semvec/pypi-packages huggingface dataset:
pymdps 0.1.3
pymdps is a Python package for working with Markov Decision Processes (MDPs) and

Partially Observable Markov Decision Processes (POMDPs). It is meant to be fast,
understandable, and easy to use.

Keywords: MDP,POMDP,Markov Decision Processes,AI,Reinforcement Learning
Maintainer-email: Duncan Eddy <duncan.eddy@gmail.com>
Requires-Python: >=3.9
Description-Content-Type: text/x-rst; charset=UTF-8
Project-URL: Documentation, https://duncaneddy.github.io/pymdps/
Project-URL: Repository, https://github.com/duncaneddy/pymdps
Project-URL: Issues, https://github.com/duncaneddy/pymdps/pulls

----

Documentation: https://duncaneddy.github.io/pymdps

Rust Library Reference: https://docs.rs/crate/pymdps/latest

Source Code: https://github.com/duncaneddy/pymdps

----

######
PyMDPs
######

PyMDPs is a Python library for working with Markov Decision Processes (MDPs) and other related models like Partially Observable MDPs (POMDPs). It is designed to be easy to use and flexible, with a focus on providing a clean and intuitive API for defining and solving MDPs.

++++++++
Features
++++++++

- **Simple API**: PyMDPs provides a simple and intuitive API for defining MDPs and solving them using a variety of algorithms.
- **Modular Design**: PyMDPs is designed to be modular and extensible, making it easy to add new algorithms and features.
- **Well-Documented**: PyMDPs comes with comprehensive documentation and examples to help you get started quickly.
- **Open Source**: PyMDPs is open source and released under the MIT license, so you are free to use it for any purpose.

+++++++++++++++
Getting Started
+++++++++++++++

For an introduction to PyMDPs and a guide on how to get started, check out the [Getting Started](getting_started/index.md) page.

To quickly install PyMDPs, you can use pip:

.. code-block:: bash

    pip install pymdps

The full documentation for PyMDPs is available at https://duncaneddy.github.io/pymdps. The documentation includes a detailed API reference, examples, and tutorials to help you get started.
django-session-switcher 0.2.3
A Django app to quickly switch between django users.
Requires Python: >=3.10

# django-session-switcher

django-session-switcher allows you to quickly switch between dummy users. Handy tool to
create dummy content for your side projects from multiple users.

## Usage


1. Install the package
```
pip install django-session-switcher
```

2. Add `django_session_switcher` to your `INSTALLED_APPS` settings.

```
INSTALLED_APPS = [
    ...,
    "django_session_switcher",
]
```

3. Add the middleware to your project

```
MIDDLEWARE = [
    ...,
    "django_session_switcher.middleware.SessionSwitcherMiddleware",
]
```

4. Include the django_session_switchers URLconf in your project urls.py like this

```
path("__dss__/", include("django_session_switcher.urls")),
```

5. Run `python manage.py migrate` to create the models.

6. You need to add the dummy users to the django_session_switcher's `Session User` model. You
   can do this through django admin.

7. Once you have added some users to `Session User` you will be able to switch between
   added users with a single click.
astock-loaders 0.1.8
A股市场分析提供强大的训练和测试数据集，支持大型模型开发。它生成适用于金融数据处理的Keras数据加载器，简化数据准备过程，使分析师和数据科学家专注于模型开发。功能包括数据标准化、增强和批处理，是在A股市场领域利用深度学习技术的必备工具。
Requires Python: <4.0,>=3.10


