Rename _ensure_f32_2d to _to_f32_2d for clarity

The function converts input to float32 contiguous 2D, not just
validates it. The new name better communicates the transformation.

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

Author: timtreis

src/squidpy/experimental/im/_sharpness_metrics.py

@@ -5,35 +5,35 @@
 from skimage.filters import laplace, sobel_h, sobel_v
 
 
-def _ensure_f32_2d(x: np.ndarray) -> np.ndarray:
+def _to_f32_2d(x: np.ndarray) -> np.ndarray:
     if x.ndim != 2:
         raise ValueError("block must be 2D")
     return np.ascontiguousarray(x.astype(np.float32, copy=False))
 
 
 def _tenengrad_mean(block: np.ndarray) -> np.ndarray:
     """Mean Tenengrad energy (sum of squared Sobel gradients)."""
-    b = _ensure_f32_2d(block)
+    b = _to_f32_2d(block)
     energy = sobel_h(b) ** 2 + sobel_v(b) ** 2
     return np.array([[float(energy.mean())]], dtype=np.float32)
 
 
 def _laplacian_variance(block: np.ndarray) -> np.ndarray:
     """Population variance of Laplacian response."""
-    b = _ensure_f32_2d(block)
+    b = _to_f32_2d(block)
     lap = laplace(b)
     var_val = float(np.var(lap))
     return np.array([[max(var_val, 0.0)]], dtype=np.float32)
 
 
 def _pop_variance(block: np.ndarray) -> np.ndarray:
     """Population variance of pixel intensities."""
-    b = _ensure_f32_2d(block)
+    b = _to_f32_2d(block)
     return np.array([[float(np.var(b))]], dtype=np.float32)
 
 
 def _fft_high_freq_energy(block: np.ndarray) -> np.ndarray:
-    x = _ensure_f32_2d(block).astype(np.float64, copy=False)
+    x = _to_f32_2d(block).astype(np.float64, copy=False)
     m = float(x.mean())
     s = float(x.std())
     x = (x - m) / s if s > 0.0 else (x - m)
@@ -63,7 +63,7 @@ def _fft_high_freq_energy(block: np.ndarray) -> np.ndarray:
 
 def _haar_wavelet_energy(block: np.ndarray) -> np.ndarray:
     """Detail-band (LH+HL+HH) energy ratio of a single-level Haar transform."""
-    x = _ensure_f32_2d(block).astype(np.float64, copy=False)
+    x = _to_f32_2d(block).astype(np.float64, copy=False)
     m = float(x.mean())
     s = float(x.std())
     x = (x - m) / s if s > 0.0 else (x - m)