semvec/pypi-packages huggingface dataset:
normtransform 0.2.1
Transformation for normalising data
License: MIT
Requires Python: <3.14,>=3.11

# Transformation

Transformation tools to normalise and stabilise the variance of non-normal data. 

![Build Status](https://img.shields.io/badge/build-passing-brightgreen)

## Table of Contents
- [Overview](#overview)
  - [Installation](#installation)
  - [Demonstration](#example-usage)
  - [Contributing](#contributing)
  - [License](#license)
  - [Contact](#contact)
  

  ---
## Overview

[**normtransform**](https://pypi.org/project/normtransform/) is a high-performance Python package (with Cython/C++ backend) for fitting univariate transformed normal distributions using maximum likelihood or maxium a posteriori (MAP) estimation, with support for left and right censoring. 
Typically, norm-transform is used to transform variables/data so that they can be used in modelling that assumed normality. The ability to handle scenarios where data may be treated as partially observed (e.g. rainfall) allows significant flexibility in applications.

---

## Interactive Demonstration of normtransform

You can explore an interactive demonstration of the normtransform tool [here](https://mybinder.org/v2/gh/csiro-hydroinformatics/hydro-forecasting-demonstrations/HEAD?urlpath=%2Fdoc%2Ftree%2F%2Fnotebooks%2Fnormtransform-demonstration.ipynb).  This link opens a Jupyter Notebook hosted on the MyBinder platform. Once it loads, click **Run-->Run All Cells** to execute the code and see the demonstration in action.

---

## Installation

A [Python virtual environment](https://packaging.python.org/en/latest/specifications/virtual-environments/) is a self-contained directory that includes a specific Python version and any additional packages you install. It helps you isolate dependencies for different projects, ensuring that changes in one environment don’t affect others. This makes it easier to manage project-specific requirements and maintain a clean development setup.

Use the package manager pip to install normtransform.

```bash
python -m venv venv # Create a virtual environment
source venv/bin/activate  # Activate the virtual enviroment, on Windows use `venv\Scripts\activate`
pip install normtransform #install normtransform
```
Once installed users can start with mimicking the [demonstration](#interactive-demonstration-of-normtransform) found above within their own environment.

---

## Contributing

Requests are welcome. Please contact the authors below.

---
## License

[MIT](https://choosealicense.com/licenses/mit/) 

---
## Contact

- Andrew Schepen - andrew.schepen@csiro.au
- David Robertson - david.robertson@csiro.au
seq-miner 1.3.1
A command-line tool to extract and filter sequence reads from BAM and FASTQ files by ID, quality score, and length.
License: MIT
Requires Python: >=3.7

# seq-miner

**seq-miner** is a lightweight tool to extract and filter reads from **BAM** or **FASTQ** files based on:

- Specific read IDs
- Mean quality score threshold
- Minimum read length
- Multi-threading (FASTQ)
- JSON/CSV-ready summary (optional)


## Installation

```bash
pip install seq-miner
```

Or clone from source:

```bash
git clone https://github.com/your-org/seq-miner.git
cd seq-miner
pip install .
```


## Usage

### Extract reads from BAM

```bash
seq-miner -i reads.bam -o filtered.bam -f bam -r read_ids.txt --min-qscore 10 --min-length 200
```

### Filter FASTQ reads in parallel

```bash
seq-miner -i reads.fastq -o filtered.fastq -f fastq --min-qscore 15 --min-length 1000 --threads 4
```

### Show version

```bash
seq-miner --version
```


## Command-line Options

| Option            | Description                                          |
|-------------------|------------------------------------------------------|
| `-i`, `--input`   | Input BAM or FASTQ file                              |
| `-o`, `--output`  | Output file for passed reads                         |
| `-f`, `--format`  | File format: `bam` or `fastq`                        |
| `-r`, `--read-ids`| Optional file with read IDs (one per line)          |
| `--min-qscore`    | Minimum mean Q-score (default: `0.0`)               |
| `--min-length`    | Minimum read length (default: `0`)                  |
| `--threads`       | Number of CPU threads (only used for FASTQ)         |
| `--verbose`       | Enable verbose logging                               |
| `--version`       | Print the current version                            |


## Output Summary

When finished, you'll see:

```
Summary:
Passed reads     : 12345
Low-quality reads : 54
Short reads      : 91
```

Optionally, you can pipe this to JSON or CSV (coming soon).


## Auto Version + Release

- Version is stored in [`seqminer/__version__.py`](seqminer/__version__.py)
- Tagged automatically with GitHub Actions on push to `main`
- Published to PyPI on GitHub release

## License

MIT License © Theerayut  
See [LICENSE](LICENSE) for full text.


## Contact

For issues, please open an issue on [GitHub](https://github.com/aeiwz/seq-miner/issues).