Complex humdrum syntax
Nathaniel Condit-Schultz
2024-10-20
Source:vignettes/ComplexSyntax.Rmd
ComplexSyntax.RmdWelcome to “Complex humdrum syntax”! This article explains how humdrum\(_{\mathbb{R}}\) handles spine paths and multi-stop data tokens.
Complex humdrum syntax
The humdrum syntax includes a few complex structures: spine paths and multi-stops (a.k.a., sub-tokens). Humdrum\(_{\mathbb{R}}\) incorporates these complexities into its data model, no problem, but they do make things more complicated, and may require some thought depending on the analyses you are trying to do. Understanding the how paths/stops are used is not at all necessary if the data you are interested in doesn’t include spine paths or multi-stops! You can always skip this article and come back to it at a later time.
The way humdrum\(_{\mathbb{R}}\) incorporates paths/stops is really just an extension of our basic data model, as described in our Data Fields article. You should definitely read and understand that article before reading this one. Each and every token, including tokens in various spine paths and various multi-stops, is recorded as a separate row in the humdrum table.
Spine paths
Humdrum\(_{\mathbb{R}}\) treats
spine paths as “sub-spines” of the main spine which they split from, and
keeps track of each path (if any) in the Path field. The
starting path (leftmost) is numbered path 0—in datasets
with no spine paths, the Path field will be all zeros.
Other paths are numbered with higher integers.
Let’s look at a simple example (found in the
humdrumRroot example files):
paths1 <- readHumdrum(humdrumRroot, "examples/Paths.krn")
paths1 |> print(view = "humdrum")
> ######################### vvv Paths.krn vvv #########################
> 1: !!!OTL: "Path" example
> 2: **kern **kern
> 3: *M4/4 *M4/4
> 4: *C: *C:
> 5: =- =-
> 6: 4C 4cc 4ee
> 7: 4D 4b 4dd
> 8: 4E 2a 2cc
> 9: 4F .
> 10: = =
> 11: * *^
> 12: 1G 8g 4cc
> 13: . 8a .
> 14: . 8g 4a
> 15: . 8f# .
> 16: . 4g 2b
> 17: . 4f .
> 18: * *v *v
> 19: = =
> 20: 1C 1e 1g 1cc
> 21: == ==
> 22: *- *-
> ######################### ^^^ Paths.krn ^^^ #########################
>
> Data fields:
> *Token :: character
paths1 |> print(view = "table")
> Piece Spine Path Record Stop Token
> ######################## vvv Paths.krn vvv #########################
> 1 <NA> <NA> 1 <NA>!!!OTL: "Path" ...mp
> 1 1 0 2 1 **kern
> 1 1 0 3 1 *M4/4
> 1 1 0 4 1 *C:
> 1 1 0 5 1 =-
> 1 1 0 6 1 4C
> 1 1 0 7 1 4D
> 1 1 0 8 1 4E
> 1 1 0 9 1 4F
> 1 1 0 10 1 =
> 1 1 0 11 1 *
> 1 1 0 12 1 1G
> 1 1 0 13 1 .
> 1 1 0 14 1 .
> 1 1 0 15 1 .
> 1 1 0 16 1 .
> 1 1 0 17 1 .
> 1 1 0 18 1 *
> 1 1 0 19 1 =
> 1 1 0 20 1 1C
> 1 1 0 21 1 ==
> 1 1 0 22 1 *-
> 1 2 0 2 1 **kern
> 1 2 0 3 1 *M4/4
> 1 2 0 4 1 *C:
> 1 2 0 5 1 =-
> 1 2 0 6 1 4cc
> 1 2 0 6 2 4ee
> 1 2 0 7 1 4b
> 1 2 0 7 2 4dd
> 1 2 0 8 1 2a
> 1 2 0 8 2 2cc
> 1 2 0 9 1 .
> 1 2 0 10 1 =
> 1 2 0 11 1 *^
> 1 2 0 12 1 8g
> 1 2 0 13 1 8a
> 1 2 0 14 1 8g
> 1 2 0 15 1 8f#
> 1 2 0 16 1 4g
> 1 2 0 17 1 4f
> 1 2 0 18 1 *v
> 1 2 0 19 1 =
> 1 2 0 20 1 1e
> 1 2 0 20 2 1g
> 1 2 0 20 3 1cc
> 1 2 0 21 1 ==
> 1 2 0 22 1 *-
> 1 2 1 12 1 4cc
> 1 2 1 13 1 .
> 1 2 1 14 1 4a
> 1 2 1 15 1 .
> 1 2 1 16 1 2b
> 1 2 1 17 1 .
