{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with Tabular Data\n", "\n", "Although MVG has been designed with vibration waveform data in mind, it still works wonderfully well with non-vibration or KPI data.\n", "We refer to this as tabular timeseries data.\n", "Each measurement consists of one or more variables captured at the same time.\n", "This is different from the vibration waveform data, where each measurement is a short measurement sample from one vibration sensor.\n", "\n", "In this example, we will show how to work with tabular sources and measurements, and how to create an analysis.\n", "If you have seen any of the previous examples, much of this one will be familiar to you.\n", "\n", "### Imports\n", "\n", "Beyond importing `MVG` and the `ModeId` analysis class, we want to use `pandas` for loading and processing the data before the analysis." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "from pprint import pprint\n", "from pathlib import Path\n", "\n", "import pandas as pd\n", "\n", "from mvg import MVG\n", "from mvg.analysis_classes import ModeId" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After importing we create the `MVG` session that let us communicate with the MVG server.\n", "For that we need an endpoint URL and a valid token.\n", "If you do not have a token, you can get one [here](https://vikinganalytics.se/multiviz-vibration-api/).\n", "\n", ".. note:: Each token is used for Authorization AND Authentication. Thus, each unique token represents a unique user, each user has it own, unique database on the VA-MVG' service.\n", "\n", "**You need to insert your token received from Viking Analytics here:**\n", "Just replace `\"os.environ['TEST_TOKEN']\"` by your token as a string." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "tags": [ "parameters" ] }, "outputs": [], "source": [ "ENDPOINT = \"http://api.beta.multiviz.com\"\n", "TOKEN = os.environ['TEST_TOKEN']" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "session = MVG(ENDPOINT, TOKEN)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will use a dataset that is collected from a heat pump in the cellar of one of the employees at Viking Analytics.\n", "The data is available in [this git repository](https://github.com/vikinganalytics/tabular-example-data). We clone the repository to get access it.\n", "If you don't have git you can download the data from the link.\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Cloning into 'tabular-example-data'...\n" ] } ], "source": [ "!git clone https://github.com/vikinganalytics/tabular-example-data.git" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": " timestamp T_out T_down T_kitchen WATT T_up \\\n0 2018-02-01T00:00:00Z 1.976053 38.367181 20.512105 2.780167 40.028018 \n1 2018-02-01T01:40:00Z 2.380000 36.940000 20.190000 3.045333 39.060000 \n2 2018-02-01T03:25:00Z 2.211223 37.715920 20.190000 3.060000 39.840240 \n3 2018-02-01T05:10:00Z 1.964956 37.940000 20.250000 3.151579 40.023789 \n4 2018-02-01T06:55:00Z 0.349134 37.551293 20.406457 3.257167 39.856860 \n\n T_brine_in T_brine_out T_cellar \n0 3.690000 2.068514 10.965206 \n1 3.560000 1.991486 10.924431 \n2 3.598392 1.961608 10.982982 \n3 3.611330 2.068670 10.888049 \n4 3.690000 2.120000 10.941380 ", "text/html": "
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timestampT_outT_downT_kitchenWATTT_upT_brine_inT_brine_outT_cellar
02018-02-01T00:00:00Z1.97605338.36718120.5121052.78016740.0280183.6900002.06851410.965206
12018-02-01T01:40:00Z2.38000036.94000020.1900003.04533339.0600003.5600001.99148610.924431
22018-02-01T03:25:00Z2.21122337.71592020.1900003.06000039.8402403.5983921.96160810.982982
32018-02-01T05:10:00Z1.96495637.94000020.2500003.15157940.0237893.6113302.06867010.888049
42018-02-01T06:55:00Z0.34913437.55129320.4064573.25716739.8568603.6900002.12000010.941380
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" }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_path = Path.cwd() / \"tabular-example-data\" / \"heatpump.csv\"\n", "data_df = pd.read_csv(data_path)\n", "data_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We loaded the `.csv` file into a dataframe object and printed out the first five rows.\n", "The timestamp was read as string timestamps, but for the MVG API we need the timestamps to be in milliseconds since EPOCH.\n", "So we need to do some data wrangling in order to get it to the correct data type.\n", "\n", "We can use the function `pandas.to_datetime()` to convert a column into datetime objects.\n", "Then we can convert the timestamps into integers, which will automatically get converted to nanoseconds since EPOCH.\n", "Then, when dividing by 1e6 we get the same time in milliseconds.\n", "We can see that the conversion was successful by printing the first five rows again." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": " timestamp T_out T_down T_kitchen WATT T_up \\\n0 1517443200000 1.976053 38.367181 20.512105 2.780167 40.028018 \n1 1517449200000 2.380000 36.940000 20.190000 3.045333 39.060000 \n2 1517455500000 2.211223 37.715920 20.190000 3.060000 39.840240 \n3 1517461800000 1.964956 37.940000 20.250000 3.151579 40.023789 \n4 1517468100000 0.349134 37.551293 20.406457 3.257167 39.856860 \n\n T_brine_in T_brine_out T_cellar \n0 3.690000 2.068514 10.965206 \n1 3.560000 1.991486 10.924431 \n2 3.598392 1.961608 10.982982 \n3 3.611330 2.068670 10.888049 \n4 3.690000 2.120000 10.941380 ", "text/html": "
