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<h1 id="test-cases">Test Cases<a class="headerlink" href="#test-cases" title="Permanent link">&para;</a></h1>
<p>Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the <code>tests/data/raw/test01/</code> contains all the test data files for testing android data formats and <code>tests/data/raw/test02/</code> contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the <code>tests/data/processed/test01/</code> and <code>tests/data/processed/test02/</code> for Android and iOS respectively. <code>tests/data/raw/test03/</code> and <code>tests/data/raw/test04/</code> contain data files for testing empty raw data files for android and iOS respectively.</p>
<p>The following is a list of the sensors that testing is currently available.</p>
<table>
<thead>
<tr>
<th>Sensor</th>
<th>Provider</th>
<th>Periodic</th>
<th>Frequency</th>
<th>Event</th>
</tr>
</thead>
<tbody>
<tr>
<td>Phone Accelerometer</td>
<td>Panda</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Accelerometer</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Activity Recognition</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Applications Foreground</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Battery</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone Bluetooth</td>
<td>Doryab</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Bluetooth</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>Y</td>
</tr>
<tr>
<td>Phone Calls</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone Conversation</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone Data Yield</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Light</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone Locations</td>
<td>Doryab</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Locations</td>
<td>Barnett</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone Messages</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone Screen</td>
<td>RAPIDS</td>
<td>Y</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Phone WiFi Connected</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Phone WiFi Visible</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Calories Intraday</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>Y</td>
</tr>
<tr>
<td>Fitbit Data Yield</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Heart Rate Summary</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Heart Rate Intraday</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Sleep Summary</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Sleep Intraday</td>
<td>RAPIDS</td>
<td>Y</td>
<td>Y</td>
<td>Y</td>
</tr>
<tr>
<td>Fitbit Sleep Intraday</td>
<td>PRICE</td>
<td>Y</td>
<td>Y</td>
<td>Y</td>
</tr>
<tr>
<td>Fitbit Steps Summary</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
<tr>
<td>Fitbit Steps Intraday</td>
<td>RAPIDS</td>
<td>N</td>
<td>N</td>
<td>N</td>
</tr>
</tbody>
</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>
</ul>
<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 call data for iOS and Android the following is the expected results the <code>calls_with_datetime_unified.csv</code>. This would give a better idea of the use cases being tested since the <code>calls_with_datetime_unified.csv</code> would make both the iOS and Android data comparable.</p>
<ul>
<li>The call data would contain data for 2 days.</li>
<li>The data for the first day contains 6 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 call data contains records for all <code>call_types</code> (i.e.
<code>incoming</code>, <code>outgoing</code> and <code>missed</code>) in both days in all epochs.
The number records with each of the <code>call_types</code> per epoch is
randomly distributed. There is at least one records with each
<code>call_types</code> per epoch.</li>
<li>There is one call 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>
</ul>
<h2 id="screen">Screen<a class="headerlink" href="#screen" title="Permanent link">&para;</a></h2>
<p>Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the <code>screen_deltas.csv</code>. This would give a better idea of the use cases being tested since the <code>screen_eltas.csv</code> would make both the iOS and Android data comparable These files are used to calculate the features for the screen sensor</p>
<ul>
<li>The screen delta data file contains data for 1 day.</li>
<li>The screen delta data contains 1 record to represent an <code>unlock</code>
episode that falls within an <code>epoch</code> for every <code>epoch</code>.</li>
<li>The screen delta data contains 1 record to represent an <code>unlock</code>
episode that falls across the boundary of 2 epochs. Namely the
<code>unlock</code> episode starts in one epoch and ends in the next, thus
there is a record for <code>unlock</code> episodes that fall across <code>night</code>
to <code>morning</code>, <code>morning</code> to <code>afternoon</code> and finally <code>afternoon</code> to
<code>night</code></li>
<li>The testing is done for <code>unlock</code> episode_type.</li>
<li>There is one screen data file each for testing both iOS and
Android data formats.</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files</li>
</ul>
<h2 id="battery">Battery<a class="headerlink" href="#battery" title="Permanent link">&para;</a></h2>
<p>Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS the following is the expected results the <code>battery_deltas.csv</code>. This would give a better idea of the use cases being tested since the <code>battery_deltas.csv</code> would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor.</p>
<ul>
<li>The battery delta data file contains data for 1 day.</li>
<li>The battery delta data contains 1 record each for a <code>charging</code> and
<code>discharging</code> episode that falls within an <code>epoch</code> for every
<code>epoch</code>. Thus, for the <code>daily</code> epoch there would be multiple
<code>charging</code> and <code>discharging</code> episodes</li>
<li>Since either a <code>charging</code> episode or a <code>discharging</code> episode and
not both can occur across epochs, in order to test episodes that
occur across epochs alternating episodes of <code>charging</code> and
<code>discharging</code> episodes that fall across <code>night</code> to <code>morning</code>,
<code>morning</code> to <code>afternoon</code> and finally <code>afternoon</code> to <code>night</code> are
present in the battery delta data. This starts with a
<code>discharging</code> episode that begins in <code>night</code> and end in <code>morning</code>.</li>
<li>There is one battery data file each, for testing both iOS and
Android data formats.</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files</li>
</ul>
<h2 id="bluetooth">Bluetooth<a class="headerlink" href="#bluetooth" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw Bluetooth data file contains data for 1 day.</li>
<li>The raw Bluetooth data contains at least 2 records for each
<code>epoch</code>. Each <code>epoch</code> has a record with a <code>timestamp</code> for the
beginning boundary for that <code>epoch</code> and a record with a
<code>timestamp</code> for the ending boundary for that <code>epoch</code>. (e.g. For
the <code>morning</code> epoch there is a record with a <code>timestamp</code> for
<code>6:00AM</code> and another record with a <code>timestamp</code> for <code>11:59:59AM</code>.
