ferray_core/creation/

mod.rs

// ferray-core: Array creation functions (REQ-16, REQ-17, REQ-18, REQ-19)
//
// Mirrors numpy's array creation routines: zeros, ones, full, empty,
// arange, linspace, logspace, geomspace, eye, identity, diag, etc.

use std::mem::MaybeUninit;

use crate::array::owned::Array;
use crate::dimension::{Dimension, Ix1, Ix2, IxDyn};
use crate::dtype::Element;
use crate::error::{FerrayError, FerrayResult};

// ============================================================================
// REQ-16: Basic creation functions
// ============================================================================

/// Create an array from a flat vector and a shape (C-order).
///
/// This is the primary "array constructor" — analogous to `numpy.array()` when
/// given a flat sequence plus a shape.
///
/// # Errors
/// Returns `FerrayError::ShapeMismatch` if `data.len()` does not equal the
/// product of the shape dimensions.
pub fn array<T: Element, D: Dimension>(dim: D, data: Vec<T>) -> FerrayResult<Array<T, D>> {
    Array::from_vec(dim, data)
}

/// Interpret existing data as an array without copying (if possible).
///
/// This is equivalent to `numpy.asarray()`. Since Rust ownership rules
/// require moving the data, this creates an owned array from the vector.
///
/// # Errors
/// Returns `FerrayError::ShapeMismatch` if lengths don't match.
pub fn asarray<T: Element, D: Dimension>(dim: D, data: Vec<T>) -> FerrayResult<Array<T, D>> {
    Array::from_vec(dim, data)
}

/// Create an array from a byte buffer, interpreting bytes as elements of type `T`.
///
/// Analogous to `numpy.frombuffer()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if the buffer length is not a multiple
/// of `size_of::<T>()`, or if the resulting length does not match the shape.
pub fn frombuffer<T: Element, D: Dimension>(dim: D, buf: &[u8]) -> FerrayResult<Array<T, D>> {
    let elem_size = std::mem::size_of::<T>();
    if elem_size == 0 {
        return Err(FerrayError::invalid_value("zero-sized type"));
    }
    if buf.len() % elem_size != 0 {
        return Err(FerrayError::invalid_value(format!(
            "buffer length {} is not a multiple of element size {}",
            buf.len(),
            elem_size,
        )));
    }
    let n_elems = buf.len() / elem_size;
    let expected = dim.size();
    if n_elems != expected {
        return Err(FerrayError::shape_mismatch(format!(
            "buffer contains {} elements but shape {:?} requires {}",
            n_elems,
            dim.as_slice(),
            expected,
        )));
    }
    // Copy bytes element-by-element via from_ne_bytes equivalent
    let mut data = Vec::with_capacity(n_elems);
    for i in 0..n_elems {
        let start = i * elem_size;
        let end = start + elem_size;
        let slice = &buf[start..end];
        // SAFETY: We're reading elem_size bytes and interpreting as T.
        // T: Element implies T: Clone + 'static, and we're copying from
        // a properly-sized byte buffer.
        let val = unsafe {
            let mut val = MaybeUninit::<T>::uninit();
            std::ptr::copy_nonoverlapping(slice.as_ptr(), val.as_mut_ptr() as *mut u8, elem_size);
            val.assume_init()
        };
        data.push(val);
    }
    Array::from_vec(dim, data)
}

/// Create a 1-D array from an iterator.
///
/// Analogous to `numpy.fromiter()`.
///
/// # Errors
/// This function always succeeds (returns `Ok`).
pub fn fromiter<T: Element>(iter: impl IntoIterator<Item = T>) -> FerrayResult<Array<T, Ix1>> {
    Array::from_iter_1d(iter)
}

/// Create an array filled with zeros.
///
/// Analogous to `numpy.zeros()`.
pub fn zeros<T: Element, D: Dimension>(dim: D) -> FerrayResult<Array<T, D>> {
    Array::zeros(dim)
}

/// Create an array filled with ones.
///
/// Analogous to `numpy.ones()`.
pub fn ones<T: Element, D: Dimension>(dim: D) -> FerrayResult<Array<T, D>> {
    Array::ones(dim)
}

/// Create an array filled with a given value.
///
/// Analogous to `numpy.full()`.
pub fn full<T: Element, D: Dimension>(dim: D, fill_value: T) -> FerrayResult<Array<T, D>> {
    Array::from_elem(dim, fill_value)
}

/// Create an array with the same shape as `other`, filled with zeros.
///
/// Analogous to `numpy.zeros_like()`.
pub fn zeros_like<T: Element, D: Dimension>(other: &Array<T, D>) -> FerrayResult<Array<T, D>> {
    Array::zeros(other.dim().clone())
}

