semvec/pypi-packages huggingface dataset:
mrseql 0.0.4
MrSEQL
Requires Python: >=3.7

# This package
A standalone Python wrapper for [MrSEQL](https://github.com/lnthach/Mr-SEQL)

## Installation

## Installation using pip
pip install mrseql

## Installation from source
```
git clone https://github.com/mlgig/mrseql.git

cd mrseql

pip install .
```

## Paper
>  T. L. Nguyen, S. Gsponer, I. Ilie, M. O'reilly and G. Ifrim [Interpretable Time Series Classification using Linear Models and Multi-resolution Multi-domain Symbolic Representations](https://www.researchgate.net/publication/332875865_Interpretable_Time_Series_Classification_using_Linear_Models_and_Multi-resolution_Multi-domain_Symbolic_Representations) in Data Mining and Knowledge Discovery (DMKD), May 2019, https://doi.org/10.1007/s10618-019-00633-3
onto-viewer-gxp 0.0.1
A package to view and write ontologies directly from draw.io diagram files.
Requires Python: >=3.9

# SDLE FAIR Package

**Description:**

The SDLE FAIR Package programmatically translates a CSV file with ontological terms and corresponding data into RDF triples. These RDF triples are then stored in OWL and JSON-LD files, facilitating data interoperability and semantic web applications.

**Output and Utility:**

An RDFLib graph with the input data:

* **An OWL File** with the ontology schema and populated ontology data.

* **A JSON-LD File** with the ontology schema and populated ontology data.


* **A Graphviz PNG** for users to visualize the ontology schema & ensure its correctness.


* **An HTML File** with the documentation for the output ontology.




## Installation

To install the SDLE FAIR Package, follow these steps:

1. **Clone the repository:**
   ```bash
   git clone https://github.com/yourusername/SDLE-FAIR-Package.git
   cd SDLE-FAIR-Package
   ```
2. **Install the necessary dependencies**:

   ```bash
   pip install rdflib==7.0.0 pylode==3.1.4 matplotlib==3.6.2 numpy==1.23.5 graphviz==0.20.1
   ```

## Usage
1. **Preparation**

   Prepare your CSV file: Ensure your CSV file contains the appropriate ontological terms and corresponding data using this template: 


   [FAIRSheetPackageTemplate](https://docs.google.com/spreadsheets/d/1k7gFRc1Yslc-m65aWfFCxqk5UtrKZl9c3GyEFQvLSFU/edit?usp=sharing)


2. **Initialization and Running Main Script**

   ```python
   import argparse
   from rdflib import Graph as rdfGraph
   import graphviz
   from fair_sheet_parser import FairSheetParser
   from rdflib_graph_saver import RDFLibGraphSaver

   python __main__.py --ontology_sheet_folder /path/to/csv --include_valuetype_graphviz_edges --include_pylode_docs
   ```
## Contributing

We welcome contributions to the SDLE FAIR Package! 

1. **Fork and Clone the repository:**
Click the "Fork" button at the top of this repository. Then clone the repository: 

   ```bash
   git clone https://github.com/yourusername/SDLE-FAIR-Package.git
   cd SDLE-FAIR-Package
   ```
2. **Create a new branch:**

   ```bash
   git checkout -b feature-or-bugfix-name
   ```
3. **Make your changes and commit them:**

   ```bash
   git add . 
   git commit -m "Description of your changes"
   ```
4. **Push your changes to your forked repository:**

   ```bash
   git push origin feature-or-bugfix-name
   ```
5. **Submit a pull request:** Go to the original repository and submit a pull request.


## License

[Open Source Initiative](https://opensource.org/licenses)

## Contact Information
If you have any questions or need further assistance, feel free to contact us at:

Email: 
[ach159@case.edu]()

GitHub Issues:
[Open an issue](https://github.com/yourusername/SDLE-FAIR-Package/issues)
draw-ionto 0.0.4
A package to view and write ontologies directly from draw.io diagram files.
Requires Python: >=3.9

# SDLE FAIR Package: DRAW.IOnto

**Description:**

This package is part of the larger SDLE FAIR application suite that features tools to create scientific ontologies faster and more efficiently. This package can provide OOP interfaces for drawing and reading ontology diagrams made using draw.io. The code is able to generate a list of relationship triples that are used to store and construct ontology diagrams.

## Installation

To install this particular package, download the latest wheel file avialable online (link not live yet), and the use pip in the following manner:

```
pip install draw_ionto-0.0.4-py3-none-any.whl
```

## Usage

One can refer to the examples folder for sample implementations of basic read and write operations with draw.io diagrams.

## License

[Open Source Initiative](https://opensource.org/licenses)

## Contact Information
If you have any questions or need further assistance, feel free to contact us at:

Email: 
[gop2@case.edu]()
zernike-descriptors 0.1.0
A Python package to compute 3D zernike descriptors.
Requires Python: >=3.10

# 3D Zernike Descriptors

pyzernike is a Python library for computing 3D Zernike invariants as descriptors for 3D shape comparison.

## Usage

Here's a basic example of how to use the library:

```python
import numpy as np
from pyzernike import ZernikeDescriptor

# Create or load your 3D array here
arr = np.zeros((50,50,50), dtype = np.float32)
arr[15:25, 5:15, 35:45] = 1

# Fit the Zernike descriptor up to order 8
descriptor = ZernikeDescriptor.fit(data = arr, order = 8)

# Get Zernike coefficients
coefficients = descriptor.get_coefficients()

# Reconstruct the array
arr_rec = descriptor.reconstruct(box_size = 50)

# Write the coefficients to a binary file
descriptor.save_invariants("coefficients.inv")

```

## Installation

We recommend installation using one of the following methods

| Method   | Command                                                 |
|----------|---------------------------------------------------------|
| PyPi     | `pip install pyzernike`                                 |
| Source   | `pip install git+https://github.com/maurerv/pyzernike`  |


> [!TIP] 
> pyzernike uses OpenMP for parallelization. On ARM MacOS systems, `brew install llvm libomp` and
> adding the llvm binary to PATH is required prior to pip install.


## Background

pyzernike provides Python bindings to C code written by Marcin Novotni, which was distributed under a GPL license and provided with the [paper](https://cg.cs.uni-bonn.de/backend/v1/files/publications/novotni-2004-shape.pdf):

M. Novotni, R. Klein "Shape Retrieval using 3D Zernike Descriptors" Computer Aided Design 2004; 36(11):1047-1062

A copy of that code serving as the basis for this project was obtained from [GitHub](https://github.com/codingforfun/ZernikeMoments). pyzernike includes a range of modifications to the original code base to improve performance but faithfully implements the original derivations.