modgraph 0.2.2

a tool to analyze sample overlap between tracker module files

modgraph

modgraph is a tool to explore a collection of tracker module files as a module->sample graph. It can function both as a CLI app, and as a library to use in notebooks. In fact, this file is a notebook!

Using through CLI

!python -m modgraph --help

usage: modgraph [-h] [-f {csv,d2}] [-r RANK] files [files ...]

positional arguments:
  files                 module files to analyze

options:
  -h, --help            show this help message and exit
  -f {csv,d2}, --format {csv,d2}
                        output format
  -r RANK, --rank RANK  min number of repeats for sample to be included

Example:

!python -m modgraph *.it --rank 6 --format csv

mod_path,sample_name,sample_hash
catherine on the waves.it,tambourin.steel.quiet     ,e1b32f84b2b788f0a58e277f4e152df5
catherine on the waves.it,piano.001                 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
dallying sadly in space.it,                          ,e1b32f84b2b788f0a58e277f4e152df5
drifting to plutonia.it,tambourine.steel.quiet    ,e1b32f84b2b788f0a58e277f4e152df5
heavenly fantasy.it,tambourin.steel.quiet     ,e1b32f84b2b788f0a58e277f4e152df5
neverending illusion.it,piano.001                 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
"so close to you, my angel.it",piano.001                 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
sorrow.it,                          ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
sylvia.it,piano.001                 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
tender storm.it,tambourin.steel.quiet     ,e1b32f84b2b788f0a58e277f4e152df5
why (enhanced version).it,piano.001                 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3
why (enhanced version).it,tambourin.steel.quiet     ,e1b32f84b2b788f0a58e277f4e152df5

Using as a library

import pandas as pd
from modgraph import modgraph
from glob import glob

# digest your library into a mod_path -> sample_hash mapping
df = pd.DataFrame(modgraph(glob("*.it")))
df = df.set_index(['mod_path', 'sample_hash']).sort_index()
df

		sample_name
mod_path	sample_hash
a day at the river.it	13dc761472f1e73cff4ed428be35a5c2	SoundWave.HiQual
29797bec77f15b782ee0d8f855720213	rimshot
3e741972e4147bfc395467a293bb11a4	Flute (Skaven)
46a82c17348315db0ec7d4558fb4a9e9	fx.750
6ce9cd4d2bd435dc6b410b4bc65eab2d	river.wav (Eagle)
...	...	...
why (enhanced version).it	d9d2074594be1e44cebafdc840c84b94	DX-Strings 1
dcacd358eb1c8a23027d1dad35e44726	osterm1bass1
e1b32f84b2b788f0a58e277f4e152df5	tambourin.steel.quiet
e4f1c0e5019b51ff947d0966eeac29f8	electric.guitar.solo1
f8d42ab1418cdbf77a53355b600fc7fe	bassdrum.459

216 rows × 1 columns

def most_used(df, cutoff):
    df = df.groupby("sample_hash")
    df = df.agg({"sample_name": [("name", lambda g: g.mode()[0]), "count"]})
    df = df.sort_values(("sample_name", "count"), ascending=False)
    df = df[df[("sample_name", "count")] >= cutoff]
    return df

most_used(df, cutoff=3).plot(kind="barh", x=('sample_name', 'name'))

<AxesSubplot: ylabel='(sample_name, name)'>