# 项目简介
astock_loads是专为深度学习模型训练和预测而设计的高质量A股市场数据集。该数据集包括丰富的金融信息，旨在帮助研究人员和开发者构建、验证及部署股市预测模型。

## 特点与优势
+ 实时的A股数据，非交易日缓存数据，交易日会实时更新，并保持节流。
+ 提供多个数据预测的数据结构，包括时间序列等
+ 数据采集周期长，覆盖个股的所有交易数据。
+ 提供简单易用的接口，便于快速集成。

# 数据集概述
包含了众多的数据集。

## 预测涨跌的数据集 TrendLoader
在股市分析中，个股的**历史交易数据**对于预测未来的股价走势具有重要意义。通过时间序列的方式，我们可以将个股的历史数据组织为有用的信息，用于构建预测模型。

### 数据特征

每一日的交易数据包含以下五个基本特征：开盘价，收盘价，最高价，最低价和成交量。

这些特征将作为模型的输入样本，以便于对未来股价走势进行准确的预测。

### 数据样本构建

我们使用连续的 `n` 个交易日数据作为一个数据样本，并使用下面的分类标签来标记未来的股价走势。

### 目标标签

标签的分类为一个**五分类问题**，包括以下类别：

- **猛涨** 🚀
- **微涨** 📈
- **走平** ⚖️
- **微跌** 📉
- **猛跌** ⬇️

通过取之后连续的 `m` 个交易日的数据平均值，我们将基于**涨幅**来定义每个类别。具体分类标准如下：

- **猛涨**：涨幅大于 `l0` 个点
- **微涨**：涨幅大于 `l1` 个点
- **走平**：涨幅大于 `l2` 个点
- **微跌**：涨幅大于 `l3` 个点
- **猛跌**：否则

其中，`l0`, `l1`, `l2`, `l3`是可配置的参数，例如可以设置为 `[5, 2, -2, -5]`。 

### 模型输出

根据上述分类标准，最终的预测结果将转化为一个标准的五分类问题，这将帮助投资者更好地理解和把握股市的动态变化。
flatmatter 0.9.1
A YAML-like data serialization language, and library.
Requires Python: >=3.10

# FlatMatter

![Coverage](https://raw.githubusercontent.com/askonomm/flatmatter/refs/heads/master/coverage-badge.svg)

A YAML-like data serialization language with support for functions. FlatMatter is considered feature-complete and is 
intentionally kept simple. Bug fixes and performance improvements are still made, of course.

Example FlatMatter:

```yaml
title: "My Blog"
last_updated: (get-content "posts") / (limit 1) / (get "publised_at") / (date "YYYY-mm-dd")
posts: "posts" / get-content
```

FlatMatter aims to be more-or-less syntactically compatible with YAML for the simple reason of not 
needing new editor plugins to have syntax highlighting, but it differs in that there is no indentation, 
but instead dots to indicate hierarchy, like `site.title` which would result in a `site` object that 
contains the `title` key.

FlatMatter also supports functions, allowing you to build your own data DSL, and functions can also be piped with the 
forward slash `/` character, meaning that the result of the left operation will be passed as the first argument 
of the next function, and so on, to produce an end result.

## Install

```shell
pip install flatmatter
``` 

## Usage

The most basic usage looks like this:

```python
from flatmatter import FlatMatter

config = FlatMatter("title: \"My blog\"").to_dict()
```
However, you most likely want to use it with functions. Those you have to create yourself. An example function
looks like this:

```python
from flatmatter import Function, fn

@fn('my-function')
class MyFunction(Function):
  def compute(self, args: list[Any]) -> Any:
    # do something with args here and return the desired result.
```

A FlatMatter function has to extend the `Function` base class, and has to use the `fn` decorator to 
name the function so that FlatMatter would know what to look for to find the function class. And like 
I mentioned before, a thing to keep in mind is that if the function is piped, its first arg will be the 
result of the previous operation.

Once you have your functions, simply pass them to FlatMatter like this:

```python
from flatmatter import FlatMatter

config = FlatMatter("...", [
  MyFunction,
  MyOtherFunction,
  etc,
])
```
clustermatic 0.0.3
Python AutoML library for clustering tasks
Requires Python: >=3.6

![clustermatic](https://raw.githubusercontent.com/AKapich/clustermatic/refs/heads/main/clustermatic/auxiliary/clustermatic.png)

# clustermatic

`clustermatic` is a Python library designed to accelerate clustering tasks using `scikit-learn`. It serves as a quick tool for selecting the optimal clustering algorithm and its hyperparameters, providing visualizations and metrics for comparison.

## Features

- **Clustering Algorithms**: Analyzes six clustering algorithms from `scikit-learn`:
    - `KMeans`
    - `DBSCAN`
    - `MiniBatchKMeans`
    - `AgglomerativeClustering`
    - `OPTICS`
    - `SpectralClustering`
- **Optimization Methods**: Includes Bayesian optimization and random search for hyperparameter tuning.
- **Flexible Preprocessing**: Allows users to customize how the data is meant to be preprocessed, adjusting methods such as scaling, normalization, and dimensionality reduction.
- **Evaluation Metrics**: Supports evaluation with `silhouette`, `calinski_harabasz`, and `davies_bouldin` scores.
- **Report Generation**: Generates reports in HTML format after optimization.