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timestampT_outT_downT_kitchenWATTT_upT_brine_inT_brine_outT_cellar
015174432000001.97605338.36718120.5121052.78016740.0280183.6900002.06851410.965206
115174492000002.38000036.94000020.1900003.04533339.0600003.5600001.99148610.924431
215174555000002.21122337.71592020.1900003.06000039.8402403.5983921.96160810.982982
315174618000001.96495637.94000020.2500003.15157940.0237893.6113302.06867010.888049
415174681000000.34913437.55129320.4064573.25716739.8568603.6900002.12000010.941380
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" }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_df[\"timestamp\"] = pd.to_datetime(data_df[\"timestamp\"]).astype(\"int64\") // 1000000\n", "data_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To upload the data to the API we need to convert the dataframe into a dictionary on the format {column: values}.\n", "Fortunately, dataframes have a method to do just that, called `to_dict()`.\n", "It has an argument for changing the orientation of the resulting dictionary.\n", "By using `to_dict(\"list\")` we will get it on the correct format.\n", "\n", ".. note:: The dictionary must have a `\"timestamp\"` key and at least one more column, corresponding to a tracked variable. The `to_dict(\"list\")` method will not include the index, so it is important to have the timestamp as a column in the data and not as an index." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "data_dict = data_df.to_dict(\"list\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that the data has been converted to the correct format we are ready to create the source and upload some measurements.\n", "When creating a tabular source, you must define the columns that the data will contain ahead of time.\n", "This is to prevent arbitrary data to be uploaded to the source.\n", "Besides the columns argument, creating a tabular source works the exact same way as creating a waveform source." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "session.create_tabular_source(\n", " sid=\"heatpump\",\n", " meta={\"location\": \"Molnlycke\"},\n", " columns=data_df.columns.tolist()\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can check that the source was created correctly by getting the source information." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'meta': {'location': 'Molnlycke'},\n", " 'properties': {'columns': ['T_out',\n", " 'T_down',\n", " 'T_kitchen',\n", " 'WATT',\n", " 'T_up',\n", " 'T_brine_in',\n", " 'T_brine_out',\n", " 'T_cellar'],\n", " 'data_class': 'tabular'},\n", " 'source_id': 'heatpump'}\n" ] } ], "source": [ "source = session.get_source(\"heatpump\")\n", "pprint(source)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's upload the data we prepared to the source" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "session.create_tabular_measurement(\n", " sid=\"heatpump\",\n", " data=data_dict\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have a source with measurements we can request an analysis of the data from the server.\n", "Requesting analyses for tabular data works the same way as it does for waveform data.\n", "Although not all features will work for tabular data.\n", "The KPIDemo feature for example will only work with waveform data.\n", "Another thing to keep in mind is that when requesting population analyses, all sources to be analyzed simultaneously must have the same data class.\n", "\n", "For now, we will simply run the mode identification algorithm." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "response = session.request_analysis(\n", " sid=\"heatpump\",\n", " feature=\"ModeId\",\n", ")\n", "request_id = response[\"request_id\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make the results easier to work with we can load them into an analysis class.\n", "There exists one for each analysis feature.\n", "Once the analysis is completed and we have received the results, we can instatiate the analysis class with the results dictionary." ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "tags": [] }, "outputs": [], "source": [ "session.wait_for_analyses([request_id])\n", "results = session.get_analysis_results(request_id)\n", "analysis = ModeId(results)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The analysis class makes it very convenient to view the results.\n", "Let's use the `ModeId.plot()` method to get an overview of the results." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": "
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\n" }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "data": { "text/plain": "''" }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "analysis.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The different modes help to identify between idle and have high-usage conditions along all seasons of the year.\n", "\n", "When you are finished with the example you can go ahead and delete the source" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "pycharm": { "name": "#%%\n" } }, "outputs": [], "source": [ "session.delete_source(\"heatpump\")\n" ] } ], "metadata": { "celltoolbar": "Tags", "interpreter": { "hash": "ee585b89b750dc1146ba89a2fe8627247123d548cada017644b010d09cffe3df" }, "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 2 }