8000 Some improvements to type hints (#230) · data-apis/array-api@30a317f · GitHub
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Some improvements to type hints (#230)
* Fix alphabetical function ordering in the linear algebra extension * Update some type hints in the spec These come from comments on the NumPy pull request numpy/numpy#18585. * Clarify that the where() result type is only based on the last two arguments * Use Literal[] for the qr() mode argument type hint * Add bool and int to the asarray type hints * Fix a typo in the __setitem__ annotations
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‎spec/API_specification/array_object.md

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@@ -780,7 +780,7 @@ The `matmul` function must implement the same semantics as the built-in `@` oper
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- **self**: _<array>_
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- array instance. Should have a numeric data type. Must have at least one dimension. If `self` is one-dimensional having shape `(M)` and `other` has more than one dimension, `self` must be promoted to a two-dimensional array by prepending `1` to its dimensions (i.e., must have shape `(1, M)`). After matrix multiplication, the prepended dimensions in the returned array must be removed. If `self` has more than one dimension (including after vector-to-matrix promotion), `self` must be compatible with `other` (see {ref}`broadcasting`). If `self` has shape `(..., M, K)`, the innermost two dimensions form matrices on which to perform matrix multiplication.
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- array instance. Should have a numeric data type. Must have at least one dimension. If `self` is one-dimensional having shape `(M)` and `other` has more than one dimension, `self` must be promoted to a two-dimensional array by prepending `1` to its dimensions (i.e., must have shape `(1, M)`). After matrix multiplication, the prepended dimensions in the returned array must be removed. If `self` has more than one dimension (including after vector-to-matrix promotion), `self` must be compatible with `other` (see {ref}`broadcasting`). If `self` has shape `(..., M, K)`, the innermost two dimensions form matrices on which to perform matrix multiplication.
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- **other**: _<array>_
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- if either `self` or `other` is a zero-dimensional array.
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- if `self` is a one-dimensional array having shape `(N)`, `other` is a one-dimensional array having shape `(M)`, and `N != M`.
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- if `self` is an array having shape `(..., M, K)`, `other` is an array having shape `(..., L, N)`, and `K != L`.
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- if `self` is an array having shape `(..., M, K)`, `other` is an array having shape `(..., L, N)`, and `K != L`.
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(method-__mod__)=
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### \_\_mod\_\_(self, other, /)
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#### Parameters
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- **self**: _<array;>_
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- **self**: _<array>_
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- array instance.
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‎spec/API_specification/creation_functions.md

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@@ -57,7 +57,7 @@ Convert the input to an array.
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#### Parameters
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- **obj**: _Union\[ float, NestedSequence\[ bool | int | float ], SupportsDLPack, SupportsBufferProtocol ]_
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- **obj**: _Union\[ <array>, bool, int, float, NestedSequence\[ bool | int | float ], SupportsDLPack, SupportsBufferProtocol ]_
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- Object to be converted to an array. Can be a Python scalar, a (possibly nested) sequence of Python scalars, or an object supporting DLPack or the Python buffer protocol.
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‎spec/API_specification/manipulation_functions.md

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#### Parameters
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- **arrays**: _Tuple\[ <array>, ... ]_
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- **arrays**: _Union\[Tuple\[ <array>, ... ], List\[ <array> ] ]_
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- input arrays to join. The arrays must have the same shape, except in the dimension specified by `axis`.
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#### Parameters
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- **arrays**: _Tuple\[ <array>, ... ]_
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- **arrays 8000 **: _Union\[Tuple\[ <array>, ... ], List\[ <array> ] ]_
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- input arrays to join. Each array must have the same shape.
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‎spec/API_specification/searching_functions.md

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- input array.
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- **axis**: _int_
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- **axis**: _Optional\[ int ]_
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- axis along which to search. If `None`, the function must return the index of the maximum value of the flattened array. Default: `None`.
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- input array.
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- **axis**: _int_
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- **axis**: _Optional\[ int ]_
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- axis along which to search. If `None`, the function must return the index of the minimum value of the flattened array. Default: `None`.
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- **out**: _<array>_
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- an array with elements from `x1` where `condition` is `True`, and elements from `x2` elsewhere. The returned array must have a data type determined by {ref}`type-promotion` rules.
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- an array with elements from `x1` where `condition` is `True`, and elements from `x2` elsewhere. The returned array must have a data type determined by {ref}`type-promotion` rules with the arrays `x1` and `x2`.

‎spec/extensions/linear_algebra_functions.md

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@@ -465,7 +465,7 @@ Computes th A3E2 e qr factorization of a matrix (or a stack of matrices), where `q` is
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- input array having shape `(..., M, N)` and whose innermost two dimensions form `MxN` matrices. Should have a floating-point data type.
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- **mode**: _str_
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- **mode**: _Literal\[ 'reduced', 'complete' ]_
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- factorization mode. Should be one of the following modes:
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#### Returns
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- **out**: _Union\[ <array>, Tuple\[ <array>, ... ] ]_
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- **out**: _Tuple\[ <array>, <array>, <array> ]_
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- a namedtuple `(u, s, vh)` whose
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Each returned array must have the same floating-point data type as `x`.
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(function-linalg-tensordot)=
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### linalg.tensordot(x1, x2, /, *, axes=2)
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Alias for {ref}`function-tensordot`.
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### linalg.svdvals(x, /)
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- an array with shape `(..., K)` that contains the vector(s) of singular values of length `K`. For each vector, the singular values must be sorted in descending order by magnitude, such that `s[..., 0]` is the largest value, `s[..., 1]` is the second largest value, et cetera. The first `x.ndim-2` dimensions must have the same shape as those of the input `x`. The returned array must have the same floating-point data type as `x`.
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(function-linalg-tensordot)=
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### linalg.tensordot(x1, x2, /, *, axes=2)
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Alias for {ref}`function-tensordot`.
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(function-linalg-trace)=
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### linalg.trace(x, /, *, offset=0)
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