DEMO_Pull_Influx_Data_Pandas.ipynb

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   "source": [
    "# Demo to download near-real time and historical wxUAS observations from the MetSprite using InfluxDB\n",
    "\n",
    "Initial commit by Ben Pickering, May 2023"
   ]
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    "import os\n",
    "from datetime import datetime, timedelta\n",
    "import numpy as np\n",
    "import numpy.ma as ma\n",
    "from pprint import pprint\n",
    "import pandas as pd\n",
    "\n",
    "from influxdb_client import InfluxDBClient, WriteOptions\n",
    "from influxdb_client .client.write_api import SYNCHRONOUS\n",
    "from pytz import UTC # For time zone tags\n",
    "from dateutil.tz import tzutc\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
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    "def Start_Influx_Client(URL, Token, Org, Bucket):\n",
    "    # Start clients through which to talk to influxDB\n",
    "    Influx_Client = InfluxDBClient(\n",
    "        url=URL, \n",
    "        token=Token, \n",
    "        org=Org, \n",
    "        bucket=Bucket)\n",
    "    \n",
    "    return Influx_Client, Bucket, Org"
   ]
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    "# Step 1: Set Up\n",
    "\n",
    "Firstly you need to set your environmental variables with the following information provided to you by Menapia, or created yourself (see manual, high risk).\n",
    "\n",
    "Do this by entering the following code into your `.bashrc` or equivalent (MacOS is now `.zshrc`)\n",
    "\n",
    "```\n",
    "export WOEST_Influx_Bucket=\"************\"\n",
    "export WOEST_Influx_Token=\"************************************************************\"\n",
    "export WOEST_Influx_Org=\"************\"\n",
    "export WOEST_Influx_URL=\"************************************\"\n",
    "```\n",
    "\n",
    "Replacing the *** with your information. This method is more secure because it avoids you hard-coding sensitive information which may be inadvertently shared in a `git push`.\n",
    "\n",
    "The variables have the following meaning:\n",
    "    \n",
    "| Variable | Description                 |\n",
    "| -------: | :--------------------------|\n",
    "| URL      | Path to the database.|\n",
    "| Token   | Equivalent to a password. This is the most sensitive variable. Typically ends with `==`.|\n",
    "| Org   | Name of the database. This will typically be the project you are associated with.|\n",
    "| Bucket   | The highest level of data structure in the database. Usually this will be either `Metsensor` or `ROS` for meteorological data and UAS telemetry data respectively. |\n",
    "\n",
    "NOTE: You will need to close the terminal window running this Python notebook, and relaunch the terminal and Python notebook for the environmental variables to be initialised."
   ]
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    "# Create the influx read-only client\n",
    "Influx_Read_Client, Read_Bucket, Read_Org = Start_Influx_Client(\n",
    "    URL=os.environ.get('WOEST_Influx_URL'),\n",
    "    Token=os.environ.get('WOEST_Influx_Token'),\n",
    "    Org=os.environ.get('WOEST_Influx_Org'),\n",
    "    Bucket=os.environ.get('WOEST_Influx_Bucket', \"Metsensor\")\n",
    ")"
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    "# Step 2: Make a Query\n",
    "\n",
