mean

Builtin index · Core maths

result = mean(x, [on_invalid=])

Input: x, on_invalid · Output: result

What it is

Arithmetic mean of every element of x — a scalar, Vec, or Mat/Signal. A boolean mask counts as 0/1, so mean(x <= c) is the fraction of elements satisfying the condition (an empirical CDF at c). With no other argument, flattens x to one scalar Num (errors on an empty vector). On a Mat, the optional axis= keyword (0 or 1) computes the mean along just that dimension, returning a length-cols or length-rows Vec — see row_mean below for a dedicated column-Mat-returning version of the row case. on_invalid= ("propagate"/"ignore"/"error", see this section's intro) controls NaN/Inf handling; under "ignore" the divisor is the count of elements that survive filtering, not the original length.

Overloads

One call shape — this function is not overloaded.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

readings = [2.1, 2.3, 2.2, 2.6, 2.4]
print("{len(readings)} readings, average {mean(readings):.2f}")

5 readings, average 2.32

from start-here

  h = spectral_entropy(emg_signal, 256, 128)
  print("mean spectral entropy: {mean(h)}")

from numerics-dsp

x = chisquare(4, 1000, seed=1)
print("sample mean: {mean(x)}")   # converges to k=4, the distribution's own mean

sample mean: 4.108075

from numerics-dsp

runs = table("
    channel, baseline, treated
    1,       0.412,    0.240
    2,       0.508,    0.301
    3,       0.377,    0.219
")
print(runs)
print(mean(runs.baseline))

channel  baseline  treated
-------  --------  -------
      1     0.412     0.24
      2     0.508    0.301
      3     0.377    0.219
0.432333

from collections-strings