These are to test edge cases)</li>
<li>An option of 5 Bluetooth devices are randomly distributed
throughout the data records.</li>
<li>There is one raw Bluetooth data file each, for testing both iOS
and Android data formats.</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files.</li>
</ul>
<h2 id="wifi">WIFI<a class="headerlink" href="#wifi" title="Permanent link">&para;</a></h2>
<ul>
<li>There are 2 data files (<code>wifi_raw.csv</code> and <code>sensor_wifi_raw.csv</code>)
for each fake participant for each phone platform. </li>
<li>The raw WIFI data files contain data for 1 day.</li>
<li>The <code>sensor_wifi_raw.csv</code> data contains at least 2 records for
each <code>epoch</code>. Each <code>epoch</code> has a record with a <code>timestamp</code> for the
beginning boundary for that <code>epoch</code> and a record with a
<code>timestamp</code> for the ending boundary for that <code>epoch</code>. (e.g. For
the <code>morning</code> epoch there is a record with a <code>timestamp</code> for
<code>6:00AM</code> and another record with a <code>timestamp</code> for <code>11:59:59AM</code>.
These are to test edge cases)</li>
<li>The <code>wifi_raw.csv</code> data contains 3 records with random timestamps
for each <code>epoch</code> to represent visible broadcasting WIFI network.
This file is empty for the iOS phone testing data.</li>
<li>An option of 10 access point devices is randomly distributed
throughout the data records. 5 each for <code>sensor_wifi_raw.csv</code> and
<code>wifi_raw.csv</code>.</li>
<li>There data files for testing both iOS and Android data formats.</li>
<li>There are also additional empty data files for both android and
iOS for testing empty data files.</li>
</ul>
<h2 id="light">Light<a class="headerlink" href="#light" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw light data file contains data for 1 day.</li>
<li>The raw light data contains 3 or 4 rows of data for each <code>epoch</code>
except <code>night</code>. The single row of data for <code>night</code> is for testing
features for single values inputs. (Example testing the standard
deviation of one input value)</li>
<li>Since light is only available for Android there is only one file
that contains data for Android. All other files (i.e. for iPhone)
are empty data files.</li>
</ul>
<h2 id="locations">Locations<a class="headerlink" href="#locations" title="Permanent link">&para;</a></h2>
<p>Description</p>
<ul>
<li>The participant&rsquo;s home location is (latitude=1, longitude=1).</li>
<li>From Sat 10:56:00 to Sat 11:04:00, the center of the cluster is (latitude=-100, longitude=-100).</li>
<li>From Sun 03:30:00 to Sun 03:47:00, the center of the cluster is (latitude=1, longitude=1). Home location is extracted from this period.</li>
<li>From Sun 11:30:00 to Sun 11:38:00, the center of the cluster is (latitude=100, longitude=100).</li>
</ul>
<h2 id="application-foreground">Application Foreground<a class="headerlink" href="#application-foreground" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw application foreground data file contains data for 1 day.</li>
<li>The raw application foreground data contains 7 - 9 rows of data
for each <code>epoch</code>. The records for each <code>epoch</code> contains apps that
are randomly selected from a list of apps that are from the
<code>MULTIPLE_CATEGORIES</code> and <code>SINGLE_CATEGORIES</code> (See
<a href="">testing_config.yaml</a>). There are also records in each epoch
that have apps randomly selected from a list of apps that are from
the <code>EXCLUDED_CATEGORIES</code> and <code>EXCLUDED_APPS</code>. This is to test
that these apps are actually being excluded from the calculations
of features. There are also records to test <code>SINGLE_APPS</code>
calculations.</li>
<li>Since application foreground is only available for Android there
is only one file that contains data for Android. All other files
(i.e. for iPhone) are empty data files.</li>
</ul>
<h2 id="activity-recognition">Activity Recognition<a class="headerlink" href="#activity-recognition" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw Activity Recognition data file contains data for 1 day.</li>
<li>The raw Activity Recognition data each <code>epoch</code> period contains
rows that records 2 - 5 different <code>activity_types</code>. The is such
that durations of activities can be tested. Additionally, there
are records that mimic the duration of an activity over the time
boundary of neighboring epochs. (For example, there a set of
records that mimic the participant <code>in_vehicle</code> from <code>afternoon</code>
into <code>evening</code>)</li>
<li>There is one file each with raw Activity Recognition data for
testing both iOS and Android data formats.
