# Use functions in formula expressions
You can use the following functions to operate on data in your formula expressions.
Transforms and metrics support different functions. The following table indicates which types of functions are compatible with each type of formula property.
**Note**
You can include a maximum of 10 functions in a formula expression.
| Function type | Transforms | Metrics |
| --- | --- | --- |
| [Use common functions in formula expressions](expression-common-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use comparison functions in formula expressions](expression-comparison-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use conditional functions in formula expressions](expression-conditional-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use string functions in formula expressions](expression-string-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use aggregation functions in formula expressions](expression-aggregation-functions.md) | ![\[A red circle with the letter x for no.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-no.png) No | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use temporal functions in formula expressions](expression-temporal-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
| [Use date and time functions in formula expressions](expression-date-and-time-functions.md) | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes | ![\[A green circle with a checkmark for yes.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/icon-yes.png) Yes |
## Function syntax
You can use the following syntax to create functions:
Regular syntax
With the regular syntax, the function name is followed by parentheses with zero or more arguments.
`function_name(argument1, argument2, argument3, ...)`. For example, functions with the regular syntax might look like `log(x)` and `contains(s, substring)`.
Uniform function call syntax (UFCS)
UFCS enables you to call functions using the syntax for method calls in object-oriented programming. With UFCS, the first argument is followed by dot (`.`), then the function name and the remaining arguments (if any) inside parentheses.
`argument1.function_name(argument2, argument3, ...)`. For example, functions with UFCS might look like `x.log()` and `s.contains(substring)`.
You can also use UFCS to chain subsequent functions. AWS IoT SiteWise uses the evaluation result of the current function as the first argument for the next function.
For example, you can use `message.jp('$.status').lower().contains('fail')` instead of `contains(lower(jp(message, '$.status')),'fail')`.
For more information, visit the [D Programming Language](https://tour.dlang.org/tour/en/gems/uniform-function-call-syntax-ufcs) website.
**Note**
You can use UFCS for all AWS IoT SiteWise functions.
AWS IoT SiteWise functions are not case sensitive. For example, you can use `lower(s)` and `Lower(s)` interchangeably.
# Use common functions in formula expressions
In [transforms](transforms.md) and [metrics](metrics.md), you can use the following functions to calculate common mathematical functions in transforms and metrics.
| Function | Description |
| --- | --- |
| `abs(x)` | Returns the absolute value of `x`. |
| `acos(x)` | Returns the arccosine of `x`. |
| `asin(x)` | Returns the arcsine of `x`. |
| `atan(x)` | Returns the arctangent of `x`. |
| `cbrt(x)` | Returns the cubic root of `x`. |
| `ceil(x)` | Returns the nearest integer greater than `x`. |
| `cos(x)` | Returns the cosine of `x`. |
| `cosh(x)` | Returns the hyperbolic cosine of `x`. |
| `cot(x)` | Returns the cotangent of `x`. |
| `exp(x)` | Returns `e` to the power of `x`. |
| `expm1(x)` | Returns `exp(x) - 1`. Use this function to more accurately calculate `exp(x) - 1` for small values of `x`. |
| `floor(x)` | Returns the nearest integer less than `x`. |
| `log(x)` | Returns the `loge` (base `e`) of `x`. |
| `log10(x)` | Returns the `log10` (base `10`) of `x`. |
| `log1p(x)` | Returns `log(1 + x)`. Use this function to more accurately calculate `log(1 + x)` for small values of `x`. |
| `log2(x)` | Returns the `log2` (base `2`) of `x`. |
| `pow(x, y)` | Returns `x` to the power of `y`. This is equivalent to `x ^ y`. |
| `signum(x)` | Returns the sign of `x` (`-1` for negative inputs, `0` for zero inputs, `+1` for positive inputs). |
| `sin(x)` | Returns the sine of `x`. |
| `sinh(x)` | Returns the hyperbolic sine of `x`. |
| `sqrt(x)` | Returns the square root of `x`. |
| `tan(x)` | Returns the tangent of `x`. |
| `tanh(x)` | Returns the hyperbolic tangent of `x`. |
# Use comparison functions in formula expressions
In [transforms](transforms.md) and [metrics](metrics.md), you can use the following comparison functions to compare two values and output `1` (true) or `0` (false). AWS IoT SiteWise compares strings by [lexicographic order](https://en.wikipedia.org/wiki/Lexicographic_order).
| Function | Description |
| --- | --- |
| `gt(x, y)` | Returns `1` if `x` is greater than `y`, otherwise `0` (`x > y`). This function doesn't return a value if `x` and `y` are incompatible types, such as a number and a string. |
| `gte(x, y)` | Returns `1` if `x` is greater than or equal to `y`, otherwise `0` (`x ≥ y`). AWS IoT SiteWise considers the arguments equal if they are within a relative tolerance of `1E-9`. This behaves similar to the [isclose](https://docs.python.org/3/library/math.html#math.isclose) function in Python. This function doesn't return a value if `x` and `y` are incompatible types, such as a number and a string. |
| `eq(x, y)` | Returns `1` if `x` is equal to `y`, otherwise `0` (`x == y`). AWS IoT SiteWise considers the arguments equal if they are within a relative tolerance of `1E-9`. This behaves similar to the [isclose](https://docs.python.org/3/library/math.html#math.isclose) function in Python. This function doesn't return a value if `x` and `y` are incompatible types, such as a number and a string. |
| `lt(x, y)` | Returns `1` if `x` is less than `y`, otherwise `0` (`x < y`). This function doesn't return a value if `x` and `y` are incompatible types, such as a number and a string. |
| `lte(x, y)` | Returns `1` if `x` is less than or equal to `y`, otherwise `0` (`x ≤ y`). AWS IoT SiteWise considers the arguments equal if they are within a relative tolerance of `1E-9`. This behaves similar to the [isclose](https://docs.python.org/3/library/math.html#math.isclose) function in Python. This function doesn't return a value if `x` and `y` are incompatible types, such as a number and a string. |
| `isnan(x)` | Returns `1` if `x` is equal to `NaN`, otherwise `0`. This function doesn't return a value if `x` is a string. |
# Use conditional functions in formula expressions
In [transforms](transforms.md) and [metrics](metrics.md), you can use the following function to check a condition and return different results, whether the condition evaluates to true or false.
| Function | Description |
| --- | --- |
| `if(condition, result_if_true, result_if_false)` | Evaluates the `condition` and returns `result_if_true` if the condition evaluates to true or `result_if_false` if the condition evaluates to `false`. `condition` must be a number. This function considers `0` and an empty string as `false` and everything else (including `NaN`) as `true`. Booleans convert to `0` (false) and `1` (true). You can return the [none constant](expression-constants.md#none-definition) from this function to discard the output for a particular condition. This means you can filter out data points that don't meet a condition. For more information, see [Filter data points](expression-tutorials.md#filter-data).
