use polars_compute::rolling::QuantileMethod;
use super::*;
use crate::prelude::*;
unsafe impl IntoSeries for DecimalChunked {
fn into_series(self) -> Series {
Series(Arc::new(SeriesWrap(self)))
}
}
impl private::PrivateSeriesNumeric for SeriesWrap<DecimalChunked> {
fn bit_repr(&self) -> Option<BitRepr> {
Some(self.0.physical().to_bit_repr())
}
}
impl SeriesWrap<DecimalChunked> {
fn apply_physical_to_s<F: Fn(&Int128Chunked) -> Int128Chunked>(&self, f: F) -> Series {
f(self.0.physical())
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series()
}
fn apply_physical<T, F: Fn(&Int128Chunked) -> T>(&self, f: F) -> T {
f(self.0.physical())
}
fn scale_factor(&self) -> u128 {
10u128.pow(self.0.scale() as u32)
}
fn apply_scale(&self, mut scalar: Scalar) -> Scalar {
if scalar.is_null() {
return scalar;
}
debug_assert_eq!(scalar.dtype(), &DataType::Float64);
let v = scalar
.value()
.try_extract::<f64>()
.expect("should be f64 scalar");
scalar.update((v / self.scale_factor() as f64).into());
scalar
}
fn agg_helper<F: Fn(&Int128Chunked) -> Series>(&self, f: F) -> Series {
let agg_s = f(self.0.physical());
match agg_s.dtype() {
DataType::Int128 => {
let ca = agg_s.i128().unwrap();
let ca = ca.as_ref().clone();
let precision = self.0.precision();
let scale = self.0.scale();
ca.into_decimal_unchecked(precision, scale).into_series()
},
DataType::List(dtype) if matches!(dtype.as_ref(), DataType::Int128) => {
let dtype = self.0.dtype();
let ca = agg_s.list().unwrap();
let arr = ca.downcast_iter().next().unwrap();
let precision = self.0.precision();
let scale = self.0.scale();
let s = unsafe {
Series::from_chunks_and_dtype_unchecked(
PlSmallStr::EMPTY,
vec![arr.values().clone()],
dtype,
)
}
.into_decimal(precision, scale)
.unwrap();
let new_values = s.array_ref(0).clone();
let dtype = DataType::Int128;
let arrow_dtype =
ListArray::<i64>::default_datatype(dtype.to_arrow(CompatLevel::newest()));
let new_arr = ListArray::<i64>::new(
arrow_dtype,
arr.offsets().clone(),
new_values,
arr.validity().cloned(),
);
unsafe {
ListChunked::from_chunks_and_dtype_unchecked(
agg_s.name().clone(),
vec![Box::new(new_arr)],
DataType::List(Box::new(DataType::Decimal(precision, scale))),
)
.into_series()
}
},
_ => unreachable!(),
}
}
}
impl private::PrivateSeries for SeriesWrap<DecimalChunked> {
fn compute_len(&mut self) {
self.0.physical_mut().compute_len()
}
fn _field(&self) -> Cow<'_, Field> {
Cow::Owned(self.0.field())
}
fn _dtype(&self) -> &DataType {
self.0.dtype()
}
fn _get_flags(&self) -> StatisticsFlags {
self.0.physical().get_flags()
}
fn _set_flags(&mut self, flags: StatisticsFlags) {
self.0.physical_mut().set_flags(flags)
}
#[cfg(feature = "zip_with")]
fn zip_with_same_type(&self, mask: &BooleanChunked, other: &Series) -> PolarsResult<Series> {
let other = other.decimal()?;
Ok(self
.0
.physical()
.zip_with(mask, other.physical())?
