Interact_with_Assets.ipynb

{
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    "id": "eAXnMj5E9jWG"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/cognitedata/WiDS-2019/blob/master/WiDS_2019_Cognite_Interact_with_Assets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
    "\n",
    "# Data Visualization and Time series prediction\n",
    "\n",
    "## What this notebook will achieve\n",
    "\n",
    "* Extract live data from an oil rig in the North Sea.\n",
    "\n",
    "* Visualize and inspect data directly from the Cognite Data Platform.\n",
    "\n",
    "* Apply Linear Regression for time series prediction.\n",
    "\n",
    "\n",
    "## Getting started\n",
    "\n",
    "* Having a basic understanding of Python concepts will help to understand the process.\n",
    "\n",
    "* Cognite has released *live* data to the public on the Cognite Data Platform streaming from [Valhall](https://www.akerbp.com/en/our-assets/production/valhall/), one of Aker's oil fields.\n",
    "\n",
    "* To access the data, generate an API Key on [Open Industrial Data](https://openindustrialdata.com/). Get your key via the Google Access platform. You will be asked to fill out some personal information to generate your personal key.\n",
    "\n",
    "* Visualize some of the machines (assets) on Valhall with Cognite's [Operational Intelligence](https://opint.cogniteapp.com/publicdata/infographics/-LOHKEJPLvt0eRIZu8mE) dashboard. This data on this page shows is streaming live data from the Valhall oil field located in the North Sea.\n",
    "\n",
    "* To understand how to interact with the data using the Python SDK ([Docs](https://cognite-sdk-python.readthedocs-hosted.com/en/latest/)) follow along in this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "k8-Oj493ReNU"
   },
   "source": [
    "## Environment Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4Lf5pnaDBLnc"
   },
   "source": [
    "#### Install the Cognite SDK package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    "colab_type": "code",
    "id": "pbgkP3p59iiK",
    "outputId": "c8220cde-0930-414d-8d53-231b1052d15b"
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   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mDEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7. More details about Python 2 support in pip, can be found at https://pip.pypa.io/en/latest/development/release-process/#python-2-support\u001b[0m\n",
      "Looking in indexes: https://pypi.python.org/simple, https://que.tran:****@cognite.jfrog.io/cognite/api/pypi/snakepit/simple\n",
      "Requirement already satisfied: cognite-sdk in /usr/local/lib/python2.7/site-packages (0.3.5)\n",
      "Requirement already satisfied: requests in /usr/local/lib/python2.7/site-packages (from cognite-sdk) (2.17.3)\n",
      "Requirement already satisfied: pandas in /usr/local/lib/python2.7/site-packages (from cognite-sdk) (0.20.3)\n",
      "Requirement already satisfied: urllib3<1.22,>=1.21.1 in /usr/local/lib/python2.7/site-packages (from requests->cognite-sdk) (1.21.1)\n",
      "Requirement already satisfied: idna<2.6,>=2.5 in /usr/local/lib/python2.7/site-packages (from requests->cognite-sdk) (2.5)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python2.7/site-packages (from requests->cognite-sdk) (2017.4.17)\n",
      "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python2.7/site-packages (from requests->cognite-sdk) (3.0.3)\n",
      "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python2.7/site-packages (from pandas->cognite-sdk) (2017.2)\n",
      "Requirement already satisfied: python-dateutil in /usr/local/lib/python2.7/site-packages (from pandas->cognite-sdk) (2.6.1)\n",
      "Requirement already satisfied: numpy>=1.7.0 in /usr/local/lib/python2.7/site-packages (from pandas->cognite-sdk) (1.12.1)\n",
      "Requirement already satisfied: six>=1.5 in /usr/local/lib/python2.7/site-packages (from python-dateutil->pandas->cognite-sdk) (1.10.0)\n",
      "\u001b[33mWARNING: You are using pip version 19.2.3, however version 19.3.1 is available.\n",
      "You should consider upgrading via the 'pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install cognite-sdk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "CN5J9FmPBRLk"
   },
   "source": [
    "#### Import the required packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "uFkQ73a44wmU"
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   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "import os\n",
    "from datetime import datetime, timedelta\n",
    "from datetime import datetime\n",
    "from getpass import getpass\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "from cognite.client import CogniteClient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Pr0CQECZBWW3"
   },
   "source": [
    "#### Connect to the Cognite Data Platform\n",
    "* This client object is how all queries will be sent to the Cognite API to retrieve data.\n",
    "\n",