**Examples**
+ `if(0, x, y)` returns the variable `y`.
+ `if(5, x, y)` returns the variable `x`.
+ `if(gt(temp, 300), x, y)` returns the variable `x` if the variable `temp` is greater than `300`.
+ `if(gt(temp, 300), temp, none)` returns the variable `temp` if it's greater than or equal to `300`, or `none` (no value) if `temp` is less than `300`. We recommend that you use UFCS for nested conditional functions where one or more arguments are conditional functions. You can use `if(condition, result_if_true)` to evaluate a condition and `elif(condition, result_if_true, result_if_false)` to evaluate additional conditions. For example, you can use `if(condition1, result1_if_true).elif(condition2, result2_if_true, result2_if_false)` instead of `if(condition1, result1_if_true, if(condition2, result2_if_true, result2_if_false))`. You can also chain additional intermediate conditional functions. For example, you can use `if(condition1, result1_if_true).elif(condition2, result2_if_true).elif(condition3, result3_if_true, result3_if_false)` instead of nesting multiple `if` statements, such as `if(condition1, result1_if_true, if(condition2, result2_if_true, if(condition3, result3_if_true result3_if_false)))`. You must use `elif(condition, result_if_true, result_if_false)` with UFCS. |
# Use string functions in formula expressions
In [transforms](transforms.md) and [metrics](metrics.md), you can use the following functions to operate on strings. For more information, see [Use strings in formulas](expression-tutorials.md#use-strings-in-formulas).
**Important**
Formula expressions can only output double or string values. Nested expressions can output other data types, such as strings, but the formula as a whole must evaluate to a number or string. You can use the [jp function](#jp-definition) to convert a string to a number. The Boolean value must be 1 (true) or 0 (false). For more information, see [Undefined, infinite, and overflow values](expression-tutorials.md#undefined-values).
| Function | Description |
| --- | --- |
| `len(s)` | Returns the length of the string `s`. |
| `find(s, substring)` | Returns the index of the string `substring` in the string `s`. |
| `contains(s, substring)` | Returns `1` if the string `s` contains the string `substring`, otherwise `0`. |
| `upper(s)` | Returns the string `s` in uppercase form. |
| `lower(s)` | Returns the string `s` in lowercase form. |
| `jp(s, json_path)` | Evaluates the string `s` with the [JsonPath](https://github.com/json-path/JsonPath) expression `json_path` and returns the result. Use this function to do the following: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-string-functions.html) To extract a string value from a JSON structure and return it as a number, you must use multiple nested `jp` functions. The outer `jp` function extracts the string from the JSON structure, and the inner `jp` function converts the string to a number. The string `json_path` must contain a string literal. This means that `json_path` can't be an expression that evaluates to a string.
**Examples**
+ `jp('{"status":"active","value":15}', '$.value')` returns `15`.
+ `jp('{"measurement":{"reading":25,"confidence":0.95}}', '$.measurement.reading')` returns `25`.
+ `jp('[2,8,23]', '$[2]')` returns `23`.
+ `jp('{"values":[3,6,7]}', '$.values[1]')` returns `6`.
+ `jp('111', '$')` returns `111`.
+ `jp(jp('{"measurement":{"reading":25,"confidence":"0.95"}}', '$.measurement.confidence'), '$')` returns `0.95`. |
| `join(s0, s1, s2, s3, ...)` | Returns a concatenated string with a delimiter. This function uses the first input string as a delimiter and joins the remaining input strings together. This behaves similar to the [join(CharSequence delimiter, CharSequence... elements)](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#join-java.lang.CharSequence-java.lang.CharSequence...-) function in Java.
**Examples**
+ `join("-", "aa", "bb", "cc")` returns `aa-bb-cc` |
| `format(expression: "format")` or `format("format", expression)` | Returns a string in the specified format. This function evaluates `expression` to a value, and then returns the value in the specified format. This behaves similar to the [format(String format, Object... args)](https://docs.oracle.com/javase/8/docs/api/java/lang/String.html#format-java.lang.String-java.lang.Object...-) function in Java. For more information about supported formats, see Conversions under [Class Formatter](https://docs.oracle.com/javase/7/docs/api/java/util/Formatter.html) in the *Java Platform, Standard Edition 7 API Specification*.
**Examples**
+ `format(100+1: "d")` returns a string, `101`.
+ `format("The result is %d", 100+1)` returns a string, `The result is 101`. |
| `f'expression'` | Returns a concatenated string. With this formatted function, you can use a simple expression to concatenate and format strings. These functions may contain nested expressions. You can use `{}` (curly braces) to interpolate expressions. This behaves similar to the [formatted string literals](https://docs.python.org/3/reference/lexical_analysis.html#f-strings) in Python.
**Examples**
+ `f'abc{1+2: "f"}d'` returns `abc3.000000d`. To evaluate this example expression, do the following:
1. `format(1+2: "f")` returns a floating point number, `3.000000`.
1. `join('', "abc", 1+2, 'd')` returns a string, `abc3.000000d`.
You can also write the expression in the following way: `join('', "abc", format(1+2: "f"), 'd')`. |
# Use aggregation functions in formula expressions
In [metrics](metrics.md) only, you can use the following functions that aggregate input values over each time interval and calculate a single output value. Aggregation functions can aggregate data from associated assets.
Aggregation function arguments can be [variables](expression-variables.md), [number literals](expression-literals.md#number-literal-definition), [temporal functions](expression-temporal-functions.md), nested expressions, or aggregation functions. The formula `max(latest(x), latest(y), latest(z))` uses an aggregation function as an argument and returns the largest current value of the `x`, `y`, and `z` properties.
You can use nested expressions in aggregation functions. When you use nested expressions, the following rules apply:
+ Each argument can have only one variable.
