semvec/pypi-packages huggingface dataset:
axonserver 1.0.1
axonserver desktop launcher
Requires Python: >=3.8


# 🚀 How to Run AxonServer

Once you have installed the package via pip install axonserver, you can launch the OS Manager using any of the following methods:

1. The Direct Command (Recommended)
Because of our setup, you can launch the menu directly from your terminal or Cloud Shell without typing "python":

>> axonserver

2. Running as a Python Module
If the direct command isn't in your system path, use the module flag. This is the most reliable way to run it on Google Cloud Shell or GitHub Codespaces:

>> python3 -m axonserver

3. Inside a Python Script (The "Import" Way)
If you want to include AxonServer as part of your own project, you can import it like any other library:

>> import axonserver
>> axonserver.main()

4. The One-Liner (Quick Start)
To run it instantly without creating a file:

python3 -c "import axonserver; axonserver.main()"
wlhosted 2026.1
Hosted Weblate Customization
Requires Python: >=3.12
Keywords: i18n, l10n, gettext, git, mercurial, translate

.. image:: https://s.weblate.org/cdn/Logo-Darktext-borders.png
   :alt: Weblate
   :target: https://weblate.org/
   :height: 80px

**Weblate is libre software web-based continuous localization system,
used by over 2500 libre projects and companies in more than 165 countries.**

Hosted Weblate customizations

.. image:: https://github.com/WeblateOrg/hosted/actions/workflows/test.yml/badge.svg
   :target: https://github.com/WeblateOrg/hosted/actions/workflows/test.yml

.. image:: https://codecov.io/gh/WeblateOrg/hosted/branch/main/graph/badge.svg
   :target: https://codecov.io/gh/WeblateOrg/hosted

These customizations are used on the `Hosted Weblate service
<https://weblate.org/hosting/>`_.

You can use them as an example how to customize `Weblate
<https://weblate.org/>`_, but there are probably not generally usable.  If you
think something should be part of Weblate, please open an issue.
mass-pkdv 0.1.1
Product Kernel Density Visualization (pKDV) algorithm
Requires Python: >=3.7

# MASS: Product Kernel Density Visualization (KDV) Package

This package provides a C++ implementation of a **Product Kernel Density Visualization (KDV)** algorithm.

---

## Input Data Format

The input data file should be plain text:

```
n
x1 y1
x2 y2
...
xn yn
```

- `n` = number of data points  
- `x` and `y` in meters (or consistent units)

---

## Input Parameters (for Python wrapper or command line)

- `dim` : data dimensionality (default: 2)  
- `method` : algorithm method (0:SCAN, 1:SLAM, 2:MASS_CR, 3:MASS_OPT, 4:RQS_kd, 5:RQS_range)  
- `n_x`, `n_y` : number of discrete regions along x/y-axis  
- `k_type_x`, `k_type_y` : kernel type for x/y-axis (1:Epanechnikov, 2:Triangular, 3:Uniform)  
- `b_x_ratio`, `b_y_ratio` : bandwidth ratio for x/y-axis kernel

---

## Usage

### Python Wrapper

```python
from mass_pkdv import run_mass

run_mass(input_file, output_file)
```

Optionally, pass custom parameters:

```python
run_mass(input_file, output_file, dim=2, method=2, n_x=800, k_type_x=3, b_x_ratio=0.8, n_y=600, k_type_y=3, b_y_ratio=0.8)
```