(plugin_google_activity_recognition_raw.csv for android and
plugin_ios_activity_recognition_raw.csv for iOS)</li>
<li>There is also an additional empty data file for both android and
iOS for testing empty data files.</li>
</ul>
<h2 id="conversation">Conversation<a class="headerlink" href="#conversation" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw conversation data file contains data for 2 day.</li>
<li>The raw conversation data contains records with a sample of both
<code>datatypes</code> (i.e. <code>voice/noise</code> = <code>0</code>, and <code>conversation</code> = <code>2</code> )
as well as rows with for samples of each of the <code>inference</code> values
(i.e. <code>silence</code> = <code>0</code>, <code>noise</code> = <code>1</code>, <code>voice</code> = <code>2</code>, and <code>unknown</code>
= <code>3</code>) for each <code>epoch</code>. The different <code>datatype</code> and <code>inference</code>
records are randomly distributed throughout the <code>epoch</code>.</li>
<li>Additionally there are 2 - 5 records for conversations (<code>datatype</code>
= 2, and <code>inference</code> = -1) in each <code>epoch</code> and for each <code>epoch</code>
except night, there is a conversation record that has a
<code>double_convo_start</code> <code>timestamp</code> that is from the previous
<code>epoch</code>. This is to test the calculations of features across
<code>epochs</code>.</li>
<li>There is a raw conversation data file for both android and iOS
platforms (<code>plugin_studentlife_audio_android_raw.csv</code> and
<code>plugin_studentlife_audio_raw.csv</code> respectively).</li>
<li>Finally, there are also additional empty data files for both
android and iOS for testing empty data files</li>
</ul>
<h2 id="keyboard">Keyboard<a class="headerlink" href="#keyboard" title="Permanent link">&para;</a></h2>
<ul>
<li>The raw keyboard data file contains data for 4 days.</li>
<li>
<p>The raw keyboard data contains records with difference in <code>timestamp</code> ranging from
milliseconds to seconds. </p>
</li>
<li>
<p>With difference in timestamps between consecutive records more than 5 seconds helps us to create separate
sessions within the usage of the same app. This helps to verify the case where sessions have to be different. </p>
</li>
<li>
<p>The raw keyboard data contains records where the difference in text is less
than 5 seconds which makes it into 1 session but because of difference of app
new session starts. This edge case determines the behaviour within particular app
and also within 5 seconds.</p>
</li>
<li>
<p>The raw keyboard data also contains the records where length of <code>current_text</code> varies between consecutive rows. This helps us to tests on the cases where input text is entered by auto-suggested
or auto-correct operations.</p>
</li>
<li>
<p>One three-minute episode with a 1-minute row on Sun 08:59:54.65 and 09:00:00,another on Sun 12:01:02 that are considering a single episode in multi-timezone event segments to showcase how
inferring time zone data for Keyboard from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.</p>
</li>
</ul>
<h2 id="fitbit-calories-intraday">Fitbit Calories Intraday<a class="headerlink" href="#fitbit-calories-intraday" title="Permanent link">&para;</a></h2>
<p>Description</p>
<ul>
<li>A five-minute sedentary episode on Fri 11:00:00</li>
<li>A one-minute sedentary episode on Sun 02:00:00. It exists in November but not in February in STZ</li>
<li>A five-minute sedentary episode on Fri 11:58:00. It is split within two 30-min segments and the morning</li>
<li>A three-minute lightly active episode on Fri 11:10:00, a one-minute at 11:18:00 and a one-minute 11:24:00. These check for start and end times of first/last/longest episode</li>
<li>A three-minute fairly active episode on Fri 11:40:00, a one-minute at 11:48:00 and a one-minute 11:54:00. These check for start and end times of first/last/longest episode</li>
<li>A three-minute very active episode on Fri 12:10:00, a one-minute at 12:18:00 and a one-minute 12:24:00. These check for start and end times of first/last/longest episode</li>
<li>A eight-minute MVPA episode with intertwined fairly and very active rows on Fri 12:30:00</li>
<li>The above episodes contain six higmet (&gt;= 3 MET) episodes and nine lowmet episodes.</li>