**Example**
For example, `avg(x*(x-1))` and `sum(x/2 )/avg(y^2 )` are supported.
For example, `min(x/y)` isn't supported.
+ Each argument can have multilevel nested expressions.
**Example**
For example, `sum(avg(x^2 )/2)` is supported.
+ Different arguments can have different variables.
**Example**
For example, `sum(x/2, y*2)` is supported.
**Note**
If your expressions contain measurements, AWS IoT SiteWise uses the last values over the current time interval for the measurements to compute aggregates.
If your expressions contain attributes, AWS IoT SiteWise uses the latest values for the attributes to compute aggregates.
| Function | Description |
| --- | --- |
| `avg(x0, ..., xn)` | Returns the mean of the given variables' values over the current time interval. This function outputs a data point only if the given variables have at least one data point over the current time interval. |
| `sum(x0, ..., xn)` | Returns the sum of the given variables' values over the current time interval. This function outputs a data point only if the given variables have at least one data point over the current time interval. |
| `min(x0, ..., xn)` | Returns the minimum of the given variables' values over the current time interval. This function outputs a data point only if the given variables have at least one data point over the current time interval. |
| `max(x0, ..., xn)` | Returns the maximum of the given variables' values over the current time interval. This function outputs a data point only if the given variables have at least one data point over the current time interval. |
| `count(x0, ..., xn)` | Returns the total number of data points for the given variables over the current time interval. For more information about how to count the number of data points that meet a condition, see [Count data points that match a condition](expression-tutorials.md#count-filtered-data). This function computes a data point for every time interval. |
| `stdev(x0, ..., xn)` | Returns the standard deviation of the given variables' values over the current time interval. This function outputs a data point only if the given variables have at least one data point over the current time interval. |
# Use temporal functions in formula expressions
Use temporal functions to return values based on timestamps of data points.
## Use temporal functions in metrics
In [metrics](metrics.md) only, you can use the following functions that return values based on timestamps of data points.
Temporal function arguments must be properties from the local asset model or nested expressions. This means that you can't use properties from child asset models in temporal functions.
You can use nested expressions in temporal functions. When you use nested expressions, the following rules apply:
+ Each argument can have only one variable.
For example, `latest( t*9/5 + 32 )` is supported.
+ Arguments can't be aggregation functions.
For example, `first( sum(x) )` isn't supported.
| Function | Description |
| --- | --- |
| `first(x)` | Returns the given variable's value with the earliest timestamp over the current time interval. |
| `last(x)` | Returns the given variable's value with the latest timestamp over the current time interval. |
| `earliest(x)` | Returns the given variable's last value before the start of the current time interval. This function computes a data point for every time interval, if the input property has at least one data point in its history. See [time-range-defintion](#time-range-def) for details. |
| `latest(x)` | Returns the given variable's last value with the latest timestamp before the end of the current time interval. This function computes a data point for every time interval, if the input property has at least one data point in its history. See [time-range-defintion](#time-range-def) for details. |
| `statetime(x)` | Returns the amount of time in seconds that the given variables are positive over the current time interval. You can use the [comparison functions](expression-comparison-functions.md) to create a transform property for the `statetime` function to consume. For example, if you have an `Idle` property that is `0` or `1`, you can calculate idle time per time interval with this expression: `IdleTime = statetime(Idle)`. For more information, see the [example statetime scenario](#statetime-example). This function doesn't support metric properties as input variables. This function computes a data point for every time interval, if the input property has at least one data point in its history. |
| `TimeWeightedAvg(x, [interpolation])` | Returns the average of input data weighted with time intervals between points.See [Time weighted functions parameters](#timeweighted-parameter) for computation and intervals details.The optional argument `interpolaton` must be a string constant:[\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-temporal-functions.html) |
| `TimeWeightedStDev(x, [algo])` | Returns the standard deviation of input data weighted with time intervals between points. See [Time weighted functions parameters](#timeweighted-parameter) for computation and intervals details. The calculation uses the Last Observed Carry Forward computation algorithm for intervals between data points. In this approach, the data point is computed as the last observed value until the next input data point time stamp. Weight is computed as time interval in seconds between data points or window boundaries. The optional argument `algo` must be a string constant: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-temporal-functions.html) The following formulas are used for computation where: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-temporal-functions.html) Equation for population standard deviation: ![\[Equation for population standard deviation.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/timeweightedstdev1.png) Equation for frequency standard deviation: ![\[Equation for frequency standard deviation.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/timeweighted-frequency-stdev.png) |
The following diagram shows how AWS IoT SiteWise computes the temporal functions `first`, `last`, `earliest`, and `latest`, relative to the current time interval.
![\[AWS IoT SiteWise temporal functions return data points based on their timestamp.\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/images/sitewise-temporal-functions.png)
**Note**
The time range for `first(x)`, `last(x)` is (current window start, current window end].
The time range for `latest(x)` is (beginning of time, current window end].
The time range for `earliest(x)` is (beginning of time, previous window end].
**Time-weighted functions parameters**
Time-weighted functions computed for the aggregate window take into account the following:
+ Data points inside the window
+ Time intervals between data points
+ Last data point before the window
+ First data point after the window (for some algorithms)
**Terms:**
+ **Bad data point** – Any data point with non-good quality or non-number value. This is not considered in a window result computation.
+ **Bad interval** – The interval after a bad data point. The interval before the first known data point is also considered a bad interval.
+ **Good data point** – Any data point with good quality and numeric value.
**Note**
AWS IoT SiteWise only consumes `GOOD` quality data when it computes transforms and metrics. It ignores `UNCERTAIN` and `BAD` data points.
The interval before the first known data point is considered a **bad interval**. See [Formula expression tutorials](expression-tutorials.md) for more information.
The interval after the last known data point continues indefinitely, affecting all following windows. When a new data point arrives, the function recomputes the interval.
Following the rules above, the aggregate window result is computed and limited to window boundaries. By default, the function only sends the window result if the whole window is a **good interval**.
If the window **good interval** is smaller than the window length, the function does not send the window.
When the data points affecting the window result change, the function recalculates the window, even if the data points are outside of the window.
If the input property has at least one data point in its history and a computation has been initiated, the function calculates the time-weighted aggregate functions for every time interval.
