2020-11-09 18:42:20 +01:00
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RAPIDS
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Add New Features
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Setup
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Example Workflows
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Example Workflows
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Minimal
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Behavioral Features
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Behavioral Features
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Introduction
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Phone
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Phone
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Phone Data Quality
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Phone Accelerometer
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Phone Activity Recognition
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Phone Applications Foreground
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Phone Battery
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Phone Bluetooth
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Phone Calls
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Phone Conversation
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Phone Light
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Phone Locations
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Phone Messages
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Phone Screen
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Phone WiFI Connected
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Phone WiFI Visible
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Add New Features
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Add New Features
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Table of contents
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New Features for Existing Sensors
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Modify the config.yaml file
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Create a provider folder, script and function
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Implement your feature extraction code
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New Features for Non-Existing Sensors
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Developers
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Developers
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Remote Support
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Virtual Environments
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Documentation
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< svg xmlns = "http://www.w3.org/2000/svg" viewBox = "0 0 24 24" > < path d = "M20.71 7.04c.39-.39.39-1.04 0-1.41l-2.34-2.34c-.37-.39-1.02-.39-1.41 0l-1.84 1.83 3.75 3.75M3 17.25V21h3.75L17.81 9.93l-3.75-3.75L3 17.25z" / > < / svg >
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< h1 id = "add-new-features" > Add New Features< a class = "headerlink" href = "#add-new-features" title = "Permanent link" > ¶ < / a > < / h1 >
< div class = "admonition hint" >
< p class = "admonition-title" > Hint< / p >
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< p > We recommend reading the < a href = "../feature-introduction/" > Behavioral Features Introduction< / a > before reading this page< / p >
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< / div >
< div class = "admonition hint" >
< p class = "admonition-title" > Hint< / p >
< p > You won’ t have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process.< / p >
< / div >
< h2 id = "new-features-for-existing-sensors" > New Features for Existing Sensors< a class = "headerlink" href = "#new-features-for-existing-sensors" title = "Permanent link" > ¶ < / a > < / h2 >
< p > You can add new features to any existing sensors (see list below) by adding a new provider in three steps:< / p >
< ol >
< li > < a href = "#modify-the-configyaml-file" > Modify< / a > the < code > config.yaml< / code > file < / li >
< li > < a href = "#create-a-provider-folder-script-and-function" > Create< / a > a provider folder, script and function< / li >
< li > < a href = "#implement-your-feature-extraction-code" > Implement< / a > your features extraction code< / li >
< / ol >
< p > As a tutorial, we will add a new provider for < code > PHONE_ACCELEROMETER< / code > called < code > VEGA< / code > that extracts < code > feature1< / code > , < code > feature2< / code > , < code > feature3< / code > in Python and that it requires a parameter from the user called < code > MY_PARAMETER< / code > .< / p >
< details class = "info" > < summary > Existing Sensors< / summary > < p > An existing sensor is any of the phone or Fitbit sensors with a configuration entry in < code > config.yaml< / code > :< / p >
< ul >
< li > Phone Accelerometer< / li >
< li > Phone Activity Recognition< / li >
< li > Phone Applications Foreground< / li >
< li > Phone Battery< / li >
< li > Phone Bluetooth< / li >
< li > Phone Calls< / li >
< li > Phone Conversation< / li >
< li > Phone Light< / li >
< li > Phone Locations< / li >
< li > Phone Messages< / li >