    "The following function is intended to simplify the query creation, but you can also learn the `flux` language with the InfluxDB documentation: https://docs.influxdata.com/flux/v0.x/get-started/ to build even more complex queries. The principal is, that the server can compute functions on the data much faster than you can locally, so better to do this on the server as part of the request first. It is of course, entirely optional.\n",
    "\n",
    "\n",
    "### Filters\n",
    "Menapia have set up specific filters within the database that need to be followed in order to get data. The following table describes these filters:\n",
    "\n",
    "| Filter | Description                 |\n",
    "| -------: | :--------------------------|\n",
    "| `_measurement` | Highest filter, enforced by InfluxDB itself. Values are `metsensor` or `ROS`.  |\n",
    "| `host`         | The name of the MetSprite wxUAS. Development names are e.g. `maggie` or `nicke` but customer wxUAS will be named differently.  |\n",
    "| `sensor`       | The type of sensor. Examples: `P14_1`, `P14_2`, `SHT85`, `BMP390`, `anemoment`, `vehicle_global_position`, `vehicle_odometry`. |\n",
    "| `_field`       | Also mandated by InfluxDB, this is the variable being measured. Examples: `PRES`,`TEMP`,`RH`, `U`, `V`, `W`, `alt`, `lat`, `lon`, `pitch`, `roll`, `yaw`, `pitch_speed`, `roll_speed`, `yaw_speed`.|\n",
    "\n",
    "If these filters are not specified, then all available data are returned. This is not recommended as some spurious data may be returned that you were not expecting to get.\n",
    "\n",
    "You cannot break the code by over-specifying filters. The worst outcome is an empty dataframe will be returned.\n",
    "\n",
    "To get a \"feel\" for what data exists before zooming into what you care about, use an aggregate window of e.g. 1 hour if pulling 1 week of data (1 minute if pulling 1 day).\n",
    "\n",
    "### Aggregate Functions\n",
    "The InfluxDB flux query language documentation has all the information you need on aggregate functions: https://docs.influxdata.com/influxdb/cloud/query-data/flux/window-aggregate/.\n",
    "\n",
    "Some basic principals are that if you don't want the raw data (which can be very large) then you can pull down a sub-sampled set of data, say at 1 second or 10 minute resolution. This is referred to as the aggregate window, and is written in flux language as `1s` or `1m` respectively. Aggregate functions such as `mean`, `last`, and `sddev` describe how to treat multiple values that exist within the aggregate window, in this case give the average of the values in the window, the last value in the window, or the standard deviation of the values within the window. This will be much faster than Python computing the standard deviation, so is worth considering for a large dataset.\n",
    "\n",
    "### Example Query\n",
    "\n",
    "For context, here is an example flux query: ()\n",
    "```\n",
    "    from(bucket: \"Metsensor\")\n",
    "      |> range(start: 2023-05-11T15:13:26Z, stop: 2023-05-11T15:15:49)\n",
    "      |> filter(fn: (r) => r[\"_measurement\"] == \"metsensor\")\n",
    "      |> filter(fn: (r) => r[\"host\"] == \"nicke\")\n",
    "      |> filter(fn: (r) => r[\"sensor\"] == \"P14_1\")\n",
    "      |> filter(fn: (r) => r[\"_field\"] == \"TEMP\")\n",
    "      |> filter(fn: (r) => r[\"deviceID\"] == \"M0026\" or r[\"deviceID\"] == \"M0028\")\n",
    "      |> aggregateWindow(every: 1s, fn: mean, createEmpty: true)\n",
    "      |> yield(name: \"mean\")\n",
    "```\n",
    "Note that the top two lines are handled by the influx Python module."