**Example statetime scenario**
Consider an example where you have an asset with the following properties:
+ `Idle` – A measurement that is `0` or `1`. When the value is `1`, the machine is idle.
+ `Idle Time` – A metric that uses the formula `statetime(Idle)` to calculate the amount of time in seconds where the machine is idle, per 1 minute interval.
The `Idle` property has the following data points.
| | | | | | |
| --- |--- |--- |--- |--- |--- |
| Timestamp | 2:00:00 PM | 2:00:30 PM | 2:01:15 PM | 2:02:45 PM | 2:04:00 PM |
| Idle | 0 | 1 | 1 | 0 | 0 |
AWS IoT SiteWise calculates the `Idle Time` property every minute from the values of `Idle`. After this calculation completes, the `Idle Time` property has the following data points.
| | | | | | |
| --- |--- |--- |--- |--- |--- |
| Timestamp | 2:00:00 PM | 2:01:00 PM | 2:02:00 PM | 2:03:00 PM | 2:04:00 PM |
| Idle Time | N/A | 30 | 60 | 45 | 0 |
AWS IoT SiteWise performs the following calculations for `Idle Time` at the end of each minute.
+ At 2:00 PM (for 1:59 PM to 2:00 PM)
+ There is no data for `Idle` before 2:00 PM, so no data point is calculated.
+ At 2:01 PM (for 2:00 PM to 2:01 PM)
+ At 2:00:00 PM, the machine is active (`Idle` is `0`).
+ At 2:00:30 PM, the machine is idle (`Idle` is `1`).
+ `Idle` doesn't change again before the end of the interval at 2:01:00 PM, so `Idle Time` is 30 seconds.
+ At 2:02 PM (for 2:01 PM to 2:02 PM)
+ At 2:01:00 PM, the machine is idle (per the last data point at 2:00:30 PM).
+ At 2:01:15 PM, the machine is still idle.
+ `Idle` doesn't change again before the end of the interval at 2:02:00 PM, so `Idle Time` is 60 seconds.
+ At 2:03 PM (for 2:02 PM to 2:03 PM)
+ At 2:02:00 PM, the machine is idle (per the last data point at 2:01:15 PM).
+ At 2:02:45 PM, the machine is active.
+ `Idle` doesn't change again before the end of the interval at 2:03:00 PM, so `Idle Time` is 45 seconds.
+ At 2:04 PM (for 2:03 PM to 2:04 PM)
+ At 2:03:00 PM, the machine is active (per the last data point at 2:02:45 PM).
+ `Idle` doesn't change again before the end of the interval at 2:04:00 PM, so `Idle Time` is 0 seconds.
**Example TimeWeightedAvg and TimeWeightedStDev scenario**
The following tables provide sample inputs and outputs for these one-minute window metrics: `Avg(x), TimeWeightedAvg(x), TimeWeightedAvg(x, "linear"), stDev(x), timeWeightedStDev(x), timeWeightedStDev(x, 'p')`.
Sample input for one-minute aggregate window:
These data points all have `GOOD` quality.
| | |
| --- |--- |
| 03:00:00 | 4.0 |
| 03:01:00 | 2.0 |
| 03:01:10 | 8.0 |
| 03:01:50 | 20.0 |
| 03:02:00 | 14.0 |
| 03:02:05 | 10.0 |
| 03:02:10 | 3.0 |
| 03:02:30 | 20.0 |
| 03:03:30 | 0.0 |
Aggregate results output:
None – Result not produced for this window.
| Time | `Avg(x)` | `TimeWeightedAvg(x)` | `TimeWeightedAvg(X, "linear")` | `stDev(X)` | `timeWeightedStDev(x)` | `timeWeightedStDev(x, 'p')` |
| --- | --- | --- | --- | --- | --- | --- |
| 3:00:00 | 4 | None | None | 0 | None | None |
| 3:01:00 | 2 | 4 | 3 | 0 | 0 | 0 |
| 3:02:00 | 14 | 9 | 13 | 6 | 5.430610041581775 | 5.385164807134504 |
| 3:03:00 | 11 | 13 | 12.875 | 8.54400374531753 | 7.724054437220943 | 7.659416862050705 |
| 3:04:00 | 0 | 10 | 2.5 | 0 | 10.084389681792215 | 10 |
| 3:05:00 | None | 0 | 0 | None | 0 | 0 |
## Use temporal functions in transforms
In [transforms](transforms.md) only, you can use the `pretrigger()` function to retrieve the `GOOD` quality value for a variable prior to the property update that initiated the current transform calculation.
Consider an example where a manufacturer uses AWS IoT SiteWise to monitor the status of a machine. The manufacturer uses the following measurements and transforms to represent the process:
+ A measurement, `current_state`, that can be 0 or 1.
+ If the machine is in the cleaning state, `current_state` equals 1.
+ If the machine is in the manufacturing state, `current_state` equals 0.
+ A transform, `cleaning_state_duration`, that equals `if(pretrigger(current_state) == 1, timestamp(current_state) - timestamp(pretrigger(current_state)), none)`. This transform returns how long the machine has been in the cleaning state in seconds, in the Unix epoch format. For more information, see [Use conditional functions in formula expressions](expression-conditional-functions.md) and the [timestamp()](expression-date-and-time-functions.md) function.
If the machine stays in the cleaning state longer than expected, the manufacturer might investigate the machine.
You can also use the `pretrigger()` function in multivariate transforms. For example, you have two measurements named `x` and `y`, and a transform, `z`, that equals `x + y + pretrigger(y)`. The following table shows the values for `x`, `y`, and `z` from 9:00 AM to 9:15 AM.
**Note**
This example assumes that the values for the measurements arrive chronologically. For example, the value of `x` for 09:00 AM arrives before the value of `x` for 09:05 AM.
If the data points for 9:05 AM arrive before the data points for 9:00 AM, `z` isn't calculated at 9:05 AM.
