# Ensemble average¶

A usual procedure employed to present the average pattern of a variable as a function of time or movement cycle across trials or across subjects is to show the ensemble average curve, which is a fancy name for (tipically) the mean $\pm$ 1 standard-deviation curve.

Let's simulate some data and explore different aesthetic variations to present the ensemble average.

In [1]:
# Import the necessary libraries
from __future__ import division, print_function
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import sys
sys.path.insert(1, r'./../functions')  # add to pythonpath

In [2]:
# slighly different data with slightly different duration (number of points)
from scipy.signal import butter, filtfilt
# Butterworth filter
b, a = butter(1, (5/(100/2)), btype = 'low')
y = np.empty([120, 20]) * np.NaN
for i in range(20):
t = np.arange(0, 100 + np.random.randint(-20, high=20)) / 100
y[0: t.size, i] = 2*np.sin(2 * np.pi * t) + np.random.randn(t.size) / 2 + t.size / 20
y[0: t.size, i] = filtfilt(b, a,  y[0: t.size, i])

plt.figure(figsize=(10, 5))
plt.plot(y)
plt.xlabel('data points')
plt.title('Plot of trials')
plt.show()


To calculate the mean and standard deviation across these trials the different trials must have the same number of points.
We can do this with the time normalization of data, where we will normalize each trial to the same percent cycle (from 0 to 100%):

In [3]:
from tnorm import tnorm
# yn, tn = tnorm(y, axis=0, step=1, k=3, smooth=0, mask=None, show=False, ax=None)
yn, tn = tnorm(y)
# plot of the normalized data
plt.figure(figsize=(10, 5))
plt.plot(yn)
plt.xlabel('Cycle [%]')
plt.title('Plot of normalized trials')
plt.show()


So, the trials have the same number of points, now we can calculate the mean and standard deviation curves and plot the ensemble average

In [4]:
ym, ysd = np.mean(yn, axis=1), np.std(yn, axis=1, ddof=1) # one line is all we need
# plot of the ensemble average
plt.figure(figsize=(10,5))
plt.errorbar(tn, ym, ysd, linewidth=2)
plt.xlabel('Cycle [%]')
plt.title('Plot of ensemble average')
plt.show()


And here are some aesthetic variations to show the ensemble average:

In [5]:
plt.rc('axes', labelsize=14,  titlesize=14)
plt.rc('xtick', labelsize=14)
plt.rc('ytick', labelsize=14)
plt.figure(figsize=(10,7))

ax1 = plt.subplot(221)
ax1.set_title('Errorbar every two data points and zero cap size')
ax1.errorbar(tn, ym, ysd, color = [0, 0, 1, 0.5], capsize=0, errorevery=2, lw=4)

ax2 = plt.subplot(222)
ax2.fill_between(tn, ym+ysd, ym-ysd, color = [0, 0, 1, 0.5])
ax2.plot(tn, ym, color = [0, 0, 1, .8], lw=2, label='Data')
ax2.legend(fontsize=14, loc='best', framealpha=.8)
ax2.grid()

ax3 = plt.subplot(223)
ax3.fill_between(tn, ym+ysd, ym-ysd, color=[0, 0, 1, 0.5], edgecolor='')
y2 = np.mean(ym) - ym + ym[0]
ax3.fill_between(tn, y2+ysd, y2-ysd, color=[1, 0, 0, 0.5], edgecolor='')
ax3.set_xlabel('Cycle [%]')
p1 = plt.Rectangle((0, 0), 1, 1, color=[0, 0, 1, 0.5])
p2 = plt.Rectangle((0, 0), 1, 1, color=[1, 0, 0, 0.5])
# fill_between() command creates a PolyCollection that is not supported by the legend()
ax3.legend([p1, p2], ['Group 1', 'Group 2'], fontsize=14, loc='best', framealpha=.8)

ax4 = plt.subplot(224)
ax4.errorbar(tn, ym, ysd, color = [0, 0, 1, 0.5], capsize=0, lw=1.5)
y2 = np.mean(ym) - ym + ym[0]
ax4.fill_between(tn, y2+ysd, y2-ysd, color=[1, 0, 0, 0.5], edgecolor='')
ax4.set_xlabel('Cycle [%]')
p1 = plt.Line2D((0, 1), (1, 1), color=[0, 0, 1, 1], lw=1.5)
p2 = plt.Rectangle((0, 0), 1, 1, color=[1, 0, 0, 0.5])
# fill_between() command creates a PolyCollection that is not supported by the legend()
ax4.legend([p1, p2], ['Group 1', 'Group 2'], fontsize=14, loc='best', framealpha=.8)
for i in range(4):
plt.gcf().add_subplot(2, 2, i + 1).set_xlim(0, 100)

plt.suptitle(r'$\mathrm{Aesthetic\,variations\,for\,the\,ensemble\,average}$', fontsize=24, y=1.04)
plt.tight_layout()
plt.show()


Instead of mean and standard deviation we can use median and the first and third quartiles:

In [6]:
ym = np.median(yn, axis=1)
yq1, yq3 = np.abs(ym-np.percentile(yn, 25, 1)), np.abs(np.percentile(yn, 75, 1)-ym)
# plot of the ensemble average
plt.figure(figsize=(10,5))
plt.errorbar(tn, ym, np.vstack((yq1, yq3)), linewidth=2)
plt.xlabel('Cycle [%]')
plt.title('Plot of ensemble average (median and quartiles)')
plt.show()


To download this notebook click on Source at the top menu or get it from the github repo.