Skip to content

Instantly share code, notes, and snippets.

@utz82 utz82/plot-results.jl
Last active Feb 21, 2019

Embed
What would you like to do?
testing dictionary compression for chipmusic modules
using DataFrames, CSV
using Cairo, Gadfly
data = CSV.read("results.csv")
p1 = layer(data, x=:module_size, y=:ratio_dict_enc0,
color=[colorant"purple"], size=[0.75mm], Geom.hair, Geom.point)
p2 = layer(data, x=:module_size, y=:ratio_dict_enc1,
color=[colorant"red"], size=[0.75mm], Geom.hair, Geom.point)
p3 = layer(data, x=:module_size, y=:ratio_dict_enc4,
color=[colorant"orange"], size=[0.75mm], Geom.hair, Geom.point)
p = plot(p1, p2, p3, Guide.xlabel("module size (words)"),
Guide.ylabel("packer efficiency (%)"),
Guide.manual_color_key("encoders",
["no recursion", "recursion depth=1",
"recursion depth=4"],
[colorant"purple", colorant"red",
colorant"orange"]))
img = SVG("results.svg", 20inch, 10inch)
draw(img, p)
;; testing dictionary encoding for module data
;;
;; Notes: For the purpose of this test, only note triggers are considered.
;; Empty pattern rows (after filtering everything but note triggers) are
;; discarded.
;; Furthermore, no module shall have more than (256 * 128) - 1 rows.
;;
;; Encoding Algorithm
;;
;; 1) Produce a dictionary of all unique rows.
;; Encoded rows consist of a control value which has bits set depending on
;; which tracks have a note trigger, followed by the note triggers
;; themselves. Data of tracks which have no note trigger is discarded.
;; 2) Produce a sequence as follows:
;; a) Replace the original module sequence with a list of the corresponding
;; pattern data.
;; b) Replace rows in the pattern data with a reference to the corresponding
;; entry in the dictionary.
;; 3) Recursively optimize the new sequence as follows:
;; a) Beginning with a window size of 3 and a nesting level of 1, find all
;; consecutive repetitions in the sequence, replacing them with a control
;; value which holds the window size and number of repetitions. Control
;; value is identified as such by having bit 15 set. A dictionary pointer
;; will never have bit 15 set, as we do not allow more than #x7fff rows of
;; input.
;; Substitution shall not take place inside compressed blocks. So,
;; assuming the following sequence
;; <ctrl window:4 len:4>1 2 3 4 5 6 7 3 4 5 6 7...
;; the packer will not replace the second occurance of 3 4 5 6 7, because
;; the first occurance is part of the compressed block
;; <ctrl window:4 len:4>1 2 3 4.
;; b) If executing step 3a replaced any chunk that already contained a
;; control value, increment nesting level by 1.
;; c) Increment window size by 1 and repeat from step 3a until reaching
;; maximum window size (set to the length of the longest source pattern
;; with empty rows discarded) or reaching maximum nesting level (set to 4
;; for this test).
(use xmkit)
(define (drop-empty-rows triggers)
(filter (lambda (row)
(any number? row))
triggers))
;; count the note triggers in the given xm
(define (note-trigger-count xm)
(apply + (map (lambda (ptn)
(apply + (map (lambda (row)
(length (filter number? row)))
(drop-empty-rows (xm:pattern-notes ptn)))))
(xm:patterns xm))))
;; pack rows of pattern data as described in Step 1.
(define (pack-rows rows)
(map (lambda (row)
(letrec ((make-ctrlval
(lambda (data cbit cval)
(if (null? data)
cval
(make-ctrlval (cdr data)
(* 2 cbit)
(if (number? (car data))
(+ cval cbit)
cval))))))
(cons (make-ctrlval row 1 0)
(filter number? row))))
rows))
;; construct a dictionary of unique packed pattern rows from a list of
;; unpacked pattern rows. init-dict should be '() when calling this proc
;; directly.
(define (make-dict init-dict packed-rows)
(if (null? packed-rows)
init-dict
(make-dict (if (member (car packed-rows) init-dict)
init-dict
(cons (car packed-rows) init-dict))
(cdr packed-rows))))
;; find and compress repetitions in data of the given window size
(define (pack-data data window)
(letrec* ((count-matches
(lambda (head tail match-count)
(if (> (length head) (length tail))
match-count
(if (not (equal? head (take tail window)))
match-count
(count-matches head (drop tail window)
(+ 1 match-count)))))))
(if (> (* 2 window) (length data))
data
(let ((matches (count-matches (take data window)
(drop data window)
0)))
(if (> matches 0)
(append (cons (+ #x8000 window (* 256 matches))
(take data window))
(pack-data (drop data (* (+ 1 matches) window)) window))
(if (= #x8000 (bitwise-and #x8000 (car data)))
;; don't search for matches inside compressed blocks
(let ((blocksize (+ 1 (bitwise-and #xff (car data)))))
(append (take data blocksize)
(pack-data (drop data blocksize) window)))
(cons (car data)
(pack-data (cdr data) window))))))))
;; pack a sequence as described in step 3
(define (pack-seq seq max-window max-depth)
(letrec
((run-packer
(lambda (sdata current-window current-depth)
(if (or (> current-depth max-depth)
(> current-window max-window)
(> current-window (round (/ (length sdata) 2))))
sdata
(let ((next-data (pack-data sdata current-window)))
(run-packer next-data (+ 1 current-window)
(if (= (length sdata)
(length next-data))
current-depth (+ 1 current-depth))))))))
(run-packer seq 3 0)))
;; construct a sequence of dictionary references as described in step 2a
(define (make-seq packed-rows dict)
(map (lambda (row)
(list-index (lambda (dict-word)
(equal? dict-word row))
dict))
packed-rows))
(define (mock-encode-dict xm max-depth)
(let* ((packed-patterns
(map (lambda (ptn)
(pack-rows (drop-empty-rows (xm:pattern-notes ptn))))
(xm:patterns xm)))
(max-ptn-len (apply max (map length packed-patterns)))
(dict (make-dict '() (concatenate packed-patterns)))
(dict-length (length (concatenate dict)))
(seq (make-seq
(concatenate
(map (lambda (pos)
(list-ref packed-patterns pos))
(filter (lambda (seqpos)
(< seqpos (xm:number-of-patterns xm)))
(xm:sequence xm))))
dict)))
(list (+ dict-length (length seq))
(+ dict-length (length (pack-seq seq max-ptn-len 1)))
(+ dict-length (length (pack-seq seq max-ptn-len max-depth))))))
;; returns the size of the module data when packed in the classic way, packing
;; rows into a control value + actual note triggers. thus, size = seq length
;; + (combined length of all patterns) + (number of note triggers)
(define (mock-encode-classic xm)
(+ (apply + (map (lambda (ptn)
(length (drop-empty-rows (xm:pattern-notes ptn))))
(xm:patterns xm)))
(xm:song-length xm)
(note-trigger-count xm)))
;; generate test results for each module listed in the given file list
(define (generate-test-results files max-depth)
(if (null? files)
'()
(let* ((xm (xm:file->module (car files)))
(dict-encode-result (mock-encode-dict xm max-depth)))
(cons (list (car files)
(xm:number-of-tracks xm)
(mock-encode-classic xm)
(car dict-encode-result)
(cadr dict-encode-result)
(caddr dict-encode-result))
(generate-test-results (cdr files) max-depth)))))
(define (get-quota i j)
(/ (round (* 1000 (/ (- i j) i)))
10))
(define (run-tests filelist-file max-depth)
(let* ((modules (filter xm:is-valid-xm-file?
(read-lines filelist-file)))
(results (generate-test-results modules max-depth))
(stats (list (string-append "modules parsed: "
(->string (length results)))
(string-append
"successful optimizations (no recursion): "
(->string
(length
(filter positive?
(map (lambda (r)
(get-quota (caddr r) (cadddr r)))
results)))))
(string-append
"successful optimizations (recursion depth = 1): "
(->string
(length
(filter positive?
(map (lambda (r)
(get-quota (caddr r) (list-ref r 4)))
results)))))
(string-append
"successful optimizations (recursion depth = 4): "
(->string
(length
(filter positive?
(map (lambda (r)
(get-quota (caddr r) (list-ref r 5)))
results)))))
(string-append
"average compression ratio (no recursion): "
(->string (/ (apply + (map (lambda (r)
(get-quota (caddr r)
(cadddr r)))
results))
(length results))))
(string-append
"average compression ratio (recursion depth = 1): "
(->string (/ (apply + (map (lambda (r)
(get-quota (caddr r)
(list-ref r 4)))
results))
(length results))))
(string-append
"average compression ratio (recursion depth = 4): "
(->string (/ (apply + (map (lambda (r)
(get-quota (caddr r)
(list-ref r 5)))
results))
(length results))))))
(csv (cons (string-append "channels," "module_size,"
"dict_enc0," "dict_enc1,"
"ratio_dict_enc0," "ratio_dict_enc1,"
"ratio_dict_enc4")
(map (lambda (r)
(string-append
;; (car r) filenames may break csv plotter
;; ","
(->string (cadr r))
"," (->string (caddr r))
"," (->string (cadddr r))
"," (->string (list-ref r 4))
"," (->string (get-quota (caddr r) (cadddr r)))
"," (->string (get-quota (caddr r)
(list-ref r 4)))
"," (->string (get-quota (caddr r)
(list-ref r 5)))))
results)))
(write-to-file (lambda (filename lines)
(call-with-output-file filename
(lambda (out)
(for-each (lambda (line)
(write-line line out))
lines))))))
(begin
(write-to-file "results.csv" csv)
(write-to-file "results.log" stats))))
@utz82

