title: Alternative to numpy roll without copying array
body: <p>I am doing something like following code and I am not happy with performance of np.roll() function. I am summing baseArray and otherArray, where baseArray is rolled by one element in each iteration. But I do not need the copy of the baseArray when I roll it, I would rather prefer a view such that for example when I sum baseArray with other array and if baseArray was rolled twice then the 2nd element of basearray is summed with 0th element of otherArray, 3rd element of baseArray is summed with 1st of otherArray etc. </p>
  
  <p>I.E. to achieve the same result as with np.roll() but without copying the array.</p>
  
  <pre><code>import numpy as np
  from numpy import random
  import cProfile
  
  def profile():
      baseArray = np.zeros(1000000)
      for i in range(1000):
          baseArray= np.roll(baseArray,1)
          otherArray= np.random.rand(1000000)
          baseArray=baseArray+otherArray
  
  cProfile.run('profile()')
  </code></pre>
  
  <p>output (note 3rd row - the roll function):</p>
  
  <pre><code>         9005 function calls in 26.741 seconds
  
     Ordered by: standard name
  
     ncalls  tottime  percall  cumtime  percall filename:lineno(function)
          1    5.123    5.123   26.740   26.740 &lt;ipython-input-101-9006a6c0d2e3&gt;:5(profile)
          1    0.001    0.001   26.741   26.741 &lt;string&gt;:1(&lt;module&gt;)
       1000    0.237    0.000    8.966    0.009 numeric.py:1327(roll)
       1000    0.004    0.000    0.005    0.000 numeric.py:476(asanyarray)
          1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}
       1000   12.650    0.013   12.650    0.013 {method 'rand' of 'mtrand.RandomState' objects}
       1000    0.005    0.000    0.005    0.000 {method 'reshape' of 'numpy.ndarray' objects}
       1000    6.390    0.006    6.390    0.006 {method 'take' of 'numpy.ndarray' objects}
       2000    1.345    0.001    1.345    0.001 {numpy.core.multiarray.arange}
       1000    0.001    0.000    0.001    0.000 {numpy.core.multiarray.array}
       1000    0.985    0.001    0.985    0.001 {numpy.core.multiarray.concatenate}
          1    0.000    0.000    0.000    0.000 {numpy.core.multiarray.zeros}
          1    0.000    0.000    0.000    0.000 {range}
  </code></pre>