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.

​fillfillfill
_missing()missing()missing()
​
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>)
Parameters
Description
Value
Mandatory
Expression
The expression to be computed
N/A
YES
first_fill
Determines how the gap should be filled. By default set to ffillffillffill
.
​ffillffillffill
 : Fills the gaps forward, starting in the most recent data point.
​
​bfillbfillbfill
: Fills in the gaps backward, starting in a oldest data point.
​
None: No fill will be done.
NO
last_fill
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 nonenonenone
.
​ffillffillffill
 : Fills the gaps forward, starting in the most recent data point.
​
​bfillbfillbfill
: Fills in the gaps backward, starting in a oldest data point.
​
​nonenonenone
: No fill will be done.
NO
fill_value
This is the value that will be used to fill in the blanks in the variables.
any integer or float
NO
Examples
Expression
Description
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 calculate 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 calculate 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 calculate its product
​where()where()where()
​
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)
Computes the operation operation_if_true, if the condition is true otherwise it will execute the operation_if_false.
​diff()diff()diff()
​
This function calculates the difference staring 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>)
Parameter
Description
Value
Mandatory
variable
It shows the measurements of a sensor as a time series of data points that vary over time, also known as "dots", and is identified by a variable label.
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
​shift()shift()shift()
​
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> )
Parameter
Description
Value
Mandatory
variable
It shows the measurements of a sensor as a time series of data points that vary over time, also known as "dots", and is identified by a variable label.
Variable label
YES
step 
Number of places (+/-) the time series will be shifted to.
Positive or negative integer
YES
​Cumsum()Cumsum()Cumsum()
​
This function returns the cumulative sum of a time series. The syntax of this function is
​cumsum(x)cumsum(x)cumsum(x)
where xxx
, is the variable to calculate.
​

Parameters	Description	Value	Mandatory
Expression	The expression to be computed	N/A	YES
`first_fill`	Determines how the gap should be filled. By default set to $ffill$ .	$ffill$ : Fills the gaps forward, starting in the most recent data point. $bfill$ : Fills in the gaps backward, starting in a oldest data point. None: No fill will be done.	NO
`last_fill`	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 $none$ .	$ffill$ : Fills the gaps forward, starting in the most recent data point. $bfill$ : Fills in the gaps backward, starting in a oldest data point. $none$ : No fill will be done.	NO
`fill_value`	This is the value that will be used to fill in the blanks in the variables.	any integer or float	NO

Expression	Description
`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 calculate 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 calculate 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 calculate its product

Parameter	Description	Value	Mandatory
variable	It shows the measurements of a sensor as a time series of data points that vary over time, also known as "dots", and is identified by a variable label.	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

Parameter	Description	Value	Mandatory
variable	It shows the measurements of a sensor as a time series of data points that vary over time, also known as "dots", and is identified by a variable label.	Variable label	YES
step	Number of places (+/-) the time series will be shifted to.	Positive or negative integer	YES

Rolling

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Specs and limits

Last modified 30d ago

Special functions

$fill$
_
$missing()$

Examples

$where()$

$diff()$

$shift()$

$Cumsum()$

Special functions

​fillfillfill_missing()missing()missing()​

Examples

​where()where()where()​

​diff()diff()diff()​

​shift()shift()shift()​

​Cumsum()Cumsum()Cumsum()​

$fill$
_
$missing()$

$where()$

$diff()$

$shift()$

$Cumsum()$