# [SOLVED] Elementwise multiplication of several arrays in Python Numpy

## Issue

Coding some Quantum Mechanics routines, I have discovered a curious behavior of Python’s NumPy. When I use NumPy’s multiply with more than two arrays, I get faulty results. In the code below, i have to write:

``````f = np.multiply(rowH,colH)
A[row][col]=np.sum(np.multiply(f,w))
``````

``````A[row][col]=np.sum(np.multiply(rowH, colH, w))
``````

which does not produce an error message, but the wrong result. Where is my fault in thinking that I could give three arrays to numpy’s multiply routine?

Here is the full code:

``````from numpy.polynomial.hermite import Hermite, hermgauss
import numpy as np
import matplotlib.pyplot as plt

dim = 3
x,w = hermgauss(dim)
A = np.zeros((dim, dim))
#build matrix
for row in range(0, dim):
rowH = Hermite.basis(row)(x)
for col in range(0, dim):
colH = Hermite.basis(col)(x)
f = np.multiply(rowH,colH)
A[row][col]=np.sum(np.multiply(f,w))
print(A)
``````

::NOTE:: this code only runs with NumPy 1.7.0 and higher!

## Solution

`numpy.multiply(x1, x2[, out])`
`multiply` takes exactly two input arrays. The optional third argument is an output array which can be used to store the result. (If it isn’t provided, a new array is created and returned.) When you passed three arrays, the third array was overwritten with the product of the first two.