Here's a technique that works: Python 2.4.2 (#5, Nov 21 2005, 23:08:11) [GCC 4.0.0 20041026 (Apple Computer, Inc. build 4061)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import numpy as np >>> a = np.array([0,4,0,11]) >>> b = np.array([-1,11,4,15]) >>> rangelen = b-a+1 >>> cumlen = rangelen.cumsum() >>> c = np.arange(cumlen[-1],dtype=np.int32) >>> c += np.repeat(a[1:]-c[cumlen[0:-1]], rangelen[1:]) >>> print c [ 4 5 6 7 8 9 10 11 0 1 2 3 4 11 12 13 14 15]
The basic idea is that the difference of your desired output from a simple range is an array with a bunch of constant values appended together, and that is what repeat() does. I'm assuming that you'll never have b < a. Notice the slight ugliness of prepending the elements at the beginning so that the cumsum starts with zero. (Maybe there is a cleaner way to do that.) This does create a second array (via the repeat) that is the same length as the result. If that uses too much memory, you could break up the repeat and update of c into segments using a loop. (You wouldn't need a loop for every a,b element -- do a bunch in each iteration.) -- Rick Raik Gruenberg wrote: > Hi there, > > perhaps someone has a bright idea for this one: > > I want to concatenate ranges of numbers into a single array (for > indexing). So I > have generated an array "a" with starting positions, for example: > > a = [4, 0, 11] > > I have an array b with stop positions: > > b = [11, 4, 15] > > and I would like to generate an index array that takes 4..11, then > 0..4, then > 11..15. > > In reality, a and b have 10000+ elements and the arrays to be > "sliced" are very > large so I want to avoid any for loops etc. Any idea how this could > be done? I > thought some combination of *repeat* and adding of *arange* should > do the trick > but just cannot nail it down. > > Thanks in advance for any hints! > > Greetings, > Raik _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://projects.scipy.org/mailman/listinfo/numpy-discussion
