root mean square in numpy and complications of matrix and arrays of numpy

Question

Can anyone direct me to the section of numpy manual where i can get functions to accomplish root mean square calculations ... (i know this can be accomplished using np.mean and

Answer 1

I use this for RMS, all using NumPy, and let it also have an optional axis similar to other NumPy functions:

import numpy as np   
rms = lambda V, axis=None: np.sqrt(np.mean(np.square(V), axis))

Answer 2

For rms, the fastest expression I have found for small x.size (~ 1024) and real x is:

def rms(x):
    return np.sqrt(x.dot(x)/x.size)

This seems to be around twice as fast as the linalg.norm version (ipython %timeit on a really old laptop).

If you want complex arrays handled more appropriately then this also would work:

def rms(x):
    return np.sqrt(np.vdot(x, x)/x.size)

However, this version is nearly as slow as the norm version and only works for flat arrays.

Answer 3

Try this:

U = np.zeros((N,N))
ind = 1
k = np.zeros(N)
k[:] = U[ind,:]

Answer 4

For the RMS, how about

norm(V)/sqrt(V.size)

Answer 5

I don't know why it's not built in. I like

def rms(x, axis=None):
    return sqrt(mean(x**2, axis=axis))

If you have nans in your data, you can do

def nanrms(x, axis=None):
    return sqrt(nanmean(x**2, axis=axis))

Answer 6

For the RMS, I think this is the clearest:

from numpy import mean, sqrt, square, arange
a = arange(10) # For example
rms = sqrt(mean(square(a)))

The code reads like you say it: "root-mean-square".