semvec/pypi-packages huggingface dataset:
bionemo-noodles 0.1.2
Python wrapper around [noodles](https://github.com/zaeleus/noodles).
Requires Python: >=3.10

# bionemo-noodles

`bionemo-noodles` is a Python wrapper of [noodles](https://github.com/zaeleus/noodles) that supports `memmap`-based file I/O for FASTA files.

## Installation

To install from PyPI, execute the following command:

```bash
pip install bionemo-noodles
```

### Compatibility

`bionemo-noodles` has pre-built wheels for Python/Cython `3.10`, `3.11`, and `3.12`, and is compatible with `manylinux_2_28` on `x86_64`.

For a custom build configuration that is not currently supported on PyPI, reach out to: bionemofeedback@nvidia.com

## Usage

An example `torch.utils.data.Dataset` using `NvFaidx` / `bionemo-noodles`:
```
import json
from pathlib import Path

import torch

from bionemo.noodles.nvfaidx import NvFaidx

class SimpleFastaDataset(torch.utils.data.Dataset):

    def __init__(self, fasta_path: Path, tokenizer):
        """Initialize the dataset."""
        super().__init__()
        self.fasta = NvFaidx(fasta_path)
        self.seqids = sorted(self.fasta.keys())
        self.tokenizer = tokenizer

    def write_idx_map(self, output_dir: Path):
        """Write the index map to the output directory."""
        with open(output_dir / "seq_idx_map.json", "w") as f:
            json.dump({seqid: idx for idx, seqid in enumerate(self.seqids)}, f)

    def __len__(self):
        """Get the length of the dataset."""
        return len(self.seqids)

    def __getitem__(self, idx: int) -> dict[str, torch.Tensor]:
        """Get an item from the dataset."""
        sequence = self.fasta[self.seqids[idx]].sequence().upper()
        tokenized_seq = self.tokenizer.text_to_ids(sequence)
        loss_mask = torch.ones_like(torch.tensor(tokenized_seq, dtype=torch.long), dtype=torch.long)
        return {
            "tokens": torch.tensor(tokenized_seq, dtype=torch.long),
            "position_ids": torch.arange(len(tokenized_seq), dtype=torch.long),
            "seq_idx": torch.tensor(idx, dtype=torch.long),
            "loss_mask": loss_mask,
        }
```

## BioNeMo Framework Ecosystem Development

To install this sub-package locally (with `--editable`):
```
pip install -e .
```

To run unit tests, execute:
```bash
pytest -v .
```

To build wheels for different Python, Linux, and system architecture configurations, run the [BioNeMo Sub-Package GitHub Actions Workflow (bionemo-subpackage-ci.yml)](https://github.com/NVIDIA/bionemo-framework/blob/main/.github/workflows/bionemo-subpackage-ci.yml)
opencv-cu126 4.12.0
Precompiled OpenCV with CUDA 12.6 support for Windows/Python3.9
Requires Python: ==3.9.*
Keywords: opencv cuda windows precompiled

针对Windows平台下Python3.9版本预编译的支持CUDA12.6的OpenCV库。

    特性包含：
    - 基于OpenCV 4.12.0源码编译
    - 集成CUDA 12.6加速支持
    - 包含cuDNN 8.x深度学习推理加速
    - 支持NVIDIA GPU硬件加速
subatomic-agents 0.1.7.1
A powerful multi-agent framework for processing and understanding multimodal information with GenAI.
Requires Python: >=3.9

# Subatomic Agents API 

**Subatomic Agents** is a modular, enterprise-grade AI framework by [Subatomic](https://www.getsubatomic.ai) for automating and scaling business intelligence workflows using agent-based architectures.

*This package provides the **Sales Proposal Agent**, which transforms meeting transcripts and summaries into structured, client-ready sales proposals.*

---

## *🚀 Features*

- *Includes the Sales Proposal Agent for generating structured, client-ready proposals from meeting inputs*

---

## *📆 Installation*

*Install via *[*PyPI*](https://pypi.org/project/subatomic-agents/)*:*

```bash
pip install subatomic-agents
```

---

## *🧠 Usage*

```python
from subatomic_agents.sales_proposal.services.agentic_generator import sales_proposal_agentic_generator

result = sales_proposal_agentic_generator(
    query = """
    Please, create a Sales Proposal with these guidelines:
    Meeting Summary URLs (and file types): https://example.com (pdf)
    Meeting Transcript URL (and file type): https://example2.com (pdf)
    """
)

print(f"FINAL RESULT => {result}")
```

