semvec/pypi-packages huggingface dataset:
tx-exchange-clip 0.1.2
Python Library for Threat Exchange CLIP Extension
Requires Python: >=3.6

CLIP Extension for Threat Exchange

# what is CLIP?
CLIP (Contrastive Language–Image Pre-training)
Model built by OpenAI: https://openai.com/research/clip

https://github.com/openai/CLIP


## Requirements

```
> python3 -m venv omm_clip
> source omm_clip/bin/activate
> (omm_clip) pip install -r requirements.txt
```
kring 0.0.1
Zig library
Requires Python: >=3.11

# The `zig-kring` Library

Written in `zig` this library useable with many tools. It is a `python` module `kring`,
but can be used directly from `zig`.

**When building with zig you may have to `zig build` twice for dependancy resolution**

Better still use `./publish.sh` from the repository root.
showme-tree 1.0.7
A command-line tool that prints the folder and file structure of your VSCode project, excluding nested items of content in .gitignore.

# showme-tree

`showme-tree` is a command-line tool that prints the folder and file structure of your VSCode project, excluding files and folders specified in `.gitignore`.

## Installation

You can install `showme-tree` using pip:

```bash
pip install showme-tree
```

## Usage

Run the following command in a Python Virtual Environment to display the folder and file structure of your VSCode project:

```bash
showme-tree
```

The command works best in a version-controlled Git project. If you are not working a Git project, you'll be asked to manually input the path to the project root directory.

Example Output:

```
|---showme-folder-structure/
    |---LICENSE
    |---dist/
    |---showme-tree/
        |---__init__.py
        |---main.py
    |---README.md
    |---setup.py
    |---.gitignore
    |---myenv/
    |---.git/
```
LinkingLines 2.0.1
Uses Hough Transform and clustering to link line segments and for feature extraction of linear and radial structures
Requires Python: >=3.7


# Welcome to LinkingLines!

[![DOI](https://zenodo.org/badge/272334230.svg)](https://zenodo.org/badge/latestdoi/272334230)

[![PyPI](https://img.shields.io/pypi/v/LinkingLines.svg)](https://pypi.org/project/LinkingLines/)

[![ReadtheDocs](https://readthedocs.org/projects/LinkingLines/badge/)](https://linkinglines.readthedocs.io/)

[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)


## 1. Introduction
Welcome to the documentation for our Python module that performs the Hough
Transform on line data from a CSV, clusters it using Agglomerative Clustering,
and provides functionality to export the results into a CSV file.
This module also includes custom plotting scripts and feature extraction
methods to help you analyze and visualize your data effectively.

This code was used to create the results published in
[Kubo Hutchison et al., 2023](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GC010842).
Initially, it was designed to link together mapped dike segments in Cartesian space
to find their true lengths. This code can be applied to any linear features including
roads, fractures, and other types of linear data.

- **Data Clustering**: Apply Agglomerative Clustering to group similar data points, this
can be used for data reduction, analysis, and mapping .

- **Data Visualization**: Custom plotting scripts help you visualize and analyze
your data, making it easier to identify patterns and anomalies.

- **Feature Extraction**: Extract meaningful features from clustered data to
perform further analysis, such as linear or radial type features.


Full documentation can be found on [ReadTheDocs](https://linkinglines.readthedocs.io/en/latest/)

## 2. Installation
To use this module, make sure you have Python installed (preferably Python 3.x).
You can install the required packages using pip:

```bash
pip install linkinglines
```

## 3. Quick Start

```python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import linkinglines as ll

data=pd.read_csv('path/to/data')
theta,rho,xc,yc=ll.HoughTransform(data)
data['theta']=theta
data['rho']=rho

dtheta=2 #degrees
drho=500 #meters

dikeset, Z=ll.AggCluster(data)
lines,evaluation=examineCluster(data)
fig,ax=DotsLines(lines, ColorBy='AvgTheta')

```

Follow this indepth [tutorial](DemoLinkingLines.md) to get started!

You are now ready to utilize the power of Hough Line Transform, Agglomerative Clustering, and custom plotting in your data analysis projects. If you have any questions or need further assistance, please refer to the detailed documentation or contact our support team.

Happy coding!