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RAPIDS
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Add New Features
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Initializing search
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RAPIDS
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Setup
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Setup
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Overview
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< li class = "md-nav__item" >
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< a href = "../../workflow-examples/minimal/" class = "md-nav__link" >
Minimal Example
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< li class = "md-nav__item" >
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Installation
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< li class = "md-nav__item" >
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Configuration
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< li class = "md-nav__item" >
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Execution
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Citation
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< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_3" type = "checkbox" id = "__nav_3" >
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Data Streams
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< / label >
< nav class = "md-nav" aria-label = "Data Streams" data-md-level = "1" >
< label class = "md-nav__title" for = "__nav_3" >
< span class = "md-nav__icon md-icon" > < / span >
Data Streams
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../datastreams/data-streams-introduction/" class = "md-nav__link" >
Introduction
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< / li >
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< label class = "md-nav__link" for = "__nav_3_2" >
Phone
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< span class = "md-nav__icon md-icon" > < / span >
< / label >
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< nav class = "md-nav" aria-label = "Phone" data-md-level = "2" >
< label class = "md-nav__title" for = "__nav_3_2" >
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< span class = "md-nav__icon md-icon" > < / span >
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Phone
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< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../../datastreams/aware-mysql/" class = "md-nav__link" >
aware_mysql
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< / li >
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< li class = "md-nav__item" >
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< a href = "../../datastreams/aware-csv/" class = "md-nav__link" >
aware_csv
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< / a >
< / li >
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< li class = "md-nav__item" >
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< a href = "../../datastreams/aware-influxdb/" class = "md-nav__link" >
aware_influxdb (beta)
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/mandatory-phone-format/" class = "md-nav__link" >
Mandatory Phone Format
< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_3_3" type = "checkbox" id = "__nav_3_3" >
< label class = "md-nav__link" for = "__nav_3_3" >
Fitbit
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< / label >
< nav class = "md-nav" aria-label = "Fitbit" data-md-level = "2" >
< label class = "md-nav__title" for = "__nav_3_3" >
< span class = "md-nav__icon md-icon" > < / span >
Fitbit
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../datastreams/fitbitjson-mysql/" class = "md-nav__link" >
fitbitjson_mysql
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< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/fitbitjson-csv/" class = "md-nav__link" >
fitbitjson_csv
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< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/fitbitparsed-mysql/" class = "md-nav__link" >
fitbitparsed_mysql
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< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/fitbitparsed-csv/" class = "md-nav__link" >
fitbitparsed_csv
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/mandatory-fitbit-format/" class = "md-nav__link" >
Mandatory Fitbit Format
< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_3_4" type = "checkbox" id = "__nav_3_4" >
< label class = "md-nav__link" for = "__nav_3_4" >
Empatica
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< / label >
< nav class = "md-nav" aria-label = "Empatica" data-md-level = "2" >
< label class = "md-nav__title" for = "__nav_3_4" >
< span class = "md-nav__icon md-icon" > < / span >
Empatica
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< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../datastreams/empatica-zip/" class = "md-nav__link" >
empatica_zip
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< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/mandatory-empatica-format/" class = "md-nav__link" >
Mandatory Empatica Format
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< / ul >
< / nav >
< / li >
< li class = "md-nav__item" >
< a href = "../../datastreams/add-new-data-streams/" class = "md-nav__link" >
Add New Data Streams
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< / a >
< / li >
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< / ul >
< / nav >
< / li >
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< li class = "md-nav__item md-nav__item--active md-nav__item--section md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_4" type = "checkbox" id = "__nav_4" checked >
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< label class = "md-nav__link" for = "__nav_4" >
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Behavioral Features
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Behavioral Features" data-md-level = "1" >
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< label class = "md-nav__title" for = "__nav_4" >
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< span class = "md-nav__icon md-icon" > < / span >
Behavioral Features
