semvec/pypi-packages huggingface dataset:
flaskbb-plugin-portal 2.0.0
A portal plugin for FlaskBB.
Requires Python: >=3.12
Keywords: flaskbb, plugin, portal

# Portal Plugin for FlaskBB

This plugin provides a simple portal for FlaskBB.

In addition to the settings that can be changed via the management panel,
one setting has to be added (if desired) to your ``flaskbb.cfg``:
```python
PLUGIN_PORTAL_URL_PREFIX="/yourportalurlprefix"
```
This setting allows you to change the URL of the portal from the default
value of ``/portal`` to something else. Be sure to **not** change it to something that
could conflict with one of the ``URL_PREFIX``es of FlaskBB.


# Installation

This plugin can be installed from PyPI via:
```bash
$ pip install flaskbb-plugin-portal
```


# License

``flaskbb-plugin-portal`` is licensed under the
[BSD License](https://github.com/flaskbb/flaskbb-plugin-portal/blob/master/LICENSE).


# Links

* [FlaskBB Website](https://flaskbb.org)
* [Source Code](https://github.com/flaskbb/flaskbb-plugin-portal)
* [Issue Tracker](https://github.com/flaskbb/flaskbb-plugin-portal/issues)
pysupertable 0.1.1
Python bindings for SuperTable - the next-gen open table format
Requires Python: >=3.8
Keywords: table-format, data-lake, parquet, arrow, iceberg, delta

# PySuperTable

<p align="center">
  <img src="https://raw.githubusercontent.com/abrahimzaman360/super-table/main/logo.png" alt="SuperTable Logo" width="200"/>
</p>

**The Rust-native open table format for Python** 🚀

PySuperTable provides Python bindings for [SuperTable](https://github.com/abrahimzaman360/super-table), bringing blazing-fast lakehouse operations to your Python workflows.

## Features

- ⚡ **Rust Performance**: Native Rust core with zero-copy Arrow integration
- 🔄 **ACID Transactions**: Full optimistic concurrency control
- 🕰️ **Time Travel**: Query any historical snapshot
- 🧬 **Schema Evolution**: Add, drop, rename columns safely
- 📊 **DataFusion & Polars**: Native query engine integration
- 🔌 **Iceberg Compatible**: REST Catalog API compatible

## Installation

```bash
pip install pysupertable
```

## Quick Start

```python
import supertable as st

# Create a table
schema = st.Schema([
    st.Field(1, "id", "long", required=True),
    st.Field(2, "name", "string", required=True),
    st.Field(3, "value", "double", required=False),
])

table = st.create_table("s3://my-bucket/warehouse/my_table", schema)

# Query with SQL
df = table.sql("SELECT * FROM my_table WHERE id > 100")

# Time travel
historical = table.snapshot_at("2024-01-01T00:00:00Z")

# Schema evolution
table.add_column("email", "string")
```

## Integration with Pandas

```python
import supertable as st
import pandas as pd

table = st.open_table("s3://my-bucket/warehouse/my_table")

# Read as Pandas DataFrame
df = table.to_pandas()

# Query with filters
df = table.sql("SELECT * FROM my_table WHERE value > 100").to_pandas()
```

## Integration with PyArrow

```python
import supertable as st
import pyarrow as pa

table = st.open_table("s3://my-bucket/warehouse/my_table")

# Read as Arrow Table
arrow_table = table.to_arrow()

# Write Arrow data
table.append(arrow_table)
```

## Why PySuperTable?

| Feature | PySuperTable | pyiceberg | delta-rs |
|---------|--------------|-----------|----------|
| Cold Start | ~10ms | ~500ms | ~200ms |
| Memory Overhead | Low | High (JVM) | Medium |
| Native Rust | ✅ | ❌ | ✅ |
| Zero-copy Arrow | ✅ | ✅ | ✅ |

## Requirements

- Python 3.8+
- Supported platforms: Linux, macOS, Windows

## License

Apache-2.0 OR MIT
algorhino-coral 0.1.11
Shared types and utilities for evalaution
Requires Python: >=3.13

# Coral

A PyTorch-based neural network library for board game evaluation.

## Overview

Coral provides a flexible framework for building and deploying neural networks to evaluate board game positions. It includes modular components for input conversion, neural network architectures, and output interpretation.

## Features

- **Multiple NN Architectures**: Multi-layer perceptrons, transformers, and custom models
- **Flexible Input/Output Conversion**: Modular converters for different board representations and evaluation formats
- **Point-of-View Evaluation**: Support for evaluating positions from different player perspectives
- **PyTorch Integration**: Built on PyTorch with JIT compilation support for optimized inference

## Installation

```bash
pip install git+https://github.com/victorgabillon/coral.git@main
```

### Requirements

- Python >= 3.13
- PyTorch
- valanga (board game library)

## Project Structure

```
src/coral/
├── board_evaluation.py          # Point-of-view and evaluation types
├── chi_nn.py                     # Base neural network class
└── neural_networks/
    ├── factory.py                # NN factory pattern
    ├── nn_content_evaluator.py   # Board evaluation with NNs
    ├── models/                   # NN architectures (MLP, Transformer)
    ├── input_converters/         # Board to tensor conversion
    └── output_converters/        # NN output to evaluation conversion
```

## License

GPL-3.0-only