//! # Functions
//!
//! Functions that might be useful.
//!
use std::ops::Add;

#[cfg(feature = "diagonal_concat")]
use ahash::AHashSet;
use arrow::compute;
use arrow::types::simd::Simd;
use num_traits::{Float, NumCast, ToPrimitive};
#[cfg(feature = "concat_str")]
use polars_arrow::prelude::ValueSize;

use crate::chunked_array::ops::sort::prepare_arg_sort;
use crate::prelude::*;
use crate::utils::coalesce_nulls;
#[cfg(feature = "diagonal_concat")]
use crate::utils::concat_df;

/// Compute the covariance between two columns.
pub fn cov_f<T>(a: &ChunkedArray<T>, b: &ChunkedArray<T>) -> Option<T::Native>
where
    T: PolarsFloatType,
    T::Native: Float,
    <T::Native as Simd>::Simd: Add<Output = <T::Native as Simd>::Simd>
        + compute::aggregate::Sum<T::Native>
        + compute::aggregate::SimdOrd<T::Native>,
{
    if a.len() != b.len() {
        None
    } else {
        let tmp = (a - a.mean()?) * (b - b.mean()?);
        let n = tmp.len() - tmp.null_count();
        Some(tmp.sum()? / NumCast::from(n - 1).unwrap())
    }
}

/// Compute the covariance between two columns.
pub fn cov_i<T>(a: &ChunkedArray<T>, b: &ChunkedArray<T>) -> Option<f64>
where
    T: PolarsIntegerType,
    T::Native: ToPrimitive,
    <T::Native as Simd>::Simd: Add<Output = <T::Native as Simd>::Simd>
        + compute::aggregate::Sum<T::Native>
        + compute::aggregate::SimdOrd<T::Native>,
{
    if a.len() != b.len() {
        None
    } else {
        let a_mean = a.mean()?;
        let b_mean = b.mean()?;
        let a = a.apply_cast_numeric::<_, Float64Type>(|a| a.to_f64().unwrap() - a_mean);
        let b = b.apply_cast_numeric(|b| b.to_f64().unwrap() - b_mean);

        let tmp = a * b;
        let n = tmp.len() - tmp.null_count();
        Some(tmp.sum()? / (n - 1) as f64)
    }
}

/// Compute the pearson correlation between two columns.
pub fn pearson_corr_i<T>(a: &ChunkedArray<T>, b: &ChunkedArray<T>, ddof: u8) -> Option<f64>
where
    T: PolarsIntegerType,
    T::Native: ToPrimitive,
    <T::Native as Simd>::Simd: Add<Output = <T::Native as Simd>::Simd>
        + compute::aggregate::Sum<T::Native>
        + compute::aggregate::SimdOrd<T::Native>,
    ChunkedArray<T>: ChunkVar<f64>,
{
    let (a, b) = coalesce_nulls(a, b);
    let a = a.as_ref();
    let b = b.as_ref();

    Some(cov_i(a, b)? / (a.std(ddof)? * b.std(ddof)?))
}

/// Compute the pearson correlation between two columns.
pub fn pearson_corr_f<T>(a: &ChunkedArray<T>, b: &ChunkedArray<T>, ddof: u8) -> Option<T::Native>
where
    T: PolarsFloatType,
    T::Native: Float,
    <T::Native as Simd>::Simd: Add<Output = <T::Native as Simd>::Simd>
        + compute::aggregate::Sum<T::Native>
        + compute::aggregate::SimdOrd<T::Native>,
    ChunkedArray<T>: ChunkVar<T::Native>,
{
    let (a, b) = coalesce_nulls(a, b);
    let a = a.as_ref();
    let b = b.as_ref();

    Some(cov_f(a, b)? / (a.std(ddof)? * b.std(ddof)?))
}

/// Find the indexes that would sort these series in order of appearance.
/// That means that the first `Series` will be used to determine the ordering
/// until duplicates are found. Once duplicates are found, the next `Series` will
/// be used and so on.
pub fn arg_sort_by(by: &[Series], descending: &[bool]) -> PolarsResult<IdxCa> {
    polars_ensure!(
        by.len() == descending.len(),
        ComputeError: "the number of ordering booleans: {} does not match the number of series: {}",
        descending.len(), by.len()
    );
    let (first, by, descending) = prepare_arg_sort(by.to_vec(), descending.to_vec()).unwrap();
    first.arg_sort_multiple(&by, &descending)
}

