Temperament addition

(THIS PAGE IS A WIP)

Temperament arithmetic is the general name for either the temperament sum or the temperament difference, which are two closely related operations on regular temperaments. Basically, to do temperament arithmetic means to match up the entries of temperament vectors then add or subtract them individually. The result is a new temperament that has similar properties to the original temperaments.

For example, the sum of 12-ET and 7-ET is 19-ET because ⟨12 19 28] + ⟨7 11 16] = ⟨(12+7) (19+11) (28+16)] = ⟨19 30 44], and the difference of 12-ET and 7-ET is 5-ET because ⟨12 19 28] - ⟨7 11 16] = ⟨(12-7) (8-11) (12-16)] = ⟨5 8 12]. We can write these using wart notation as 12p + 7p = 19p and 12p - 7p = 5p, respectively. The similarity in these temperaments can be seen in how, like both 12-ET and 7-ET, 19-ET also supports meantone temperament.

Temperament sums and differences can also be found using commas; for example meantone + porcupine = tetracot because [4 -4 1⟩ + [1 -5 3⟩ = [(4+1) (-4+-5) (1+3)⟩ = [5 -9 4⟩ and meantone - porcupine = dicot because [4 -4 1⟩ - [1 -5 3⟩ = [(4-1) (-4--5) (1-3)⟩ = [3 1 -2⟩. We could write this in ratio form — replacing addition with multiplication and subtraction with division — as 80/81 × 250/243 = 20000/19683 and 80/81 ÷ 250/243 = 25/24, respectively. The similarity in these temperaments can be seen in how all of them are supported by 7-ET.

Temperament arithmetic is simplest for temperaments which can be represented by single vectors such as demonstrated in these examples. In other words, it is simplest for temperaments that are either rank-1 (ETs) or nullity-1 (having only a single comma). Because grade is the generic word for rank and nullity, we could define the minimum grade of a temperament as the minimum of its rank and nullity, and so for convenience in this article we will refer to rank-1 or nullity-1 temperaments as min-grade-1 temperaments.

For non-min-grade-1 temperaments, arithmetic gets a little trickier. This is discussed in the beyond min-grade-1 section later.

Visualizing temperament arithmetic

One way we can visualize temperament arithmetic is on projective tuning space.

A visualization of temperament arithmetic on projective tuning space.

This shows both the sum and the difference of porcupine and meantone. All four temperaments — the two input temperaments, porcupine and meantone, as well as the sum, tetracot, and the diff, dicot — can be seen to intersect at 7-ET. This is because all four temperaments' mappings can be expressed with the map for 7-ET as one of their mapping rows.

These are all rank-2 temperaments, so their mappings each have one other row besides the one reserved for 7-ET. Any line that we draw across these four temperament lines will strike four ETs whose maps have a sum and difference relationship. On this diagram, two such lines have been drawn. The first one runs through 5-ET, 20-ET, 15-ET, and 10-ET. We can see that 5 + 15 = 20, which corresponds to the fact that 20-ET is the ET on the line for tetracot, which is the sum of porcupine and meantone, while 5-ET and 15-ET are the ETs on their lines. Similarly, we can see that 15 - 5 = 10, which corresponds to the fact that 10-ET is the ET on the line for dicot, which is the difference of porcupine and meantone.

The other line runs through the ETs 12, 41, 29, and 17, and we can see again that 12 + 29 = 41 and 29 - 12 = 17.

A visualization of temperament arithmetic on projective tone space.

We can also visualize temperament arithmetic on projective tone space. Here relationships are inverted: points are lines, and lines are points. So all four temperaments are found along the line for 7-ET.

Note that when viewed in tuning space, the sum is found between the two input temperaments, and the difference is found on the outside of them, to one side or the other. While in tone space, it's the difference that's found between the two input temperaments, and its the sum that's found outside. In either situation when a temperament is on the outside and may be on one side or the other, the explanation for this can be inferred from behavior of the scale tree on any temperament line, where e.g. if 5-ET and 7-ET support a rank-2 temperament, then so will 5 + 7 = 12-ET, and then so will 5 + 12 and 7 + 12 in turn, and so on and so on recursively; when you navigate like this, what we could call down the scale tree, children are always found between their parents. But when you try to go back up the scale tree, to one or the other parent, you may not immediately know which side of the child to go.

Conditions on temperament arithmetic

Temperament arithmetic is only possible for temperaments with the same rank and dimensionality (and therefore, by the rank-nullity theorem, also the same nullity). The reason for this is visually obvious: without the same [math]\displaystyle{ d }[/math], [math]\displaystyle{ r }[/math], and [math]\displaystyle{ n }[/math] (dimensionality, rank, and nullity), the numeric representations of the temperament — such as matrices and multivectors — will not have the same proportions, and therefore their entries will be unable to be matched up one-to-one. From this condition it also follows that the result of temperament arithmetic will be a new temperament with the same [math]\displaystyle{ d }[/math], [math]\displaystyle{ r }[/math], and [math]\displaystyle{ n }[/math] as the input temperaments.

