Crate numpy

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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};

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};

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

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.

Macros

  • array
    Create an Array with one, two or three dimensions.
  • einsum
    Return the Einstein summation convention of given tensors.
  • pyarray
    Create a PyArray with one, two or three dimensions.

Structs

Enums

Traits

  • Element
    Represents that a type can be an element of PyArray.

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.
  • dtype
    Returns the type descriptor (“dtype”) for a registered type.
  • einsum
    Return the Einstein summation convention of given tensors.
  • inner
    Return the inner product of two arrays.

Type Aliases

  • Complex32
  • Complex64
  • 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.