// utility to be able to also add literals to concat_str function
#[cfg(feature = "concat_str")]
enum IterBroadCast<'a> {
    Column(Box<dyn PolarsIterator<Item = Option<&'a str>> + 'a>),
    Value(Option<&'a str>),
}

#[cfg(feature = "concat_str")]
impl<'a> IterBroadCast<'a> {
    fn next(&mut self) -> Option<Option<&'a str>> {
        use IterBroadCast::*;
        match self {
            Column(iter) => iter.next(),
            Value(val) => Some(*val),
        }
    }
}

/// Casts all series to string data and will concat them in linear time.
/// The concatenated strings are separated by a `delimiter`.
/// If no `delimiter` is needed, an empty &str should be passed as argument.
#[cfg(feature = "concat_str")]
pub fn concat_str(s: &[Series], delimiter: &str) -> PolarsResult<Utf8Chunked> {
    polars_ensure!(!s.is_empty(), NoData: "expected multiple series in `concat_str`");
    if s.iter().any(|s| s.is_empty()) {
        return Ok(Utf8Chunked::full_null(s[0].name(), 0));
    }

    let len = s.iter().map(|s| s.len()).max().unwrap();

    let cas = s
        .iter()
        .map(|s| {
            let s = s.cast(&DataType::Utf8)?;
            let mut ca = s.utf8()?.clone();
            // broadcast
            if ca.len() == 1 && len > 1 {
                ca = ca.new_from_index(0, len)
            }

            Ok(ca)
        })
        .collect::<PolarsResult<Vec<_>>>()?;

    polars_ensure!(
        s.iter().all(|s| s.len() == 1 || s.len() == len),
        ComputeError: "all series in `concat_str` should have equal or unit length"
    );
    let mut iters = cas
        .iter()
        .map(|ca| match ca.len() {
            1 => IterBroadCast::Value(ca.get(0)),
            _ => IterBroadCast::Column(ca.into_iter()),
        })
        .collect::<Vec<_>>();

    let bytes_cap = cas.iter().map(|ca| ca.get_values_size()).sum();
    let mut builder = Utf8ChunkedBuilder::new(s[0].name(), len, bytes_cap);

    // use a string buffer, to amortize alloc
    let mut buf = String::with_capacity(128);

    for _ in 0..len {
        let mut has_null = false;

        iters.iter_mut().enumerate().for_each(|(i, it)| {
            if i > 0 {
                buf.push_str(delimiter);
            }

            match it.next() {
                Some(Some(s)) => buf.push_str(s),
                Some(None) => has_null = true,
                None => {
                    // should not happen as the out loop counts to length
                    unreachable!()
                }
            }
        });

        if has_null {
            builder.append_null();
        } else {
            builder.append_value(&buf)
        }
        buf.truncate(0)
    }
    Ok(builder.finish())
}

/// Concat `[DataFrame]`s horizontally.
#[cfg(feature = "horizontal_concat")]
/// Concat horizontally and extend with null values if lengths don't match
pub fn hor_concat_df(dfs: &[DataFrame]) -> PolarsResult<DataFrame> {
    let max_len = dfs
        .iter()
        .map(|df| df.height())
        .max()
        .ok_or_else(|| polars_err!(ComputeError: "cannot concat empty dataframes"))?;

    let owned_df;

    // if not all equal length, extend the DataFrame with nulls
    let dfs = if !dfs.iter().all(|df| df.height() == max_len) {
        owned_df = dfs
            .iter()
            .cloned()
            .map(|mut df| {
                if df.height() != max_len {
                    let diff = max_len - df.height();
                    df.columns
                        .iter_mut()
                        .for_each(|s| *s = s.extend_constant(AnyValue::Null, diff).unwrap());
                }
                df
            })
            .collect::<Vec<_>>();
        owned_df.as_slice()
    } else {
        dfs
    };

    let mut first_df = dfs[0].clone();

    for df in &dfs[1..] {
        first_df.hstack_mut(df.get_columns())?;
    }
    Ok(first_df)
}

