Daily Sleep Analysis
import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.Sleep.col("efficiency").mean(),
va.Sleep.score().mean(),
va.Sleep.chronotype().newest()
).where(
"type = 'long_sleep'"
).group_by(
va.date_trunc(va.Sleep.index(), 1, "day")
).finalize()
{
"where": "type = 'long_sleep'",
"select": [
{
"group_key": "*"
},
{
"arg": {
"sleep": "efficiency"
},
"func": "mean"
},
{
"arg": {
"value_macro": "sleep_score",
"version": "automatic"
},
"func": "mean"
},
{
"arg": {
"value_macro": "chronotype",
"version": "automatic"
},
"func": "newest"
}
],
"group_by": [
{
"date_trunc": {
"value": 1,
"unit": "day"
},
"arg": {
"index": "sleep"
}
}
]
}
Weekly Insights Into Users’ Activity
import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.Activity.col("heart_rate_resting").mean(),
va.Activity.col("calories_total").max(),
va.Activity.col("steps").min(),
va.Activity.col("duration_active_second").mean()
).group_by(
va.date_trunc(va.Activity.index(), 1, "week")
).finalize()
{
"select": [
{
"group_key": "*"
},
{
"arg": {
"activity": "heart_rate_resting"
},
"func": "mean"
},
{
"arg": {
"activity": "calories_total"
},
"func": "max"
},
{
"arg": {
"activity": "steps"
},
"func": "min"
},
{
"arg": {
"activity": "duration_active_second"
},
"func": "mean"
}
],
"group_by": [
{
"date_trunc": {
"value": 1,
"unit": "week"
},
"arg": {
"index": "activity"
}
}
]
}
First Glucose Measurement Of The Day Grouped By Source Type and Provider
import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.Timeseries.col("glucose").field("value").oldest()
).group_by(
va.date_trunc(va.Timeseries.index(), 1, "day"),
va.Source.col("source_provider"),
va.Source.col("source_type")
).finalize()
{
"select": [
{
"group_key": "*"
},
{
"arg": {
"field": "value",
"timeseries": "glucose"
},
"func": "oldest"
}
],
"group_by": [
{
"arg": {
"index": "timeseries"
},
"date_trunc": {"unit": "day", "value": 1}
},
{"source": "source_provider"},
{"source": "source_type"}
]
}
Daily Summaries of Metabolic Biomarkers Grouped By Source Type and Provider
import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.Timeseries.col("glucose").field("value").mean(),
va.Timeseries.col("heartrate").field("value").mean(),
va.Timeseries.col("steps").field("value").sum(),
va.Timeseries.col("hrv").field("value").mean(),
va.Timeseries.col("calories_active").field("value").sum(),
va.Timeseries.col("body_temperature").field("value").mean(),
va.Timeseries.col("body_temperature").field("value").min(),
va.Timeseries.col("body_temperature").field("value").max(),
).group_by(
va.date_trunc(va.Timeseries.index(), 1, "day"),
va.Source.col("source_provider"),
va.Source.col("source_type"),
).finalize()
{
"select": [
{
"group_key": "*"
},
{
"arg": {
"timeseries": "glucose",
"field": "value"
},
"func": "mean"
},
{
"arg": {
"timeseries": "heartrate",
"field": "value"
},
"func": "mean"
},
{
"arg": {
"timeseries": "steps",
"field": "value"
},
"func": "sum"
},
{
"arg": {
"timeseries": "hrv",
"field": "value"
},
"func": "mean"
},
{
"arg": {
"timeseries": "calories_active",
"field": "value"
},
"func": "sum"
},
{
"arg": {
"timeseries": "body_temperature",
"field": "value"
},
"func": "mean"
},
{
"arg": {
"timeseries": "body_temperature",
"field": "value"
},
"func": "min"
},
{
"arg": {
"timeseries": "body_temperature",
"field": "value"
},
"func": "max"
}
],
"group_by": [
{
"arg": {
"index": "timeseries"
},
"date_trunc": {"unit": "day", "value": 1}
},
{"source": "source_provider"},
{"source": "source_type"}
]
}
Weekly Exercise Summary
import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.Workout.col("calories").max(),
va.Workout.col("calories").min(),
va.Workout.col("heart_rate_zone_1").mean(),
va.Workout.col("heart_rate_zone_2").mean(),
va.Workout.col("heart_rate_zone_3").mean(),
va.Workout.col("heart_rate_zone_4").mean(),
va.Workout.col("heart_rate_zone_5").mean(),
va.Workout.col("heart_rate_zone_6").mean(),
va.Workout.col("distance_meter").max(),
va.Workout.col("duration_active_second").mean()
).group_by(
va.date_trunc(va.Workout.index(), 1, "week")
).finalize()
{
"select": [
{
"group_key": "*"
},
{
"arg": {
"workout": "calories"
},
"func": "max"
},
{
"arg": {
"workout": "calories"
},
"func": "min"
},
{
"arg": {
"workout": "heart_rate_zone_1"
},
"func": "mean"
},
{
"arg": {
"workout": "heart_rate_zone_2"
},
"func": "mean"
},
{
"arg": {
"workout": "heart_rate_zone_3"
},
"func": "mean"
},
{
"arg": {
"workout": "heart_rate_zone_4"
},
"func": "mean"
},
{
"arg": {
"workout": "heart_rate_zone_5"
},
"func": "mean"
},
{
"arg": {
"workout": "heart_rate_zone_6"
},
"func": "mean"
},
{
"arg": {
"workout": "distance_meter"
},
"func": "max"
},
{
"arg": {
"workout": "duration_active_second"
},
"func": "mean"
}
],
"group_by": [
{
"date_trunc": {
"value": 1,
"unit": "week"
},
"arg": {
"index": "workout"
}
}
]
}
Menstrual Cycle Summary
Period end, cycle end, mean basal body temperature, and number of meaningful flow days — one row per cycle.import vitalx.aggregation as va
va.select(
va.group_key("*"),
va.MenstrualCycle.col("period_end").newest(),
va.MenstrualCycle.col("cycle_end").newest(),
va.MenstrualCycle.col("basal_body_temperature")
.unnest_and_select(lambda col: col.field("value").mean())
.mean(),
va.MenstrualCycle.col("menstrual_flow")
.unnest_and_select(lambda col: col.count())
.where("flow != 'none'")
.mean(),
).group_by(
va.date_trunc(va.MenstrualCycle.index(), 1, "day")
).finalize()
{
"select": [
{ "group_key": "*" },
{ "func": "newest", "arg": { "menstrual_cycle": "period_end" } },
{ "func": "newest", "arg": { "menstrual_cycle": "cycle_end" } },
{
"func": "mean",
"arg": {
"select": {
"func": "mean",
"arg": { "field_for": "menstrual_cycle", "basal_body_temperature": "value" }
},
"from": { "unnest": { "menstrual_cycle": "basal_body_temperature" } }
}
},
{
"func": "mean",
"arg": {
"select": { "func": "count", "arg": null },
"from": { "unnest": { "menstrual_cycle": "menstrual_flow" } },
"where": "flow != 'none'"
}
}
],
"group_by": [
{
"date_trunc": { "value": 1, "unit": "day" },
"arg": { "index": "menstrual_cycle" }
}
]
}