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1.4/change-log/index.html
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@ -1716,6 +1716,13 @@
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#v141" class="md-nav__link">
v1.4.1
</a>
</li>
<li class="md-nav__item">
<a href="#v140" class="md-nav__link">
v1.4.0
@ -1910,6 +1917,13 @@
</label>
<ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
<li class="md-nav__item">
<a href="#v141" class="md-nav__link">
v1.4.1
</a>
</li>
<li class="md-nav__item">
<a href="#v140" class="md-nav__link">
v1.4.0
@ -2083,6 +2097,11 @@
<h1 id="change-log">Change Log<a class="headerlink" href="#change-log" title="Permanent link">&para;</a></h1>
<h2 id="v141">v1.4.1<a class="headerlink" href="#v141" title="Permanent link">&para;</a></h2>
<ul>
<li>Update home page</li>
<li>Add PHONE_MESSAGES tests</li>
</ul>
<h2 id="v140">v1.4.0<a class="headerlink" href="#v140" title="Permanent link">&para;</a></h2>
<ul>
<li>Add new Application Foreground episode features and tests</li>

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1.4/developers/test-cases/index.html
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@ -2108,7 +2108,7 @@
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
<td>Y</td>
</tr>
<tr>
<td>Phone Screen</td>
@ -2198,21 +2198,68 @@
</table>
<h2 id="messages-sms">Messages (SMS)<a class="headerlink" href="#messages-sms" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw message data file contains data for 2 separate days.</li>
<li>The data for the first day contains records 5 records for every
<code>epoch</code>.</li>
<li>The second day's data contains 6 records for each of only 2
<code>epoch</code> (currently <code>morning</code> and <code>evening</code>)</li>
<li>The raw message data contains records for both <code>message_types</code>
(i.e. <code>recieved</code> and <code>sent</code>) in both days in all epochs. The
number records with each <code>message_types</code> per epoch is randomly
distributed There is at least one records with each
<code>message_types</code> per epoch.</li>
<li>There is one raw message data file each, as described above, for
testing both iOS and Android data.</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files</li>
<li>The raw message data file, <code>phone_messages_raw.csv</code>, contains data for 4 separate days.</li>
<li>One episode for each daily segment (night, morning, afternoon and evening)</li>
<li>Two <code>sent</code> episodes locate in the same 30-min segment (<code>Fri 16:08:03.000</code> and <code>Fri 16:19:35.000</code>)</li>
<li>Two <code>received</code> episodes locate in the same 30-min segment (<code>Sat 06:45:05.000</code> and <code>Fri 06:45:05.000</code>)</li>
<li>Two episodes locate in the same daily segment (<code>Fri 11:57:56.385</code> and <code>Sat 10:54:10.000</code>)</li>
<li>One episode before the time switch (<code>Sun 00:48:01.000</code>) and one episode after the time switch (<code>Sun 06:21:01.000</code>)</li>
</ul>
<p>Checklist:</p>
<table>
<thead>
<tr>
<th>time segment</th>
<th>single tz</th>
<th>multi tz</th>
<th>platform</th>
</tr>
</thead>
<tbody>
<tr>
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>android</td>
</tr>
</tbody>
</table>
<h2 id="calls">Calls<a class="headerlink" href="#calls" title="Permanent link">&para;</a></h2>
<p>Due to the difference in the format of the raw data for iOS and Android the following is the expected results
the <code>phone_calls.csv</code>. </p>
@ -2269,43 +2316,43 @@ When generating test data, all traces for iOS device need to be unique otherwise
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
</tbody>
</table>
@ -2358,43 +2405,43 @@ When generating test data, all traces for iOS device need to be unique otherwise
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android, iOS</td>
<td>android, iOS</td>
</tr>
</tbody>
</table>
@ -2442,43 +2489,43 @@ When generating test data, all traces for iOS device need to be unique otherwise
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
</tbody>
</table>
@ -2699,43 +2746,43 @@ When generating test data, all traces for iOS device need to be unique otherwise
<td>30min</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>Android</td>
<td>android</td>
</tr>
</tbody>
</table>

