Exercise: Filtering n-dimensional arrays

5.12.3.1. Exercise: Filtering n-dimensional arrays#

We recorded this series of forces using a gait force platform, where the first axis corresponds to time and the second axis corresponds to the three force components \(F_x\), \(F_y\) and \(F_z\).

import numpy as np

forces = np.array(
    [
        [0.17619048, 0.82380952, 17.61904762],
        [0.21428571, 0.78571429, 21.42857143],
        [0.17619048, 0.82380952, 17.61904762],
        [0.17619048, 0.82380952, 17.61904762],
        [0.17619048, 0.82380952, 17.61904762],
        [0.32857143, 0.67142857, 32.85714286],
        [0.25238095, 0.74761905, 25.23809524],
        [0.17619048, 0.82380952, 17.61904762],
        [0.29047619, 0.70952381, 29.04761905],
        [0.32857143, 0.67142857, 32.85714286],
        [0.25238095, 0.74761905, 25.23809524],
        [0.21428571, 0.78571429, 21.42857143],
        [0.67142857, 0.32857143, 67.14285714],
        [8.1, -7.1, 810.0],
        [8.70952381, -7.70952381, 870.95238095],
        [8.02380952, -7.02380952, 802.38095238],
        [7.26190476, -6.26190476, 726.19047619],
        [7.75714286, -6.75714286, 775.71428571],
        [9.47142857, -8.47142857, 947.14285714],
        [9.85238095, -8.85238095, 985.23809524],
        [9.54761905, -8.54761905, 954.76190476],
        [8.63333333, -7.63333333, 863.33333333],
        [7.83333333, -6.83333333, 783.33333333],
        [6.76666667, -5.76666667, 676.66666667],
        [5.2047619, -4.2047619, 520.47619048],
        [2.95714286, -1.95714286, 295.71428571],
        [1.50952381, -0.50952381, 150.95238095],
        [0.48095238, 0.51904762, 48.0952381],
        [-0.01428571, 1.01428571, -1.42857143],
        [0.02380952, 0.97619048, 2.38095238],
        [0.17619048, 0.82380952, 17.61904762],
        [0.02380952, 0.97619048, 2.38095238],
        [0.06190476, 0.93809524, 6.19047619],
        [0.06190476, 0.93809524, 6.19047619],
        [0.06190476, 0.93809524, 6.19047619],
        [0.02380952, 0.97619048, 2.38095238],
        [0.06190476, 0.93809524, 6.19047619],
        [0.13809524, 0.86190476, 13.80952381],
    ]
)
_images/6bf2dd0b78f69f752098696996665208c61948f8c637a52550f207dcfa61475a.png

Write a code that calculates the mean of \(F_x\), but only during the weight support phase. We consider that the weight support phase consists in any sample where \(F_z > 10\).

Follow these steps:

  1. Isolate the weight support phase using \(F_z\);

  2. Isolate \(F_x\) during this phase;

  3. Calculate the average of \(F_x\).

Hide code cell content 
# Step 1

# We create a mask to keep only the weight support phase
is_weight_support_phase = forces[:, 2] > 10

# Step 2

# We isolate Fx during this phase
f_x = forces[is_weight_support_phase, 0]

# Step 3

# We calculate the average
print(np.mean(f_x))

3.528571428666667