<｜begin▁of▁sentence｜>### Instruction:
As an ecologist, you have been tracking two different animal species in their respective habitats over a period of time. You have represented the spatial coordinates of their movement patterns in their habitats as two separate matrices. Now, suppose, for comparative analysis between these species and understanding the influences of their habitats on their behaviors, you want to align these two separate sets of coordinates representing the species' movements.  

The challenge here is, how can you identify the best suitable alignment between these two different sets of coordinates? In other words, what could be the ideal linear transformation that would precisely map each point from the first species' movement coordinates onto its corresponding point in the second species' movement coordinates? This transformation should be orthogonal to preserve the distances and angles between the points. 

Just a note, we are not looking for any guidance on solving this challenge, nor we are providing any data at this point. We are just rephrasing a theoretical concept into a practical ecological scenario.
### Response:
import numpy as np
from graspologic.align import OrthogonalProcrustes

# Given two matrices X and Y of equal shape (n, d)
X = np.array([[1, 2], [3, 4], [5, 6]])
Y = np.array([[4, -2], [3, 5], [6, 1]])

# Instantiate the OrthogonalProcrustes class
op = OrthogonalProcrustes()

# Learn the orthogonal matrix Q_ that most closely maps X to Y
op.fit(X, Y)

# Transform the first dataset X using the learned orthogonal matrix
X_prime = op.transform(X)

# Print the transformed dataset
print('Transformed X:', X_prime)
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