title: root mean square in numpy and complications of matrix and arrays of numpy
body: <p>Can anyone direct me to the section of numpy manual where i can get functions to accomplish root mean square calculations ... 
  (i know this can be accomplished using np.mean and np.abs .. isn't there a built in ..if no why?? .. just curious ..no offense)</p>
  
  <p>can anyone explain the complications of matrix and arrays (just in the following case):</p>
  
  <p><code>U</code> is a matrix(T-by-N,or u say T cross N) , <code>Ue</code> is another matrix(T-by-N)
  I define <code>k</code> as a numpy array</p>
  
  <p><code>U[ind,:]</code> is still matrix</p>
  
  <p>in the following fashion
  <code>k = np.array(U[ind,:])</code></p>
  
  <p>when I print <code>k</code> or type <code>k</code> in ipython</p>
  
  <p>it displays following</p>
  
  <pre><code>K = array ([[2,.3 .....
                ......
                  9]])
  </code></pre>
  
  <p>You see the double square brackets (which makes it multi-dim i guess)
  which gives it the shape = (1,N)</p>
  
  <p>but I can't assign it to array defined in this way</p>
  
  <pre><code>l = np.zeros(N)
  shape = (,N) or perhaps (N,) something like that
  
  l[:] = k[:]
  error:
  matrix dimensions incompatible
  </code></pre>
  
  <p>Is there a way to accomplish the vector assignment which I intend to do ... Please don't tell me do this <code>l = k</code> (that defeats the purpose ... I get different errors in program .. I know the reasons ..If you need I may attach the piece of code)</p>
  
  <p>writing a loop is the dumb way .. which I'm using for the time being ...</p>
  
  <p>I hope I was able to explain .. the problems I'm facing ..</p>
  
  <p>regards ...</p>