crab-lib 1.0.0

A Python library for analyzing GPS data from fishing grounds.

Crab-Lib

Crab-Lib is a Python library designed to simplify the processing, analysis, and visualization of GPS data, particularly for fishing grounds in the Gulf of Mexico. It includes tools for cleaning data, calculating distances, detecting clusters, and generating insightful visualizations. Whether you're a fisheries scientist, data analyst, or GIS enthusiast, Crab-Lib helps you make sense of complex geospatial datasets.

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Features

• Data I/O:
  • Load and save GPS data in CSV format.
  • Validate and handle missing or malformed data.
• Data Preprocessing:
  • Remove duplicates and invalid rows.
  • Filter data based on keywords in comments.
  • Standardize comment text for consistency.
• Geospatial Analysis:
  • Calculate great-circle distances using the Haversine formula.
  • Identify clusters of GPS points based on proximity.
  • Summarize datasets with averages and bounding boxes.
• Visualization:
  • Plot points, clusters, and density heatmaps.
  • Create interactive and informative visualizations.
• Utilities:
  • Convert coordinates from DMS to decimal degrees.
  • Calculate initial bearings between two GPS points.
  • Validate if a point lies within a bounding box.

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Table of Contents

1. Installation
2. Quick Start
3. Detailed Examples
   • Data I/O
   • Preprocessing
   • Analysis
   • Visualization
   • Utilities
4. API Reference
5. Troubleshooting
6. Contributing
7. License
8. Contact

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Installation

Install via Source

1. Clone the repository:

   git clone https://github.com/RedGloveProductions/crab-lib.git
   cd crab-lib
   

Install the library: pip install -e . Dependencies Crab-Lib requires the following Python packages: matplotlib >= 3.0.0 For development, additional tools like pytest and black are recommended. Quick Start

Here's a quick guide to get started with Crab-Lib: Load Data: from crab_lib.io import load_csv data = load_csv("fishing_data.csv") Clean Data: from crab_lib.preprocessing import clean_data cleaned_data = clean_data(data) Analyze Data: from crab_lib.analysis import calculate_distances, find_clusters distances = calculate_distances(cleaned_data) clusters = find_clusters(cleaned_data, radius=100) Visualize Data: from crab_lib.visualization import plot_points plot_points(cleaned_data) Detailed Examples

Data I/O Load and Save Data: from crab_lib.io import load_csv, save_csv

Load data from a CSV file

data = load_csv("fishing_data.csv")

Save cleaned data to a new file

save_csv("cleaned_data.csv", data) Preprocessing Clean, Filter, and Standardize Data: from crab_lib.preprocessing import clean_data, filter_data, standardize_comments

Remove duplicates and invalid rows

cleaned_data = clean_data(data)

Filter rows containing the keyword 'hotspot'

filtered_data = filter_data(cleaned_data, "hotspot")

Standardize comments for consistency

standardized_data = standardize_comments(filtered_data) Analysis Calculate Distances and Find Clusters: from crab_lib.analysis import calculate_distances, find_clusters, summarize_data

Calculate pairwise distances

distances = calculate_distances(cleaned_data)

Identify clusters within a 50 km radius

clusters = find_clusters(cleaned_data, radius=50)

Summarize the dataset

summary = summarize_data(cleaned_data) print(summary) Visualization Visualize Data: from crab_lib.visualization import plot_points, plot_clusters, create_heatmap

Plot individual points

plot_points(cleaned_data)

Plot clusters

plot_clusters(clusters)

Create a heatmap of point density

create_heatmap(cleaned_data) Utilities Convert Coordinates and Calculate Bearings: from crab_lib.utils import convert_coordinates, calculate_bearing, is_within_bounds

Convert DMS to decimal degrees

decimal_coord = convert_coordinates("25°46'26.5"N")

Calculate bearing between two points

bearing = calculate_bearing((25.774, -80.19), (27.345, -82.567)) print("Bearing:", bearing)

Check if a point is within bounds

bounds = (25.0, 26.0, -81.0, -79.0) print("Is within bounds:", is_within_bounds((25.774, -80.19), bounds)) API Reference

I/O Module load_csv(file_path: str) -> List[Dict[str, str]]: Load GPS data from a CSV file. save_csv(file_path: str, data: List[Dict[str, str]]) -> None: Save data to a CSV file. Preprocessing Module clean_data(data: List[Dict[str, str]]) -> List[Dict[str, str]]: Remove duplicates and invalid rows. filter_data(data: List[Dict[str, str]], keyword: str) -> List[Dict[str, str]]: Filter rows by keyword. standardize_comments(data: List[Dict[str, str]]) -> List[Dict[str, str]]: Standardize comment text. Analysis Module calculate_distances(data: List[Dict[str, float]]) -> List[Dict[str, float]]: Compute pairwise distances. find_clusters(data: List[Dict[str, float]], radius: float) -> List[List[Dict[str, float]]]: Detect clusters. summarize_data(data: List[Dict[str, float]]) -> Dict[str, float]: Summarize dataset metrics. Visualization Module plot_points(data: List[Dict[str, float]]) -> None: Scatter plot of GPS points. plot_clusters(clusters: List[List[Dict[str, float]]]) -> None: Visualize clusters. create_heatmap(data: List[Dict[str, float]], bins: int) -> None: Generate a heatmap. Utils Module convert_coordinates(dms: str) -> float: Convert DMS to decimal degrees. calculate_bearing(coord1: Tuple[float, float], coord2: Tuple[float, float]) -> float: Compute initial bearing. is_within_bounds(coord: Tuple[float, float], bounds: Tuple[float, float, float, float]) -> bool: Check if a point is in bounds. Troubleshooting

Issue: FileNotFoundError when loading a CSV. Solution: Verify the file path and ensure the file exists. Issue: ValueError when cleaning or processing data. Solution: Ensure your data matches the required format (x, y, comment). Contributing

Contributions are welcome! To contribute: Fork the repository. Create a feature branch. Submit a pull request with a clear description of changes. License

This project is licensed under the MIT License. Contact

Author: Your Name Email: [e-mail] GitHub: RedGloveProductions Project Links

GitHub Repository Issue Tracker

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This README.md includes the additional details and provides a comprehensive introduction to your library.