semvec/pypi-packages huggingface dataset:
rl-practicum-I054 0.1
A simple package providing common Reinforcement Learning utility functions
Requires Python: >=3.6

# RL Utils

A simple and useful Python package providing common Reinforcement Learning utility functions.

## Installation

```bash
pip install rl-utils
```

## Features

This package provides essential utility functions for implementing Reinforcement Learning algorithms:

- **Epsilon-Greedy Action Selection**: Classic exploration-exploitation strategy
- **Q-Learning Update**: Implementation of the Q-learning algorithm update rule
- **Softmax Action Selection**: Boltzmann exploration strategy
- **Q-Table Initialization**: Helper function to initialize Q-tables

## Usage

### Epsilon-Greedy Action Selection

```python
from rl_utils import epsilon_greedy_action
import numpy as np

q_values = [0.5, 0.3, 0.8, 0.2]
action = epsilon_greedy_action(q_values, epsilon=0.1, num_actions=4)
```

### Q-Learning Update

```python
from rl_utils import q_learning_update, initialize_q_table
import numpy as np

# Initialize Q-table
q_table = initialize_q_table(num_states=10, num_actions=4)

# Update Q-value
q_table, new_q = q_learning_update(
    q_table=q_table,
    state=0,
    action=1,
    reward=10.0,
    next_state=2,
    alpha=0.1,
    gamma=0.9,
    num_actions=4
)
```

### Softmax Action Selection

```python
from rl_utils import softmax_action_selection

q_values = [0.5, 0.3, 0.8, 0.2]
action = softmax_action_selection(q_values, temperature=1.0)
```

## Requirements

- numpy >= 1.11.1

## License

MIT License

## Author

Created for RL Practicum course.
importsearch 0.2.0
A package to track your imports
Requires Python: >=3.10

# importsearch

importsearch is a CLI tool that inspects Python files and reports the import dependency graph for a given entry point.  
Specify a target file and instantly view the downstream files and modules that it touches.

> Looking for the Japanese guide? See [README-ja.md](./README-ja.md).

## Features

- **Rich CLI report:** Presents dependencies and visited files in a styled table.
- **Multiple output formats:** Choose between `print` (decorated), `text`, and `json`.
- **Stable traversal:** Works on `Path` objects, avoiding cycles and duplicates during analysis.

## Installation

```bash
pip install importsearch
```

## CLI Usage

```bash
importsearch path/to/main.py --root . --output-format print
```

### Common options

- `--root / -r`: Root directory for the analysis (defaults to the current directory)  
- `--output-format / -o`: `print` | `text` | `json`  
- `--output-file / -of`: Base name for `text` / `json` reports (extension is added automatically)  
- `--verbose / -v`: Display progress logs during the walk

### Example: Save a text report

```bash
importsearch src/main.py --root . --output-format text --output-file report
```

The command prints the summary to stdout and writes it to `report.txt`.

## Optional: Programmatic Usage

While the CLI is the primary interface, you can instantiate the searcher directly:

```python
import argparse
from pathlib import Path
from importsearch import importsearch

args = argparse.Namespace(
    file_path=Path("src/main.py"),
    root_path=Path("."),
    output_format="json",
    output_path=Path("dependencies"),
    verbose=False,
)

searcher = importsearch(args)
searcher.search()
searcher.summary()
```

## Development

Run the test suite with `pytest`:

```bash
pytest
```

Contributions are welcome! If you have ideas for improvements, please open an issue or submit a pull request.
ayame 0.4
An Apache Wicket-like component based WSGI framework
Requires Python: >=3.10

Ayame
=====

Ayame is a component based WSGI framework. It is inspired by
`Apache Wicket`_, `Apache Click`_ and Flask_.

