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Author SHA1 Message Date
8ef21489a3
Be explicit about datalad run inputs and outputs 2025-10-01 10:25:04 +02:00
75586854de
Add validation routine to justfile 2025-10-01 10:24:53 +02:00
2faeda87c3
Validate CSV output schemas
Also moved code dir to src.
There are reasons to do standard things in standard ways. While it is
possible to get the `code/` directory to work, and recognize it as a
package path, this requires wrangling the pyproject.toml file.
Additionally, any import from the `code.something` path automatically
shadows the python stdlib `code` module. While it may not be necessary,
it still is good to not shadow standard library modules.
2025-10-01 10:23:10 +02:00
de96b67fac
Add validation dependencies to venv 2025-10-01 10:22:54 +02:00
7ceeb11b42
Add uv skeleton 2025-09-30 21:40:18 +02:00
42e706080a
Add code README and automatic CHANGELOG entries 2025-09-30 21:39:22 +02:00
fbba2d2992
Update README.md and CHANGELOG.md 2025-09-30 21:13:19 +02:00
3beb496680
Update justfile with versioned run 2025-09-30 20:58:36 +02:00
3e084abbc1
[DATALAD RUNCMD] Create updated output data
=== Do not change lines below ===
{
 "chain": [],
 "cmd": "just",
 "dsid": "186d720c-0862-453d-8b94-5140f0d67d2e",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [
  "input/"
 ],
 "outputs": [
  "output/"
 ],
 "pwd": "."
}
^^^ Do not change lines above ^^^
2025-09-30 20:53:50 +02:00
5525debd8d
Remove empty raw 0byte files 2025-09-30 20:45:29 +02:00
38 changed files with 599 additions and 38 deletions

2
.gitattributes vendored
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@ -3,3 +3,5 @@
CHANGELOG.md annex.largefiles=nothing
README.md annex.largefiles=nothing
justfile annex.largefiles=nothing
.python-version annex.largefiles=nothing
pyproject.toml annex.largefiles=nothing

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.gitignore vendored Normal file
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# Created by https://www.toptal.com/developers/gitignore/api/python,linux,vim
# Edit at https://www.toptal.com/developers/gitignore?templates=python,linux,vim
### Linux ###
*~
# temporary files which can be created if a process still has a handle open of a deleted file
.fuse_hidden*
# KDE directory preferences
.directory
# Linux trash folder which might appear on any partition or disk
.Trash-*
# .nfs files are created when an open file is removed but is still being accessed
.nfs*
### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/
cover/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
.pybuilder/
target/
# Jupyter Notebook
.ipynb_checkpoints
# IPython
profile_default/
ipython_config.py
# pyenv
# For a library or package, you might want to ignore these files since the code is
# intended to run in multiple environments; otherwise, check them in:
# .python-version
# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock
# poetry
# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
# This is especially recommended for binary packages to ensure reproducibility, and is more
# commonly ignored for libraries.
# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
#poetry.lock
# pdm
# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
#pdm.lock
# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
# in version control.
# https://pdm.fming.dev/#use-with-ide
.pdm.toml
# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
__pypackages__/
# Celery stuff
celerybeat-schedule
celerybeat.pid
# SageMath parsed files
*.sage.py
# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/
# Spyder project settings
.spyderproject
.spyproject
# Rope project settings
.ropeproject
# mkdocs documentation
/site
# mypy
.mypy_cache/
.dmypy.json
dmypy.json
# Pyre type checker
.pyre/
# pytype static type analyzer
.pytype/
# Cython debug symbols
cython_debug/
# PyCharm
# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
# and can be added to the global gitignore or merged into this file. For a more nuclear
# option (not recommended) you can uncomment the following to ignore the entire idea folder.
#.idea/
### Python Patch ###
# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration
poetry.toml
# ruff
.ruff_cache/
# LSP config files
pyrightconfig.json
### Vim ###
# Swap
[._]*.s[a-v][a-z]
!*.svg # comment out if you don't need vector files
[._]*.sw[a-p]
[._]s[a-rt-v][a-z]
[._]ss[a-gi-z]
[._]sw[a-p]
# Session
Session.vim
Sessionx.vim
# Temporary
.netrwhist
# Auto-generated tag files
tags
# Persistent undo
[._]*.un~
# End of https://www.toptal.com/developers/gitignore/api/python,linux,vim

