title: Recursively defining an N-dimensional numpy array
body: <p>I am trying to recursively define a numpy array of N dimensions. After researching for several hours, I have came across a couple of ways this might work (np.append and np.concatenate), however neither of these has given me the desired output. I've been getting either:
  <code>[-0.6778734  -0.73517866 -0.73517866  0.6778734 ]</code> (1-d array) or
  <code>[array([-0.6778734 , -0.73517866]), array([-0.73517866,  0.6778734 ])]</code> (a list of arrays)</p>
  
  <p>My Input: </p>
  
  <pre><code>[(1.2840277121727839, array([-0.6778734, -0.73517866])), 
      (0.049083398938327472, array([-0.73517866, 0.6778734 ]))]
  </code></pre>
  
  <p>Desired output: </p>
  
  <pre><code>array([-0.6778734, -0.73517866], [-0.73517866, 0.6778734])
  </code></pre>
  
  <p>Is it possible to create a numpy array from arrays, because converting them to lists and back to arrays seems computationally inefficient?</p>
  
  <p>Thanks in advance!</p>