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../feature-introduction/" class = "md-nav__link" >
Introduction
< / a >
< / li >
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< li class = "md-nav__item md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_4_2" type = "checkbox" id = "__nav_4_2" >
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< label class = "md-nav__link" for = "__nav_4_2" >
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Phone
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Phone" data-md-level = "2" >
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< label class = "md-nav__title" for = "__nav_4_2" >
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< span class = "md-nav__icon md-icon" > < / span >
Phone
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../phone-accelerometer/" class = "md-nav__link" >
Phone Accelerometer
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-activity-recognition/" class = "md-nav__link" >
Phone Activity Recognition
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-applications-crashes/" class = "md-nav__link" >
Phone Applications Crashes
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-applications-foreground/" class = "md-nav__link" >
Phone Applications Foreground
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-applications-notifications/" class = "md-nav__link" >
Phone Applications Notifications
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-battery/" class = "md-nav__link" >
Phone Battery
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-bluetooth/" class = "md-nav__link" >
Phone Bluetooth
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-calls/" class = "md-nav__link" >
Phone Calls
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-conversation/" class = "md-nav__link" >
Phone Conversation
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-data-yield/" class = "md-nav__link" >
Phone Data Yield
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-keyboard/" class = "md-nav__link" >
Phone Keyboard
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-light/" class = "md-nav__link" >
Phone Light
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-locations/" class = "md-nav__link" >
Phone Locations
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-log/" class = "md-nav__link" >
Phone Log
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-messages/" class = "md-nav__link" >
Phone Messages
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-screen/" class = "md-nav__link" >
Phone Screen
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-wifi-connected/" class = "md-nav__link" >
Phone WiFI Connected
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../phone-wifi-visible/" class = "md-nav__link" >
Phone WiFI Visible
< / a >
< / li >
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< / ul >
< / nav >
< / li >
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< li class = "md-nav__item md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_4_3" type = "checkbox" id = "__nav_4_3" >
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< label class = "md-nav__link" for = "__nav_4_3" >
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Fitbit
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Fitbit" data-md-level = "2" >
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< label class = "md-nav__title" for = "__nav_4_3" >
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< span class = "md-nav__icon md-icon" > < / span >
Fitbit
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../fitbit-data-yield/" class = "md-nav__link" >
Fitbit Data Yield
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-heartrate-summary/" class = "md-nav__link" >
Fitbit Heart Rate Summary
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-heartrate-intraday/" class = "md-nav__link" >
Fitbit Heart Rate Intraday
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-sleep-summary/" class = "md-nav__link" >
Fitbit Sleep Summary
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-sleep-intraday/" class = "md-nav__link" >
Fitbit Sleep Intraday
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-steps-summary/" class = "md-nav__link" >
Fitbit Steps Summary
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../fitbit-steps-intraday/" class = "md-nav__link" >
Fitbit Steps Intraday
< / a >
< / li >
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< / ul >
< / nav >
< / li >
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< li class = "md-nav__item md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_4_4" type = "checkbox" id = "__nav_4_4" >
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< label class = "md-nav__link" for = "__nav_4_4" >
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Empatica
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Empatica" data-md-level = "2" >
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< label class = "md-nav__title" for = "__nav_4_4" >
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< span class = "md-nav__icon md-icon" > < / span >
Empatica
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../empatica-accelerometer/" class = "md-nav__link" >
Empatica Accelerometer
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-heartrate/" class = "md-nav__link" >
Empatica Heart Rate
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-temperature/" class = "md-nav__link" >
Empatica Temperature
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-electrodermal-activity/" class = "md-nav__link" >
Empatica Electrodermal Activity
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-blood-volume-pulse/" class = "md-nav__link" >
Empatica Blood Volume Pulse
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-inter-beat-interval/" class = "md-nav__link" >
Empatica Inter Beat Interval
< / a >
< / li >
< li class = "md-nav__item" >
< a href = "../empatica-tags/" class = "md-nav__link" >
Empatica Tags
< / a >
< / li >
< / ul >
< / nav >
< / li >
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< li class = "md-nav__item md-nav__item--active" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "toc" type = "checkbox" id = "__toc" >
< label class = "md-nav__link md-nav__link--active" for = "__toc" >
Add New Features