< li > Phone Screen< / li >
< li > Phone WiFI Connected< / li >
< li > Phone WiFI Visible< / li >
< / ul >
< / details >
< h3 id = "modify-the-configyaml-file" > Modify the < code > config.yaml< / code > file< a class = "headerlink" href = "#modify-the-configyaml-file" title = "Permanent link" > ¶ < / a > < / h3 >
< p > In this step you need to add your provider configuration section under the relevant sensor in < code > config.yaml< / code > . See our example for our tutorial’ s < code > VEGA< / code > provider for < code > PHONE_ACCELEROMETER< / code > :< / p >
< details class = "example" > < summary > Example configuration for a new accelerometer provider < code > VEGA< / code > < / summary > < div class = "highlight" > < pre > < span > < / span > < code > < span class = "nt" > PHONE_ACCELEROMETER< / span > < span class = "p" > :< / span >
< span class = "nt" > TABLE< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > accelerometer< / span >
< span class = "nt" > PROVIDERS< / span > < span class = "p" > :< / span >
< span class = "nt" > RAPIDS< / span > < span class = "p" > :< / span >
< span class = "nt" > COMPUTE< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > False< / span >
< span class = "l l-Scalar l-Scalar-Plain" > ...< / span >
< span class = "nt" > PANDA< / span > < span class = "p" > :< / span >
< span class = "nt" > COMPUTE< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > False< / span >
< span class = "l l-Scalar l-Scalar-Plain" > ...< / span >
< span class = "nt" > VEGA< / span > < span class = "p" > :< / span >
< span class = "nt" > COMPUTE< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > False< / span >
< span class = "nt" > FEATURES< / span > < span class = "p" > :< / span > < span class = "p p-Indicator" > [< / span > < span class = "s" > " feature1" < / span > < span class = "p p-Indicator" > ,< / span > < span class = "s" > " feature2" < / span > < span class = "p p-Indicator" > ,< / span > < span class = "s" > " feature3" < / span > < span class = "p p-Indicator" > ]< / span >
< span class = "nt" > MY_PARAMTER< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > a_string< / span >
< span class = "nt" > SRC_FOLDER< / span > < span class = "p" > :< / span > < span class = "s" > " vega" < / span >
< span class = "nt" > SRC_LANGUAGE< / span > < span class = "p" > :< / span > < span class = "s" > " python" < / span >
< / code > < / pre > < / div >
< / details >
< table >
< thead >
< tr >
< th > Key < / th >
< th > Description< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > < code > [COMPUTE]< / code > < / td >
< td > Flag to activate/deactivate your provider< / td >
< / tr >
< tr >
< td > < code > [FEATURES]< / code > < / td >
< td > List of features your provider supports. Your provider code should only return the features on this list< / td >
< / tr >
< tr >
< td > < code > [MY_PARAMTER]< / code > < / td >
< td > An arbitrary parameter that our example provider < code > VEGA< / code > needs. This can be a boolean, integer, float, string or an array of any of such types.< / td >
< / tr >
< tr >
< td > < code > [SRC_LANGUAGE]< / code > < / td >
< td > The programming language of your provider script, it can be < code > python< / code > or < code > r< / code > , in our example < code > python< / code > < / td >
< / tr >
< tr >
< td > < code > [SRC_FOLDER]< / code > < / td >
< td > The name of your provider in lower case, in our example < code > vega< / code > (this will be the name of your folder in the next step)< / td >
< / tr >
< / tbody >
< / table >
< h3 id = "create-a-provider-folder-script-and-function" > Create a provider folder, script and function< a class = "headerlink" href = "#create-a-provider-folder-script-and-function" title = "Permanent link" > ¶ < / a > < / h3 >
< p > In this step you need to add a folder, script and function for your provider.< / p >
< ol >
< li > Create your provider < strong > folder< / strong > under < code > src/feature/DEVICE_SENSOR/YOUR_PROVIDER< / code > , in our example < code > src/feature/phone_accelerometer/vega< / code > (same as < code > [SRC_FOLDER]< / code > in the step above).< / li >
< li > Create your provider < strong > script< / strong > inside your provider folder, it can be a Python file called < code > main.py< / code > or an R file called < code > main.R< / code > .< / li >
< li >
< p > Add your provider < strong > function< / strong > in your provider script. The name of such function should be < code > [providername]_features< / code > , in our example < code > vega_features< / code > < / p >
< div class = "admonition info" >
< p class = "admonition-title" > Python function< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > def< / span > < span class = "p" > [< / span > < span class = "n" > providername< / span > < span class = "p" > ]< / span > < span class = "n" > _features< / span > < span class = "p" > (< / span > < span class = "n" > sensor_data_files< / span > < span class = "p" > ,< / span > < span class = "n" > day_segment< / span > < span class = "p" > ,< / span > < span class = "n" > provider< / span > < span class = "p" > ,< / span > < span class = "n" > filter_data_by_segment< / span > < span class = "p" > ,< / span > < span class = "o" > *< / span > < span class = "n" > args< / span > < span class = "p" > ,< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< / code > < / pre > < / div >
