use std::borrow::Cow;
use ahash::RandomState;
use polars_arrow::prelude::QuantileInterpolOptions;
use super::{private, IntoSeries, SeriesTrait, SeriesWrap, *};
use crate::chunked_array::comparison::*;
use crate::chunked_array::ops::aggregate::{ChunkAggSeries, QuantileAggSeries, VarAggSeries};
use crate::chunked_array::ops::compare_inner::{
IntoPartialEqInner, IntoPartialOrdInner, PartialEqInner, PartialOrdInner,
};
use crate::chunked_array::ops::explode::ExplodeByOffsets;
use crate::chunked_array::AsSinglePtr;
#[cfg(feature = "algorithm_group_by")]
use crate::frame::group_by::*;
#[cfg(feature = "algorithm_join")]
use crate::frame::hash_join::ZipOuterJoinColumn;
use crate::prelude::*;
#[cfg(feature = "checked_arithmetic")]
use crate::series::arithmetic::checked::NumOpsDispatchChecked;
macro_rules! impl_dyn_series {
($ca: ident) => {
impl private::PrivateSeries for SeriesWrap<$ca> {
fn compute_len(&mut self) {
self.0.compute_len()
}
fn _field(&self) -> Cow<Field> {
Cow::Borrowed(self.0.ref_field())
}
fn _dtype(&self) -> &DataType {
self.0.ref_field().data_type()
}
fn _set_flags(&mut self, flags: Settings) {
self.0.set_flags(flags)
}
fn _get_flags(&self) -> Settings {
self.0.get_flags()
}
fn explode_by_offsets(&self, offsets: &[i64]) -> Series {
self.0.explode_by_offsets(offsets)
}
#[cfg(feature = "cum_agg")]
fn _cummax(&self, reverse: bool) -> Series {
self.0.cummax(reverse).into_series()
}
#[cfg(feature = "cum_agg")]
fn _cummin(&self, reverse: bool) -> Series {
self.0.cummin(reverse).into_series()
}
unsafe fn equal_element(
&self,
idx_self: usize,
idx_other: usize,
other: &Series,
) -> bool {
self.0.equal_element(idx_self, idx_other, other)
}
#[cfg(feature = "zip_with")]
fn zip_with_same_type(
&self,
mask: &BooleanChunked,
other: &Series,
) -> PolarsResult<Series> {
ChunkZip::zip_with(&self.0, mask, other.as_ref().as_ref())
.map(|ca| ca.into_series())
}
fn into_partial_eq_inner<'a>(&'a self) -> Box<dyn PartialEqInner + 'a> {
(&self.0).into_partial_eq_inner()
}
fn into_partial_ord_inner<'a>(&'a self) -> Box<dyn PartialOrdInner + 'a> {
(&self.0).into_partial_ord_inner()
}
fn vec_hash(&self, random_state: RandomState, buf: &mut Vec<u64>) -> PolarsResult<()> {
self.0.vec_hash(random_state, buf)?;
Ok(())
}
fn vec_hash_combine(
&self,
build_hasher: RandomState,
hashes: &mut [u64],
) -> PolarsResult<()> {
self.0.vec_hash_combine(build_hasher, hashes)?;
Ok(())
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_min(&self, groups: &GroupsProxy) -> Series {
self.0.agg_min(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_max(&self, groups: &GroupsProxy) -> Series {
self.0.agg_max(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_sum(&self, groups: &GroupsProxy) -> Series {
self.0.agg_sum(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_std(&self, groups: &GroupsProxy, ddof: u8) -> Series {
self.agg_std(groups, ddof)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_var(&self, groups: &GroupsProxy, ddof: u8) -> Series {
self.agg_var(groups, ddof)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_list(&self, groups: &GroupsProxy) -> Series {
self.0.agg_list(groups)
}
#[cfg(feature = "algorithm_join")]
unsafe fn zip_outer_join_column(
&self,
right_column: &Series,
opt_join_tuples: &[(Option<IdxSize>, Option<IdxSize>)],
