> > I gave two counterexamples of why. > The examples you gave aren't counterexamples. See below...
On Wed, May 19, 2010 at 7:06 PM, Darren Dale <dsdal...@gmail.com> wrote: > On Wed, May 19, 2010 at 4:19 PM, <josef.p...@gmail.com> wrote: > > On Wed, May 19, 2010 at 4:08 PM, Darren Dale <dsdal...@gmail.com> wrote: > >> I have a question about creation of numpy arrays from a list of > >> objects, which bears on the Quantities project and also on masked > >> arrays: > >> > >>>>> import quantities as pq > >>>>> import numpy as np > >>>>> a, b = 2*pq.m,1*pq.s > >>>>> np.array([a, b]) > >> array([ 12., 1.]) > >> > >> Why doesn't that create an object array? Similarly: > >> > Consider the use case of a person creating a 1-D numpy array: > np.array([12.0, 1.0]) array([ 12., 1.]) How is python supposed to tell the difference between > np.array([a, b]) and > np.array([12.0, 1.0]) ? It can't, and there are plenty of times when one wants to explicitly initialize a small numpy array with a few discrete variables. > >>>>> m = np.ma.array([1], mask=[True]) > >>>>> m > >> masked_array(data = [--], > >> mask = [ True], > >> fill_value = 999999) > >> > >>>>> np.array([m]) > >> array([[1]]) > >> > Again, this is expected behavior. Numpy saw an array of an array, therefore, it produced a 2-D array. Consider the following: > np.array([[12, 4, 1], [32, 51, 9]]) I, as a user, expect numpy to create a 2-D array (2 rows, 3 columns) from that array of arrays. > >> This has broader implications than just creating arrays, for example: > >> > >>>>> np.sum([m, m]) > >> 2 > >>>>> np.sum([a, b]) > >> 13.0 > >> > If you wanted sums from each object, there are some better (i.e., more clear) ways to go about it. If you have a predetermined number of numpy-compatible objects, say a, b, c, then you can explicitly call the sum for each one: > a_sum = np.sum(a) > b_sum = np.sum(b) > c_sum = np.sum(c) Which I think communicates the programmer's intention better than (for a numpy array, x, composed of a, b, c): > object_sums = np.sum(x) # <--- As a numpy user, I would expect a scalar out of this, not an array If you have an arbitrary number of objects (which is what I suspect you have), then one could easily produce an array of sums (for a list, x, of numpy-compatible objects) like so: > object_sums = [np.sum(anObject) for anObject in x] Performance-wise, it should be no more or less efficient than having numpy somehow produce an array of sums from a single call to sum. Readability-wise, it makes more sense because when you are treating objects separately, a *list* of them is more intuitive than a numpy.array, which is more-or-less treated as a single mathematical entity. I hope that addresses your concerns. Ben Root
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