Allows you to look for strange or missing values in SkySpark.

sfxDataQualityExtAxon funcs

Registered StackHub users may elect to receive email notifications whenever a new package version is released or a comment is posted on the forum.

There are **9** watchers.

v2.4.3

sfxDQFindNoise

sfxDQFindNoise(thePoint, dates, delta: 0%, dur: 16min)

Find Noise

- This rule runs at the point level.
- This rule looks for data groups that have changed by a certain percentage from a previous group.
- The percentage will be determined automatically if no delta and dur parameters are given.
- Side Effects: None.
- Input: A point, a date/date range, an optional delta, and an optional duration.
- Output: hisDurGrid
- sfxDQFindNoise(read(temp and discharge), pastMonth)
- sfxDQFindNoise(read(temp and discharge), pastMonth, 5%, 30min)

sfxDQFindNulls

sfxDQFindNulls(thePoint, dates, interval: 15min)

Find Missing Values

- This rule runs at the point level.
- Do rollups of the expected interval of the data. Then, look for empty periods.
`sfxDQTSGap()`

does the same thing, but is more advanced.- Side Effects: None.
- Input: A point, a date/date range, and an expected interval.
- Output: hisDurGrid
`sfxDQFindNulls(read(temp and discharge), pastMonth, 20min)`

sfxDQFindOddities

sfxDQFindOddities(thePoint, dates)

Find Oddities

- This rule runs at the point level.
- Finds nulls, NAs, or numbers less than or equal to 0.
- Side Effects: None.
- Input: A point and a date/date range.
- Output: hisDurGrid
`sfxDQFindOddities(read(temp and discharge), pastMonth)`

sfxDQFindOutliers

sfxDQFindOutliers(thePoint, dates, sDs: 3)

Find Outliers Based on Standard Deviation

- This rule runs at the point level.
- This rule has been modified to rely on the hisDQAverage and hisDQstdDev tags that come from the DQJob.
- sDs is the number of standard deviations you want this rule to allow for before a spark is detected. If a history record is below or above 3 stdDev, it will cause a spark in this form (you can change this).
- Side Effects: None.
- Input: A point, a date/date range, and optionally, how many standard deviations to go out.
- Output: hisDurGrid
`sfxDQFindOutliers(read(temp and discharge), pastMonth, 4)`

sfxDQJob

sfxDQJob(reInitialize: 1mo, dateRange: pastYear, filter: "point and kind==\"Number\" and hisSize > 1", modeInterval: 1)

Data Quality Job

- Run as task or job
- Adds Descriptive Tags to Numeric Points
- Run this as a job weekly
- reInitialize tells SkySpark how often to update tags
- Descriptive tags can be modified by user on the fly or with tuning function.
- Monthly running yearly total normalizes for many things.
- Dependencies: Calculus and Signal Analysis Pods.
- Side Effects: Adds several statistical tags to all numeric points that can be used for complex rules.
- Input: Optional reInitialize interval and option how far back to go (default is 1mo and pastYear).
- Output: None.
`sfxDQJob()`

sfxDQMismatchedEnergyUsage

sfxDQMismatchedEnergyUsage(thePoint, dates, buffer: 0.8)

Find Where Energy Out is not in Line with other Records

- This rule runs at the point level.
- Use tags from data quality job to look for energy in/out outliers.
- This rule is currently designed to run on
`zone air temp sensor`

points and then go to the equip (usually ahu) level, find the`weatherPoint temp`

(via`siteRef`

and`weatherRef`

) and finally the`siteMeter energy point`

- Side Effects: None.
- Input: A
`zone air temp point`

, a date/date range, and optional buffer. - The buffer determines what percentage of the pastYear's absolute min or max is acceptable. if old: {min: 50, max: 100, buffer: 0.8} then run against: {minThresh: 62.5, maxThresh: 80} (50/0.8 = 62.5 and 100*0.8 = 80)
- Output: hisDurGrid
`sfxDQMismatchedEnergyUsage(read(zone and air and temp and sensor), pastMonth, 0.7)`

sfxDQOutlierThresh

sfxDQOutlierThresh(thePoint, dates, threshold: 1000)

Find Values too High.

- This rule runs at the point level.
- Find Outliers based on threshold and its inverse.
- Dependencies: None.
- Side Effects: None.
- Input: A point, a date/date range, and a threshold.
- Output: hisDurGrid
`sfxDQOutlierThresh(read(temp and discharge), pastMonth, 100)`

sfxDQOutofRange

sfxDQOutofRange(thePoint, dates, buffer: 0.8)

Find Outliers

- This rule runs at the point level.
- Use tags from data quality job to look for outliers.
- Side Effects: None.
- Input: A point, a date/date range, and optional buffer.
- The buffer determines what percentage of the pastYear's absolute min or max is acceptable.
- if old: {min: 50, max: 100, buffer: 0.8} then run against: {minThresh: 62.5, maxThresh: 80} (50/0.8 = 62.5 and 100*0.8 = 80)
- Output: hisDurGrid
`sfxDQOutofRange(read(temp and discharge), pastMonth, 0.7)`

sfxDQRateofChange

sfxDQRateofChange(thePoint, dates, buffer: 0.8)

Find Rate of Change too High

- This rule runs at the point level.
- sfxDQRateofChange() does the same thing, but is more advanced.
- Use tags from data quality job to look for rate-of-change outliers.
- Dependencies: Calculus and Signal Analysis Pods.
- Side Effects: None.
- Input: A point, a date/date range, and an optional buffer.
- The buffer determines what percentage of the pastYear's absolute max negative or max positive rate of change is acceptable.
- if oldROC: {maxNegROC: -10, maxPosROC: 10, buffer: 0.8} then run against: {maxNegROCThresh: -8, maxPosROCThresh: 8} (-10*0.8 = -8 and 10*0.8 = 8)
- Output: hisDurGrid
`sfxDQRateofChange(read(temp and discharge), pastMonth, 0.7)`

sfxDQRateofChangeThresh

sfxDQRateofChangeThresh(thePoint, dates, threshold: 5)

Find Rate of Change too High

- This rule runs at the point level.
- Use tags from data quality job to look for rate-of-change outliers.
- Dependencies: Calculus and Signal Analysis Pods.
- Side Effects: None.
- Input: A point, a date/date range, and a threshold.
- Output: hisDurGrid
`sfxDQRateofChangeThresh(read(temp and discharge), pastMonth, 10)`

sfxDQTSGap

sfxDQTSGap(thePoint, dates, buffer: 1.9)

Find Gaps in Data

- This rule runs at the point level.
- Use tags from data quality job to look for missing data/intervals.
- Side Effects: None.
- Input: A point, a date/date range, and an optional buffer.
- The buffer determines what percentage of the pastYear's median ts interval is acceptable.
- if oldTSInt: {medTSInt: 15min, buffer: 1.9} then run against: {medTSIntThresh: 28.5min} (15min*1.9 = 28.5min)
- Output: hisDurGrid
`sfxDQTSGap(read(temp and discharge), pastMonth, 1.8)`

sfxDQZScoreOutofBounds

sfxDQZScoreOutofBounds(thePoint, dates, buffer: 0.8)

Find Outliers using ZScore

- This rule runs at the point level.
- Use tags from data quality job to look for outliers.
- Side Effects: None.
- Input: A point, a date/date range, and optional ZScore override.
- Output: hisDurGrid
`sfxDQZScoreOutofBounds(read(temp and discharge), pastMonth, 0.9)`

Published by SkyFoundry