   ]
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    "def Create_Influx_Query(bucket, t_start, t_stop, _measurement, host, sensor, _field, agg_time=None, agg_func=None):\n",
    "    '''\n",
    "    Build a string which is compatible with the flux language for interacting with influx database.\n",
    "    Specify the time range, device(s), field(s), and aggregation of the data.\n",
    "    Inputs:\n",
    "        bucket       string describing the influxdb bucket where the desired data exists.\n",
    "        t_start      datetime object.\n",
    "        t_stop       datetime object.\n",
    "        device_IDs   list of strings of metsensor device names (e.g. [\"metsensor_0028\"]).\n",
    "        fields       list of strings of field names (e.g. [\"p14_2_humidity\",\"bmp390_temp\"]).\n",
    "        agg_time     string with the desired time window, such that '1s' will return 3600 values\n",
    "                     in a 1 hour t_start to t_stop time window. If the agg_time is coarser than\n",
    "                     the raw data, then multiple devices will be time-synced automatically.\n",
    "        agg_func     string to describe how the raw data should be binned in to the agg_time\n",
    "                     window. For example: 'mean', 'last', 'sddev'.\n",
    "                     \n",
    "    For context, here is an example flux query:\n",
    "    \n",
    "        from(bucket: \"Metsensor\")\n",
    "          |> range(start: 2023-05-11T15:13:26Z, stop: 2023-05-11T15:15:49)\n",
    "          |> filter(fn: (r) => r[\"_measurement\"] == \"metsensor\")\n",
    "          |> filter(fn: (r) => r[\"host\"] == \"nicke\")\n",
    "          |> filter(fn: (r) => r[\"sensor\"] == \"P14_1\")\n",
    "          |> filter(fn: (r) => r[\"_field\"] == \"TEMP\")\n",
    "          |> filter(fn: (r) => r[\"deviceID\"] == \"M0026\" or r[\"deviceID\"] == \"M0028\")\n",
    "          |> aggregateWindow(every: 1s, fn: mean, createEmpty: true)\n",
    "    \n",
    "    '''\n",
    "    # Create _measurement selection filter in flux query language\n",
    "    _measurement_filter = '|> filter(fn: (r) => '\n",
    "    for i in range(len(_measurement)):\n",
    "        _measurement_filter += 'r._measurement == \"' + _measurement[i] + '\" or '\n",
    "    _measurement_filter = _measurement_filter[:-5] + '\")'\n",
    "    \n",
    "    # Create host selection filter in flux query language\n",
    "    host_filter = '|> filter(fn: (r) => '\n",
    "    for i in range(len(host)):\n",
    "        host_filter += 'r.host == \"' + host[i] + '\" or '\n",
    "    host_filter = host_filter[:-5] + '\")'\n",
    "    \n",
    "    # Create sensor selection filter in flux query language\n",
    "    sensor_filter = '|> filter(fn: (r) => '\n",
    "    for i in range(len(sensor)):\n",
    "        sensor_filter += 'r.sensor == \"' + sensor[i] + '\" or '\n",
    "    sensor_filter = sensor_filter[:-5] + '\")'\n",
    "    \n",
    "    # Create field selection filter in flux query language\n",
    "    _field_filter = '|> filter(fn: (r) => '\n",
    "    for i in range(len(_field)):\n",
    "        _field_filter += 'r._field == \"' + _field[i] + '\" or '\n",
    "    _field_filter = _field_filter[:-5] + '\")'\n",
    "    \n",
    "    # Create the full flux string command (required terms only)\n",
    "    query = (\n",
    "        'from(bucket: \"'+bucket+'\") '\n",
    "        +'|> range(start:'+t_start.strftime('%Y-%m-%dT%H:%M:%SZ')+', stop: '+t_stop.strftime('%Y-%m-%dT%H:%M:%SZ')+') '\n",
    "        + _measurement_filter\n",
    "        + host_filter\n",
    "        + sensor_filter\n",
    "        + _field_filter\n",
    "    )\n",
    "    \n",
    "    # Do aggregateWindow if specified\n",
    "    if agg_time != None and agg_func != None:\n",
    "        query = query +'|> aggregateWindow(every: '+agg_time+', fn: '+agg_func+', createEmpty: true)'\n",
    "    \n",
    "    # If you are experiencing issues with pandas dataframes, try these custom flux commands for pandas compatiblity: https://www.influxdata.com/blog/tldr-python-pandas-dataframes-influxdb/\n",