< / div >
< div class = "admonition info" >
< p class = "admonition-title" > R function< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "p" > [< / span > < span class = "n" > providername< / span > < span class = "p" > ]< / span > _< span class = "n" > features< / span > < span class = "o" > < -< / span > < span class = "nf" > function< / span > < span class = "p" > (< / span > < span class = "n" > sensor_data< / span > < span class = "p" > ,< / span > < span class = "n" > day_segment< / span > < span class = "p" > ,< / span > < span class = "n" > provider< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div >
< / div >
< / li >
< / ol >
< h3 id = "implement-your-feature-extraction-code" > Implement your feature extraction code< a class = "headerlink" href = "#implement-your-feature-extraction-code" title = "Permanent link" > ¶ < / a > < / h3 >
< p > The provider function that you created in the step above will receive the following parameters:< / p >
< table >
< thead >
< tr >
< th > Parameter < / th >
< th > Description< / th >
< / tr >
< / thead >
< tbody >
< tr >
< td > < code > sensor_data_files< / code > < / td >
< td > Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the < code > [PIDS]< / code > array in < code > config.yaml< / code > )< / td >
< / tr >
< tr >
< td > < code > day_segment< / code > < / td >
< td > The label of the day segment that should be processed.< / td >
< / tr >
< tr >
< td > < code > provider< / code > < / td >
< td > The parameters you configured for your provider in < code > config.yaml< / code > will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains < code > {MY_PARAMETER:"a_string"}< / code > < / td >
< / tr >
< tr >
< td > < code > filter_data_by_segment< / code > < / td >
< td > Python only. A function that you will use to filter your data. In R this function is already available in the environment.< / td >
< / tr >
< tr >
< td > < code > *args< / code > < / td >
< td > Python only. Not used for now< / td >
< / tr >
< tr >
< td > < code > **kwargs< / code > < / td >
< td > Python only. Not used for now< / td >
< / tr >
< / tbody >
< / table >
< p > The code to extract your behavioral features should be implemented in your provider function and in general terms it will have three stages:< / p >
< details class = "info" > < summary > 1. Read a participant’ s data by loading the CSV data stored in the file pointed by < code > sensor_data_files< / code > < / summary > < div class = "highlight" > < pre > < span > < / span > < code > < span class = "n" > acc_data< / span > < span class = "o" > =< / span > < span class = "n" > pd< / span > < span class = "o" > .< / span > < span class = "n" > read_csv< / span > < span class = "p" > (< / span > < span class = "n" > sensor_data_files< / span > < span class = "p" > [< / span > < span class = "s2" > " sensor_data" < / span > < span class = "p" > ])< / span >
< / code > < / pre > < / div >
< p > Note that phone’ s battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on)< / p >
< / details >
< details class = "info" > < summary > 2. Filter your data to process only those rows that belong to < code > day_segment< / code > < / summary > < p > This step is only one line of code, but to undersand why we need it, keep reading.
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "n" > acc_data< / span > < span class = "o" > =< / span > < span class = "n" > filter_data_by_segment< / span > < span class = "p" > (< / span > < span class = "n" > acc_data< / span > < span class = "p" > ,< / span > < span class = "n" > day_segment< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div > < / p >
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< p > You should use the < code > filter_data_by_segment()< / code > function to process and group those rows that belong to each of the < a href = "../../setup/configuration/#day-segments" > day segments RAPIDS could be configured with< / a > .< / p >
< p > Let’ s understand the < code > filter_data_by_segment()< / code > function with an example. A RAPIDS user can extract features on any arbitrary < a href = "../../setup/configuration/#day-segments" > day segment< / a > . A day segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for < code > p01< / code > . The labels are arbritrary and the instances depend on the days a participant was monitored for: < / p >
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< ul >
< li > the daily segment could be named < code > my_days< / code > and if < code > p01< / code > was monitored for 14 days, it would have 14 instances< / li >
< li > the weekly segment could be named < code > my_weeks< / code > and if < code > p01< / code > was monitored for 14 days, it would have 2 instances.< / li >
< li > the weekend segment could be named < code > my_weekends< / code > and if < code > p01< / code > was monitored for 14 days, it would have 2 instances.< / li >
< / ul >