    "When prompted for your API key, use the key generated by open industrial data as mentioned in the Getting Started steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "E8CdoRFu9n2t",
    "outputId": "43130697-9674-407a-cac4-c871a9c82757"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Open Industrial Data API-KEY: ········\n"
     ]
    }
   ],
   "source": [
    "client = CogniteClient(api_key=getpass(\"Open Industrial Data API-KEY: \"), \n",
    "                       project=\"publicdata\", \n",
    "                       client_name=\"OID_example\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "VKFPBwYsRjHL"
   },
   "source": [
    "## Accessing Cognite Data Platform (CDP)\n",
    "* The CDP organizes digital information about the physical world.\n",
    "* There are 6 resource types stored on the CDP. Each of these objects in the CDP are labelled with a unique ID. Information regarding a specific Asset, Event, etc are often retrieved using this ID.\n",
    "\n",
    "  * [Assets](https://docs.cognite.com/api/v1/#tag/Assets) are digital representations of physical objects or groups of objects, and assets are organized into an asset hierarchy. For example, an asset can represent a water pump which is part of a subsystem on an oil platform.\n",
    "  \n",
    "  * [Events](https://docs.cognite.com/api/v1/#tag/Events) objects store complex information about multiple assets over a time period. For example, an event can describe two hours of maintenance on a water pump and some associated pipes.\n",
    "  \n",
    "  * A [Files](https://docs.cognite.com/api/v1/#tag/Files) stores a sequence of bytes connected to one or more assets. For example, a file can contain a piping and instrumentation diagram (P&IDs) showing how multiple assets are connected.\n",
    "  \n",
    "  * A [Time Series](https://docs.cognite.com/api/v1/#tag/Time-series) consists of a sequence of data points connected to a single asset. For example: A water pump asset can have a temperature time series that records a data point in units of °C every second.\n",
    "  \n",
    "  * [Sequences](https://docs.cognite.com/api/v1/#tag/Sequences) are similar to time series in that they are a key value pair, but rather than using a timestamp as the key, another measurment such as depth could be the key. For example, this is used in practice when drilling and taking measurments at various depths.\n",
    "  \n",
    "  * A [3D models](https://docs.cognite.com/api/v1/#tag/3D-Models) model is typically built up by a hierarchical structure. This looks very similar to how we organize our internal asset hierarchy. 3D models are visualized via Cognite's dashboards.\n",
    "  \n",
    "* It is important to refer back to the [SDK](https://cognite-sdk-python.readthedocs-hosted.com/en/latest/index.html) for specific details on arguments on all avaiable methods on how to access these objects."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Fn9FD2lMWUwI"
   },
   "source": [
    "### Collecting Asset Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "dRiusGapFmT8"
   },
   "source": [
    "#### Retrieve a list of all Assets\n",
    "\n",
    "* There are thousands of Assets in the CDP, we can have a look at a few examples.\n",
    "\n",
    "* This will generate a list of assets from the CDP with no particular filters, this is a random result. Generally we would want to apply filters when retrieving records.\n"
   ]
  },
  {
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   "execution_count": 4,
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    "id": "_sACMOGWFmna",
    "outputId": "41e482b7-70b4-41f7-bfc9-014e77205a5c"
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       "\n",
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     "execution_count": 4,
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   ],
   "source": [
    "client.assets.list(limit=5).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ysq_kFWLGYFV"
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   "source": [
    "#### Decide on which asset we want to explore\n",
    "* To get started exploring data in the CDP, we first need to decide on which Asset we want to gather information from.\n",
    "\n",
    "* Some asset names may be retrieved from the [Op Int](https://opint.cogniteapp.com/publicdata/infographics/-LOHKEJPLvt0eRIZu8mE) dashboard.\n",
    "\n",
    "* Some example asset names are:\n",
    "  * 23-HA-9103\n",
    "  * 23-PV-92583\n",
    "  * 23-VG-9101\n",
    "  \n",
    "The *fuzzy* search for an asset can be performed as followed\n"
   ]
  },
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       "4  2137557577165478  6687602007296940  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_name = \"23-HA-9103\"\n",
    "asset_df = client.assets.search(name=asset_name).to_pandas()\n",
    "asset_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "THRHdIXwS-bf"
   },
   "source": [
    "#### Get information on the asset of interest\n",
    "\n",
    "* We can filter the assets to get asset-specific details based on asset_name\n",
