{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Resample data\n",
    "> Marcos Duarte  \n",
    "> [Laboratory of Biomechanics and Motor Control](http://demotu.org/)  \n",
    "> Federal University of ABC, Brazil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "%matplotlib notebook\n",
    "# tk qt notebook inline ipympl\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import sys, os\n",
    "sys.path.insert(1, r'./../functions')\n",
    "\n",
    "from resample import resample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>0.00</th>\n",
       "      <td>1.203531</td>\n",
       "      <td>-1.026002</td>\n",
       "      <td>0.345930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.01</th>\n",
       "      <td>0.745963</td>\n",
       "      <td>0.944473</td>\n",
       "      <td>-0.054512</td>\n",
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       "    <tr>\n",
       "      <th>0.02</th>\n",
       "      <td>0.789788</td>\n",
       "      <td>-0.049776</td>\n",
       "      <td>-1.072692</td>\n",
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       "    <tr>\n",
       "      <th>0.03</th>\n",
       "      <td>2.005024</td>\n",
       "      <td>-1.013743</td>\n",
       "      <td>-0.354158</td>\n",
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       "    <tr>\n",
       "      <th>0.04</th>\n",
       "      <td>-1.185777</td>\n",
       "      <td>-0.818023</td>\n",
       "      <td>-1.550145</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             0         1         2\n",
       "0.00  1.203531 -1.026002  0.345930\n",
       "0.01  0.745963  0.944473 -0.054512\n",
       "0.02  0.789788 -0.049776 -1.072692\n",
       "0.03  2.005024 -1.013743 -0.354158\n",
       "0.04 -1.185777 -0.818023 -1.550145"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.random.randn(100, 3)\n",
    "freq_new = 100\n",
    "y = pd.DataFrame(data=y, columns=None)\n",
    "y.index = y.index/freq_new\n",
    "y.index.name = y.index.name\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.000</th>\n",
       "      <td>1.204336</td>\n",
       "      <td>-1.026688</td>\n",
       "      <td>0.346161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.001</th>\n",
       "      <td>1.298574</td>\n",
       "      <td>-0.925828</td>\n",
       "      <td>0.396714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.002</th>\n",
       "      <td>1.364617</td>\n",
       "      <td>-0.776893</td>\n",
       "      <td>0.436292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.003</th>\n",
       "      <td>1.398068</td>\n",
       "      <td>-0.586546</td>\n",
       "      <td>0.459980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.004</th>\n",
       "      <td>1.396121</td>\n",
       "      <td>-0.364184</td>\n",
       "      <td>0.463271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.995</th>\n",
       "      <td>-0.672521</td>\n",
       "      <td>0.643513</td>\n",
       "      <td>-0.066679</td>\n",
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       "    <tr>\n",
       "      <th>0.996</th>\n",
       "      <td>-0.541424</td>\n",
       "      <td>0.524998</td>\n",
       "      <td>-0.065225</td>\n",
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       "    <tr>\n",
       "      <th>0.997</th>\n",
       "      <td>-0.399792</td>\n",
       "      <td>0.391877</td>\n",
       "      <td>-0.055730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.998</th>\n",
       "      <td>-0.256741</td>\n",
       "      <td>0.253914</td>\n",
       "      <td>-0.040121</td>\n",
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       "    <tr>\n",
       "      <th>0.999</th>\n",
       "      <td>-0.120937</td>\n",
       "      <td>0.120501</td>\n",
       "      <td>-0.020723</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0         1         2\n",
       "0.000  1.204336 -1.026688  0.346161\n",
       "0.001  1.298574 -0.925828  0.396714\n",
       "0.002  1.364617 -0.776893  0.436292\n",
       "0.003  1.398068 -0.586546  0.459980\n",
       "0.004  1.396121 -0.364184  0.463271\n",
       "...         ...       ...       ...\n",
       "0.995 -0.672521  0.643513 -0.066679\n",
       "0.996 -0.541424  0.524998 -0.065225\n",
       "0.997 -0.399792  0.391877 -0.055730\n",
       "0.998 -0.256741  0.253914 -0.040121\n",
       "0.999 -0.120937  0.120501 -0.020723\n",
       "\n",
       "[1000 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = resample(y, freq_old=100, freq_new=1000, limit=1000, method='resample_poly')\n",
    "y2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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