.. image:: https://img.shields.io/pypi/v/ayame.svg
   :target: https://pypi.org/project/ayame

.. image:: https://github.com/hattya/ayame/actions/workflows/ci.yml/badge.svg
   :target: https://github.com/hattya/ayame/actions/workflows/ci.yml

.. image:: https://ci.appveyor.com/api/projects/status/67nbqb4ej84liu9m?svg=true
   :target: https://ci.appveyor.com/project/hattya/ayame

.. image:: https://codecov.io/gh/hattya/ayame/branch/master/graph/badge.svg
   :target: https://codecov.io/gh/hattya/ayame

.. _Apache Wicket: https://wicket.apache.org/
.. _Apache Click: https://click.apache.org/
.. _Flask: https://palletsprojects.com/p/flask


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

- Python 3.10+
- Werkzeug
- secure-cookie


Example Application
-------------------

::

    app.wsgi
    app/
        HelloWorld.html


app.wsgi
~~~~~~~~

.. code:: python

    from ayame import Ayame, Page
    from ayame.basic import Label


    class HelloWorld(Page):

        def __init__(self):
            super().__init__()
            self.add(Label('message', 'Hello World!'))


    application = Ayame(__name__)

    map = application.config['ayame.route.map']
    map.connect('/', HelloWorld)


HelloWorld.html
~~~~~~~~~~~~~~~

.. code:: html

    <?xml version="1.0"?>
    <!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
        "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
    <html xmlns="http://www.w3.org/1999/xhtml"
          xmlns:ayame="http://hattya.github.io/ayame">
      <head>
        <title>HelloWorld</title>
      </head>
      <body>
        <p ayame:id="message">...</p>
      </body>
    </html>


License
-------

Ayame is distributed under the terms of the MIT License.
install-k2 0.1.0
Install k2 package
Requires Python: >=3.9

# install-k2

[![PyPI version](https://badge.fury.io/py/install-k2.svg)](https://badge.fury.io/py/install-k2)

A simple utility to automatically install the correct k2 wheel for your platform and environment.

## Installation

```bash
pip install install-k2
```

## Usage

After installation, you can run the installer using either command:

```bash
# Using hyphen (recommended)
install-k2

# Or using underscore
install_k2
```

### Options

- `--dry-run`: Show what would be installed without making changes

```bash
install-k2 --dry-run
```

## What It Does

The installer automatically:
- Detects your operating system (Linux, macOS, Windows)
- Identifies your Python version
- Detects CUDA version (if available on Linux)
- Finds the appropriate PyTorch version
- Downloads and installs the compatible k2 wheel from official sources

### Supported Platforms

- **Linux**: CPU and CUDA wheels
- **macOS**: CPU wheels (Intel and Apple Silicon)
- **Windows**: CPU wheels

## Wheel Sources

The installer fetches wheels from official k2 repositories:
- Linux CUDA: https://k2-fsa.github.io/k2/installation/pre-compiled-cuda-wheels-linux/
- macOS CPU: https://k2-fsa.github.io/k2/installation/pre-compiled-cpu-wheels-macos/
- Windows CPU: https://k2-fsa.github.io/k2/installation/pre-compiled-cpu-wheels-windows/

## Requirements

- Python 3.9 or higher
- PyTorch (automatically detected)

## License

Apache License 2.0

## Author

The Lattifai Development Team
pyshallow 2025.11.1
PyShallow - prevents screen saver / screen lock / turn off from kicking in
Requires Python: >=3
Keywords: development, productivity, tools, utilities

# *PyShallow*

*PyShallow* - **prevents** (when possible):
- Screen saver
- Screen lock
- Turn off

from kicking in


## Install

Use *PIP* (**starting with *v2025.08.23***):

```commandline
python -m pip install pyshallow
```


## Run

1. Invoke entry-point:
    ```commandline
    pyshallow
    ```
    entry-point path must be present in *PATH* environment variable (otherwise its fullpath must be specified)
2. Manually:
    ```commandline
    python -m pyshallow
    ```


## Notes

- Depends on [\[PyPI\]: pycfutils](https://pypi.org/project/pycfutils).
- *Linux* (this is the fallback):
    - Relies on *X11* server running
- **Use it responsibly** (be aware of all implications)!