use ndarray::prelude::*;
use rayon::prelude::*;
#[cfg(feature = "serde")]
use serde::{Deserialize, Serialize};

use crate::prelude::*;
use crate::POOL;

#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Default)]
#[cfg_attr(feature = "serde", derive(Serialize, Deserialize))]
pub enum IndexOrder {
    C,
    #[default]
    Fortran,
}

impl<T> ChunkedArray<T>
where
    T: PolarsNumericType,
{
    /// If data is aligned in a single chunk and has no Null values a zero copy view is returned
    /// as an [ndarray]
    pub fn to_ndarray(&self) -> PolarsResult<ArrayView1<T::Native>> {
        let slice = self.cont_slice()?;
        Ok(aview1(slice))
    }
}

impl ListChunked {
    /// If all nested [`Series`] have the same length, a 2 dimensional [`ndarray::Array`] is returned.
    pub fn to_ndarray<N>(&self) -> PolarsResult<Array2<N::Native>>
    where
        N: PolarsNumericType,
    {
        polars_ensure!(
            self.null_count() == 0,
            ComputeError: "creation of ndarray with null values is not supported"
        );

        // first iteration determine the size
        let mut iter = self.into_no_null_iter();
        let series = iter
            .next()
            .ok_or_else(|| polars_err!(NoData: "unable to create ndarray of empty ListChunked"))?;

        let width = series.len();
        let mut row_idx = 0;
        let mut ndarray = ndarray::Array::uninit((self.len(), width));

        let series = series.cast(&N::get_dtype())?;
        let ca = series.unpack::<N>()?;
        let a = ca.to_ndarray()?;
        let mut row = ndarray.slice_mut(s![row_idx, ..]);
        a.assign_to(&mut row);
        row_idx += 1;

        for series in iter {
            polars_ensure!(
                series.len() == width,
                ShapeMismatch: "unable to create a 2-D array, series have different lengths"
            );
            let series = series.cast(&N::get_dtype())?;
            let ca = series.unpack::<N>()?;
            let a = ca.to_ndarray()?;
            let mut row = ndarray.slice_mut(s![row_idx, ..]);
            a.assign_to(&mut row);
            row_idx += 1;
        }

        debug_assert_eq!(row_idx, self.len());
        // SAFETY:
        // We have assigned to every row and element of the array
        unsafe { Ok(ndarray.assume_init()) }
    }
}

impl DataFrame {
    /// Create a 2D [`ndarray::Array`] from this [`DataFrame`]. This requires all columns in the
    /// [`DataFrame`] to be non-null and numeric. They will be casted to the same data type
    /// (if they aren't already).
    ///
    /// For floating point data we implicitly convert `None` to `NaN` without failure.
    ///
    /// ```rust
    /// use polars_core::prelude::*;
    /// let a = UInt32Chunked::new("a", &[1, 2, 3]).into_series();
    /// let b = Float64Chunked::new("b", &[10., 8., 6.]).into_series();
    ///
    /// let df = DataFrame::new(vec![a, b]).unwrap();
    /// let ndarray = df.to_ndarray::<Float64Type>(IndexOrder::Fortran).unwrap();
    /// println!("{:?}", ndarray);
    /// ```
    /// Outputs:
    /// ```text
    /// [[1.0, 10.0],
    ///  [2.0, 8.0],
    ///  [3.0, 6.0]], shape=[3, 2], strides=[1, 3], layout=Ff (0xa), const ndim=2
    /// ```
    pub fn to_ndarray<N>(&self, ordering: IndexOrder) -> PolarsResult<Array2<N::Native>>
    where
        N: PolarsNumericType,
    {
        let columns = POOL.install(|| {
            self.get_columns()
                .par_iter()
                .map(|s| {
                    let s = s.cast(&N::get_dtype())?;
                    let s = match s.dtype() {
                        DataType::Float32 => {
                            let ca = s.f32().unwrap();
                            ca.none_to_nan().into_series()
                        },
                        DataType::Float64 => {
                            let ca = s.f64().unwrap();
                            ca.none_to_nan().into_series()
                        },
                        _ => s,
                    };
                    Ok(s.rechunk())
                })
                .collect::<PolarsResult<Vec<_>>>()
        })?;

        let shape = self.shape();
        let height = self.height();
        let mut membuf = Vec::with_capacity(shape.0 * shape.1);
        let ptr = membuf.as_ptr() as usize;

        POOL.install(|| {
            columns
                .par_iter()
                .enumerate()
                .map(|(col_idx, s)| {
                    polars_ensure!(
                        s.null_count() == 0,
                        ComputeError: "creation of ndarray with null values is not supported"
                    );

