chunking-data

chunking.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nilmtk import DataSet\n",
    "ds = DataSet(\"/home/nipunbatra-pc/Downloads/iawe.h5\")\n",
    "\n",
    "elec = ds.buildings[1].elec\n",
    "\n",
    "df = next(elec.meters[0].load(chunksize=10000, sample_period=3600))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import preprocessing\n",
    "def preprocess(x):\n",
    "    scalar = preprocessing.StandardScaler().fit(x)\n",
    "    return scalar.transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/nipunbatra-pc/anaconda3/envs/nilmtk-env/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "preprocessed = pd.DataFrame(preprocess(df[('power','active')].reshape(-1, 1)), index=df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f3d49b5c0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[('power','active')].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7f3c3a5518>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "preprocessed.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2013-05-24 05:00:00+05:30</th>\n",
       "      <td>-0.717642</td>\n",
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       "    <tr>\n",
       "      <th>2013-05-24 06:00:00+05:30</th>\n",
       "      <td>-0.715534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-24 07:00:00+05:30</th>\n",
       "      <td>-0.696334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-24 08:00:00+05:30</th>\n",
       "      <td>-0.696334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-24 09:00:00+05:30</th>\n",
       "      <td>1.340332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013-05-24 10:00:00+05:30</th>\n",
       "      <td>1.485513</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  0\n",
       "2013-05-24 05:00:00+05:30 -0.717642\n",
       "2013-05-24 06:00:00+05:30 -0.715534\n",
       "2013-05-24 07:00:00+05:30 -0.696334\n",
       "2013-05-24 08:00:00+05:30 -0.696334\n",
       "2013-05-24 09:00:00+05:30  1.340332\n",
       "2013-05-24 10:00:00+05:30  1.485513"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = pd.HDFStore(\"abc.h5\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "st['/iawe/building1/meter1/chunk1'] = preprocessed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "st.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "st = pd.HDFStore(\"abc.h5\", \"r\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<class 'pandas.io.pytables.HDFStore'>\n",
       "File path: abc.h5"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/iawe/building1/meter1/chunk1']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "st.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "                                  0\n",
       "2013-05-24 05:00:00+05:30 -0.717642\n",
       "2013-05-24 06:00:00+05:30 -0.715534\n",
       "2013-05-24 07:00:00+05:30 -0.696334\n",
       "2013-05-24 08:00:00+05:30 -0.696334\n",
       "2013-05-24 09:00:00+05:30  1.340332\n",
       "2013-05-24 10:00:00+05:30  1.485513"
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     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "st['/iawe/building1/meter1/chunk1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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