| Name | modgraph JSON |
| Version |
0.2.2
JSON |
| download |
| home_page | |
| Summary | a tool to analyze sample overlap between tracker module files |
| upload_time | 2022-12-22 14:01:34 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.10 |
| license | MIT |
| keywords |
tracker
music
samples
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# `modgraph`
`modgraph` is a tool to explore a collection of tracker <i>mod</i>ule 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](README.ipynb)!
## Using through CLI
```python
!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
!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
```python
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
```
<div>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th></th>
<th>sample_name</th>
</tr>
<tr>
<th>mod_path</th>
<th>sample_hash</th>
<th></th>
</tr>
</thead>
<tbody>
<tr>
<th rowspan="5" valign="top">a day at the river.it</th>
<th>13dc761472f1e73cff4ed428be35a5c2</th>
<td>SoundWave.HiQual</td>
</tr>
<tr>
<th>29797bec77f15b782ee0d8f855720213</th>
<td>rimshot</td>
</tr>
<tr>
<th>3e741972e4147bfc395467a293bb11a4</th>
<td>Flute (Skaven)</td>
</tr>
<tr>
<th>46a82c17348315db0ec7d4558fb4a9e9</th>
<td>fx.750</td>
</tr>
<tr>
<th>6ce9cd4d2bd435dc6b410b4bc65eab2d</th>
<td>river.wav (Eagle)</td>
</tr>
<tr>
<th>...</th>
<th>...</th>
<td>...</td>
</tr>
<tr>
<th rowspan="5" valign="top">why (enhanced version).it</th>
<th>d9d2074594be1e44cebafdc840c84b94</th>
<td>DX-Strings 1</td>
</tr>
<tr>
<th>dcacd358eb1c8a23027d1dad35e44726</th>
<td>osterm1bass1</td>
</tr>
<tr>
<th>e1b32f84b2b788f0a58e277f4e152df5</th>
<td>tambourin.steel.quiet</td>
</tr>
<tr>
<th>e4f1c0e5019b51ff947d0966eeac29f8</th>
<td>electric.guitar.solo1</td>
</tr>
<tr>
<th>f8d42ab1418cdbf77a53355b600fc7fe</th>
<td>bassdrum.459</td>
</tr>
</tbody>
</table>
<p>216 rows × 1 columns</p>
</div>
```python
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)'>
Raw data
{
"_id": null,
"home_page": "",
"name": "modgraph",
"maintainer": "",
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": "",
"keywords": "tracker,music,samples",
"author": "",
"author_email": "Ilia Pozdnyakov <ilia.pozdnyakov@ya.ru>",
"download_url": "https://files.pythonhosted.org/packages/da/ec/170a880a060f65734cf549728baf3cb476a8c9035c2105ce53aeca8a5c25/modgraph-0.2.2.tar.gz",
"platform": null,
"description": "# `modgraph`\n\n`modgraph` is a tool to explore a collection of tracker <i>mod</i>ule files as a module->sample _graph_.\nIt can function both as a CLI app, and as a library to use in notebooks.\nIn fact, this file is a [notebook](README.ipynb)!\n\n## Using through CLI\n\n\n```python\n!python -m modgraph --help\n```\n\n usage: modgraph [-h] [-f {csv,d2}] [-r RANK] files [files ...]\n \n positional arguments:\n files module files to analyze\n \n options:\n -h, --help show this help message and exit\n -f {csv,d2}, --format {csv,d2}\n output format\n -r RANK, --rank RANK min number of repeats for sample to be included\n\n\n### Example:\n\n\n```python\n!python -m modgraph *.it --rank 6 --format csv\n```\n\n mod_path,sample_name,sample_hash\n catherine on the waves.it,tambourin.steel.quiet ,e1b32f84b2b788f0a58e277f4e152df5\n catherine on the waves.it,piano.001 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n dallying sadly in space.it, ,e1b32f84b2b788f0a58e277f4e152df5\n drifting to plutonia.it,tambourine.steel.quiet ,e1b32f84b2b788f0a58e277f4e152df5\n heavenly fantasy.it,tambourin.steel.quiet ,e1b32f84b2b788f0a58e277f4e152df5\n neverending illusion.it,piano.001 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n \"so close to you, my angel.it\",piano.001 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n sorrow.it, ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n sylvia.it,piano.001 