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< a href = "./" class = "md-nav__link md-nav__link--active" >
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Add New Features
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< / a >
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< nav class = "md-nav md-nav--secondary" aria-label = "Table of contents" >
< label class = "md-nav__title" for = "__toc" >
< span class = "md-nav__icon md-icon" > < / span >
Table of contents
< / label >
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< ul class = "md-nav__list" data-md-component = "toc" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "#new-features-for-existing-sensors" class = "md-nav__link" >
New Features for Existing Sensors
< / a >
< nav class = "md-nav" aria-label = "New Features for Existing Sensors" >
< ul class = "md-nav__list" >
< li class = "md-nav__item" >
< a href = "#modify-the-configyaml-file" class = "md-nav__link" >
Modify the config.yaml file
< / a >
< / li >
< li class = "md-nav__item" >
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< a href = "#create-a-feature-provider-script" class = "md-nav__link" >
Create a feature provider script
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< / a >
< / li >
< li class = "md-nav__item" >
< a href = "#implement-your-feature-extraction-code" class = "md-nav__link" >
Implement your feature extraction code
< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item" >
< a href = "#new-features-for-non-existing-sensors" class = "md-nav__link" >
New Features for Non-Existing Sensors
< / a >
< / li >
< / ul >
< / nav >
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< / li >
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< / ul >
< / nav >
< / li >
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2021-02-09 20:54:36 +01:00
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_5" type = "checkbox" id = "__nav_5" >
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< label class = "md-nav__link" for = "__nav_5" >
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Visualizations
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Visualizations" data-md-level = "1" >
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< label class = "md-nav__title" for = "__nav_5" >
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< span class = "md-nav__icon md-icon" > < / span >
Visualizations
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../../visualizations/data-quality-visualizations/" class = "md-nav__link" >
Data Quality
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../../visualizations/feature-visualizations/" class = "md-nav__link" >
Features
< / a >
< / li >
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< / ul >
< / nav >
< / li >
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< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
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< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_6" type = "checkbox" id = "__nav_6" >
< label class = "md-nav__link" for = "__nav_6" >
Analysis Workflows
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Analysis Workflows" data-md-level = "1" >
< label class = "md-nav__title" for = "__nav_6" >
< span class = "md-nav__icon md-icon" > < / span >
Analysis Workflows
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
< li class = "md-nav__item" >
< a href = "../../workflow-examples/analysis/" class = "md-nav__link" >
Complete Example
< / a >
< / li >
< / ul >
< / nav >
< / li >
< li class = "md-nav__item md-nav__item--section md-nav__item--nested" >
< input class = "md-nav__toggle md-toggle" data-md-toggle = "__nav_7" type = "checkbox" id = "__nav_7" >
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< label class = "md-nav__link" for = "__nav_7" >
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Developers
< span class = "md-nav__icon md-icon" > < / span >
< / label >
< nav class = "md-nav" aria-label = "Developers" data-md-level = "1" >
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< label class = "md-nav__title" for = "__nav_7" >
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< span class = "md-nav__icon md-icon" > < / span >
Developers
< / label >
< ul class = "md-nav__list" data-md-scrollfix >
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< li class = "md-nav__item" >
< a href = "../../developers/git-flow/" class = "md-nav__link" >
Git Flow
< / a >
< / li >
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2020-12-08 03:12:19 +01:00
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< li class = "md-nav__item" >
< a href = "../../developers/remote-support/" class = "md-nav__link" >
Remote Support
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../../developers/virtual-environments/" class = "md-nav__link" >
Virtual Environments
< / a >
< / li >
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< li class = "md-nav__item" >
< a href = "../../developers/documentation/" class = "md-nav__link" >
Documentation
< / a >
< / li >
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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 >
< / a >
< 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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< ul >
< li > We recommend reading the < a href = "../feature-introduction/" > Behavioral Features Introduction< / a > before reading this page.< / li >
< li > You can implement new features in Python or R scripts.< / li >
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< li > 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 are ready to process.< / li >
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< / ul >
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< / 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 >
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< li > < a href = "#create-a-feature-provider-script" > Create< / a > your feature provider script< / li >
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< li > < a href = "#implement-your-feature-extraction-code" > Implement< / a > your features extraction code< / li >
< / ol >
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< 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 > with a Python script that requires a parameter from the user called < code > MY_PARAMETER< / code > .< / p >