/// Create an array with the same shape as `other`, filled with ones.
///
/// Analogous to `numpy.ones_like()`.
pub fn ones_like<T: Element, D: Dimension>(other: &Array<T, D>) -> FerrayResult<Array<T, D>> {
    Array::ones(other.dim().clone())
}

/// Create an array with the same shape as `other`, filled with `fill_value`.
///
/// Analogous to `numpy.full_like()`.
pub fn full_like<T: Element, D: Dimension>(
    other: &Array<T, D>,
    fill_value: T,
) -> FerrayResult<Array<T, D>> {
    Array::from_elem(other.dim().clone(), fill_value)
}

// ============================================================================
// REQ-17: empty() returning MaybeUninit
// ============================================================================

/// An array whose elements have not been initialized.
///
/// The caller must call [`assume_init`](UninitArray::assume_init) after
/// filling all elements.
pub struct UninitArray<T: Element, D: Dimension> {
    data: Vec<MaybeUninit<T>>,
    dim: D,
}

impl<T: Element, D: Dimension> UninitArray<T, D> {
    /// Shape as a slice.
    #[inline]
    pub fn shape(&self) -> &[usize] {
        self.dim.as_slice()
    }

    /// Total number of elements.
    #[inline]
    pub fn size(&self) -> usize {
        self.data.len()
    }

    /// Number of dimensions.
    #[inline]
    pub fn ndim(&self) -> usize {
        self.dim.ndim()
    }

    /// Get a mutable raw pointer to the underlying data.
    ///
    /// Use this to fill the array element-by-element before calling
    /// `assume_init()`.
    #[inline]
    pub fn as_mut_ptr(&mut self) -> *mut MaybeUninit<T> {
        self.data.as_mut_ptr()
    }

    /// Write a value at a flat index.
    ///
    /// # Errors
    /// Returns `FerrayError::IndexOutOfBounds` if `flat_index >= size()`.
    pub fn write_at(&mut self, flat_index: usize, value: T) -> FerrayResult<()> {
        let size = self.size();
        if flat_index >= size {
            return Err(FerrayError::IndexOutOfBounds {
                index: flat_index as isize,
                axis: 0,
                size,
            });
        }
        self.data[flat_index] = MaybeUninit::new(value);
        Ok(())
    }

    /// Convert to an initialized `Array<T, D>`.
    ///
    /// # Safety
    /// The caller must ensure that **all** elements have been initialized
    /// (e.g., via `write_at` or raw pointer writes). Reading uninitialized
    /// memory is undefined behavior.
    pub unsafe fn assume_init(self) -> Array<T, D> {
        let nd_dim = self.dim.to_ndarray_dim();
        let len = self.data.len();

        // Transmute Vec<MaybeUninit<T>> to Vec<T>.
        // SAFETY: MaybeUninit<T> has the same layout as T, and the caller
        // guarantees all elements are initialized.
        let mut raw_vec = std::mem::ManuallyDrop::new(self.data);
        let data: Vec<T> =
            unsafe { Vec::from_raw_parts(raw_vec.as_mut_ptr() as *mut T, len, raw_vec.capacity()) };

        let inner = ndarray::Array::from_shape_vec(nd_dim, data)
            .expect("UninitArray assume_init: shape/data mismatch (this is a bug)");
        Array::from_ndarray(inner)
    }
}

/// Create an uninitialized array.
///
/// Analogous to `numpy.empty()`, but returns a [`UninitArray`] that must
/// be explicitly initialized via [`UninitArray::assume_init`].
///
/// This prevents accidentally reading uninitialized memory — a key safety
/// improvement over NumPy's `empty()`.
pub fn empty<T: Element, D: Dimension>(dim: D) -> UninitArray<T, D> {
    let size = dim.size();
    let mut data = Vec::with_capacity(size);
    // SAFETY: MaybeUninit does not require initialization.
    // We set the length to match the capacity; each element is MaybeUninit.
    unsafe {
        data.set_len(size);
    }
    UninitArray { data, dim }
}

// ============================================================================
// REQ-18: Range functions
// ============================================================================

/// Trait for types usable with `arange` — numeric types that support
/// stepping and comparison.
pub trait ArangeNum: Element + PartialOrd {
    /// Convert from f64 for step calculations.
    fn from_f64(v: f64) -> Self;
    /// Convert to f64 for step calculations.
    fn to_f64(self) -> f64;
}

macro_rules! impl_arange_int {
    ($($ty:ty),*) => {
        $(
            impl ArangeNum for $ty {
                #[inline]
                fn from_f64(v: f64) -> Self { v as Self }
                #[inline]
                fn to_f64(self) -> f64 { self as f64 }
            }
        )*
    };
}

macro_rules! impl_arange_float {
    ($($ty:ty),*) => {
        $(
            impl ArangeNum for $ty {
                #[inline]
                fn from_f64(v: f64) -> Self { v as Self }
                #[inline]
                fn to_f64(self) -> f64 { self as f64 }
            }
        )*
    };
}

impl_arange_int!(u8, u16, u32, u64, i8, i16, i32, i64);
impl_arange_float!(f32, f64);