If the value of `x` for 9:05 AM arrives before the value of `x` for 09:00 AM and the values of `y` arrive chronologically, `z` equals `22 = 20 + 1 + 1` at 9:05 AM.
| | 09:00 AM | 09:05 AM | 09:10 AM | 09:15 AM |
| --- | --- | --- | --- | --- |
| `x` | 10 | 20 | | 30 |
| `y` | 1 | 2 | 3 | |
| `z = x + y + pretrigger(y)` | `y` doesn't receive any data point before 09:00 AM. Therefore, `z` isn't calculated at 09:00 AM. | 23 = 20 \$1 2 \$1 1 `pretrigger(y)` equals 1. | 25 = 20 \$1 3 \$1 2 `x` doesn't receive a new data point. `pretrigger(y)` equals 2. | 36 = 30 \$1 3 \$1 3 `y` doesn't receive a new data point. Therefore, `pretrigger(y)` equals 3 at 09:15 AM. |
# Use date and time functions in formula expressions
In [transforms](transforms.md) and [metrics](metrics.md), you can use the date and time functions in the following ways:
+ Retrieve the current timestamp of a data point in UTC or in the local time zone.
+ Construct timestamps with arguments, such as `year`, `month`, and `day_of_month`.
+ Extract a time period such as a year or month with the `unix_time` argument.
| Function | Description |
| --- | --- |
| `now()` | Returns the current date and time, in seconds, in the Unix epoch format. |
| `timestamp()` | [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) |
| `mktime(time_zone, year, month, day_of_month, hour, minute, second)` | Returns the input time in seconds, in the Unix epoch format. The following requirements apply for using this function: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) The following limits apply for using this function: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) Examples: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) |
| `localtime(unix_time, time_zone)` | Returns the year, the day of the month, the day of the week, the day of the year, the hour, the minute, or the second in the specified time zone from the Unix time. The following requirements apply for using this function: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) Example response: `2007-12-03T10:15:30+01:00[Europe/Paris]` `localtime(unix_time, time_zone)` isn't a standalone function. The `year()`, `mon()`, `mday`, `wday()`, `yday()`, `hour()`, `minute()`, and `sec()` functions take `localtime(unix_time, time_zone)` as an argument. Examples: [\[See the AWS documentation website for more details\]](http://docs.aws.amazon.com/iot-sitewise/latest/userguide/expression-date-and-time-functions.html) |
| `year(localtime(unix_time, time_zone)` | Returns the year from `localtime(unix_time, time_zone)`. |
| `mon(localtime(unix_time, time_zone))` | Returns the month from `localtime(unix_time, time_zone)`. |
| `mday(localtime(unix_time, time_zone))` | Returns the day of the month from `localtime(unix_time, time_zone)`. |
| `wday(localtime(unix_time, time_zone))` | Returns the day of the week from `localtime(unix_time, time_zone)`. |
| `yday(localtime(unix_time, time_zone))` | Returns the day of the year from `localtime(unix_time, time_zone)`. |
| `hour(localtime(unix_time, time_zone))` | Returns the hour from `localtime(unix_time, time_zone)`. |
| `minute(localtime(unix_time, time_zone))` | Returns the minute from `localtime(unix_time, time_zone)`. |
| `sec(localtime(unix_time, time_zone))` | Returns the second from `localtime(unix_time, time_zone)`. |
## Supported time zone formats
You can specify the time zone argument in the following ways:
+ Time zone offset - Specify `'Z'` for UTC or an offset (`'+2'` or `'-5'`).
+ Offset IDs - Combine a time zone abbreviation and an offset. For example, `'GMT+2'` and `'UTC-01:00'`. The time zone abbreviation must contain only three letters.
+ Region based IDs - For example, `'Etc/GMT+12'` and `'Pacific/Pago_Pago'`.
### Supported time zone abbreviations
The date and time functions support the following three-letter time zone abbreviations:
+ EST - -05:00
+ HST - -10:00
+ MST - -07:00
+ ACT - Australia/Darwin
+ AET - Australia/Sydney
+ AGT - America/Argentina/Buenos\$1Aires
+ ART - Africa/Cairo
+ AST - America/Anchorage
+ BET - America/Sao\$1Paulo
+ BST - Asia/Dhaka
+ CAT - Africa/Harare
+ CET - Europe/Paris
+ CNT - America/St\$1Johns
+ CST - America/Chicago
+ CTT - Asia/Shanghai
+ EAT - Africa/Addis\$1Ababa
