FastLine

FastLine is a Python module for performing geometric line operations. Implemented in C++ and bound to Python, it is optimized for speed, making it suitable for projects that require numerous line calculations.

Installation

You can install FastLine using pip:

pip install FastLine

Alternatively, install it directly from the source repository:

git clone https://github.com/MrGolden1/FastLine.git
cd FastLine
pip install .

Usage

Import the Line class from the FastLine module and create line instances using either two points or slope-intercept form.

from FastLine import Line

# Define a line by two points
line1 = Line(p1=(0, 0), p2=(10, 10))

# Define a line by its slope and intercept
line2 = Line(m=4, b=-1)

Methods

solve

Calculates the corresponding y for a given x or x for a given y.

>>> line1.solve(x=20)
20.0

>>> line2.solve(y=20)
5.25

distance_to

Computes the shortest distance from a given point to the line.

>>> line1.distance_to((20, 50))
21.213203435596427

>>> line2.distance_to((-15, 17))
18.91777875283397

crossed_by

Determines the side of the line on which a point lies. Returns -1, 0, or 1 indicating the opposite side, on the line, or a specific side, respectively. You will need to define the orientation of each side based on your application's context.

>>> line1.crossed_by((20, 50))  # Specific side
1

>>> line1.crossed_by((50, 50))  # On the line
0

>>> line1.crossed_by((-20, -50))  # Opposite side
-1

intersection

Finds the intersection point with another line. Returns None if the lines are parallel.

>>> line1.intersection(line2)
(0.3333333333333333, 0.3333333333333333)

Acknowledgements

• Intersection of Two Lines
• Distance from a Point to a Line
• Line Side Determination

Author

M. Ali Zarrinzadeh

• Email: ali.zarrinzadeh@gmail.com

Speed Benchmarks

FastLine has been benchmarked against pure Python, NumPy, and Numba implementations to evaluate its performance in checking intersections between M and N line segments.

Running the Benchmarks

1. Install Dependencies:

   Ensure you have the necessary dependencies installed:

   pip install numpy numba

2. Execute the Benchmark Script:

   Run the benchmark script to compare performance:

   python benchmark.py

Benchmark Results

The following table summarizes the benchmark results comparing FastLine with pure Python, NumPy, and Numba implementations:

Implementation	Time (seconds)
FastLine	0.1523
Pure Python	0.4266
NumPy (Non-Vectorized)	2.8718
Numba	0.6909

---

For any questions or contributions, feel free to contact the author or submit an issue on the GitHub repository.