NEW_Theil-Sen.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Trying Theil-Sen"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "file='/home/denis/pcloud/PB_carlos/SQL/drought_consultas_br_gpp_muni.csv'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "gpp = pd.read_csv(file).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>geocodig_m</th>\n",
       "      <th>2100055</th>\n",
       "      <th>2100105</th>\n",
       "      <th>2100154</th>\n",
       "      <th>2100204</th>\n",
       "      <th>2100303</th>\n",
       "      <th>2100402</th>\n",
       "      <th>2100436</th>\n",
       "      <th>2100477</th>\n",
       "      <th>2100501</th>\n",
       "      <th>2100550</th>\n",
       "      <th>...</th>\n",
       "      <th>2933109</th>\n",
       "      <th>2933158</th>\n",
       "      <th>2933174</th>\n",
       "      <th>2933208</th>\n",
       "      <th>2933257</th>\n",
       "      <th>2933307</th>\n",
       "      <th>2933406</th>\n",
       "      <th>2933455</th>\n",
       "      <th>2933505</th>\n",
       "      <th>2933604</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>acquisition</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-07-04</th>\n",
       "      <td>623.619832</td>\n",
       "      <td>712.007343</td>\n",
       "      <td>659.526469</td>\n",
       "      <td>628.322112</td>\n",
       "      <td>715.647473</td>\n",
       "      <td>676.897400</td>\n",
       "      <td>731.059168</td>\n",
       "      <td>687.427156</td>\n",
       "      <td>153.676466</td>\n",
       "      <td>604.551091</td>\n",
       "      <td>...</td>\n",
       "      <td>437.252955</td>\n",
       "      <td>256.538398</td>\n",
       "      <td>476.211679</td>\n",
       "      <td>473.362426</td>\n",
       "      <td>420.565293</td>\n",
       "      <td>337.572629</td>\n",
       "      <td>465.152353</td>\n",
       "      <td>137.226114</td>\n",
       "      <td>579.634938</td>\n",
       "      <td>141.556499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-12</th>\n",
       "      <td>579.595267</td>\n",
       "      <td>729.132844</td>\n",
       "      <td>606.340896</td>\n",
       "      <td>611.580198</td>\n",
       "      <td>752.098401</td>\n",
       "      <td>700.182916</td>\n",
       "      <td>742.318596</td>\n",
       "      <td>643.244144</td>\n",
       "      <td>152.318737</td>\n",
       "      <td>607.382440</td>\n",
       "      <td>...</td>\n",
       "      <td>500.007092</td>\n",
       "      <td>267.898506</td>\n",
       "      <td>427.837956</td>\n",
       "      <td>443.102071</td>\n",
       "      <td>330.225713</td>\n",
       "      <td>336.551118</td>\n",
       "      <td>497.273529</td>\n",
       "      <td>132.248317</td>\n",
       "      <td>526.964260</td>\n",
       "      <td>133.475723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-20</th>\n",
       "      <td>579.869725</td>\n",
       "      <td>639.371829</td>\n",
       "      <td>555.031995</td>\n",
       "      <td>515.031683</td>\n",
       "      <td>684.469738</td>\n",
       "      <td>622.751161</td>\n",
       "      <td>653.302341</td>\n",
       "      <td>620.006178</td>\n",
       "      <td>136.351363</td>\n",
       "      <td>557.612599</td>\n",
       "      <td>...</td>\n",
       "      <td>388.632388</td>\n",
       "      <td>246.532586</td>\n",
       "      <td>376.420438</td>\n",
       "      <td>466.202663</td>\n",
       "      <td>341.544826</td>\n",
       "      <td>339.123053</td>\n",
       "      <td>379.871765</td>\n",
       "      <td>129.278054</td>\n",
       "      <td>343.902991</td>\n",
       "      <td>133.377567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-28</th>\n",
       "      <td>535.971155</td>\n",
       "      <td>569.187583</td>\n",
       "      <td>298.303083</td>\n",
       "      <td>343.808581</td>\n",
       "      <td>596.861548</td>\n",
       "      <td>516.162024</td>\n",
       "      <td>508.422627</td>\n",
       "      <td>492.747619</td>\n",
       "      <td>94.947945</td>\n",
       "      <td>249.924613</td>\n",
       "      <td>...</td>\n",
       "      <td>127.277778</td>\n",
       "      <td>215.777293</td>\n",
       "      <td>376.254015</td>\n",
       "      <td>445.707101</td>\n",
       "      <td>440.945806</td>\n",
       "      <td>310.367232</td>\n",
       "      <td>486.704118</td>\n",
       "      <td>87.941964</td>\n",
       "      <td>546.141867</td>\n",
       "      <td>93.531177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-08-05</th>\n",
       "      <td>540.475777</td>\n",
       "      <td>636.289720</td>\n",
       "      <td>510.112275</td>\n",
       "      <td>580.946095</td>\n",
       "      <td>719.769290</td>\n",
       "      <td>569.366295</td>\n",
       "      <td>611.176853</td>\n",
