36 lines
3.5 KiB
Markdown
36 lines
3.5 KiB
Markdown
# Welcome to RAPIDS documentation
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!!! warning
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The functionality outlined in these docs is implemented in the branch `time_segments` which we will merge to `master` soon as release `0.1`. The previous (first) release of RAPIDS along with the old [docs](https://rapidspitt.readthedocs.io/en/latest/) will be labeled `beta`. If you landed on this page feel free to look around, just have in mind that we are polishing the last rough patches before we advertise `0.1` (Nov 16, 2020)
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Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to extract **behavioral features** (a.k.a. digital biomarkers/phenotypes).
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RAPIDS is open source, documented, modular, tested, and reproducible. At the moment we support smartphone data collected with [AWARE](https://awareframework.com/) and wearable data from Fitbit devices.
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:material-slack: Questions or feedback can be posted on \#rapids in AWARE Framework\'s [slack](http://awareframework.com:3000/).
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:material-github: Bugs should be reported on [Github](https://github.com/carissalow/rapids/issues).
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:fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion).
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:fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/)
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## How does it work?
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RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://snakemake.readthedocs.io/en/stable/). We suggest you read Snakemake's docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant.
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## What are the benefits of using RAPIDS?
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1. **Consistent analysis**. Every participant sensor dataset is analyzed in the exact same way and isolated from each other.
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2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated.
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5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code.
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6. **Extensible code**. You can easily add your own behavioral features in R or Python and keep authorship and citations.
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3. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*).
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4. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses).
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7. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct.
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8. **Reproducible code**. You can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead.
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9. **Private**. All your data is processed locally.
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## How is it organized?
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In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). |