Plot MatrixProfile Discords - Anomalies

While we provide some default plotting functionality through visualize, it may not suite your needs. This example shows you how to create various plots displaying anomalies.

import matrixprofile as mp
import numpy as np
from matplotlib import pyplot as plt

%matplotlib inline

dataset = mp.datasets.load('motifs-discords-small')
window_size = 32

profile = mp.compute(dataset['data'], window_size)
profile = mp.discover.discords(profile)

Plot with Stars

This example shows how you can plot discords on the matrix profile with red stars.

# We have to adjust the matrix profile to match the dimensions of the original
# time series
mp_adjusted = np.append(profile['mp'], np.zeros(profile['w'] - 1) + np.nan)

# Create a plot with three subplots
fig, axes = plt.subplots(2, 1, sharex=True, figsize=(20,7))
axes[0].plot(np.arange(len(profile['data']['ts'])), profile['data']['ts'])
axes[0].set_title('Raw Data', size=22)

#Plot the Matrix Profile
axes[1].plot(np.arange(len(mp_adjusted)), mp_adjusted)
axes[1].set_title('Matrix Profile', size=22)

for discord in profile['discords']:
    x = discord
    y = profile['mp'][discord]

    axes[1].plot(x, y, marker='*', markersize=10, c='r')

plt.show()

Plot with Color

This example shows how you can plot on top of the original time series.

# We have to adjust the matrix profile to match the dimensions of the original
# time series
mp_adjusted = np.append(profile['mp'], np.zeros(profile['w'] - 1) + np.nan)

# Create a plot with three subplots
fig, ax = plt.subplots(1, 1, sharex=True, figsize=(20,7))
ax.plot(np.arange(len(profile['data']['ts'])), profile['data']['ts'])
ax.set_title('Raw Data', size=22)

for discord in profile['discords']:
    x = np.arange(discord, discord + profile['w'])
    y = profile['data']['ts'][discord:discord + profile['w']]

    ax.plot(x, y, c='r')

plt.show()

Plot with Heatmap

Display a colored heatmap over the original time series.

# We have to adjust the matrix profile to match the dimensions of the original
# time series
mp_adjusted = np.append(profile['mp'], np.zeros(profile['w'] - 1) + np.nan)

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(20, 7), gridspec_kw={'height_ratios': [3, 25]})
pos = axes[0].imshow([mp_adjusted,], aspect='auto', cmap='coolwarm')
axes[0].set_yticks([])

axes[1].plot(np.arange(len(profile['data']['ts'])), profile['data']['ts'])
axes[1].set_title('Raw Data', size=22)

cbar_ax = fig.add_axes([0.92, 0.36, 0.01, 0.5])
fig.colorbar(pos, orientation='vertical', cax=cbar_ax, use_gridspec=True)
plt.show()