Elementwise Minimum

This is an example of computing the elementwise minimum from multiple input tensors of the same size.

from openmdao.api import Problem
from omtools.api import Group
import omtools.api as ot
import numpy as np


class ExampleElementwise(Group):
    def setup(self):

        m = 2
        n = 3
        # Shape of the three tensors is (2,3)
        shape = (m, n)

        # Creating the values for two tensors
        val1 = np.array([[1, 5, -8], [10, -3, -5]])
        val2 = np.array([[2, 6, 9], [-1, 2, 4]])

        # Declaring the two input tensors
        tensor1 = self.declare_input('tensor1', val=val1)
        tensor2 = self.declare_input('tensor2', val=val2)

        # Creating the output for matrix multiplication
        self.register_output('ElementwiseMin', ot.min(tensor1, tensor2))


prob = Problem()
prob.model = ExampleElementwise()
prob.setup(force_alloc_complex=True)
prob.run_model()

print('tensor1', prob['tensor1'].shape)
print(prob['tensor1'])
print('tensor2', prob['tensor2'].shape)
print(prob['tensor2'])
print('ElementwiseMin', prob['ElementwiseMin'].shape)
print(prob['ElementwiseMin'])

tensor1 (2, 3)
[[ 1.  5. -8.]
 [10. -3. -5.]]
tensor2 (2, 3)
[[ 2.  6.  9.]
 [-1.  2.  4.]]
ElementwiseMin (2, 3)
[[ 1.  5. -8.]
 [-1. -3. -5.]]