ndarrays extended to have an explicit “hypercross” of axes, each with names (possibly defaulted).
for methods in which an “axis” is denoted, an axis name may be used
indexing/slicing along a named axis returns that slicing, at that axis, along with slice(None) slicing along all other axes
for all arithmetic/binary-op matters under which dimension numbers and lengths must match, also the hypercrosses must be consistent
broadcasting will “inherit” labels from the super-hyper-cross (look at np.broadcast)
padding dimensions will insert “dummy” dimensions, eg:
a = datarray( np.random.randn(10,10), ('time', 'temp') )
a[:,None,:].axes --> ('time', None, 'temp')
axes may be transposed
each named axis has tick labels
numpy, fancy and slice-like indexing on each axis:
x.named_axis[...]
--> does any kind of numpy indexing on the axis
x.named_axis.at( *args )
--> returns essentially "fancy" indexing along the axis, at valid ticks in args
x.named_axis.t_slice( start, stop, [step])
--> where arguments are valid ticks, performs a slicing-like operation along the axis
mixed indexing on the array:
x.at( *args )
--> len(args) <= x.ndim -- for each indexing spec in args, perform that indexing
on the enumerated axes
x.t_slice( *args )
--> same as above, but perform t_slice slicing on the enumerated axes
The smart indexing implemented by Larry is very full featured. I believe the design of using lists to separating labels from integers in mixed indexing is a good choice (and necessary). However, I think it illustrates the potential confusion created by mixed indexing and is a good argument for discouraging/not allowing it.
slicing on an axis returns a new DataArray
arr = DataArray(np.random.randn(10,10), labels=('time', 'freq'))
arr.axis.time[:5] --> new DataArray with (time, freq) axes
However, slicing on the special slicing object “aix” returns a new Special Tuple (stuple).
Stuple:
s1 --> ( slice(0,4), slice(None) )
s2 --> ( slice(None), slice(3,10) )
s1 <compose> s2 --> ( slice(0,4), slice(3,10) )
Constructor prototype:
def __init__(self, *args, parent=None, index=None, name=None) ??
To chain slicing, the syntax would be like this:
arr.aix.time[:4].freq[3:8]
--OR--
arr[ arr.aix.time[:4].freq[3:8] ]
Chaining an axis on itself will not be implemented yet (possibly ever):
arr.aix.time[:4].time[:2] --> raise error