Douglas Blumeyer's RTT HowTo
This is the reference I wish I had when I was learning RTT, or Regular Temperament Theory. There are other great resources out there, but this is how I would have liked to have learned it myself. I might say these materials lean more visual and geometric than others I've seen, and focus on elementary computation and representation rather than theory. It's not really a big picture introduction, it doesn't explore musical applications, and its algorithms are for humans, not computers. In any case, I hope others are able to benefit from these tools and explanations.
There's still a ton more to say here, though, and I hope to get to completing this material soon.
Intro
What’s tempering, you ask, and why temper? I won’t be answering those questions in depth here. Plenty has been said about the “what” and “why” elsewhere^{[1]}. These materials are about the “how”.
But I will at least give brief answers. In the most typical case, tempering means adjusting the tuning of the prime numbers that make up the frequency ratios of just intonation — the harmonic building blocks of your music — only a little bit, so that you can still sense what chords and melodies are “supposed” to be, but in just such a way that the interval math “adds up” in more practical ways than it does in pure just intonation (JI). This is also what equal divisions (EDs) do, but where EDs go “all the way”, compromising more JI accuracy for more ease of use, RTT finds a “middle path”: minimizing the accuracies you sacrifice, while maximizing ease of use. Understanding that much of the “what”, you can refer to this table to see basically “why”:
ED  RTT (middle path)  JI  

ease of use  ★★★★  ★★★  ★ 
harmonic accuracy  ★  ★★★  ★★★★ 
The point is that a tempered tuning manages to score high for both usability and harmonic accuracy, and therefore the case can be made that it is better overall than either a straight ED or straight JI. On this table (which reflects my opinion), RTT got six total stars while ED and JI each only got five. (And this doesn't even account for the power RTT has to create fascinating new harmonic effects, like comma pumps and essentially tempered chords, which EDs can do to a lesser extent.) (And this table doesn't account for how many don’t find the distinctive buzzing sound of perfectly accurate JI to be a desirable effect.)
But, you protest: this tutorial is pretty long, and it contains a bunch of gnarly diagrams and advanced math concepts, so how could RTT possibly be easier to use than JI? Well, what I’ve rated above is the ease of use after you’ve chosen your particular ED, RTT, or JI tuning. It’s the ease of writing, reading, reasoning about, communicating about, teaching, performing, listening to, and analyzing the music in said tuning. This is different from how simple it is to determine a desirable tuning up front.
Determining desirable tunings is a whole other beast. Perhaps contrary to popular belief, xenharmonic musicians — composers and performers alike — can mostly insulate themselves from this stuff if they like. It’s fine to nab a popular and wellreviewed tuning off the shelf, without deeply understanding how or why it’s there, and just pump, jam, or riff away. There's a good chance you could naturally pick up what's cool about a tuning without ever learning the definition of "temper out" or "generator". But if you do want to be deliberate about it, to mod something, rifle through the obscure section, or even discover your own tuning, then you must prepare to delve deeper into the xenharmonic fold. That’s why this resource is here, for RTT.
As for whether determining a middle path tuning is any harder than determining an ED or JI tuning, I think it would be fair to say that in the exact same way that a middle path tuning — once attained — combines the strengths of ED and of JI, determining a middle path tuning combines the challenges of determining good ED tunings and of determining good JI tunings. You have been warned.
Maps
In this first section, you will learn about maps — one of the basic building blocks of temperaments — and the effect maps have on musical intervals.
Vectors and covectors
It’s hard to get too far with RTT before you understand vectors and covectors, so let’s start there.
Until stated otherwise, this material will assume the 5 primelimit.
If you’ve previously worked with JI, you may already be familiar with vectors. Vectors are a compact way to express JI intervals in terms of their prime factorization, or in other words, their harmonic building blocks. In JI, and in most contexts in RTT, vectors simply list the count of each prime, in order. For example, 16/15 is [4 1 1⟩ because it has four 2’s in its numerator, one 3 in its denominator, and also one 5 in its denominator. You can look at each term as an exponent: 2⁴ × 3⁻¹ × 5⁻¹ = 16/15. If we need to distinguish these vectors from other kinds of vectors used in RTT, we call these prime count vectors or PCvectors.
And if you’ve previously worked with EDOs, you may already be familiar with covectors. Covectors are a compact way to express EDOs in terms of the count of its steps it takes to reach its approximation of each prime harmonic, in order. For example, 12EDO is ⟨12 19 28]. The first term is the same as the name of the EDO, because the first prime harmonic is 2/1, or in other words: the octave. So this covector tells us that it takes 12 steps to reach 2/1 (the octave), 19 steps to reach 3/1 (the tritave), and 28 steps to each 5/1 (the pentave). Any or all of those intervals may be approximate.
If the mathematical structure called a vector represents the musical structure called an interval, the mathematical structure called a covector represents the musical structure called a map. Elsewhere you may see these referred to as “monzos” or “vals”, respectively, but we will be avoiding unhelpful jargon like that here^{[2]}.
Note the different direction of the brackets between covectors and vectors: covectors ⟨] point left, vectors [⟩ point right.
Covectors and vectors give us a way to bridge JI and EDOs. If the vector gives us a list of primes in a JI interval, and the covector tells us how many steps it takes to reach the approximation of each of those primes individually in an EDO, then when we put them together, we can see what step of the EDO should give the closest approximation of that JI interval. We say that the JI interval maps to that number of steps in the EDO. Calculating this looks like ⟨12 19 28][4 1 1⟩, and all that means is to multiply matching terms and sum the results (this is called the dot product).
So, 16/15 maps to one step in 12EDO (see Figure 2a).
For another example, we can quickly find the fifth size for 12EDO from its map, because 3/2 is [1 1 0⟩, and so ⟨12 19 28][1 1 0⟩ = (12 × 1) + (19 × 1) = 7. Similarly, the major third — 5/4, or [2 0 1⟩ — is simply 28  12  12 = 4.
Throughout this article I will be referring to examples implemented in Wolfram Language (formerly Mathematica), a popular and capable programming language for working with math. I encourage you to try them out, to get a feel for things in another way, and get yourself started exploring temperaments yourself! If you're interested, you can run them right on the web without downloading or setting anything up on your computer: just go to https://www.wolframcloud.com, sign up for free, create a new computational notebook, paste in the contents from this file, and Shift+Enter to run it, which will load up all the functions. Then open a new tab to use them; you'll be computing in no time. (And of course you're encouraged to look over the implementations of the functions if that may help you.) FYI, any notebook you create has a lifespan of 60 days before Wolfram Cloud will recycle it, so you'll have to copy and paste them to new notebooks or wherever if you don't want to lose your work.^{[3]}
If, on the other hand, you're not interested in code examples, that's no big deal. They're not necessary to follow along.
So here's the first:
In: {12,19,28}.{1,1,0} Out: 7
Wolfram Language's syntax is a bit different than what we use in RTT, which can take a little getting used to. As you can see here, both our vector and covector use the same curly brackets instead of angle and square brackets. This is a very simple example, though, and the actual difference between RTT and Wolfram Language syntax gets more complicated than just replacing all brackets with curlies; but we won't have to worry about that for a while.
Tempering out commas
Here’s where things start to get really interesting.
We can also see that the JI interval 81/80 maps to zero steps in 12EDO, because ⟨12 19 28][4 4 1⟩ = 0; we therefore say this JI interval vanishes in 12EDO, or that it is tempered out. This type of JI interval is called a comma, and this particular one is called the meantone comma.
The immediate conclusion is that 12EDO is not equipped to approximate the meantone comma directly as a melodic or harmonic interval, and this shouldn’t be surprising because 81/80 is only around 20¢, while the (smallest) step in 12EDO is five times that.
But a more interesting way to think about this result involves treating [4 4 1⟩ not as a single interval, but as the end result of moving by a combination of intervals. For example, moving up four fifths, 4 × [1 1 0⟩ = [4 4 0⟩, and then moving down one pentave [0 0 1⟩, gets you right back where you started in 12EDO. Or, in other words, moving by one pentave is the same thing as moving by four fifths (see Figure 2b). One can make compelling music that exploits such harmonic mechanisms.
From this perspective, the disappearance of 81/80 is not a shortcoming, but a fascinating feature of 12EDO; we say that 12EDO supports the meantone temperament. And 81/80 in 12EDO is only the beginning of that journey. For many people, tempering commas is one of the biggest draws to RTT.
But we’re still only talking about JI and EDOs. If you’re familiar with meantone as a historical temperament, you may be aware already that it is neither JI nor an EDO. Well, we’ve got a ways to go yet before we get there.
One thing we can easily begin to do now, though, is this: refer to EDOs instead as ETs, or equal temperaments. The two terms are roughly synonymous, but have different implications and connotations. To put it briefly, the difference can be found in the names: 12 Equal Divisions of the Octave suggests only that your goal is equally dividing the octave, while 12 Equal Temperament suggests that your goal is to temper and that you have settled on a single equal step to accomplish that. Because we’re learning about temperament theory here, it would be more appropriate and accurate to use the local terminology. 12ET it is, then.
Approximating JI
If you’ve seen one map before, it’s probably ⟨12 19 28]. That’s because this map is the foundation of conventional Western tuning: 12 equal temperament. A major reason it stuck is because — for its low complexity — it can closely approximate all three of the 5 primelimit harmonics 2, 3, and 5 at the same time.
One way to think of this is that 12:19:28 is an excellent low integer approximation of log(2:3:5). That's a really compact way of saying that each of these sets of three numbers has the same ratio between each pair of them:
 [math]\frac{19}{12} = 1.583 ≈ \frac{\log(3)}{\log(2)} = 1.585[/math]
 [math]\frac{28}{12} = 2.333 ≈ \frac{\log(5)}{\log(2)} = 2.322[/math]
 [math]\frac{28}{19} = 1.474 ≈ \frac{\log(5)}{\log(3)} = 1.465[/math]
You may be more familiar with seeing the base specified for a logarithm, but in this case the base is irrelevant as long as you use the same base for both numbers. If you don't see why, try experimenting with different bases and see that the ratio comes out the same^{[4]}.
But why take the logarithm at all? Because a) 2, 3, and 5 are not exponents, b) 12, 19, and 28 are exponents, and c) logarithms give exponents.
 2, 3, and 5 are not exponents. They’re multipliers. To be specific, they’re multipliers of frequency. If the root pitch 1(/1) is 440Hz, then 2(/1) is 880Hz, 3(/1) is 1320Hz, and 5(/1) is 2200Hz.
 12, 19, and 28 are exponents. Think of it this way: the map tells us to find some shared number g, called a generator, such that g¹² ≈ 2, g¹⁹ ≈ 3, and g²⁸ ≈ 5. It doesn’t tell us whether all of those approximations can be good at the same time, but it tells us that’s what we’re aiming for. For this map, it happens to be the case that a generator of around 1.059 will be best. Note that this generator is the same thing as one step of our ET. Also note that by thinking this way, we are thinking in terms of frequency (e.g. in Hz), not pitch (e.g. in cents): when we move repeatedly in pitch, we repeatedly add, which can be expressed as multiplication, e.g. 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ + 100¢ = 12×100¢ = 1200¢, while when we move repeatedly in frequency, we repeatedly multiply, which can be expressed as exponentiation, e.g. 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 × 1.059 = 1.059¹² ≈ 2. We can therefore say that frequency and pitch are two realms separated by one logarithmic order.
 logarithms give exponents. A logarithm answers the question, “What exponent do I raise this base to in order to get this value?” So when I say 12 = log_{g}2 I’m saying there’s some base g which to the twelfth power gives 2, and when I say 19 = log_{g}3 I’m saying there’s some base g which to the nineteenth power gives 3, etc. (That’s how I found 1.059, by the way; if g¹² ≈ 2, and I take the twelfth root of both sides, I get g = ¹²√2 ≈ 1.05946, and I could have just easily taken ¹⁹√3 ≈ 1.05952 or ²⁸√5 ≈ 1.05916).
So when I say 12:19:28 ≈ log(2:3:5) what I’m saying is that there is indeed some shared generator g for which log_{g}2 ≈ 12, log_{g}3 ≈ 19, and log_{g}5 ≈ 28 are all good approximations all at the same time, or, equivalently, a shared generator g for which g¹² ≈ 2, g¹⁹ ≈ 3, and g²⁸ ≈ 5 are all good approximations at the same time (see Figure 2c). And that’s a pretty cool thing to find! To be clear, with g = 1.059, we get g¹² ≈ 1.9982, g¹⁹ ≈ 2.9923, and g²⁸ ≈ 5.0291.
Another glowing example is the map ⟨53 84 123], for which a good generator will give you g⁵³ ≈ 2.0002, g⁸⁴ ≈ 3.0005, g¹²³ ≈ 4.9974. This speaks to historical attention given to 53ET. So while 53:84:123 is an even better approximation of log(2:3:5) (and you won’t find a better one until 118:187:274, though you’ll have to zoom in on this linked image and scroll across it horizontally to convince yourself of it, because it’s an extremely wide image), of course its integers aren’t as low, so that lessens its appeal.
Why is this rare? Well, it’s like a game of trying to get these numbers to line up (see Figure 2d):
If the distance between entries in the row for 2 are defined as 1 unit apart, then the distance between entries in the row for prime 3 are 1/log₂3 units apart, and 1/log₂5 units apart for the prime 5. So, nearlinings up don’t happen all that often!^{[5]} (By the way, any vertical line drawn through a chart like this is what we'll be calling here a uniform map; elsewhere you may find this called a “generalized patent val”.^{[6]})
And why is this cool? Well, if ⟨12 19 28] approximates the harmonic building blocks well individually, then JI intervals built out of them, like 16/15, 5/4, 10/9, etc. should also be reasonably closely approximated overall, and thus recognizable as their JI counterparts in musical context. You could think of it like taking all the primes in a prime factorization and swapping in their approximations. For example, if 16/15 = 2⁴ × 3⁻¹ × 5⁻¹ ≈ 1.067, and ⟨12 19 28] approximates 2, 3, and 5 by 1.059¹² ≈ 1.998, 1.059¹⁹ ≈ 2.992, and 1.059²⁸ ≈ 5.029, respectively, then ⟨12 19 28] maps 16/15 to 1.998⁴ × 2.992⁻¹ × 5.029⁻¹ ≈ 1.059, which is indeed pretty close to 1.067. Of course, we should also note that 1.059 is the same as our step of ⟨12 19 28], which checks out with our calculation we made in the previous section that the best approximation of 16/15 in ⟨12 19 28] would be 1 step.
