Struct PyReadonlyArray

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pub struct PyReadonlyArray<'py, T, D>where
    T: Element,
    D: Dimension,
{ /* private fields */ }

Expand description

Read-only borrow of an array.

An instance of this type ensures that there are no instances of PyReadwriteArray, i.e. that only shared references into the interior of the array can be created safely.

See the module-level documentation for more.

Implementations§

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impl<'py, T, D> PyReadonlyArray<'py, T, D>
where T: Element, D: Dimension,

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pub fn as_array(&self) -> ArrayView<'_, T, D>

Provides an immutable array view of the interior of the NumPy array.

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pub fn as_slice(&self) -> Result<&[T], NotContiguousError>

Provide an immutable slice view of the interior of the NumPy array if it is contiguous.

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pub fn get(&self, index: I) -> Option<&T>
where I: NpyIndex<Dim = D>,

Provide an immutable reference to an element of the NumPy array if the index is within bounds.

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impl<'py, N, D> PyReadonlyArray<'py, N, D>
where N: Scalar + Element, D: Dimension,

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pub fn try_as_matrix<R, C, RStride, CStride>( &self, ) -> Option<MatrixView<'_, N, R, C, RStride, CStride>>
where R: Dim, C: Dim, RStride: Dim, CStride: Dim,

Try to convert this array into a nalgebra::MatrixView using the given shape and strides.

Note that nalgebra’s types default to Fortan/column-major standard strides whereas NumPy creates C/row-major strides by default. Furthermore, array views created by slicing into existing arrays will often have non-standard strides.

If you do not fully control the memory layout of a given array, e.g. at your API entry points, it can be useful to opt into nalgebra’s support for dynamic strides, for example

use pyo3::py_run;
use numpy::{get_array_module, PyReadonlyArray2};
use nalgebra::{MatrixView, Const, Dyn};

#[pyfunction]
fn sum_standard_layout<'py>(py: Python<'py>, array: PyReadonlyArray2<'py, f64>) -> Option<f64> {
    let matrix: Option<MatrixView<f64, Const<2>, Const<2>>> = array.try_as_matrix();
    matrix.map(|matrix| matrix.sum())
}

#[pyfunction]
fn sum_dynamic_strides<'py>(py: Python<'py>, array: PyReadonlyArray2<'py, f64>) -> Option<f64> {
    let matrix: Option<MatrixView<f64, Const<2>, Const<2>, Dyn, Dyn>> = array.try_as_matrix();
    matrix.map(|matrix| matrix.sum())
}

Python::with_gil(|py| {
    let np = py.eval_bound("__import__('numpy')", None, None).unwrap();
    let sum_standard_layout = wrap_pyfunction!(sum_standard_layout)(py).unwrap();
    let sum_dynamic_strides = wrap_pyfunction!(sum_dynamic_strides)(py).unwrap();

    py_run!(py, np sum_standard_layout, r"assert sum_standard_layout(np.ones((2, 2), order='F')) == 4.");
    py_run!(py, np sum_standard_layout, r"assert sum_standard_layout(np.ones((2, 2, 2))[:,:,0]) is None");

    py_run!(py, np sum_dynamic_strides, r"assert sum_dynamic_strides(np.ones((2, 2), order='F')) == 4.");
    py_run!(py, np sum_dynamic_strides, r"assert sum_dynamic_strides(np.ones((2, 2, 2))[:,:,0]) == 4.");
});

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impl<'py, N> PyReadonlyArray<'py, N, Ix1>
where N: Scalar + Element,

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pub fn as_matrix(&self) -> DMatrixView<'_, N, Dyn, Dyn>

Convert this one-dimensional array into a nalgebra::DMatrixView using dynamic strides.

§Panics

Panics if the array has negative strides.

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impl<'py, N> PyReadonlyArray<'py, N, Ix2>
where N: Scalar + Element,

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pub fn as_matrix(&self) -> DMatrixView<'_, N, Dyn, Dyn>

Convert this two-dimensional array into a nalgebra::DMatrixView using dynamic strides.

§Panics

Panics if the array has negative strides.

Methods from Deref<Target = Bound<'py, PyArray<T, D>>>§

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pub fn borrow(&self) -> PyRef<'py, T>

Immutably borrows the value T.

This borrow lasts while the returned PyRef exists. Multiple immutable borrows can be taken out at the same time.

For frozen classes, the simpler get is available.

§Examples

#[pyclass]
struct Foo {
    inner: u8,
}

Python::with_gil(|py| -> PyResult<()> {
    let foo: Bound<'_, Foo> = Bound::new(py, Foo { inner: 73 })?;
    let inner: &u8 = &foo.borrow().inner;

    assert_eq!(*inner, 73);
    Ok(())
})?;

§Panics

Panics if the value is currently mutably borrowed. For a non-panicking variant, use try_borrow.

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pub fn borrow_mut(&self) -> PyRefMut<'py, T>
where T: PyClass<Frozen = False>,

Mutably borrows the value T.

This borrow lasts while the returned PyRefMut exists.

§Examples

#[pyclass]
struct Foo {
    inner: u8,
}

Python::with_gil(|py| -> PyResult<()> {
    let foo: Bound<'_, Foo> = Bound::new(py, Foo { inner: 73 })?;
    foo.borrow_mut().inner = 35;

    assert_eq!(foo.borrow().inner, 35);
    Ok(())
})?;

§Panics

Panics if the value is currently borrowed. For a non-panicking variant, use try_borrow_mut.

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pub fn try_borrow(&self) -> Result<PyRef<'py, T>, PyBorrowError>

Attempts to immutably borrow the value T, returning an error if the value is currently mutably borrowed.

The borrow lasts while the returned PyRef exists.

This is the non-panicking variant of borrow.

For frozen classes, the simpler get is available.

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pub fn try_borrow_mut(&self) -> Result<PyRefMut<'py, T>, PyBorrowMutError>
where T: PyClass<Frozen = False>,

Attempts to mutably borrow the value T, returning an error if the value is currently borrowed.

The borrow lasts while the returned PyRefMut exists.

This is the non-panicking variant of borrow_mut.

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pub fn get(&self) -> &T
where T: PyClass<Frozen = True> + Sync,

Provide an immutable borrow of the value T without acquiring the GIL.

This is available if the class is frozen and Sync.

§Examples

use std::sync::atomic::{AtomicUsize, Ordering};

#[pyclass(frozen)]
struct FrozenCounter {
    value: AtomicUsize,
}

Python::with_gil(|py| {
    let counter = FrozenCounter { value: AtomicUsize::new(0) };

    let py_counter = Bound::new(py, counter).unwrap();

    py_counter.get().value.fetch_add(1, Ordering::Relaxed);
});

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