semvec/pypi-packages huggingface dataset:
pqfilt 0.1.0
Generic Parquet filtering tool (CLI + API)
Requires Python: >=3.10
Keywords: data, filter, parquet, predicate-pushdown, pyarrow

# pqfilt

Generic Parquet predicate-pushdown filter tool — CLI and Python API.

`pqfilt` wraps `pyarrow.dataset` to let you filter Parquet files **before** they
are fully read into memory, using row-group-level predicate pushdown.

## Installation

```bash
pip install pqfilt
# or
uv add pqfilt
```

## Python API

```python
import pqfilt

# Simple filter
df = pqfilt.read("data.parquet", filters="vmag < 20")

# AND + OR with expression syntax
df = pqfilt.read("data.parquet", filters="(a < 30 & b > 50) | c == 1")

# Tuple syntax (flat AND)
df = pqfilt.read("data.parquet", filters=[("a", "<", 30), ("b", ">", 50)])

# DNF syntax (OR of ANDs)
df = pqfilt.read("data.parquet", filters=[
    [("a", "<", 30)],
    [("b", ">", 50)],
])

# Column selection + output
df = pqfilt.read("data/*.parquet", columns=["a", "b"], output="out.parquet")
```

## CLI

```bash
# Basic filter
pqfilt data/*.parquet -f "vmag < 20" -o filtered.parquet

# AND + OR expression
pqfilt data/*.parquet -f "(a < 30 & b > 50) | c == 1" -o filtered.parquet

# Multiple -f flags (AND-ed together)
pqfilt data/*.parquet -f "vmag < 20" -f "dec > 30" -o filtered.parquet

# Column selection
pqfilt data/*.parquet -f "vmag < 20" --columns vmag,ra,dec -o filtered.parquet

# Membership filter
pqfilt data/*.parquet -f "desig in 1,2,3" -o filtered.parquet
```

### Column names with special characters

Columns containing operator characters can be backtick-quoted:

```python
pqfilt.read("data.parquet", filters="`alpha*360` > 100")
```

## License

MIT
jax2onnx 0.12.1
export JAX to ONNX
Requires Python: >=3.11

# jax2onnx 🌟

[![CI](https://github.com/enpasos/jax2onnx/actions/workflows/ci.yml/badge.svg)](https://github.com/enpasos/jax2onnx/actions/workflows/ci.yml)
[![PyPI version](https://img.shields.io/pypi/v/jax2onnx.svg)](https://pypi.org/project/jax2onnx/) 
[![License](https://img.shields.io/badge/License-Apache_2.0-blue.svg)](https://opensource.org/licenses/Apache-2.0)
[![Documentation](https://img.shields.io/badge/docs-live-blue)](https://enpasos.github.io/jax2onnx/)

`jax2onnx` converts your [JAX](https://docs.jax.dev/),  [Flax NNX](https://flax.readthedocs.io/en/latest/), [Flax Linen](https://flax-linen.readthedocs.io/en/latest/), [Equinox](https://docs.kidger.site/equinox/) functions directly into the ONNX format.


![jax2onnx.svg](https://enpasos.github.io/jax2onnx/images/jax2onnx.svg)

## 📚 Documentation

**[Read the full documentation here](https://enpasos.github.io/jax2onnx/)**

## 🚀 Quick Install

```bash
pip install jax2onnx
```

## ⚡ Quick Usage

```python
from jax2onnx import to_onnx
from flax import nnx

model = MyFlaxModel(...)
to_onnx(model, [("B", 32)], return_mode="file", output_path="model.onnx")
```

## 🤝 Contributing

We warmly welcome contributions! Please check our [Developer Guide](https://enpasos.github.io/jax2onnx/developer_guide/plugin_system/) for plugin tutorials and architecture details.

## 📜 License

Apache License, Version 2.0. See [`LICENSE`](./LICENSE).


## 🌟 Special Thanks

A huge thank you to all [our contributors and the community](https://enpasos.github.io/jax2onnx/about/acknowledgements/) for their help and inspiration!

---

**Happy converting! 🎉**
agent-integrity-proto 0.2.0
Agent Integrity Protocol — real-time thinking block analysis for AI agent alignment
Requires Python: >=3.10
Keywords: agent, ai, alignment, conscience, integrity, llm, safety, thinking

# agent-integrity-proto

Python SDK for the Agent Integrity Protocol — real-time thinking block analysis for AI agent alignment.

## Installation

```bash
pip install agent-integrity-proto
```

## Usage

```python
from aip import check_integrity, build_signal, AdapterRegistry

# Extract thinking block from LLM response
registry = AdapterRegistry()
thinking = registry.extract(response, provider="anthropic")

# Run integrity analysis
checkpoint = check_integrity(
    thinking_block=thinking.content,
    card=alignment_card,
    config={
        "agent_id": "my-agent",
        "analysis_llm": {
            "model": "claude-haiku-4-5-20251001",
            "base_url": "https://api.anthropic.com",
            "api_key": os.environ["ANTHROPIC_API_KEY"],
            "max_tokens": 1024,
        },
    },
)

# Act on the verdict
if checkpoint.verdict == "clear":
    proceed()
else:
    escalate(checkpoint.concerns)
```

## API

See the [full documentation](https://github.com/mnemom/aip#readme) and [specification](https://github.com/mnemom/aip/blob/main/docs/SPEC.md).

## Requirements

- Python >= 3.10

## License

Apache 2.0. See [LICENSE](../../LICENSE) for details.