Tuning & pure octaves
Now, because the octave is the interval of equivalence in terms of human pitch perception, it’s a major convenience to enforce pure octaves, and so many people prefer the first term to be exact. In fact, I’ll bet many readers have never even heard of or imagined impure octaves, if my own anecdotal experience is any indicator; the idea that I could temper octaves to optimize tunings came rather late to me.
Well, you’ll notice that in the previous section, we did approximate the octave, using 1.998 instead of 2. But another thing ⟨12 19 28] has going for it is that it excels at approximating 5limit JI even if we constrain ourselves to pure octaves, locking g¹² to exactly 2: (¹²√2)¹⁹ ≈ 2.997 and (¹²√2)²⁸ ≈ 5.040. You can see that actually the approximation of 3 is even better here, marginally; it’s the error on 5 which is lamentable.
When we don’t enforce pure octaves, tuning becomes a more interesting problem. Approximating all three primes at once with the same generator is a balancing act. At least one of the primes will be tuned a bit sharp while at least one of them will be tuned a bit flat. In the case of ⟨12 19 28], the 5 is a bit sharp, and the 2 and 3 are each a tiny bit flat (as you can see in Figure 2c).
If you think about it, you would never want to tune all the primes sharp at the same time, or all of them flat; if you care about this particular proportion of their tunings, why wouldn’t you shift them all in the same direction, toward accuracy, while maintaining that proportion? (see Figure 2e)
This matter of choosing the exact generator for a map is called tuning, and if you’ll believe it, we won’t actually talk about that in detail again until much later. Temperament — the second ‘T’ in “RTT” — is the discipline concerned with choosing an interesting map, and tuning can remain largely independent from it. The temperament is only concerned with the fact that — no matter what exact size you ultimately make the generator — it is the case e.g. that 12 of them make a 2, 19 of them make a 3, and 28 of them make a 5. So, for now, whenever we show a value for g, assume we’ve given a computer a formula for optimizing the tuning to approximate all three primes equally well. As for us humans, let’s stay focused on tempering.
A multitude of maps
Suppose we want to experiment with the ⟨12 19 28] map a bit. We’ll change one of the terms by 1, so now we have ⟨12 20 28]. Because the previous map did such a great job of approximating the 5limit (i.e. log(2:3:5)), though, it should be unsurprising that this new map cannot achieve that feat. The proportions, 12:20:28, should now be about as out of whack as they can get. The best generator we can do here is about 1.0583 (getting a little more precise now), and 1.0583¹² ≈ 1.9738 which isn’t so bad, but 1.0583¹⁹ = 3.1058 and 1.0583²⁸ = 4.8870 which are both way off! And they’re way off in the opposite direction — 3.1058 is too big and 4.8870 is too small — which is why our tuning formula for g, which is designed to make the approximation good for every prime at once, can’t improve the situation: either sharpening or flattening helps one but hurts the other.
The results of such inaccurate approximation are a bit chaotic. A ratio like 16/15 — where the factors of 3 and 5 are on the same side of the fraction bar and therefore cancel out each other’s error — fares relatively alright, if by “alright” we mean it gets tempered out despite being about 112¢ in JI. On the other hand, an interval like 27/25 where the factors of 3 and 5 are on opposite sides of the fraction bar and thus their errors compound, gets mapped to a whopping 4 steps, despite only being about 133¢ in JI.
If your goal is to evoke JIlike harmony, then, ⟨12 20 28] is not your friend. Feel free to work out some other variations on ⟨12 19 28] if you like, such as ⟨12 19 29] maybe, but I guarantee you won’t find a better one that starts with 12 than ⟨12 19 28].
So the case is cutanddry for ⟨12 19 28], and therefore from now on I'm simply going to refer to this ET by "12ET" rather than spelling out its map. But other ETs find themselves in trickier situations. Consider 17ET. One option we have is the map ⟨17 27 39], with a generator of about 1.0418, and prime approximations of 2.0045, 3.0177, 4.9302. But we have a second reasonable option here, too, where ⟨17 27 40] gives us a generator of 1.0414, and prime approximations of 1.9929, 2.9898, and 5.0659. In either case, the approximations of 2 and 3 are close, but the approximation of 5 is way off. For ⟨17 27 39], it’s way small, while for ⟨17 27 40] it’s way big. The conundrum could be described like this: any generator we could find that divides 2 into about 17 equal steps can do a good job dividing 3 into about 27 equal steps, too, but it will not do a good job of dividing 5 into equal steps; 5 is going to land, unfortunately, right about in the middle between the 39th and 40th steps, as far as possible from either of these two nearest approximations. To do a good job approximating prime 5, we’d really just want to subdivide each of these steps in half, or in other words, we’d want 34ET.
Curiously, ⟨17 27 39] is the map for which each prime individually is as closely approximated as possible when prime 2 is exact, so it is in a sense the naively best map for 17ET, however, if that constraint is lifted, and we’re allowed to either temper prime 2 and/or choose the nextclosest approximations for prime 5, the overall approximation can be improved; in other words, even though 39 steps can take you just a tiny bit closer to prime 5 than 40 steps can, the tiny amount by which it is closer is less than the improvements to the tuning of primes 2 and 3 you can get by using ⟨17 27 40]. So again, the choice is not always cutanddry; there’s still a lot of personal preference going on in the tempering process.
So some musicians may conclude “17ET is clearly not cut out for 5limit music,” and move on to another ET. Other musicians may snicker maniacally, and choose one or the other map, and begin exploiting the profound and unusual 5limit harmonic mechanisms it affords. ⟨17 27 40], like ⟨12 19 28], tempers out the meantone comma [4 4 1⟩, so even though fifths and major thirds are different sizes in these two ETs, the relationship that four fifths equals one major third is shared. ⟨17 27 39], on the other hand, does not work like that, but what it does do is temper out 25/24, [3 1 2⟩, or in other words, it equates one fifth with two major thirds.
If you’re enforcing pure octaves, the difference between ⟨17 27 39] and ⟨17 27 40] is nominal, or contextual. The steps in either case are identical: exactly ¹⁷√2, or 1200/17=70.588¢. You simply choose to think of 5 as being approximated by either 39 or 40 of those steps, or imply it in your composition. But when octaves are freed to temper, then the difference between these two maps becomes pronounced. When optimizing for ⟨17 27 39], the best step size is 70.225¢, but when optimizing for ⟨17 27 40], the best step size is more like 70.820¢.
You will sometimes see maps like 17ET’s distinguished from each other using names like 17p and 17c. This is called wart notation.
At this point you should have a pretty good sense for why choosing a map makes an important impact on how your music sounds. Now we just need to help you find and compare maps! Or, similarly, how to find and compare intervals to temper. To do this, we need to give you the ability to navigate tuning space.
Projective tuning space
In this section, we will be going into potentially excruciating detail about how to read the projective tuning space diagram featured prominently in Paul Erlich's Middle Path paper. For me personally, attaining total understanding of this diagram was critical before the linear algebra stuff (that we'll discuss afterwards) started to mean much to me. But other people might not work that way, and the extent of detail I go into in this section is not necessary to become competent with RTT (in fact, to my delight, one of the points I make in this section was news to Paul himself). So if you're already confident about reading the PTS diagram, you may try skipping ahead.
Intro to PTS
This is 5limit projective tuning space, or PTS for short (see Figure 3a). This diagram was created by RTT pioneer Paul Erlich. It compresses a huge amount of valuable information into a small space. If at first it looks overwhelming or insane, do not despair. It may not be instantly easy to understand, but once you learn the tricks for navigating it from these materials, you will find it is very powerful. Perhaps you will even find patterns in it which others haven’t found yet.
I suggest you open this diagram in another window and keep it open as you proceed through these next few sections, as we will be referring to it frequently.
If you’ve worked with 5limit JI before, you’re probably aware that it is threedimensional. You’ve probably reasoned about it as a 3D lattice, where one axis is for the factors of prime 2, one axis is for the factors of prime 3, and one axis is for the factors of prime 5. This way, you can use vectors, such as [4 4 1⟩ or [1 2 1⟩, just like coordinates.
PTS can be thought of as a projection of 5limit JI map space, which similarly has one axis each for 2, 3, and 5. But it is no JI pitch lattice. In fact, in a sense, it is the opposite! This is because the coordinates in map space aren’t prime count lists, but maps, such as ⟨12 19 28]. That particular map is seen here as the biggish, slightly tilted numeral 12 just to the left of the center point.
And the two 17ETs we looked at can be found here too. ⟨17 27 40] is the slightly smaller numeral 17 found on the line labeled “meantone” which the 12 is also on, thus representing the fact we mentioned earlier that they both temper it out. The other 17, ⟨17 27 39], is found on the other side of the center point, aligned horizontally with the first 17. So you could say that map space plots ETs, showing how they are related to each other.
Of course, PTS looks nothing like this JI lattice (see Figure 3b). This diagram has a ton more information, and as such, Paul needed to get creative about how to structure it. It’s a little tricky, but we’ll get there. For starters, the axes are not actually shown on the PTS diagram; if they were, they would look like this (see Figure 3c).
The 2axis points toward the bottom right, the 3axis toward the top right, and the 5axis toward the left. These are the positive halves of each of these axes; we don’t need to worry about the negative halves of any of them, because every term of every ET map is positive.
And so it makes sense that ⟨17 27 40] and ⟨17 27 39] are aligned horizontally, because the only difference between their maps is in the 5term, and the 5axis is horizontal.
Scaled axes
You might guess that to arrive at that tilted numeral 12, you would start at the origin in the center, move 12 steps toward the bottom right (along the 2axis), 19 steps toward the top right (not along, but parallel to the 3axis), and then 28 steps toward the left (parallel to the 5axis). And if you guessed this, you’d probably also figure that you could perform these moves in any order, because you’d arrive at the same ending position regardless (see Figure 3d).
If you did guess this, you are on the right track, but the full truth is a bit more complicated than that.
The first difference to understand is that each axis’s steps have been scaled proportionally according to their prime (see Figure 3e). We will see in a moment that the scaling factor, to be precise, is the inverse of the logarithm of the prime. To illustrate this, let’s choose an example ET and compare its position with the positions of three other closelyrelated ETs:
 the one which is one step away from it on the 5axis,
 the one which is one step away from it on the 3axis, and
 the one which is one step away from it on the 2axis.
Our example ET will be 40. We'll start out at the map ⟨40 63 93]. This map is a default of sorts for 40ET, because it’s the map where all three terms are as close as possible to JI when prime 2 is exact (we'll be calling it a simple map here, though elsewhere you may find it called a "patent val"^{[7]}).
From here, let’s move by a single step on the 5axis by adding 1 to the 5term of our map, from 93 to 94, therefore moving to the map ⟨40 63 94]. This map is found directly to the left. This makes sense because the orientation of the 5axis is horizontal, and the positive direction points out from the origin toward the left, so increases to the 5term move us in that direction.
Back from our starting point, let’s move by a single step again, but this time on the 3axis, by adding 1 to the 3term of our map, from 63 to 64, therefore moving to the map ⟨40 64 93]. This map is found up and to the right. Again, this direction makes sense, because it’s the direction the 3axis points.
Finally, let’s move by a single step on the 2axis, from 40 to 41, moving to the map ⟨41 63 93], which unsurprisingly is in the direction the 2axis points. This move actually takes us off the chart, way down here.
Now let’s observe the difference in distances (see Figure 3f). Notice how the distance between the maps separated by a change in 5term is the smallest, the maps separated by a change in 3term have the mediumsized distance, and maps separated by a change in the 2term have the largest distance. This tells us that steps along the 3axis are larger than steps along the 5axis, and steps along the 2axis are larger still. The relationship between these sizes is that the 3axis step has been divided by the binary logarithm of 3, written log₂3, which is approximately 1.585, while the 5axis step has been divided by the binary logarithm of 5, written log₂5, and which is approximately 2.322. The 2axis step can also be thought of as having been divided by the binary logarithm of its prime, but because log₂2 is exactly 1, and dividing by 1 does nothing, the scaling has no effect on the 2axis.
The reason Paul chose this particular scaling scheme is that it causes those ETs which are closer to JI to appear closer to the center of the diagram (and this is a useful property to organize ETs by). How does this work? Well, let’s look into it.
Remember that nearjust ETs have maps whose terms are in close proportion to log(2:3:5). ET maps use only integers, so they can only approximate this ideal, but a theoretical pure JI map would be ⟨log₂2 log₂3 log₂5]. If we scaled this theoretical JI map by this scaling scheme, then, we’d get 1:1:1, because we’re just dividing things by themselves: log₂2/log₂2:log₂3/log₂3:log₂5/log₂5 = 1:1:1. This tells us that we should find this theoretical JI map at the point arrived at by moving exactly the same amount along the 2axis, 3axis, and 5axis. Well, if we tried that, these three movements would cancel each other out: we’d draw an equilateral triangle and end up exactly where we started, at the origin, or in other words, at pure JI. Any other ET approximating but not exactly log(2:3:5) will be scaled to proportions not exactly 1:1:1, but approximately so, like maybe 1:0.999:1.002, and so you’ll move in something close to an equilateral triangle, but not exactly, and land in some interesting spot that’s not quite in the center. In other words, we scale the axes this way so that we can compare the maps not in absolute terms, but in terms of what direction and by how much they deviate from JI (see Figure 3g).
For example, let’s scale our 12ET example:
 12/log₂2 = 12
 19/log₂3 ≈ 11.988
 28/log₂5 ≈ 12.059
Clearly, 12:11.988:12.059 is quite close to 1:1:1. This checks out with our knowledge that it is close to JI, at least in the 5limit.