> 1 2 1 18 1 *v
> ######################## ^^^ Paths.krn ^^^ #########################
> Piece Spine Path Record Stop Token
>
> Data fields:
> *Token :: characterHere is a more complex example:
paths2 <- readHumdrum('examples/Paths2.krn')
paths2
> ######################### vvv Paths2.krn vvv #########################
> 1: !!!OTL: complex "Path" example
> 2: **path
> 3: a
> 4: *^
> 5: b c
> 6: d e
> 7: *^ *
> 8: f g h
> 9: i j k
> 10: *v *v *
> 11: l m
> 12: n o
> 13: * *^
> 14: p q r
> 15: s t u
> 16: * *v *v
> 17: v w
> 18: x y
> 19: *v *v
> 20: z
> 21: *-
> ######################### ^^^ Paths2.krn ^^^ #########################
>
> Data fields:
> *Token :: characterNotice that humdrum\(_{\mathbb{R}}\)
prints paths in a way that is more readable than reading humdrum syntax
directly: paths are “shifted” over into columns that align. This is an
option to the function as.matrix.humdrumR().
Working with Paths
How you work with spine paths depends, as always, on the nature of
the data. If you are simply doing global counting of notes, for example,
tokens in paths might be treated no differently than any other data. In
some data sets, information in spine paths might not be relevant to your
analyses—for example, if the paths are used to store ossia. In other
analyses—especially, if you are analyzing data in a linear/melodic way—,
incorporating/handling spine paths may be very difficult, with no
obviously correct way to do it. In fact, many of humdrum\(_{\mathbb{R}}\)’s standard “lagged”
functions—like ditto(), mint(), and
timeline()—can give weird results in the presence of spine
paths. Often, the simplest solution is to simply ignore/remove spine
paths from your data. Since the Path field is numbered
starting from 0, this can be achieved easily by filtering
out anywhere where Path > 0.
paths1 |>
filter(Path == 0) |>
removeEmptyPaths()
> ############### vvv Paths.krn vvv ################
> 1: !!!OTL: "Path" example
> 2: **kern **kern
> 3: *M4/4 *M4/4
> 4: *C: *C:
> 5: =- =-
> 6: 4C 4cc 4ee
> 7: 4D 4b 4dd
> 8: 4E 2a 2cc
> 9: 4F .
> 10: = =
> 11: * *^
> 12: 1G 8g
> 13: . 8a
> 14: . 8g
> 15: . 8f#
> 16: . 4g
> 17: . 4f
> 18: * *v
> 19: = =
> 20: 1C 1e 1g 1cc
> 21: == ==
> 22: *- *-
> ############### ^^^ Paths.krn ^^^ ################
>
> Data fields:
> *Token :: characterExpanding Paths
One way of doing linear/melodic analyses in the presence of spine
paths is to “expand” the paths into full spines. The
expandPaths() function will “expand” paths by copying the
parts of spines that are shared by multiple paths into separate paths
(or spines). Observe:
paths2 |> expandPaths(asSpines = TRUE)
> ######################### vvv Paths2.krn vvv #########################
> 1: !!!OTL: complex "Path" example
> 2: **path **path **path **path
> 3: a a a a
> 4: *^ *^ *^ *^
> 5: b b c c
> 6: d d e e
> 7: *^ *^ * *
> 8: f g h h
> 9: i j k k
> 10: *v *v * *
> 11: l l m m
> 12: n n o o
> 13: * * *^ *^
> 14: p p q r
> 15: s s t u
> 16: * * *v *v
> 17: v v w w
> 18: x x y y
> 19: *v *v *v *v
> 20: z z z z
> 21: *- *- *- *-
> ######################### ^^^ Paths2.krn ^^^ #########################
>
> Data fields:
> *Token :: characterHumdrum\(_{\mathbb{R}}\)’s
[tidyverse methods][withinHumdrum], like mutate() and
within(), also have an expandPaths argument.
If expandPaths = TRUE, these functions will expand spine
paths, apply their expression, then “unexpand” the paths back to normal.
Look at the differnce between these two calls:
paths2 |>
group_by(Spine, Path) |>
mutate(Enumerate = seq_along(Token))
> ######################### vvv Paths2.krn vvv #########################
> 1: 1:NA:11:NA
> 2: 1:1:21:1
> 3: 1:1:31:11
> 4: *^
> 5: 1:1:51:12 1:1:51:11
> 6: 1:1:61:13 1:1:61:12
> 7: *^ *
> 8: 1:1:81:14 1:1:81:11 1:1:81:13
> 9: 1:1:91:15 1:1:91:12 1:1:91:14
> 10: *v *v *
> 11: 1:1:111:16 1:1:111:15
> 12: 1:1:121:17 1:1:121:16
> 13: * *^
> 14: 1:1:141:18 1:1:141:17 1:1:141:11
> 15: 1:1:151:19 1:1:151:18 1:1:151:12
> 16: * *v *v
> 17: 1:1:171:110 1:1:171:19
> 18: 1:1:181:111 1:1:181:110
> 19: *v *v
> 20: 1:1:201:112
> 21: *-
> ######################### ^^^ Paths2.krn ^^^ #########################
>
> Data fields:
> *Enumerate :: integer
> *Piece.Spine :: character
> *Piece.Spine.Record :: character
> Token :: character
>
> Grouping fields: (four groups)
> Path :: integer
> Spine :: integer
paths2 |>
group_by(Spine, Path) |>
mutate(Enumerate = seq_along(Token), expandPaths = TRUE)
> ######################### vvv Paths2.krn vvv #########################
> 2: **path
> 3: 1
> 4: *^
> 5: 2 2
> 6: 3 3
> 7: *^ *
> 8: 4 4 4
> 9: 5 5 5
> 10: *v *v *
> 11: 6 6
> 12: 7 7
> 13: * *^
> 14: 8 8 8
> 15: 9 9 9
> 16: * *v *v
> 17: 10 10
> 18: 11 11
> 19: *v *v
> 20: 12
> 21: *-
> ######################### ^^^ Paths2.krn ^^^ #########################
>
> Data fields:
> *Enumerate :: integer
> Token :: character
>
> Grouping fields: (four groups)
> Path :: integer
> Spine :: integerNotice how, when we don’t use expandPaths, each
path is counted entirely separately from the rest.