### Command Line

```bash
bin\mass_pkdv.exe data\data.dat data\result.txt
selfusepy 0.4.0
Self-Use Python lib
Requires Python: >=3.10

Self-Use Python Lib
=

[![PyPI](https://badge.fury.io/py/selfusepy.svg)](https://pypi.org/project/selfusepy/)
[![image](https://github.com/LuomingXu/selfusepy/workflows/Python%20Test/badge.svg)](https://github.com/LuomingXu/selfusepy/actions?query=workflow%3A%22Python+Test%22)
[![image](https://github.com/LuomingXu/selfusepy/workflows/Pypi%20Release/badge.svg)](https://github.com/LuomingXu/selfusepy/actions?query=workflow%3A%22Pypi+Release%22)
[![image](https://img.shields.io/badge/License-Apache_v2-blue.svg)](http://www.apache.org/licenses/LICENSE-2.0)

### Json To Object

#### Usage
check in [json_test_cases]

[json_test_cases]:test/jsontest/__init__.py
amdreader 0.9.7
A Markdown document viewer for the terminal.
Requires Python: >=3.10.0


<p align="center">
  <img src="https://raw.githubusercontent.com/eivl/amdreader/6f27ff68b130ea43303a7a8a90cc07776e446cd8/amd-logo.png"  width="600" align="center"/>
</p>


# AMD Reader


AMD reader is a Markdown viewer / browser for your terminal, built with [Textual](https://github.com/Textualize/textual).
It is a hard fork from Textualize and all credits goes to their work. This derived work adds custom features and improvements.
amd can open `*.md` files locally or via a URL.
There is a familiar browser-like navigation stack, history, bookmarks, and table of contents.


## Compatibility

amd runs on Linux, macOS, and Windows. amd requires Python **3.8** or above.


## Installing

The easiest way to install amd is with [pipx](https://pypa.github.io/pipx/) (particularly if you aren't a Python developer).

```
pipx install amdreader
```

You can also install amd with `pip`:

```
pip install amdreader
```

Whichever method you use, you should have a `amd` command on your path.

## Running

Enter `amd` at the prompt to run the app, optionally followed by a path to a Markdown file:

```
amd README.md
```

You can navigate with the mouse or the keyboard.
Use <kbd>tab</kbd> and <kbd>shift</kbd>+<kbd>tab</kbd> to navigate between the various controls on screen.

## Features

You can load README files direct from GitHub repositories with the `gh` command.
Use the following syntax:

```
amd gh textualize/textual
```

This also works with the address bar in the app.
See the help (<kbd>F1</kbd>) in the app for details.
torch-batteries 0.5.2
A lightweight Python package that supplies batteries-included abstractions for PyTorch workflows
Requires Python: >=3.12
Keywords: pytorch, machine learning, deep learning, training, evaluation

# torch-batteries

<p align="center">
   <a href="https://pypi.org/project/torch-batteries/"><img src="https://img.shields.io/pypi/v/torch-batteries" alt="PyPI version"></a>
   <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="License: MIT"></a>
</p>

<p align="center">
   <img src="https://raw.githubusercontent.com/michalszc/torch-batteries/refs/heads/master/assets/logo.png" alt="torch-batteries" style="max-height:512px; height:auto;" />
   <br/>
   <em>Image generated by AI</em>
</p>

A lightweight Python package that supplies batteries-included abstractions for:
- Data loading pipelines
- Model training loops
- Evaluation workflows
- Metrics computation
- Experiment tracking (Weights & Biases)

Designed to reduce boilerplate and standardize experiment code.

## Installation

```bash
pip install torch-batteries
```

### Optional Dependencies

For experiment tracking with Weights & Biases:

```bash
pip install torch-batteries[wandb]
```

## Examples

Explore practical examples demonstrating torch-batteries features:

| Example | Description | Notebook | Colab |
|---------|-------------|----------|-------|
| **Function Fitting with MLP** | Train a neural network to approximate a polynomial function using the event-driven training approach | [function_fitting.ipynb](notebooks/function_fitting.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/michalszc/torch-batteries/blob/master/notebooks/function_fitting.ipynb) |
| **Image Classification with CNN** | Train a Convolutional Neural Network (CNN) for image classification | [image_classification.ipynb](notebooks/image_classification.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/michalszc/torch-batteries/blob/master/notebooks/image_classification.ipynb) |
| **Learning Rate Sweep with Early Stopping** | Conduct a learning rate sweep on MNIST classification with aggressive early stopping and log results to Weights & Biases | [lr_sweep_early_stopping.ipynb](notebooks/lr_sweep_early_stopping.ipynb) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/michalszc/torch-batteries/blob/master/notebooks/lr_sweep_early_stopping.ipynb) |