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series())
}
fn into_total_eq_inner<'a>(&'a self) -> Box<dyn TotalEqInner + 'a> {
self.0.physical().into_total_eq_inner()
}
fn into_total_ord_inner<'a>(&'a self) -> Box<dyn TotalOrdInner + 'a> {
self.0.physical().into_total_ord_inner()
}
fn vec_hash(
&self,
random_state: PlSeedableRandomStateQuality,
buf: &mut Vec<u64>,
) -> PolarsResult<()> {
self.0.physical().vec_hash(random_state, buf)?;
Ok(())
}
fn vec_hash_combine(
&self,
build_hasher: PlSeedableRandomStateQuality,
hashes: &mut [u64],
) -> PolarsResult<()> {
self.0.physical().vec_hash_combine(build_hasher, hashes)?;
Ok(())
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_sum(&self, groups: &GroupsType) -> Series {
self.agg_helper(|ca| ca.agg_sum(groups))
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_min(&self, groups: &GroupsType) -> Series {
self.agg_helper(|ca| ca.agg_min(groups))
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_max(&self, groups: &GroupsType) -> Series {
self.agg_helper(|ca| ca.agg_max(groups))
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_arg_min(&self, groups: &GroupsType) -> Series {
self.0.physical().agg_arg_min(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_arg_max(&self, groups: &GroupsType) -> Series {
self.0.physical().agg_arg_max(groups)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_list(&self, groups: &GroupsType) -> Series {
self.agg_helper(|ca| ca.agg_list(groups))
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_var(&self, groups: &GroupsType, ddof: u8) -> Series {
self.0
.cast(&DataType::Float64)
.unwrap()
.agg_var(groups, ddof)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_std(&self, groups: &GroupsType, ddof: u8) -> Series {
self.0
.cast(&DataType::Float64)
.unwrap()
.agg_std(groups, ddof)
}
fn subtract(&self, rhs: &Series) -> PolarsResult<Series> {
let rhs = rhs.decimal()?;
((&self.0) - rhs).map(|ca| ca.into_series())
}
fn add_to(&self, rhs: &Series) -> PolarsResult<Series> {
let rhs = rhs.decimal()?;
((&self.0) + rhs).map(|ca| ca.into_series())
}
fn multiply(&self, rhs: &Series) -> PolarsResult<Series> {
let rhs = rhs.decimal()?;
((&self.0) * rhs).map(|ca| ca.into_series())
}
fn divide(&self, rhs: &Series) -> PolarsResult<Series> {
let rhs = rhs.decimal()?;
((&self.0) / rhs).map(|ca| ca.into_series())
}
#[cfg(feature = "algorithm_group_by")]
fn group_tuples(&self, multithreaded: bool, sorted: bool) -> PolarsResult<GroupsType> {
self.0.physical().group_tuples(multithreaded, sorted)
}
fn arg_sort_multiple(
&self,
by: &[Column],
options: &SortMultipleOptions,
) -> PolarsResult<IdxCa> {
self.0.physical().arg_sort_multiple(by, options)
}
}
impl SeriesTrait for SeriesWrap<DecimalChunked> {
fn rename(&mut self, name: PlSmallStr) {
self.0.rename(name)
}
fn chunk_lengths(&self) -> ChunkLenIter<'_> {
self.0.physical().chunk_lengths()
}
fn name(&self) -> &PlSmallStr {
self.0.name()
}
fn chunks(&self) -> &Vec<ArrayRef> {
self.0.physical().chunks()
}
unsafe fn chunks_mut(&mut self) -> &mut Vec<ArrayRef> {
self.0.physical_mut().chunks_mut()
}
fn slice(&self, offset: i64, length: usize) -> Series {
self.apply_physical_to_s(|ca| ca.slice(offset, length))
}
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);
let mut other = other.to_physical_repr().into_owned();
self.0
.physical_mut()
.append_owned(std::mem::take(other._get_inner_mut().as_mut()))
}
fn append_owned(&mut self, mut other: Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), append);
self.0.physical_mut().append_owned(std::mem::take(
&mut other
._get_inner_mut()
.as_any_mut()
.downcast_mut::<DecimalChunked>()
.unwrap()
.phys,
))
}
fn extend(&mut self, other: &Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), extend);
let other = other.to_physical_repr();
self.0
.physical_mut()
.extend(other.as_ref().as_ref().as_ref())?;
Ok(())
}
fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Series> {
Ok(self
.0
.physical()
.filter(filter)?
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series())
}
fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
Ok(self
.0
.physical()
.take(indices)?
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series())
}
unsafe fn take_unchecked(&self, indices: &IdxCa) -> Series {
self.0
.physical()
.take_unchecked(indices)
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series()
}
fn take_slice(&self, indices: &[IdxSize]) -> PolarsResult<Series> {
Ok(self
.0
.physical()
.take(indices)?
