On Di, 2015-11-10 at 10:24 -0500, Benjamin Root wrote: > Just pointing out np.loadtxt(..., ndmin=2) will always return a 2D > array. Notice that without that option, the result is effectively > squeezed. So if you don't specify that option, and you load up a CSV > file with only one row, you will get a very differently shaped array > than if you load up a CSV file with two rows. >
Oh, well I personally think that default squeeze is an abomination :).
Anyway, I just wanted to point out that it is two different possible
logics, and we have to pick one.
I have a slight preference for the indexing/array-like interpretation,
but I am aware that from a usage point of view the sequence one is
likely better.
I could throw in another option: Throw an explicit error instead of the
general.
Anyway, I *really* do not have an opinion about what is better.
Array-like would only suggest that you also accept buffer interface
objects or array_interface stuff. Which in this case is really
unnecessary I think.
- Sebastian
>
> Ben Root
>
>
> On Tue, Nov 10, 2015 at 10:07 AM, Irvin Probst
> <irvin.pro...@ensta-bretagne.fr> wrote:
> On 10/11/2015 14:17, Sebastian Berg wrote:
> Actually, it is the "sequence special case" type ;).
> (matlab does not
> have this, since matlab always returns 2-D I
> realized).
>
> As I said, if usecols is like indexing, the result
> should mimic:
>
> arr = np.loadtxt(f)
> arr = arr[usecols]
>
> in which case a 1-D array is returned if you put in a
> scalar into
> usecols (and you could even generalize usecols to
> higher dimensional
> array-likes).
> The way you implemented it -- which is fine, but I
> want to stress that
> there is a real decision being made here --, you
> always see it as a
> sequence but allow a scalar for convenience (i.e.
> always return a 2-D
> array). It is a `sequence of ints or int` type
> argument and not an
> array-like argument in my opinion.
>
> I think we have two separate problems here:
>
> The first one is whether loadtxt should always return a 2D
> array or should it match the shape of the usecol argument.
> From a CS guy point of view I do understand your concern here.
> Now from a teacher point of view I know many people expect to
> get a "matrix" (thank you Matlab...) and the "purity" of
> matching the dimension of the usecol variable will be seen by
> many people [1] as a nerdy useless heavyness noone cares of
> (no offense). So whatever you, seadoned numpy devs from this
> mailing list, decide I think it should be explained in the
> docstring with a very clear wording.
>
> My own opinion on this first problem is that loadtxt() should
> always return a 2D array, no less, no more. If I write
> np.loadtxt(f)[42] it means I want to read the whole file and
> then I explicitely ask for transforming the 2-D array
> loadtxt() returned into a 1-D array. Otoh if I write
> loadtxt(f, usecol=42) it means I don't want to read the other
> columns and I want only this one, but it does not mean that I
> want to change the returned array from 2-D to 1-D. I know this
> new behavior might break a lot of existing code as
> usecol=(42,) used to return a 1-D array, but
> usecol=((((42,)))) also returns a 1-D array so the current
> behavior is not consistent imho.
>
> The second problem is about the wording in the docstring, when
> I see "sequence of int or int" I uderstand I will have to cast
> into a 1-D python list whatever wicked N-dimensional object I
> use to store my column indexes, or hope list(my_object) will
> do it fine. On the other hand when I read "array-like" the
> function is telling me I don't have to worry about my object,
> as long as numpy knows how to cast it into an array it will be
> fine.
>
> Anyway I think something like that:
>
> import numpy as np
> a=[[[2,],[],[],],[],[],[]]
> foo=np.loadtxt("CONCARNEAU_2010.txt", usecols=a)
>
> should just work and return me a 2-D (or 1-D if you like)
> array with the data I asked for and I don't think "a" here is
> an int or a sequence of int (but it's a good example of why
> loadtxt() should not match the shape of the usecol argument).
>
> To make it short, let the reading function read the data in a
> consistent and predictible way and then let the user
> explicitely change the data's shape into anything he likes.
>
> Regards.
>
> [1] read non CS people trying to switch to numpy/scipy
>
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