1
.python-version Normal file
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3.12

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@ -0,0 +1,10 @@
# CHANGELOG
## 2025-09-30
- Clean output data until 2025-09-24
- Removal of 0-byte empty raw data files
## 2025-09-29
- Dirty output data until 2025-09-25

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@ -1,7 +1,57 @@
# Project <insert name>
# Project Popcorn Voidlinux
Contains the gathered data of the <https://popcorn.voidlinux.org> statistics collection for all
available dates (2025-09-24 as of today) in easier to work with CSV form.
Data can be cleaned and processed with the available code.
Any action can easily be started using [`just`](https://github.com/casey/just) with the available `justfile`.
## Dataset structure
- All inputs (i.e. building blocks from other sources) are located in
`inputs/`.
- All custom code is located in `code/`.
- All inputs (i.e. building blocks from other sources) are located in `input/`.
- All custom code is located in `src/`.
- All final output data is located in `output/`
## Output data structure
### Files
Represents information about the individual JSON files available in the raw dataset.
Contained in `files.csv`, 4 columns:
- `date`: the date a specific file is relevant for
- `filename`: the full filename as it exists in the `input/` directory
- `mtime`: the last modification time of the file on the system
- `filesize`: the size of the file, in bytes
### Kernels
Represents information about the kernel versions represented in the raw dataset.
Contained in `kernels.csv`, 3 columns:
- `date`: the date a specific file is relevant for
- `kernel`: the full kernel name that is available in the raw data, including major version, minor
version and suffix
- `downloads`: the amount of times the kernel has been seen on the observation date
### Packages
Represents information about the package versions represented in the raw dataset.
Contained in `packages.csv`, 4 columns:
- `date`: the date a specific file is relevant for
- `package`: the full package name as it is available in the raw data
- `version`: the full package version as it is available in the raw data
- `count`: the amount of times the package and version combination has been seen on the observation date
### Unique installs
Represents information about the unique system installations represented in the raw dataset.
Contained in `unique_installs.csv`, 2 columns:
- `date`: the date a specific file is relevant for
- `unique`: the amount of unique installations counted on the observation date

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@ -1,3 +0,0 @@
All custom code goes into this directory. All scripts should be written such
that they can be executed from the root of the dataset, and are only using
relative paths for portability.

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1 +0,0 @@
../.git/annex/objects/fm/24/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json/MD5E-s0--d41d8cd98f00b204e9800998ecf8427e.json

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@ -1,13 +1,23 @@
default: files kernels unique packages
default: versioned validate
all: files kernels unique packages
files:
python code/files.py input output
python src/files.py input output
kernels:
python code/kernels.py input output
python src/kernels.py input output
unique:
python code/unique.py input output
python src/unique.py input output
packages:
python code/packages.py input output
python src/packages.py input output
versioned:
datalad run -m "Create updated output data" -i "input/*.json" -o "output/*.csv" just all
printf "\n## %s\n\n- auto generated\n" $(date +%F) | sed -i '/CHANGELOG/r /dev/stdin' CHANGELOG.md
# requires python dependencies, thus to be running in venv
validate:
uv run pytest

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@ -1 +1 @@
../.git/annex/objects/84/5J/MD5E-s144471--7ffa8c5e2554bd2017ec2b3efd79e61d.csv/MD5E-s144471--7ffa8c5e2554bd2017ec2b3efd79e61d.csv
../.git/annex/objects/5g/QF/MD5E-s143687--a8cd063146eb3003968900a3b7aff0e8.csv/MD5E-s143687--a8cd063146eb3003968900a3b7aff0e8.csv
1 ../.git/annex/objects/84/5J/MD5E-s144471--7ffa8c5e2554bd2017ec2b3efd79e61d.csv/MD5E-s144471--7ffa8c5e2554bd2017ec2b3efd79e61d.csv ../.git/annex/objects/5g/QF/MD5E-s143687--a8cd063146eb3003968900a3b7aff0e8.csv/MD5E-s143687--a8cd063146eb3003968900a3b7aff0e8.csv

13
pyproject.toml Normal file
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[project]
name = "popcorn-dataset"
version = "0.1.0"
description = "A full CSV representation of the Voidlinux popcorn data"
readme = "README.md"
requires-python = ">=3.12"
dependencies = []
[dependency-groups]
dev = [
"dataframely>=1.13.0",
"pytest>=8.4.2",
]

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src/README.md Normal file
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# Popcorn dataset code
Each script can be run stand-alone like `python src/files.py <input-dir> <output-dir>`,
exchanging the script file for the one intended.
It is suggested, however, to run the scripts using the `just` command runner from the
dataset root, such as `just files` for the same effect as above.
This will automatically populate the correct input and output directories.
To create new `datalad` versioned output data, run `just versioned` or `just` without any arguments.
A new commit containing the updated data will be created,
and an automatic entry in the CHANGELOG made.