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series())
}
unsafe fn take_slice_unchecked(&self, indices: &[IdxSize]) -> Series {
self.0
.physical()
.take_unchecked(indices)
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series()
}
fn deposit(&self, validity: &Bitmap) -> Series {
self.0
.physical()
.deposit(validity)
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series()
}
fn len(&self) -> usize {
self.0.len()
}
fn rechunk(&self) -> Series {
let ca = self.0.physical().rechunk().into_owned();
ca.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series()
}
fn new_from_index(&self, index: usize, length: usize) -> Series {
self.0
.physical()
.new_from_index(index, length)
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.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(self
.0
.physical()
.sort_with(options)
.into_decimal_unchecked(self.0.precision(), self.0.scale())
.into_series())
}
fn arg_sort(&self, options: SortOptions) -> IdxCa {
self.0.physical().arg_sort(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> {
Ok(self.apply_physical_to_s(|ca| ca.unique().unwrap()))
}
#[cfg(feature = "algorithm_group_by")]
fn n_unique(&self) -> PolarsResult<usize> {
self.0.physical().n_unique()
}
#[cfg(feature = "algorithm_group_by")]
fn arg_unique(&self) -> PolarsResult<IdxCa> {
self.0.physical().arg_unique()
}
fn unique_id(&self) -> PolarsResult<(IdxSize, Vec<IdxSize>)> {
ChunkUnique::unique_id(self.0.physical())
}
fn is_null(&self) -> BooleanChunked {
self.0.is_null()
}
fn is_not_null(&self) -> BooleanChunked {
self.0.is_not_null()
}
fn reverse(&self) -> Series {
self.apply_physical_to_s(|ca| ca.reverse())
}
fn shift(&self, periods: i64) -> Series {
self.apply_physical_to_s(|ca| ca.shift(periods))
}
#[cfg(feature = "approx_unique")]
fn approx_n_unique(&self) -> PolarsResult<IdxSize> {
Ok(ChunkApproxNUnique::approx_n_unique(self.0.physical()))
}
fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
Arc::new(SeriesWrap(Clone::clone(&self.0)))
}
fn sum_reduce(&self) -> PolarsResult<Scalar> {
Ok(self.apply_physical(|ca| {
let sum = ca.sum();
let DataType::Decimal(prec, scale) = self.dtype() else {
unreachable!()
};
let av = AnyValue::Decimal(sum.unwrap(), *prec, *scale);
Scalar::new(self.dtype().clone(), av)
}))
}
fn min_reduce(&self) -> PolarsResult<Scalar> {
Ok(self.apply_physical(|ca| {
let min = ca.min();
let DataType::Decimal(prec, scale) = self.dtype() else {
unreachable!()
};
let av = if let Some(min) = min {
AnyValue::Decimal(min, *prec, *scale)
} else {
AnyValue::Null
};
Scalar::new(self.dtype().clone(), av)
}))
}
fn max_reduce(&self) -> PolarsResult<Scalar> {
Ok(self.apply_physical(|ca| {
let max = ca.max();
let DataType::Decimal(prec, scale) = self.dtype() else {
unreachable!()
};
let av = if let Some(m) = max {
AnyValue::Decimal(m, *prec, *scale)
} else {
AnyValue::Null
};
Scalar::new(self.dtype().clone(), av)
}))
}
fn _sum_as_f64(&self) -> f64 {
self.0.physical()._sum_as_f64() / self.scale_factor() as f64
}
fn mean(&self) -> Option<f64> {
self.0
.physical()
.mean()
.map(|v| v / self.scale_factor() as f64)
}
fn mean_reduce(&self) -> PolarsResult<Scalar> {
Ok(Scalar::new(DataType::Float64, self.mean().into()))
}
fn median(&self) -> Option<f64> {
self.0
.physical()
.median()
.map(|v| v / self.scale_factor() as f64)
}
fn median_reduce(&self) -> PolarsResult<Scalar> {
Ok(self.apply_scale(self.0.physical().median_reduce()))
}
fn std(&self, ddof: u8) -> Option<f64> {
self.0.cast(&DataType::Float64).ok()?.std(ddof)
}
fn std_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
self.0.cast(&DataType::Float64)?.std_reduce(ddof)
}
fn var(&self, ddof: u8) -> Option<f64> {
self.0.cast(&DataType::Float64).ok()?.var(ddof)
}
fn var_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
self.0.cast(&DataType::Float64)?.var_reduce(ddof)
}
fn quantile_reduce(&self, quantile: f64, method: QuantileMethod) -> PolarsResult<Scalar> {
self.0
.physical()
.quantile_reduce(quantile, method)
.map(|v| self.apply_scale(v))
}
fn quantiles_reduce(&self, quantiles: &[f64], method: QuantileMethod) -> PolarsResult<Scalar> {
let result = self.0.physical().quantiles_reduce(quantiles, method)?;
if let AnyValue::List(float_s) = result.value() {
let scale_factor = self.scale_factor() as f64;
let float_ca = float_s.f64().unwrap();
let scaled_s = float_ca
.iter()
.map(|v: Option<f64>| v.map(|f| f / scale_factor))
.collect::<Float64Chunked>()
.into_series();
Ok(Scalar::new(
DataType::List(Box::new(self.dtype().clone())),
AnyValue::List(scaled_s),
))
} else {
polars_bail!(ComputeError: "expected list scalar from quantiles_reduce")
}
}
fn find_validity_mismatch(&self, other: &Series, idxs: &mut Vec<IdxSize>) {
self.0.physical().find_validity_mismatch(other, idxs)
}
fn as_any(&self) -> &dyn Any {
&self.0
}
fn as_any_mut(&mut self) -> &mut dyn Any {
&mut self.0
}
fn as_phys_any(&self) -> &dyn Any {
self.0.physical()
}
fn as_arc_any(self: Arc<Self>) -> Arc<dyn Any + Send + Sync> {
self as _
}
}