/// Create a 1-D array with evenly spaced values within a given interval.
///
/// Analogous to `numpy.arange(start, stop, step)`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if `step` is zero.
pub fn arange<T: ArangeNum>(start: T, stop: T, step: T) -> FerrayResult<Array<T, Ix1>> {
    let step_f = step.to_f64();
    if step_f == 0.0 {
        return Err(FerrayError::invalid_value("step cannot be zero"));
    }
    let start_f = start.to_f64();
    let stop_f = stop.to_f64();
    let n = ((stop_f - start_f) / step_f).ceil();
    let n = if n < 0.0 { 0 } else { n as usize };

    let mut data = Vec::with_capacity(n);
    for i in 0..n {
        data.push(T::from_f64(start_f + (i as f64) * step_f));
    }
    let dim = Ix1::new([data.len()]);
    Array::from_vec(dim, data)
}

/// Trait for float-like types used in linspace/logspace/geomspace.
pub trait LinspaceNum: Element + PartialOrd {
    /// Convert from f64.
    fn from_f64(v: f64) -> Self;
    /// Convert to f64.
    fn to_f64(self) -> f64;
}

impl LinspaceNum for f32 {
    #[inline]
    fn from_f64(v: f64) -> Self {
        v as f32
    }
    #[inline]
    fn to_f64(self) -> f64 {
        self as f64
    }
}

impl LinspaceNum for f64 {
    #[inline]
    fn from_f64(v: f64) -> Self {
        v
    }
    #[inline]
    fn to_f64(self) -> f64 {
        self
    }
}

/// Create a 1-D array with `num` evenly spaced values between `start` and `stop`.
///
/// If `endpoint` is true (the default in NumPy), `stop` is the last sample.
/// Otherwise, it is not included.
///
/// Analogous to `numpy.linspace()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if `num` is 0 and `endpoint` is true
/// (cannot produce an empty array with an endpoint).
pub fn linspace<T: LinspaceNum>(
    start: T,
    stop: T,
    num: usize,
    endpoint: bool,
) -> FerrayResult<Array<T, Ix1>> {
    if num == 0 {
        return Array::from_vec(Ix1::new([0]), vec![]);
    }
    if num == 1 {
        return Array::from_vec(Ix1::new([1]), vec![start]);
    }
    let start_f = start.to_f64();
    let stop_f = stop.to_f64();
    let divisor = if endpoint {
        (num - 1) as f64
    } else {
        num as f64
    };
    let step = (stop_f - start_f) / divisor;
    let mut data = Vec::with_capacity(num);
    for i in 0..num {
        data.push(T::from_f64(start_f + (i as f64) * step));
    }
    Array::from_vec(Ix1::new([num]), data)
}

/// Create a 1-D array with values spaced evenly on a log scale.
///
/// Returns `base ** linspace(start, stop, num)`.
///
/// Analogous to `numpy.logspace()`.
///
/// # Errors
/// Propagates errors from `linspace`.
pub fn logspace<T: LinspaceNum>(
    start: T,
    stop: T,
    num: usize,
    endpoint: bool,
    base: f64,
) -> FerrayResult<Array<T, Ix1>> {
    let lin = linspace(start, stop, num, endpoint)?;
    let data: Vec<T> = lin
        .iter()
        .map(|v| T::from_f64(base.powf(v.clone().to_f64())))
        .collect();
    Array::from_vec(Ix1::new([num]), data)
}

/// Create a 1-D array with values spaced evenly on a geometric (log) scale.
///
/// The values are `start * (stop/start) ** linspace(0, 1, num)`.
///
/// Analogous to `numpy.geomspace()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if `start` or `stop` is zero or
/// if they have different signs.
pub fn geomspace<T: LinspaceNum>(
    start: T,
    stop: T,
    num: usize,
    endpoint: bool,
) -> FerrayResult<Array<T, Ix1>> {
    let start_f = start.clone().to_f64();
    let stop_f = stop.clone().to_f64();
    if start_f == 0.0 || stop_f == 0.0 {
        return Err(FerrayError::invalid_value(
            "geomspace: start and stop must be non-zero",
        ));
    }
    if (start_f < 0.0) != (stop_f < 0.0) {
        return Err(FerrayError::invalid_value(
            "geomspace: start and stop must have the same sign",
        ));
    }
    if num == 0 {
        return Array::from_vec(Ix1::new([0]), vec![]);
    }
    if num == 1 {
        return Array::from_vec(Ix1::new([1]), vec![start]);
    }
    let log_start = start_f.abs().ln();
    let log_stop = stop_f.abs().ln();
    let sign = if start_f < 0.0 { -1.0 } else { 1.0 };
    let divisor = if endpoint {
        (num - 1) as f64
    } else {
        num as f64
    };
    let step = (log_stop - log_start) / divisor;
    let mut data = Vec::with_capacity(num);
    for i in 0..num {
        let log_val = log_start + (i as f64) * step;
        data.push(T::from_f64(sign * log_val.exp()));
    }
    Array::from_vec(Ix1::new([num]), data)
}

