scirs2_numpy/
lib.rs

1//! This crate provides Rust interfaces for [NumPy C APIs][c-api],
2//! especially for the [ndarray][ndarray] class.
3//!
4//! It uses [`pyo3`] for Rust bindings to CPython, and uses
5//! [`ndarray`] as the Rust matrix library.
6//!
7//! To resolve its dependency on NumPy, it calls `import numpy.core` internally.
8//! This means that this crate should work if you can use NumPy in your Python environment,
9//! e.g. after installing it by `pip install numpy`. It does not matter whether you use
10//! the system environment or a dedicated virtual environment.
11//!
12//! Loading NumPy is done automatically and on demand. So if it is not installed, the functions
13//! provided by this crate will panic instead of returning a result.
14//!
15#![cfg_attr(
16    feature = "nalgebra",
17    doc = "Integration with [`nalgebra`] is provided via an implementation of [`ToPyArray`] for [`nalgebra::Matrix`] to convert nalgebra matrices into NumPy arrays
18as well as the [`PyReadonlyArray::try_as_matrix`] and [`PyReadwriteArray::try_as_matrix_mut`] methods to treat NumPy array as nalgebra matrix slices.
19"
20)]
21//! # Example
22//!
23//! ```
24//! use numpy::pyo3::Python;
25//! use numpy::ndarray::array;
26//! use numpy::{ToPyArray, PyArray, PyArrayMethods};
27//!
28//! Python::attach(|py| {
29//!     let py_array = array![[1i64, 2], [3, 4]].to_pyarray(py);
30//!
31//!     assert_eq!(
32//!         py_array.readonly().as_array(),
33//!         array![[1i64, 2], [3, 4]]
34//!     );
35//! });
36//! ```
37//!
38#![cfg_attr(feature = "nalgebra", doc = "```")]
39#![cfg_attr(not(feature = "nalgebra"), doc = "```rust,ignore")]
40//! use numpy::pyo3::Python;
41//! use numpy::nalgebra::Matrix3;
42//! use numpy::{pyarray, ToPyArray, PyArrayMethods};
43//!
44//! Python::attach(|py| {
45//!     let py_array = pyarray![py, [0, 1, 2], [3, 4, 5], [6, 7, 8]];
46//!
47//!     let py_array_square;
48//!
49//!     {
50//!         let py_array = py_array.readwrite();
51//!         let mut na_matrix = py_array.as_matrix_mut();
52//!
53//!         na_matrix.add_scalar_mut(1);
54//!
55//!         py_array_square = na_matrix.pow(2).to_pyarray(py);
56//!     }
57//!
58//!     assert_eq!(
59//!         py_array.readonly().as_matrix(),
60//!         Matrix3::new(1, 2, 3, 4, 5, 6, 7, 8, 9)
61//!     );
62//!
63//!     assert_eq!(
64//!         py_array_square.readonly().as_matrix(),
65//!         Matrix3::new(30, 36, 42, 66, 81, 96, 102, 126, 150)
66//!     );
67//! });
68#![doc = "```"]
69//!
70//! [c-api]: https://numpy.org/doc/stable/reference/c-api
71//! [ndarray]: https://numpy.org/doc/stable/reference/arrays.ndarray.html
72#![deny(missing_docs)]
73
74pub mod array;
75mod array_like;
76pub mod array_protocol;
77pub mod array_subclass;
78pub mod borrow;
79pub mod convert;
80pub mod datetime;
81pub mod dlpack;
82/// GPU tensor passthrough via DLPack without CPU roundtrip.
83///
84/// Provides device-aware dispatch: CPU tensors are zero-copy viewed as
85/// `ndarray`, while CUDA/ROCm/Metal tensors are returned as [`dlpack_cuda::CudaTensorInfo`]
86/// without touching device memory.
87pub mod dlpack_cuda;
88mod dtype;
89mod error;
90pub mod extended_int;
91pub mod masked;
92pub mod npyffi;
93/// SIMD-accelerated copy for non-contiguous-to-contiguous coercion.
94///
95/// Use these routines when a Python caller passes a strided (non-contiguous)
96/// NumPy array that must be gathered into a flat, contiguous `Vec` before
97/// further processing.
