semvec/pypi-packages huggingface dataset:
ebat 0.2.8
EBAT: Evaluation of Behaviuoral Authentication Toolbox
Requires Python: >=3.8

# EBAT: Evaluation of Behavioural Authentication approaches Toolbox

Contemporary user authentication systems based on behavioural patterns of users are limited in 
terms of evaluation approaches, prohibiting them from gaining widespread adoption outside of the
research community. Therefore, we develop EBAT: a behavioural authentication toolbox that provides
a standardised way to evaluate and compare such authentication systems in a more comprehensive
manner and standardised. We source most popular behaviour authentication dataset, most popular evaluation metrics,
and develop new metrics that take into account aspects of evaluation so far neglected in the 
behavioural user authentication space.
path-loader 0.1.1
自動從配置檔載入 Python 路徑到 sys.path
Requires Python: >=3.7
Keywords: path, sys.path, pythonpath, auto-load

# Path Loader

自動從配置檔載入 Python 路徑到 `sys.path`。

[![Python Version](https://img.shields.io/badge/python-3.7%2B-blue.svg)](https://www.python.org/downloads/)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## 安裝

```bash
pip install path-loader
```

## 快速開始

### 1. 建立配置檔

在專案根目錄建立 `.pypath`:

```
# .pypath
src
lib
```

### 2. 使用

Python 啟動時會自動載入:

```python
# 直接 import 你的模組
import my_module
```

## 配置檔格式

```
# 支援註解

# 相對路徑 (相對於 .pypath 所在目錄)
src
lib/modules

# 絕對路徑
/usr/local/custom_lib
C:\Projects\Common

# 空白行會被忽略
```

## 手動控制

```python
import path_loader

# 基本使用
path_loader.load_paths()

# 自訂配置檔名
path_loader.load_paths(config_names=['my_config.txt'])

# 從特定目錄開始搜尋
path_loader.load_paths(start_dir='/project/root')

# 只搜尋不載入
config = path_loader.find_pypath_file()
if config:
    paths = path_loader.parse_pypath_file(config)
    print(paths)
```

## 範例

### 專案結構

```
my_project/
├── .pypath          # 內容: src\nlib
├── src/
│   └── utils.py
├── lib/
│   └── helper.py
└── main.py
```

### 使用

```python
# main.py
# 不需要任何額外程式碼,直接 import
from utils import my_function
from helper import HelperClass
```
causalspyne 0.1.6
hierarchical data generation for causal discovery and abstraction
Requires Python: >=3.12

# CausalSpyne
[![PyPI version](https://badge.fury.io/py/causalspyne.svg)](https://badge.fury.io/py/causalspyne)  [![test coverage](https://marrlab.github.io/causalspyne/coverage-badge.svg)](https://marrlab.github.io/causalspyne/)

A Python package for simulating data from confounded causal models.


## Quick start
Install with: `pip install causalspyne`

Generate some data:
```
from causalspyne import gen_partially_observed


gen_partially_observed(size_micro_node_dag=4,
                       num_macro_nodes=4,
                       degree=2,  # average vertex/node degree
                       list_confounder2hide=[0.5, 0.9], # choie of confounder to hide: percentile or index of all toplogically sorted confounders
                       num_sample=200,
                       output_dir="output",
                       rng=0)
```

## Submodules

```
git submodule update --init
```
mdformat-gfm 1.0.0
Mdformat plugin for GitHub Flavored Markdown compatibility
Requires Python: >=3.10
Keywords: mdformat, markdown, formatter, gfm

[![Build Status](https://github.com/hukkin/mdformat-gfm/actions/workflows/tests.yaml/badge.svg?branch=master)](https://github.com/hukkin/mdformat-gfm/actions?query=workflow%3ATests+branch%3Amaster+event%3Apush)
[![PyPI version](https://img.shields.io/pypi/v/mdformat-gfm)](https://pypi.org/project/mdformat-gfm)

# mdformat-gfm

> Mdformat plugin for GitHub Flavored Markdown compatibility

## Description

[Mdformat](https://github.com/executablebooks/mdformat) is a formatter for
[CommonMark](https://spec.commonmark.org/current/)
compliant Markdown.