---

## *📂 Output*

*Returns a structured JSON dictionary representing the sales proposal (excluding internal vector data).*\
*The result is also saved to **`sales_proposal_{id}.pdf`** and **`sales_proposal_{id}.docx`**.*

---

## *➕ Extending the Framework*

*This framework is modular and designed for easy extension:*

- *Add new agents in **`agents/`***
- *Define prompt strategies in **`factories/`***
- *Extend the generation flow via **`SalesProposalGeneratorService`***

---

## *🧪 Requirements*

- *Python 3.9+*
- *Compatible with **`uv`**, **`pip`**, or **`poetry`**-based environments*

---

## *👥 Authors*

*Built by *[*Subatomic*](https://www.getsubatomic.ai)*

- *Karl Simon · *[*karl@getsubatomic.ai*](mailto:karl@getsubatomic.ai)*
- *Aaron Sosa · *[*wilfredo@getsubatomic.ai*](mailto:wilfredo@getsubatomic.ai)*

---
manaflow 0.1.0
Async connection management for Manaflow services
Requires Python: >=3.7

# Manaflow

Async connection management for Manaflow services. This package provides utilities for managing database connections and other services with dependency injection.

## Installation

```bash
pip install manaflow
```

## Usage

```python
import asyncio
import manaflow
from manaflow import secrets_manager

# Pre-populate secrets manager with scoped keys
secrets_manager.set_secret("postgres_conn_db_uri", "postgresql://user:pass@localhost/mydb")
secrets_manager.set_secret("redis_conn_cache_uri", "redis://localhost:6379")

# Define connection factories with scoped secrets
@manaflow.connection("postgres_conn_db", secret_keys=["uri"])
async def postgres_factory(uri: str):
    # Use your preferred async PostgreSQL driver
    return await create_postgres_pool(uri)

@manaflow.connection("redis_conn_cache", secret_keys=["uri"])
async def redis_factory(uri: str):
    # Use your preferred async Redis driver
    return await create_redis_connection(uri)

# Use connections with dependency injection
async def demo_postgres(db=manaflow.Depends("postgres_conn_db")):
    # The connection is automatically retrieved and injected
    connection = await db()
    # Use the connection
    return {"status": "success"}

# Run everything
async def main():
    result = await demo_postgres()
    print(result)
    # Close all connections when done
    await manaflow.connection.close_connections()

if __name__ == "__main__":
    asyncio.run(main())
```

## Features

- Async connection management with dependency injection
- Secret management for connection credentials
- Support for multiple connection types (PostgreSQL, MySQL, MongoDB, Redis, HTTP, etc.)
- No external dependencies required

## License

MIT
redplanet 0.2.2
User-friendly accessors/calculations/plotting for various Mars datasets and derived geophysics quantities.
Requires Python: <3.13,>=3.10

<!-- Logo -->
<p align="center">
  <a href="https://humboldt-penguin.github.io/redplanet/" target="_blank" rel="noopener noreferrer">
    <img src="https://files.catbox.moe/ueo0kd.png" style="width:700px; height:auto;">
    <!-- NOTE: ^THIS URL MUST BE MANUALLY EDITED IF LOGO IS UPDATED!!! -->
  </a>
</p>



<!-- Badges (useful: https://naereen.github.io/badges/ ) -->
<div align="center">

  <a href="https://pypi.org/project/redplanet/" target="_blank" rel="noopener noreferrer">
    <img src="https://img.shields.io/pypi/v/redplanet.svg?color=blue"/>
  </a>

  <a href="https://github.com/Humboldt-Penguin/redplanet/actions/workflows/test.yml" target="_blank" rel="noopener noreferrer">
    <img src="https://github.com/Humboldt-Penguin/redplanet/actions/workflows/test.yml/badge.svg?branch=main"/>
  </a>

  <a href="https://github.com/humboldt-penguin/" target="_blank" rel="noopener noreferrer">
    <img src="https://img.shields.io/badge/Ask%20me-anything-1abc9c.svg"/>
  </a>

  <!-- Template:
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    <img src=""/>
  </a>
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</div>

&nbsp;



<!-- Content -->

*RedPlanet* is an open-source Python library for working with various Mars geophysical datasets. We aim to streamline data analysis/visualization workflows for beginners and experts alike, so you spend less time hunting/wrangling data and more time doing cool science! :)

**MORE INFO ON OUR WEBSITE: https://humboldt-penguin.github.io/redplanet/**

&nbsp;

Huge thanks to my incredible advisor [Prof. Lujendra Ojha](https://www.lujendraojha.com/) for all his support, guidance, patience, and kindness over the past four years.