/// Return coordinate arrays from coordinate vectors.
///
/// Analogous to `numpy.meshgrid(*xi, indexing='xy')`.
///
/// Given N 1-D arrays, returns N N-D arrays, where each output array
/// has the shape `(len(x1), len(x2), ..., len(xN))` for 'xy' indexing
/// or `(len(x1), ..., len(xN))` transposed for 'ij' indexing.
///
/// `indexing` should be `"xy"` (default Cartesian) or `"ij"` (matrix).
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if `indexing` is not `"xy"` or `"ij"`,
/// or if there are fewer than 2 input arrays.
pub fn meshgrid(
    arrays: &[Array<f64, Ix1>],
    indexing: &str,
) -> FerrayResult<Vec<Array<f64, IxDyn>>> {
    if indexing != "xy" && indexing != "ij" {
        return Err(FerrayError::invalid_value(
            "meshgrid: indexing must be 'xy' or 'ij'",
        ));
    }
    let ndim = arrays.len();
    if ndim == 0 {
        return Ok(vec![]);
    }

    let mut shapes: Vec<usize> = arrays.iter().map(|a| a.shape()[0]).collect();
    if indexing == "xy" && ndim >= 2 {
        shapes.swap(0, 1);
    }

    let total: usize = shapes.iter().product();
    let mut results = Vec::with_capacity(ndim);

    for (k, arr) in arrays.iter().enumerate() {
        let src_data: Vec<f64> = arr.iter().copied().collect();
        let mut data = Vec::with_capacity(total);
        // For 'xy' indexing, the first two dimensions are swapped
        let effective_k = if indexing == "xy" && ndim >= 2 {
            match k {
                0 => 1,
                1 => 0,
                other => other,
            }
        } else {
            k
        };

        // Build the output by iterating over all indices in the output shape
        for flat in 0..total {
            // Compute the index along dimension effective_k
            let mut rem = flat;
            let mut idx_k = 0;
            for (d, &s) in shapes.iter().enumerate().rev() {
                if d == effective_k {
                    idx_k = rem % s;
                }
                rem /= s;
            }
            data.push(src_data[idx_k]);
        }

        let dim = IxDyn::new(&shapes);
        results.push(Array::from_vec(dim, data)?);
    }
    Ok(results)
}

/// Create a dense multi-dimensional "meshgrid" with matrix ('ij') indexing.
///
/// Analogous to `numpy.mgrid[start:stop:step, ...]`.
///
/// Takes a slice of `(start, stop, step)` tuples, one per dimension.
/// Returns a vector of arrays, one per dimension.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if any step is zero.
pub fn mgrid(ranges: &[(f64, f64, f64)]) -> FerrayResult<Vec<Array<f64, IxDyn>>> {
    let mut arrs: Vec<Array<f64, Ix1>> = Vec::with_capacity(ranges.len());
    for &(start, stop, step) in ranges {
        arrs.push(arange(start, stop, step)?);
    }
    meshgrid(&arrs, "ij")
}

/// Create a sparse (open) multi-dimensional "meshgrid" with 'ij' indexing.
///
/// Analogous to `numpy.ogrid[start:stop:step, ...]`.
///
/// Returns arrays that are broadcastable to the full grid shape.
/// Each returned array has shape 1 in all dimensions except its own.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if any step is zero.
pub fn ogrid(ranges: &[(f64, f64, f64)]) -> FerrayResult<Vec<Array<f64, IxDyn>>> {
    let ndim = ranges.len();
    let mut results = Vec::with_capacity(ndim);
    for (i, &(start, stop, step)) in ranges.iter().enumerate() {
        let arr1d = arange(start, stop, step)?;
        let n = arr1d.shape()[0];
        let data: Vec<f64> = arr1d.iter().copied().collect();
        // Build shape: all ones except dimension i = n
        let mut shape = vec![1usize; ndim];
        shape[i] = n;
        let dim = IxDyn::new(&shape);
        results.push(Array::from_vec(dim, data)?);
    }
    Ok(results)
}

// ============================================================================
// REQ-19: Identity/diagonal functions
// ============================================================================

/// Create a 2-D identity matrix of size `n x n`.
///
/// Analogous to `numpy.identity()`.
pub fn identity<T: Element>(n: usize) -> FerrayResult<Array<T, Ix2>> {
    eye(n, n, 0)
}