## Installation

To install `clustermatic`, use pip:

```bash
pip install clustermatic
```
signallib 1.0.0
A Python library for signals and observers
Keywords: python, reactivity, signals, observers


# signalLib

<div align="center"><img src="./signallib.png" width="100" /></div>

<div align="center">
    <img alt="MIT Licensed" src="https://img.shields.io/badge/license-MIT-blue.svg">
    <img alt="Version Badge" src="https://img.shields.io/badge/version-1..0.0-brightgreen.svg">
</div>

<br>

> **signalLib** is a small, simple library for intergrating reactivity concepts like signals and observables.

## Installation

```bash
pip install signallib
```

## Getting Started

### Making Values Reactive

```python
from signallib import signal

name = signal('oarabile')

# this method takes in a function and
# when the value is changed, it passes
# the new variable into that function.
name.subscribe(lambda x: print(x))

# this is both our setter and getter
name.value = 'averylongnamecausewhynot'

# the "name" is printed onto the console
```

### Observing Changes To A Dict

```python
from signallib import observe

user = { "name": "oarabile", "age": 19 }

user = observe(user)

user.observer(lambda prop, value: print('The Prop Is ${prop}, and Value Of ${value}'))

user.name = 'john'

# we can even add custom properties
user.location = 'botswana'

# this is printed to the console 👇
# The Prop Is name, and Value Of john
```

## Contributing

We welcome contributions to **rosana.ds**! To contribute:

1. Fork the repository.
2. Create a new branch (`git checkout -b feature-branch`).
3. Make changes and commit (`git commit -am 'Add new feature'`).
4. Push to your fork (`git push origin feature-branch`).
5. Open a Pull Request.

Feel free to suggest new features and improvements.

## License

This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
pytest-aioworkers 0.5.0
A plugin to test aioworkers project with pytest
Requires Python: >=3.9
Keywords: aioworkers, pytest

pytest-aioworkers
=================

.. image:: https://img.shields.io/pypi/v/pytest-aioworkers.svg
  :target: https://pypi.org/project/pytest-aioworkers

.. image:: https://github.com/aioworkers/pytest-aioworkers/workflows/Tests/badge.svg
  :target: https://github.com/aioworkers/pytest-aioworkers/actions?query=workflow%3ATests

.. image:: https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/charliermarsh/ruff/main/assets/badge/v0.json
  :target: https://github.com/charliermarsh/ruff
  :alt: Code style: ruff

.. image:: https://img.shields.io/badge/types-Mypy-blue.svg
  :target: https://github.com/python/mypy
  :alt: Code style: Mypy

.. image:: https://readthedocs.org/projects/pytest-aioworkers/badge/?version=latest
  :target: https://github.com/aioworkers/pytest-aioworkers#readme
  :alt: Documentation Status

.. image:: https://img.shields.io/pypi/pyversions/pytest-aioworkers.svg
  :target: https://pypi.org/project/pytest-aioworkers
  :alt: Python versions

.. image:: https://img.shields.io/pypi/dm/pytest-aioworkers.svg
  :target: https://pypistats.org/packages/pytest-aioworkers

.. image:: https://img.shields.io/badge/%F0%9F%A5%9A-Hatch-4051b5.svg
  :alt: Hatch project
  :target: https://github.com/pypa/hatch

A plugin to test aioworkers projects with pytest



Features
--------

* fixtures: aioworkers, context, config_yaml


Requirements
------------

* pytest
* aioworkers


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

You can install "pytest-aioworkers" via `pip`_ from `PyPI`_::

    $ pip install pytest-aioworkers[asyncio]


Contributing
------------
Contributions are very welcome. Tests can be run with `hatch`_, please ensure
the coverage at least stays the same before you submit a pull request.

Check code:

.. code-block:: shell

    hatch run lint:all


Format code:

.. code-block:: shell

    hatch run lint:fmt


Run tests:

.. code-block:: shell

    hatch run pytest


Run tests with coverage:

.. code-block:: shell

    hatch run cov

License
-------

Distributed under the terms of the `Apache Software License 2.0`_ license, "pytest-aioworkers" is free and open source software


Issues
------

If you encounter any problems, please `file an issue`_ along with a detailed description.

.. _`Apache Software License 2.0`: http://www.apache.org/licenses/LICENSE-2.0
.. _`file an issue`: https://github.com/aioworkers/pytest-aioworkers/issues
.. _`pytest`: https://github.com/pytest-dev/pytest
.. _`hatch`: https://hatch.pypa.io/latest/environment/#scripts
.. _`pip`: https://pypi.org/project/pip/
.. _`PyPI`: https://pypi.org/project