I have several pairs of arrays of measurements and the times at which the measurements were taken that I want to average. Unfortunately the times at which these measurements were taken isn't regular or the same for each pair.
My idea for averaging them is to create a new array with the value at each second then average these. It works but it seems a bit clumsy and means I have to create many unnecessarily long arrays.
Example Inputs
m1 = [0.4, 0.6, 0.2]
t1 = [0.0, 2.4, 5.2]
m2 = [1.0, 1.4, 1.0]
t2 = [0.0, 3.6, 4.8]
Generated Regular Arrays for values at each second
r1 = [0.4, 0.4, 0.4, 0.6, 0.6, 0.6, 0.2]
r2 = [1.0, 1.0, 1.0, 1.0, 1.4, 1.0]
Average values up to length of shortest array
a = [0.7, 0.7, 0.7, 0.8, 1.0, 0.8]
My attempt given list of measurement arrays measurements and respective list of time interval arrays times
def granulate(values, times):
count = 0
regular_values = []
for index, x in enumerate(times):
while count <= x:
regular_values.append(values[index])
count += 1
return np.array(regular_values)
processed_measurements = [granulate(m, t) for m, t in zip(measurements, times)]
min_length = min(len(m) for m in processed_measurements )
processed_measurements = [m[:min_length] for m in processed_measurements]
average_measurement = np.mean(processed_measurements, axis=0)
Is there a better way to do it, ideally using numpy functions?
This will average to closest second:
time_series = np.arange(np.stack((t1, t2)).max())
np.mean([m1[abs(t1-time_series[:,None]).argmin(axis=1)], m2[abs(t2-time_series[:,None]).argmin(axis=1)]], axis=0)
If you want to floor times to each second (with possibility of generalizing to more arrays):
m = [m1, m2]
t = [t1, t2]
m_t=[]
time_series = np.arange(np.stack(t).max())
for i in range(len(t)):
time_diff = time_series-t[i][:,None]
m_t.append(m[i][np.where(time_diff > 0, time_diff, np.inf).argmin(axis=0)])
average = np.mean(m_t, axis=0)
output:
[0.7 0.7 0.7 0.8 1. 0.8]