Testing if all values in a numpy array are equal [duplicate]

Question

I have a numpy one dimensional array c that is supposed to be filled with the contents of a + b. I'm first executing a + b on a device using PyOpenCL.

I want to quickly determine the correctness of the result array c in python using numpy slicing.

This is what I currently have

def python_kernel(a, b, c):
    temp = a + b
    if temp[:] != c[:]:
        print "Error"
    else:
        print "Success!"

But I get the error:

ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()

But it seems a.any or a.all will just determine whether the values aren't 0.

What should I do if I want to test if all of the scalers in the numpy array temp are equal to every value in the numpy array c?

Answer 1

Why not just use numpy.array_equal(a1, a2)^[docs] from NumPy's functions?

Answer 2

np.allclose is a good choice if np.array data type is floats. np.array_equal does not always work properly. For example:

import numpy as np
def get_weights_array(n_recs):
    step = - 0.5 / n_recs
    stop = 0.5
    return np.arange(1, stop, step)

a = get_weights_array(5)
b = np.array([1.0, 0.9, 0.8, 0.7, 0.6])

Result:

>>> a
array([ 1. ,  0.9,  0.8,  0.7,  0.6])
>>> b
array([ 1. ,  0.9,  0.8,  0.7,  0.6])
>>> np.array_equal(a, b)
False
>>> np.allclose(a, b)
True

>>> import sys
>>> sys.version
'2.7.3 (default, Apr 10 2013, 05:13:16) \n[GCC 4.7.2]'
>>> np.version.version
'1.6.2'

Answer 3

You would call any on the result of the comparison: if np.any(a+b != c): or equivalently if np.all(a+b == c):. a+b != c creates an array of dtype=bool, and then any looks at that array to see if any member is True.

>>> import numpy as np
>>> a = np.array([1,2,3])
>>> b = np.array([4,5,2])
>>> c = a+b
>>> c
array([5, 7, 5]) # <---- numeric, so any/all not useful
>>> a+b == c
array([ True,  True,  True], dtype=bool) # <---- BOOLEAN result, not numeric
>>> all(a+b == c)
True

Having said all that, though, Amber's solution is probably faster since it doesn't have to create the whole boolean result array.