But if instead we picked some random alternate mapping of 12ET, like ⟨12 23 25], looking at those integer terms directly, it may not be obvious how close to JI this map is. However, upon scaling them:
 12/log₂2 = 12
 23/log₂3 ≈ 14.511
 25/log₂5 ≈ 10.767
It becomes clear how far this map is from JI.
So what really matters here are the little differences between these numbers. Everything else cancels out. That 12ET’s scaled 3term, at ≈11.988, is eversoslightly less than 12, indicates that prime 3 is mapped eversoslightly flat. And that its 5term, at ≈12.059, is slightly more than 12, indicates that prime 5 is mapped slightly sharp in 12. This checks out with the placement of 12 on the diagram: eversoslightly below and to the left of the horizontal midline, due to the flatness of the 3, and slightly further still to the left, due to the sharpness of the 5.
We can imagine that if we hadn’t scaled the steps, as in our initial naive guess, we’d have ended up nowhere near the center of the diagram. How could we have, if the steps are all the same size, but we’re moving 28 of them to the left, but only 12 and 19 of them to the bottom left and top right? We’d clearly end up way, way further to the left, and also above the horizontal midline. And this is where pretty much any nearjust ET would get plotted, because 3 being bigger than 2 would dominate its behavior, and 5 being larger still than 3 would dominate its behavior.
Perspective
The truth about distances between related ETs on the PTS diagram is actually slightly even more complicated than that, though; as we mentioned, the scaled axes are only the first difference from our initial guess. In addition to the effect of the scaling of the axes, there is another effect, which is like a perspective effect. Basically, as ETs get more complex, you can think of them as getting farther and farther away; to suggest this, they are printed smaller and smaller on the page, and the distances between them appear smaller and smaller too.
Remember that 5limit JI is 3D, but we’re viewing it on a 2D page. It’s not the case that its axes are flat on the page. They’re not literally occupying the same plane, 120° apart from each other. That’s just not how axes typically work, and it’s not how they work here either! The 5axis is perpendicular to the 2axis and 3axis just like typical Cartesian space. Again, we’re looking only at the positive coordinates, which is to say that this is only the +++ octant of space, which comes to a point at the origin (0,0,0) like the corner of a cube. So you should think of this diagram as showing that cubic octant sticking its corner straight out of the page at us, like a triangular pyramid. So we’re like a tiny little bug, situated right at the tip of that corner, pointing straight down the octant’s interior diagonal, or in other words the line equidistant from three axes, the line which we understand represents theoretically pure JI. So we see that in the center of the page, represented as a red hexagram, and then toward the edges of the page is our peripheral vision. (See Figure 3h.)
PTS doesn’t show the entire tuning cube. You can see evidence of this in the fact that some numerals have been cut off on its edges. We’ve cropped things around the central region of information, which is where the ETs best approximating JI are found (note how close 53ET is to the center!). Paul added some concentric hexagons to the center of his diagram, which you could think of as concentric around that interior diagonal, or in other words, are defined by gradually increasing thresholds of deviations from JI for any one prime at a time.
No maps past 99ET are drawn on this diagram. ETs with that many steps are considered too complex (read: big numbers, impractical) to bother cluttering the diagram with. Better to leave the more useful information easier to read.
Okay, but what about the perspective effect? Right. So every step further away on any axis, then, appears a bit smaller than the previous step, because it’s just a bit further away from us. And how much smaller? Well, the perspective effect is such that, as seen on this diagram, the distances between nETs are twice the size of the distances between 2nETs.
Moreover, there’s a special relationship between the positions of nETs and 2nETs, and indeed between nETs and 3nETs, 4nETs, etc. To understand why, it’s instructive to plot it out (see Figure 3i).
For simplicity, we’re looking at the octant cube here from the angle straight on to the 2axis, so changes to the 2terms don’t matter here. At the top is the origin; that’s the point at the center of PTS. Closeby, we can see the map ⟨3 5 7], and two closely related maps ⟨3 4 7] and ⟨3 5 8]. Colored lines have been drawn from the origin through these points to the black line in the topright, which represents the page; this is portraying how if our eye is at that origin, where on the page these points would appear to be.
In between where the colored lines touch the maps themselves and the page, we see a cluster of more maps, each of which starts with 6. In other words, these maps are about twice as far away from us as the others. Let’s consider ⟨6 10 14] first. Notice that each of its terms is exactly 2x the corresponding term in ⟨3 5 7]. In effect, ⟨6 10 14] is redundant with ⟨3 5 7]. If you imagine doing a mapping calculation or two, you can easily convince yourself that you’ll get the same answer as if you’d just done it with ⟨3 5 7] instead and then simply divided by 2 one time at the end. It behaves in the exact same way as ⟨3 5 7] in terms of the relationships between the intervals it maps, the only difference being that it needlessly includes twice as many steps to do so, never using every other one. So we don’t really care about ⟨6 10 14]. Which is great, because it’s hidden exactly behind ⟨3 5 7] from where we’re looking.
The same is true of the map pair ⟨3 4 7] and ⟨6 8 14], as well as of ⟨3 5 8] and ⟨6 10 16]. Any map whose terms have a common factor other than 1 is going to be redundant in this sense, and therefore hidden. You can imagine that even further past ⟨3 5 7] you’ll find ⟨9 15 21], ⟨12 20 28], and so on, and these we could call “enfactored” maps.^{[8]}^{[9]}. More on those later. What’s important to realize here is that Paul found a way to collapse 3 dimensions worth of information down to 2 dimensions without losing anything important. Each of these lines connecting redundant ETs have been projected onto the page as a single point. That’s why the diagram is called "projective" tuning space.
Now, to find a 6ET with anything new to bring to the table, we’ll need to find one whose terms don’t share a common factor. That’s not hard. We’ll just take one of the ones halfway between the ones we just looked at. How about ⟨6 11 14], which is halfway between ⟨6 10 14] and ⟨6 12 14]. Notice that the purple line that runs through it lands halfway between the red and blue lines on the page. Similarly, ⟨6 10 15] is halfway between ⟨6 10 14] and ⟨6 10 16], and its yellow line appears halfway between the red and green lines on the page. What this is demonstrating is that halfway between any pair of nETs on the diagram, whether this pair is separated along the 3axis or 5axis, you will find a 2nET. We can’t really demonstrate this with 3ET and 6ET on the diagram, because those ETs are too inaccurate; they’ve been cropped off. But if we return to our 40ET example, that will work just fine.
I’ve circled every 40ET visible in the chart (see Figure 3j). And you can see that halfway between each one, there’s an 80ET too. Well, sometimes it’s not actually printed on the diagram^{[10]}, but it’s still there. You will also notice that if we also land right about on top of 20ET and 10ET. That’s no coincidence! Hiding behind that 20ET is a redundant 40ET whose terms are all 2x the 20ET’s terms, and hiding behind the 10ET is a redundant 40ET whose terms are all 4x the 40ET’s terms (and also a redundant 20ET and a 30ET, and 50ET, 60ET, etc. etc. etc.)
Also, check out the spot halfway between our two 17ETs: there’s the 34ET we briefly mused about earlier, which would solve 17’s problem of approximating prime 5 by subdividing each of its steps in half. We can confirm now that this 34ET does a superb job at approximating prime 5, because it is almost vertically aligned with the JI red hexagram.
Just as there are 2nETs halfway between nETs, there are 3nETs a third of the way between nETs. Look at these two 29ETs here. The 58ET is here halfway between them, and two 87ETs are here each a third of the way between.
Map space vs. tuning space
So far, we’ve been describing PTS as a projection of map space, which is to say that we’ve been thinking of maps as the coordinates. We should be aware that tuning space is a slightly different structure. In tuning space, coordinates are not maps, but tunings, specified in cents, octaves, or some other unit of pitch. So a coordinate might be ⟨6 10 14] in map space, but ⟨1200 2000 2800] in tuning space.
Both tuning space and map space project to the identical result as seen in Paul’s diagram, which is how we’ve been able to get away without distinguishing them thus far.
Why did I do this to you? Well, I decided map space was conceptually easier to introduce than tuning space. Paul himself prefers to think of this diagram as a projection of tuning space, however, so I don’t want to leave this material before clarifying the difference. Also, there are different helpful insights you can get from thinking of PTS as tuning space. Let’s consider those now.
The first key difference to notice is that we can normalize coordinates in tuning space, so that the first term of every coordinate is the same, namely, one octave, or 1200 cents. For example, note that while in map space, ⟨3 5 7] is located physically in front of ⟨6 10 14], in tuning space, these two points collapse to literally the same point, ⟨1200 2000 2800]. This can be helpful in a similar way to how the scaled axes of PTS help us visually compare maps’ proximity to the central JI spoke: they are now expressed closer to in terms of their deviation from JI, so we can more immediately compare maps to each other, as well as individually directly to the pure JI primes, as long as we memorize the cents values of those (they’re 1200, 1901.955, and 2786.314). For example, in map space, it may not be immediately obvious that ⟨6 9 14] is halfway between ⟨3 5 7] and ⟨3 4 7], but in tuning space it is immediately obvious that ⟨1200 1800 2800] is halfway between ⟨1200 2000 2800] and ⟨1200 1600 2800].
So if we take a look at a crosssection of projection again, but in terms of tuning space now (see Figure 3k), we can see how every point is about the same distance from us.
The other major difference is that tuning space is continuous, where map space is discrete. In other words, to find a map between ⟨6 10 14] and ⟨6 9 14], you’re subdividing it by 2 or 3 and picking a point in between, that sort of thing. But between ⟨1200 2000 2800] and ⟨1200 1800 2800] you’ve got an infinitude of choices smoothly transitioning between each other; you’ve basically got knobs you can turn on the proportions of the tuning of 2, 3, and 5. Everything from from ⟨1200 1999.999 2800] to ⟨1200 1901.955 2800] to ⟨1200 1817.643 2800] is along the way.
But perhaps even more interesting than this continuous tuning space that appears in PTS between points is the continuous tuning space that does not appear in PTS because it exists within each point, that is, exactly out from and deeper into the page at each point. In tuning space, as we’ve just established, there are no maps in front of or behind each other that get collapsed to a single point. But there are still many things that get collapsed to a single point like this, but in tuning space they are different tunings (see Figure 3l). For example, ⟨1200 1900 2800] is the way we’d write 12ET in tuning space. But there are other tunings represented by this same point in PTS, such as ⟨1200.12 1900.19 2800.28] (note that in order to remain at the same point, we’ve maintained the exact proportions of all the prime tunings). That tuning might not be of particular interest. I just used it as a simple example to illustrate the point. A more useful example would be ⟨1198.440 1897.531 2796.361], which by some algorithm is the optimal tuning for 12ET (minimizes error across primes or intervals); it may not be as obvious from looking at that one, but if you check the proportions of those terms with each other, you will find they are still exactly 12:19:28.
The key point here is that, as we mentioned before, the problems of tuning and tempering are largely separate. PTS projects all tunings of the same temperament to the same point. This way, issues of tuning are completely hidden and ignored on PTS, so we can focus instead on tempering.
Regions
We’ve shown that ETs with the same number that are horizontally aligned differ in their mapping of 5, and ETs with the same number that are aligned on the 3axis running bottom left to top right differ in their mapping of 3. These basic relationships can be extrapolated to be understood in a general sense. ETs found in the centerleft map 5 relatively big and 2 and 3 relatively small. ETs found in the topright map 3 relatively big and 2 and 5 relatively small. ETs found in the bottomright map 2 relatively big and 3 and 5 relatively small. And for each of these three statements, the region on the opposite side maps things in the opposite way.
So: we now know which point is ⟨12 19 28], and we know a couple of 17’s, 40’s and a 41. But can we answer in the general case? Given an arbitrary map, like ⟨7 11 16], can we find it on the diagram? Well, you may look to the first term, 7, which tells you it’s 7ET. There’s only one big 7 on this diagram, so it’s probably that. (You’re right). But that one’s easy. The 7 is huge.
What if I gave you ⟨43 68 100]. Where’s 43ET? I’ll bet you’re still complaining: the map expresses the tempering of 2, 3, and 5 in terms of their shared generator, but doesn’t tell us directly which primes are sharp, and which primes are flat, so how could we know in which region to look for this ET?
The answer to that is, unfortunately: that’s just how it is. It can be a bit of a hunt sometimes. But the chances are, in the real world, if you’re looking for a map or thinking about it, then you probably already have at least some other information about it to help you find it, whether it’s memorized in your head, or you’re reading it off the results page for an automatic temperament search tool.
Probably you have the information about the primes’ tempering; maybe you get lucky and a 43 jumps out at you but it’s not the one you’re looking for, but you can use what you know about the perspectival scaling and axis directions and logofprime scaling to find other 43’s relative to it.
Or maybe you know which commas ⟨43 68 100] tempers out, so you can find it along the line for that comma’s temperament.
Linear temperaments
We're about to take our first look at temperaments beyond mere equal temperaments. By the end of this section, you'll be able to explain the musical meaning of the patterns in the numerals along lines in PTS, the labels of these lines, as well as what's happening at their intersections and what their slopes mean. In other words, pretty much all of the major remaining visual elements on PTS should make sense to you.
Temperament lines
So we understand the shape of projective tuning space. And we understand what points are in this space. But what about the magenta lines, now?
So far, we’ve only mentioned one of these lines: the one labelled “meantone”, noting that the fact that 12ET and 17ET appear on it means that either of them tempers out the meantone comma. In other words, this line represents the meantone temperament.
For another example, the line on the right side of the diagram running almost vertically which has the other 17ET we looked at, as well as 10ET and 7ET, is labeled “dicot”, and so this line represents the dicot temperament, and unsurprisingly all of these ET’s temper out the dicot comma.
Simply put, lines on PTS are temperaments. Specifically, they are abstract regular temperaments. If you are a student of historical temperaments, you may be familiar with e.g. quartercomma meantone; to an RTT practitioner, this is actually a specific tuning of the meantone temperament. Meantone is an abstract temperament, which encompasses a range of other possible temperaments and tunings.