+ IET - America/Indiana/Indianapolis
+ IST - Asia/Kolkata
+ JST - Asia/Tokyo
+ MIT - Pacific/Apia
+ NET - Asia/Yerevan
+ NST - Pacific/Auckland
+ PLT - Asia/Karachi
+ PRT - America/Puerto\$1Rico
+ PST - America/Los\$1Angeles
+ SST - Pacific/Guadalcanal
+ VST - Asia/Ho\$1Chi\$1Minh
### Supported Region-based IDs
The date and time functions support the following Region-based IDs, organized by their relation to UTC\$100:00:
+ Etc/GMT\$112 (UTC-12:00)
+ Pacific/Pago\$1Pago (UTC-11:00)
+ Pacific/Samoa (UTC-11:00)
+ Pacific/Niue (UTC-11:00)
+ US/Samoa (UTC-11:00)
+ Etc/GMT\$111 (UTC-11:00)
+ Pacific/Midway (UTC-11:00)
+ Pacific/Honolulu (UTC-10:00)
+ Pacific/Rarotonga (UTC-10:00)
+ Pacific/Tahiti (UTC-10:00)
+ Pacific/Johnston (UTC-10:00)
+ US/Hawaii (UTC-10:00)
+ SystemV/HST10 (UTC-10:00)
+ Etc/GMT\$110 (UTC-10:00)
+ Pacific/Marquesas (UTC-09:30)
+ Etc/GMT\$19 (UTC-09:00)
+ Pacific/Gambier (UTC-09:00)
+ America/Atka (UTC-09:00)
+ SystemV/YST9 (UTC-09:00)
+ America/Adak (UTC-09:00)
+ US/Aleutian (UTC-09:00)
+ Etc/GMT\$18 (UTC-08:00)
+ US/Alaska (UTC-08:00)
+ America/Juneau (UTC-08:00)
+ America/Metlakatla (UTC-08:00)
+ America/Yakutat (UTC-08:00)
+ Pacific/Pitcairn (UTC-08:00)
+ America/Sitka (UTC-08:00)
+ America/Anchorage (UTC-08:00)
+ SystemV/PST8 (UTC-08:00)
+ America/Nome (UTC-08:00)
+ SystemV/YST9YDT (UTC-08:00)
+ Canada/Yukon (UTC-07:00)
+ US/Pacific-New (UTC-07:00)
+ Etc/GMT\$17 (UTC-07:00)
+ US/Arizona (UTC-07:00)
+ America/Dawson\$1Creek (UTC-07:00)
+ Canada/Pacific (UTC-07:00)
+ PST8PDT (UTC-07:00)
+ SystemV/MST7 (UTC-07:00)
+ America/Dawson (UTC-07:00)
+ Mexico/BajaNorte (UTC-07:00)
+ America/Tijuana (UTC-07:00)
+ America/Creston (UTC-07:00)
+ America/Hermosillo (UTC-07:00)
+ America/Santa\$1Isabel (UTC-07:00)
+ America/Vancouver (UTC-07:00)
+ America/Ensenada (UTC-07:00)
+ America/Phoenix (UTC-07:00)
+ America/Whitehorse (UTC-07:00)
+ America/Fort\$1Nelson (UTC-07:00)
+ SystemV/PST8PDT (UTC-07:00)
+ America/Los\$1Angeles (UTC-07:00)
+ US/Pacific (UTC-07:00)
+ America/El\$1Salvador (UTC-06:00)
+ America/Guatemala (UTC-06:00)
+ America/Belize (UTC-06:00)
+ America/Managua (UTC-06:00)
+ America/Tegucigalpa (UTC-06:00)
+ Etc/GMT\$16 (UTC-06:00)
+ Pacific/Easter (UTC-06:00)
+ Mexico/BajaSur (UTC-06:00)
+ America/Regina (UTC-06:00)
+ America/Denver (UTC-06:00)
+ Pacific/Galapagos (UTC-06:00)
+ America/Yellowknife (UTC-06:00)
+ America/Swift\$1Current (UTC-06:00)
+ America/Inuvik (UTC-06:00)
+ America/Mazatlan (UTC-06:00)
+ America/Boise (UTC-06:00)
+ America/Costa\$1Rica (UTC-06:00)
+ MST7MDT (UTC-06:00)
+ SystemV/CST6 (UTC-06:00)
+ America/Chihuahua (UTC-06:00)
+ America/Ojinaga (UTC-06:00)
+ Chile/EasterIsland (UTC-06:00)
+ US/Mountain (UTC-06:00)
+ America/Edmonton (UTC-06:00)
+ Canada/Mountain (UTC-06:00)
+ America/Cambridge\$1Bay (UTC-06:00)
+ Navajo (UTC-06:00)
+ SystemV/MST7MDT (UTC-06:00)
+ Canada/Saskatchewan (UTC-06:00)
+ America/Shiprock (UTC-06:00)
+ America/Panama (UTC-05:00)
+ America/Chicago (UTC-05:00)
+ America/Eirunepe (UTC-05:00)
+ Etc/GMT\$15 (UTC-05:00)
+ Mexico/General (UTC-05:00)
+ America/Porto\$1Acre (UTC-05:00)
+ America/Guayaquil (UTC-05:00)
+ America/Rankin\$1Inlet (UTC-05:00)
+ US/Central (UTC-05:00)
+ America/Rainy\$1River (UTC-05:00)
+ America/Indiana/Knox (UTC-05:00)
+ America/North\$1Dakota/Beulah (UTC-05:00)
+ America/Monterrey (UTC-05:00)
+ America/Jamaica (UTC-05:00)
+ America/Atikokan (UTC-05:00)
+ America/Coral\$1Harbour (UTC-05:00)
+ America/North\$1Dakota/Center (UTC-05:00)
+ America/Cayman (UTC-05:00)
+ America/Indiana/Tell\$1City (UTC-05:00)
+ America/Mexico\$1City (UTC-05:00)
+ America/Matamoros (UTC-05:00)
+ CST6CDT (UTC-05:00)
+ America/Knox\$1IN (UTC-05:00)
+ America/Bogota (UTC-05:00)
+ America/Menominee (UTC-05:00)
+ America/Resolute (UTC-05:00)
+ SystemV/EST5 (UTC-05:00)
+ Canada/Central (UTC-05:00)
+ Brazil/Acre (UTC-05:00)
+ America/Cancun (UTC-05:00)
+ America/Lima (UTC-05:00)
+ America/Bahia\$1Banderas (UTC-05:00)
+ US/Indiana-Starke (UTC-05:00)
+ America/Rio\$1Branco (UTC-05:00)
+ SystemV/CST6CDT (UTC-05:00)
+ Jamaica (UTC-05:00)
+ America/Merida (UTC-05:00)
+ America/North\$1Dakota/New\$1Salem (UTC-05:00)
+ America/Winnipeg (UTC-05:00)
+ America/Cuiaba (UTC-04:00)
+ America/Marigot (UTC-04:00)
