semvec/pypi-packages huggingface dataset:
imagefeatures 0.0.2
A library for processing image features in a dataframe.
License: MIT
Keywords: features, image

# image-features

<a href="https://pypi.org/project/image-features/">
    <img alt="PyPi" src="https://img.shields.io/pypi/v/image-features">
</a>

A library for processing image features in a dataframe. 

## Dependencies :globe_with_meridians:

Python 3.11.6:

- [pandas](https://pandas.pydata.org/)
- [pyarrow](https://arrow.apache.org/docs/python/index.html)
- [imgembeddings](https://github.com/minimaxir/imgbeddings)
- [pillow](https://python-pillow.github.io/)
- [requests](https://requests.readthedocs.io/en/latest/)
- [numpy](https://numpy.org/)
- [torch](https://pytorch.org/)

## Raison D'être :thought_balloon:

`image-features` transforms URLs in a dataframe into image features.

## Architecture :triangular_ruler:

`image-features` is a functional library, meaning that each phase of feature extraction gets put through a different function until the final output. The features its computes are as follows:

1. Embeddings - An embedding for each image in the row.
2. Luminosity - A measure of the luminosity of the image.
3. Mean Channels - The mean of each R/G/B channel.

## Installation :inbox_tray:

This is a python package hosted on pypi, so to install simply run the following command:

`pip install image-features`

or install using this local repository:

`python setup.py install --old-and-unmanageable`

## Usage example :eyes:

The use of `image-features` is entirely through code due to it being a library. It attempts to hide most of its complexity from the user, so it only has a few functions of relevance in its outward API.

### Generating Features

To generate features:

```python
import datetime

import pandas as pd

from imagefeatures.process import process

df = ... # Your timeseries dataframe
df = process(df, {"image_url"})
```

This will produce a dataframe that contains the new image related features.

## License :memo:

The project is available under the [MIT License](LICENSE).
inm-rspace 0.4.1
Tools for working with the RSpace ELN
License: MIT
Requires Python: >=3.9
Keywords: eln, rspace, science, notebook

# inm-rspace

Python tools for interaction with the Rspace Electronic Lab Notebook. Developed at INM - Leibniz Institute for New Materials in Saarbrücken, Germany.

`inm-rspace` adds functionality to the `rspace-client` API to make it easier to include personal scientific data workflows.
The main feature is the `Workflow` class, with which you can wrap arbitrary existing Python code in order to execute it on RSpace documents.


## Installation and Setup

You can install `inm-rspace` through PyPI, which should automatically take care of installing dependencies (especially `rspace-client`). Simply type:

`pip install inm-rspace`

However, to use the API, you need to first create an API key as explained [here](https://documentation.researchspace.com/article/v0dxtfvj7u-rspace-api-introduction).
Furthermore, `inm-rspace` assumes you have saved this API key as `RSPACE_API_KEY` along with your RSpace URL as `RSPACE_URL` (e.g. https://leibniz-inm.researchspace.com) as environment variables in your terminal.


## Examples

To learn how to generally use the `rspace-client` API, please refer to their [examples](https://github.com/rspace-os/rspace-client-python/tree/master/examples).

As for how to use the `inm-rspace` extension for workflows, check the `examples` folder of this repository.


## Documentation

The full API documentation is still in progress, but the current state is already available [here](https://sintharic.github.io/inm-rspace/).