/// Create a 2-D array with ones on the diagonal and zeros elsewhere.
///
/// `k` is the diagonal offset: 0 = main diagonal, positive = above, negative = below.
///
/// Analogous to `numpy.eye(N, M, k)`.
pub fn eye<T: Element>(n: usize, m: usize, k: isize) -> FerrayResult<Array<T, Ix2>> {
    let mut data = vec![T::zero(); n * m];
    for i in 0..n {
        let j = i as isize + k;
        if j >= 0 && (j as usize) < m {
            data[i * m + j as usize] = T::one();
        }
    }
    Array::from_vec(Ix2::new([n, m]), data)
}

/// Extract a diagonal or construct a diagonal array.
///
/// If `a` is 2-D, extract the `k`-th diagonal as a 1-D array.
/// If `a` is 1-D, construct a 2-D array with `a` on the `k`-th diagonal.
///
/// Analogous to `numpy.diag()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if `a` is not 1-D or 2-D.
pub fn diag<T: Element>(a: &Array<T, IxDyn>, k: isize) -> FerrayResult<Array<T, IxDyn>> {
    let shape = a.shape();
    match shape.len() {
        1 => {
            // Construct a 2-D diagonal array
            let n = shape[0];
            let size = n + k.unsigned_abs();
            let mut data = vec![T::zero(); size * size];
            let src: Vec<T> = a.iter().cloned().collect();
            for (i, val) in src.into_iter().enumerate() {
                let row = if k >= 0 { i } else { i + k.unsigned_abs() };
                let col = if k >= 0 { i + k as usize } else { i };
                data[row * size + col] = val;
            }
            Array::from_vec(IxDyn::new(&[size, size]), data)
        }
        2 => {
            // Extract the k-th diagonal
            let (n, m) = (shape[0], shape[1]);
            let src: Vec<T> = a.iter().cloned().collect();
            let mut diag_vals = Vec::new();
            for i in 0..n {
                let j = i as isize + k;
                if j >= 0 && (j as usize) < m {
                    diag_vals.push(src[i * m + j as usize].clone());
                }
            }
            let len = diag_vals.len();
            Array::from_vec(IxDyn::new(&[len]), diag_vals)
        }
        _ => Err(FerrayError::invalid_value("diag: input must be 1-D or 2-D")),
    }
}

/// Create a 2-D array with the flattened input as a diagonal.
///
/// Analogous to `numpy.diagflat()`.
///
/// # Errors
/// Propagates errors from the underlying construction.
pub fn diagflat<T: Element>(a: &Array<T, IxDyn>, k: isize) -> FerrayResult<Array<T, IxDyn>> {
    // Flatten a to 1-D, then call diag
    let flat: Vec<T> = a.iter().cloned().collect();
    let n = flat.len();
    let arr1d = Array::from_vec(IxDyn::new(&[n]), flat)?;
    diag(&arr1d, k)
}

/// Create a lower-triangular matrix of ones.
///
/// Returns an `n x m` array where `a[i, j] = 1` if `i >= j - k`, else `0`.
///
/// Analogous to `numpy.tri(N, M, k)`.
pub fn tri<T: Element>(n: usize, m: usize, k: isize) -> FerrayResult<Array<T, Ix2>> {
    let mut data = vec![T::zero(); n * m];
    for i in 0..n {
        for j in 0..m {
            if (i as isize) >= (j as isize) - k {
                data[i * m + j] = T::one();
            }
        }
    }
    Array::from_vec(Ix2::new([n, m]), data)
}

/// Return the lower triangle of a 2-D array.
///
/// `k` is the diagonal above which to zero elements. 0 = main diagonal.
///
/// Analogous to `numpy.tril()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if input is not 2-D.
pub fn tril<T: Element>(a: &Array<T, IxDyn>, k: isize) -> FerrayResult<Array<T, IxDyn>> {
    let shape = a.shape();
    if shape.len() != 2 {
        return Err(FerrayError::invalid_value("tril: input must be 2-D"));
    }
    let (n, m) = (shape[0], shape[1]);
    let src: Vec<T> = a.iter().cloned().collect();
    let mut data = vec![T::zero(); n * m];
    for i in 0..n {
        for j in 0..m {
            if (i as isize) >= (j as isize) - k {
                data[i * m + j] = src[i * m + j].clone();
            }
        }
    }
    Array::from_vec(IxDyn::new(&[n, m]), data)
}