+ America/Indiana/Petersburg (UTC-04:00)
+ Chile/Continental (UTC-04:00)
+ America/Grand\$1Turk (UTC-04:00)
+ Cuba (UTC-04:00)
+ Etc/GMT\$14 (UTC-04:00)
+ America/Manaus (UTC-04:00)
+ America/Fort\$1Wayne (UTC-04:00)
+ America/St\$1Thomas (UTC-04:00)
+ America/Anguilla (UTC-04:00)
+ America/Havana (UTC-04:00)
+ US/Michigan (UTC-04:00)
+ America/Barbados (UTC-04:00)
+ America/Louisville (UTC-04:00)
+ America/Curacao (UTC-04:00)
+ America/Guyana (UTC-04:00)
+ America/Martinique (UTC-04:00)
+ America/Puerto\$1Rico (UTC-04:00)
+ America/Port\$1of\$1Spain (UTC-04:00)
+ SystemV/AST4 (UTC-04:00)
+ America/Indiana/Vevay (UTC-04:00)
+ America/Indiana/Vincennes (UTC-04:00)
+ America/Kralendijk (UTC-04:00)
+ America/Antigua (UTC-04:00)
+ America/Indianapolis (UTC-04:00)
+ America/Iqaluit (UTC-04:00)
+ America/St\$1Vincent (UTC-04:00)
+ America/Kentucky/Louisville (UTC-04:00)
+ America/Dominica (UTC-04:00)
+ America/Asuncion (UTC-04:00)
+ EST5EDT (UTC-04:00)
+ America/Nassau (UTC-04:00)
+ America/Kentucky/Monticello (UTC-04:00)
+ Brazil/West (UTC-04:00)
+ America/Aruba (UTC-04:00)
+ America/Indiana/Indianapolis (UTC-04:00)
+ America/Santiago (UTC-04:00)
+ America/La\$1Paz (UTC-04:00)
+ America/Thunder\$1Bay (UTC-04:00)
+ America/Indiana/Marengo (UTC-04:00)
+ America/Blanc-Sablon (UTC-04:00)
+ America/Santo\$1Domingo (UTC-04:00)
+ US/Eastern (UTC-04:00)
+ Canada/Eastern (UTC-04:00)
+ America/Port-au-Prince (UTC-04:00)
+ America/St\$1Barthelemy (UTC-04:00)
+ America/Nipigon (UTC-04:00)
+ US/East-Indiana (UTC-04:00)
+ America/St\$1Lucia (UTC-04:00)
+ America/Montserrat (UTC-04:00)
+ America/Lower\$1Princes (UTC-04:00)
+ America/Detroit (UTC-04:00)
+ America/Tortola (UTC-04:00)
+ America/Porto\$1Velho (UTC-04:00)
+ America/Campo\$1Grande (UTC-04:00)
+ America/Virgin (UTC-04:00)
+ America/Pangnirtung (UTC-04:00)
+ America/Montreal (UTC-04:00)
+ America/Indiana/Winamac (UTC-04:00)
+ America/Boa\$1Vista (UTC-04:00)
+ America/Grenada (UTC-04:00)
+ America/New\$1York (UTC-04:00)
+ America/St\$1Kitts (UTC-04:00)
+ America/Caracas (UTC-04:00)
+ America/Guadeloupe (UTC-04:00)
+ America/Toronto (UTC-04:00)
+ SystemV/EST5EDT (UTC-04:00)
+ America/Argentina/Catamarca (UTC-03:00)
+ Canada/Atlantic (UTC-03:00)
+ America/Argentina/Cordoba (UTC-03:00)
+ America/Araguaina (UTC-03:00)
+ America/Argentina/Salta (UTC-03:00)
+ Etc/GMT\$13 (UTC-03:00)
+ America/Montevideo (UTC-03:00)
+ Brazil/East (UTC-03:00)
+ America/Argentina/Mendoza (UTC-03:00)
+ America/Argentina/Rio\$1Gallegos (UTC-03:00)
+ America/Catamarca (UTC-03:00)
+ America/Cordoba (UTC-03:00)
+ America/Sao\$1Paulo (UTC-03:00)
+ America/Argentina/Jujuy (UTC-03:00)
+ America/Cayenne (UTC-03:00)
+ America/Recife (UTC-03:00)
+ America/Buenos\$1Aires (UTC-03:00)
+ America/Paramaribo (UTC-03:00)
+ America/Moncton (UTC-03:00)
+ America/Mendoza (UTC-03:00)
+ America/Santarem (UTC-03:00)
+ Atlantic/Bermuda (UTC-03:00)
+ America/Maceio (UTC-03:00)
+ Atlantic/Stanley (UTC-03:00)
+ America/Halifax (UTC-03:00)
+ Antarctica/Rothera (UTC-03:00)
+ America/Argentina/San\$1Luis (UTC-03:00)
+ America/Argentina/Ushuaia (UTC-03:00)
+ Antarctica/Palmer (UTC-03:00)
+ America/Punta\$1Arenas (UTC-03:00)
+ America/Glace\$1Bay (UTC-03:00)
+ America/Fortaleza (UTC-03:00)
+ America/Thule (UTC-03:00)
+ America/Argentina/La\$1Rioja (UTC-03:00)
+ America/Belem (UTC-03:00)
+ America/Jujuy (UTC-03:00)
+ America/Bahia (UTC-03:00)
+ America/Goose\$1Bay (UTC-03:00)
+ America/Argentina/San\$1Juan (UTC-03:00)
+ America/Argentina/ComodRivadavia (UTC-03:00)
+ America/Argentina/Tucuman (UTC-03:00)
+ America/Rosario (UTC-03:00)
+ SystemV/AST4ADT (UTC-03:00)
+ America/Argentina/Buenos\$1Aires (UTC-03:00)
+ America/St\$1Johns (UTC-02:30)
+ Canada/Newfoundland (UTC-02:30)
+ America/Miquelon (UTC-02:00)
+ Etc/GMT\$12 (UTC-02:00)
+ America/Godthab (UTC-02:00)
+ America/Noronha (UTC-02:00)
+ Brazil/DeNoronha (UTC-02:00)
+ Atlantic/South\$1Georgia (UTC-02:00)
+ Etc/GMT\$11 (UTC-01:00)
+ Atlantic/Cape\$1Verde (UTC-01:00)
+ Pacific/Kiritimati (UTC\$114:00)
+ Etc/GMT-14 (UTC\$114:00)
+ Pacific/Fakaofo (UTC\$113:00)
+ Pacific/Enderbury (UTC\$113:00)
+ Pacific/Apia (UTC\$113:00)
+ Pacific/Tongatapu (UTC\$113:00)
+ Etc/GMT-13 (UTC\$113:00)
+ NZ-CHAT (UTC\$112:45)
+ Pacific/Chatham (UTC\$112:45)
