Non permutational serialism (2): Static and dynamic intervals

Though it is permuting notes, the function Train() is not ‘permutational’ because it does not compose permutations but only iterate through the notes of some series in order to modify the corresponding values in another one. This could certainly be done ‘permutationally’ but with much more computation and it’d be useless in this context, since the goal of this set of functions is precisely to obtain sequences different from typical permutational ones. With Trainsposition() we had an already interesting example since each modifying note was given two parameters (in addition to its index): an integer value and a sign. The amount of strictly permutational manipulations needed to perform such a computation would be fairly large.

Static intervals

Now instead of applying one modification to each note in a series and take a picture of the result after each step in the process, we want to have a sequence of intervals (they can be represented by a series too) that will run through our original series and stop at some stage. Each interval will be applied to a gradually decreasing number of notes in the original row. Let’s name ‘origin’ the series to be modified. The number of concerned notes in the origin is decreasing because each time an interval has reached the last available note, is remains as a permanent modification. So for a twelve-tone origin, as soon as the first interval has reached the last note, it remains there and this note index is not available anymore for further modification. Then the second interval only touches eleven notes in the origin, and so on till the last change which only concerns one note. This is a train of static intervals.

These schoenbergian examples are taken from a 2008 Etude for clarinet.

Series S:   05814B62793A
Reverse R:  A39726B41850
Inverse I:  074B816A5392
0pposite O: 2935A618B470

Ex. 1:

*** DESCR: seq_obj: Seq_2 after Build_tree: object tree: 
funt: Static(A=05814B62793A,oct,B=A39726B41850,oct,ord=05814B62793A) ready: 1 ***
*** seq_proc : Tkrow_2 Sequence object: recorded

In this sequence we reach at step 12 the schoenbergian Reverse of Series S. The notes are modified following the order given by the same Series S. Now we could be interessed by the series of intervals that is applied in this case. To acquire this knowledge, Csgrouper provides – on the ‘Series’ tab – an analytic function called Statana(). Putting our origin (05814B62793A) into fields A and ord and our target (A39726B41850) into field B and applying function Statana() we obtain the resulting series (261215A2265A) and a sequence of signs (++-+++—–+) that have to be applied to S in order to get R under the static intervals sequential method. You can verify that the first value (2) has to be added (+) to note index (A=10, i.e. the eleventh note in S = 3) in order to get (5) which is the eleventh note in R. To get there, (2) had first to change the value of each other note in S. This knowledge isn’t required to run Static() though, but the same sequence of values can then be devoted to another role somewhere else in a work for instance.

Dynamic intervals

Now we can also have our origin modified permanently by each interval. Instead of falling back to the original value when a note has been modified, we keep the modified value. Then next time the interval changes this note, it is no more the value that was shown in the origin. This is a train of dynamic intervals, and it almost makes melodies sound like random sequences. However, the ultimate state of the series is completely determined too. Thus sequences of dynamic intervals are useful in music because from an original pattern they create complications leading to other defined musical patterns. The complication itself shows properties that the ear may perhaps sometimes apprehend or recognize, most of all when this form is used in several places in a piece. Again the internal parameters of Dynamic() can be known by feeding the same fields and appying Dynana() (checking the box ‘exp’ will further expand the results).

Ex. 2:

Now we start from where we left off in Static(), i.e. R (A39726B41850) and we want to reach O (2935A618B470) according to order (A39726B41850):

*** DESCR: seq_obj: Seq_2 after Build_tree: object tree: 
funt: Dynamic(A=A39726B41850,oct,B=2935A618B470,oct,ord=A39726B41850) ready: 1 ***
*** seq_proc : Tkrow_2 Sequence object: recorded

Non permutational serialism (1): Trainsposition()

Aside from what is explained in the ‘code’ section, Csgrouper offers non permutational functions, that were the origin of the whole program back in 2008. Their purpose is to transform gradually one series to another by arithmetic means.


While mere ‘Transposition’ is easily replaced by setting the correct ‘tone’ value for a ‘Suite’ in the corresponding row (and could therefore be removed from the function menu if it wasn’t for the possibility of giving a negative interval value to the field ‘n’),  ‘Trainsposition’ represents the first in a set of non permutational functions I wish to describe here. However there is a last semi-permutational function to describe before, because it makes the link between permutational and non permutational functions by giving to the latter ones a general action scheme.


The Train function is quite simple, provided you give it two Series, no matter whether they are made of distinct elements, and one Order Series, all three in the same base, it will yield a gradual replacement of the values of the first one by the values of the second one following the indexical order given as third series.

The Order Series (ord) must be made of distinct elements, and that feature is shared by all Train-like functions explained from now on.


*** DESCR: seq_obj: Seq_1 after Build_tree: 
object tree: 102145 112145 111145 111145 111115 111111
funt: Train(A=302145,oct,B=111111,oct,ord=012345) [n=1] ready: 1 
*** seq_proc : Tkrow_1 Sequence object: recorded

This last example taken from the output in Csgrouper’s terminal after row validation, shows well the process that will be shared by all non permutational functions. The original Series sees its first element replaced by the corresponding element in B, and so on till the whole Series is replaced (here the repetitions come from the fact that we have chosen a replacing Series made of one sole element) . Now if we had put 3 into field ‘n’ we’d have obtained:

object tree: 111145 111110

And this is a (small) bug since last sign should be one! But we see that the number of intermediary series made by the train is different. This feature will be further described below.


This function will take a Series (A) as first argument (a twelve-tone row in our example) and transpose each of its notes according to the values in a second Series (B) in the same base. This transposition will however not be done at once but note by note, so if the Series has twelve notes, it will then be repeated twelve times, each time showing a new differing note till reaching a totally different Series, possibly with non-distinct signs. This behaviour makes the ear gradually used to a different note pattern.

To make things a little less predictable, we add three factors to our transformation: first, the order in which the notes are taken for transposition is not necessarily ascending but represented by any Series in the same base put into field ‘ord’. Second, unlike normal transposition, the  value combined with the original note is not necessarily unique, i.e. you can have a different transposition value for each note of your base Series. This is the content of field (B). Of course you can also choose to have the transpositions done with a sole value, then your field (B) should be filled with 12 times that value, for example, ‘111111111111’ will transpose all your original row one semitone higher. The third factor is the sense of transposition, and it will define whether you’d like to have your transposition value added or subtracted from your original note. This is the content of field ‘signs’ and it be filled with signs corresponding to the values in field (B). For example ‘-+++++++++++’ will make the function subtract from your initial note, and then add to the other ones.

Another way of modifying the behaviour of this function is to change the value of the field ‘n’ to one of the divisors of your Series base. For a twelve-tone row we see that this value can be set to 1, 2, 3, 4 or 6. Each one of these choices will have the effect of grouping together as many changes or steps in the gradual process. Having 3 in ‘n’ for instance, will limit the repetitions of a twelve-tone row to 4, each one containing 3 notes transposed. This value is also set as a global fallback value for each row by meta-field ‘Steps’, but the utility of this field raises a question: to be efficient in each possible case, it should always be set to one (because a series base can be a prime number) and thus it isn’t requiring an independant field.

Unfortunately – unlike what exists for the Static and Dynamic trains of intervals we’ll examine later –  Csgrouper doesn’t provide any analytic tool to tell quickly which Series of transpositions (B) you have to use in order to obtain a certain Series as last output of this function, but this is not very difficult to calculate.

Finally as with any Series manipulation in Csgrouper, one has to be aware of the octavial range attributed jointly (correlative fields ‘Aoct’ and ‘Boct’).