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))
which produces the correct result. However, my initial formulation was this:
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) #gaussian quadrature in vectorized form 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!
Your fault is in not reading the documentation:
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.
Answered By – BrenBarn
Answer Checked By – Mary Flores (BugsFixing Volunteer)