Inner Product between a Tensor and a Vector

This is an example of how to use the omtools inner function to compute the inner product between a tensor and a vector.

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


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

        m = 3
        n = 4
        p = 5

        # Shape of the vectors
        vec_shape = (m, )

        # Shape of the tensors
        ten_shape = (m, n, p)

        # Values for the two vectors
        vec1 = np.arange(m)

        # Number of elements in the tensors
        num_ten_elements = np.prod(ten_shape)

        # Values for the two tensors
        ten1 = np.arange(num_ten_elements).reshape(ten_shape)

        # Adding the vector and tensor to omtools
        vec1 = self.declare_input('vec1', val=vec1)
        ten1 = self.declare_input('ten1', val=ten1)

        # Tensor-Vector Inner Product specifying the first axis for
        # Vector and Tensor
        self.register_output(
            'TenVecInner',
            ot.inner(ten1, vec1, axes=([0], [0])),
        )


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

print('vec1', prob['vec1'].shape)
print(prob['vec1'])
print('ten1', prob['ten1'].shape)
print(prob['ten1'])
print('TenVecInner', prob['TenVecInner'].shape)
print(prob['TenVecInner'])

vec1 (3,)
[0. 1. 2.]
ten1 (3, 4, 5)
[[[ 0.  1.  2.  3.  4.]
  [ 5.  6.  7.  8.  9.]
  [10. 11. 12. 13. 14.]
  [15. 16. 17. 18. 19.]]

 [[20. 21. 22. 23. 24.]
  [25. 26. 27. 28. 29.]
  [30. 31. 32. 33. 34.]
  [35. 36. 37. 38. 39.]]

 [[40. 41. 42. 43. 44.]
  [45. 46. 47. 48. 49.]
  [50. 51. 52. 53. 54.]
  [55. 56. 57. 58. 59.]]]
TenVecInner (4, 5)
[[100. 103. 106. 109. 112.]
 [115. 118. 121. 124. 127.]
 [130. 133. 136. 139. 142.]
 [145. 148. 151. 154. 157.]]