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on– The variables it applies to. They can be selected usingallorfixedin the same way the inputs of computed variables are selected.group– Defines how to group the selected variables so each group can be ordered.type– The way we decide things are equal (for grouping them); the options are:strict– The standard group by the operation that groups strictly equal things.by– The list of dimensions or data fields to group by.
custom– This lets the user define for each feature how to decide if two elements are equal. It is useful to group things that are similar rather than strictly equal. It only works with doubles.by– The list of dimensions or data fields to group by with instructions on how to group them.feature– A dimension that is parsable to a double or a data field that contains a double.type– The type of equality for this feature, eitherstrictorapproximate(strict will be used if no type is specified). In the case ofapproximate, the user has to set the parametertolerance_as_amountortolerance_as_rateto define the approximation as needed.
comparator– The comparator that defines the order.type– There are three types currently available:inc_natural_order– Increasing natural order over dimension or data field of any type.dec_natural order– Decreasing natural order over strings dimension or data field of any type.explicit– the user explicitly states the order within a list.E.g. if you want to order prices by their color where blue is the lowest and red is the highest, then state the following
order:["blue", "red"].This order can either be defined inline or through data in the alignment’s scope.
By using data, you can specify partial orders with a different
min_gapfor each.
Two other types of comparators exist but are deprecated:
double_inc_natural_order– Increasing natural order over string-typed dimension or data field that will be cast to double.double_dec_natural_order– Decreasing natural order over string-typed dimension or data field that will be cast to double.
over– The feature over which the comparator is applied. It is usually a dimension, but it can be any data field in the scope where the variables are selected.
The minimum gap between two consecutive variables is required. There are two options:
min_gap_as_amountif the gap is expressed as a fixed amount.E.g. applied to
A<B<C:A + min_gap_as_amount < B & B + min_gap_as_amount < C
min_gap_as_rateif the gap is expressed as a rate of the previous variable in the alignment.E.g. applied to
A<B<C:A + A*min_gap_as_rate < B & B + B*min_gap_as_rate < C
Each can be defined inline or through data.
priority– low, medium, or high.
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on– The variables it applies to. They can be selected usingallorfixedin the same way the inputs of the computed variables are selected.group– Defines the groups within which the variables should be equal. This can be omitted if you want all the selected variables to be equal to each other. Works in the same way as with the Alignment criterion.
precision– The expected precision of the equality. It should be set with technical or domain-related limitations in mind, it has nothing to do with a user being okay with the criterion not being exactly met.Equality with a precision of 0.1 will be considered satisfied if the difference between the variables is in
]-0.1; 0.1[.Usually, we set the precision to half the smallest relevant amount of change for the variable.
E.g. half a cent for a variable in Euros.
acceptable_delta– The acceptable margin of error. It is related to the end-users' tolerance to error.Equality with an acceptable delta of 0.5 will be considered acceptable (but not satisfied) if the difference between the variables is in
[-0.5,-precision[ U ]+precision, +0.5].
priority– low, medium, or high.
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