semvec/pypi-packages huggingface dataset:
polapy 0.0.2
PolaPy (Polarization for Python) is a collection of algorithmic implementations of polarization metrics in Python.

# PolaPy

PolaPy (Polarization for Python) is a collection of algorithmic implementations of polarization metrics in Python.

It provides:

- algorithmic implementations for measurements of polarizations introduced in the literature of economics and political science
- useful tools to manage data for research
- full integration with Pandas DataFrames

## Installation

The simplest way to install PolaPy in your laptop is via PyPI:

```
pip install polapy
```

As this project is in its early stages, please let us know if you have issues installing or running it.

## About PolaPy

The PolaPy project is developed and maintained by researchers at the University of ConcepciÃ³n. Currently, both the library and the documentation are constantly under update, please let us know if you have any questions or special inquiries.
rdbex 0.2.6
Replit DataBase EXtended
Requires Python: >=3.9

# RdbEX
## Replit DataBase EXtended

Replit database extended is a package designed to expand the abilities of the default replit database.

it adds things such as:
<br>
**emulated folders**
<br>
**ease of access**
<br>
**value sharing**
<br>
**safe secret storage**
<br>
*and much more...*

## how to use: a basic rundown

### (NOTE: does NOT support versions below 0.2)

after installing the package, import using <code>import rdbex</code>.

to create a new key in the root directory, use <code> rdbex.set("keyname")</code>.

to delete a key, use <code> rdbex.delete("keyname")</code>.

to create a new key within a folder, use <code> rdbex.set("keyname", "folderA")</code>. (same thing for deleting keys within folders)

to create keys within nested folders, use <code> rdbex.set("keyname", "folderA|folderB")</code>. (same thing for deleting keys within folders)

to list keys and list keys within a folder(s), use <code> rdbex.list("folderA|folderB (optional)")</code>.

and to drop your database (with an optional path), use <code> rdbex.drop("folderpath (optional)")</code>

### key sharing

to share a key with another value, use <code>rdbex.reference("FROM", "key", "path (optional)"</code> in the <code>value</code> section of <code>rdbex.set()</code>

### secret storage

to safely store replit secrets and os environment variables within your database, use <code>rdbex.reference("ENV", "variable name")</code> in the <code>value</code> section of <code>rdbex.set()</code>

## Recovery.

### warning!

to repair anything without reinstalling the package, you need to make sure the "recovery.py" and "__internal/_recovery.py" files are accessible.
<br>
<br>
if you delete a required file from the package or the package gets corrupted and if fails to load on import, you will get an ImportError that says *"The package has been installed incorrectly and/or has been corrupted. please reinstall the package."*, to fix this, just uninstall & reinstall the package using the package manager of your choice.

if the database is corrupted, you will get an ImportError saying *"critical damage to rdbex detected. a repair has been ran, please restart. if the error persists, use "from rdbex import recovery" and run recovery.rebuild() to attempt to fix the issue"*, before doing anything, try rerunning the code, as it attempts to repair the database. if the error still persists, run <code>from rdbex import recovery</code>. after importing recovery, run <code>recovery.rebuild()</code> to completely rebuild the database metadata. 
<br>
***this WILL wipe ALL user-set config, but NOT the db***

<br>
<br>
<br>
<sub>*keep in mind that by default, "|" is the spacing character for folders, this can be changed via the <code>configure()</code> command in utils*

<sub>*this is not full documentation, and it will be coming soon.*

<sub>*converting databases is currently not implemented, but might happen in the future.*
switch-hacker 0.0.1
A hack way to play switch game
Requires Python: >=3.7

# hit-and-blow

<!-- *Hit and Blow* is a game of Clubhouse Games: 51 Worldwide Classics. -->
Switch游戏 - 猜颜色 自动解算器。

猜颜色是《世界游戏大全51》中的一款小游戏。本仓库复现了该游戏的玩法，并添加了“自动解算”功能。

## Functions

已支持四种模式：

- 游戏模式 (`game.py`)：复现基本功能，可在命令行体验游戏玩法
- 自动解算 (`auto_play.py`)：计算答案的可行域，使用寻优策略逐步逼近答案
- 实验模式 (`experiment.py`)：多次重复自动解算过程，用于统计寻优策略的性能
- 作弊模式 (`hacker.py`)：和朋友联机时获得指引，快人一步得出答案 ( `д´)

## Usage

可通过两种方式运行代码：

1）通过 [pypi](https://test.pypi.org/project/switch_hacker) 下载运行

```python
import switch_hacker

g = switch_hacker.game.Game()
g.main()
```

2）本地运行：以作弊模式为例

1. 在 `src` 目录下新建 `hack.py` 文件
2. 将以下代码贴入文件中保存
3. 在命令行中执行 `python hack.py`

```python
# hack.py
import switch_hacker

hk = switch_hacker.hacker.Hacker()
hk.main()
```