Mdformat-gfm is an mdformat plugin that changes the target specification to
[GitHub Flavored Markdown (GFM)](https://github.github.com/gfm/),
making the tool able to format the following syntax extensions:

- [tables](https://github.github.com/gfm/#tables-extension-)
- [task list items](https://github.github.com/gfm/#task-list-items-extension-)
- [strikethroughs](https://github.github.com/gfm/#strikethrough-extension-)
- [autolinks](https://github.github.com/gfm/#autolinks-extension-)
- [disallowed raw HTML](https://github.github.com/gfm/#disallowed-raw-html-extension-)
  (note that no changes are required from a formatter to support this extension)

## Install

```sh
pipx install mdformat
pipx inject mdformat mdformat-gfm
```

## Usage

```sh
mdformat <filename>
```

## Configuration

Mdformat-gfm distribution includes two plugins identified as `gfm` and `tables`.
The `gfm` plugin adds support for all GFM syntax (including tables).
Enabling `tables` only adds tables support.

Mdformat-gfm adds a `--compact-tables` CLI option and a corresponding `compact_tables` TOML boolean.
Turning this on will strip extra spaces from GFM tables that are otherwise used to align table columns.

To use the option on the command line, do

```sh
mdformat --compact-tables <filename>
```

Alternatively add the following in a `.mdformat.toml` configuration file

```toml
[plugin.tables]
compact_tables = true
```
lzma-cf 0.0.4
LZMA and XZ compression for control freaks
Requires Python: >=3.10
Keywords: compression, lzma, xz, xz-utils

# lzma-cf

A Python LZMA & XZ library with greater control over compression parameters.

In addition to CPython's stdlib [`lzma`][CPython-lzma], lzma-cf can

- Compress and decompress XZ files in multithreaded mode


## Usage

```pycon
>>> import lzma_cf
>>> uncompressed = b'qwertyuiopasdfghjklzxcvbnm1234567890' * 10_000_000
>>> compressed_singlethreaded = lzma_cf.compress(uncompressed)
>>> compressed_multithreaded = lzma_cf.compress(uncompressed, threads=0)
>>> compressed_singlethreaded == compressed_multithreaded
False
>>> uncompressed == lzma_cf.decompress(compressed_singlethreaded)
True
>>> uncompressed == lzma_cf.decompress(compressed_multithreaded)
True
```

```console
$ python -mtimeit -s "import lzma_cf" -s "s=b'qwertyuiopasdfghjklzxcvbnm1234567890' * 10_000_000"  "lzma_cf.compress(s)"
1 loop, best of 5: 3.74 sec per loop
$ python -mtimeit -s "import lzma_cf" -s "s=b'qwertyuiopasdfghjklzxcvbnm1234567890' * 10_000_000" "lzma_cf.compress(s, threads=0)"
1 loop, best of 5: 685 msec per loop
```


## Installation

```
pip install lzma-cf
```

or

```
uv pip install lzma-cf
```


## License

<!-- SPDX-FileCopyrightText: 2025 Alex Willmer <alex@moreati.org.uk> -->
<!-- SPDX-License-Identifier: MIT -->

BSD-3-Clause AND MIT AND PSF-2.0


## History

lzma-cf is derived from code that has passed through multiple projects by
multiple authors

1. [CPython] 3.3.0 added `lzma` to the stdlib in `_lzmamodule.c` & `lzma.py`
1. [backports.lzma] repackaged CPython C-API code & adapted it
1. [lzmaffi] forked backports.lzma & changed C-API bindings to [CFFI] bindings
1. [PyPy] incorporated lzmaffi code into their stdlib & updated it
1. lzma-cf (this project) uses PyPy code and builds on it

[backports.lzma]: http://pypi.python.org/pypi/backports.lzma/
[CFFI]: https://pypi.org/project/cffi/
[CPython]: https://python.org
[CPython-lzma]: https://docs.python.org/3/library/lzma.html
[lzmaffi]: http://pypi.python.org/pypi/lzmaffi/
[PyPy]: https://pypy.org/