/// Return the upper triangle of a 2-D array.
///
/// `k` is the diagonal below which to zero elements. 0 = main diagonal.
///
/// Analogous to `numpy.triu()`.
///
/// # Errors
/// Returns `FerrayError::InvalidValue` if input is not 2-D.
pub fn triu<T: Element>(a: &Array<T, IxDyn>, k: isize) -> FerrayResult<Array<T, IxDyn>> {
    let shape = a.shape();
    if shape.len() != 2 {
        return Err(FerrayError::invalid_value("triu: input must be 2-D"));
    }
    let (n, m) = (shape[0], shape[1]);
    let src: Vec<T> = a.iter().cloned().collect();
    let mut data = vec![T::zero(); n * m];
    for i in 0..n {
        for j in 0..m {
            if (i as isize) <= (j as isize) - k {
                data[i * m + j] = src[i * m + j].clone();
            }
        }
    }
    Array::from_vec(IxDyn::new(&[n, m]), data)
}

// ============================================================================
// Tests
// ============================================================================

#[cfg(test)]
mod tests {
    use super::*;
    use crate::dimension::{Ix1, Ix2, IxDyn};

    // -- REQ-16 tests --

    #[test]
    fn test_array_creation() {
        let a = array(Ix2::new([2, 3]), vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0]).unwrap();
        assert_eq!(a.shape(), &[2, 3]);
        assert_eq!(a.size(), 6);
    }

    #[test]
    fn test_asarray() {
        let a = asarray(Ix1::new([3]), vec![1, 2, 3]).unwrap();
        assert_eq!(a.as_slice().unwrap(), &[1, 2, 3]);
    }

    #[test]
    fn test_frombuffer() {
        let bytes: Vec<u8> = vec![1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0];
        let a = frombuffer::<i32, Ix1>(Ix1::new([3]), &bytes).unwrap();
        assert_eq!(a.as_slice().unwrap(), &[1, 2, 3]);
    }

    #[test]
    fn test_frombuffer_bad_length() {
        let bytes: Vec<u8> = vec![1, 0, 0];
        assert!(frombuffer::<i32, Ix1>(Ix1::new([1]), &bytes).is_err());
    }

    #[test]
    fn test_fromiter() {
        let a = fromiter((0..5).map(|x| x as f64)).unwrap();
        assert_eq!(a.shape(), &[5]);
        assert_eq!(a.as_slice().unwrap(), &[0.0, 1.0, 2.0, 3.0, 4.0]);
    }

    #[test]
    fn test_zeros() {
        let a = zeros::<f64, Ix2>(Ix2::new([3, 4])).unwrap();
        assert_eq!(a.shape(), &[3, 4]);
        assert!(a.iter().all(|&v| v == 0.0));
    }

    #[test]
    fn test_ones() {
        let a = ones::<f64, Ix1>(Ix1::new([5])).unwrap();
        assert!(a.iter().all(|&v| v == 1.0));
    }

    #[test]
    fn test_full() {
        let a = full(Ix1::new([4]), 42i32).unwrap();
        assert!(a.iter().all(|&v| v == 42));
    }

    #[test]
    fn test_zeros_like() {
        let a = ones::<f64, Ix2>(Ix2::new([2, 3])).unwrap();
        let b = zeros_like(&a).unwrap();
        assert_eq!(b.shape(), &[2, 3]);
        assert!(b.iter().all(|&v| v == 0.0));
    }

    #[test]
    fn test_ones_like() {
        let a = zeros::<f64, Ix1>(Ix1::new([4])).unwrap();
        let b = ones_like(&a).unwrap();
        assert!(b.iter().all(|&v| v == 1.0));
    }

    #[test]
    fn test_full_like() {
        let a = zeros::<i32, Ix1>(Ix1::new([3])).unwrap();
        let b = full_like(&a, 7).unwrap();
        assert!(b.iter().all(|&v| v == 7));
    }

    // -- REQ-17 tests --

    #[test]
    fn test_empty_and_init() {
        let mut u = empty::<f64, Ix1>(Ix1::new([3]));
        assert_eq!(u.shape(), &[3]);
        u.write_at(0, 1.0).unwrap();
        u.write_at(1, 2.0).unwrap();
        u.write_at(2, 3.0).unwrap();
        // SAFETY: all elements initialized
        let a = unsafe { u.assume_init() };
        assert_eq!(a.as_slice().unwrap(), &[1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_empty_write_oob() {
        let mut u = empty::<f64, Ix1>(Ix1::new([2]));
        assert!(u.write_at(5, 1.0).is_err());
    }

    // -- REQ-18 tests --

    #[test]
    fn test_arange_int() {
        let a = arange(0i32, 5, 1).unwrap();
        assert_eq!(a.as_slice().unwrap(), &[0, 1, 2, 3, 4]);
    }

    #[test]
    fn test_arange_float() {
        let a = arange(0.0_f64, 1.0, 0.25).unwrap();
        assert_eq!(a.shape(), &[4]);
        let data = a.as_slice().unwrap();
        assert!((data[0] - 0.0).abs() < 1e-10);
        assert!((data[1] - 0.25).abs() < 1e-10);
        assert!((data[2] - 0.5).abs() < 1e-10);
        assert!((data[3] - 0.75).abs() < 1e-10);
    }