                    // this is an Arc clone if already of type N
                    let s = s.cast(&N::get_dtype())?;
                    let ca = s.unpack::<N>()?;
                    let vals = ca.cont_slice().unwrap();

                    // Depending on the desired order, we add items to the buffer.
                    // SAFETY:
                    // We get parallel access to the vector by offsetting index access accordingly.
                    // For C-order, we only operate on every num-col-th element, starting from the
                    // column index. For Fortran-order we only operate on n contiguous elements,
                    // offset by n * the column index.
                    match ordering {
                        IndexOrder::C => unsafe {
                            let num_cols = columns.len();
                            let mut offset = (ptr as *mut N::Native).add(col_idx);
                            for v in vals.iter() {
                                *offset = *v;
                                offset = offset.add(num_cols);
                            }
                        },
                        IndexOrder::Fortran => unsafe {
                            let offset_ptr = (ptr as *mut N::Native).add(col_idx * height);
                            // SAFETY:
                            // this is uninitialized memory, so we must never read from this data
                            // copy_from_slice does not read
                            let buf = std::slice::from_raw_parts_mut(offset_ptr, height);
                            buf.copy_from_slice(vals)
                        },
                    }
                    Ok(())
                })
                .collect::<PolarsResult<Vec<_>>>()
        })?;

        // SAFETY:
        // we have written all data, so we can now safely set length
        unsafe {
            membuf.set_len(shape.0 * shape.1);
        }
        // Depending on the desired order, we can either return the array buffer as-is or reverse
        // the axes.
        match ordering {
            IndexOrder::C => Ok(Array2::from_shape_vec((shape.0, shape.1), membuf).unwrap()),
            IndexOrder::Fortran => {
                let ndarr = Array2::from_shape_vec((shape.1, shape.0), membuf).unwrap();
                Ok(ndarr.reversed_axes())
            },
        }
    }
}

#[cfg(test)]
mod test {
    use super::*;

    #[test]
    fn test_ndarray_from_ca() -> PolarsResult<()> {
        let ca = Float64Chunked::new("", &[1.0, 2.0, 3.0]);
        let ndarr = ca.to_ndarray()?;
        assert_eq!(ndarr, ArrayView1::from(&[1.0, 2.0, 3.0]));

        let mut builder =
            ListPrimitiveChunkedBuilder::<Float64Type>::new("", 10, 10, DataType::Float64);
        builder.append_opt_slice(Some(&[1.0, 2.0, 3.0]));
        builder.append_opt_slice(Some(&[2.0, 4.0, 5.0]));
        builder.append_opt_slice(Some(&[6.0, 7.0, 8.0]));
        let list = builder.finish();

        let ndarr = list.to_ndarray::<Float64Type>()?;
        let expected = array![[1.0, 2.0, 3.0], [2.0, 4.0, 5.0], [6.0, 7.0, 8.0]];
        assert_eq!(ndarr, expected);

        // test list array that is not square
        let mut builder =
            ListPrimitiveChunkedBuilder::<Float64Type>::new("", 10, 10, DataType::Float64);
        builder.append_opt_slice(Some(&[1.0, 2.0, 3.0]));
        builder.append_opt_slice(Some(&[2.0]));
        builder.append_opt_slice(Some(&[6.0, 7.0, 8.0]));
        let list = builder.finish();
        assert!(list.to_ndarray::<Float64Type>().is_err());
        Ok(())
    }

    #[test]
    fn test_ndarray_from_df_order_fortran() -> PolarsResult<()> {
        let df = df!["a"=> [1.0, 2.0, 3.0],
            "b" => [2.0, 3.0, 4.0]
        ]?;

        let ndarr = df.to_ndarray::<Float64Type>(IndexOrder::Fortran)?;
        let expected = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];
        assert!(!ndarr.is_standard_layout());
        assert_eq!(ndarr, expected);

        Ok(())
    }

    #[test]
    fn test_ndarray_from_df_order_c() -> PolarsResult<()> {
        let df = df!["a"=> [1.0, 2.0, 3.0],
            "b" => [2.0, 3.0, 4.0]
        ]?;

        let ndarr = df.to_ndarray::<Float64Type>(IndexOrder::C)?;
        let expected = array![[1.0, 2.0], [2.0, 3.0], [3.0, 4.0]];
        assert!(ndarr.is_standard_layout());
        assert_eq!(ndarr, expected);

        Ok(())
    }
}