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@ -1,6 +1,4 @@
import csv
from collections.abc import Generator
from datetime import datetime
from pathlib import Path
@ -29,7 +27,6 @@ def main(input: str, output: str) -> None:
input_dir = Path(input)
output_dir = Path(output)
ensure_dirs(input_dir, output_dir)
filesize_csv(input_dir, output_dir)

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@ -1,7 +1,5 @@
import csv
import json
from collections.abc import Generator
from datetime import datetime
from pathlib import Path
from typing import Any, cast
@ -33,7 +31,6 @@ def package_kernel_csv(input_dir: Path, output_dir: Path) -> None:
p_count = cast(int, data["XuKernel"][entry])
p_date = date
writer.writerow([p_date, p_name, p_count])
# print(output_file, p_date, p_name, p_count)
def ensure_dirs(input_dir: Path, output_dir: Path):
@ -46,9 +43,6 @@ def main(input: str, output: str) -> None:
input_dir = Path(input)
output_dir = Path(output)
ensure_dirs(input_dir, output_dir)
# json_to_daily_pkg(Path(input) / "daily", Path(output) / "daily", force=True)
# json_to_unique_csv(Path(input) / "daily", Path(output), force=True)
package_kernel_csv(input_dir, output_dir)

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import dataframely as dy
import polars as pl
class DateSchema(dy.Schema):
date: dy.Date = dy.Date(nullable=False)
@dy.rule()
def minimum_starting_date() -> pl.Expr:
return pl.col("date") > pl.date(2018, 5, 8)

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@ -0,0 +1,24 @@
import dataframely as dy
import polars as pl
from tests.test_validate_date_col import DateSchema
class FilesSchema(DateSchema):
filename: dy.String = dy.String(nullable=False)
mtime: dy.Float = dy.Float(nullable=False)
filesize: dy.Integer = dy.Integer(nullable=False)
def test_files_schema():
_ = FilesSchema.validate(
pl.scan_csv(
"output/files.csv",
schema={
"date": pl.Date,
"filename": pl.String,
"mtime": pl.Float32,
"filesize": pl.UInt32,
},
).collect(engine="streaming")
)

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@ -0,0 +1,22 @@
import dataframely as dy
import polars as pl
from tests.test_validate_date_col import DateSchema
class KernelsSchema(DateSchema):
kernel: dy.String = dy.String(nullable=False)
downloads: dy.Integer = dy.Integer(nullable=False)
def test_kernels_schema():
_ = KernelsSchema.validate(
pl.scan_csv(
"output/kernels.csv",
schema={
"date": pl.Date,
"kernel": pl.String,
"downloads": pl.UInt32,
},
).collect(engine="streaming")
)

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@ -0,0 +1,24 @@
import dataframely as dy
import polars as pl
from tests.test_validate_date_col import DateSchema
class PackagesSchema(DateSchema):
package: dy.String = dy.String(nullable=False)
version: dy.String = dy.String(nullable=False)
count: dy.Integer = dy.Integer(nullable=False)
def test_packages_schema():
_ = PackagesSchema.validate(
pl.scan_csv(
"output/packages.csv",
schema={
"date": pl.Date,
"package": pl.String,
"version": pl.String,
"count": pl.UInt16,
},
).collect(engine="streaming")
)

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@ -0,0 +1,24 @@
import dataframely as dy
import polars as pl
from tests.test_validate_date_col import DateSchema
class UniquesSchema(DateSchema):
unique: dy.Integer = dy.Integer(nullable=False)
@dy.rule()
def cannot_be_zero() -> pl.Expr:
return pl.col("unique") > 0
def test_uniques_schema():
_ = UniquesSchema.validate(
pl.scan_csv(
"output/unique_installs.csv",
schema={
"date": pl.Date,
"unique": pl.UInt16,
},
).collect(engine="streaming")
)

176
uv.lock generated Normal file
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