< details class = "info" > < summary > Existing Sensors< / summary > < p > An existing sensor of any device with a configuration entry in < code > config.yaml< / code > :< / p >
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< p > Smartphone (AWARE)< / p >
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< ul >
< li > Phone Accelerometer< / li >
< li > Phone Activity Recognition< / li >
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< li > Phone Applications Crashes< / li >
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< li > Phone Applications Foreground< / li >
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< li > Phone Applications Notifications< / li >
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< li > Phone Battery< / li >
< li > Phone Bluetooth< / li >
< li > Phone Calls< / li >
< li > Phone Conversation< / li >
< li > Phone Data Yield< / li >
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< li > Phone Keyboard< / li >
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< li > Phone Light< / li >
< li > Phone Locations< / li >
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< li > Phone Log< / li >
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< li > Phone Messages< / li >
< li > Phone Screen< / li >
< li > Phone WiFI Connected< / li >
< li > Phone WiFI Visible< / li >
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< / ul >
< p > Fitbit< / p >
< ul >
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< li > Fitbit Data Yield< / li >
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< li > Fitbit Heart Rate Summary< / li >
< li > Fitbit Heart Rate Intraday< / li >
< li > Fitbit Sleep Summary< / li >
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< li > Fitbit Sleep Intraday< / li >
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< li > Fitbit Steps Summary< / li >
< li > Fitbit Steps Intraday< / li >
< / ul >
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< p > Empatica< / p >
< ul >
< li > Empatica Accelerometer< / li >
< li > Empatica Heart Rate< / li >
< li > Empatica Temperature< / li >
< li > Empatica Electrodermal Activity< / li >
< li > Empatica Blood Volume Pulse< / li >
< li > Empatica Inter Beat Interval< / li >
< li > Empatica Tags< / li >
< / ul >
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< / 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 >
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< 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 >
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< 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 >
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< span class = "nt" > CONTAINER< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > accelerometer< / span >
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< span class = "nt" > PROVIDERS< / span > < span class = "p" > :< / span >
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< span class = "nt" > RAPIDS< / span > < span class = "p" > :< / span > < span class = "c1" > # this is a feature provider< / span >
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< 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 >
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< span class = "nt" > PANDA< / span > < span class = "p" > :< / span > < span class = "c1" > # this is another feature provider< / span >
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< 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 >
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< span class = "hll" > < span class = "nt" > VEGA< / span > < span class = "p" > :< / span > < span class = "c1" > # this is our new feature provider< / span >
< / span > < span class = "hll" > < span class = "nt" > COMPUTE< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > False< / span >
< / span > < span class = "hll" > < 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 > < span class = "hll" > < span class = "nt" > MY_PARAMTER< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > a_string< / span >
< / span > < span class = "hll" > < span class = "nt" > SRC_SCRIPT< / span > < span class = "p" > :< / span > < span class = "l l-Scalar l-Scalar-Plain" > src/features/phone_accelerometer/vega/main.py< / span >
< / span > < / code > < / pre > < / div >
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< / 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 >
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< 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 >
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< / tr >
< tr >
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< td > < code > [SRC_SCRIPT]< / code > < / td >
< td > The relative path from RAPIDS’ root folder to an script that computes the features for this provider. It can be implemented in R or Python.< / td >
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< / tr >
< / tbody >
< / table >
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< h3 id = "create-a-feature-provider-script" > Create a feature provider script< a class = "headerlink" href = "#create-a-feature-provider-script" title = "Permanent link" > ¶ < / a > < / h3 >
< p > Create your feature Python or R script called < code > main.py< / code > or < code > main.R< / code > in the correct folder, < code > src/feature/[sensorname]/[providername]/< / code > . RAPIDS automatically loads and executes it based on the config key < code > [SRC_SCRIPT]< / code > you added in the last step. For our example, this script is:
< div class = "highlight" > < pre > < span > < / span > < code > src/feature/phone_accelerometer/vega/main.py
< / code > < / pre > < / div > < / p >
< 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 > Every feature script (< code > main.[py|R]< / code > ) needs a < code > [providername]_features< / code > function with specific parameters. RAPIDS calls this function with the sensor data ready to process and with other functions and arguments you will need.< / p >
< div class = "tabbed-set" data-tabs = "1:2" > < input checked = "checked" id = "__tabbed_1_1" name = "__tabbed_1" type = "radio" / > < label for = "__tabbed_1_1" > Python function< / label > < div class = "tabbed-content" >
