AN_cattle-long.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xuleta as xu\n",
    "import numpy as np\n",
    "import pylab as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = \"/home/denis/pcloud/PB_carlos/data/cattle_headless_muni.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y=[[str(x)+'B',str(x)+'C',str(x)+'O'] for x in range(1991,2017)]\n",
    "y = np.asarray(y)\n",
    "y = y.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['1991B', '1991C', '1991O', '1992B', '1992C', '1992O', '1993B', '1993C',\n",
       "       '1993O', '1994B', '1994C', '1994O', '1995B', '1995C', '1995O', '1996B',\n",
       "       '1996C', '1996O', '1997B', '1997C', '1997O', '1998B', '1998C', '1998O',\n",
       "       '1999B', '1999C', '1999O', '2000B', '2000C', '2000O', '2001B', '2001C',\n",
       "       '2001O', '2002B', '2002C', '2002O', '2003B', '2003C', '2003O', '2004B',\n",
       "       '2004C', '2004O', '2005B', '2005C', '2005O', '2006B', '2006C', '2006O',\n",
       "       '2007B', '2007C', '2007O', '2008B', '2008C', '2008O', '2009B', '2009C',\n",
       "       '2009O', '2010B', '2010C', '2010O', '2011B', '2011C', '2011O', '2012B',\n",
       "       '2012C', '2012O', '2013B', '2013C', '2013O', '2014B', '2014C', '2014O',\n",
       "       '2015B', '2015C', '2015O', '2016B', '2016C', '2016O'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(data)\n",
    "df.set_index(df['code'],inplace=True,drop=True)\n",
    "df.drop('code',inplace=True,axis=1)\n",
    "df.columns = y\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 5.63 s, sys: 60.2 ms, total: 5.69 s\n",
      "Wall time: 7.09 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "\n",
    "df2 =pd.DataFrame(data=None,index=df.index)\n",
    "for i in range(1991,2017,1):\n",
    "    tosum = [str(x) for x in df.columns if x[:4] == str(i)]\n",
    "    df2[i] = df[tosum].sum(axis=1)\n",
    "df2.to_csv('/home/denis/pcloud/PB_carlos/data/cattle_muni_sum_long.csv')\n",
    "\n",
    "ts = df2.T\n",
    "\n",
    "trends = pd.DataFrame(index=ts.columns)\n",
    " \n",
    "trends['1991-2009-z'],trends['1991-2009-trend'],trends['2009-2016-z'],trends['2009-2016-trend'] = None,None,None,None\n",
    "for i in ts.columns:\n",
    "    trends['1991-2009-z'][i] = xu.mk_test(np.asarray(ts[i].loc[1991:2009]),alpha=0.05)[3]\n",
    "    trends['1991-2009-trend'][i] = xu.mk_test(np.asarray(ts[i].loc[1991:2009]),alpha=0.05)[0]\n",
    "    trends['2009-2016-z'][i] = xu.mk_test(np.asarray(ts[i].loc[2009:2016]),alpha=0.05)[3]\n",
    "    trends['2009-2016-trend'][i] = xu.mk_test(np.asarray(ts[i].loc[2009:2016]),alpha=0.05)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.52 s, sys: 13.8 ms, total: 2.54 s\n",
      "Wall time: 2.67 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "start = 1999\n",
    "stop = 2012\n",
    "\n",
    "df2 =pd.DataFrame(data=None,index=df.index)\n",
    "for i in range(start,stop+1,1):\n",
    "    tosum = [str(x) for x in df.columns if x[:4] == str(i)]\n",
    "    df2[i] = df[tosum].sum(axis=1)\n",
    "df2.to_csv('/home/denis/pcloud/PB_carlos/data/cattle_muni_sum_long.csv')\n",
    "\n",
    "ts = df2.T\n",
    "\n",
    "trends = pd.DataFrame(index=ts.columns)\n",
    " \n",
    "trends[str(start)+'-'+str(stop+1)+'-z'],trends[str(start)+'-'+str(stop+1)+'-trend'] = None,None\n",
    "for i in ts.columns:\n",
    "    trends[str(start)+'-'+str(stop+1)+'-z'][i] = xu.mk_test(np.asarray(ts[i].loc[1991:2009]),alpha=0.05)[3]\n",
    "    trends[str(start)+'-'+str(stop+1)+'-trend'][i] = xu.mk_test(np.asarray(ts[i].loc[1991:2009]),alpha=0.05)[0]\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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g+3VJ5+tb+K8Pynjrg9NU1bYvuLt2dBILpo9k+rVOXccqIkNOaqKN0hPVtLZ5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\nMKewFxERCXP/P1SZUE1BTVP+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0e71639e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ts[2915353].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trends.to_csv('/home/denis/pcloud/PB_carlos/data/trends_cattle_muni_'+str(start)+'-'+str(stop+1)+'.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1999-2013-z</th>\n",
       "      <th>1999-2013-trend</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2100055</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100105</th>\n",
       "      <td>-2.491</td>\n",
       "      <td>decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100154</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100204</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100303</th>\n",
       "      <td>-0.311</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100402</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100436</th>\n",
       "      <td>1.246</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100477</th>\n",
       "      <td>3.27</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100501</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100550</th>\n",
       "      <td>2.803</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100600</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100709</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100808</th>\n",
       "      <td>0.311</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100832</th>\n",
       "      <td>3.892</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100873</th>\n",
       "      <td>-0.623</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100907</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2100956</th>\n",
       "      <td>3.737</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101004</th>\n",
       "      <td>3.27</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101103</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101202</th>\n",
       "      <td>2.647</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101251</th>\n",
       "      <td>4.204</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101301</th>\n",
       "      <td>4.048</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101350</th>\n",
       "      <td>-2.647</td>\n",
       "      <td>decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101400</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101509</th>\n",
       "      <td>0.467</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101608</th>\n",
       "      <td>3.892</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101707</th>\n",
       "      <td>1.557</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101731</th>\n",
       "      <td>-0.934</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101772</th>\n",