If you’re new to RTT, all of the other temperaments besides meantone, like “dicot”, “porcupine”, and “mavila”, are probably unfamiliar and their names may seem sort of random or bizarre looking. Well, you’re not wrong about the names being random and bizarre. But mathematically and musically, these temperaments are every bit as much real and of interest as meantone. One day you too may compose a piece or write an academic paper about porcupine temperament.
But hold up now: points are ETs, which are temperaments, too, right? Well, yes, that’s still true. But while points are equal temperaments, or rank1 temperaments, the lines represent what we call rank2 temperaments. It may be helpful to differentiate the names in your mind in terms of their geometric dimensionality. Recall that projective tuning space has compressed all our information by one dimension; every point on our diagram is actually a line radiating out from our eye. So a rank1 temperament is really a line, which is onedimensional; rank1, 1D. And the rank2 temperaments, which are seen as lines in our diagram, are truly planes coming up out of the page, and planes are of course twodimensional; rank2, 2D. If you wanted to, you could even say 5limit JI was a rank3 temperament, because that’s this entire space, which is 3dimensional; rank3, 3D.
“Rank” has a slightly different meaning than dimension, but that’s not important yet. We’ll define rank, and discuss what exactly a rank2 or 3 temperament means later. For now, it’s enough to know that each temperament line on this 5limit PTS diagram is defined by tempering out a comma which has the same name. For now, we’re still focusing on visually how to navigate PTS. So the natural thing to wonder next, then, is what’s up with the slopes of all these temperament lines?
Let’s begin with a simple example: the perfectly horizontal line that runs through just about the middle of the page, through the numeral 12, labelled “compton”. What’s happening along this line? Well, as we know, moving to the left means tuning 5 sharper, and moving to the right means tuning 5 flatter. But what about 2 and 3? Well, they are changing as well: 2 is sharp in the bottom right, and 3 is sharp in the top right, so when we move exactly rightward, 2 and 3 are both getting sharper (though not as directly as 5 is getting flatter). But the critical thing to observe here is that 2 and 3 are sharpening at the exact same rate. Therefore the approximations of primes 2 and 3 are in a constant ratio with each other along horizontal lines like this. Said another way, if you look at the 2 and 3 terms for any ET’s map on this line, the ratio between its term for 2 and 3 will be identical.
Let’s grab some samples to confirm. We already know that 12ET here looks like ⟨12 19] (I’m dropping the 5 term for now). The 24ET here looks like ⟨24 38], which is simply 2×⟨12 19]. The 36ET here looks like ⟨36 57] = 3×⟨12 19]. And so on. So that’s why we only see multiples of 12 along this line: because 12 and 19 are coprime, so the only other maps which could have them in the same ratio would be multiples of them.
Let’s look at the other perfectly horizontal line on this diagram. It’s found about a quarter of the way down the diagram, and runs through the 10ET and 20ET we looked at earlier. This one’s called “blackwood”. Here, we can see that all of its ETs are all multiples of 5. In fact, 5ET itself is on this line, though we can only see a sliver of its giant numeral off the left edge of the diagram. Again, all of its maps have locked ratios between their mappings of prime 2 and prime 3: ⟨5 8], ⟨10 16], ⟨15 24], ⟨20 32], ⟨40 64], ⟨80 128], etc. You get the idea.
So what do these two temperaments have in common such that their lines are parallel? Well, they’re defined by commas, so why don’t we compare their commas. The compton comma is [19 12 0⟩, and the blackwood comma is [8 5 0⟩^{[11]}. What sticks out about these two commas is that they both have a 5term of 0. This means that when we ask the question “how many steps does this comma map to in a given ET”, the ET’s mapping of 5 is irrelevant. Whether we check it in ⟨40 63 93] or ⟨40 63 94], the result is going to be the same. So if ⟨40 63 93] tempers out the blackwood comma, then ⟨40 63 94] also tempers out the blackwood comma. And if ⟨24 38 56] tempers out compton, then ⟨24 38 55] tempers out compton. And so on.
Similar temperaments can be found which include only 2 of the 3 primes at once. Take “augmented”, for instance, running from bottomleft to topright. This temperament is aligned with the 3axis. This tells us several equivalent things: that relative changes to the mapping of 3 are irrelevant for augmented temperament, that the augmented comma has no 3’s in its prime factorization, and the ratios of the mappings of 2 and 5 are the same for any ET along this line. Indeed we find that the augmented comma is [7 0 3⟩, or 128/125, which has no 3’s. And if we sample a few maps along this line, we find ⟨12 19 28], ⟨9 14 21], ⟨15 24 35], ⟨21 33 48], ⟨27 43 63], etc., for which there is no pattern to the 3term, but the 2 and 5terms for each are in a 3:7 ratio.
There are even temperaments whose comma includes only 3’s and 5’s, such as “bug” temperament, which tempers out 27/25, or [0 3 2⟩. If you look on this PTS diagram, however, you won’t find bug. Paul chose not to draw it. There are infinite temperaments possible here, so he had to set a threshold somewhere on which temperaments to show, and bug just didn’t make the cut in terms of how much it distorts harmony from JI. If he had drawn it, it would have been way out on the left edge of the diagram, completely outside the concentric hexagons. It would run parallel to the 2axis, or from topleft to bottomright, and it would connect the 5ET (the huge numeral which is cut off the left edge of the diagram so that we can only see a sliver of it) to the 9ET in the bottom left, running through the 19ET and 14ET inbetween. Indeed, these ET maps — ⟨9 14 21], ⟨5 8 12], ⟨19 30 45], and ⟨14 22 33] — lock the ratio between their 3terms and 5terms, in this case to 2:3.
Those are the three simplest slopes to consider, i.e. the ones which are exactly parallel to the axes (see Figure 4a). But all the other temperament lines follow a similar principle. Their slopes are a manifestation of the prime factors in their defining comma. If having zero 5’s means you are perfectly horizontal, then having only one 5 means your slope will be close to horizontal, such as meantone [4 4 1⟩ or helmholtz [15 8 1⟩. Similarly, magic [10 1 5⟩ and würschmidt [17 1 8⟩, having only one 3 apiece, are close to parallel with the 3axis, while porcupine [1 5 3⟩ and ripple [1 8 5⟩, having only one 2 apiece, are close to parallel with the 2axis.
Think of it like this: for meantone, a change to the mapping of 5 doesn’t make near as much of a difference to the outcome as does a change to the mapping of 2 or 3, therefore, changes along the 5axis don’t have near as much of an effect on that line, so it ends up roughly parallel to it.
Scale trees
Patterns, patterns, everywhere. PTS is chock full of them. One pattern we haven’t discussed yet is the pattern made by the ETs that fall along each temperament line.
Let’s consider meantone as our first example. Notice that between 12 and 7, the nextbiggest numeral we find is 19, and 12+7=19. Notice in turn that between 12 and 19 the nextbiggest numeral is 31, and 12+19=31, and also that between 19 and 7 the nextbiggest numeral is 26, and 19+7=26. You can continue finding deeper ETs indefinitely following this pattern: 43 between 12 and 31, 50 between 31 and 19, 45 between 19 and 26, 33 between 26 and 7. In fact, if we step back a bit, remembering that the huge numeral just off the left edge is a 5, we can see that 12 is there in the first place because 5+7=12.
This effect is happening on every other temperament line. Look at dicot. 10+7=17. 10+17=27. 17+7=24. Etc.^{[12]}
To fully understand why this is happening, we need a crash course in mediants, and the The SternBrocot tree.
The mediant of two fractions [math]\frac ab[/math] and [math]\frac cd[/math] is [math]\frac{a+c}{b+d}[/math]. It’s sometimes called the freshman’s sum because it’s an easy mistake to make when first learning how to add fractions. And while this operation is certainly not equivalent to adding two fractions, it does turn out to have other important mathematical properties. The one we’re leveraging here is that the mediant of two numbers is always greater than one and less than the other. For example, the mediant of [math]\frac 35[/math] and [math]\frac 23[/math] is [math]\frac 58[/math], and it’s easy to see in decimal form that 0.625 is between 0.6 and 0.666.
The SternBrocot tree is a helpful visualization of all these mediant relations. Flanking the part of the tree we care about — which comes up in the closelyrelated theory of MOS scales, where it is often referred to as the “scale tree” — are the extreme fractions [math]\frac 01[/math] and [math]\frac 11[/math]. Taking the mediant of these two gives our first node: [math]\frac 12[/math]. Each new node on the tree drops an infinitely descending line of copies of itself on each new tier. Then, each node branches to either side, connecting itself to a new node which is the mediant of its two adjacent values. So [math]\frac 01[/math] and [math]\frac 12[/math] become [math]\frac 13[/math], and [math]\frac 12[/math] and [math]\frac 11[/math] become [math]\frac 23[/math]. In the next tier, [math]\frac 01[/math] and [math]\frac 13[/math] become [math]\frac 14[/math], [math]\frac 13[/math] and [math]\frac 12[/math] become [math]\frac 25[/math], [math]\frac 12[/math] and [math]\frac 23[/math] become [math]\frac 35[/math], and [math]\frac 23[/math] and [math]\frac 11[/math] become [math]\frac 34[/math].^{[13]} The tree continues forever.
So what does this have to do with the patterns along the temperament lines in PTS? Well, each temperament line is kind of like its own section of the scale tree. The key insight here is that in terms of meantone temperament, there’s more to 7ET than simply the number 7. The 7 is just a fraction’s denominator. The numerator in this case is 3. So imagine a [math]\frac 37[/math] floating on top of the 7ET there. And there’s more to 5ET than simply the number 5, in that case, the fraction is the [math]\frac 25[/math]. So the mediant of [math]\frac 25[/math] and [math]\frac 37[/math] is [math]\frac{5}{12}[/math]. And if you compare the decimal values of these numbers, we have 0.4, 0.429, and 0.417. Success: [math]\frac{5}{12}[/math] is between [math]\frac 25[/math] and [math]\frac 37[/math] on the meantone line. You may verify yourself that the mediant of [math]\frac{5}{12}[/math] and [math]\frac 37[/math], [math]\frac{8}{19}[/math], is between them in size, as well as [math]\frac{7}{17}[/math] being between [math]\frac 25[/math] and [math]\frac{5}{12}[/math] in size.
In fact, if you followed this value along the meantone line all the way from [math]\frac 25[/math] to [math]\frac 37[/math], it would vary continuously from 0.4 to 0.429; the ET points are the spots where the value happens to be rational.
Okay, so it’s easy to see how all this follows from here. But where the heck did I get [math]\frac 25[/math] and [math]\frac 37[/math] in the first place? I seemed to pull them out of thin air. And what the heck is this value?
Generators
The answer to both of those questions is: it’s the generator (in this case, the meantone generator).
A generator is an interval which generates a temperament. Again, if you’re already familiar with MOS scales, this is the same concept. If not, all this means is that if you repeatedly move by this interval, you will visit the pitches you can include in your tuning.
We briefly looked at generators earlier. We saw how the generator for 12ET was about 1.059, because repeated movement is like repeated multiplication (1.059 × 1.059 × 1.059 ...) and 1.059¹² ≈ 2, 1.059¹⁹ ≈ 3, and 1.059²⁸ ≈ 5. This meantone generator is the same basic idea, but there’s a couple of important differences we need to cover.
First of all, and this difference is superficial, it’s in a different format. We were expressing 12ET’s generator 1.059 as a frequency multiplier; it’s like 2, 3, or 5, and this could be measured in Hz, say, by multiplying by 440 if A4 was our 1/1 (1.059 away from A is 466Hz, which is #A). But the meantone generators we’re looking at now in forms like [math]\frac 25[/math], [math]\frac 37[/math], or [math]\frac{5}{12}[/math], are expressed as fractional octaves, i.e. they’re in terms of pitch, something that could be measured in cents if we multiplied by 1200 (2/5 × 1200¢ = 480¢). We have a special way of writing fractional octaves, and that’s with a backslash instead of a slash, like this: 2\5, 3\7, 5\12.
Cents and hertz values can readily be converted between one form and the other, so it’s the second difference which is more important. It’s their size. If we do convert 12ET’s generator to cents so we can compare it with meantone’s generator at 12ET, we can see that 12ET’s generator is 100¢ (log₂1.059 × 1200¢ = 100¢) while meantone’s generator at 12ET is 500¢ (5/12 × 1200¢ = 500¢). What is the explanation for this difference?
Well, notice that meantone is not the only temperament which passes through 12ET. Consider augmented temperament. It has a generator at 12ET that is 400¢^{[14]}. What's key here is that all three of these generators — 100¢, 500¢, and 400¢ — are multiples of 100¢.
Let’s put it this way. When we look at 12ET in terms of itself, rather than in terms of any particular rank2 temperament, its generator is 1\12. That’s the simplest, smallest generator which if we iterate it 12 times will touch every pitch in 12ET. But when we look at 12ET not as the end goal, but rather as a foundation upon which we could work with a given temperament, things change. We don’t necessarily need to include every pitch in 12ET to realize a temperament it supports.
For example, for meantone, even if I iterated the generator only four times, starting at step 0, touching steps 5, 10, 3 (it would be 15, but we octavereduce here, subtracting 12 to stay within 12, landing back at 15  12 = 3), and 8, we’d realize meantone. That’s because fourths and fifths are octavecomplements, and so in a sense they are equivalent. So, moving four fourths up like this is the same thing as moving four fifths down, and we can see that gets me to the same place as if I moved one major third down, which — being 4 steps — would also take me to step 8. That's the central idea of meantone temperament, and so this is what I mean by we've "realized" it.
If we continued to iterate this 12ET meantone generator, we would happen to eventually touch every pitch in 12ET, because 5 and 12 are coprime; we’d continue onward from 8 to 1 (13  12 = 1), then 6, 11, 4, 9, 2, 7, and circle back to 0. On the other hand, augmented temperament in 12ET could never reach most of the pitches, because 4 is not coprime with 12; the 4\12 generator is essentially 1\3, and can only reach 0, 4, and 8. From augmented temperament’s perspective, that’s acceptable, though: this set of pitches still realizes the fact that three major thirds get you back where you started, which is its whole point.
