polars-expr 0.53.0

use polars_core::POOL;
use polars_core::prelude::*;
use polars_ops::chunked_array::ListNameSpaceImpl;
use polars_utils::idx_vec::IdxVec;
use rayon::prelude::*;

use super::*;
use crate::expressions::{AggState, AggregationContext, PhysicalExpr};

pub struct SortExpr {
    pub(crate) physical_expr: Arc<dyn PhysicalExpr>,
    pub(crate) options: SortOptions,
    expr: Expr,
}

impl SortExpr {
    pub fn new(physical_expr: Arc<dyn PhysicalExpr>, options: SortOptions, expr: Expr) -> Self {
        Self {
            physical_expr,
            options,
            expr,
        }
    }
}

/// Map arg_sort result back to the indices on the `GroupIdx`
pub(crate) fn map_sorted_indices_to_group_idx(sorted_idx: &IdxCa, idx: &[IdxSize]) -> IdxVec {
    sorted_idx
        .cont_slice()
        .unwrap()
        .iter()
        .map(|&i| unsafe { *idx.get_unchecked(i as usize) })
        .collect()
}

pub(crate) fn map_sorted_indices_to_group_slice(sorted_idx: &IdxCa, first: IdxSize) -> IdxVec {
    sorted_idx
        .cont_slice()
        .unwrap()
        .iter()
        .map(|&i| i + first)
        .collect()
}

impl PhysicalExpr for SortExpr {
    fn as_expression(&self) -> Option<&Expr> {
        Some(&self.expr)
    }

    fn evaluate(&self, df: &DataFrame, state: &ExecutionState) -> PolarsResult<Column> {
        let series = self.physical_expr.evaluate(df, state)?;
        series.sort_with(self.options)
    }

    #[allow(clippy::ptr_arg)]
    fn evaluate_on_groups<'a>(
        &self,
        df: &DataFrame,
        groups: &'a GroupPositions,
        state: &ExecutionState,
    ) -> PolarsResult<AggregationContext<'a>> {
        let mut ac = self.physical_expr.evaluate_on_groups(df, groups, state)?;
        match ac.agg_state() {
            AggState::AggregatedList(s) => {
                let ca = s.list().unwrap();
                let out = ca.lst_sort(self.options)?;
                ac.with_values(out.into_column(), true, Some(&self.expr))?;
            },
            _ => {
                let series = ac.flat_naive().into_owned();

                let mut sort_options = self.options;
                sort_options.multithreaded = false;
                let groups = POOL.install(|| {
                    match ac.groups().as_ref().as_ref() {
                        GroupsType::Idx(groups) => {
                            groups
                                .par_iter()
                                .map(|(first, idx)| {
                                    // SAFETY: group tuples are always in bounds.
                                    let group = unsafe { series.take_slice_unchecked(idx) };

                                    let sorted_idx = group.arg_sort(sort_options);
                                    let new_idx = map_sorted_indices_to_group_idx(&sorted_idx, idx);
                                    (new_idx.first().copied().unwrap_or(first), new_idx)
                                })
                                .collect()
                        },
                        GroupsType::Slice { groups, .. } => groups
                            .par_iter()
                            .map(|&[first, len]| {
                                let group = series.slice(first as i64, len as usize);
                                let sorted_idx = group.arg_sort(sort_options);
                                let new_idx = map_sorted_indices_to_group_slice(&sorted_idx, first);
                                (new_idx.first().copied().unwrap_or(first), new_idx)
                            })
                            .collect(),
                    }
                });
                let groups = GroupsType::Idx(groups);
                ac.with_groups(groups.into_sliceable());
            },
        }

        Ok(ac)
    }

    fn to_field(&self, input_schema: &Schema) -> PolarsResult<Field> {
        self.physical_expr.to_field(input_schema)
    }

    fn is_scalar(&self) -> bool {
        false
    }
}