fabaff · July 9, 2016 08:52
diff --git a/home-assistant-sensor-graph.ipynb b/home-assistant-sensor-graph.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "# matplotlib for plotting the data\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine\n",
    "from sqlalchemy.orm import scoped_session, sessionmaker\n",
    "\n",
    "from homeassistant.components.recorder.models import Base, Events, States, RecorderRuns\n",
    "import homeassistant.util.dt as dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Your database url as specified in configuration.yaml\n",
    "# If using default settings, it's sqlite:///<path to config dir>/home-assistant_v2.db\n",
    "DB_URL = 'sqlite:///<path to config dir>/.homeassistant/home-assistant_v2.db'\n",
    "SENSOR = 'sensor.aare'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "engine = create_engine(DB_URL)\n",
    "Base.metadata.create_all(engine)\n",
    "session_factory = sessionmaker(bind=engine)\n",
    "Session = scoped_session(session_factory)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = list(engine.execute(\"SELECT state, last_changed FROM states where entity_id = '{}'\".format(SENSOR)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prepare data for graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "values = []\n",
    "timestamps = []\n",
    "for x in data:\n",
    "    timestamps.append(dates.date2num(dt.parse_datetime(x[1])))\n",
    "    values.append(float(x[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x7f902a92f668>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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uwFJgeAihZ5XXuwB3AD2An2ZWZ+f6fHKzmL77zruPRo+GjTf2chWdO0O7dj88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\nREQkJyUIERHJSQlCRERy+n9RogPAvC+RKgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f902aa5b7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot_date(x=timestamps, y=values, fmt=\"r-\")\n",
    "plt.ylabel('Temperature')\n",
    "plt.xlabel('Time line')"
   ]
  }
 ],
 "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": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
 }
	{
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Setup"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 25,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"Populating the interactive namespace from numpy and matplotlib\n"
	]
	}
	],
	"source": [
	"# matplotlib for plotting the data\n",
	"%pylab inline"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 26,
	"metadata": {
	"collapsed": false
	},
	"outputs": [],
	"source": [
	"from sqlalchemy import create_engine\n",
	"from sqlalchemy.orm import scoped_session, sessionmaker\n",
	"\n",
	"from homeassistant.components.recorder.models import Base, Events, States, RecorderRuns\n",
	"import homeassistant.util.dt as dt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 27,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"# Your database url as specified in configuration.yaml\n",
	"# If using default settings, it's sqlite:///<path to config dir>/home-assistant_v2.db\n",
	"DB_URL = 'sqlite:///<path to config dir>/.homeassistant/home-assistant_v2.db'\n",
	"SENSOR = 'sensor.aare'"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 28,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"engine = create_engine(DB_URL)\n",
	"Base.metadata.create_all(engine)\n",
	"session_factory = sessionmaker(bind=engine)\n",
	"Session = scoped_session(session_factory)"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Query"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 29,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"data = list(engine.execute(\"SELECT state, last_changed FROM states where entity_id = '{}'\".format(SENSOR)))"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Prepare data for graph"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 30,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"values = []\n",
	"timestamps = []\n",
	"for x in data:\n",
	" timestamps.append(dates.date2num(dt.parse_datetime(x[1])))\n",
	" values.append(float(x[0]))"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"### Plot the graph"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 31,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
	"text/plain": [
	"<matplotlib.text.Text at 0x7f902a92f668>"
	]
	},
	"execution_count": 31,
	"metadata": {},
	"output_type": "execute_result"
	},
	{
	"data": {
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uwFJgeAihZ5XXuwB3AD2An2ZWZ+f6fHKzmL77zruPRo+GjTf2chWdO0O7dj88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\nREQkJyUIERHJSQlCRERy+n9RogPAvC+RKgAAAABJRU5ErkJggg==\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7f902aa5b7b8>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.plot_date(x=timestamps, y=values, fmt=\"r-\")\n",
	"plt.ylabel('Temperature')\n",
	"plt.xlabel('Time line')"
	]
	}
	],
	"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": "3.4.3"
	}
	},
	"nbformat": 4,
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