    "#     query = (query\n",
    "#              +'|> pivot(rowKey: [“_time”], ColumnKey: [“tag”], ValueColumn: “value”)'\n",
    "        \n",
    "    return query"
   ]
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   "source": [
    "# Example single flight:\n",
    "t_start = datetime(2023, 5, 11, 15, 13, 26)\n",
    "t_stop = datetime(2023, 5, 11, 15, 15, 49)\n",
    "\n",
    "_measurement = [\"metsensor\", \"ROS\"]\n",
    "host = [\"nicke\"]\n",
    "sensor = [\"P14_1\", \"P14_2\", \"SHT85\", \"BMP390\", \"anemoment\", \"vehicle_global_position\", \"vehicle_odometry\"]\n",
    "_field = [\"PRES\",\"TEMP\",\"RH\", \"U\", \"V\", \"W\", \"alt\", \"lat\", \"lon\", \"pitch\", \"roll\", \"yaw\", \"pitch_speed\", \"roll_speed\", \"yaw_speed\"]\n",
    "\n",
    "Agg_Time = None #'1s'\n",
    "Agg_Func = None #'mean'"
   ]
  },
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   "source": [
    "# Step 3: Request Data\n",
    "\n",
    "Metsensor and ROS (telemetry) are in separate influx buckets, so must be requested separately"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8c40b3b5-22df-4e72-9ca4-20f76d43b8bb",
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   "source": [
    "# Send query for ROS (telemetry) data\n",
    "Influx_DF_ROS = Influx_Read_Client.query_api().query_data_frame(\n",
    "    query=Create_Influx_Query(\n",
    "        bucket=\"ROS\",\n",
    "        t_start=t_start, \n",
    "        t_stop=t_stop, \n",
    "        _measurement=_measurement, \n",
    "        host=host, \n",
    "        sensor=sensor, \n",
    "        _field=_field, \n",
    "        agg_time=None, \n",
    "        agg_func=None\n",
    "    ), \n",
    "    org=\"WOEST\"\n",
    ")\n",
    "\n",
    "# Send query for Metsensor (atmospheric) data\n",
    "Influx_DF_Metsensor = Influx_Read_Client.query_api().query_data_frame(\n",
    "    query=Create_Influx_Query(\n",
    "        bucket=\"Metsensor\",\n",
    "        t_start=t_start, \n",
    "        t_stop=t_stop, \n",
    "        _measurement=_measurement, \n",
    "        host=host, \n",
    "        sensor=sensor, \n",
    "        _field=_field, \n",
    "        agg_time=None, \n",
    "        agg_func=None\n",
    "    ), \n",
    "    org=\"WOEST\"\n",
    ")\n",
    "\n",
    "# The returned tables don't have time zone. We must add this manually\n",
    "Influx_DF_ROS[\"_time\"] = Influx_DF_ROS[\"_time\"].dt.tz_convert(\"UTC\")\n",
    "Influx_DF_Metsensor[\"_time\"] = Influx_DF_Metsensor[\"_time\"].dt.tz_convert(\"UTC\")\n",
    "\n",
    "# Merge the two returned pandas dataframes into one dataframe\n",
    "DFs = [Influx_DF_Metsensor, Influx_DF_ROS]\n",
    "Influx_DF = pd.concat(DFs)"
   ]
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       "      <td>shaq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128860</th>\n",
       "      <td>_result</td>\n",
       "      <td>8</td>\n",
       "      <td>2023-05-11 15:13:26+00:00</td>\n",
       "      <td>2023-05-11 15:15:49+00:00</td>\n",
       "      <td>2023-05-11 15:15:48.985207+00:00</td>\n",
       "      <td>0.111364</td>\n",
       "      <td>yaw_speed</td>\n",
       "      <td>ROS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nicke</td>\n",
       "      <td>vehicle_odometry</td>\n",
       "      <td>shaq</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128861</th>\n",
       "      <td>_result</td>\n",
       "      <td>8</td>\n",
       "      <td>2023-05-11 15:13:26+00:00</td>\n",
       "      <td>2023-05-11 15:15:49+00:00</td>\n",
       "      <td>2023-05-11 15:15:48.995169+00:00</td>\n",
       "      <td>-0.149992</td>\n",
       "      <td>yaw_speed</td>\n",
       "      <td>ROS</td>\n",
       "      <td>NaN</td>\n",
       "      <td>nicke</td>\n",
       "      <td>vehicle_odometry</td>\n",
       "      <td>shaq</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>169084 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         result table                    _start                     _stop  \\\n",