       "      <td>-1.246</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2101806</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29003</th>\n",
       "      <td>2.18</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29004</th>\n",
       "      <td>-0.778</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29005</th>\n",
       "      <td>2.491</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29006</th>\n",
       "      <td>0.934</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29007</th>\n",
       "      <td>3.581</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29008</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29009</th>\n",
       "      <td>1.09</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29010</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29011</th>\n",
       "      <td>2.18</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29012</th>\n",
       "      <td>4.048</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29013</th>\n",
       "      <td>3.581</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29014</th>\n",
       "      <td>-0.934</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29015</th>\n",
       "      <td>3.581</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29016</th>\n",
       "      <td>-2.335</td>\n",
       "      <td>decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29017</th>\n",
       "      <td>2.958</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29018</th>\n",
       "      <td>2.335</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29019</th>\n",
       "      <td>3.27</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29020</th>\n",
       "      <td>-0.623</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29021</th>\n",
       "      <td>-0.156</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29022</th>\n",
       "      <td>-1.868</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29023</th>\n",
       "      <td>1.246</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29024</th>\n",
       "      <td>-0.467</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29025</th>\n",
       "      <td>-3.337</td>\n",
       "      <td>decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29026</th>\n",
       "      <td>3.425</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29027</th>\n",
       "      <td>0.778</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29028</th>\n",
       "      <td>0.623</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29029</th>\n",
       "      <td>2.491</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29030</th>\n",
       "      <td>-2.647</td>\n",
       "      <td>decreasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29031</th>\n",
       "      <td>2.647</td>\n",
       "      <td>increasing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29032</th>\n",
       "      <td>0.467</td>\n",
       "      <td>no trend</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1982 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        1999-2013-z 1999-2013-trend\n",
       "code                               \n",
       "2100055       3.425      increasing\n",
       "2100105      -2.491      decreasing\n",
       "2100154       4.204      increasing\n",
       "2100204       4.204      increasing\n",
       "2100303      -0.311        no trend\n",
       "2100402       4.204      increasing\n",
       "2100436       1.246        no trend\n",
       "2100477        3.27      increasing\n",
       "2100501       4.204      increasing\n",
       "2100550       2.803      increasing\n",
       "2100600       4.204      increasing\n",
       "2100709       4.204      increasing\n",
       "2100808       0.311        no trend\n",
       "2100832       3.892      increasing\n",
       "2100873      -0.623        no trend\n",
       "2100907       4.204      increasing\n",
       "2100956       3.737      increasing\n",
       "2101004        3.27      increasing\n",
       "2101103       4.204      increasing\n",
       "2101202       2.647      increasing\n",
       "2101251       4.204      increasing\n",
       "2101301       4.048      increasing\n",
       "2101350      -2.647      decreasing\n",
       "2101400       3.425      increasing\n",
       "2101509       0.467        no trend\n",
       "2101608       3.892      increasing\n",
       "2101707       1.557        no trend\n",
       "2101731      -0.934        no trend\n",
       "2101772      -1.246        no trend\n",
       "2101806       3.425      increasing\n",
       "...             ...             ...\n",
       "29003          2.18      increasing\n",
       "29004        -0.778        no trend\n",
       "29005         2.491      increasing\n",
       "29006         0.934        no trend\n",
       "29007         3.581      increasing\n",
       "29008         3.425      increasing\n",
       "29009          1.09        no trend\n",
       "29010         3.425      increasing\n",
       "29011          2.18      increasing\n",
       "29012         4.048      increasing\n",
       "29013         3.581      increasing\n",
       "29014        -0.934        no trend\n",
       "29015         3.581      increasing\n",
       "29016        -2.335      decreasing\n",
       "29017         2.958      increasing\n",
       "29018         2.335      increasing\n",
       "29019          3.27      increasing\n",
       "29020        -0.623        no trend\n",
       "29021        -0.156        no trend\n",
       "29022        -1.868        no trend\n",
       "29023         1.246        no trend\n",
       "29024        -0.467        no trend\n",
       "29025        -3.337      decreasing\n",
       "29026         3.425      increasing\n",
       "29027         0.778        no trend\n",
       "29028         0.623        no trend\n",
       "29029         2.491      increasing\n",
       "29030        -2.647      decreasing\n",
       "29031         2.647      increasing\n",
       "29032         0.467        no trend\n",
       "\n",
       "[1982 rows x 2 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002-2011-z           3.935\n",
       "2002-2011-trend    no trend\n",
       "2011-2016-z            2.63\n",
       "2011-2016-trend    no trend\n",
       "dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends.max(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2002-2011-z            -3.935\n",
       "2002-2011-trend    decreasing\n",
       "2011-2016-z             -2.63\n",
       "2011-2016-trend    decreasing\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trends.min(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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