The fact that both the augmented and meantone temperament lines pass through 12ET doesn’t mean that you need the entirety of 12ET to play either one; it means something more like this: if you had an instrument locked into 12ET, you could use it to play some kind of meantone and some kind of augmented. 12ET is not necessarily the most interesting manifestation of either meantone or augmented; it’s merely the case that it technically supports either one. The most interesting manifestations of meantone or augmented may lay between ETs, and/or boast far more than 12 notes.
We mentioned that the generator value changes continuously as we move along a temperament line. So just to either side of 12ET along the meantone line, the tuning of 2, 3, and 5 supports a generator size which in turn supports meantone, but it wouldn’t support augmented. And just to either side of 12ET along the augmented line, the tuning of 2, 3, and 5 supports a generator which still supports augmented, but not meantone. 12ET, we could say, is a convergence point between the meantone generator and the augmented generator. But it is not a convergence point because the two generators become identical in 12ET, but rather because they can both be achieved in terms of 12ET’s generator. In other words, 5\12 ≠ 4\12, but they are both multiples of 1\12.
Here’s a diagram that shows how the generator size changes gradually across each line in PTS. It may seem weird how the same generator size appears in multiple different places across the space. But keep in mind that pretty much any generator is possible pretty much anywhere here. This is simply the generator size pattern you get when you lock the octave to exactly 1200 cents, to establish a common basis for comparison. This is what enables us to produce maps of temperaments such as the one found at this Xen wiki page, or this chart here (see Figure 4b).
Periods and generators
Earlier we mentioned the term “rank”. I warned you then that it wasn’t actually the same thing as dimensionality, even though we could use dimensionality in the PTS to help differentiate rank2 from rank1 temperaments. Now it’s time to learn the true meaning of rank: it’s how many generators a temperament has. So, it is the dimensionality of the tempered lattice; but it's still important to stay clear about the fact that it's different from the dimensionality of the original system from which you are tempering.
When we spoke of “the” generator for a rank2 temperament such as meantone, we were taking advantage of the fact that the other generator is generally assumed to be the octave (or a unit fraction of the octave) and it gets its own special name: the period. It’s technically a generator too, but when we say “the” generator of a rank2 temperament, we mean the one that’s not the period.
The period, usually being the largest generator, serves as the interval of repetition. Rank1 temperaments have only one generator, so that generator technically is the period, though we usually don’t think of it that way; that’s because the word “period” is designed to convey the way in which the other smaller generators can be understood to cycle within it, and so without any smaller generators doing that (see Figure 4c), it sort of loses its meaning. Sometimes the octave (or a fraction thereof) is treated as the “period” of an ET, especially when comparing the ET with a related rank2 temperament; that may make sense in context, though technically this interval could only be the interval of equivalence, not also the interval of repetition (see Figure 4d).
As we’ll soon see, there’s more than one way to generate a given rank2 temperament. For example, meantone can be generated by an octave and a fourth. But it could equivalently be generated by an octave and a fifth. Or an octave and an augmented unison. It could even be generated by cycling a fourth against a fifth. And so on.
And so it’s good to have a standard form for the generators of a linear temperament. One excellent standard is to set the period to an octave and the generator set to anything less than half the size of the period, as we did earlier.
Let’s bring up MOS theory again. We mentioned earlier that you might have been familiar with the scale tree if you’d worked with MOS scales before, and if so, the connection was scale cardinalities, or in other words, how many notes are in the resultant scales when you continuously iterate the generator until you reach points where there are only two scale step sizes. At these points scales tend to sound relatively good, and this is in fact the definition of a MOS scale. There’s a mathematical explanation for how to know, given a ratio between the size of your generator and period, the cardinalities of scales possible; we won’t reexplain it here. The point is that the scale tree can show you that pattern visually. And so if each temperament line in PTS is its own segment of the scale tree, then we can use it in a similar way.
For example, if we pick a point along the meantone line between 12 and 19, the cardinalities will be 5, 7, 12, 19, 31, 50, etc. If we chose exactly the point at 12 then the cardinality pattern would terminate there, or in other words, eventually we’ll hit a scale with 12 notes and instead of two different step sizes there would only be one, i.e. you've got an ET, and there’s no place else to go from there. The system has circled back around to its starting point, so it’s a closed system. Further generator iterations will only retread notes you’ve already touched. The same would be true if you chose exactly the point at 19, except that’s where you’d hit an ET instead, at 19 notes.
Between ETs, in the stretches of rank2 temperament lines where the generator is not a rational fraction of the octave, theoretically those temperaments could have infinite pitches; you could continuously iterate the generator and you’d never exactly circle back to the point where you started. If bigger numbers were shown on PTS, you could continue to use those numbers to guide your cardinalities forever.
The structure when you stop iterating the meantone generator with five notes is called meantone[5]. If you were to use the entirety of 12ET as meantone then that’d be meantone[12]. But you can also realize meantone[12] in 19ET; in the former you have only one step size, but in the latter you have two. You can’t realize meantone[19] in 12ET, but you could also realize it in 31ET.
Meet and join
We’ve seen how 12ET is found at the convergence of meantone and augmented temperaments, and therefore supports both at the same time. In fact, no other ET can boast this feat. Therefore, we can even go so far as to describe 12ET as the meeting of the meantone line and the augmented line. Using the pipe operator “” to mean “meet”, then, we could call 12ET “meantoneaugmented”, read "meantone meet augmented". In other words, we express a rank1 temperament in terms of two rank2 temperaments.
For another rank1 example, we could call 7ET “meantonedicot”, because it is the meeting of meantone and dicot temperaments.
We can conclude that there’s no “blackwoodcompton” temperament, because those two lines are parallel. In other words, it’s impossible to temper out the blackwood comma and compton comma simultaneously. How could it ever be the case that 12 fifths take you back where you started yet also 5 fifths take you back where you started?^{[15]}
Similarly, we can express rank2 temperaments in terms of rank1 temperaments. Have you ever heard the expression “two points make a line”? Well, if we choose two ETs from PTS, then there is one and only one line that runs through both of them. So, by choosing those ETs, we can be understood to be describing the rank2 temperament along that line, or in other words, the one and only temperament whose comma both of those ETs temper out.
For example, we could choose 7ET and 12ET. Looking at either 12ET or 7ET, we can see that many, many temperament lines pass through them individually. Even more pass through them which Paul chose (via a complexity threshold) not to show. But there’s only one line which runs through both 7ET and 12ET, and that’s the meantone line. So of all the commas that 7ET tempers out, and all the commas that 12ET tempers out, there’s only a single one which they have in common, and that’s the meantone comma. Therefore we could give meantone temperament another name, and that’s “7&12”; in this case we use the ampersand operator, not the pipe. This operator is called "join", so we can read that "7 join 12".^{[16]}
When specifying a rank1 temperament in terms of two rank2 temperaments, an obvious constraint is that the two rank2 temperaments cannot be parallel. When specifying a rank2 temperament in terms of two rank1 temperaments, it seems like things should be more openended. Indeed, however, there is a special additional constraint on either method, and they’re related to each other. Let’s look at rank2 as the join of rank1 first.
7&12 is valid for meantone. So is 5&7, and 7&12. 12&19 and 19&7 are both fine too, and so are 5&17 and 17&12. Yes, these are all literally the same thing (though you may connote a meantone generator size on the meantone line somewhere between these two ETs). So how could we mess this one up, then? Well, here are our first counterexamples: 5&19, 7&17, and 17&19. And what problem do all these share in common? The problem is that between 5 and 19 on the meantone line we find 12, and 12 is a smaller number than 19 (or, if you prefer, on PTS, it is printed as a larger numeral). It’s the same problem with 17&19, and with 7&17 the problem is that 12 is smaller than 17. It’s tricky, but you have to make sure that between the two ETs you join there’s not a smaller ET (which you should be joining instead). The reason why is out of scope to explain here, but we’ll get to it eventually.
I encourage you to spend some time playing around with Graham Breed's online RTT tool. For example, at http://x31eq.com/temper/net.html you can enter 12&19
in the "list of steps to the octave" field and 5
in the "limit" field and Submit, and you'll be taken to a results page for meantone.
And the related constraint for rank1 from two rank2 is that you can’t choose two temperaments whose names are printed smaller on the page than another temperament between them. More on that later.
Matrices
From the PTS diagram, we can visually pick out rank1 temperaments at the meetings of rank2 temperaments as well as rank2 temperaments as the joinings of rank1 temperaments. But we can also understand these results through covectors and vectors. And we're going to need to learn how, because PTS can only take us so far. 5limit PTS is good for humans because we live in a physically 3dimensional world (and spend a lot of time sitting in front of 2D pages on paper and on computer screens), but as soon as you want to start working in 7limit harmony, which is 4D, visual analogies will begin to fail us, and if we’re not equipped with the necessary mathematical abstractions, we’ll no longer be able to effectively navigate.
Don’t worry: we’re not going 4D just yet. We’ve still got plenty we can cover using only the 5limit. But we may put away PTS for a couple sections. It’s matrix time. By the end of this section, you'll understand how to represent a temperament in matrix form, how to interpret them, notate them, and use them, as well as how to apply important transformations between different kinds of these matrices.
Mappingrowbases and comma bases
19ET. Its map is ⟨19 30 44]. We also now know that we could call it “meantonemagic”, because we find it at the meeting of the meantone and magic temperament lines. But how would we mathematically, nonvisually make this connection?
The first critical step is to recall that temperaments are defined by commas, which can be expressed as vectors. So, we can represent meantone using the meantone comma, [4 4 1⟩, and magic using the magic comma [10 1 5⟩.
The meet of two vectors can be represented as a matrix. If a vector is like a list of numbers, a matrix is a table of them. Technically, vectors are vertical lists of numbers, or columns, so when we put meantone and magic together, we get a matrix that looks like this:
[math] \left[ \begin{array} {rrr} 4 & 10 \\ 4 & 1 \\ 1 & 5 \end{array} \right] [/math]
We call such a matrix a comma basis. The plural of “basis” is “bases”, but pronounced like BAYsees (/ˈbeɪ siz/).
Now how in the world could that matrix represent the same temperament as ⟨19 30 44]? Well, they’re two different ways of describing it. ⟨19 30 44], as we know, tells us how many generator steps it takes to reach each prime approximation. This matrix, it turns out, is an equivalent way of stating the same information. This matrix is a minimal representation of the nullspace of that mapping, or in other words, of all the commas it tempers out. (Don't worry about the word "mapping" just yet; for now, just imagine I'm writing "map". We'll explain the difference very soon.).
This was a bit tricky for me to get my head around, so let me hammer this point home: when you say "the nullspace", you're referring to the entire infinite set of all commas that a mapping tempers out, not only the two commas you see in any given basis for it. Think of the comma basis as one of many valid sets of instructions to find every possible comma, by adding or subtracting (integer multiples of) these two commas from each other^{[17]}. The math term for adding and subtracting vectors like this, which you will certainly see plenty of as you explore RTT, is "linear combination". It should be visually clear from the PTS diagram that this 19ET comma basis couldn't be listing every single comma 19ET tempers out, because we can see there are at least four temperament lines that pass through it (there are actually infinity of them!). But so it turns out that picking two commas is perfectly enough; every other comma that 19ET tempers out could be expressed in terms of these two!
Try one. How about the hanson comma, [6 5 6⟩. Well that one’s too easy! Clearly if you go down by one magic comma to [10 1 5⟩ and then up by one meantone comma you get one hanson comma. What you’re doing when you’re adding and subtracting multiples of commas from each other like this are technically called column operations. Feel free to work through any other examples yourself.
A good way to explain why we don’t need three of these commas is that if you had three of them, you could use any two of them to create the third, and then subtract the result from the third, turning that comma into a zero vector, or a vector with only zeroes, which is pretty useless, so we could just discard it.
And a potentially helpful way to think about why any other interval arrived at through linear combinations of the commas in a basis would also be a valid column in the basis is this: any of these interval vectors, by definition, is mapped to zero steps by the mapping. So any combination of them will also map to zero steps, and thus be a comma that is tempered out by the temperament.
When written with the ⟨] notation, we’re expressing maps in “covector” form, or in other words, as the opposite of vectors. But we can also think of maps in terms of matrices. If vectors are like matrix columns, maps are like matrix rows. So while we have to write [4 4 1⟩ vertically when in matrix form, ⟨19 30 44] stays horizontal.
We can extend our angle bracket notation (technically called braket notation, or Dirac notation^{[18]}) to handle matrices by nesting rows inside columns, or columns inside rows (see Figure 5a). For example, we could have written our comma basis like this: ⟨[4 4 1⟩ [10 1 5⟩]. Starting from the outside, the ⟨] tells us to think in terms of a row. It's just that this row isn't a row of numbers, like the ones we've gotten used to by now, but rather a row of columns of numbers. So this row houses two such columns. Alternatively, we could have written this same matrix like [⟨4 10] ⟨4 1] ⟨1 5]⟩, but that would obscure the fact that it is the combination of two familiar commas (but that notation would be useful for expressing a matrix built out of multiple maps, as we will soon see).
Sometimes a comma basis may have only a single comma. That’s okay. A single vector can become a matrix. To disambiguate this situation, you could put the vector inside row brackets, like this: ⟨[4 4 1⟩]. Similarly, a single covector can become a matrix, by nesting inside column brackets, like this: [⟨19 30 44]⟩.
If a comma basis is the name for the matrix made out of commas, then we could say a “mapping” is the name for the matrix made out of maps^{[19]}. Why isn't this one a "basis", you ask? Well, it can be thought of as a basis too. It depends on the context. When you use the word "mapping" for it, you're treating it like a function, or a machine: it takes in intervals, and spits out new forms of intervals. That's how we've been using it here. But in other places, you may be thinking of this matrix as a basis for the infinite space of possible maps that could be combined to produce a matrix which works the same way as a given mapping, i.e. it tempers out the same commas. In these contexts, it might make more sense to call such a mapping matrix a "mappingrowbasis".
And now you wonder why it's not just "map basis". Well, that's answerable too. It's because "map" is the analogous term to an "interval", but we're looking for the analogous term to a "comma". A comma is an interval which is tempered out. So we need a word that means a map which tempers out, and that term is "mappingrow".