       "      <td>534.068855</td>\n",
       "      <td>101.854093</td>\n",
       "      <td>522.397817</td>\n",
       "      <td>...</td>\n",
       "      <td>328.361702</td>\n",
       "      <td>221.806351</td>\n",
       "      <td>454.816058</td>\n",
       "      <td>501.599112</td>\n",
       "      <td>428.796195</td>\n",
       "      <td>306.276176</td>\n",
       "      <td>451.086471</td>\n",
       "      <td>113.601635</td>\n",
       "      <td>611.279358</td>\n",
       "      <td>128.981437</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1792 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "geocodig_m      2100055     2100105     2100154     2100204     2100303  \\\n",
       "acquisition                                                               \n",
       "2002-07-04   623.619832  712.007343  659.526469  628.322112  715.647473   \n",
       "2002-07-12   579.595267  729.132844  606.340896  611.580198  752.098401   \n",
       "2002-07-20   579.869725  639.371829  555.031995  515.031683  684.469738   \n",
       "2002-07-28   535.971155  569.187583  298.303083  343.808581  596.861548   \n",
       "2002-08-05   540.475777  636.289720  510.112275  580.946095  719.769290   \n",
       "\n",
       "geocodig_m      2100402     2100436     2100477     2100501     2100550  \\\n",
       "acquisition                                                               \n",
       "2002-07-04   676.897400  731.059168  687.427156  153.676466  604.551091   \n",
       "2002-07-12   700.182916  742.318596  643.244144  152.318737  607.382440   \n",
       "2002-07-20   622.751161  653.302341  620.006178  136.351363  557.612599   \n",
       "2002-07-28   516.162024  508.422627  492.747619   94.947945  249.924613   \n",
       "2002-08-05   569.366295  611.176853  534.068855  101.854093  522.397817   \n",
       "\n",
       "geocodig_m      ...         2933109     2933158     2933174     2933208  \\\n",
       "acquisition     ...                                                       \n",
       "2002-07-04      ...      437.252955  256.538398  476.211679  473.362426   \n",
       "2002-07-12      ...      500.007092  267.898506  427.837956  443.102071   \n",
       "2002-07-20      ...      388.632388  246.532586  376.420438  466.202663   \n",
       "2002-07-28      ...      127.277778  215.777293  376.254015  445.707101   \n",
       "2002-08-05      ...      328.361702  221.806351  454.816058  501.599112   \n",
       "\n",
       "geocodig_m      2933257     2933307     2933406     2933455     2933505  \\\n",
       "acquisition                                                               \n",
       "2002-07-04   420.565293  337.572629  465.152353  137.226114  579.634938   \n",
       "2002-07-12   330.225713  336.551118  497.273529  132.248317  526.964260   \n",
       "2002-07-20   341.544826  339.123053  379.871765  129.278054  343.902991   \n",
       "2002-07-28   440.945806  310.367232  486.704118   87.941964  546.141867   \n",
       "2002-08-05   428.796195  306.276176  451.086471  113.601635  611.279358   \n",
       "\n",
       "geocodig_m      2933604  \n",
       "acquisition              \n",
       "2002-07-04   141.556499  \n",
       "2002-07-12   133.475723  \n",
       "2002-07-20   133.377567  \n",
       "2002-07-28    93.531177  \n",
       "2002-08-05   128.981437  \n",
       "\n",
       "[5 rows x 1792 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats\n",
    "import pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = gpp[2933307]\n",
    "res = theilslopes(y)\n",
    "lsq_res = stats.linregress(x, y)\n",
    "x = np.arange(len(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-0.11327273102764579,\n",
       " 414.53906301630127,\n",
       " -0.14553193371841783,\n",
       " -0.080983975494384158)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.theilslopes(y, alpha=0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinregressResult(slope=-0.095303739554821659, intercept=426.91170672759051, rvalue=-0.15007848762544601, pvalue=9.073603732707308e-05, stderr=0.024201212005751704)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.linregress(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u09EQkZwiiTpGqw3eVsLKHZrUi+gkGshjiSndBUo0si1arFqnrNyOw+kNVrRi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\nfxnCrONJj6x/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4715f45240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,6))\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(x, y, 'b.')\n",
    "ax.plot(x, res[1] + res[0] * x, 'r-', label='Theil-Sen')\n",
    "ax.plot(x, res[1] + res[2] * x, 'r--')\n",
    "ax.plot(x, res[1] + res[3] * x, 'r--')\n",
    "ax.plot(x, lsq_res[1] + lsq_res[0] * x, 'g-', label='Linear Regression')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}