linkinglines 2.1.1

Process linear geospatial data and link them using the Hough Transform and Agglomerative Clustering

Welcome to LinkingLines!

Read the Full documentation on ReadtheDocs!

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. 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

2. Installation

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

pip install linkinglines

3. Quick Start

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

data=ll.readFile('path/to/data')

dtheta=2 #degrees
drho=500 #meters

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

We have three examples:

1. Indepth tutorial with hough transform, clustering, and feature extraction using Spanish Peaks Data CSV file.
2. Hough Transform and feature extraction on Venus lineament data shape file.
3. Hough transform on fracture data geoJSON.

Data from:

1. https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022GC010842
2. https://pubs.usgs.gov/sim/3121/
3. https://doi.org/10.5281/zenodo.7919843

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.

4. Contributing Guidelines

Thank you for your interest in contributing to linkinglines. Please feel free to open up issues with bugs or requested features. Any contributions you make will benefit everybody else and are greatly appreciated.

We recommend using a virutal environment to manage packages virtualenv, see here.

# Install virtualenv if you haven't already
pip install virtualenv

# Navigate to the project directory
cd path/to/LinkingLines

# Create a virtual environment
virtualenv venv

# Activate the virtual environment
# On Windows
venv\Scripts\activate
# On Unix or MacOS
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

If you would like to contribute code please do so in a seperate branch and open up an issue describing your contribution.

git clone [e-mail]:aikubo/LinkingLines.git
git checkout my-development-branch

Before submitting your pull request please verify the following:

1. Code is documented in NumPy Docstring Style
2. Code is tested and passes test
   • To run the tests please go to "/tests" and run pytest
   • Add your test code to any file with the name test
   • More here on pytest and testing practices
3. Open an issue and pull request

After your pull request the code will be reviewed by maintainers. After the sucessful merge the documentation will be regenerated.