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1.4/index.html
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@ -1802,7 +1802,7 @@
<h1 id="welcome-to-rapids-documentation">Welcome to RAPIDS documentation<a class="headerlink" href="#welcome-to-rapids-documentation" title="Permanent link">&para;</a></h1>
<p>Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to <a href="features/feature-introduction/">extract</a> and <a href="features/add-new-features/">create</a> <strong>behavioral features</strong> (a.k.a. digital biomarkers), <a href="visualizations/data-quality-visualizations/">visualize</a> mobile sensor data, and <a href="workflow-examples/analysis/">structure</a> your analysis into reproducible workflows.</p>
<p>RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support <a href="datastreams/data-streams-introduction">data streams</a> logged by smartphones, Fitbit wearables, and Empatica wearables in collaboration with the <a href="https://dbdp.org/">DBDP</a>. </p>
<p>RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support <a href="datastreams/data-streams-introduction">data streams</a> logged by smartphones, Fitbit wearables, and Empatica wearables (the latter in collaboration with the <a href="https://dbdp.org/">DBDP</a>). </p>
<div class="admonition tip">
<p class="admonition-title">Where do I start?</p>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 3h-2v10h2V3m4.83 2.17-1.42 1.42A6.944 6.944 0 0 1 19 12a7 7 0 0 1-7 7A6.995 6.995 0 0 1 7.58 6.58L6.17 5.17a9.001 9.001 0 0 0-1.03 12.69c3.22 3.78 8.9 4.24 12.69 1.02A9.003 9.003 0 0 0 21 12c0-2.63-1.16-5.13-3.17-6.83z"/></svg></span> New to RAPIDS? Check our <a href="setup/overview/">Overview + FAQ</a> and <a href="workflow-examples/minimal">minimal example</a></p>
@ -1817,7 +1817,7 @@
<ol>
<li><strong>Consistent analysis</strong>. Every participant sensor dataset is analyzed in the same way and isolated from each other.</li>
<li><strong>Efficient analysis</strong>. Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated.</li>
<li><strong>Parallel execution</strong>. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code.</li>
<li><strong>Parallel execution</strong>. Thanks to <a href="https://snakemake.github.io/">Snakemake</a>, your analysis can be executed over multiple cores without changing your code.</li>
<li><strong>Code-free features</strong>. Extract any of the behavioral features offered by RAPIDS without writing any code.</li>
<li><strong>Extensible code</strong>. You can easily add your own data streams or behavioral features in R or Python, share them with the community, and keep authorship and citations.</li>
<li><strong>Time zone aware</strong>. Your data is adjusted to one or more time zones per participant.</li>
@ -1827,7 +1827,7 @@
<li><strong>Private</strong>. All your data is processed locally.</li>
</ol>
<h2 id="users-and-contributors">Users and Contributors<a class="headerlink" href="#users-and-contributors" title="Permanent link">&para;</a></h2>
<details class="quote"><summary>Community Contributors</summary><p>Many thanks to our community contributions and the <a href="../team">whole team</a>:</p>
<details class="quote"><summary>Community Contributors</summary><p>Many thanks to the <a href="./team">whole team</a> and our community contributions:</p>
<ul>
<li>Agam Kumar (CMU)</li>
<li>Yasaman S. Sefidgar (University of Washington)</li>
@ -1836,7 +1836,7 @@
<li>Stephen Price (CMU)</li>
<li>Neil Singh (University of Virginia)</li>
</ul>
<p>Many thanks to the researchers that made <a href="../citation">their work</a> open source:</p>
<p>Many thanks to the researchers that made <a href="./citation">their work</a> open source:</p>
<ul>
<li>Panda et al. <a href="https://pubmed.ncbi.nlm.nih.gov/31657854/">paper</a></li>
<li>Stachl et al. <a href="https://www.pnas.org/content/117/30/17680">paper</a></li>
@ -1850,6 +1850,7 @@
<li>Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyper-parameter Optimization, and Feature Importance Analysis: An Exploratory Study <a href="https://preprints.jmir.org/preprint/26540">link</a></li>
<li>Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study <a href="https://cancer.jmir.org/2021/2/e27975/">link</a></li>
<li>An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data <a href="https://edas.info/showManuscript.php?m=1570708269&amp;random=750318666&amp;type=final&amp;ext=pdf&amp;title=PDF+file">link</a></li>
<li>Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity <a href="https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract">link</a></li>
</ul>
</details>
<div class="users">

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@ -2,382 +2,382 @@
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