So, yes, that's right: maps are similar to commas insofar as — once you have more than one of them in your matrix — the possibilities for individual members immediately go infinite. Technically speaking, though, while a comma basis is a basis of the nullspace of the mapping, a mappingrowbasis is a rowbasis of the rowspace of the mapping.
One last note back on the bracket notation before we proceed: you will regularly see matrices across the wiki that use only square brackets on the outside, e.g. [⟨5 8 12] ⟨7 11 16]] or [[4 4 1⟩ [10 1 5⟩]. That's fine because it's unambiguous; if you have a list of rows, it's fairly obvious you've arranged them vertically, and if you've got a list of columns, it's fairly obvious you've arranged them horizontally. I personally prefer the style of using angle brackets at both levels — for slightly more effort, it raises slightly less questions — but using only square brackets on the outside should not be said to be wrong^{[20]}.
Nullspace
There’s nothing special about the pairing of meantone and magic. We could have chosen meantonehanson, or magicnegri, etc. A matrix formed out of the meet of any two of these particular commas will capture the same exact nullspace of [⟨19 30 44]⟩.
We already have the tools to check that each of these commas’ vectors is tempered out individually by the mappingrow ⟨19 30 44]; we learned this bit in the very first section: all we have to do is make sure that the comma maps to zero steps in this ET. But that's not a special relationship between 19ET and any of these commas individually; each of these commas are tempered out by many different ETs, not just 19ET. The special relationship 19ET has is to a nullspace which can be expressed in basis form as the meet of two commas (at least in the 5limit; more on this later). In this way, the comma bases which represent the meet of two commas are greater than the sum of their individual parts.
We can confirm the relationship between an ET and its nullspace by converting back and forth between them. There exists a mathematical function which — when input any one of these comma bases — will output [⟨19 30 44]⟩, thus demonstrating the various bases' equivalence with respect to it. If the operation called "taking the nullspace" is what gets you from [⟨19 30 44]⟩ to one basis for the nullspace, then this mathematical function is in effect undoing the nullspace operation; maybe we could call it an antinullspace operation.
And interestingly enough, as you'll soon see, the process is almost the same to take the nullspace as it is to undo it.
Working this out by hand goes like this (it is a standard linear algebra operation, so if you're comfortable with it already, you can skip this and other similar parts of these materials).
So here's our starting point, the meantonemagic comma basis:
[math] \left[ \begin{array} {rrr} 4 & \color{blue}10 \\ \color{blue}4 & 1 \\ 1 & 5 \\ \end{array} \right] [/math]
First, take its antitranspose. Or in other words, flip it across its antidiagonal, the terms coming out from the topright corner (highlighted in blue here and in the previous matrix to help demonstrate):
[math] \left[ \begin{array} {rrr} 5 & 1 & \color{blue}10 \\ 1 & \color{blue}4 & 4 \end{array} \right] [/math]
Now, augment it with an “identity matrix”.
[math] \left[ \begin{array} {rrr} 5 & 1 & 10 \\ 1 & 4 & 4 \\ \hline 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] [/math]
Now, do GaussJordan elimination on columns until you can get one of the columns in the top half to be all zeroes:
[math] \left[ \begin{array} {rrr} 5 & 1 & 30 \\ 1 & 4 & 0 \\ \hline 1 & 0 & 4 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 19 & 30 \\ 1 & 0 & 0 \\ \hline 1 & 4 & 4 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 19 & 570 \\ 1 & 0 & 0 \\ \hline 1 & 4 & 76 \\ 0 & 1 & 0 \\ 0 & 0 & 19 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 19 & \color{lime}0 \\ 1 & 0 & \color{lime}0 \\ \hline 1 & 4 & \color{green}44 \\ 0 & 1 & \color{green}30 \\ 0 & 0 & \color{green}19 \end{array} \right] [/math]
Grab the corresponding column from the bottom half:
[math] \left[ \begin{array} {rrr} \color{green}44 \\ \color{green}30 \\ \color{green}19 \end{array} \right] [/math]
Antitranspose it:
[math] \left[ \begin{array} {rrr} 19 & 30 & 44 \end{array} \right] [/math]
And tada! You’ve found a mapping from a comma basis, and it is [⟨19 30 44]⟩. In other words, for this temperament, you have converted a basis for its nullspace to a rowbasis for its mapping rowspace. Feel free to try this with any other combination of two commas tempered out by this mappingrow.
So why the antitranspose sandwich? What we (and everyone) want in a mapping is to have a triangle of zeros in the bottom left corner. What we want in a comma basis is also to have a triangle of zeros in the bottom left corner. But the comma basis is on the other side of duality from the mapping, so it must be transposed before we can apply our nullspacebasis finding process to it, because that process is designed for mappings. But when we transpose the comma basis we end up with the triangle of zeros in the top right. If we take the nullspace of that and then transpose it back again, we don't get a nice form of the mapping, we get a mapping with a triangle of zeros in the top right. The way to fix this is to antitranspose instead of transpose, before and after taking the nullspace. Because when you antitranspose the comma basis, you still turn columns into rows, but this time the triangle of zeros stays on the bottom left.
Let's try it out in Wolfram Language:
In: antiNullSpaceBasis[{{4,4,1},{10,1,5}}] Out: {{19,30,44}}
Note that this time we used two sets of curlies around our numbers. That's because these are not technically vectors and covectors, but matrices now. A matrix can be thought of as a list of lists. The comma basis here is a list of two lists. The output mapping is a list with only one list in it.
Now the nullspace function, to take you from [⟨19 30 44]⟩ back to the matrix, is pretty much the same thing, but simpler! No need to antitranspose. Just start at the augmentation step:
[math] \left[ \begin{array} {rrr} 19 & 30 & 44 \\ \hline 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] [/math]
This time try to get two of the (1row) columns in the top half to be (all) zeroes.
[math] \left[ \begin{array} {rrr} 19 & 30 & 836 \\ \hline 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 19 \end{array} \right] → \left[ \begin{array} {rrr} 19 & 30 & 0 \\ \hline 1 & 0 & 44 \\ 0 & 1 & 0 \\ 0 & 0 & 19 \end{array} \right] → \left[ \begin{array} {rrr} 19 & 570 & \color{lime}0 \\ \hline 1 & 0 & \color{green}44 \\ 0 & 19 & \color{green}0 \\ 0 & 0 & \color{green}19 \end{array} \right] → \left[ \begin{array} {rrr} 19 & \color{lime}0 & \color{lime}0 \\ \hline 1 & \color{green}30 & \color{green}44 \\ 0 & \color{green}19 & \color{green}0 \\ 0 & \color{green}0 & \color{green}19 \end{array} \right] [/math]
Now grab the corresponding columns from the bottom half
[math] \left[ \begin{array} {rrr} \color{green}30 & \color{green}44 \\ \color{green}19 & \color{green}0 \\ \color{green}0 & \color{green}19 \end{array} \right] [/math]
So that’s not any of the commas we’ve looked at so far (it’s the 19comma and the acute limma). But it is clear to see that either of them would be tempered out by 19ET (no need to map by hand — just look at these commas sidebyside with the mappingrow [⟨19 30 44]⟩ and it should be apparent). We're done!
And let's try that one in Wolfram Language, too:
In: nullSpaceBasis[{{19,30,44}}] Out: {{44,0,19},{30,19,0}}
So we've gotten right back where we've started.
The RTT library for Wolfram Language includes a function called dual[]
. This will give you the comma basis for a mapping, or the mapping for a comma basis, depending on which you put it. There's a catch, though. You have to tell it which type you're putting in! Otherwise it can't tell the difference. And of course it needs to know whether or not it needs to do an antitranspose sandwich. So here's how we provide that:
In: dual[{{{19,30,44}},"mapping"}] Out: {{{44,0,19},{30,19,0}},"comma basis"} In: dual[{{{4,4,1},{10,1,5}},"comma basis"}] Out: {{{19,30,44}},"mapping"}
Note that we're now using a data structure which is an ordered pair: the first entry is the matrix, and the second entry is a string which says which type of matrix it is.
It's great to have Wolfram and other such tools to compute these things for us, once we understand them. But I think it’s a very good idea to work through these operations by hand at least a couple times, to demystify them and give you a feel for them.
The other side of duality
So we can now convert back and forth between a mappingrowbasis and a comma basis. We could imagine drawing a diagram with a line of duality down the center, with a temperament's mappingrowbasis on the left, and its comma basis on the right. Either side ultimately gives the same information, but sometimes you want to come at it in terms of the maps, and sometimes in terms of the commas.
So far we've looked at how to meet comma vectors to form a comma basis. Next, let's look at the other side of duality, and see how to form a mappingrowbasis out of joining maps. In many ways, the approaches are similar; the line of duality is a lot like a mirror in that way.
When we join two maps, we put them together into a matrix, just like how we put two vectors together into a matrix. But again, where vectors are vertical columns, maps are horizontal rows. So when we combine ⟨5 8 12] and ⟨7 11 16], we get a matrix that looks like
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \end{array} \right] [/math]
This matrix represents meantone. In our angle bracket notation, we would write it as two covectors inside a column bracket (one column of two rows), like this: [⟨5 8 12] ⟨7 11 16]⟩.
Again, we find ourselves in the position where we must reconcile a strange new representation of an object with an existing one. We already know that meantone can be represented by the vector for the comma it tempers out, [4 4 1⟩. How are these two representations related?
Well, it’s actually quite simple! They’re related in the same way as [⟨19 30 44]⟩ was related to ⟨[4 4 1⟩ [10 1 5⟩]: by the nullspace operation. Specifically, ⟨[4 4 1⟩] is a basis for the nullspace of the temperament with mappingrowbasis [⟨5 8 12] ⟨7 11 16]⟩, because it is the minimal representation of all the commas tempered out by meantone temperament, which means mapped to zeros by the meantone mapping.
We can work this one out by hand too:
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \\ \hline 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 8 & 60 \\ 7 & 11 & 80 \\ \hline 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 5 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 8 & 0 \\ 7 & 11 & 4 \\ \hline 1 & 0 & 12 \\ 0 & 1 & 0 \\ 0 & 0 & 5 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 40 & 0 \\ 7 & 55 & 4 \\ \hline 1 & 0 & 12 \\ 0 & 5 & 0 \\ 0 & 0 & 5 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 0 & 0 \\ 7 & 1 & 4 \\ \hline 1 & 8 & 12 \\ 0 & 5 & 0 \\ 0 & 0 & 5 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 0 & 0 \\ 7 & 1 & 0 \\ \hline 1 & 8 & 20 \\ 0 & 5 & 20 \\ 0 & 0 & 5 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 0 & 0 \\ 7 & 1 & 0 \\ \hline 1 & 8 & 4 \\ 0 & 5 & 4 \\ 0 & 0 & 1 \end{array} \right] [/math]
input  output 

nullSpaceBasis[{{5,8,12},{7,11,16}}]

{{4},{4},{1}} 
And there’s our ⟨[4 4 1⟩]^{[21]}. Feel free to try reversing the operation by working out the mappingrowbasis from this if you like. And/or you could try working out that ⟨[4 4 1⟩] is a basis for the nullspace of any other combination of ETs we found that could specify meantone, such as 7&12, or 12&19.
It’s worth noting that, just as 2 commas were exactly enough to define a rank1 temperament, though there were an infinitude of equivalent pairs of commas we could choose to fill that role, there’s a similar thing happening here, where 2 maps are exactly enough to define a rank2 temperament, but an infinitude of equivalent pairs of them. We can even see that we can convert between these maps using GaussJordan addition and subtraction, just like we could manipulate commas to get from one to the other. For example, the map for 12ET ⟨12 19 28] is exactly what you get from summing the terms of 5ET ⟨5 8 12] and 7ET ⟨7 11 16]: ⟨5+7 8+11 12+16] = ⟨12 19 28]. Cool!
Probably the biggest thing you’re in suspense about now, though, is: how the heck is
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \end{array} \right] [/math]
supposed to be a mappingrowbasis, or mapping, for meantone? What does that even mean?
Rank2 mappings
Let’s consider some facts:
 [⟨19 30 44]⟩ is the mapping for a rank1 temperament.
 [⟨5 8 12] ⟨7 11 16]⟩ is the mapping for a rank2 temperament.
 A rank1 temperament has one generator.
 A rank2 temperament has two generators.
 ⟨19 30 44] asks us to imagine a generator g for which g¹⁹ ≈ 2, g³⁰ ≈ 3, and g⁴⁴ ≈ 5.
From these facts, we can see that what the mapping
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \end{array} \right] [/math]
is trying to tell us is: we have two generators. And the first generator has something to do with 5ET, and the second generator has something to do with 7ET.
And somehow… from this… we can generate meantone?! This is true, but it’s not immediately easy to see how that would happen.
First we should show how to actually use rank2 mappings. It’s actually not that complicated. It’s just like using a rank1 mapping, except you have to find each of them separately, and then put them back together at the end. Let’s see how this plays out for 10/9, or [1 2 1⟩.
⟨5 8 12]:
 ⟨5 8 12][1 2 1⟩
 5×1 + 8×2 + 12×1
 5 + 16 + 12
 1
⟨7 11 16]:
 ⟨7 11 16][1 2 1⟩
 7×1 + 11×2 + 16×1
 7 + 22 + 16
 1
So in this meantone mapping, the best approximation of the JI interval 10/9 is found by moving 1 step in each generator. We could write this in vector form as [1 1⟩.
If the familiar usage of vectors has been as prime count vectors or PCvectors, we can now generalize that definition to things like this [1 1⟩: generator count vectors or GCvectors. Since interval vectors are often called monzos, you’ll often see these called tempered monzos or tmonzos for short. There’s very little difference. We can use these vectors as coordinates in a lattice just the same as before. The main difference is that the nodes we visit on this lattice aren’t pure JI; they’re a tempered lattice.
