how should i compare more than 2 numpy arrays?
import numpy
a = numpy.zeros((512,512,3),dtype=numpy.uint8)
b = numpy.zeros((512,512,3),dtype=numpy.uint8)
c = numpy.zeros((512,512,3),dtype=numpy.uint8)
if (a==b==c).all():
pass
this give a valueError, and i am not interested in comparing arrays two at a time.
For three arrays, you can check for equality among the corresponding elements between the first and second arrays and then second and third arrays to give us two boolean scalars and finally see if both of these scalars are True for final scalar output, like so -
np.logical_and( (a==b).all(), (b==c).all() )
For more number of arrays, you could stack them, get the differentiation along the axis of stacking and check if all of those differentiations are equal to zeros. If they are, we have equality among all input arrays, otherwise not. The implementation would look like so -
L = [a,b,c] # List of input arrays
out = (np.diff(np.vstack(L).reshape(len(L),-1),axis=0)==0).all()
a = np.random.randint(0,9,(4,5,3)); b = a.copy(); c = a.copy().For three arrays, you should really just compare them two at a time:
if np.array_equal(a, b) and np.array_equal(b, c):
do_whatever()
For a variable number of arrays, let's suppose they're all combined into one big array arrays. Then you could do
if np.all(arrays[:-1] == arrays[1:]):
do_whatever()
arrays a list or something.To expand on previous answers, I would use combinations from itertools to construct all pairs, then run your comparison on each pair. For example, if I have three arrays and want to confirm that they're all equal, I'd use:
from itertools import combinations
for pair in combinations([a, b, c], 2):
assert np.array_equal(pair[0], pair[1])
float arrays where you'd want to use allclose instead of array_equal; allclose is not transitive. @Elsewhere Maybe you could expand your answer based on that since the question doesn't explicitly ask for comparing for equality and neither restricts to int arrays.compare against first element of array-list:
import numpy as np
a = np.arange(3)
b = np.arange(3)
c = np.arange(3)
d = np.arange(4)
lst_eq = [a, b, c]
lst_neq = [a, b, d]
def all_equal(lst):
for arr in lst[1:]:
if not np.array_equal(lst[0], arr, equal_nan=True):
return False
return True
print('all_equal(lst_eq)=', all_equal(lst_eq))
print('all_equal(lst_neq)=', all_equal(lst_neq))
output
all_equal(lst_eq)= True
all_equal(lst_neq)= False
Combine everything into one array, calculate the absolute diff along the new axis and check if the maximum element along the new dimension is equal 0 or lower than some threshold. This should be quite fast.
import numpy as np
a = np.arange(3)
b = np.arange(3)
c = np.arange(3)
d = np.array([0, 1, 3])
lst_eq = [a, b, c]
lst_neq = [a, b, d]
def all_equal(lst, threshold = 0):
arr = np.stack(lst, axis=0)
return np.max(np.abs(np.diff(arr, axis=0))) <= threshold
print('all_equal(lst_eq)=', all_equal(lst_eq))
print('all_equal(lst_neq)=', all_equal(lst_neq))
output
all_equal(lst_eq)= True
all_equal(lst_neq)= False
one-liner solution:
arrays = [a, b, c]
all([np.array_equal(a, b) for a, b in zip(arrays, arrays[1:])])
We test the equality of consecutive pairs of arrays
This might work.
import numpy
x = np.random.rand(10)
arrays = [x for _ in range(10)]
print(np.allclose(arrays[:-1], arrays[1:])) # True
arrays.append(np.random.rand(10))
print(np.allclose(arrays[:-1], arrays[1:])) # False