) -> Series {
ZipOuterJoinColumn::zip_outer_join_column(&self.0, right_column, opt_join_tuples)
}
fn subtract(&self, rhs: &Series) -> PolarsResult<Series> {
NumOpsDispatch::subtract(&self.0, rhs)
}
fn add_to(&self, rhs: &Series) -> PolarsResult<Series> {
NumOpsDispatch::add_to(&self.0, rhs)
}
fn multiply(&self, rhs: &Series) -> PolarsResult<Series> {
NumOpsDispatch::multiply(&self.0, rhs)
}
fn divide(&self, rhs: &Series) -> PolarsResult<Series> {
NumOpsDispatch::divide(&self.0, rhs)
}
fn remainder(&self, rhs: &Series) -> PolarsResult<Series> {
NumOpsDispatch::remainder(&self.0, rhs)
}
#[cfg(feature = "algorithm_group_by")]
fn group_tuples(&self, multithreaded: bool, sorted: bool) -> PolarsResult<GroupsProxy> {
IntoGroupsProxy::group_tuples(&self.0, multithreaded, sorted)
}
fn arg_sort_multiple(&self, options: &SortMultipleOptions) -> PolarsResult<IdxCa> {
self.0.arg_sort_multiple(options)
}
}
impl SeriesTrait for SeriesWrap<$ca> {
#[cfg(feature = "rolling_window")]
fn rolling_map(
&self,
_f: &dyn Fn(&Series) -> Series,
_options: RollingOptionsFixedWindow,
) -> PolarsResult<Series> {
ChunkRollApply::rolling_map(&self.0, _f, _options).map(|ca| ca.into_series())
}
fn rename(&mut self, name: &str) {
self.0.rename(name);
}
fn chunk_lengths(&self) -> ChunkIdIter {
self.0.chunk_id()
}
fn name(&self) -> &str {
self.0.name()
}
fn chunks(&self) -> &Vec<ArrayRef> {
self.0.chunks()
}
unsafe fn chunks_mut(&mut self) -> &mut Vec<ArrayRef> {
self.0.chunks_mut()
}
fn shrink_to_fit(&mut self) {
self.0.shrink_to_fit()
}
fn slice(&self, offset: i64, length: usize) -> Series {
return self.0.slice(offset, length).into_series();
}
fn append(&mut self, other: &Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), append);
self.0.append(other.as_ref().as_ref());
Ok(())
}
fn extend(&mut self, other: &Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), extend);
self.0.extend(other.as_ref().as_ref());
Ok(())
}
fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Series> {
ChunkFilter::filter(&self.0, filter).map(|ca| ca.into_series())
}
fn mean(&self) -> Option<f64> {
self.0.mean()
}
fn median(&self) -> Option<f64> {
self.0.median().map(|v| v as f64)
}
#[cfg(feature = "chunked_ids")]
unsafe fn _take_chunked_unchecked(&self, by: &[ChunkId], sorted: IsSorted) -> Series {
self.0.take_chunked_unchecked(by, sorted).into_series()
}
#[cfg(feature = "chunked_ids")]
unsafe fn _take_opt_chunked_unchecked(&self, by: &[Option<ChunkId>]) -> Series {
self.0.take_opt_chunked_unchecked(by).into_series()
}
fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
let indices = if indices.chunks.len() > 1 {
Cow::Owned(indices.rechunk())
} else {
Cow::Borrowed(indices)
};
Ok(self.0.take((&*indices).into())?.into_series())
}
fn take_iter(&self, iter: &mut dyn TakeIterator) -> PolarsResult<Series> {
Ok(self.0.take(iter.into())?.into_series())
}
unsafe fn take_iter_unchecked(&self, iter: &mut dyn TakeIterator) -> Series {
self.0.take_unchecked(iter.into()).into_series()
}
unsafe fn take_unchecked(&self, idx: &IdxCa) -> PolarsResult<Series> {
let idx = if idx.chunks.len() > 1 {
Cow::Owned(idx.rechunk())
} else {
Cow::Borrowed(idx)
};
let mut out = self.0.take_unchecked((&*idx).into());
if self.0.is_sorted_ascending_flag()
&& (idx.is_sorted_ascending_flag() || idx.is_sorted_descending_flag())