< p > For this example, RAPIDS will call your provider function three times for < code > p01< / code > , once where < code > day_segment< / code > is < code > my_days< / code > , once where < code > day_segment< / code > is < code > my_weeks< / code > and once where < code > day_segment< / code > is < code > my_weekends< / code > . In this example not every row in < code > p01< / code > ‘ s data needs to take part in the feature computation for either segment < strong > and< / strong > the rows need to be grouped differently. < / p >
< p > Thus < code > filter_data_by_segment()< / code > comes in handy, it will return a data frame that contains the rows that were logged during a day segment plus an extra column called < code > local_segment< / code > . This new column will have as many unique values as day segment instances exist (14, 2, and 2 for our < code > p01< / code > ‘ s < code > my_days< / code > , < code > my_weeks< / code > , and < code > my_weekends< / code > examples). After filtering, < strong > you should group the data frame by this column and compute any desired features< / strong > , for example:< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_maxmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > ()< / span >
< / code > < / pre > < / div >
< p > The reason RAPIDS does not filter the participant’ s data set for you is because your code might need to compute something based on a participant’ s complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number.< / p >
< / details >
< details class = "info" > < summary > 3. Return a data frame with your features< / summary > < p > After filtering, grouping your data, and computing your features, your provider function should return a data frame that has:< / p >
< ul >
< li > One row per day segment instance (e.g. 14 our < code > p01< / code > ‘ s < code > my_days< / code > example)< / li >
< li > The < code > local_segment< / code > column added by < code > filter_data_by_segment()< / code > < / li >
< li > One column per feature. Your feature columns should be named < code > SENSOR_PROVIDER_FEATURE< / code > , for example < code > accelerometr_vega_feature1< / code > < / li >
< / ul >
< / details >
< details class = "example" > < summary > < code > PHONE_ACCELEROMETER< / code > Provider Example< / summary > < p > For your reference, this a short example of our own provider (< code > RAPIDS< / code > ) for < code > PHONE_ACCELEROMETER< / code > that computes five acceleration features< / p >
< div class = "highlight" > < pre > < span > < / span > < code > < span class = "k" > def< / span > < span class = "nf" > rapids_features< / span > < span class = "p" > (< / span > < span class = "n" > sensor_data_files< / span > < span class = "p" > ,< / span > < span class = "n" > day_segment< / span > < span class = "p" > ,< / span > < span class = "n" > provider< / span > < span class = "p" > ,< / span > < span class = "n" > filter_data_by_segment< / span > < span class = "p" > ,< / span > < span class = "o" > *< / span > < span class = "n" > args< / span > < span class = "p" > ,< / span > < span class = "o" > **< / span > < span class = "n" > kwargs< / span > < span class = "p" > ):< / span >
< span class = "n" > acc_data< / span > < span class = "o" > =< / span > < span class = "n" > pd< / span > < span class = "o" > .< / span > < span class = "n" > read_csv< / span > < span class = "p" > (< / span > < span class = "n" > sensor_data_files< / span > < span class = "p" > [< / span > < span class = "s2" > " sensor_data" < / span > < span class = "p" > ])< / span >
< span class = "n" > requested_features< / span > < span class = "o" > =< / span > < span class = "n" > provider< / span > < span class = "p" > [< / span > < span class = "s2" > " FEATURES" < / span > < span class = "p" > ]< / span >
< span class = "c1" > # name of the features this function can compute< / span >
< span class = "n" > base_features_names< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s2" > " maxmagnitude" < / span > < span class = "p" > ,< / span > < span class = "s2" > " minmagnitude" < / span > < span class = "p" > ,< / span > < span class = "s2" > " avgmagnitude" < / span > < span class = "p" > ,< / span > < span class = "s2" > " medianmagnitude" < / span > < span class = "p" > ,< / span > < span class = "s2" > " stdmagnitude" < / span > < span class = "p" > ]< / span >
< span class = "c1" > # the subset of requested features this function can compute< / span >
< span class = "n" > features_to_compute< / span > < span class = "o" > =< / span > < span class = "nb" > list< / span > < span class = "p" > (< / span > < span class = "nb" > set< / span > < span class = "p" > (< / span > < span class = "n" > requested_features< / span > < span class = "p" > )< / span > < span class = "o" > & < / span > < span class = "nb" > set< / span > < span class = "p" > (< / span > < span class = "n" > base_features_names< / span > < span class = "p" > ))< / span >
< span class = "n" > acc_features< / span > < span class = "o" > =< / span > < span class = "n" > pd< / span > < span class = "o" > .< / span > < span class = "n" > DataFrame< / span > < span class = "p" > (< / span > < span class = "n" > columns< / span > < span class = "o" > =< / span > < span class = "p" > [< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ]< / span > < span class = "o" > +< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_" < / span > < span class = "o" > +< / span > < span class = "n" > x< / span > < span class = "k" > for< / span > < span class = "n" > x< / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > ])< / span >
