forcelayout 1.0.5

Force-directed layout algorithms for Python

Force Directed Layout Algorithms for Python

This package provides 3 force directed layout algorithms implemented in Python.

Basic Usage

These algorithms are designed for the visualisation of high-dimensional datasets using force-directed graph drawing methods. Datasets are expected to be a numpy array with shape (N, D) where N is the size of the dataset and D is the number of dimensions.

# other imports
import matplotlib.pyplot as plt
import numpy as np

# import the package
import forcelayout as fl

# Need a dataset to visualise
dataset = np.array([[1, 1],
                    [1, 3],
                    [2, 2]])

# Need to use the brute force algorithm on a dataset this small
# (not recommended for larger datasets)
layout = fl.draw_spring_layout(dataset=dataset, algorithm=fl.SpringForce)

plt.show()

Functions

fl.draw_spring_layout():

Draw a spring layout diagram of the dataset. Returns the instance of the algorithm.

• dataset: np.ndarray: 2d array of the data to lay out
• algorithm: fl.BaseSpringLayout: The algorithm class to draw the spring layout diagram. One of the following:
  • fl.SpringForce
  • fl.NeighbourSampling
  • fl.Hybrid
  • fl.Pivot
• iterations: int: number of force iterations to perform on the layout (when using the Hybrid algorithm this is the number of force iterations performed on the original sample).
• hybrid_remainder_layout_iterations: int: number of force iterations to perform on each child of the remainder subset when using the Hybrid or Pivot algorithm.
• hybrid_refine_layout_iterations: int: number of force iterations to perform on the whole layout after the remainder layout stage.
• pivot_set_size: int: number of pivot nodes if algorithm=fl.Pivot.
• sample_set_size: int: size of random sample set.
• neighbour_set_size: int: size of neighbour set for neighbour sampling stages.
• distance: Callable[[np.ndarray, np.ndarray], float]: function to determine the high dimensional distance between two datapoints of shape (1, D) and return a float representing the dissimilarity.
• alpha: float: opacity of nodes in the diagram in range [0-1].
• size: float: size of node to draw passed to matplotlib.pyplot.scatter(s=size).
• color_by: Callable[[np.ndarray], float]: function to colour nodes by converting a (1, D) datapoint to a float to be expressed through the color_map.
• color_map: str: string name or matplotlib colour map to use with color_by function.
• annotate: Callable[[Node, int], str]: callback function for annotating nodes when hovered, given the node being hovered and the index in dataset.
• algorithm_highlights: bool: allow on click of data point to highlight important nodes for the algorithm.
  • fl.NeighbourSampling: neighbour nodes.
  • fl.Hybrid: sample set nodes.
  • fl.Pivot: pivot nodes.

fl.draw_spring_layout_animated()

Draw a spring layout diagram of the data using the given algorithm. Returns a matplotlib.animation.FuncAnimation. Call plt.show() to view the animation. Parameters are the same as fl.draw_spring_layout() with one extra:

• interval: int: milliseconds between each frame. If this is set low it will be limited by the run time of the algorithm.

fl.spring_layout()

Create an instance of a spring layout algorithm without running it. Uses the same parameters as fl.draw_spring_layout without ones specific to drawing.