       "0       _result     0 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "1       _result     0 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "2       _result     0 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "3       _result     0 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "4       _result     0 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "...         ...   ...                       ...                       ...   \n",
       "128857  _result     8 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "128858  _result     8 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "128859  _result     8 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "128860  _result     8 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "128861  _result     8 2023-05-11 15:13:26+00:00 2023-05-11 15:15:49+00:00   \n",
       "\n",
       "                                  _time         _value     _field  \\\n",
       "0      2023-05-11 15:13:26.025000+00:00  100752.120000       PRES   \n",
       "1      2023-05-11 15:13:26.127000+00:00  100752.380000       PRES   \n",
       "2      2023-05-11 15:13:26.228000+00:00  100752.510000       PRES   \n",
       "3      2023-05-11 15:13:26.329000+00:00  100752.760000       PRES   \n",
       "4      2023-05-11 15:13:26.430000+00:00  100752.640000       PRES   \n",
       "...                                 ...            ...        ...   \n",
       "128857 2023-05-11 15:15:48.955454+00:00      -0.197032  yaw_speed   \n",
       "128858 2023-05-11 15:15:48.965363+00:00      -0.109307  yaw_speed   \n",
       "128859 2023-05-11 15:15:48.975221+00:00       0.182451  yaw_speed   \n",
       "128860 2023-05-11 15:15:48.985207+00:00       0.111364  yaw_speed   \n",
       "128861 2023-05-11 15:15:48.995169+00:00      -0.149992  yaw_speed   \n",
       "\n",
       "       _measurement deviceID   host            sensor  user  \n",
       "0         metsensor    M0026  nicke            BMP390  shaq  \n",
       "1         metsensor    M0026  nicke            BMP390  shaq  \n",
       "2         metsensor    M0026  nicke            BMP390  shaq  \n",
       "3         metsensor    M0026  nicke            BMP390  shaq  \n",
       "4         metsensor    M0026  nicke            BMP390  shaq  \n",
       "...             ...      ...    ...               ...   ...  \n",
       "128857          ROS      NaN  nicke  vehicle_odometry  shaq  \n",
       "128858          ROS      NaN  nicke  vehicle_odometry  shaq  \n",
       "128859          ROS      NaN  nicke  vehicle_odometry  shaq  \n",
       "128860          ROS      NaN  nicke  vehicle_odometry  shaq  \n",
       "128861          ROS      NaN  nicke  vehicle_odometry  shaq  \n",
       "\n",
       "[169084 rows x 12 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Influx_DF"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d5e04ed-a33c-4d93-950d-40b78891f856",
   "metadata": {},
   "source": [
    "## Print some statistics about the returned data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ad2ec88c-109d-4b5c-af74-2bfa40228a69",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of datapoints in record: 169084 \n",
      "\n",
      "Columns in pandas datafrme:\n",
      " ['result', 'table', '_start', '_stop', '_time', '_value', '_field', '_measurement', 'deviceID', 'host', 'sensor', 'user'] \n",
      "\n",
      "Metsensors in dataframe:\n",
      " ['M0026' 'M0028' 'M0200' nan] \n",
      "\n",
      "Sensors in dataframe:\n",
      " ['BMP390' 'P14_1' 'P14_2' 'SHT85' 'anemoment' 'vehicle_global_position'\n",
      " 'vehicle_odometry'] \n",
      "\n",
      "Fields in dataframe:\n",
      " ['PRES' 'TEMP' 'RH' 'U' 'V' 'W' 'alt' 'lat' 'lon' 'pitch' 'pitch_speed'\n",
      " 'roll' 'roll_speed' 'yaw' 'yaw_speed'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of datapoints in record:\", len(Influx_DF), \"\\n\")\n",
    "\n",
    "print(\"Columns in pandas datafrme:\\n\", list(Influx_DF.columns), \"\\n\")\n",
