chunk_episodes of utils.py for multi time zone data[DEVICE_IDS][PLATFORMS][DEVICE_IDS]device_id on this list.[LABEL]This example will create eight segments for a single participant (a748ee1a...), five independent stressX segments with various lengths (1,4,3,7, and 9 hours). Segments stress1, stress3, and stress5 are shifted forwards by 5 minutes and stress2 and stress4 are shifted backwards by 4 hours (that is, if the stress4 event happened on March 15th at 1pm EST (1584291600000), the time segment will start on that day at 9am and end at 4pm).
The three mood segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these mood segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant.
In the final feature file, you will find a row per event segment. The local_segment column of each row has a label, a start date-time string, and an end date-time string.
weeklysurvey2060#2020-09-12 01:00:00,2020-09-18 23:59:59
+All sensor data is always segmented based on timestamps, and the date-time strings are attached for informative purposes. For example, you can plot your features based on these strings.
+When you configure RAPIDS to work with a single time zone, such tz code will be used to convert start/end timestamps (the ones you typed in the event segments file) into start/end date-time strings. However, when you configure RAPIDS to work with multiple time zones, RAPIDS will use the most common time zone across all devices of every participant to do the conversion. The most common time zone is the one in which a participant spent the most time.
+In practical terms, this means that the date-time strings of event segments that happened in uncommon time zones will have shifted start/end date-time labels. However, the data within each segment was correctly filtered based on timestamps.
+If your participants lived on different time zones or they travelled across time zones, and you know when participants’ devices were in a specific time zone, RAPIDS can use this data to process your data streams with the correct date-time. You need to provide RAPIDS with the time zone data in a CSV file ([TZCODES_FILE]) in the format described below.
If your participants lived in different time zones or they traveled across time zones, and you know when participants’ devices were in a specific time zone, RAPIDS can use this data to process your data streams with the correct date-time. You need to provide RAPIDS with the time zone data in a CSV file ([TZCODES_FILE]) in the format described below.
TIMEZONE:
TYPE: MULTIPLE
SINGLE:
@@ -2389,7 +2396,7 @@ survey2,1584291600000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3
[MULTIPLE][TZCODES_FILE][MULTIPLE][IF_MISSING_TZCODE]1587400000000 will be discarded because it was logged outside any interval.TZCODES_FILE from the time zone table collected automatically by the AWARE app?Sure. You can put your timezone table ( Sure. You can put your timezone table (timezone.csv) collected by AWARE app under data/external folder and run:
+Can I get the
TZCODES_FILE from the time zone table collected automatically by the AWARE app?timezone.csv) collected by the AWARE app under data/external folder and run:
python tools/create_multi_timezones_file.py
TZCODES_FILE will be saved as data/external/multiple_timezones.csv file.
chunk_episodes of utils.py for multi time zone data[DEVICE_IDS][PLATFORMS][DEVICE_IDS]device_id on this list.[LABEL]This example will create eight segments for a single participant (a748ee1a...), five independent stressX segments with various lengths (1,4,3,7, and 9 hours). Segments stress1, stress3, and stress5 are shifted forwards by 5 minutes and stress2 and stress4 are shifted backwards by 4 hours (that is, if the stress4 event happened on March 15th at 1pm EST (1584291600000), the time segment will start on that day at 9am and end at 4pm).
The three mood segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these mood segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant.
In the final feature file, you will find a row per event segment. The local_segment column of each row has a label, a start date-time string, and an end date-time string.
weeklysurvey2060#2020-09-12 01:00:00,2020-09-18 23:59:59
+All sensor data is always segmented based on timestamps, and the date-time strings are attached for informative purposes. For example, you can plot your features based on these strings.
+When you configure RAPIDS to work with a single time zone, such tz code will be used to convert start/end timestamps (the ones you typed in the event segments file) into start/end date-time strings. However, when you configure RAPIDS to work with multiple time zones, RAPIDS will use the most common time zone across all devices of every participant to do the conversion. The most common time zone is the one in which a participant spent the most time.
+In practical terms, this means that the date-time strings of event segments that happened in uncommon time zones will have shifted start/end date-time labels. However, the data within each segment was correctly filtered based on timestamps.
+If your participants lived on different time zones or they travelled across time zones, and you know when participants’ devices were in a specific time zone, RAPIDS can use this data to process your data streams with the correct date-time. You need to provide RAPIDS with the time zone data in a CSV file ([TZCODES_FILE]) in the format described below.
If your participants lived in different time zones or they traveled across time zones, and you know when participants’ devices were in a specific time zone, RAPIDS can use this data to process your data streams with the correct date-time. You need to provide RAPIDS with the time zone data in a CSV file ([TZCODES_FILE]) in the format described below.
TIMEZONE:
TYPE: MULTIPLE
SINGLE:
@@ -2389,7 +2396,7 @@ survey2,1584291600000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3
[MULTIPLE][TZCODES_FILE][MULTIPLE][IF_MISSING_TZCODE]1587400000000 will be discarded because it was logged outside any interval.TZCODES_FILE from the time zone table collected automatically by the AWARE app?Sure. You can put your timezone table ( Sure. You can put your timezone table (timezone.csv) collected by AWARE app under data/external folder and run:
+Can I get the
TZCODES_FILE from the time zone table collected automatically by the AWARE app?timezone.csv) collected by the AWARE app under data/external folder and run:
python tools/create_multi_timezones_file.py
TZCODES_FILE will be saved as data/external/multiple_timezones.csv file.