+ Pacific/Kwajalein (UTC\$112:00)
+ Antarctica/McMurdo (UTC\$112:00)
+ Pacific/Wallis (UTC\$112:00)
+ Pacific/Fiji (UTC\$112:00)
+ Pacific/Funafuti (UTC\$112:00)
+ Pacific/Nauru (UTC\$112:00)
+ Kwajalein (UTC\$112:00)
+ NZ (UTC\$112:00)
+ Pacific/Wake (UTC\$112:00)
+ Antarctica/South\$1Pole (UTC\$112:00)
+ Pacific/Tarawa (UTC\$112:00)
+ Pacific/Auckland (UTC\$112:00)
+ Asia/Kamchatka (UTC\$112:00)
+ Etc/GMT-12 (UTC\$112:00)
+ Asia/Anadyr (UTC\$112:00)
+ Pacific/Majuro (UTC\$112:00)
+ Pacific/Ponape (UTC\$111:00)
+ Pacific/Bougainville (UTC\$111:00)
+ Antarctica/Macquarie (UTC\$111:00)
+ Pacific/Pohnpei (UTC\$111:00)
+ Pacific/Efate (UTC\$111:00)
+ Pacific/Norfolk (UTC\$111:00)
+ Asia/Magadan (UTC\$111:00)
+ Pacific/Kosrae (UTC\$111:00)
+ Asia/Sakhalin (UTC\$111:00)
+ Pacific/Noumea (UTC\$111:00)
+ Etc/GMT-11 (UTC\$111:00)
+ Asia/Srednekolymsk (UTC\$111:00)
+ Pacific/Guadalcanal (UTC\$111:00)
+ Australia/Lord\$1Howe (UTC\$110:30)
+ Australia/LHI (UTC\$110:30)
+ Australia/Hobart (UTC\$110:00)
+ Pacific/Yap (UTC\$110:00)
+ Australia/Tasmania (UTC\$110:00)
+ Pacific/Port\$1Moresby (UTC\$110:00)
+ Australia/ACT (UTC\$110:00)
+ Australia/Victoria (UTC\$110:00)
+ Pacific/Chuuk (UTC\$110:00)
+ Australia/Queensland (UTC\$110:00)
+ Australia/Canberra (UTC\$110:00)
+ Australia/Currie (UTC\$110:00)
+ Pacific/Guam (UTC\$110:00)
+ Pacific/Truk (UTC\$110:00)
+ Australia/NSW (UTC\$110:00)
+ Asia/Vladivostok (UTC\$110:00)
+ Pacific/Saipan (UTC\$110:00)
+ Antarctica/DumontDUrville (UTC\$110:00)
+ Australia/Sydney (UTC\$110:00)
+ Australia/Brisbane (UTC\$110:00)
+ Etc/GMT-10 (UTC\$110:00)
+ Asia/Ust-Nera (UTC\$110:00)
+ Australia/Melbourne (UTC\$110:00)
+ Australia/Lindeman (UTC\$110:00)
+ Australia/North (UTC\$109:30)
+ Australia/Yancowinna (UTC\$109:30)
+ Australia/Adelaide (UTC\$109:30)
+ Australia/Broken\$1Hill (UTC\$109:30)
+ Australia/South (UTC\$109:30)
+ Australia/Darwin (UTC\$109:30)
+ Etc/GMT-9 (UTC\$109:00)
+ Pacific/Palau (UTC\$109:00)
+ Asia/Chita (UTC\$109:00)
+ Asia/Dili (UTC\$109:00)
+ Asia/Jayapura (UTC\$109:00)
+ Asia/Yakutsk (UTC\$109:00)
+ Asia/Pyongyang (UTC\$109:00)
+ ROK (UTC\$109:00)
+ Asia/Seoul (UTC\$109:00)
+ Asia/Khandyga (UTC\$109:00)
+ Japan (UTC\$109:00)
+ Asia/Tokyo (UTC\$109:00)
+ Australia/Eucla (UTC\$108:45)
+ Asia/Kuching (UTC\$108:00)
+ Asia/Chungking (UTC\$108:00)
+ Etc/GMT-8 (UTC\$108:00)
+ Australia/Perth (UTC\$108:00)
+ Asia/Macao (UTC\$108:00)
+ Asia/Macau (UTC\$108:00)
+ Asia/Choibalsan (UTC\$108:00)
+ Asia/Shanghai (UTC\$108:00)
+ Antarctica/Casey (UTC\$108:00)
+ Asia/Ulan\$1Bator (UTC\$108:00)
+ Asia/Chongqing (UTC\$108:00)
+ Asia/Ulaanbaatar (UTC\$108:00)
+ Asia/Taipei (UTC\$108:00)
+ Asia/Manila (UTC\$108:00)
+ PRC (UTC\$108:00)
+ Asia/Ujung\$1Pandang (UTC\$108:00)
+ Asia/Harbin (UTC\$108:00)
+ Singapore (UTC\$108:00)
+ Asia/Brunei (UTC\$108:00)
+ Australia/West (UTC\$108:00)
+ Asia/Hong\$1Kong (UTC\$108:00)
+ Asia/Makassar (UTC\$108:00)
+ Hongkong (UTC\$108:00)
+ Asia/Kuala\$1Lumpur (UTC\$108:00)
+ Asia/Irkutsk (UTC\$108:00)
+ Asia/Singapore (UTC\$108:00)
+ Asia/Pontianak (UTC\$107:00)
+ Etc/GMT-7 (UTC\$107:00)
+ Asia/Phnom\$1Penh (UTC\$107:00)
+ Asia/Novosibirsk (UTC\$107:00)
+ Antarctica/Davis (UTC\$107:00)
+ Asia/Tomsk (UTC\$107:00)
+ Asia/Jakarta (UTC\$107:00)
+ Asia/Barnaul (UTC\$107:00)
+ Indian/Christmas (UTC\$107:00)
+ Asia/Ho\$1Chi\$1Minh (UTC\$107:00)
+ Asia/Hovd (UTC\$107:00)
+ Asia/Bangkok (UTC\$107:00)
+ Asia/Vientiane (UTC\$107:00)
+ Asia/Novokuznetsk (UTC\$107:00)
+ Asia/Krasnoyarsk (UTC\$107:00)
+ Asia/Saigon (UTC\$107:00)
+ Asia/Yangon (UTC\$106:30)
+ Asia/Rangoon (UTC\$106:30)
+ Indian/Cocos (UTC\$106:30)
+ Asia/Kashgar (UTC\$106:00)
+ Etc/GMT-6 (UTC\$106:00)
+ Asia/Almaty (UTC\$106:00)
+ Asia/Dacca (UTC\$106:00)
+ Asia/Omsk (UTC\$106:00)
+ Asia/Dhaka (UTC\$106:00)
+ Indian/Chagos (UTC\$106:00)
+ Asia/Qyzylorda (UTC\$106:00)
+ Asia/Bishkek (UTC\$106:00)
+ Antarctica/Vostok (UTC\$106:00)
+ Asia/Urumqi (UTC\$106:00)
+ Asia/Thimbu (UTC\$106:00)
+ Asia/Thimphu (UTC\$106:00)
+ Asia/Kathmandu (UTC\$105:45)
+ Asia/Katmandu (UTC\$105:45)
+ Asia/Kolkata (UTC\$105:30)
+ Asia/Colombo (UTC\$105:30)
+ Asia/Calcutta (UTC\$105:30)
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+
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