    "\n",
    "print(\"Metsensors in dataframe:\\n\", Influx_DF[\"deviceID\"].unique(), \"\\n\")\n",
    "\n",
    "print(\"Sensors in dataframe:\\n\", Influx_DF[\"sensor\"].unique(), \"\\n\")\n",
    "\n",
    "print(\"Fields in dataframe:\\n\", Influx_DF[\"_field\"].unique(), \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d7687a8-9dc5-4420-b40a-df97d23914d3",
   "metadata": {},
   "source": [
    "# Step 4: Plot Data\n",
    "\n",
    "The returned data is now in Pandas DF format, so any plotting code you prefer can be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f0e2cd5-b5a5-4d32-ae68-a41552ca0096",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# More readable to extract the desired field from the pandas dataframe first\n",
    "alt = Influx_DF[Influx_DF['_field'] == 'alt']\n",
    "\n",
    "# Plot the variable of interest using '_time' and '_value' keys\n",
    "# This can all be done in one line, but is not as readable to humans\n",
    "plt.plot(alt['_time'], alt['_value'])\n",
    "plt.ylabel(\"Altitude (m AMSL)\")\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "65fa3135-1c7d-456f-8032-f1295897baec",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Metsensor M0026\n",
    "\n",
    "# More readable to extract the desired field from the pandas dataframe first\n",
    "# 1) Metsensor ID — one MetSprite carries multiple Metsensors\n",
    "# 2) Sensor name — Metsensor carries multiple sensor chips\n",
    "# 3) Field name — each sensor tends to observe multiple variables\n",
    "SHT85_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0026') & (Influx_DF['sensor'] == 'SHT85') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "P14_1_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0026') & (Influx_DF['sensor'] == 'P14_1') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "P14_2_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0026') & (Influx_DF['sensor'] == 'P14_2') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "\n",
    "# Plot the variable of interest using '_time' and '_value' keys\n",
    "# This can all be done in one line, but is not as readable to humans\n",
    "plt.scatter(SHT85_TEMP['_time'], SHT85_TEMP['_value'], label=\"SHT85_TEMP\", alpha=0.1, s=1)\n",
    "plt.scatter(P14_1_TEMP['_time'], P14_1_TEMP['_value'], label=\"P14_1_TEMP\", alpha=0.1, s=1)\n",
    "plt.scatter(P14_2_TEMP['_time'], P14_2_TEMP['_value'], label=\"P14_2_TEMP\", alpha=0.1, s=1)\n",
    "plt.ylabel(\"Metsensor M0026 Temperature (ºC)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "\n",
    "# Metsensor M0028\n",
    "\n",
    "# More readable to extract the desired field from the pandas dataframe first\n",
    "# 1) Metsensor ID — one MetSprite carries multiple Metsensors\n",
    "# 2) Sensor name — Metsensor carries multiple sensor chips\n",
    "# 3) Field name — each sensor tends to observe multiple variables\n",
    "SHT85_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0028') & (Influx_DF['sensor'] == 'SHT85') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "P14_1_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0028') & (Influx_DF['sensor'] == 'P14_1') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "P14_2_TEMP = Influx_DF[ (Influx_DF['deviceID'] == 'M0028') & (Influx_DF['sensor'] == 'P14_2') & (Influx_DF['_field'] == 'TEMP') ]\n",
    "\n",
    "# Plot the variable of interest using '_time' and '_value' keys\n",
    "# This can all be done in one line, but is not as readable to humans\n",
    "plt.scatter(SHT85_TEMP['_time'], SHT85_TEMP['_value'], label=\"SHT85_TEMP\", alpha=0.1, s=1)\n",
    "plt.scatter(P14_1_TEMP['_time'], P14_1_TEMP['_value'], label=\"P14_1_TEMP\", alpha=0.1, s=1)\n",
    "plt.scatter(P14_2_TEMP['_time'], P14_2_TEMP['_value'], label=\"P14_2_TEMP\", alpha=0.1, s=1)\n",
    "plt.ylabel(\"Metsensor M0028 Temperature (ºC)\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:menapia] *",
   "language": "python",
   "name": "conda-env-menapia-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}