/// Concat `[DataFrame]`s diagonally.
#[cfg(feature = "diagonal_concat")]
/// Concat diagonally thereby combining different schemas.
pub fn diag_concat_df(dfs: &[DataFrame]) -> PolarsResult<DataFrame> {
    // TODO! replace with lazy only?
    let upper_bound_width = dfs.iter().map(|df| df.width()).sum();
    let mut column_names = AHashSet::with_capacity(upper_bound_width);
    let mut schema = Vec::with_capacity(upper_bound_width);

    for df in dfs {
        df.get_columns().iter().for_each(|s| {
            let name = s.name();
            if column_names.insert(name) {
                schema.push((name, s.dtype()))
            }
        });
    }

    let dfs = dfs
        .iter()
        .map(|df| {
            let height = df.height();
            let mut columns = Vec::with_capacity(schema.len());

            for (name, dtype) in &schema {
                match df.column(name).ok() {
                    Some(s) => columns.push(s.clone()),
                    None => columns.push(Series::full_null(name, height, dtype)),
                }
            }
            DataFrame::new_no_checks(columns)
        })
        .collect::<Vec<_>>();

    concat_df(&dfs)
}

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

    #[test]
    fn test_cov() {
        let a = Series::new("a", &[1.0f32, 2.0, 5.0]);
        let b = Series::new("b", &[1.0f32, 2.0, -3.0]);
        let out = cov_f(a.f32().unwrap(), b.f32().unwrap());
        assert_eq!(out, Some(-5.0));
        let a = a.cast(&DataType::Int32).unwrap();
        let b = b.cast(&DataType::Int32).unwrap();
        let out = cov_i(a.i32().unwrap(), b.i32().unwrap());
        assert_eq!(out, Some(-5.0));
    }

    #[test]
    fn test_pearson_corr() {
        let a = Series::new("a", &[1.0f32, 2.0]);
        let b = Series::new("b", &[1.0f32, 2.0]);
        assert!((cov_f(a.f32().unwrap(), b.f32().unwrap()).unwrap() - 0.5).abs() < 0.001);
        assert!(
            (pearson_corr_f(a.f32().unwrap(), b.f32().unwrap(), 1).unwrap() - 1.0).abs() < 0.001
        );
    }

    #[test]
    #[cfg(feature = "concat_str")]
    fn test_concat_str() {
        let a = Series::new("a", &["foo", "bar"]);
        let b = Series::new("b", &["spam", "ham"]);

        let out = concat_str(&[a.clone(), b.clone()], "_").unwrap();
        assert_eq!(Vec::from(&out), &[Some("foo_spam"), Some("bar_ham")]);

        let c = Series::new("b", &["literal"]);
        let out = concat_str(&[a, b, c], "_").unwrap();
        assert_eq!(
            Vec::from(&out),
            &[Some("foo_spam_literal"), Some("bar_ham_literal")]
        );
    }

    #[test]
    #[cfg(feature = "diagonal_concat")]
    fn test_diag_concat() -> PolarsResult<()> {
        let a = df![
            "a" => [1, 2],
            "b" => ["a", "b"]
        ]?;

        let b = df![
            "b" => ["a", "b"],
            "c" => [1, 2]
        ]?;

        let c = df![
            "a" => [5, 7],
            "c" => [1, 2],
            "d" => [1, 2]
        ]?;

        let out = diag_concat_df(&[a, b, c])?;

        let expected = df![
            "a" => [Some(1), Some(2), None, None, Some(5), Some(7)],
            "b" => [Some("a"), Some("b"), Some("a"), Some("b"), None, None],
            "c" => [None, None, Some(1), Some(2), Some(1), Some(2)],
            "d" => [None, None, None, None, Some(1), Some(2)]
        ]?;

        assert!(out.frame_equal_missing(&expected));

        Ok(())
    }
}