We haven’t specified the size of either of these generators, but that’s not important here. These mappings are just like a set of requirements for any pair of generators that might implement this temperament. This is as good a time as any to emphasize the fact that temperaments are abstract; they are not readytogo tunings, but more like instructions for a tuning to follow. This can sometimes feel frustrating or hard to understand, but ultimately it’s a big part of the power of temperament theory. In this case, a common method for optimizing tunings from temperaments would give these two generators as 73.756¢ and 118.945¢, respectively, which gives the tempered 10/9 as 73.756¢ + 118.945¢ = 192.701¢, which is about 10¢ sharp from its JI cents value of 182.404¢. We’ll learn more about tuning methods later.
The critical thing here is that if [4 4 1⟩ is mapped to 0 steps by ⟨5 8 12] individually and to 0 steps by ⟨7 11 16] individually, then in total it comes out to 0 steps in the temperament, and thus is tempered out, or has vector [0 0⟩.
Previously we mentioned that any given rank2 temperaments can be generated by a wide variety of combinations of intervals. In other words, the absolute size of the intervals is not the important part, in terms of their potential for generating the temperament; only their relative size matters. However, for us humans, it’s much easier to make sense of these things if we get them in a good old standard form, by locking one generator to the octave to establish a common basis for comparison, and the other generator to a size less than half of the octave (because anything past the halfway point and it would be the octavecomplement of a smaller and therefore in some sense simpler interval). And there’s a way to find this form by transforming our matrix. This time we’ll use elementary row operations (which are more common than elementary column operations, anyway). Our target this time is a bit harder to explain ahead of time, so this first time through, just watch, and we’ll review the result.
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \end{array} \right] → \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 2 & 3 & 4 \end{array} \right] → \left[ \begin{array} {rrr} 1 & 2 & 4 \\ 2 & 3 & 4 \end{array} \right] → \left[ \begin{array} {rrr} 1 & 2 & 4 \\ 1 & 1 & 0 \end{array} \right] → \left[ \begin{array} {rrr} 0 & 1 & 4 \\ 1 & 1 & 0 \end{array} \right] [/math]
And I’m just going to switch the order of those two:
[math] \left[ \begin{array} {rrr} 1 & 1 & 0 \\ 0 & 1 & 4 \end{array} \right] [/math]
So this is still meantone! But now it’s a bit more practical to think about. Because notice what happens to the octave, [1 0 0⟩. To approximate the octave, you simply move by one of the first generator, or [1 0⟩. The second generator has nothing to do with it. And how about the fifth, [1 1 0⟩? Well, the first generator maps that to 0 steps, and the second generator maps that to 1 step, or [0 1⟩. So that tells us our second generator is the fifth. Which is… almost perfect! I would have preferred a fourth, which is the octavecomplement of the fifth which is less than half of an octave. But it’s basically the same thing. Good enough.
Hopefully manipulating these rows like this gives you some sort of feel for how what matters in a mapping is not so much the values themselves but their relationship with each other.
To conclude this section, I have a barrage of unrelated points of order:
 We’ve made it to a critical point here: we are now able to explain why RTT is called “regular” temperament theory. Regular here is a mathematical term, and I don’t have a straightforward definition of it for you, but it apparently refers to the fact that all intervals in the tuning are combinations of only these specified generators. So there you go.
 Both [⟨5 8 12] ⟨7 11 16]⟩ and [⟨1 1 0] ⟨0 1 4]⟩ are equivalent mappings, then. In other words, they are both mappingrowbases of the same mapping rowspace. Converting between them is sometimes called a “change of basis”, though this is a different sort of “basis” than the basis of a space, like comma bases or mappingrowbases. Using what we know now, it may be clearer to think of it as a “change of generators”. We’ll look at this idea more in the tuning section later.
 Note well: this is not to say that ⟨1 1 0] or ⟨0 1 4] are the generators for meantone. They are generator mappings: when assembled together, they collectively describe behavior of the generators, but they are not themselves the generators. At least, the map ⟨1 1 0] in isolation does not inherently have a connection with the octave generator of meantone. This situation can be confusing; it confused me for many weeks. I thought of it this way: because the first generator is 2/1 — i.e. [1 0 0⟩ maps to [1 0⟩ — referring to ⟨1 1 0] as the octave or period seems reasonable and is effective when the context is clear. And similarly, because the second generator is 3/2 — i.e. [1 1 0⟩ maps to [0 1⟩ — referring to ⟨0 1 4] as the fifth or the generator seems reasonable as is effective when the context is clear. But it's critical to understand that the first generator "being" the octave here is contingent upon the definition of the second generator, and vice versa, the second generator "being" the fifth here is contingent upon the definition of the first generator. Considering ⟨1 1 0] or ⟨0 1 4] individually, we cannot say what intervals the generators are. What if the mapping was [⟨0 1 4] ⟨1 2 4]⟩ instead? We'd still have the first generator mapping as ⟨1 1 0], but now that the second generator mapping is ⟨1 2 4], the two generators must be the fourth and the fifth. In summary, neither mappingrow describes a generator in a vacuum, but does so in the context of all the other mappingrows.
 This also gives us a new way to think about the scale tree patterns. Remember how earlier we pointed out that ⟨12 19 28] was simply ⟨5 8 12] + ⟨7 11 16]? Well, if [⟨5 8 12] ⟨7 11 16]⟩ is a way of expressing meantone in terms of its two generators, you can imagine that 12ET is the point where those two generators converge on being the same exact size^{[22]}. If they become the same size, then they aren’t truly two separate generators, or at least there’s no effect in thinking of them as separate. And so for convenience you can simply combine their mappings into one. You could imagine gradually increasing the size of one generator and decreasing the size of the other until they were both 100¢. As long as you maintain the correct proportion, you'll stay along the meantone line..
 Technically speaking, when we first learned how to map vectors with ETs, we could think of those outputs as vectors too, but they'd be 1dimensional vectors, i.e. if 12ET maps 16/15 to 1 step, we could write that as ⟨12 19 28][4 1 1⟩ = [1⟩, where writing the answer as [1⟩ expresses that 1 step as 1 of the only generator in this equal temperament.
JI as a temperament
Two points make a line. By the same logic, three points make a plane. Does this carry any weight in RTT? Yes it does.
Our hypothesis might be: this represents the entirety of 5limit JI. If two rank1 temperaments — each of which can be described as tempering out 2 commas — when joined result in a rank2 temperament — which is defined as tempering out 1 comma — then when we join three rank1 temperaments, we should expect to get a rank3 temperament, which tempers out 0 commas. The rank1 temperaments appear as 0D points in PTS but are understood to be a 1D line coming straight at us; the rank2 temperaments appear as 1D points in PTS but are understood to be 2D planes coming straight at us; the rank3 temperament appear as the 2D plane of the entire PTS diagram but is understood to be the entire 3D space.
Let’s check our hypothesis using the PTS navigation techniques and matrix math we’ve learned.
Let’s say we pick three ETs from PTS: 12, 15, and 22. The same constraint applies here that we can’t choose ETs for which there is a smaller number between them on the line that connects them. Each pair of these pass that test. Done.
Their combined matrix is:
[math] \left[ \begin{array} {rrr} 12 & 19 & 28 \\ 15 & 24 & 35 \\ 22 & 35 & 51 \end{array} \right] [/math]
I won’t work through this by hand, but if you’re feeling up to it, you could do GaussJordan elimination on this thing, and what you’d achieve is this:
[math] \left[ \begin{array} {rrr} 1 & 0 & 0 \\ 0 & 1 & 0 \\ 0 & 0 & 1 \end{array} \right] [/math]
That looks like an identity matrix! Well, in this case the best interpretation can be found by checking its mapping of 2/1, 3/1, and 5/1, or in other words [1⟩, [0 1⟩, and [0 0 1⟩. Each prime is generated by a different generator, independently. And if you think about the implications of that, you’ll realize that this is simply another way of expressing the idea of 5limit JI! Because the three generators are entirely independent, we are capable of exactly generating literally any 5limit interval. Which is another way of confirming our hypothesis that no commas are tempered out.
Tempered lattice
Let’s make sure we establish what exactly the tempered lattice is. This is something like the JI lattice we looked at very early on, except instead of one axis per prime, we have one axis per generator. As we saw just a moment ago, these two situations are not all that different; the JI lattice could be viewed as a tempered lattice, where each prime is a generator.
In this rank2 example of 5limit meantone, we have 2 generators, so the lattice is 2D, and can therefore be viewed on a simple square grid on the page. Up and down correspond to movements by one generator, and left and right correspond to movements by the other generator.
The next step is to understand our primes in terms of this temperament’s generators. Meantone’s mapping is [⟨1 0 4] ⟨0 1 4]⟩. This maps prime 2 to one of the first generators and zero of the second generators. This can be seen plainly by slicing the first column from the matrix; we could even write it as the vector [1 0⟩. Similarly, this mapping maps prime 3 to zero of the first generator and one of the second generator, or in vector form [0 1⟩. Finally, this mapping maps prime 5 to negative four of the first generator and four of the second generator, or [4 4⟩.
So we could label the nodes with a list of approximations. For example, the node at [4 4⟩ would be ~5. We could label ~9/8 on [3 2⟩ just the same as we could label [3 2⟩ 9/8 in JI, however, here, we can also label that node ~10/9, because [1 2 1⟩ → 1×[1 0⟩ + 2×[0 1⟩ + 1×[4 4⟩ = [1 0⟩ + [0 2⟩ + [4 4⟩ = [3 2⟩. Cool, huh? Because conflating 9/8 and 10/9 is a quintessential example of the effect of tempering out the meantone comma (see Figure 5b).
Sometimes it may be more helpful to imagine slicing your mapping matrix the other way, by columns (vectors) corresponding to the different primes, rather than rows (covectors) corresponding to generators. Meaning we can look at [⟨1 0 4] ⟨0 1 4]⟩ as a matrix of three vectors, ⟨[1 0⟩ [0 1⟩ [4 4⟩] which tells us that 2/1 is [1 0⟩, 3/1 is [0 1⟩, and 5/1 is [4 4⟩}}.
And so we can see that tempering has reduced the dimensionality of our lattice by 1. Or in other words, the dimensionality of our lattice was always the rank; it’s just that in JI, the rank was equal to the dimensionality. And what’s happened by reducing this rank is that we eliminated one of the primes in a sense, by making it so we can only express things in terms of it via combinations of the other remaining primes.
Rank and nullity
Let’s review what we’ve seen so far. 5limit JI is 3dimensional. When we have a rank3 temperament of 5limit JI, 0 commas are tempered out. When we have a rank2 temperament of 5limit JI, 1 comma is tempered out. When we have a rank1 temperament of 5limit JI, 2 commas are tempered out.^{[23]}
There’s a straightforward formula here: [math]d  n = r[/math], where [math]d[/math] is dimensionality, [math]n[/math] is nullity, and [math]r[/math] is rank. We’ve seen every one of those words so far except nullity. Nullity simply means the count of commas tempered out, or in other words, the count of commas in a basis for the nullspace (see Figure 5c).
So far, everything we’ve done has been in terms of 5limit, which has dimensionality of 3. Before we generalize our knowledge upwards, into the 7limit, let’s take a look at how things one step downwards, in the simpler direction, in the 3limit, which is only 2dimensional.
We don’t have a ton of options here! The PTS diagram for 3limit JI could be a simple line. This axis would define the relative tuning of primes 2 and 3, which are the only harmonic building blocks available. Along this line we’ll find some points, which familiarly are ETs. For example, we find 12ET. Its map here is ⟨12 19]; no need to mention the 5term because we have no vectors that will use it here. At this ET, being a rank1 temperament, [math]r[/math] = 1. So if [math]d[/math] = 2, then solve for [math]n[/math] and we find that it only tempers out a single comma (unlike the rank1 temperaments in 5limit JI, which tempered out two commas). We can use our familiar nullspace function to find what this comma is:
[math] \left[ \begin{array} {rrr} 12 & 19 \\ \hline 1 & 0 \\ 0 & 1 \end{array} \right] → \left[ \begin{array} {rrr} 12 & 228 \\ \hline 1 & 0 \\ 0 & 12 \end{array} \right] → \left[ \begin{array} {rrr} 12 & 0 \\ \hline 1 & 19 \\ 0 & 12 \end{array} \right] [/math]
Let's try it out in Wolfram Language:
In: nullSpaceBasis[{{12,19}}] Out: {{19, 12}}
Unsurprisingly, the comma is [19 12⟩, the compton comma. Basically, any comma we could temper out in 3limit JI is going to be obvious from the ET’s map. Another option would be the blackwood comma, [8 5⟩ tempered out in 5ET, ⟨5 8]. Exciting stuff! Okay, not really. But good to ground yourself with.
But now you shouldn’t be afraid even of 11limit or beyond. 11limit is 5D. So if you temper 2 commas there, you’ll have a rank3 temperament.
Beyond the 5limit
So far we’ve only been dealing with RTT in terms of prime limits, which is by far the most common and simplest way to use it. But nothing is stopping you from using other JI groups. What is a JI group? Well, I'll explain in terms of what we already know: prime limits. Prime limits are basically the simplest type of JI group. A prime limit is shorthand for the JI group consisting of all the primes up to that prime which is your limit; for example, the 7limit is the same thing as the JI group "2.3.5.7". So JI groups are just sets of harmonics, and they are notated by separating the selected harmonics with dots.
Sometimes you may want to use a JI [[1]]. For example, you could create a 3D tuning space out of primes 2, 3, and 7 instead, skipping prime 5. You would call it “the 2.3.7 subgroup”. Or you could just call it "the 2.3.7 group", really. Nobody really cares that it's a subgroup of another group.
You could even choose a JI group with combinations of primes, such as the 2.5/3.7 group. Here, we still care about approximating primes 2, 3, 5, and 7, however there's something special about 3 and 5: we don't specifically care about approximating 3 or 5 individually, but only about approximating their combination. Note that this is different yet from the 2.15.7 group, where the combinations of 3 and 5 we care about approximating are when they're on the same side of the fraction bar.
As you can see from the 2.15.7 example, you don't even have to use primes. Simple and common examples of this situation are the 2.9.5 or the 2.3.25 groups, where you're targeting multiples of the same prime, rather than combinations of different primes.
