This only allows for indexing with a constant value (e.g. x[0]).
While slices would be possible to implement, it's not clear how to
preserve type inference here. The current typing rule is:
Γ ⊢ x : τ Γ ⊢ a : Int Γ ⊢ b : Int
------------------------------------
Γ ⊢ x[a:b] : τ
However, tuples would require a different typing rule, and so we'd need
to defer type inference if τ is a tyvar. I'm not confident that this
won't change behaviour, so we leave as-is for now.
Signed-off-by: Jonathan Coates <jonathan.coates@oxionics.com>
Previously, any type would be accepted for the test expression,
leading to internal errors in the code generator if the passed
value wasn't in fact a bool.
array([...]), the constructor for NumPy arrays, currently has the
status of some weird kind of macro in ARTIQ Python, as it needs
to determine the number of dimensions in the resulting array
type, which is a fixed type parameter on which inference cannot
be performed.
This leads to an ambiguity for empty lists, which could contain
elements of arbitrary type, including other lists (which would
add to the number of dimensions).
Previously, I had chosen to make array([]) to be of completely
indeterminate type for this reason. However, this is different
to how the call behaves in host NumPy, where this is a well-formed
call creating an empty 1D array (or 2D for array([[], []]), etc.).
This commit adds special matching for (recursive lists of) empty
ListT AST nodes to treat them as scalar dimensions, with the
element type still unknown.
This also happens to fix type inference for embedding empty 1D
NumPy arrays from host object attributes, although multi-dimensional
arrays will still require work (see GitHub #1633).
GitHub: Fixes#1626.
Strided slicing of one-dimensional arrays (i.e. with non-trivial
steps) might have previously been working, but would have had
different semantics, as all slices were copies rather than a view
into the original data.
Fixing this in the future will require adding support for an index
stride field/tuple to our array representation (and all the
associated indexing logic).
GitHub: Fixes#1627.
This was a long-standing issue affecting both lists and
the new NumPy array implementation, just caused by the
generic inference passes not being run on the slice
subexpressions (and thus e.g. ints not being monomorphized).
GitHub: Fixes#1632.
This generates rather more code than necessary, but has
the advantage of automatically handling incomplete
multi-dimensional subscripts which still leave arrays
behind.
Left generic transpose (shape order inversion) for now, as that
would be less ugly if we implement forwarding to Python function
bodies for array function implementations.
Needs a runtime test case.
Still needs support through all the rest of the compiler, and
support for higher-dimensional arrays.
Alternatively, we could always assume ndarrays of ndarrays
are rectangular (i.e. ban array/list element types), and
detect mismatch at runtime. This might turn out to be
preferrable to be able to construct matrices from rows/columns.
`array()` is disallowed for no particularly good reason but
numpy API compatibility.
Before this commit, it displayed incorrect output if an error
appeared on 2nd run and beyond, and displayed messages for trying
to do "num32 -= num64" that made very little sense.
mwojcik
Jonathan Coates
pca006132
David Nadlinger
Sebastien Bourdeauducq
whitequark