Stops
In humdrum syntax, multiple tokens can be placed “in the same place”
(i.e., same record, same spine) by simply separating them with spaces.
(This is most commonly used to represent chords in **kern
data.) In humdrum\(_{\mathbb{R}}\), we
call these “Stops”—as always, every humdrum token,
including stops, get their own row in a humdrum\(_{\mathbb{R}}\) [humdrum table][humTable].
Thus, we need the Stop field to tell us which stop a token
came from! In much data, all/most tokens are simply
Stop == 1 (the first position), but if there are more than
one tokens in the same record/spine, they will be numbered ascending
from one.
Let’s look at an example to make sense of this! Let’s start by looking at our humdrum-data view.
stops <- readHumdrum(humdrumRroot, 'examples/Stops.krn')
stops |> print(view = 'humdrum')
> ############### vvv Stops.krn vvv ################
> 1: !!!OTL: "Stop" example
> 2: **kern **kern
> 3: *M4/4 *M4/4
> 4: *C: *C:
> 5: =- =-
> 6: 2G 4g cc ee
> 7: . 4f b dd
> 8: 2C; 2e; cc;
> 9: = =
> 10: *- *-
> ############### ^^^ Stops.krn ^^^ ################
>
> Data fields:
> *Token :: character
stops |> print(view = 'data.frame')
> Piece Spine Record Stop Token
> ######################## vvv Stops.krn vvv #########################
> 1 <NA> 1 <NA>!!!OTL: "Stop" ...mp
> 1 1 2 1 **kern
> 1 1 3 1 *M4/4
> 1 1 4 1 *C:
> 1 1 5 1 =-
> 1 1 6 1 2G
> 1 1 7 1 .
> 1 1 8 1 2C;
> 1 1 9 1 =
> 1 1 10 1 *-
> 1 2 2 1 **kern
> 1 2 3 1 *M4/4
> 1 2 4 1 *C:
> 1 2 5 1 =-
> 1 2 6 1 4g
> 1 2 6 2 cc
> 1 2 6 3 ee
> 1 2 7 1 4f
> 1 2 7 2 b
> 1 2 7 3 dd
> 1 2 8 1 2e;
> 1 2 8 2 cc;
> 1 2 9 1 =
> 1 2 10 1 *-
> ######################## ^^^ Stops.krn ^^^ #########################
> Piece Spine Record Stop Token
>
> Data fields:
> *Token :: characterHere we have a file with chords in the second spine: individual note
tokens separated by spaces. Now, we can switch back to table view: You
can see that each note of the chords gets its own row, numbered
1, 2, and 3 in the
Stop field!
Working with Multi-Stops
Working with multi-stops is often even more challenging than working
with spine paths. How you work with stops depends, as always, on the
nature of the data. Again, If you are simply doing global counting of
notes, for example, tokens in stops might be treated no differently than
any other data. In other analyses—especially, if you are analyzing data
in a linear/melodic way—, incorporating/handling spine paths may be very
difficult, with no obviously correct way to do it. In fact, many of
humdrum\(_{\mathbb{R}}\)’s standard
“lagged” functions—like ditto(), mint(), and
timeline()—can give weird results in the presence of stops.
Often, the simplest solution is to simply ignore/remove stops from your
data. Since the Stop field is numbered starting from
1, this can be achieved easily by filtering out anywhere
where Stop > 1.
stops |>
filter(Stop == 1) |>
removeEmptyStops()
> ############### vvv Stops.krn vvv ################
> 1: !!!OTL: "Stop" example
> 2: **kern **kern
> 3: *M4/4 *M4/4
> 4: *C: *C:
> 5: =- =-
> 6: 2G 4g
> 7: . 4f
> 8: 2C; 2e;
> 9: = =
> 10: *- *-
> ############### ^^^ Stops.krn ^^^ ################
>
> Data fields:
> *Token :: character