    "\n",
    "* The *retrieve()* interface provides the same information for 1 specific asset based on the provided ID\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 328
    },
    "colab_type": "code",
    "id": "kxnrtYoYSBGo",
    "outputId": "f1d57fa0-30e0-4f21-ff1b-1f7c5d9d6c32"
   },
   "outputs": [
    {
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       "      <td>Y</td>\n",
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       "      <th>WMT_TAG_ID</th>\n",
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       "                                                                      value\n",
       "name                                                             23-HA-9103\n",
       "parentId                                                   2513266419866445\n",
       "description                                  VRD - 1ST STAGE SUCTION COOLER\n",
       "id                                                         2861239574637735\n",
       "createdTime                                                               0\n",
       "lastUpdatedTime                                                           0\n",
       "rootId                                                     6687602007296940\n",
       "ELC_STATUS_ID                                                          1211\n",
       "RES_ID                                                               531306\n",
       "SOURCE_DB                                                          workmate\n",
       "SOURCE_TABLE                                              wmate_dba.wmt_tag\n",
       "WMT_AREA_ID                                                            1585\n",
       "WMT_CATEGORY_ID                                                        1120\n",
       "WMT_CONTRACTOR_ID                                                      1686\n",
       "WMT_FUNC_CODE_ID                                                       4757\n",
       "WMT_LOCATION_ID                                                        1004\n",
       "WMT_PO_ID                                                              8309\n",
       "WMT_SYSTEM_ID                                                          4440\n",
       "WMT_TAG_COMMENT           Load Distribution: 1 x 100%, Capacity: 231800 ...\n",
       "WMT_TAG_CREATED_DATE                                    2009-06-26 15:36:37\n",
       "WMT_TAG_CRITICALLINE                                                      N\n",
       "WMT_TAG_DESC                                 VRD - 1ST STAGE SUCTION COOLER\n",
       "WMT_TAG_GLOBALID                                           1000000000706279\n",
       "WMT_TAG_HISTORYREQUIRED                                                   Y\n",
       "WMT_TAG_ID                                                           345949\n",
       "WMT_TAG_ID_ANCESTOR                                                  346905\n",
       "WMT_TAG_ISACTIVE                                                          1\n",
       "WMT_TAG_ISOWNEDBYPROJECT                                                  0\n",
       "WMT_TAG_MAINID                                                       681760\n",
       "WMT_TAG_NAME                                                     23-HA-9103\n",
       "WMT_TAG_UPDATED_BY                                                     1001\n",
       "WMT_TAG_UPDATED_DATE                                    2015-10-08 15:05:08"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asset_id = int(asset_df[asset_df[\"name\"] == asset_name].iloc[0]['id'])\n",
    "asset = client.assets.retrieve(id=asset_id).to_pandas()\n",
    "asset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X75GOITO_m1q"
   },
   "source": [
    "#### How do we get Asset relationships?\n",
    "\n",
    "* The interface *get_asset_subtree()* can be used to retrieve the *children* of an Asset. \n",
    "\n",
    "* Each Asset is given various properties, some of the useful ones for this method are:\n",
    "\n",
    "  * Depth: The number of edges from the parent node\n",
    "  \n",
    "  * Description: Includes information such as the platform and type of sensor being monitored\n",
    "  \n",
    "We will generate a list of all children of the main asset of interest. This is done by specifying a depth of 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "colab_type": "code",
    "id": "1OPecRPv6xsU",
    "outputId": "aa33544c-6a3c-4cb8-f3cc-3933bb116289"
   },
   "outputs": [
    {
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      "text/plain": [
       "   createdTime                            description                id  \\\n",
       "0            0         VRD - 1ST STAGE SUCTION COOLER  2861239574637735   \n",
       "1            0   VRD - PH 1STSTGSUCTCOOL SHELL PSV IN   274450897701725   \n",
       "2            0          VRD - PH 1STSTGSUCTCLR GAS IN   576308321452985   \n",
       "3            0  VRD - PH 1STSTGSUCTCOOL SHELL PSV OUT   619750565594754   \n",