## Tree

文件结构

```
├── README.md
├── pyproject.toml
└── src
    ├── switch_hacker
    │   ├── __init__.py
    │   ├── base.py: 通用函数集合
    │   ├── game.py: 命令行游戏
    │   ├── auto_play.py: 自动解算器
    │   ├── experiment.py: 实验统计模块
    │   ├── hacker.py: 作弊模块
    │   └── strategy.py: 寻优策略
    └── test
        └── example.py: 示例代码
```

## Log

- [x] 命令行游戏
- [x] 自动解算器
- [x] 实验统计模块
- [x] 作弊模块
- [ ] 强化学习

## Appendix

游戏的命令行实现：

![游戏的命令行实现.gif](./img/demo.gif)
BrainLes-preprocessing 0.0.2
TODO.
Requires Python: >=3.5
Keywords: registration,brain extraction

# niftyreg_preprocessor
does the following
* co-registration
* atlas registration
* transformation
* skullstripping in brats-space
* apply masking
* result: 4niftis, skullstripped brats-space -> save to path

## atlas
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2915788/

## installation:
```
pip install -e "git+https://github.com/neuronflow/BrainLes.git@main#egg=HD_BET&subdirectory=preprocessing/brainles_preprocessing/brain_extraction/HD_BET"
```


/home/florian/flow/BrainLesion/BrainLes/preprocessing
iclik 0.1.10a0
Information criteria for composite likelihood models

IClik: Information Criteria for Composite Likelihoods
=====================================================

``IClik`` is a minimal package for evaluating composite likelihood
models using Composite Likelihood AIC and BIC (CLAIC/CLBIC).

For a review of these information criteria, please see: *Ng, C. T., &
Joe, H. (2014). Model comparison with composite likelihood information
criteria. Bernoulli, 20(4), 1738–1764.
http://www.jstor.org/stable/43590422*

**This package is in development. Please let me know about any bugs or
problems by raising an issue on GitHub.**

Available information criteria
------------------------------

Composite Likelihood AIC (CLAIC)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

The composite likelihood version of the Akaike information criterion
(AIC) was proposed by Varin et al (2011). It is calculated as:

:math:`$CLAIC = -2L_{CL}(\hat\theta_{CL}) + 2tr[\mathbf{J}(\hat\theta_{CL})\mathbf{H}^{-1}(\hat\theta_{CL})]$` 

Where :math:`$\mathbf{J(\theta)}$` and :math:`$\mathbf{H(\theta)}$` are the
Jacobian and Hessian matrices of the likelihood function, and
:math:`$\hat\theta_{CL}$` represents the composite maximum likelihood
estimate.

**Reference:** *Varin, C., Reid, N., & Firth, D. (2011). AN OVERVIEW OF
COMPOSITE LIKELIHOOD METHODS. Statistica Sinica, 21(1), 5–42.
http://www.jstor.org/stable/24309261*

Composite Likelihood BIC (CLBIC)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

CLBIC, formulated by Gao and Song (2010), is similar to CLAIC, but
adjusts for sample size *n*:

:math:`$CLBIC = -2L_{CL}(\hat\theta_{CL}) + log(n) tr[\mathbf{J}(\hat\theta_{CL})\mathbf{H}^{-1}(\hat\theta_{CL})]$` 

**Reference:** *Gao, X., & Song, P. X.-K. (2010). Composite Likelihood
Bayesian Information Criteria for Model Selection in High-Dimensional
Data. Journal of the American Statistical Association, 105(492),
1531–1540. http://www.jstor.org/stable/27920184*

Installation
------------

``IClik`` is available via PyPi: ``pip install iclik``.

Syntax
------

``IClik`` is very easy to use, provided that you have a correctly
formulated likelihood function. A simple example of how to use it is
provided here.

Import ``claic``:

.. code:: python

   from iclik.inform_crit import claic

Things are much easier if we ignore the data, so it's best to design a wrapper function that only takes parameters as input. In reality, we would embed the fitted model in the wrapper function, but here we'll just work with a simple dummy function:

.. code:: python

   def f(params):
       """I'm a dummy function"""
       x, y, z = params
       return x**2 + y**2 + z**2

Running ``IClik`` is then a one-liner:

.. code:: python

   claic(f, (1,2,3))

Output:

.. code:: python

   -26.000000000000004
cbra 2.0.0a143
A class-based REStful API framework built on FastAPI




# OAuth 2.x

- https://www.rfc-editor.org/rfc/rfc8707.html

# Resources

- https://tyk.io/blog/your-data-model-is-not-an-api/
- https://shekhargulati.com/2021/10/15/web-api-design-anti-pattern-exposing-your-database-model
- https://cloud.google.com/blog/products/api-management/why-your-web-apis-should-be-entity-oriented
- https://cloud.google.com/blog/products/application-development/api-design-why-you-should-use-links-not-keys-to-represent-relationships-in-apis
- https://tools.ietf.org/html/rfc7807
- https://www.iana.org/assignments/link-relations/link-relations.xhtml
- https://thorben-janssen.com/dont-expose-entities-in-api/
- https://vaadin.com/blog/ddd-part-3-domain-driven-design-and-the-hexagonal-architecture