Special functions
We developed functions to facilitate working with time series, fill in gaps or missing data points, allow conditional statements, cumulative sums and shift the position of the elements inside.
Last updated
We developed functions to facilitate working with time series, fill in gaps or missing data points, allow conditional statements, cumulative sums and shift the position of the elements inside.
Last updated
Many synthetic expressions require two or more variables. In those cases, each of the values to be computed must have the same timestamp, otherwise the engine will not compute the expression. Fill missing() can help in these cases, as it fills the gaps where there is a missing value in any of the variables used within the expression. The syntax for this function is as follows:
fill_missing (expression, <first_fill>, <last_fill>, <fill_value>)
Expression
The expression to be computed
N/A
YES
first_fill
None: No fill will be done.
NO
last_fill
NO
fill_value
This is the value that will be used to fill in the blanks in the variables.
Any integer or float.
NO
fill_missing(temperature * humidity)
Fills the missing gaps starting in the most recent data in the time series for the temperature
and humidity
variables and then calculates its product.
fill_missing(temperature * humidity, fill_value = 1)
Fills the missing gaps with the value "1" starting in the most recent data in the time series for the temperature
and humidity
variables and then calculates its product.
fill_missing(temperature * humidity, "first_fill" = "bfill")
Fills the missing gaps starting in the oldest data in the time series for the temperature
and humidity
variables and then calculates its product.
A conditional expression is required in some cases to generate an outcome. The where() function is an if-else statement that executes one of two actions depending on whether the input condition is true or false. The syntax for this function is as follows:
where(condition, operation if true, operation if false)
It computes the operation operation_if_true, if the condition is true. Otherwise it will execute the operation_if_false.
This function calculates the difference starting at the last element in a time series and the next separated by a specified number of steps. The syntax of this function is as follows.
diff(<variable>,<steps>)
Variable
Variable label
YES
Steps
Number of places separating two components in a time series, starting with the most recent element.
Any positive integer no bigger than the total of elements in the time series
YES
The function returns the variable's values in the time series shifted by the given number of steps. The syntax of this function is as follows.
shift(<variable>,<step> )
Variable
Variable label
YES
Step
Number of places (+/-) the time series will be shifted to.
Positive or negative integer
YES
This function returns the cumulative sum of a time series. The syntax of this function is
Determines how the gap should be filled. By default set to .
: Fills the gaps forward, starting in the most recent data point.
: Fills in the gaps backward, starting in the oldest data point.
Determines how the gap should be filled at the end of the time series. Comes in handy when you have to fill all gaps in the variables. By default the function is set to .
: Fills the gaps forward, starting in the most recent data point.
: Fills in the gaps backward, starting in the oldest data point.
: No fill will be done.
It shows the measurements of a sensor as a of data points that vary over time. Also known as , it's identified by a variable label.
It shows the measurements of a sensor as a of data points that vary over time. Also known as , it's identified by a variable label.
, where is the variable to be calculated.