ProTrackR vs ProTrackR2 • ProTrackR2

ProTracker2 and its predecessor

The predecessor to this package (ProTrackR) was entirely programmed in R. Although technically possible, it was challenging and slow with recursive algorithms. The new version is a complete overhaul in C/C++, based on Olav Sørensen’s ProTracker clone. With it, came some design changes which are worth mentioning here.

Overview of differences

The table below summarises the differences between ProTrackR2 and its predecessor.

Feature	ProTrackR	ProTrackR2
Effect commands	Limited set implemented	All PT2.3d effects implemented
Infrastructure	R script	Compiled C/C++
File readers	Optimized for format preservation	Optimized for PT2.3d compatibility
Audio output	tuneR S4 Wave class	audio s3 audioSample class
OpenMPT test cases	Passes 6 out of 12 selected tests	Passes at least 17 of the 23 tests

Effect commands

ProTracker uses specific codes to apply certain effects or position jumps. ProTrackR implements only a subset of these effects, whereas ProTrackR2 has implemented all ProTracker compatible effects.

The predecessor only partly implemented arpeggio and setting finetune. It did not implement glissando, sample filtering (E8) and loop reversal (EF). All these effects are implemented in the current package.

For a full overview of effect commands see vignette("effect_commands").

Infrastructure

By switching to C and C++ compiled code, the new package gained a significant performance boost (see benchmark results). Where the in the predecessor the module was represented by a vector of raw data, an externalptr to a C struct is used in the current package. This required a slightly different approach to handle these objects. In order to avoid confusion about the syntax, it was completely redesigned in the successor.

Class type objects

ProTrackR uses s4 class objects. It has elegant semantics and due to its strict nature, has a lot of build-in safety mechanisms. But consequently, they are not straightforward and easy to work with.

Therefore, in ProTrackR2 I chose to work with the s3 class objects. They are easier to work with and perhaps more intuitive to most R users. If you are coming to ProTrackR2 from ProTrackR, here is an overview of equivalent classes:

ProTrackR s4 class	ProTrackR equivalent s3 class	Description and remarks
`?ProTrackR::PTModule`	`pt2_new_mod()`	Overarching class representing a module
`ProTrackR::PTCell()`	`pt2_cell()`	Smallest structure in a pattern table
No explicit class	`pt2_note()`	Code representing a specific note
No explicit class	`pt2_command()`	Code representing sound effect or trigger
`ProTrackR::PTBlock()` `ProTrackR::PTTrack()`	`pt2_celllist()`	ProTrackR distinguishes between blocks (matrices) and tracks (columns) of cells. ProTrackR2 only considers a list of cells
`ProTrackR::PTPattern()`	`pt2_pattern()`	A pattern table
`ProTrackR::PTSample()`	`pt2_sample()`	An audio sample

These different class styles also have consequences for you workflow. Below are some examples of how to achieve similar operations using the different packages.

library(ProTrackR)
library(ProTrackR2)

## *Old* way of creating a blank module:
mod_old <- new("PTModule")
## *New* way of creating a blank module:
mod_new <- pt2_new_mod("my mod")

## *Old* reading a module:
mod_old <- read.module(pt2_demo())
## *New* way of creating a blank module:
mod_new <- pt2_read_mod(pt2_demo())

## *Old* way of getting a sample
samp_old <- PTSample(mod_old, 1L)
## *New* way of getting a sample
samp_new <- mod_new$samples[[1L]]

## *Old* way of getting a pattern
patt_old <- PTPattern(mod_old, 1L)
## *New* way of getting a pattern
patt_new <- mod_new$patterns[[1L]]

## *Old* way of getting a cell
cell_old <- PTCell(mod_old, 1L, 1L, 1L)
## *New* way of getting a cell
##
##   First cell in cell-list ---------------+
##   First column in pattern ----------+    |
##      First row in pattern ------+   |    |
##             First pattern -+    |   |    |
##                            |    |   |    |
##                            V    V   V    V
cell_new <- mod_new$patterns[[1L]][1L, 1L][[1L]]

print(cell_old)
#> [1] "C-3 01 A08"
print(cell_new)
#> C-3 01 A08

Benchmark

A benchmark test where the same module (the one provided with this package) is rendered with both ProTrackR and ProTrackR2. The settings for both tests were similar and performed on the same system and repeated 10 times. On average ProTrackR2 renders 8.8 times faster than ProTrackR.

File readers

While reading ProTracker modules, the predecessor preserved the data in the file. It only modified / fixed data when requested by the user. The current package will always sanitise data while reading it, making it compatible with ProTracker 2.3d. The current reader is also a bit more flexible and allows to read more exotic formats. It even allows you to read files compressed with PowerPacker.

Alternatives

If you want even more flexibility, check out the openmpt package. It uses libopenmpt to read and play modules. This library has a more extensive set of supported file formats. The downside is that it does not allow you to modify or save modules.

Audio output

The predecessor used tuneR objects to store rendered audio. In the current package we use audio objects. This switch was made as the S3 class objects from ‘audio’ are easier to handle than the stricter and formal S4 class objects from ‘tuneR’. If you wish to use the advanced features from the ‘tuneR’ package, this is still possible as both formats can be converted relatively easy.

## Load demo module
mod <- pt2_read_mod(pt2_demo())

## render 'audioSample' object
mod_audio <- pt2_render(mod)

## Convert from 'audio::audioSample' to 'tuneR::Wave' object:
if (requireNamespace("tuneR")) {
  mod_tuneR <-
    tuneR::Wave(
      left      = as.integer(2^15*unclass(mod_audio[1,])),
      right     = as.integer(2^15*unclass(mod_audio[2,])),
      samp.rate = attributes(mod_audio)$rate,
      bit       = attributes(mod_audio)$bits
    )
}