98pub mod simd_copy;
99mod slice_container;
100mod strings;
101mod structured;
102mod sum_products;
103pub mod untyped;
104mod untyped_array;
105
106pub use ndarray;
107pub use pyo3;
108
109#[cfg(feature = "nalgebra")]
110pub use nalgebra;
111
112pub use crate::array::{
113    get_array_module, PyArray, PyArray0, PyArray0Methods, PyArray1, PyArray2, PyArray3, PyArray4,
114    PyArray5, PyArray6, PyArrayDyn, PyArrayMethods,
115};
116pub use crate::array_like::{
117    AllowTypeChange, PyArrayLike, PyArrayLike0, PyArrayLike1, PyArrayLike2, PyArrayLike3,
118    PyArrayLike4, PyArrayLike5, PyArrayLike6, PyArrayLikeDyn, TypeMustMatch,
119};
120pub use crate::array_protocol::{
121    parse_typestr, register_array_protocol_module, ArrayInterfaceDict, ArrayProtocol,
122    ArrayProtocolError, NdArrayWrapper,
123};
124pub use crate::array_subclass::{
125    from_array_like_f32, from_array_like_f64, register_array_subclass_module, SubclassArrayWrapper,
126};
127pub use crate::borrow::{
128    PyReadonlyArray, PyReadonlyArray0, PyReadonlyArray1, PyReadonlyArray2, PyReadonlyArray3,
129    PyReadonlyArray4, PyReadonlyArray5, PyReadonlyArray6, PyReadonlyArrayDyn, PyReadwriteArray,
130    PyReadwriteArray0, PyReadwriteArray1, PyReadwriteArray2, PyReadwriteArray3, PyReadwriteArray4,
131    PyReadwriteArray5, PyReadwriteArray6, PyReadwriteArrayDyn,
132};
133pub use crate::convert::{IntoPyArray, NpyIndex, ToNpyDims, ToPyArray};
134pub use crate::dlpack::{
135    array_from_dlpack_f32, array_from_dlpack_f64, check_tensor_contiguous, dlarray_from_torch_f32,
136    dlarray_from_torch_f64, dlpack_from_slice, dlpack_to_vec_f64, jax_device_type,
137    register_dlpack_module, validate_dlpack_tensor, validate_jax_dlpack_tensor,
138    validate_torch_dlpack_tensor, DLDataType, DLDataTypeCode, DLDevice, DLDeviceType,
139    DLManagedTensor, DLPackCapsule, DLTensor, DLTensorInfo, DlpackError, JaxDeviceType,
140    DL_DEVICE_TYPE_TPU,
141};
142pub use crate::dlpack_cuda::{
143    cuda_tensor_info, cuda_tensor_info_from_dltensor, get_cuda_tensor_info,
144    register_dlpack_cuda_module, CudaTensorInfo, DLPackDispatchResult,
145};
146pub use crate::dtype::{dtype, Complex32, Complex64, Element, PyArrayDescr, PyArrayDescrMethods};
147pub use crate::error::{BorrowError, FromVecError, NotContiguousError};
148pub use crate::extended_int::{
149    from_i128, from_u128, register_extended_int_module, to_i128, to_u128, I128Array, U128Array,
150};
151pub use crate::masked::{masked_array, masked_less, register_masked_module, MaskedArray};
152pub use crate::npyffi::{PY_ARRAY_API, PY_UFUNC_API};
153pub use crate::strings::{PyFixedString, PyFixedUnicode};
154pub use crate::structured::{
155    register_structured_module, DtypeField, StructuredArray, StructuredDtype,
156};
157pub use crate::sum_products::{dot, einsum, inner};
158pub use crate::untyped::{register_untyped_module, UntypedArray};
159pub use crate::untyped_array::{PyUntypedArray, PyUntypedArrayMethods};
160
161pub use ndarray::{array, Ix1, Ix2, Ix3, Ix4, Ix5, Ix6, IxDyn};
162
163/// A prelude
164///
165/// The purpose of this module is to avoid direct imports of
166/// the method traits defined by this crate via a glob import:
167///
168/// ```
169/// # #![allow(unused_imports)]
170/// use numpy::prelude::*;
171/// ```
172pub mod prelude {
173    pub use crate::array::{PyArray0Methods, PyArrayMethods};
174    pub use crate::convert::{IntoPyArray, ToPyArray};
175    pub use crate::dtype::PyArrayDescrMethods;
176    pub use crate::untyped_array::PyUntypedArrayMethods;
177}
178
179#[cfg(doctest)]
180mod doctest {
181    macro_rules! doc_comment {
182        ($doc_string:expr, $mod_name:ident) => {
183            #[doc = $doc_string]
184            mod $mod_name {}
185        };
186    }
187    doc_comment!(include_str!("../README.md"), readme);
188}
189
190#[cold]
191#[inline(always)]
192fn cold() {}
193
194/// Create a [`PyArray`] with one, two or three dimensions.
195///
196/// This macro is backed by [`ndarray::array`].
197///
198/// # Example
199///
200/// ```
201/// use numpy::pyo3::Python;
202/// use numpy::ndarray::array;
203/// use numpy::{pyarray, PyArrayMethods};
204///
205/// Python::attach(|py| {
206///     let array = pyarray![py, [1, 2], [3, 4]];
207///
208///     assert_eq!(
209///         array.readonly().as_array(),
210///         array![[1, 2], [3, 4]]
211///     );
212/// });
213#[macro_export]
214macro_rules! pyarray {
215    ($py: ident, $([$([$($x:expr),* $(,)*]),+ $(,)*]),+ $(,)*) => {{
216        $crate::IntoPyArray::into_pyarray($crate::array![$([$([$($x,)*],)*],)*], $py)
217    }};
218    ($py: ident, $([$($x:expr),* $(,)*]),+ $(,)*) => {{
219        $crate::IntoPyArray::into_pyarray($crate::array![$([$($x,)*],)*], $py)
220    }};
221    ($py: ident, $($x:expr),* $(,)*) => {{
222        $crate::IntoPyArray::into_pyarray($crate::array![$($x,)*], $py)
223    }};
224}
scirs2_numpy/lib.rs

scirs2_numpy/
lib.rs
