semvec/pypi-packages huggingface dataset:
celescope-mobiu 1.4.2
MobiuSCOPE Analysis Pipelines
Requires Python: >=3.9

# Introduction
celescope-mobiu is a bioinfomatics analysis pipeline to process [MobiuSCOPE(3' + 5' scRNA-Seq)](https://singleron.bio/products/mobiuscope-full-length-single-cell-rna-sequencing-kit/) data.

# Documentation
[User guide](./doc/user_guide.md)

# Support
Please use the issue tracker for quesdions and malfunctions.
yara-mail 3.3.0
A Python package and command line utility for scanning emails with YARA rules
Requires Python: >=3.7
Keywords: YARA, email, information security, infosec, security

<div align="center">
  <img src="https://seanthegeek.github.io/yaramail/_static/yaramail-logo.png" alt="yaramail logo">
<h1 style=>yaramail</h1>

  [![Python tests](https://github.com/seanthegeek/yaramail/actions/workflows/python-tests.yaml/badge.svg)](https://github.com/seanthegeek/yaramail/actions/workflows/python-tests.yaml)
  [![PyPI](https://img.shields.io/pypi/v/yara-mail)](https://pypi.org/project/yara-mail/)
  [![PyPI - Downloads](https://img.shields.io/pypi/dm/yara-mail?color=blue)](https://pypistats.org/packages/yara-mail)
</div>

`yaramail` is a Python package and command line utility for scanning emails with
[YARA rules][yara]. It is ideal for automated triage of phishing reports.

## CLI Demo

[![asciicast](https://asciinema.org/a/529801.svg)][cli-demo]

## Features

- Scans all parts of an email via API or CLI
  - Headers
    - Removes header indents by default for consistent scanning
  - Plain text and HTML body content
    - Converts body content to Markdown by default for consistent scanning
  - Attachments
    - Raw file content
    - Emails attached to emails
    - PDF document text
    - ZIP file contents, including nested ZIP files
      - Uses message body content as a list of possible ZIP passwords
      - Customizable list of passwords to use when attempting to scan encrypted ZIP files
- Provides a built-in methodology for categorizing emails
- Parses `Authentication-Results` headers

[yara]: https://yara.readthedocs.io/en/stable/writingrules.html
[cli-demo]: https://seanthegeek.github.io/yaramail/tutorial.html#using-the-cli
percently 0.1.4
A fast and efficient percentile calculator written in Rust.
Requires Python: >=3.9
Keywords: percentile, statistics, Rust, Python

<div align="center">
  <h1>percently</h1>
  <p><strong>Fast percentile calculator written in Rust — shipped as a Python package.</strong></p>

  <p>
    <a href="https://github.com/4thel00z/percently/actions/workflows/CI.yml">
      <img alt="CI" src="https://github.com/4thel00z/percently/actions/workflows/CI.yml/badge.svg" />
    </a>
    <a href="https://pypi.org/project/percently/">
      <img alt="PyPI" src="https://img.shields.io/pypi/v/percently" />
    </a>
    <a href="https://pypi.org/project/percently/">
      <img alt="Python versions" src="https://img.shields.io/pypi/pyversions/percently" />
    </a>
    <a href="LICENSE">
      <img alt="License" src="https://img.shields.io/github/license/4thel00z/percently" />
    </a>
    <img alt="Rust" src="https://img.shields.io/badge/Rust-2021-000000?logo=rust" />
    <img alt="maturin" src="https://img.shields.io/badge/built%20with-maturin-2A2A2A" />
  </p>
</div>

## Motivation

When you’re processing load test timings (or any latency distribution), you typically want buckets like p50/p95/p99 quickly. `percently` provides a small, fast Rust implementation exposed to Python.

## Features

- **Fast**: Rust core
- **Simple API**: one call to compute a percentile
- **Python-friendly**: works with regular lists/iterables of floats

## Installation

Using `uv`:

```bash
uv add percently
```

Using `pip`:

```bash
pip install percently
```

## 🧮 Usage

```python
import percently

data = [10.5, 22.0, 18.7, 19.2, 30.1, 25.3]

# Calculate the 95th percentile (f64)
p95 = percently.percentile(data, 95)
print(f"95th percentile: {p95:.2f}")
```

### Streaming (iterative) percentile estimates

If you have a generator/iterable and want **running percentile estimates** without loading all values into memory, use `stream`.

```python
import percently

def timings():
    for i in range(1, 1_000_001):
        yield float(i % 10_000) / 10_000.0

# Yield an updated p95 estimate every 1000 samples (approximate, bounded memory)
for est in percently.stream(timings(), 95, every=1000):
    last = est

print("final approx p95:", last)
```

Notes:
- `stream(..., 0, ...)` yields the running **min** (exact)
- `stream(..., 100, ...)` yields the running **max** (exact)
- All values must be finite floats (no NaN/inf)

## Development

Build/install locally (editable) with `maturin`:

```bash
uv sync
uv run maturin develop
```

## Releasing

This repo is configured for **signed** release commits/tags and tag-based publishing:
- Create a signed tag (e.g. `0.1.4`)
- GitHub Actions (tag-triggered) builds wheels/sdist and publishes to PyPI

Recommended (cargo-style) tool: `cargo-release`

```bash
cargo install cargo-release

# Patch release: bumps Cargo.toml version, commits (signed), creates signed tag, pushes
cargo release patch --execute
```

## License

MIT — see `LICENSE`.