    #[test]
    fn test_arange_negative_step() {
        let a = arange(5.0_f64, 0.0, -1.0).unwrap();
        assert_eq!(a.shape(), &[5]);
    }

    #[test]
    fn test_arange_zero_step() {
        assert!(arange(0.0_f64, 1.0, 0.0).is_err());
    }

    #[test]
    fn test_arange_empty() {
        let a = arange(5i32, 0, 1).unwrap();
        assert_eq!(a.shape(), &[0]);
    }

    #[test]
    fn test_linspace() {
        let a = linspace(0.0_f64, 1.0, 5, true).unwrap();
        assert_eq!(a.shape(), &[5]);
        let data = a.as_slice().unwrap();
        assert!((data[0] - 0.0).abs() < 1e-10);
        assert!((data[4] - 1.0).abs() < 1e-10);
        assert!((data[2] - 0.5).abs() < 1e-10);
    }

    #[test]
    fn test_linspace_no_endpoint() {
        let a = linspace(0.0_f64, 1.0, 4, false).unwrap();
        assert_eq!(a.shape(), &[4]);
        let data = a.as_slice().unwrap();
        assert!((data[0] - 0.0).abs() < 1e-10);
        assert!((data[1] - 0.25).abs() < 1e-10);
    }

    #[test]
    fn test_linspace_single() {
        let a = linspace(5.0_f64, 10.0, 1, true).unwrap();
        assert_eq!(a.as_slice().unwrap(), &[5.0]);
    }

    #[test]
    fn test_linspace_empty() {
        let a = linspace(0.0_f64, 1.0, 0, true).unwrap();
        assert_eq!(a.shape(), &[0]);
    }

    #[test]
    fn test_logspace() {
        let a = logspace(0.0_f64, 2.0, 3, true, 10.0).unwrap();
        let data = a.as_slice().unwrap();
        assert!((data[0] - 1.0).abs() < 1e-10); // 10^0
        assert!((data[1] - 10.0).abs() < 1e-10); // 10^1
        assert!((data[2] - 100.0).abs() < 1e-10); // 10^2
    }

    #[test]
    fn test_geomspace() {
        let a = geomspace(1.0_f64, 1000.0, 4, true).unwrap();
        let data = a.as_slice().unwrap();
        assert!((data[0] - 1.0).abs() < 1e-10);
        assert!((data[1] - 10.0).abs() < 1e-8);
        assert!((data[2] - 100.0).abs() < 1e-6);
        assert!((data[3] - 1000.0).abs() < 1e-4);
    }

    #[test]
    fn test_geomspace_zero_start() {
        assert!(geomspace(0.0_f64, 1.0, 5, true).is_err());
    }

    #[test]
    fn test_geomspace_different_signs() {
        assert!(geomspace(-1.0_f64, 1.0, 5, true).is_err());
    }

    #[test]
    fn test_meshgrid_xy() {
        let x = Array::from_vec(Ix1::new([3]), vec![1.0, 2.0, 3.0]).unwrap();
        let y = Array::from_vec(Ix1::new([2]), vec![4.0, 5.0]).unwrap();
        let grids = meshgrid(&[x, y], "xy").unwrap();
        assert_eq!(grids.len(), 2);
        assert_eq!(grids[0].shape(), &[2, 3]);
        assert_eq!(grids[1].shape(), &[2, 3]);
        // X grid: rows are [1,2,3], [1,2,3]
        let xdata: Vec<f64> = grids[0].iter().copied().collect();
        assert_eq!(xdata, vec![1.0, 2.0, 3.0, 1.0, 2.0, 3.0]);
        // Y grid: rows are [4,4,4], [5,5,5]
        let ydata: Vec<f64> = grids[1].iter().copied().collect();
        assert_eq!(ydata, vec![4.0, 4.0, 4.0, 5.0, 5.0, 5.0]);
    }

    #[test]
    fn test_meshgrid_ij() {
        let x = Array::from_vec(Ix1::new([3]), vec![1.0, 2.0, 3.0]).unwrap();
        let y = Array::from_vec(Ix1::new([2]), vec![4.0, 5.0]).unwrap();
        let grids = meshgrid(&[x, y], "ij").unwrap();
        assert_eq!(grids.len(), 2);
        assert_eq!(grids[0].shape(), &[3, 2]);
        assert_eq!(grids[1].shape(), &[3, 2]);
    }

    #[test]
    fn test_meshgrid_bad_indexing() {
        assert!(meshgrid(&[], "zz").is_err());
    }

    #[test]
    fn test_mgrid() {
        let grids = mgrid(&[(0.0, 3.0, 1.0), (0.0, 2.0, 1.0)]).unwrap();
        assert_eq!(grids.len(), 2);
        assert_eq!(grids[0].shape(), &[3, 2]);
    }

