Matplotlib Time Series Histogram

Introduction

This lab demonstrates how to efficiently visualize large numbers of time series in a way that could potentially reveal hidden substructure and patterns that are not immediately obvious and display them in a visually appealing way.

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Skills Graph

Import Required Libraries

In this step, we will import the required libraries for this lab.

import time
import matplotlib.pyplot as plt
import numpy as np

Generate Data

In this step, we will generate multiple sinusoidal "signal" series that are buried under a larger number of random walk "noise/background" series. We will generate unbiased Gaussian random walks and sinusoidal signals.

## Fix random state for reproducibility
np.random.seed(19680801)

## Make some data; a 1D random walk + small fraction of sine waves
num_series = 1000
num_points = 100
SNR = 0.10  ## Signal to Noise Ratio
x = np.linspace(0, 4 * np.pi, num_points)

## Generate unbiased Gaussian random walks
Y = np.cumsum(np.random.randn(num_series, num_points), axis=-1)

## Generate sinusoidal signals
num_signal = round(SNR * num_series)
phi = (np.pi / 8) * np.random.randn(num_signal, 1)  ## small random offset
Y[-num_signal:] = (
    np.sqrt(np.arange(num_points))  ## random walk RMS scaling factor
    * (np.sin(x - phi)
       + 0.05 * np.random.randn(num_signal, num_points))  ## small random noise
)

Visualize Data with Line Plot

In this step, we will visualize the generated data with a line plot.

## Plot series using `plot` and a small value of `alpha`.
## With this view, it is very difficult to observe the sinusoidal behavior because of how many overlapping series there are.
## It also takes a bit of time to run because so many individual artists need to be generated.
tic = time.time()
plt.plot(x, Y.T, color="C0", alpha=0.1)
toc = time.time()
plt.title("Line plot with alpha")
plt.show()
print(f"{toc-tic:.3f} sec. elapsed")

Visualize Data with 2D Histogram - Log Color Scale

In this step, we will convert the multiple time series into a histogram. Not only will the hidden signal be more visible, but it is also a much quicker procedure. We will plot (x, y) points in 2D histogram with a log color scale.

tic = time.time()

## Linearly interpolate between the points in each time series
num_fine = 800
x_fine = np.linspace(x.min(), x.max(), num_fine)
y_fine = np.concatenate([np.interp(x_fine, x, y_row) for y_row in Y])
x_fine = np.broadcast_to(x_fine, (num_series, num_fine)).ravel()

## Plot (x, y) points in 2d histogram with log colorscale
## It is pretty evident that there is some kind of structure under the noise
## You can tune vmax to make signal more visible
cmap = plt.colormaps["plasma"]
cmap = cmap.with_extremes(bad=cmap(0))
h, xedges, yedges = np.histogram2d(x_fine, y_fine, bins=[400, 100])
pcm = plt.pcolormesh(xedges, yedges, h.T, cmap=cmap,
                         norm="log", vmax=1.5e2, rasterized=True)
plt.colorbar(pcm, label="## points", pad=0)
plt.title("2D Histogram and Log Color Scale")
plt.show()

toc = time.time()
print(f"{toc-tic:.3f} sec. elapsed")

Visualize Data with 2D Histogram - Linear Color Scale

In this step, we will visualize data with a linear color scale.

## Same data but on linear color scale
pcm = plt.pcolormesh(xedges, yedges, h.T, cmap=cmap,
                         vmax=1.5e2, rasterized=True)
plt.colorbar(pcm, label="## points", pad=0)
plt.title("2D Histogram and Linear Color Scale")
plt.show()

Summary

In this lab, we learned how to efficiently visualize large numbers of time series in a way that could potentially reveal hidden substructure and patterns that are not immediately obvious and display them in a visually appealing way. We generated multiple sinusoidal "signal" series that are buried under a larger number of random walk "noise/background" series, and we visualized the data with a line plot and 2D histogram with log and linear color scales.