semvec/pypi-packages huggingface dataset:
OGPy 0.2.0
Modern consumer of Open Graph protocol
Requires Python: >=3.10

====
OGPy
====

Modern consumer of `Open Graph protocol <https://ogp.me/>`_ for Python.

First goal
==========

The first purpose of this project is to provide new role and directive
that can translate to figure element form content URL with Open Graph protocol.

Example:

.. code:: rst

   .. ogp:image-link:: http://example.com

docutils handles it as this:

.. code:: rst

   .. figure:: http://example.com/OGP-IMAGE
      :target: http://example.com
      :alt: EXAMPLE.COM

      DESCRIPTION or TITLE

For implement these,
it provides simple consumer for ogp contents.
cm-feathers 1.0.2
Matplotlib colormaps inspired by bird feathers
Requires Python: >=3.9
Keywords: Visualization, Color, Plotting

# Feathers

A simple Python package providing colormaps inspired by the colours of bird feathers.
Based on the [R package](https://github.com/shandiya/feathers).

### Demonstration
![Image demonstrating the Feathers colormaps](docs/assets/feathers.svg)

### Installation
Install the package from PyPI:
```bash
pip install cm_feathers
```

Or to develop it, clone this repository and install locally:
```bash
pip install -e .
```

### Usage
Simply import the package to make the colormaps available for use with Matplotlib:
```python
import feathers
```
-----------------------------
Available under the GNU General Public License v3.

Copyright (C) 2025 Harrison Nicholls
django-simple-toc 0.0.3
A Django app for generating a table of contents whenever you want to put it. It is customizable via template variables. Easy to set up and configure
Requires Python: >=3.10
Keywords: django, django app, index, table of content, timthewebmaster, toc

# Django simple table of content generator

This project is intended to be a plug-and-play Django app. It is generating a table of contents for the specified page.
To see more details, go to the homepage.
Kgen 1.0.1
Python functions for calculating stoichiometric speciation constants for seawater.

<div style="float: right">
<a href="https://badge.fury.io/py/kgen"><img src="https://badge.fury.io/py/kgen.svg" alt="PyPI version" height="18"></a>
</div>

# Kgen in Python

For details, see the [manual](https://palaeocarb.github.io/Kgen/use_kgen/python).
openai-to-codex-wrapper 0.1.3
CLI to manage the OpenAI to Codex Wrapper API server
Requires Python: >=3.7

# Note - changed to openai-responses-server to better reflect the nature of this library.


# openai-to-codex-wrapper
Wraps any OpenAI API interface so it supports Codex. Adding any missing stateful features. Ollama and Vllm compliant 

OpenAI had released their version of coding assistant as open source.

No big model library supports their Resources api yet, so they can’t work with it.

This library wraps any OpenAI compliant library and gaps resources api via chat completions endpoint.

It is still missing some features, but I would appreciate your support in stars, issues, suggestions and even pull requests if you are inclined for it.

I want to support the stateful features the other libraries don’t want to support and are needed for Codex (and more).

I verified it works in my main repo, in my [demo AI assistant that can hear, think and speak](https://github.com/OriNachum/autonomous-intelligence/tree/main/baby-tau) with the docker-compose-codex.yaml

# Roadmap

- [x] Tool run support (Tested with llama 3.2 3b on Ollama)
- [ ] Validate work from CLI
- [ ] dotenv support
- [ ] State management (long term, not just in-memory)
- [ ] Web search support
- [ ] File upload + search 
- [ ] Code interpreter 
- [ ] Computer use
hangovergames-assetgen 0.1.0
Generate game graphics using OpenAI's image generation APIs
Requires Python: >=3.8

# Hangover Games Asset Generator

A command-line tool for generating game graphics using OpenAI's image generation APIs.

## Installation

### Option 1: Clone from Repository
```bash
# Clone the repository
git clone https://github.com/hangovergames/assetgen.git
cd assetgen

# Initialize and update the assets submodule
git submodule init
git submodule update
```

### Option 2: Install via pip
```bash
pip install hangovergames-assetgen
```

## Usage

Create a spec file (e.g., `assets.txt`) with your image generation instructions:

```text
PROMPT Create a clean top‑down 2‑D sprite on a transparent background.
MODEL gpt-image-1
SIZE 1024x1024
BACKGROUND transparent
ASSET road_straight_ns.png A seamless 256×256 asphalt road …
ASSET road_corner_ne.png  A 256×256 90‑degree bend …
```

Then run:

```bash
assetgen assets.txt
```

### Command Line Options

- `-c, --count`: Maximum number of images to generate this run (default: 1)
- `-o, --output-dir`: Output directory (relative to spec file location)
- `--continue-on-error`: Continue processing on API errors
- `-v, --verbose`: Show detailed API response information