<li>One two-minute sedentary episode with a 1-minute row on Sun 09:00:00 and another on Sun 12:01:01 that are considering a single episode in multi-timezone event segments to showcase how inferring time zone data for Fitbit from phone data can produce inaccurate results around the tz change. This happens because the device was on LA time until 11:59 and switched to NY time at 12pm, in terms of actual time 09 am LA and 12 pm NY represent the same moment in time so 09:00 LA and 12:01 NY are consecutive minutes.</li>
<li>A three-minute sedentary episode on Sat 08:59 that will be ignored for multi-timezone event segments.</li>
<li>A three-minute sedentary episode on Sat 12:59 of which the first minute will be ignored for multi-timezone event segments since the test segment starts at 13:00</li>
<li>A three-minute sedentary episode on Sat 16:00</li>
<li>A four-minute sedentary episode on Sun 10:01 that will be ignored for Novembers&rsquo;s multi-timezone event segments since the test segment ends at 10am on that weekend.</li>
<li>A three-minute very active episode on Sat 16:03. This episode and the one at 16:00 are counted as one for lowmet episodes</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>fitbit</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
</tbody>
</table>
<h2 id="fitbit-sleep-summary">Fitbit Sleep Summary<a class="headerlink" href="#fitbit-sleep-summary" title="Permanent link">&para;</a></h2>
<p>Description</p>
<ul>
<li>A main sleep episode that starts on Fri 20:00:00 and ends on Sat 02:00:00. This episode starts after 11am (Last Night End) which will be considered as today&rsquo;s (Fri) data.</li>
<li>A nap that starts on Sat 04:00:00 and ends on Sat 06:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday&rsquo;s (Fri) data.</li>
<li>A nap that starts on Sat 13:00:00 and ends on Sat 15:00:00. This episode starts after 11am (Last Night End) which will be considered as today&rsquo;s (Sat) data.</li>
<li>A main sleep that starts on Sun 01:00:00 and ends on Sun 12:00:00. This episode starts before 11am (Last Night End) which will be considered as yesterday&rsquo;s (Sat) data.</li>
<li>A main sleep that starts on Sun 23:00:00 and ends on Mon 07:00:00. This episode starts after 11am (Last Night End) which will be considered as today&rsquo;s (Sun) data.</li>
<li>Any segment shorter than one day will be ignored for sleep RAPIDS features.</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>fitbit</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
</tbody>
</table>
<h2 id="fitbit-sleep-intraday">Fitbit Sleep Intraday<a class="headerlink" href="#fitbit-sleep-intraday" title="Permanent link">&para;</a></h2>
<p>Description</p>
<ul>
<li>A five-minute main sleep episode with asleep-classic level on Fri 11:00:00.</li>
<li>An eight-hour main sleep episode on Fri 17:00:00. It is split into 2 parts for daily segment: a seven-hour sleep episode on Fri 17:00:00 and an one-hour sleep episode on Sat 00:00:00.</li>
<li>A two-hour nap on Sat 01:00:00 that will be ignored for main sleep features.</li>
<li>An one-hour nap on Sat 13:00:00 that will be ignored for main sleep features.</li>
<li>An eight-hour main sleep episode on Sat 22:00:00. This episode ends on Sun 08:00:00 (NY) for March and Sun 06:00:00 (NY) for Novembers due to daylight savings. It will be considered for <code>beforeMarchEvent</code> segment and ignored for <code>beforeNovemberEvent</code> segment.</li>
<li>A nine-hour main sleep episode on Sun 11:00:00. Start time will be assigned as NY time zone and converted to 14:00:00.</li>
<li>A seven-hour main sleep episode on Mon 06:00:00. This episode will be split into two parts: a five-hour sleep episode on Mon 06:00:00 and a two-hour sleep episode on Mon 11:00:00. The first part will be discarded as it is before 11am (Last Night End)</li>
<li>Any segment shorter than one day will be ignored for sleep PRICE features.</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>fitbit</td>
</tr>
<tr>
<td>morning</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>daily</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>threeday</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>weekend</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeMarchEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
<tr>
<td>beforeNovemberEvent</td>
<td>OK</td>
<td>OK</td>
<td>fitbit</td>
</tr>
</tbody>
</table>
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