notebooks/TimeStamps/Detect_time_resolution.ipynb

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   "source": [
    "![lop](../../images/logo_diive1_128px.png)"
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   "source": [
    "<span style='font-size:40px; display:block;'>\n",
    "<b>\n",
    "    Detect time resolution from data\n",
    "</b>\n",
    "</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "**Notebook version**: `2` (20 Apr 2024)  \n",
    "**Author**: Lukas Hörtnagl (holukas@ethz.ch) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-23T13:13:22.306270Z",
     "start_time": "2023-04-23T13:13:20.453340Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "diive version: v0.74.0\n"
     ]
    }
   ],
   "source": [
    "import importlib.metadata\n",
    "from datetime import datetime\n",
    "from diive.configs.exampledata import load_exampledata_parquet\n",
    "from diive.core.times.times import DetectFrequency\n",
    "version_diive = importlib.metadata.version(\"diive\")\n",
    "print(f\"diive version: v{version_diive}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load example data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-23T13:13:22.447247Z",
     "start_time": "2023-04-23T13:13:22.309261Z"
    },
    "collapsed": false,
    "jupyter": {
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    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded .parquet file F:\\Sync\\luhk_work\\20 - CODING\\21 - DIIVE\\diive\\diive\\configs\\exampledata\\exampledata_PARQUET_CH-DAV_FP2022.5_2013-2022_ID20230206154316_30MIN.parquet (0.052 seconds). Detected time resolution of <30 * Minutes> / 30min \n"
     ]
    }
   ],
   "source": [
    "data_df = load_exampledata_parquet()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detect time resolution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-23T13:13:22.509345Z",
     "start_time": "2023-04-23T13:13:22.449242Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detecting time resolution from timestamp TIMESTAMP_MIDDLE ... OK\n",
      "   Detected 30min time resolution with MAXIMUM confidence.\n",
      "   All approaches yielded the same result:\n",
      "       from full data = 30min / full data (OK)\n",
      "       from timedelta = 30min / >90% occurrence (OK)\n",
      "       from progressive = 30min / data 1000+1000 (OK)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "f = DetectFrequency(index=data_df.index, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-23T13:13:22.527966Z",
     "start_time": "2023-04-23T13:13:22.464985Z"
    },
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'30min'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "freq = f.get()\n",
    "freq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# End of notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished 2024-04-20 14:34:11\n"
     ]
    }
   ],
   "source": [
    "dt_string = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n",
    "print(f\"Finished {dt_string}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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