Matching rank and dimensionality is only the first of two conditions on the possibility of temperament arithmetic. The second condition is that the temperaments must all be monononcollinear. This condition is trickier, though, and so a detailed discussion of it will be deferred to a later section (here: Temperament arithmetic#Monononcollinearity). But we can at least say here that any set of min-grade-1 temperaments are monononcollinear, fortunately, so we don't need to worry about it in that case.

Versus meet and join

Like meet and join, temperament arithmetic takes temperaments as inputs and finds a new temperament sharing properties of the inputs. And they both can be understood as, in some sense, adding these input temperaments together.

But there is a big difference between temperament arithmetic and meet/join. Temperament arithmetic is done using entry-wise addition (or subtraction), whereas meet/join are done using concatenation. So the temperament sum of mappings with two rows each is a new mapping that still has exactly two rows, while the other hand, the join of mappings with two rows each is a new mapping that has a total of four rows.

The collinearity connection

Another connection between temperament arithmetic and meet/join is that they may involve checks for collinearity.

Temperament arithmetic, as stated earlier, requires always monononcollinearity, which is a more complex property involving collinearity.

Meet and join does not necessarily involve collinearity. Collinearity only matters for meet and join if you attempt to do it using exterior algebra, that is, by using the wedge product, rather than the linear algebra approach, which is just to concatenate the vectors as a matrix and reduce. For more information on this, see Douglas Blumeyer and Dave Keenan's Intro to exterior algebra for RTT#Collinearity exception.

Beyond min-grade-1

As stated above, temperament arithmetic is simplest for temperaments which can be represented by single vectors, or in other words, temperaments that are either rank-1 (ETs) or nullity-1 (having only a single comma), and for other temperaments, the computation gets a little trickier. Here we'll look at how to handle it.

Multivector approach

The simplest approach is to use multivectors. This is discussed in more detail here: Douglas Blumeyer and Dave Keenan's Intro to exterior algebra for RTT#Temperament arithmetic.

Matrix approach

Temperament arithmetic for temperaments with both [math]\displaystyle{ r\gt 1 }[/math] and [math]\displaystyle{ n\gt 1 }[/math] can also be done using matrices, but it's significantly more involved than it is with multivectors. It works in essentially the same way — entry-wise addition or subtraction — but for matrices, it is necessary to surface the collinear vectors before performing the arithmetic. In other words, any vectors that can be found through linear combinations of each matrices' vectors must appear explicitly in the same position of each matrix before the sum or difference is taken. But it is not as simple as determining these explicit vectors and pasting them over existing vectors, because the results may then be enfactored. Defactoring them without compromising the explicit vectors cannot be done using existing defactoring algorithms.

(Examples WIP)

Monononcollinearity

Verbal explanation

In order to understand monononcollinearity, we must work up to it, understanding these concepts in this order:

1. collinearity
2. temperament collinearity
3. temperament noncollinearity
4. temperament monononcollinearity

1. Collinearity

This is explained here: collinearity.

2. Temperament collinearity

Collinearity has been defined for the matrices and multivectors that represent temperaments, but it can also be defined for temperaments themselves. The conditions of temperament arithmetic motivate a definition of collinearity for temperaments whereby temperaments are considered collinear if either of their mappings or their comma bases are collinear.

For example, 5-limit 5-ET and 5-limit 7-ET, represented by the mappings [⟨5 8 12]⟩ and [⟨7 11 16]⟩ may at first seem to be noncollinear, because the vectors visible in their mappings are clearly noncollinear (when comparing two vectors, the only way they could be collinear is if they are multiples of each other, as discussed here). And indeed their mappings are noncollinear. But these two temperaments are collinear, because if we consider their corresponding comma bases, we will find that they share the vector of the meantone comma [4 -4 1⟩.

3. Temperament noncollinearity

Collinearity may be considered as a boolean (yes/no, collinear/noncollinear) or it may be considered as an integer count of collinear vectors (e.g. 5-ET and 7-ET, per the example in the previous section, are collinearity-1 temperaments).

It does not make sense to speak of temperament collinearity in this integer count sense. Here's an example that illustrates why. Consider two different 11-limit rank-2 temperaments. Both their mappings and comma bases are collinear, but their mappings have collinearity of 1, while their comma bases have collinearity of 2. So what could the collinearity of a temperament be? We could, of course, define "min-collinearity" and "max-collinearity", as we defined "min-grade" and "max-grade", but this does not turn out to be helpful.

However, it turns out that it does make sense to speak of the noncollinearity of the temperament as an integer count. This is because the noncollinearity of two temperaments' mappings and the noncollinearity of their comma bases will always be the same. So the temperament noncollinearity is simply this number. In the 11-limit rank-2 example from the previous paragraph, these would be noncollinearity-1 temperaments.

4. Temperament monononcollinearity

Two temperaments are monononcollinear if they are noncollinearity-1. In other words, both their mappings and their comma bases share all but one vector.

Diagrammatic explanation

(WIP)

Geometric explanation

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Algebraic explanation

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Applications

The temperament that results from summing or diffing two temperaments, as stated above, has similar properties to the original two temperaments. According to some sources, these properties are discussed in terms of "Fokker groups" on this page: Fokker block.