faer 0.20.2

#[allow(unused_imports)]
use super::*;
#[allow(unused_imports)]
use crate::assert;
#[allow(unused_imports)]
use complex_native::{c32, c64};

/// Memory view over a buffer in `npy` format.
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
pub struct Npy<'a> {
    aligned_bytes: &'a [u8],
    nrows: usize,
    ncols: usize,
    prefix_len: usize,
    dtype: NpyDType,
    fortran_order: bool,
}

/// Data type of an `npy` buffer.
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
#[derive(Debug, Copy, Clone, PartialEq, Eq)]
pub enum NpyDType {
    /// 32-bit floating point.
    F32,
    /// 64-bit floating point.
    F64,
    /// 32-bit complex floating point.
    C32,
    /// 64-bit complex floating point.
    C64,
    /// Unknown type.
    Other,
}

/// Trait implemented for native types that can be read from a `npy` buffer.
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
pub trait FromNpy: faer_entity::SimpleEntity {
    /// Data type of the buffer data.
    const DTYPE: NpyDType;
}

#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
impl FromNpy for f32 {
    const DTYPE: NpyDType = NpyDType::F32;
}
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
impl FromNpy for f64 {
    const DTYPE: NpyDType = NpyDType::F64;
}
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
impl FromNpy for c32 {
    const DTYPE: NpyDType = NpyDType::C32;
}
#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
impl FromNpy for c64 {
    const DTYPE: NpyDType = NpyDType::C64;
}

#[cfg(feature = "npy")]
#[cfg_attr(docsrs, doc(cfg(feature = "npy")))]
impl<'a> Npy<'a> {
    fn parse_npyz(
        data: &[u8],
        npyz: npyz::NpyFile<&[u8]>,
    ) -> Result<(NpyDType, usize, usize, usize, bool), std::io::Error> {
        let ver_major = data[6] - b'\x00';
        let length = if ver_major <= 1 {
            2usize
        } else if ver_major <= 3 {
            4usize
        } else {
            return Err(std::io::Error::new(
                std::io::ErrorKind::Other,
                "unsupported version",
            ));
        };
        let header_len = if length == 2 {
            u16::from_le_bytes(data[8..10].try_into().unwrap()) as usize
        } else {
            u32::from_le_bytes(data[8..12].try_into().unwrap()) as usize
        };
        let dtype = || -> NpyDType {
            match npyz.dtype() {
                npyz::DType::Plain(str) => {
                    let is_complex = match str.type_char() {
                        npyz::TypeChar::Float => false,
                        npyz::TypeChar::Complex => true,
                        _ => return NpyDType::Other,
                    };

                    let byte_size = str.size_field();
                    if byte_size == 8 && is_complex {
                        NpyDType::C32
                    } else if byte_size == 16 && is_complex {
                        NpyDType::C64
                    } else if byte_size == 4 && !is_complex {
                        NpyDType::F32
                    } else if byte_size == 16 && !is_complex {
                        NpyDType::F64
                    } else {
                        NpyDType::Other
                    }
                }
                _ => NpyDType::Other,
            }
        };

        let dtype = dtype();
        let order = npyz.header().order();
        let shape = npyz.shape();
        let nrows = shape.get(0).copied().unwrap_or(1) as usize;
        let ncols = shape.get(1).copied().unwrap_or(1) as usize;
        let prefix_len = 8 + length + header_len;
        let fortran_order = order == npyz::Order::Fortran;
        Ok((dtype, nrows, ncols, prefix_len, fortran_order))
    }

    /// Parse a npy file from a memory buffer.
    #[inline]
    pub fn new(data: &'a [u8]) -> Result<Self, std::io::Error> {
        let npyz = npyz::NpyFile::new(data)?;

        let (dtype, nrows, ncols, prefix_len, fortran_order) = Self::parse_npyz(data, npyz)?;

        Ok(Self {
            aligned_bytes: data,
            prefix_len,
            nrows,
            ncols,
            dtype,
            fortran_order,
        })
    }

    /// Returns the data type of the memory buffer.
    #[inline]
    pub fn dtype(&self) -> NpyDType {
        self.dtype
    }

    /// Checks if the memory buffer is aligned, in which case the data can be referenced in-place.
    #[inline]
    pub fn is_aligned(&self) -> bool {
        self.aligned_bytes.as_ptr().align_offset(64) == 0
    }

    /// If the memory buffer is aligned, and the provided type matches the one stored in the buffer,
    /// returns a matrix view over the data.
    #[inline]
    pub fn as_aligned_ref<E: FromNpy>(&self) -> MatRef<'_, E> {
        assert!(self.is_aligned());
        assert!(self.dtype == E::DTYPE);

        if self.fortran_order {
            crate::mat::from_column_major_slice_generic(
                bytemuck::cast_slice(&self.aligned_bytes[self.prefix_len..]),
                self.nrows,
                self.ncols,
            )
        } else {
            crate::mat::from_row_major_slice_generic(
                bytemuck::cast_slice(&self.aligned_bytes[self.prefix_len..]),
                self.nrows,
                self.ncols,
            )
        }
    }

    /// If the provided type matches the one stored in the buffer, returns a matrix containing the
    /// data.
    #[inline]
    pub fn to_mat<E: FromNpy>(&self) -> Mat<E> {
        assert!(self.dtype == E::DTYPE);

        let mut mat = Mat::<E>::with_capacity(self.nrows, self.ncols);
        unsafe { mat.set_dims(self.nrows, self.ncols) };

        let data = &self.aligned_bytes[self.prefix_len..];

        if self.fortran_order {
            for j in 0..self.ncols {
                bytemuck::cast_slice_mut(mat.col_as_slice_mut(j)).copy_from_slice(
                    &data[j * self.nrows * core::mem::size_of::<E>()..]
                        [..self.nrows * core::mem::size_of::<E>()],
                )
            }
        } else {
            for j in 0..self.ncols {
                for i in 0..self.nrows {
                    bytemuck::cast_slice_mut(&mut mat.col_as_slice_mut(j)[i..i + 1])
                        .copy_from_slice(
                            &data[(i * self.ncols + j) * core::mem::size_of::<E>()..]
                                [..core::mem::size_of::<E>()],
                        )
                }
            }
        };

        mat
    }
}