PyDLC - Density Line Charts with Python

Python implementation of the Density Line Chart (Moritz & Fisher, 2018) to visualize time series collections.

Installation

Python Package Index

$ pip install pydlc

Requirements

• numpy
• matplotlib

Usage

Example

The following example shows how to import and use the dense_lines plotting function.

import numpy as np
import matplotlib.pyplot as plt
from pydlc import dense_lines

# Generate random synthetic time series
x = np.linspace(0, 90, 25)
ys = []
for _ in range(10000):
    ys.append(np.random.randn(1)*np.exp(-x/100))

# Plot here
fig, axs = plt.subplots(1, 2, figsize=(8, 3), sharey=True, sharex=True)
axs[0].plot(x, np.array(ys).T, lw=1)  # this is slow and cluttered
axs[0].set_title('Line Chart')
im = dense_lines(ys, x=x, ax=axs[1], cmap='magma')  # this is fast and clean
axs[1].set_title('Density Lines Chart')
fig.colorbar(im)
fig.tight_layout()
plt.show()

Arguments

• ys (list of 1darray): The lines to plot. Can also be passed as a 2darray.
• x (1darray, optional): The x values corresponding to the data passed with ys. If not provided, range(0, len(ys)) is used.
• ax (matplotlib axes, optional): The axes to plot on. If not provided a new figure will be created.
• ny (int, optional): The vertical grid size. Higher values yield a smoother density estimation. Default: 100.
• y_pad (float, optional): The padding fraction to set the grid limits past the data values. Must be greater than 0. Default: 0.01.
• normalize (bool, optional): Normalize the plot so the density is between 0 and 1. Default: True.
• **kwargs: Arbitrary keyword arguments to pass to plt.imshow().

Limitations

• All series to be included in the density estimation and passed in the ys argument must have the same length.
• The vertical grid size can be adjusted with the ny parameter. Higher values of ny yield a smoother density visualization. However, the horizontal grid size is currently limited to the same size as the input sequences and there is no parameter to adjust it (yet).

Algorithm

This graphical abstract explains the algorithm (source).