Crate numpy

Expand description

This crate provides Rust interfaces for NumPy C APIs, especially for the ndarray class.

It uses pyo3 for Rust bindings to CPython, and uses ndarray as the Rust matrix library.

To resolve its dependency on NumPy, it calls import numpy.core internally. This means that this crate should work if you can use NumPy in your Python environment, e.g. after installing it by pip install numpy. It does not matter whether you use the system environment or a dedicated virtual environment.

Loading NumPy is done automatically and on demand. So if it is not installed, the functions provided by this crate will panic instead of returning a result.

Integration with nalgebra is provided via an implementation of ToPyArray for nalgebra::Matrix to convert nalgebra matrices into NumPy arrays as well as the PyReadonlyArray::try_as_matrix and PyReadwriteArray::try_as_matrix_mut methods to treat NumPy array as nalgebra matrix slices.

§Example

use numpy::pyo3::Python;
use numpy::ndarray::array;
use numpy::{ToPyArray, PyArray, PyArrayMethods};

Python::with_gil(|py| {
    let py_array = array![[1i64, 2], [3, 4]].to_pyarray(py);

    assert_eq!(
        py_array.readonly().as_array(),
        array![[1i64, 2], [3, 4]]
    );
});

use numpy::pyo3::Python;
use numpy::nalgebra::Matrix3;
use numpy::{pyarray, ToPyArray, PyArrayMethods};

Python::with_gil(|py| {
    let py_array = pyarray![py, [0, 1, 2], [3, 4, 5], [6, 7, 8]];

    let py_array_square;

    {
        let py_array = py_array.readwrite();
        let mut na_matrix = py_array.as_matrix_mut();

        na_matrix.add_scalar_mut(1);

        py_array_square = na_matrix.pow(2).to_pyarray(py);
    }

    assert_eq!(
        py_array.readonly().as_matrix(),
        Matrix3::new(1, 2, 3, 4, 5, 6, 7, 8, 9)
    );

    assert_eq!(
        py_array_square.readonly().as_matrix(),
        Matrix3::new(30, 36, 42, 66, 81, 96, 102, 126, 150)
    );
});

Re-exports§

pub use crate::array::get_array_module;
pub use crate::array::PyArray;
pub use crate::array::PyArray0;
pub use crate::array::PyArray0Methods;
pub use crate::array::PyArray1;
pub use crate::array::PyArray2;
pub use crate::array::PyArray3;
pub use crate::array::PyArray4;
pub use crate::array::PyArray5;
pub use crate::array::PyArray6;
pub use crate::array::PyArrayDyn;
pub use crate::array::PyArrayMethods;
pub use crate::borrow::PyReadonlyArray;
pub use crate::borrow::PyReadonlyArray0;
pub use crate::borrow::PyReadonlyArray1;
pub use crate::borrow::PyReadonlyArray2;
pub use crate::borrow::PyReadonlyArray3;
pub use crate::borrow::PyReadonlyArray4;
pub use crate::borrow::PyReadonlyArray5;
pub use crate::borrow::PyReadonlyArray6;
pub use crate::borrow::PyReadonlyArrayDyn;
pub use crate::borrow::PyReadwriteArray;
pub use crate::borrow::PyReadwriteArray0;
pub use crate::borrow::PyReadwriteArray1;
pub use crate::borrow::PyReadwriteArray2;
pub use crate::borrow::PyReadwriteArray3;
pub use crate::borrow::PyReadwriteArray4;
pub use crate::borrow::PyReadwriteArray5;
pub use crate::borrow::PyReadwriteArray6;
pub use crate::borrow::PyReadwriteArrayDyn;
pub use crate::convert::IntoPyArray;
pub use crate::convert::NpyIndex;
pub use crate::convert::ToNpyDims;
pub use crate::convert::ToPyArray;
pub use crate::npyffi::PY_ARRAY_API;
pub use crate::npyffi::PY_UFUNC_API;
pub use ndarray;
pub use pyo3;
pub use nalgebra;

Modules§

array: Safe interface for NumPy’s N-dimensional arrays
borrow: Types to safely create references into NumPy arrays
convert: Defines conversion traits between Rust types and NumPy data types.
datetime: Support datetimes and timedeltas
npyffi: Low-Level bindings for NumPy C API.
prelude: A prelude

Macros§

array: Create an Array with one, two, three, four, five, or six dimensions.
einsum: Return the Einstein summation convention of given tensors.
einsum_boundDeprecated: Deprecated name for [einsum!].
pyarray: Create a PyArray with one, two or three dimensions.
pyarray_boundDeprecated: Deprecated name for pyarray.

Structs§

AllowTypeChange: Marker type to indicate that the element type received via PyArrayLike can be cast to the specified type by NumPy’s asarray.
FromVecError: Represents that given Vec cannot be treated as an array.
NotContiguousError: Represents that the given array is not contiguous.
PyArrayDescr: Binding of numpy.dtype.
PyArrayLike: Receiver for arrays or array-like types.
PyFixedString: A newtype wrapper around [u8; N] to handle byte scalars while satisfying coherence.
PyFixedUnicode: A newtype wrapper around [PyUCS4; N] to handle str_ scalars while satisfying coherence.
PyUntypedArray: A safe, untyped wrapper for NumPy’s ndarray class.
TypeMustMatch: Marker type to indicate that the element type received via PyArrayLike must match the specified type exactly.

Enums§

BorrowError: Inidcates why borrowing an array failed.

Traits§

Element: Represents that a type can be an element of PyArray.
PyArrayDescrMethods: Implementation of functionality for PyArrayDescr.
PyUntypedArrayMethods: Implementation of functionality for PyUntypedArray.

Functions§

Ix1: Create a one-dimensional index
Ix2: Create a two-dimensional index
Ix3: Create a three-dimensional index
Ix4: Create a four-dimensional index
Ix5: Create a five-dimensional index
Ix6: Create a six-dimensional index
IxDyn: Create a dynamic-dimensional index
dot: Return the dot product of two arrays.
dot_boundDeprecated: Deprecated name for dot.
dtype: Returns the type descriptor (“dtype”) for a registered type.
dtype_boundDeprecated: Deprecated name for dtype.
einsum: Return the Einstein summation convention of given tensors.
einsum_boundDeprecated: Deprecated name for einsum.
inner: Return the inner product of two arrays.
inner_boundDeprecated: Deprecated name for inner.

Type Aliases§

Complex32: Alias for a Complex<f32>
Complex64: Alias for a Complex<f64>
Ix1: one-dimensional
Ix2: two-dimensional
Ix3: three-dimensional
Ix4: four-dimensional
Ix5: five-dimensional
Ix6: six-dimensional
IxDyn: dynamic-dimensional
PyArrayLike0: Receiver for zero-dimensional arrays or array-like types.
PyArrayLike1: Receiver for one-dimensional arrays or array-like types.
PyArrayLike2: Receiver for two-dimensional arrays or array-like types.
PyArrayLike3: Receiver for three-dimensional arrays or array-like types.
PyArrayLike4: Receiver for four-dimensional arrays or array-like types.
PyArrayLike5: Receiver for five-dimensional arrays or array-like types.
PyArrayLike6: Receiver for six-dimensional arrays or array-like types.
PyArrayLikeDyn: Receiver for arrays or array-like types whose dimensionality is determined at runtime.

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