< span class = "k" > if< / span > < span class = "ow" > not< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > empty< / span > < span class = "p" > :< / span >
< span class = "n" > acc_data< / span > < span class = "o" > =< / span > < span class = "n" > filter_data_by_segment< / span > < span class = "p" > (< / span > < span class = "n" > acc_data< / span > < span class = "p" > ,< / span > < span class = "n" > day_segment< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "ow" > not< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > empty< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "o" > =< / span > < span class = "n" > pd< / span > < span class = "o" > .< / span > < span class = "n" > DataFrame< / span > < span class = "p" > ()< / span >
< span class = "c1" > # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2)< / span >
< span class = "n" > magnitude< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > apply< / span > < span class = "p" > (< / span > < span class = "k" > lambda< / span > < span class = "n" > row< / span > < span class = "p" > :< / span > < span class = "n" > np< / span > < span class = "o" > .< / span > < span class = "n" > sqrt< / span > < span class = "p" > (< / span > < span class = "n" > row< / span > < span class = "p" > [< / span > < span class = "s2" > " double_values_0" < / span > < span class = "p" > ]< / span > < span class = "o" > **< / span > < span class = "mi" > 2< / span > < span class = "o" > +< / span > < span class = "n" > row< / span > < span class = "p" > [< / span > < span class = "s2" > " double_values_1" < / span > < span class = "p" > ]< / span > < span class = "o" > **< / span > < span class = "mi" > 2< / span > < span class = "o" > +< / span > < span class = "n" > row< / span > < span class = "p" > [< / span > < span class = "s2" > " double_values_2" < / span > < span class = "p" > ]< / span > < span class = "o" > **< / span > < span class = "mi" > 2< / span > < span class = "p" > ),< / span > < span class = "n" > axis< / span > < span class = "o" > =< / span > < span class = "mi" > 1< / span > < span class = "p" > )< / span >
< span class = "n" > acc_data< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > assign< / span > < span class = "p" > (< / span > < span class = "n" > magnitude< / span > < span class = "o" > =< / span > < span class = "n" > magnitude< / span > < span class = "o" > .< / span > < span class = "n" > values< / span > < span class = "p" > )< / span >
< span class = "k" > if< / span > < span class = "s2" > " maxmagnitude" < / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_maxmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > max< / span > < span class = "p" > ()< / span >
< span class = "k" > if< / span > < span class = "s2" > " minmagnitude" < / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_minmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > min< / span > < span class = "p" > ()< / span >
< span class = "k" > if< / span > < span class = "s2" > " avgmagnitude" < / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_avgmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > mean< / span > < span class = "p" > ()< / span >
< span class = "k" > if< / span > < span class = "s2" > " medianmagnitude" < / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_medianmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > median< / span > < span class = "p" > ()< / span >
< span class = "k" > if< / span > < span class = "s2" > " stdmagnitude" < / span > < span class = "ow" > in< / span > < span class = "n" > features_to_compute< / span > < span class = "p" > :< / span >
< span class = "n" > acc_features< / span > < span class = "p" > [< / span > < span class = "s2" > " acc_rapids_stdmagnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > =< / span > < span class = "n" > acc_data< / span > < span class = "o" > .< / span > < span class = "n" > groupby< / span > < span class = "p" > ([< / span > < span class = "s2" > " local_segment" < / span > < span class = "p" > ])[< / span > < span class = "s2" > " magnitude" < / span > < span class = "p" > ]< / span > < span class = "o" > .< / span > < span class = "n" > std< / span > < span class = "p" > ()< / span >
< span class = "n" > acc_features< / span > < span class = "o" > =< / span > < span class = "n" > acc_features< / span > < span class = "o" > .< / span > < span class = "n" > reset_index< / span > < span class = "p" > ()< / span >
< span class = "k" > return< / span > < span class = "n" > acc_features< / span >
< / code > < / pre > < / div >
< / details >
< h2 id = "new-features-for-non-existing-sensors" > New Features for Non-Existing Sensors< a class = "headerlink" href = "#new-features-for-non-existing-sensors" title = "Permanent link" > ¶ < / a > < / h2 >
2020-11-09 19:24:54 +01:00
< p > If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the < code > "Existing Sensors"< / code > list above), < a href = "../../team" > contact us< / a > or request it on < a href = "http://awareframework.com:3000/" > Slack< / a > and we can add the necessary code so you can follow the instructions above.< / p >
2020-11-09 18:42:20 +01:00
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