use super::*;
use crate::chunked_array::comparison::*;
#[cfg(feature = "algorithm_group_by")]
use crate::frame::group_by::*;
use crate::prelude::*;
macro_rules! impl_dyn_series {
($ca: ident, $pdt:ident) => {
impl private::PrivateSeries for SeriesWrap<$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().dtype()
}
fn _set_flags(&mut self, flags: StatisticsFlags) {
self.0.set_flags(flags)
}
fn _get_flags(&self) -> StatisticsFlags {
self.0.get_flags()
}
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_total_eq_inner<'a>(&'a self) -> Box<dyn TotalEqInner + 'a> {
(&self.0).into_total_eq_inner()
}
fn into_total_ord_inner<'a>(&'a self) -> Box<dyn TotalOrdInner + 'a> {
(&self.0).into_total_ord_inner()
}
fn vec_hash(
&self,
random_state: PlRandomState,
buf: &mut Vec<u64>,
) -> PolarsResult<()> {
self.0.vec_hash(random_state, buf)?;
Ok(())
}
fn vec_hash_combine(
&self,
build_hasher: PlRandomState,
hashes: &mut [u64],
) -> PolarsResult<()> {
self.0.vec_hash_combine(build_hasher, hashes)?;
Ok(())
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_min(&self, groups: &GroupsType) -> Series {
self.0.agg_min(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_max(&self, groups: &GroupsType) -> Series {
self.0.agg_max(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_sum(&self, groups: &GroupsType) -> Series {
self.0.agg_sum(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_std(&self, groups: &GroupsType, ddof: u8) -> Series {
self.agg_std(groups, ddof)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_var(&self, groups: &GroupsType, ddof: u8) -> Series {
self.agg_var(groups, ddof)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_list(&self, groups: &GroupsType) -> Series {
self.0.agg_list(groups)
}
#[cfg(feature = "bitwise")]
unsafe fn agg_and(&self, groups: &GroupsType) -> Series {
self.0.agg_and(groups)
}
#[cfg(feature = "bitwise")]
unsafe fn agg_or(&self, groups: &GroupsType) -> Series {
self.0.agg_or(groups)
}
#[cfg(feature = "bitwise")]
unsafe fn agg_xor(&self, groups: &GroupsType) -> Series {
self.0.agg_xor(groups)
}
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<GroupsType> {
IntoGroupsType::group_tuples(&self.0, multithreaded, sorted)
}
fn arg_sort_multiple(
&self,
by: &[Column],
options: &SortMultipleOptions,
) -> PolarsResult<IdxCa> {
self.0.arg_sort_multiple(by, 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: PlSmallStr) {
self.0.rename(name);
}
fn chunk_lengths(&self) -> ChunkLenIter {
self.0.chunk_lengths()
}
fn name(&self) -> &PlSmallStr {
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 split_at(&self, offset: i64) -> (Series, Series) {
let (a, b) = self.0.split_at(offset);
(a.into_series(), b.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 append_owned(&mut self, other: Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), append);
self.0.append_owned(other.take_inner())
}
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 _sum_as_f64(&self) -> f64 {
self.0._sum_as_f64()
}
fn mean(&self) -> Option<f64> {
self.0.mean()
}
fn median(&self) -> Option<f64> {
self.0.median().map(|v| v as f64)
}
fn std(&self, ddof: u8) -> Option<f64> {
self.0.std(ddof)
}
fn var(&self, ddof: u8) -> Option<f64> {
self.0.var(ddof)
}
fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
Ok(self.0.take(indices)?.into_series())
}
unsafe fn take_unchecked(&self, indices: &IdxCa) -> Series {
self.0.take_unchecked(indices).into_series()
}
fn take_slice(&self, indices: &[IdxSize]) -> PolarsResult<Series> {
Ok(self.0.take(indices)?.into_series())
}
unsafe fn take_slice_unchecked(&self, indices: &[IdxSize]) -> Series {
self.0.take_unchecked(indices).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, dtype: &DataType, cast_options: CastOptions) -> PolarsResult<Series> {
self.0.cast_with_options(dtype, cast_options)
}
#[inline]
unsafe fn get_unchecked(&self, index: usize) -> AnyValue {
self.0.get_any_value_unchecked(index)
}
fn sort_with(&self, options: SortOptions) -> PolarsResult<Series> {
Ok(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_nulls(&self) -> bool {
self.0.has_nulls()
}
#[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_reduce(&self) -> PolarsResult<Scalar> {
Ok(ChunkAggSeries::sum_reduce(&self.0))
}
fn max_reduce(&self) -> PolarsResult<Scalar> {
Ok(ChunkAggSeries::max_reduce(&self.0))
}
fn min_reduce(&self) -> PolarsResult<Scalar> {
Ok(ChunkAggSeries::min_reduce(&self.0))
}
fn median_reduce(&self) -> PolarsResult<Scalar> {
Ok(QuantileAggSeries::median_reduce(&self.0))
}
fn var_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
Ok(VarAggSeries::var_reduce(&self.0, ddof))
}
fn std_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
Ok(VarAggSeries::std_reduce(&self.0, ddof))
}
fn quantile_reduce(
&self,
quantile: f64,
method: QuantileMethod,
) -> PolarsResult<Scalar> {
QuantileAggSeries::quantile_reduce(&self.0, quantile, method)
}
#[cfg(feature = "bitwise")]
fn and_reduce(&self) -> PolarsResult<Scalar> {
let dt = <$pdt as PolarsDataType>::get_dtype();
let av = self.0.and_reduce().map_or(AnyValue::Null, Into::into);
Ok(Scalar::new(dt, av))
}
#[cfg(feature = "bitwise")]
fn or_reduce(&self) -> PolarsResult<Scalar> {
let dt = <$pdt as PolarsDataType>::get_dtype();
let av = self.0.or_reduce().map_or(AnyValue::Null, Into::into);
Ok(Scalar::new(dt, av))
}
#[cfg(feature = "bitwise")]
fn xor_reduce(&self) -> PolarsResult<Scalar> {
let dt = <$pdt as PolarsDataType>::get_dtype();
let av = self.0.xor_reduce().map_or(AnyValue::Null, Into::into);
Ok(Scalar::new(dt, av))
}
#[cfg(feature = "approx_unique")]
fn approx_n_unique(&self) -> PolarsResult<IdxSize> {
Ok(ChunkApproxNUnique::approx_n_unique(&self.0))
}
fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
Arc::new(SeriesWrap(Clone::clone(&self.0)))
}
#[cfg(feature = "checked_arithmetic")]
fn checked_div(&self, rhs: &Series) -> PolarsResult<Series> {
self.0.checked_div(rhs)
}
fn as_any(&self) -> &dyn Any {
&self.0
}
fn as_any_mut(&mut self) -> &mut dyn Any {
&mut self.0
}
fn as_arc_any(self: Arc<Self>) -> Arc<dyn Any + Send + Sync> {
self as _
}
}
};
}
impl_dyn_series!(Float32Chunked, Float32Type);
impl_dyn_series!(Float64Chunked, Float64Type);