Rolling aggregations not working for various integer types

[lukemanley]

Checks

• I have checked that this issue has not already been reported.
• I have confirmed this bug exists on the latest version of Polars.

Reproducible example

import polars as pl

s = pl.Series("a", [1, 2, 3, 4], pl.Int8)
s.rolling_sum(2)

Log output

thread '<unnamed>' panicked at /Users/runner/work/polars/polars/crates/polars-time/src/chunkedarray/rolling_window/dispatch.rs:218:9:
not implemented for dtype Int8

Issue description

The example above panics for i8, i16, i128, u8, u16 - all of which are feature gated inside the macro below. The python example works as expected if I remove the feature gates from this macro and rebuild, though I'm not sure that is the correct fix?

polars/crates/polars-core/src/utils/mod.rs

Lines 505 to 530 in c88911f

	#[macro_export]
	macro_rules! with_match_physical_numeric_polars_type {(
	$key_type:expr, | $_:tt $T:ident | $($body:tt)*
	) => ({
	macro_rules! __with_ty__ {( $_ $T:ident ) => ( $($body)* )}
	use $crate::datatypes::DataType::*;
	match $key_type {
	#[cfg(feature = "dtype-i8")]
	Int8 => __with_ty__! { Int8Type },
	#[cfg(feature = "dtype-i16")]
	Int16 => __with_ty__! { Int16Type },
	Int32 => __with_ty__! { Int32Type },
	Int64 => __with_ty__! { Int64Type },
	#[cfg(feature = "dtype-i128")]
	Int128 => __with_ty__! { Int128Type },
	#[cfg(feature = "dtype-u8")]
	UInt8 => __with_ty__! { UInt8Type },
	#[cfg(feature = "dtype-u16")]
	UInt16 => __with_ty__! { UInt16Type },
	UInt32 => __with_ty__! { UInt32Type },
	UInt64 => __with_ty__! { UInt64Type },
	Float32 => __with_ty__! { Float32Type },
	Float64 => __with_ty__! { Float64Type },
	dt => panic!("not implemented for dtype {:?}", dt),
	}
	})}

Expected behavior

Works without panicking

Installed versions

--------Version info---------
Polars:              1.18.0
Index type:          UInt32
Platform:            macOS-14.6.1-arm64-arm-64bit
Python:              3.12.7 (main, Oct 26 2024, 21:27:43) [Clang 15.0.0 (clang-1500.3.9.4)]
LTS CPU:             False

----Optional dependencies----
adbc_driver_manager  <not installed>
altair               <not installed>
azure.identity       <not installed>
boto3                <not installed>
cloudpickle          <not installed>
connectorx           <not installed>
deltalake            <not installed>
fastexcel            <not installed>
fsspec               <not installed>
gevent               <not installed>
google.auth          <not installed>
great_tables         <not installed>
matplotlib           <not installed>
nest_asyncio         <not installed>
numpy                <not installed>
openpyxl             <not installed>
pandas               <not installed>
pyarrow              <not installed>
pydantic             <not installed>
pyiceberg            <not installed>
sqlalchemy           <not installed>
torch                <not installed>
xlsx2csv             <not installed>
xlsxwriter           <not installed>

[ritchie46]

I suspect the feature gates aren't propagated properly to the polars-time crate.

[Chuck321123]

The same problem occurs for rolling_min and rolling_max

[lukemanley]

The same problem occurs for rolling_min and rolling_max

yep, I think #20512 fixes (and tests) min/max/sum