semvec/pypi-packages huggingface dataset:
bm3dornl-gui 0.8.1
GUI application for BM3D ring artifact removal in neutron imaging
Requires Python: >=3.10
Keywords: BM3D, image processing, neutron imaging, GUI, ring artifacts

# BM3D GUI

GUI application for BM3D ring artifact removal in neutron imaging.

## Installation

```bash
pip install bm3dornl-gui
```

Or install together with the bm3dornl library:

```bash
pip install bm3dornl[gui]
```

## Usage

After installation, run:

```bash
bm3dornl-gui
```

## Features

- Load HDF5 and TIFF volume data
- Interactive slice viewing with adjustable window/level
- Multiple colormaps (Grayscale, Viridis, Plasma, etc.)
- BM3D processing with configurable parameters
- Side-by-side comparison of original and processed data
- Histogram analysis with ROI selection
- Export results to HDF5 or TIFF format

## Requirements

- Linux x86_64 or macOS ARM64
- No additional dependencies (HDF5 is bundled)

## License

MIT License - see the main [bm3dornl repository](https://github.com/ornlneutronimaging/bm3dornl).
zynamon 0.2.0
Generalized & powerful time-series class and related functions
Requires Python: >=3.12
Keywords: timeseries, analysis, monitoring, prediction, visualisation

# zynamon

## Synopsis

Generalized & powerful time-series class and related functions.

## Contents

This library defines a powerful time-series representation based on the well-known ```pandas``` DataFrames. However, it adds meta-data as well as conveniently built-in filtering & alignment routines for both value and time dimension. Moreover, the central time-series objects can be constructed from *nearly any 1D data* such that handling of various sources in a standardised way is made possible (e.g. regularly-sampled sensor data, event logs as well as audio streams).

As of now, this package is structured into the following modules:

**zeit**: Main class ```TimeSeries``` containing time-, value- and samples-related methods and algorithmic operators (+, -, *, /)

**zeitspec**: Class ```TimeSpec``` and conversion routine for a unified treatment of various timestamp representations as settings or from data (types and strings)

**imex**: Routines for the automatic bulk import, conversion & aggregation of data from (a lot of) CSV-like files, e.g. proper extraction of all individual time-series hidden in lengthy event logs

**utils**: Utilities working on ```TimeSeries``` objects such as helpers for general analysis (e.g. outliers, predictions, history etc) and operators on two arbitrary objects (incl. routines to make objects "coherent" in time)

**xutils**: Special conversion utilities for CSV-files containing DFT spectra as generated by the "CMS X-Tools" real-time multi-channel analysis software (Siemens AG).

[ Dr. Marcus Zeller | <dsp4444@gmail.com> | Erlangen, Germany | 2022-2026 ]
likeness-vitals 1.4.9
Shared utility functionality for Likeness modules.
Requires Python: >=3.11
Keywords: population-synthesis-utilities

# Likeness-Vitals: Shared utilities for the Likeness ecosystem

![tag](https://img.shields.io/github/v/release/likeness-pop/likeness-vitals?include_prereleases&sort=semver)
[![PyPI version](https://badge.fury.io/py/likeness-vitals.svg)](https://badge.fury.io/py/likeness-vitals)
[![Conda Version](https://img.shields.io/conda/vn/conda-forge/likeness_vitals.svg)](https://anaconda.org/conda-forge/likeness_vitals)

[![Continuous Integration](https://github.com/likeness-pop/likeness-vitals/actions/workflows/continuous_integration.yml/badge.svg)](https://github.com/likeness-pop/likeness-vitals/actions/workflows/continuous_integration.yml)
[![codecov](https://codecov.io/gh/likeness-pop/likeness-vitals/branch/develop/graph/badge.svg)](https://codecov.io/gh/likeness-pop/likeness-vitals)
[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff)

## Installation

### Conda-forge (recommended)

The `likeness-vitals` feedstock is available via the [conda-forge channel](https://github.com/conda-forge/likeness_vitals-feedstock).

```
$ conda install --channel conda-forge likeness_vitals
```

### PyPI

`likeness-vitals` is available on the [Python Package Index](https://pypi.org/project/likeness-vitals/).

```
$ pip install likeness_vitals
```

### Source

#### Directly via GitHub + `pip`

```
$ pip install git+https://github.com/likeness-pop/likeness-vitals.git@develop
```

#### Download + `pip`

Download the source distribution (``.tar.gz``) and decompress where desired. From that location:

```
$ pip install .
```

## Development

1. Clone the repository to the desired location.
2. Install in editable mode
   * Navigate to where the repo was cloned locally.
   * Within that directory:
      ```
      $ pip install -e .
      ```
3. Open an Issue for discussion
4. In a branch off `develop`, implement update/bug fix/etc.
5. Create a minimal Pull Request with clear description linked back to the associated issue from (3.)
6. Wait for review from maintainers
7. Adjust as directed
8. Once merged, fetch down `origin/develop` and merge into the local `develop`
9. Delete the branch created in (4.)
10. Start over at (2.)

## Ecosystem-level conda environments

The conda environments provided in `./envs/*` contain all dependencies required to use `livelike`, `pymedm` / `pmedm-legacy`, and `likeness-vitals`.

The install script will create a Python 3.12 environment and automatically choose between the vanilla and CUDA environment based on system type. To run it:

```
bash setup.sh
```

If a newer version of Python is desired, simply update `setup.sh` appropriately, though the CUDA version may not be available.