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< 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" > time_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 >
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< span class = "c1" > # empty for now< / span >
< span class = "k" > return< / span > < span class = "p" > (< / span > < span class = "n" > your_features_df< / span > < span class = "p" > )< / span >
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< / code > < / pre > < / div >
< / div >
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< input id = "__tabbed_1_2" name = "__tabbed_1" type = "radio" / > < label for = "__tabbed_1_2" > R function< / label > < div class = "tabbed-content" >
< 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" > time_segment< / span > < span class = "p" > ,< / span > < span class = "n" > provider< / span > < span class = "p" > ){< / span >
< span class = "c1" > # empty for now< / span >
< span class = "nf" > return< / span > < span class = "p" > (< / span > < span class = "n" > your_features_df< / span > < span class = "p" > )< / span >
< span class = "p" > }< / span >
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< / code > < / pre > < / div >
< / div >
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< / div >
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< 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 > time_segment< / code > < / td >
< td > The label of the time 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 >
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< p > The next step is to implement the code that computes your behavioral features in your provider script’ s function. As with any other script, this function can call other auxiliary methods, but in general terms, it should have three stages:< / p >
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< 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 >
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< p > Note that the phone’ s battery, screen, and activity recognition data are given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on)< / p >
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< / details >
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< details class = "info" > < summary > 2. Filter your data to process only those rows that belong to < code > time_segment< / code > < / summary > < p > This step is only one line of code, but keep reading to understand why we need it.
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< 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" > time_segment< / span > < span class = "p" > )< / span >
< / code > < / pre > < / div > < / p >
< 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/#time-segments" > time segments RAPIDS could be configured with< / a > .< / p >
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< 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/#time-segments" > time segment< / a > . A time segment is a period that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and weekend basis for < code > p01< / code > . The labels are arbitrary, 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 >
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< p > For this example, RAPIDS will call your provider function three times for < code > p01< / code > , once where < code > time_segment< / code > is < code > my_days< / code > , once where < code > time_segment< / code > is < code > my_weeks< / code > , and once where < code > time_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 >
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< 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 time segment plus an extra column called < code > local_segment< / code > . This new column will have as many unique values as time 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" > " 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 >
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< 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 that number.< / p >
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< / 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 >
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< li > One row per time segment instance (e.g., 14 our < code > p01< / code > ‘ s < code > my_days< / code > example)< / li >
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< li > The < code > local_segment< / code > column added by < code > filter_data_by_segment()< / code > < / li >
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< li > One column per feature. The name of your features should only contain letters or numbers (< code > feature1< / code > ) by convention. RAPIDS automatically adds the correct sensor and provider prefix; in our example, this prefix is < code > phone_accelerometr_vega_< / code > .< / li >
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< / ul >
< / details >
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< details class = "example" > < summary > < code > PHONE_ACCELEROMETER< / code > Provider Example< / summary > < p > For your reference, this 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 = "kn" > import< / span > < span class = "nn" > pandas< / span > < span class = "k" > as< / span > < span class = "nn" > pd< / span >
< span class = "kn" > import< / span > < span class = "nn" > numpy< / span > < span class = "k" > as< / span > < span class = "nn" > np< / span >
< 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" > time_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 >
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< 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 = "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" > time_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" > " 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" > " 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" > " 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" > " 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" > " 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 >
< 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 >
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< script src = "../../javascripts/extra.js" > < / script >
< / body >
< / html >
