__getitem__ and scalar for neo objects #490
Description
here an issue from #454 by @bjoern1001001.
I have a question about this, because I have been working on SpikeTrain.__getitem__ for the last days. It's rather general, but I noticed this here.
I noticed here that what you wrote works in Epoch for things like epoch[1], while it would raise an error in SpikeTrain, because when you create the new Epoch in __getitem__, times is a quantity scalar, NOT an array or Epoch. Creating a SpikeTrain like this in __getitem__ via SpikeTrain(times=super(...).__getitem, ...) would raise an error here saying it's testing the length of an unsized object. Is it supposed to be okay that epochs are created with a scalar quantity instead of a quantity array? And if that is the case, shouldn't it be converted to an array then in order to ensure consistency?
A little example of what happens:
epoch = neo.Epoch([1,2,3]*pq.s)
ep1 = epoch[1] # ep1 is now an Epoch with times=2*pq.s, not [2]*pq.s or array([2]*pq.s)
This is because the following happens in __getitem__:
times = super(...).__getitem__(1)
Times now becomes a scalar quantity, because numpy __getitem__ returns a scalar instead of an array when called with int index. From this pq.Quantity.getitem creates a quantity, because it is not yet a quantity.
obj = Epoch(times=times, ...)
This is not a problem, because there is no test about whether times is an array or not
...
return obj # This returns obj, with times=2*pq.s
Should it be like this or should a test for this be introduced?
Apart from that I'd like to know if any of you has deeper knowledge of __getitem__ because I noticed together with @JuliaSprenger that there sometimes a scalar quantity is returned instead of a SpikeTrain object. I've been trying to change this but it fails. Trying to copy what you did, I noticed that this works for Epoch, but not for SpikeTrain.
Again some code to illustrate what I mean:
What currently happens:
st1 = new SpikeTrain(times=[1,2,3]*pq.s, ...)
testobj = st1[1]
testobj is now a (scalar) quantity object, not a SpikeTrain
print(testobject) # This prints 2 s
new_st = st1[0:1]
new_st is a SpikeTrain, because numpy returns a SpikeTrain array
print(new_st) # This prints [1] s
If one would do the same as you did in Epoch for SpikeTrain, it would raise an error
st2 = st1[1] # This would raise an error
What happens SpikeTrain __getitem__:
times = super(...).__getitem__(1)
times is now a scalar quantity just like in Epoch
obj = SpikeTrain(times=times, ...)
This will raise an error in SpikeTrain.__new__ (line 220), because times is a scalar, and thus an unsized object: TypeError: len() of unsized object
On the other hand, calling things like st1[0:1] will work just fine, because numpy then returns an array (or a subclass, which is SpikeTrain in this case)
I then tried creating a new SpikeTrain if I get a quantity by creating a list with a single entry:
if not isinstance(obj, SpikeTrain):
obj = SpikeTrain(times=obj.magnitude, t_stop=self.t_stop, units=self.units) # Error happens here
This works for most cases, but it fails whenever calling sorted(np.concatenate((st1, st2))), because apparently in concatenate a kind of 'intermediate' SpikeTrain is generated that doesn't contain any attributes.
The error I get then is that SpikeTrain object [referring to self here] has no attribute t_stop.
If anybody knows more about this stuff, I'd really appreciate your help.