On Feb 10, 2010, at 7:26 PM, David Carmean wrote: >> > > Got this to "work", but now it's revealed my lack of understanding of the > shape > of arrays; I'd hoped that the results would look like (be the same shape > as?) > the column_stack results.
You're misunderstanding what structured arrays / recarrays are. Imagine a structured array of N records with 3 fields A, B and C. The shape of this array is (N,), but each element of the array is a special numpy object (numpy.void) with three elements (named A, B and C. The basic differences with an array of shape (N,3), where you expect the three columns to correspond to the fields A, B, C are that: (1): the types of fields of a structured array are not necessarily homogeneous (you can have A int, B float and C string, eg) whereas for a standard array each column has the same type. (2): the organization in memory is slighlty different. Anyway, you're working with functions that return structured arrays, not standard arrays, so you end up with a 1D structured array. > I wanted to be able to take slices of the > results. Quite doable, depending on how you wanna slice: if you wanna take, say, the 2nd to 5th entries, just use [1:5]: the result will be a structured array with the same fields as the original. > I created the original arrays from a list of tuples of the form > > [(1265184061, 0.02), (1265184121, 0.0), (1265184181, 0.31), ] > > so the resulting arrays had the shape (n,2); What function did you use to create this array ? If you end up with a (n,2), something went probably wrong and you're dealing with a standard array. > these seemed easy to > manipulate by slicing, and my recollection was that this was a > useful format to feed mplotlib.plot. > > The result looks like: > > array([ (1265184061.0, 0.0, 0.029999999999999999, 152.0, 1.5600000000000001, > \ > 99.879999999999995, 0.02, 3.0, 0.0, 0.040000000000000001, > 0.070000000000000007, \ > 0.68999999999999995),\ > (1265184121.0, 0.0, 0.01, 148.0, 1.46, 99.950000000000003, 0.0, 0.0, 0.0, > 0.01, \ > 0.040000000000000001, 0.56000000000000005), ] ) > > with shape (n,) be more specific: dtype ? > These 1-dimensional results give me nice text output, I can't/don't know > how to slice them; Well, once again, that depends what you wanna do. Please be more specific. > this form may work for one of my use cases, but my > main use case is to reprocess this data--which is for one server--by > taking one field from about 60 servers worth of this data (saved to disk > as binary pickles) and plot them all to a single canvas. > > In other words, from sixty sets of this: > > tposix ldavg-15 ldavg-1 ldavg-5 > 1265184061.00 0.00 0.03 1.56 > 1265184121.00 0.00 0.01 1.46 > 1265184181.00 0.00 0.65 1.37 > > I need to collect and plot ldavg-1 as separate time-series plots. you can access each field independently as youarray["tposic"], yourarray["ldavg-15"], .... _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
