semvec/pypi-packages huggingface dataset:
figeno 1.9.0
Package for generating genomics figures.
Requires Python: >=3.7

# figeno

```
  FIGENO is a
  FIGure
    GENerator
for GENOmics
```
With figeno, you can plot various types of sequencing data along genomic coordinates. Video overview: https://www.youtube.com/watch?v=h1cBeXoSYTA.

![figeno](docs/content/images/figeno.png)
Example figures generated with figeno. Left: allele-specific methylation with nanopore data. Right: HiC data across a structural rearrangement. See more examples [here](https://figeno.readthedocs.io/en/latest/content/examples.html).

## Features
- Large collection of tracks (bigwig, HiC, alignments with base modifications, WGS with copy numbers and SV…)
- Multi-region figures with interactions across regions
- Graphical user interface, command line interface and python API
- Output figures in vector graphics (svg, pdf) or bitmap (png)

## Quick start
### Linux, MacOS
In an environment with python>=3.7:
```
pip install figeno
figeno gui
```
This will install figeno and run the graphical user interface (GUI). From the GUI, you can configure the figure and generate it, as well as save the JSON config file which fully defines the figure. The GUI is optional and you can instead use the command line interface: use [figeno init](https://figeno.readthedocs.io/en/latest/content/usage.html#figeno-init) to initialize a config file, edit the config file manually, and generate the figure with [figeno make](https://figeno.readthedocs.io/en/latest/content/usage.html#figeno-make).

### Windows
Download figeno_windows.zip from https://github.com/CompEpigen/figeno/releases/latest, unzip it and launch the graphical user interface by executing `figeno.exe`.

## Documentation
For more information on how to use figeno, please read the documentation at 
https://figeno.readthedocs.io/en/latest/.

## Test data
Example input files to test figeno are provided in [test_data](https://github.com/CompEpigen/figeno/tree/main/test_data).

## Feedback
If you encounter a bug or would like to have a new feature added, please do not hesitate to [raise an issue](https://github.com/CompEpigen/figeno/issues/new) or to [contact me directly](https://www.dkfz.de/en/CanEpi/staff/kontakt/Sollier_Etienne.php). You can also provide feedback by completing this [anonymous form](https://forms.gle/xb9Ygk6zsJCJUQYm9), which would help me improve figeno!

## Citation
If you use figeno in your research, please consider citing:

Etienne Sollier, Jessica Heilmann, Clarissa Gerhauser, Michael Scherer, Christoph Plass, Pavlo Lutsik. Figeno: multi-region genomic figures with long-read support, Bioinformatics 2024. [https://doi.org/10.1093/bioinformatics/btae354](https://doi.org/10.1093/bioinformatics/btae354)
granite-common 0.3.6
Input and output processing for IBM Granite models
Requires Python: >=3.10

# granite-common


Python library that provides enhanced prompt creation and output parsing for IBM
Granite models.


## Installation

To install from the main development branch, type:

```
pip install git+https://github.com/ibm-granite/granite-common.git
```

## Developer setup

For compatibility with different underlying operating system versions, we recommend using `conda` to create a consistent base Python environment for development and testing.

Detailed instructions:

1. Install [MiniForge](https://github.com/conda-forge/miniforge) or another package that provides the `conda` command-line utility.
1. Set the environment variable `CONDA_HOME` to point to the root of your `conda` install. If you installed MiniForge in your home directory, this value should be `${HOME}/miniforge3`.
1. Check out a copy of this repository.
1. Run the script [`scripts/env.sh`](scripts/env.sh) from the root of your local copy of the repository. The script will create a Conda environment in `./env` and will install the source code of your local copy as an editable Pip package. The script will also install and enable pre-commit hooks with [pre-commit](https://pre-commit.com/).
1. Before running commands such as `python` or `jupyter` from the command line, activate the Conda environment by typing `conda activate ./env` from the root of your local copy of this repository.
1. If you are using Visual Studio Code or a similar IDE, configure your IDE to use the environment at `./env`

## Running tests

After following the instructions in the previous section, you should be able to run tests on your local machine by typing:
```
pytest tests
```
from the root of your local copy of this repository, using the conda environment
described in the previous section.

The build automation for this project uses the [`tox`](https://tox.wiki/en) environment manager. Sometimes you will need to run tests from inside a `tox` managed environment to replicate issues from the continuous integration environment. To run tests with `tox`, activate the Python environment `./env` created earlier, then choose from among the following:

* Run regression tests: `tox -e unit`
* Run `pylint` checks: `tox -e lint`
* Run `ruff` formatter: `tox -e ruff`
* Run a full continuous integration suite: `tox`
PyBioImageUtils 0.0.1
Utilities for bioimaging and bioimage analysis workflows.
Requires Python: >=3.9

# Bioimage Utilities for Python

``pybioimageutils`` is a Python package designed to provide convenience utilities for bioimaging and bioimage analysis workflows. While foundational libraries like NumPy and scikit-image are built for general purpose use, common tasks — such as overlaying image masks or performing watershed segmentation — often require redundant code copied across multiple projects. This toolbox aims to capture these frequent utility functions in a single, easy-to-include package, streamlining your bioimage analysis pipeline.

## Usage

1. Install the package using pip


## Developers Guide

To test the visualizations, download the [BBBC033 dataset](https://bbbc.broadinstitute.org/BBBC033) from the Broad Bioimage Benchmark Collection. Download both the Images and the