Calculate mean of list of arrays with different lengths (useful for plotting progress of incomplete simulation runs)

np_tolerant_mean.py

import numpy as np

x = [1, 2, 3.5, 4]
y = [1, 2, 3, 3, 4, 5, 3]
z = [7, 8]
arrs = [x, y, z]

def tolerant_mean(arrs):
    # arrs = [x, y, z]
    lens = [len(i) for i in arrs]
    arr = np.ma.empty((np.max(lens),len(arrs)))
    arr.mask = True
    for idx, l in enumerate(arrs):
        arr[:len(l),idx] = l
    return arr.mean(axis = -1), arr.std(axis=-1)

y, error = tolerant_mean(arrs)

x = np.arange(len(y))+1

from matplotlib import pyplot as pl
fig, ax = pl.subplots(figsize=(4, 3.5))
ax.plot(x, y)
ax.fill_between(x, y-error, y+error, alpha=0.3)

pl.tight_layout()
pl.show()