Factors and Levels
- class sweetpea.Factor(name, levels)
A factor for use in an experiment design.
The levels of a factor can be plain
Levelvalues, any kind of non-Levelvalue (which is implicitly coerced to aLevelvalue), orDerivedLevelvalues. In the last case, the result is a derived factor. The levels list must either contain all derived levels or all values that are not derived levels, and derived levels must all use a compatible derivation as described in Derivations. The names of the levels must be distinct; create a level with a weight to get the effect of multiple levels with he same name.- Parameters:
name – the factor’s name
levels (List[Level]) – the factor’s levels
- get_level(name)
Finds a returns a level of the factor with a given name. If the factor has multiple levels with the same name, any one of them might be returned.
Indexing a factor with [] is the same as calling the get_level method.
- Parameters:
name – the level’s name
- Returns:
a level with the given name
- Return type:
- class sweetpea.Level(name, weight=1)
A level for use in a non-derived factor. A level object can be used for only one factor.
If weight is provided as a value greater than 1, it affects how the level is used in crossings, causing it to be combined weight times with each combination of other factors’ levels in a crossing. That’s conceptually similar to having multiple levels with the same name, but as long as the level’s factor is part of a block’s crossing, the weight crossing occurrences of the level are not considered distinct. Consequently, a sampling strategy without replacement (see
Gen) will produce fewer samples than it would for separate levels. Along similar lines, aDerivedLevelcan have a weight greater than 1 to affect crossings, but cannot be included in a level multiple times, because each derived level’s predicate must match a different set of inputs.For a non-derived level whose factor is not crossed (or, more generally, is not in all crossings in a
MultiCrossBlock), a weight value has the same effect as duplicating the level’s name. That is, the would-be copies are treated as distinct, which means that sampling with replacement is biased toward levels with greater weight. For sampling strategies without replacement, the weight thus increases the number of samples that are considered distinct.- Parameters:
name – the level’s name, which can be any value
weight (int) – the level’s weight
- Return type:
- property name
The level’s name, which can be any kind of value.
- class sweetpea.DerivedLevel(name, derivation, weight=1)
Creates a derived level, which depends on the levels of other factors in a design. All derived levels for one factor must use compatible derivations as described in Derivations.
- Parameters:
name – the level’s name, which can be any value
derivation (Derivation) – a condition on other factors’ levels; see Derivations
weight (int) – the level’s weight
- Returns:
a derived level
- Return type:
- class sweetpea.ElseLevel(name, weight=1)
Creates a derived level that acts as an “else” case, matching any arguments that other derived levels do not match. An “else” derived level can appear only once among the levels supplied to
Factor, and only in combination with other derived levels. It is compatible with any derivation described in Derivations.