### API Configuration

You can configure the API using environment variables:

- `OPENAI_API_KEY`: Your OpenAI API key (required)
- `OPENAI_ORGANIZATION`: Your OpenAI organization ID
- `OPENAI_PROJECT`: Your OpenAI project ID
- `OPENAI_API_BASE`: API base URL (default: https://api.openai.com)
- `OPENAI_API_PATH`: API path (default: /v1/images/generations)

### Spec File Format

Each line (ignoring leading whitespace) must begin with one of these tokens (case-insensitive):

```
PROMPT <text …>
ASSET  <filename> <asset‑specific prompt>
MODEL  <dall-e-2|dall-e-3|gpt-image-1>
BACKGROUND <transparent|opaque|auto>
MODERATION <low|auto>
OUTPUT_COMPRESSION <0‑100>
OUTPUT_FORMAT <png|jpeg|webp>
QUALITY <auto|high|medium|low|hd|standard>
SIZE <WxH|auto>
STYLE <vivid|natural>
USER <identifier>
```

## License

MIT License - see LICENSE file for details
api-client-lnx-edge 0.3.11
Client for the LNX Edge API
Requires Python: >=3.7

# LNX Edge API Client
##### Written for Python ≥ 3.7

---

## Quickstart

Install using pip:

```shell
pip install api-client-lnx-edge
```

Set the following environment variables:

- `EDGE_API_KEY`: your production API Key (will be looked up automatically on init)
- `EDGE_STAGING_API_KEY`: your staging API Key (also will be looked up automatically on init, requires the use of the `staging=True` flag. see below)


Then you're off:

```python
# import
from edge_api_client import EdgeAPI

# initialize
edge = EdgeAPI()  # uses the environment variable `EDGE_API_KEY`
# or
edge = EdgeAPI(api_key='XXX')  # try not to do this, but if you have to...
# or
edge = EdgeAPI(staging=True)  # uses the environment variable `EDGE_STAGING_API_KEY`, targets the staging backend
# or
edge = EdgeAPI(api_key='XXX', staging=True)  # provide a staging API key and target staging backend

# and you're off:
resp = edge.get_offers()
```


#### Behavioral rules of thumb:

When requesting a report:
  - Pass _date_ (YYYY-MM-DD) strings instead of _datetime_ strings (YYYY-MM-DDTHH:MM:SS.sss) to the `dateRange` field.  # TODO FIX THIS
  - Timezone should _always_ be specified in the request body. `EdgeAPI._get_report(...)` defaults to 
  `America/New_York`, but can be overridden.

---

#### Test script:
```shell script
(venv) > pytest -v
```


Now using marshmallow for data validation on the client-side, allowing users to either specify content as JSON or as 
python arguments for Create Offer endpoint.


Packaging instructions found here: https://packaging.python.org/en/latest/tutorials/packaging-projects/
alpha-utils 2.2.2
alpha utils
Requires Python: >=3.6

# Cross-section

| Function| Definition |
|:---|:---|
|rank(x) | Cross-sectional rank|
|scale(x, a) | Rescaled x such that sum(abs(x)) = a (the default is a = 1)|
|indneutralize(x, g) | x cross-sectionally demeaned within groups g (subindustries, industries, etc.)

# Operators

- abs(x), log(x), sign(x), power(x, a) = standard definitions
- same for the operators “+”, “-”, “*”, “/”, “>”, “<”, “==”, “||”, “x ? y : z”

# Time Series

| Function                | Definition                                                   |
| :---------------------- | :----------------------------------------------------------- |
| ts_{O}(x, d)            | Operator O applied to the time-series for the past d days; non-integer number of days d is converted to floor(d) |
| ts_lag(x, d)            | Value of x d days ago                                        |
| ts_delta(x, d)          | Difference between the value of x today and d days ago       |
| ts_weighted_mean(x, d)  | Weighted moving average over the past d days with linearly decaying weights d, d – 1, …, 1 (rescaled to sum up to 1) |
| ts_sum(x, d)            | Rolling sum over the past d days                             |
| ts_product(x, d)        | Rolling product over the past d days                         |
| ts_stddev(x, d)         | Moving standard deviation over the past d days               |
| ts_rank(x, d)           | Rank over the past d days                                    |
| ts_min(x, d)            | Rolling min over the past d days \[alias: min(x, d)\]        |
| ts_max(x, d)            | Rolling max over the past d days \[alias: max(x, d)\]        |
| ts_argmax(x, d)         | Day of ts_max(x, d)                                          |
| ts_argmin(x, d)         | Day of ts_min(x, d)                                          |
| ts_correlation(x, y, d) | Correlation of x and y for the past d days                   |
| ts_covariance(x, y, d)  | Covariance of x and y for the past d days                    |