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n tender storm.it,tambourin.steel.quiet ,e1b32f84b2b788f0a58e277f4e152df5\n why (enhanced version).it,piano.001 ,8ef52cdf9c20c9ada9df7bf4d3b59fc3\n why (enhanced version).it,tambourin.steel.quiet ,e1b32f84b2b788f0a58e277f4e152df5\n\n\n## Using as a library\n\n\n```python\nimport pandas as pd\nfrom modgraph import modgraph\nfrom glob import glob\n\n# digest your library into a mod_path -> sample_hash mapping\ndf = pd.DataFrame(modgraph(glob(\"*.it\")))\ndf = df.set_index(['mod_path', 'sample_hash']).sort_index()\ndf\n```\n\n\n\n\n<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th>sample_name</th>\n </tr>\n <tr>\n <th>mod_path</th>\n <th>sample_hash</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"5\" valign=\"top\">a day at the river.it</th>\n <th>13dc761472f1e73cff4ed428be35a5c2</th>\n <td>SoundWave.HiQual</td>\n </tr>\n <tr>\n <th>29797bec77f15b782ee0d8f855720213</th>\n <td>rimshot</td>\n </tr>\n <tr>\n <th>3e741972e4147bfc395467a293bb11a4</th>\n <td>Flute (Skaven)</td>\n </tr>\n <tr>\n <th>46a82c17348315db0ec7d4558fb4a9e9</th>\n <td>fx.750</td>\n </tr>\n <tr>\n <th>6ce9cd4d2bd435dc6b410b4bc65eab2d</th>\n <td>river.wav (Eagle)</td>\n </tr>\n <tr>\n <th>...</th>\n <th>...</th>\n <td>...</td>\n </tr>\n <tr>\n <th rowspan=\"5\" valign=\"top\">why (enhanced version).it</th>\n <th>d9d2074594be1e44cebafdc840c84b94</th>\n <td>DX-Strings 1</td>\n </tr>\n <tr>\n <th>dcacd358eb1c8a23027d1dad35e44726</th>\n <td>osterm1bass1</td>\n </tr>\n <tr>\n <th>e1b32f84b2b788f0a58e277f4e152df5</th>\n <td>tambourin.steel.quiet</td>\n </tr>\n <tr>\n <th>e4f1c0e5019b51ff947d0966eeac29f8</th>\n <td>electric.guitar.solo1</td>\n </tr>\n <tr>\n <th>f8d42ab1418cdbf77a53355b600fc7fe</th>\n <td>bassdrum.459</td>\n </tr>\n </tbody>\n</table>\n<p>216 rows \u00d7 1 columns</p>\n</div>\n\n\n\n\n```python\ndef most_used(df, cutoff):\n df = df.groupby(\"sample_hash\")\n df = df.agg({\"sample_name\": [(\"name\", lambda g: g.mode()[0]), \"count\"]})\n df = df.sort_values((\"sample_name\", \"count\"), ascending=False)\n df = df[df[(\"sample_name\", \"count\")] >= cutoff]\n return df\n\nmost_used(df, cutoff=3).plot(kind=\"barh\", x=('sample_name', 'name'))\n```\n\n\n\n\n <AxesSubplot: ylabel='(sample_name, name)'>\n\n\n\n\n \n\n \n\n",
"bugtrack_url": null,
"license": "MIT",
"summary": "a tool to analyze sample overlap between tracker module files",
"version": "0.2.2",
"split_keywords": [
"tracker",
"music",
"samples"
],
"urls": [
{
"comment_text": "",
"digests": {
"md5": "b9aaa9bf198741345aaea0c262394796",
"sha256": "fa2561cd0efa25d4d3c515a89e1422aaf6d8394998350ece7fb14f63a458a186"
},
"downloads": -1,
"filename": "modgraph-0.2.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b9aaa9bf198741345aaea0c262394796",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 5549,
"upload_time": "2022-12-22T14:01:33",
"upload_time_iso_8601": "2022-12-22T14:01:33.278190Z",
"url": "https://files.pythonhosted.org/packages/26/11/ac816785593a8497d0cdacfad9c5d8b7e7cac6b3f8e2deea21181a53d574/modgraph-0.2.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"md5": "457f4e3351e7402cdd2dfb1a068bb459",
"sha256": "0761997b7c4af3e54b481c0191f9155e61507965f51b0618c4e2a2ee52a830be"
},
"downloads": -1,
"filename": "modgraph-0.2.2.tar.gz",
"has_sig": false,
"md5_digest": "457f4e3351e7402cdd2dfb1a068bb459",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 5256,
"upload_time": "2022-12-22T14:01:34",
"upload_time_iso_8601": "2022-12-22T14:01:34.734573Z",
"url": "https://files.pythonhosted.org/packages/da/ec/170a880a060f65734cf549728baf3cb476a8c9035c2105ce53aeca8a5c25/modgraph-0.2.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2022-12-22 14:01:34",
"github": false,
"gitlab": false,
"bitbucket": false,
"lcname": "modgraph"
}