       "4            0    VRD - PH 1STSTGSUCTCOOL COOLMED OUT   705952550422793   \n",
       "\n",
       "   lastUpdatedTime                                           metadata  \\\n",
       "0                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '531306', ...   \n",
       "1                0  {'ELC_STATUS_ID': '1225', 'RES_ID': '444134', ...   \n",
       "2                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '609895', ...   \n",
       "3                0  {'ELC_STATUS_ID': '1225', 'RES_ID': '510103', ...   \n",
       "4                0  {'ELC_STATUS_ID': '1211', 'RES_ID': '485917', ...   \n",
       "\n",
       "             name          parentId            rootId  \n",
       "0      23-HA-9103  2513266419866445  6687602007296940  \n",
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      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "subtree_df = client.assets.retrieve_subtree(id=asset_id, depth=1).to_pandas()\n",
    "subtree_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KeCoOb_AWPv_"
   },
   "source": [
    "### Collecting Time Series Information and Data Points\n",
    "\n",
    "* Time Series objects contain the bulk of information in the CDP.\n",
    "\n",
    "* Time Series objects are generally linked to an asset through the asset_id field.\n",
    "\n",
    "* The *list()* method has a variety of filters, all are listed in the SDK.\n",
    "\n",
    "* By specifying **asset_ids** we will get a dataframe of all the associated time series objects with at least one of the specified assets. This is seen below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oGg2xxBQUQUa"
   },
   "source": [
    "#### Compile a list of time series objects under the asset\n",
    "* Get the associated time series objects for al the children assets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <td>PH 1stStgSuctClr CoolMed Out</td>\n",
       "      <td>VAL_45-TT-92508:X.Value</td>\n",
       "      <td>3929156348065703</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_45-TT-92508:X.Value', 'scan': '1'...</td>\n",
       "      <td>VAL_45-TT-92508:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4840206559741735</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Gas In</td>\n",
       "      <td>VAL_23-TT-92502:X.Value</td>\n",
       "      <td>5474031062875475</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_23-TT-92502:X.Value', 'scan': '1'...</td>\n",
       "      <td>VAL_23-TT-92502:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5552927149248373</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Gas In ESDV</td>\n",
       "      <td>VAL_23-PDT-92501:X.Value</td>\n",
       "      <td>5880632484472759</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_23-PDT-92501:X.Value', 'scan': '1...</td>\n",
       "      <td>VAL_23-PDT-92501:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2814662602621825</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Gas In</td>\n",
       "      <td>VAL_23-PT-92512:X.Value</td>\n",
       "      <td>6156871056679530</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_23-PT-92512:X.Value', 'scan': '1'...</td>\n",
       "      <td>VAL_23-PT-92512:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5193727763959013</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Gas Out</td>\n",
       "      <td>VAL_23-PT-92504:X.Value</td>\n",
       "      <td>7491681405625794</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_23-PT-92504:X.Value', 'scan': '1'...</td>\n",
       "      <td>VAL_23-PT-92504:X.Value</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6681756879590530</td>\n",
       "      <td>0</td>\n",
       "      <td>PH 1stStgSuctCool Tubes</td>\n",
       "      <td>VAL_23-PDT-92502:X.Value</td>\n",
       "      <td>7891376783606949</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>0</td>\n",
       "      <td>{'tag': 'VAL_23-PDT-92502:X.Value', 'scan': '1...</td>\n",
       "      <td>VAL_23-PDT-92502:X.Value</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             assetId  createdTime                    description  \\\n",
       "0   5439867226448359            0       PH 1stStgSuctCool Gas In   \n",
       "1   7835659687560027            0        PH 1stStgSuctCool Shell   \n",
       "2   5497674254221099            0   PH 1stStgSuctCool CoolMed In   \n",
       "3   7390359759479147            0  PH 1stStgSuctClr CoolMed Sply   \n",
       "4    786220428505816            0       PH 1stStgSuctCool Gas In   \n",
       "5    705952550422793            0  PH 1stStgSuctCool CoolMed Out   \n",
       "6   5891006566061532            0   PH 1stStgSuctClr CoolMed Out   \n",
       "7   4840206559741735            0       PH 1stStgSuctCool Gas In   \n",
       "8   5552927149248373            0  PH 1stStgSuctCool Gas In ESDV   \n",
       "9   2814662602621825            0       PH 1stStgSuctCool Gas In   \n",
       "10  5193727763959013            0      PH 1stStgSuctCool Gas Out   \n",