And these are no longer JI groups, of course, but you can even use irrationals, like the 2.ɸ.5.7 group! The sky is the limit. Whatever you choose, though, this core structural rule [math]d  n = r[/math] holds strong (see Figure 5d).
The order you list the pitches you're approximating with your temperament is not standardized; generally you increase them in size from left to right, though as you can see from the 2.9.5 and 2.15.7 examples above it can often be less surprising to list the numbers in prime limit order instead. Whatever order you choose, the important thing is that you stay consistent about it, because that's the only way any of your vectors and covectors are going to match up correctly!
Alright, here’s where things start to get pretty fun. 7limit JI is 4D. We can no longer refer to our 5limit PTS diagram for help. Maps and vectors here will have four terms; the new fourth term being for prime 7. So the map for 12ET here is ⟨12 19 28 34].
Because we're starting in 4D here, if we temper out one comma, we still have a rank3 temperament, with 3 independent generators. Temper out two commas, and we have a rank2 temperament, with 2 generators (remember, one of them is the period, which is usually the octave). And we’d need to temper out 3 commas here to pinpoint a single ET.
The particular case I’d like to focus our attention on here is the rank2 case. This is the first situation we’ve been able to achieve which boasts both an infinitude of matrices made from comma vectors which can represent the temperament by its comma basis, as well as an infinitude of matrices made from ET maps which can represent a temperament by its mappingrowbasis. These are not contradictory. Let’s look at an example: septimal meantone.
Septimal meantone may be thought of as the temperament which tempers out the meantone comma and the starling comma (126/125), or “meantonestarling”. But it may also be thought of as “meantonemarvel”, where the marvel comma is 225/224. We don’t even necessarily need the meantone comma at all: it can even be “starlingmarvel”! This speaks to the fact that any temperament with a nullity greater than 1 has an infinitude of equivalent comma bases. It’s up to you which one to use.
On the other side of duality, septimal meantone’s mappingrowbasis has two rows, corresponding to its two generators. We don’t have PTS for 7limit JI handy, but because septimal meantone includes, or extends plain meantone, we can still refer to 5limit PTS, and pick ETs from the meantone line there. The difference is that this time we need to include their 7term. So the join of ⟨12 19 28 34] and ⟨19 30 44 53] would work. But so would ⟨19 30 44 53] and ⟨31 49 72 87]. We have an infinitude of options on this side of duality too, but here it’s not because our nullity is greater than 1, but because our rank is greater than 1.
Canonical form
Recently we reduced
[math] \left[ \begin{array} {rrr} 5 & 8 & 12 \\ 7 & 11 & 16 \\ \end{array} \right] [/math]
to
[math] \left[ \begin{array} {rrr} 1 & 1 & 0 \\ 0 & 1 & 4 \end{array} \right] [/math]
In this form, as we observed, the period is an octave and the generator is a fifth, which is a popular and convenient way to think about meantone. But there are other good forms this mappingrowbasis could be put into.
For example, you might want the form that Graham Breed's temperament finder puts them in, where all values in a mappingrowbasis row may be negative, but this is in the service of the generator being positive, and less than half the size of the period. For example, for meantone, we'd want the fourth instead of the fifth, and we can see that
[math] \left[ \begin{array} {rrr} 1 & 2 & 4 \\ 0 & 1 & 4 \end{array} \right] [/math]
maps the fourth (4/3, [2 1 0⟩) to [0 1⟩. That form is called mingen form.
But there are still more forms! One very important form is called defactored Hermite form, or we may call it here canonical form for short.
It’s often the case that a temperament’s nullity is greater than 1 or its rank is greater than 1, and therefore we have an infinitude of equivalent ways of expressing the comma basis or the mappingrowbasis. This can be problematic, if we want to efficiently communicate about and catalog temperaments. It’s good to have a standardized form in these cases. The approach RTT takes here is to get these matrices into canonical form. In plain words, this just means: we have a function which takes in a matrix and spits out a matrix of the same shape, and no matter which matrix we input from a set of matrices which we consider all to be equivalent to each other, it will spit out the same result. This output is thereby “canonical”, and it can therefore uniquely identify a temperament.
To be clear, canonical form isn’t necessary to avoid ambiguity: you will never find a comma basis that could represent more than one temperament.
For example, the canonical form of meantone is:
[math] \left[ \begin{array} {rrr} 1 & 0 & 4 \\ 0 & 1 & 4 \end{array} \right] [/math]
So if you take the canonical form of [⟨5 8 12] ⟨7 11 16]⟩, that’s what you get. It’s also what you get if you take the canonical form of [⟨12 19 28] ⟨19 30 44]⟩, or any equivalent other mappingrowbasis. That’s the power of canonicalization.
Let's try it out in Wolfram Language:
In: canonicalForm[{{{5,8,12},{7,11,16}},"mapping"}] Out: {{{1,0,4},{0,1,4}},"mapping"}
Canonical form can be done by hand, but it's a bit involved, because it requires first defactoring and then putting into Hermite Normal Form. I've demonstrated how to do these processes at the links provided.
Canonicalization used to be achieved in RTT through the use of the "wedgie", an object that involves more advanced math. So while you may see "wedgies" around on the wiki and elsewhere, don't worry — you don't need to worry about them in order to do RTT. If you want to learn more anyway, I've gathered up everything I figured out about those here: Intro to exterior algebra for RTT.
Other topics (TBD)
Tuning
Timbre
Scales
Lattices
Notation
Outro
terminology category  building block →  temperament ID  temperament ID dual  ← building block 

RTT application  map, mappingrow (often an ET)  mapping, mappingrowbasis  comma basis  interval, comma 
RTT structure  map  list of maps  list of prime count lists  prime count list 
linear algebra structure  row vector, matrix row, covector  matrix, list of covectors  matrix, list of vectors  (column) vector, matrix column, vector 
extended braket notation representation  bra  ket of bras  bra of kets  ket 
RTT jargon  val  list of vals  list of monzos  monzo 
You’ve made it to the end. This is pretty much everything that I understand about RTT at this point (May 2021). This took me a little over a month of fulltime funemployed exploration to learn and put together; at the onset, while having considered myself a xenharmonicist for 15 years, I only understood a few scattered things about RTT, such as ET maps and commas and the overlapping MOS concepts. I hope that equipped with my results here it shouldn’t take a newcomer nearly that much time to get going. And of course I hope to continue learning more myself and perhaps even contributing to the theory one day, since there’s still plenty of frontier here.
I couldn’t have put this together without the help of:
 Dave Keenan
 Paul Erlich
 Mike Battaglia
 Graham Breed
 Steve Martin
 Herman Miller
 Keenan Pepper
 Flora Canou
 Inthar
 Scott Thompson
 Joshua Sanchez
 Vincenzo Sicurella
 Petr Pařízek
 Margo Schulter
 Stephen Weigel
plus many many more. And of course I owe a big debt to Gene Ward Smith.
I take full responsibility for any errors or shortcomings of this work. Please feel free to edit this stuff yourself if you have something you'd like to correct, revise, or contribute.
Happy tempering!
 ↑ And curiously little about the history.
 ↑ "Val" creates an unnecessary barrier to understanding for those already familiar with established terms for concepts RTT borrows from linear algebra. Even if it does have some connection to the mathematical concept of a "valuation", this is of no help in illuminating its musictheory meaning, which is simply that of being a mapping from a single generator to primes. The first sentence in the Xenharmonic Wiki article for "val" says, "a val is a linear map". And the first sentence of the Wikipedia article where "covector" redirects to is: "... a covector is a linear map from a vector space to its field of scalars". So that is why we use "map" in this material instead.
As for “monzo”, I feel I must say that the man Joe Monzo deserves to be celebrated. He is a friend and has even sung for me in a recording of a piece of music I composed. But I simply prefer descriptive, established, obvious names for things, and eponyms do not fit that bill.  ↑ It's not as simple as selectall, copy, paste, because of how computational notebooks can (and should) be broken down into many cells. However there is a handy way to copy all cells, including all of each of their output: just click in the top right to select the first cell (it should highlight along the right edge in blue), then shiftclick the same area but for the bottom cell, copy, and paste. Voilà!
 ↑ This list of logarithmic identities has been an excellent resource for me in getting my head around logarithmic thinking. As you can see there, [math]\frac{log_{10}{a}}{log_{10}{b}} = log_{b}{a}[/math], so the base doesn't matter; you could put anything in there — 10, 2, e — and it still reduces to [math]log_{b}{a}[/math].
 ↑ For more information, see: The Riemann zeta function and tuning.
 ↑ See my proposal to rename this object here: https://en.xen.wiki/w/Talk:Patent_val
 ↑ See my proposal to rename this object here: https://en.xen.wiki/w/Talk:Patent_val
 ↑ Elsewhere you may see these called "contorted", but as you can read on the page defactoring, this is not technically correct, but has historically been frequently confused.
 ↑ On some versions of PTS which Paul prepared, these enfactored ETs are actually printed on the page.
 ↑ The reason is that Paul’s diagram, in addition to cutting off beyond 99ET, also filters out maps that aren’t uniform maps.
 ↑ Yes, these are the same as the Pythagorean comma and Pythagorean diatonic semitone, respectively.
 ↑ There’s an extension of this pattern. Pick any ET. Maybe start with a prominent one like 7, or 12. Notice that you can find lines radiating out from it of aligned ETs. These would all be rank2 temperaments, though they’re not all drawn. You’ll see that if you pick any size of numeral and follow consecutive numerals of continuously changing size, that the values decrease by the ET number you’re radiating out from. That’s because each step you can think of subtracting that ET number over and over, because moving inward you’d be doing the opposite: repeatedly adding that ET number, per the rules of the scale tree.
 ↑ Each tier of the SternBrocot tree is the next Farey sequence.
 ↑ This statement is slightly misleading, in order to help make the more important point it's in the context of. The full truth is that augmented's period is 400¢ at 12ET. As you'll soon see, the period is just a special name for the first one of a temperament's generators, but because in rank2 situations like this where there are only two generators, referring to the generator typically implies the second generator, the one which is not the period. For augmented, this generator is 100¢ at 12ET.
 ↑ As you can confirm using the matrix tools you'll learn soon, technically speaking you can temper them both out at the same time... but it'll only be by using 0EDO, i.e. a system with only a single pitch. For more information see trivial temperaments.
 ↑ Elsewhere, you may see this described as "crossbreeding", where an ET map is a "breed".
 ↑ To be clear, because what you are adding and subtracting in interval vectors are exponents (as you know), the commas are actually being multiplied by each other; e.g. [4 4 1⟩ + [10 1 5⟩ = [6 5 6⟩, which is the same thing as [math]\frac{81}{80} × \frac{3072}{3125} = \frac{15552}{15625}[/math]
 ↑ Braket notation comes to RTT from quantum mechanics, not algebra.
 ↑ The following notes are adapted from research by Dave Keeanan:
While it is true that, in mathematics generally, mapping and map, when used as nouns, are synonyms, and both are synonymous with function. But there is very little difference between an individual row of a mapping, and a mapping with only one row. So if we were to agree that, in RTT, only an individual row should be called a map, and someone new to the field assumes that a map is the same as a mapping, then there are almost no consequences of that temporary confusion, if it can even be called confusion. For 12edo, its 5limit map is ⟨12 19 28], and its 5limit mapping is [⟨12 19 28]⟩. The mnemonic is simple: The shorter term applies to the smaller object. The difference rarely matters to anyone.
Dave Keenan is one of the founders of regular temperament theory along with Paul Erlich, Graham Breed, Gene Smith and others, since 1998. In online discussions of regular temperaments, and in our writings, all four of them have referred to any array of numbers whose units are "generators per prime", as a mapping, ever since we first referred to them as anything at all, which seems to have been in early 2001. Only rarely has this been shortened to "map" — typically only as a heading in tables of temperament data generated by Gene Ward Smith. But even Gene is on record as defining a "prime mapping" as a "list of vals", here: http://www.tonalsoft.com/enc/p/primemapping.aspx Evidence of this history of usage of map and mapping can be found in the Yahoo tuning groups archive.
Most of the temperament data in the Xen Wiki was generated by Gene, so it is not surprising if it contained "map" as an abbreviation of "mapping".
In the Xen Wiki and Graham Breed's temperament finder and the tuning archives, the term "map" (and not "mapping") already consistently refers to an individual row of the form ⟨...]. This is in the case of a "tuning map", which maps from generators to cents. This is a map in "tuning space". By analogy, a row of a mapping is therefore a map in "temperament space", and so it would be perfectly consistent with existing terminology, to refer to a mappingrow or onerow mapping as a "temperament map" as opposed to a temperament mapping. So an unqualified "map" should be assumed to be a temperament map, not a tuning map. Or at least that when it is clear from the context that it is a temperament map, the qualifier "temperament" can be dropped.  ↑ Besides, in most contexts the nullspace of a linear mapping is thought of as a list of vectors, rather than a matrix, but it’s generally more helpful for us here to think of it smooshed together as a matrix.
 ↑ Interestingly, the other two columns in the bottom half of this matrix are valuable too. They tell you prime count vectors that would work for your generators. In this case, the two vectors are [1 0 0⟩ and [8 5 0⟩, so that tells you that the octave and the diesis could generate meantone. They're not necessarily the best generators, though. You can find other generators from these by adding or subtracting temperament commas, because of course — being tempered out — don't change anything.
 ↑ For real numbers [math]p,q[/math] we can make the two generators respectively [math]\frac{p}{5p+7q}[/math] and [math]\frac{q}{5p+7q}[/math] of an octave, e.g. [math](p,q)=(1,0)[/math] for 5ET, [math](0,1)[/math] for 7ET, [math](1,1)[/math] for 12ET, and many other possibilities.
 ↑ Probably, a rank0 temperament of 5limit JI would temper 3 commas out. All I can think a rank0 temperament could be is a single pitch, or in other words, everything is tempered out. So perhaps in some theoretical sense, a comma basis in 5limit made out of 3 vectors, thus a square 3×3 matrix, as long as none of the lines are parallel, should minimally represent every interval in the space.