This comment has been minimized.

Copy link
Owner Author

commented Feb 21, 2019

Module Encoder Test Suite

The purpose of this crude and simple test suite was to verify the efficiency of a dictionary based compression algorithm for chiptune modules.

Perhaps you may find it useful for testing your own module encoding algorithms.

Requirements

  • Chicken Scheme 4 to run the tests
  • xmkit, a Chicken egg for parsing XM files (install with chicken-install xmkit)
  • Optional: Julia to generate a plot graph of the results. Requires the DataFrames, CSV, Cairo, and Gadfly packages.

Usage

  • Obtain a set of modules to test against, for example from ftp://ftp.modland.com/pub/
  • Create a plaintext file that contains the list of all modules you wish to test against. One entry per line, with paths relative to where test-encoder.scm is located. On *nix systems, you could run find ./my_modules -type f -print > filelist.

Now you can fire up csi and run

(load "test-encoder.scm")
(run-tests "filelist" nesting-level)

where filelist is your list of module files, and nesting-level is the maximum recursion depth that the encoder should apply. Evaluating run-tests will yield two files, results.csv, and results.log. The latter has a short summary of the test results, while the former contains a list of comma seperated values with all the test results. This can then be plotted. An example Julia script for plotting is provided.

The encoder is not optimized at all, and thus very slow. On my Core i7 machine, it takes about half an hour to run through a test case of around 2000 modules. You may consider compiling the script into a shared library or stand-alone application for better results.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
You can’t perform that action at this time.