
       "      <td>685.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438</td>\n",
       "      <td>1587</td>\n",
       "      <td>716.09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data_mes  AQUA_M-T  NPP-375  ref_calc\n",
       "0 2020-01-01       768     2344    848.59\n",
       "1 2020-02-01       807      742    568.20\n",
       "2 2020-03-01      1271     1419    686.69\n",
       "3 2020-04-01       992     1415    685.99\n",
       "4 2020-05-01       438     1587    716.09"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mes1_5 4276 3505 -0.1803086997193639\n"
     ]
    }
   ],
   "source": [
    "total20_aqua = acum20 [(acum20.data_mes.dt.year == 2020)][\"AQUA_M-T\"].sum()\n",
    "total20_calc = int(acum20 [(acum20.data_mes.dt.year == 2020)][\"ref_calc\"].sum())\n",
    "dif = (total20_calc - total20_aqua) / total20_aqua \n",
    "print('mes1_5' , total20_aqua, total20_calc, dif)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusão  \n",
    "  Conforme foi testado utilizando como referencia os valores do ano anterior foi um bom melhor r2 e a diferença do acumulado anual foi de -18%\n",
    "  \n",
    "  # Análise considerando dias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine.connect()\n",
    "sql = \"\"\"\n",
    "\n",
    "select\n",
    "\tdata_hora_gmt::date as mdata,\n",
    "\tsatelite,\n",
    "\tcount(1) as qtd\n",
    "from\n",
    "\tcollection2.focos_bdq_c2\n",
    "where\n",
    "\t--extract(month from data_hora_gmt) = 1 and \n",
    "\tdata_hora_gmt > '20130101'\n",
    "\tand satelite in ('AQUA_M-T', 'NPP-375')\n",
    "\tand id_0 = 33\n",
    "\tand id_1 = 51\n",
    "\tand data_hora_gmt::time >= '12:00:00'\n",
    "group by 1,2\n",
    "order by 1,2\n",
    "\n",
    ";\n",
    "\"\"\"\n",
    "focos = pd.read_sql(sql, engine)\n",
    "focos[\"mdata\"] = pd.to_datetime(focos.mdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum = focos.pivot_table(index='mdata', columns=['satelite'], aggfunc=\"mean\", values='qtd', margins=False )\n",
    "acum = pd.DataFrame(acum.to_records())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "metadata": {},
   "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>mdata</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>2.00</td>\n",
       "      <td>74.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2013-01-02</td>\n",
       "      <td>49.00</td>\n",
       "      <td>92.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2013-01-03</td>\n",
       "      <td>5.00</td>\n",
       "      <td>85.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2013-01-04</td>\n",
       "      <td>25.00</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-01-05</td>\n",
       "      <td>8.00</td>\n",
       "      <td>46.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>2020-05-17</td>\n",
       "      <td>1.00</td>\n",
       "      <td>38.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2634</th>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>58.00</td>\n",
       "      <td>88.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2635</th>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>nan</td>\n",
       "      <td>43.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2636</th>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>34.00</td>\n",
       "      <td>78.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2637</th>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>20.00</td>\n",
       "      <td>114.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2638 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          mdata  AQUA_M-T  NPP-375\n",
       "0    2013-01-01      2.00    74.00\n",
       "1    2013-01-02     49.00    92.00\n",
       "2    2013-01-03      5.00    85.00\n",
       "3    2013-01-04     25.00    38.00\n",
       "4    2013-01-05      8.00    46.00\n",
       "...         ...       ...      ...\n",
       "2633 2020-05-17      1.00    38.00\n",
       "2634 2020-05-18     58.00    88.00\n",
       "2635 2020-05-19       nan    43.00\n",
       "2636 2020-05-20     34.00    78.00\n",
       "2637 2020-05-21     20.00   114.00\n",
       "\n",
       "[2638 rows x 3 columns]"
      ]
     },
     "execution_count": 556,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mdata       0\n",
       "AQUA_M-T    0\n",
       "NPP-375     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 563,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "acum = acum[~acum['NPP-375'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "acum = acum[~acum['AQUA_M-T'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sns' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-44ad3bece543>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'NPP-375'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'AQUA_M-T'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0macum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"+\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'sns' is not defined"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 595.44x595.44 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.jointplot('NPP-375', 'AQUA_M-T', data=acum, kind=\"reg\",\n",
    "                  color=\"r\", height=8.27)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=0.17925002598674725, intercept=12.129282227570755, rvalue=0.8514894239188792, pvalue=0.0, stderr=0.002238859287352067)"
      ]
     },
     "execution_count": 565,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9899328859034556"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.rvalue**2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "a = linregress(acum['NPP-375'] , acum['AQUA_M-T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [],
   "source": [
    "acum20['ref_calc'] = acum20['NPP-375']*a.slope + a.intercept"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "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>data_mes</th>\n",
       "      <th>AQUA_M-T</th>\n",
       "      <th>NPP-375</th>\n",
       "      <th>ref_calc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>768</td>\n",
       "      <td>2344</td>\n",
       "      <td>432.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020-02-01</td>\n",
       "      <td>807</td>\n",
       "      <td>742</td>\n",
       "      <td>145.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-03-01</td>\n",
       "      <td>1271</td>\n",
       "      <td>1419</td>\n",
       "      <td>266.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020-04-01</td>\n",
       "      <td>992</td>\n",
       "      <td>1415</td>\n",
       "      <td>265.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-05-01</td>\n",
       "      <td>438</td>\n",
       "      <td>1587</td>\n",
       "      <td>296.60</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    data_mes  AQUA_M-T  NPP-375  ref_calc\n",
       "0 2020-01-01       768     2344    432.29\n",
       "1 2020-02-01       807      742    145.13\n",
       "2 2020-03-01      1271     1419    266.49\n",
       "3 2020-04-01       992     1415    265.77\n",
       "4 2020-05-01       438     1587    296.60"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acum20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 570,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mes1_5 4276 1406 -0.6711880261927035\n"
     ]
    }
   ],
   "source": [
    "total20_aqua = acum20 [(acum20.data_mes.dt.year == 2020)][\"AQUA_M-T\"].sum()\n",
    "total20_calc = int(acum20 [(acum20.data_mes.dt.year == 2020)][\"ref_calc\"].sum())\n",
    "dif = (total20_calc - total20_aqua) / total20_aqua \n",
    "print('mes1_5' , total20_aqua, total20_calc, dif)"
   ]
  },
  {
   "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": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}