Get data

tmpo.ipynb

@@ -3,20 +3,21 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "%matplotlib inline"
+    "import pandas as pd\n",
+    "import os\n",
+    "import tmpo\n",
+    "\n",
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "token = \"b371402dc767cc83e41bc294b63f9586\"\n",
@@ -27,17 +28,19 @@
     "}"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Init session, add sensors and sync"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import os\n",
-    "import tmpo\n",
-    "\n",
     "s = tmpo.Session(path=os.getcwd())\n",
     "for room in house:\n",
     "    for sensor in house[room]:\n",
@@ -47,9 +50,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "s.debug = True"
@@ -58,50 +59,78 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "s.sync()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get info about sensor"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "start = pd.Timestamp('20160101')"
+    "sensor = house[room][\"electricity\"]\n",
+    "\n",
+    "type, data_type, subtype = s.get_types(sensor)\n",
+    "unit = s.get_unit(sensor)\n",
+    "\n",
+    "print(type, data_type, subtype, unit)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Get raw data"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "s.debug = False"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "df = s.dataframe([house[room][\"electricity\"]], head=start)"
+    "start = pd.Timestamp('20160101')\n",
+    "end = pd.Timestamp('20160501')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = s.dataframe(sids=[house[room][\"electricity\"]], head=start, tail=end)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "collapsed": false,
     "scrolled": false
    },
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "room = \"cellar\"\n",
-    "plt.figure(figsize=(16,10))\n",
+    "plt.figure(figsize=(16,5))\n",
     "ax1=plt.subplot()\n",
     "ax1.grid()\n",
     "ax1.set_ylabel(u'Wh')\n",
@@ -110,11 +139,147 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {
     "collapsed": true
    },
+   "source": [
+    "## Get resampled data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The difference between raw and resampled data is that raw data has data with an uneven time axis. This makes it difficult to calculate power values or flow rates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = s.get_data(sids=[house[room][\"electricity\"]], head=start, tail=end, resolution='1h')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "As resolution you can pick from these resolutions, and you can precede them with a number:\n",
+    "* B   business day frequency\n",
+    "* C   custom business day frequency (experimental)\n",
+    "* D   calendar day frequency\n",
+    "* W   weekly frequency\n",
+    "* M   month end frequency\n",
+    "* BM  business month end frequency\n",
+    "* MS  month start frequency\n",
+    "* BMS business month start frequency\n",
+    "* Q   quarter end frequency\n",
+    "* BQ  business quarter endfrequency\n",
+    "* QS  quarter start frequency\n",
+    "* BQS business quarter start frequency\n",
+    "* A   year end frequency\n",
+    "* BA  business year end frequency\n",
+    "* AS  year start frequency\n",
+    "* BAS business year start frequency\n",
+    "* H   hourly frequency\n",
+    "* T   minutely frequency\n",
+    "* S   secondly frequency\n",
+    "* L   milliseconds\n",
+    "* U   microseconds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "room = \"cellar\"\n",
+    "plt.figure(figsize=(16,5))\n",
+    "ax1=plt.subplot()\n",
+    "ax1.grid()\n",
+    "ax1.set_ylabel(u'Wh')\n",
+    "plt.title(room + \" electricity\")\n",
+    "df.plot(ax=ax1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can easily calculate power by taking the difference between values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P = df.diff()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "room = \"cellar\"\n",
+    "plt.figure(figsize=(16,5))\n",
+    "ax1=plt.subplot()\n",
+    "ax1.grid()\n",
+    "ax1.set_ylabel(u'W')\n",
+    "plt.title(room + \" electricity\")\n",
+    "P.plot(ax=ax1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mind your dimensions! Since we had Wh values resampled to hourly basis, the `diff` gets us Wh/h, or W! If you choose another resolution you need to bring a factor into account."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Export to CSV, Excel..."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P.to_csv('tmpo_example.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "P.to_excel('tmpo_example.xlsx')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Pandas has many other export methods, see https://pandas.pydata.org/pandas-docs/stable/io.html"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -139,5 +304,5 @@
   }
  },
  "nbformat": 4,
- "nbformat_minor": 0
+ "nbformat_minor": 1
 }