       "11  6681756879590530            0        PH 1stStgSuctCool Tubes   \n",
       "\n",
       "                  externalId                id  isStep  isString  \\\n",
       "0    VAL_23-TT-92512:X.Value   988967451935968   False     False   \n",
       "1   VAL_45-PDT-92506:X.Value  1804879717768950   False     False   \n",
       "2    VAL_45-FT-92506:X.Value  1920137775628302   False     False   \n",
       "3    VAL_45-TT-92506:X.Value  2288456938237513   False     False   \n",
       "4    VAL_23-FT-92512:X.Value  3518012501014915   False     False   \n",
       "5    VAL_45-PT-92508:X.Value  3529821833330815   False     False   \n",
       "6    VAL_45-TT-92508:X.Value  3929156348065703   False     False   \n",
       "7    VAL_23-TT-92502:X.Value  5474031062875475   False     False   \n",
       "8   VAL_23-PDT-92501:X.Value  5880632484472759   False     False   \n",
       "9    VAL_23-PT-92512:X.Value  6156871056679530   False     False   \n",
       "10   VAL_23-PT-92504:X.Value  7491681405625794   False     False   \n",
       "11  VAL_23-PDT-92502:X.Value  7891376783606949   False     False   \n",
       "\n",
       "    lastUpdatedTime                                           metadata  \\\n",
       "0                 0  {'tag': 'VAL_23-TT-92512:X.Value', 'scan': '1'...   \n",
       "1                 0  {'tag': 'VAL_45-PDT-92506:X.Value', 'scan': '1...   \n",
       "2                 0  {'tag': 'VAL_45-FT-92506:X.Value', 'scan': '1'...   \n",
       "3                 0  {'tag': 'VAL_45-TT-92506:X.Value', 'scan': '1'...   \n",
       "4                 0  {'tag': 'VAL_23-FT-92512:X.Value', 'scan': '1'...   \n",
       "5                 0  {'tag': 'VAL_45-PT-92508:X.Value', 'scan': '1'...   \n",
       "6                 0  {'tag': 'VAL_45-TT-92508:X.Value', 'scan': '1'...   \n",
       "7                 0  {'tag': 'VAL_23-TT-92502:X.Value', 'scan': '1'...   \n",
       "8                 0  {'tag': 'VAL_23-PDT-92501:X.Value', 'scan': '1...   \n",
       "9                 0  {'tag': 'VAL_23-PT-92512:X.Value', 'scan': '1'...   \n",
       "10                0  {'tag': 'VAL_23-PT-92504:X.Value', 'scan': '1'...   \n",
       "11                0  {'tag': 'VAL_23-PDT-92502:X.Value', 'scan': '1...   \n",
       "\n",
       "                        name  \n",
       "0    VAL_23-TT-92512:X.Value  \n",
       "1   VAL_45-PDT-92506:X.Value  \n",
       "2    VAL_45-FT-92506:X.Value  \n",
       "3    VAL_45-TT-92506:X.Value  \n",
       "4    VAL_23-FT-92512:X.Value  \n",
       "5    VAL_45-PT-92508:X.Value  \n",
       "6    VAL_45-TT-92508:X.Value  \n",
       "7    VAL_23-TT-92502:X.Value  \n",
       "8   VAL_23-PDT-92501:X.Value  \n",
       "9    VAL_23-PT-92512:X.Value  \n",
       "10   VAL_23-PT-92504:X.Value  \n",
       "11  VAL_23-PDT-92502:X.Value  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.time_series.list(asset_ids=subtree_df.id.tolist()).to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YIejfGjYWwNY"
   },
   "source": [
    "#### View datapoints for one of the time series\n",
    "\n",
    "* Now that we have a list of all of the time series directly related to the immediete childern of our asset, we can retrieve datapoints for some of the them.\n",
    "\n",
    "* A Datapoint in the CDP is stored as a key value pair\n",
    "\n",
    "  * timestamp is the time since epoch in milliseconds\n",
    "  \n",
    "  * value is the reading from the sensor\n",
    "  \n",
    "The identifier to retrieve Datapoints is the **name** column from the DataFrame above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "d4wYaEyJWtrq",
    "outputId": "665ed30e-17b3-4d24-d7dc-59d680dfe00d"
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>VAL_23-TT-92512:X.Value</th>\n",
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       "      <th>2019-11-04 08:50:21.205</th>\n",
       "      <td>39.820095</td>\n",
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       "      <th>2019-11-04 08:50:40.247</th>\n",
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       "      <th>2019-11-04 08:51:22.215</th>\n",
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       "      <th>2019-11-04 08:51:32.235</th>\n",
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       "      <th>2019-11-04 08:51:43.221</th>\n",
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       "                         VAL_23-TT-92512:X.Value\n",
       "2019-11-04 08:50:21.205                39.820095\n",
       "2019-11-04 08:50:40.247                39.792019\n",
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       "2019-11-04 08:51:43.221                39.932404"
      ]
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     "metadata": {},
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   ],
   "source": [
    "ts_id = 988967451935968\n",
    "client.datapoints.retrieve(id=ts_id, start=\"10d-ago\", end=\"now\").to_pandas().head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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