{
out.set_sorted_flag(idx.is_sorted_flag())
}
Ok(out.into_series())
}
unsafe fn take_opt_iter_unchecked(&self, iter: &mut dyn TakeIteratorNulls) -> Series {
self.0.take_unchecked(iter.into()).into_series()
}
#[cfg(feature = "take_opt_iter")]
fn take_opt_iter(&self, iter: &mut dyn TakeIteratorNulls) -> PolarsResult<Series> {
Ok(self.0.take(iter.into())?.into_series())
}
fn len(&self) -> usize {
self.0.len()
}
fn rechunk(&self) -> Series {
self.0.rechunk().into_series()
}
fn new_from_index(&self, index: usize, length: usize) -> Series {
ChunkExpandAtIndex::new_from_index(&self.0, index, length).into_series()
}
fn cast(&self, data_type: &DataType) -> PolarsResult<Series> {
self.0.cast(data_type)
}
fn get(&self, index: usize) -> PolarsResult<AnyValue> {
self.0.get_any_value(index)
}
#[inline]
unsafe fn get_unchecked(&self, index: usize) -> AnyValue {
self.0.get_any_value_unchecked(index)
}
fn sort_with(&self, options: SortOptions) -> Series {
ChunkSort::sort_with(&self.0, options).into_series()
}
fn arg_sort(&self, options: SortOptions) -> IdxCa {
ChunkSort::arg_sort(&self.0, options)
}
fn null_count(&self) -> usize {
self.0.null_count()
}
fn has_validity(&self) -> bool {
self.0.has_validity()
}
#[cfg(feature = "algorithm_group_by")]
fn unique(&self) -> PolarsResult<Series> {
ChunkUnique::unique(&self.0).map(|ca| ca.into_series())
}
#[cfg(feature = "algorithm_group_by")]
fn n_unique(&self) -> PolarsResult<usize> {
ChunkUnique::n_unique(&self.0)
}
#[cfg(feature = "algorithm_group_by")]
fn arg_unique(&self) -> PolarsResult<IdxCa> {
ChunkUnique::arg_unique(&self.0)
}
fn is_null(&self) -> BooleanChunked {
self.0.is_null()
}
fn is_not_null(&self) -> BooleanChunked {
self.0.is_not_null()
}
fn reverse(&self) -> Series {
ChunkReverse::reverse(&self.0).into_series()
}
fn as_single_ptr(&mut self) -> PolarsResult<usize> {
self.0.as_single_ptr()
}
fn shift(&self, periods: i64) -> Series {
ChunkShift::shift(&self.0, periods).into_series()
}
fn _sum_as_series(&self) -> Series {
ChunkAggSeries::sum_as_series(&self.0)
}
fn max_as_series(&self) -> Series {
ChunkAggSeries::max_as_series(&self.0)
}
fn min_as_series(&self) -> Series {
ChunkAggSeries::min_as_series(&self.0)
}
fn median_as_series(&self) -> Series {
QuantileAggSeries::median_as_series(&self.0)
}
fn var_as_series(&self, ddof: u8) -> Series {
VarAggSeries::var_as_series(&self.0, ddof)
}
fn std_as_series(&self, ddof: u8) -> Series {
VarAggSeries::std_as_series(&self.0, ddof)
}
fn quantile_as_series(
&self,
quantile: f64,
interpol: QuantileInterpolOptions,
) -> PolarsResult<Series> {
QuantileAggSeries::quantile_as_series(&self.0, quantile, interpol)
}
fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
Arc::new(SeriesWrap(Clone::clone(&self.0)))
}
fn peak_max(&self) -> BooleanChunked {
self.0.peak_max()
}
fn peak_min(&self) -> BooleanChunked {
self.0.peak_min()
}
#[cfg(feature = "repeat_by")]
fn repeat_by(&self, by: &IdxCa) -> PolarsResult<ListChunked> {
RepeatBy::repeat_by(&self.0, by)
}
#[cfg(feature = "checked_arithmetic")]
fn checked_div(&self, rhs: &Series) -> PolarsResult<Series> {
self.0.checked_div(rhs)
}
#[cfg(feature = "mode")]
fn mode(&self) -> PolarsResult<Series> {
Ok(self.0.mode()?.into_series())
}
}
};
}
impl_dyn_series!(Float32Chunked);
impl_dyn_series!(Float64Chunked);