    #[test]
    fn test_ogrid() {
        let grids = ogrid(&[(0.0, 3.0, 1.0), (0.0, 2.0, 1.0)]).unwrap();
        assert_eq!(grids.len(), 2);
        assert_eq!(grids[0].shape(), &[3, 1]);
        assert_eq!(grids[1].shape(), &[1, 2]);
    }

    // -- REQ-19 tests --

    #[test]
    fn test_identity() {
        let a = identity::<f64>(3).unwrap();
        assert_eq!(a.shape(), &[3, 3]);
        let data = a.as_slice().unwrap();
        assert_eq!(data, &[1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0]);
    }

    #[test]
    fn test_eye() {
        let a = eye::<f64>(3, 4, 0).unwrap();
        assert_eq!(a.shape(), &[3, 4]);
        let data = a.as_slice().unwrap();
        assert_eq!(
            data,
            &[1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]
        );
    }

    #[test]
    fn test_eye_positive_k() {
        let a = eye::<f64>(3, 3, 1).unwrap();
        let data = a.as_slice().unwrap();
        assert_eq!(data, &[0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]);
    }

    #[test]
    fn test_eye_negative_k() {
        let a = eye::<f64>(3, 3, -1).unwrap();
        let data = a.as_slice().unwrap();
        assert_eq!(data, &[0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0]);
    }

    #[test]
    fn test_diag_from_1d() {
        let a = Array::from_vec(IxDyn::new(&[3]), vec![1.0, 2.0, 3.0]).unwrap();
        let d = diag(&a, 0).unwrap();
        assert_eq!(d.shape(), &[3, 3]);
        let data: Vec<f64> = d.iter().copied().collect();
        assert_eq!(data, vec![1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 3.0]);
    }

    #[test]
    fn test_diag_from_2d() {
        let a = Array::from_vec(
            IxDyn::new(&[3, 3]),
            vec![1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0, 3.0],
        )
        .unwrap();
        let d = diag(&a, 0).unwrap();
        assert_eq!(d.shape(), &[3]);
        let data: Vec<f64> = d.iter().copied().collect();
        assert_eq!(data, vec![1.0, 2.0, 3.0]);
    }

    #[test]
    fn test_diag_k_positive() {
        let a = Array::from_vec(IxDyn::new(&[2]), vec![1.0, 2.0]).unwrap();
        let d = diag(&a, 1).unwrap();
        assert_eq!(d.shape(), &[3, 3]);
        let data: Vec<f64> = d.iter().copied().collect();
        assert_eq!(data, vec![0.0, 1.0, 0.0, 0.0, 0.0, 2.0, 0.0, 0.0, 0.0]);
    }

    #[test]
    fn test_diagflat() {
        let a = Array::from_vec(IxDyn::new(&[2, 2]), vec![1.0, 2.0, 3.0, 4.0]).unwrap();
        let d = diagflat(&a, 0).unwrap();
        assert_eq!(d.shape(), &[4, 4]);
        // Diagonal should be [1, 2, 3, 4]
        let extracted = diag(&d, 0).unwrap();
        let data: Vec<f64> = extracted.iter().copied().collect();
        assert_eq!(data, vec![1.0, 2.0, 3.0, 4.0]);
    }

    #[test]
    fn test_tri() {
        let a = tri::<f64>(3, 3, 0).unwrap();
        let data = a.as_slice().unwrap();
        assert_eq!(data, &[1.0, 0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0, 1.0]);
    }

    #[test]
    fn test_tril() {
        let a = Array::from_vec(
            IxDyn::new(&[3, 3]),
            vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
        )
        .unwrap();
        let t = tril(&a, 0).unwrap();
        let data: Vec<f64> = t.iter().copied().collect();
        assert_eq!(data, vec![1.0, 0.0, 0.0, 4.0, 5.0, 0.0, 7.0, 8.0, 9.0]);
    }

    #[test]
    fn test_triu() {
        let a = Array::from_vec(
            IxDyn::new(&[3, 3]),
            vec![1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0],
        )
        .unwrap();
        let t = triu(&a, 0).unwrap();
        let data: Vec<f64> = t.iter().copied().collect();
        assert_eq!(data, vec![1.0, 2.0, 3.0, 0.0, 5.0, 6.0, 0.0, 0.0, 9.0]);
    }

    #[test]
    fn test_tril_not_2d() {
        let a = Array::from_vec(IxDyn::new(&[3]), vec![1.0, 2.0, 3.0]).unwrap();
        assert!(tril(&a, 0).is_err());
    }

    #[test]
    fn test_triu_not_2d() {
        let a = Array::from_vec(IxDyn::new(&[3]), vec![1.0, 2.0, 3.0]).unwrap();
        assert!(triu(&a, 0).is_err());
    }
}