From 47fa08d3558aea8c5b7232ce08bb1b70280bb9fb Mon Sep 17 00:00:00 2001
From: thodson-usgs <thodson@usgs.gov>
Date: Fri, 17 Apr 2026 21:05:30 -0500
Subject: [PATCH] Collapse duplicated accessor methods

The three interp methods (linear/nearest/cubic) had identical 3-line
bodies differing only in the method string; extract a private `_interp`.
most_common/least_common were near-identical with an `anti_mode` flag
already threaded through to `compute_mode`; extract a private `_mode`
that also builds the error label from the same flag.

No behavior change; public API unchanged.
---
 src/xarray_regrid/regrid.py | 61 ++++++++++++++++++-------------------
 1 file changed, 29 insertions(+), 32 deletions(-)

diff --git a/src/xarray_regrid/regrid.py b/src/xarray_regrid/regrid.py
index b2ed389..872253a 100644
--- a/src/xarray_regrid/regrid.py
+++ b/src/xarray_regrid/regrid.py
@@ -1,5 +1,5 @@
 from collections.abc import Hashable
-from typing import Any, overload
+from typing import Any, Literal, overload
 
 import numpy as np
 import xarray as xr
@@ -7,6 +7,8 @@
 from xarray_regrid.methods import conservative, flox_reduce, interp
 from xarray_regrid.utils import format_for_regrid
 
+InterpMethod = Literal["linear", "nearest", "cubic"]
+
 
 @xr.register_dataarray_accessor("regrid")
 @xr.register_dataset_accessor("regrid")
@@ -40,9 +42,7 @@ def linear(
         Returns:
             Data regridded to the target dataset coordinates.
         """
-        ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
-        ds_formatted = format_for_regrid(self._obj, ds_target_grid)
-        return interp.interp_regrid(ds_formatted, ds_target_grid, "linear")
+        return self._interp(ds_target_grid, "linear", time_dim)
 
     def nearest(
         self,
@@ -59,9 +59,7 @@ def nearest(
         Returns:
             Data regridded to the target dataset coordinates.
         """
-        ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
-        ds_formatted = format_for_regrid(self._obj, ds_target_grid)
-        return interp.interp_regrid(ds_formatted, ds_target_grid, "nearest")
+        return self._interp(ds_target_grid, "nearest", time_dim)
 
     def cubic(
         self,
@@ -78,9 +76,17 @@ def cubic(
         Returns:
             Data regridded to the target dataset coordinates.
         """
+        return self._interp(ds_target_grid, "cubic", time_dim)
+
+    def _interp(
+        self,
+        ds_target_grid: xr.Dataset,
+        method: InterpMethod,
+        time_dim: str | None,
+    ) -> xr.DataArray | xr.Dataset:
         ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
         ds_formatted = format_for_regrid(self._obj, ds_target_grid)
-        return interp.interp_regrid(ds_formatted, ds_target_grid, "cubic")
+        return interp.interp_regrid(ds_formatted, ds_target_grid, method)
 
     def conservative(
         self,
@@ -160,27 +166,7 @@ def most_common(
         Returns:
             Regridded data.
         """
-        ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
-
-        if isinstance(self._obj, xr.Dataset):
-            msg = (
-                "The 'most common value' regridder is not implemented for\n",
-                "xarray.Dataset, as it requires specifying the expected labels.\n"
-                "Please select only a single variable (as DataArray),\n"
-                " and regrid it separately.",
-            )
-            raise ValueError(msg)
-
-        ds_formatted = format_for_regrid(self._obj, ds_target_grid, stats=True)
-
-        return flox_reduce.compute_mode(
-            ds_formatted,
-            ds_target_grid,
-            values,
-            time_dim,
-            fill_value,
-            anti_mode=False,
-        )
+        return self._mode(ds_target_grid, values, time_dim, fill_value, anti_mode=False)
 
     def least_common(
         self,
@@ -212,14 +198,25 @@ def least_common(
         Returns:
             Regridded data.
         """
+        return self._mode(ds_target_grid, values, time_dim, fill_value, anti_mode=True)
+
+    def _mode(
+        self,
+        ds_target_grid: xr.Dataset,
+        values: np.ndarray,
+        time_dim: str | None,
+        fill_value: None | Any,
+        anti_mode: bool,
+    ) -> xr.DataArray:
         ds_target_grid = validate_input(self._obj, ds_target_grid, time_dim)
 
         if isinstance(self._obj, xr.Dataset):
+            label = "least common value" if anti_mode else "most common value"
             msg = (
-                "The 'least common value' regridder is not implemented for\n",
+                f"The '{label}' regridder is not implemented for\n"
                 "xarray.Dataset, as it requires specifying the expected labels.\n"
                 "Please select only a single variable (as DataArray),\n"
-                " and regrid it separately.",
+                " and regrid it separately."
             )
             raise ValueError(msg)
 
@@ -231,7 +228,7 @@ def least_common(
             values,
             time_dim,
             fill_value,
-            anti_mode=True,
+            anti_mode=anti_mode,
         )
 
     def stat(