Merge branch 'master' into nd-support-to-trim_zero

Author: antonwolfy

dpnp/dpnp_utils/dpnp_utils_statistics.py

@@ -57,10 +57,10 @@ def _calc_median(a, axis, out=None):
     return res
 
 
-def _calc_nanmedian(a, axis, out=None):
+def _calc_nanmedian(a, out=None):
     """Compute the median of an array along a specified axis, ignoring NaNs."""
     mask = dpnp.isnan(a)
-    valid_counts = dpnp.sum(~mask, axis=axis)
+    valid_counts = dpnp.sum(~mask, axis=-1)
     if out is None:
         res = dpnp.empty_like(valid_counts, dtype=a.dtype)
     else:
@@ -76,27 +76,19 @@ def _calc_nanmedian(a, axis, out=None):
             )
         res = out
 
-    # Iterate over all indices of the output shape
-    for idx in dpnp.ndindex(res.shape):
-        current_valid_counts = valid_counts[idx]
+    left = (valid_counts - 1) // 2
+    right = valid_counts // 2
 
-        if current_valid_counts > 0:
-            # Extract the corresponding slice from the last axis of `a`
-            data = a[idx][:current_valid_counts]
-            left = (current_valid_counts - 1) // 2
-            right = current_valid_counts // 2
+    left_data = dpnp.take_along_axis(a, left[..., None], axis=-1)
+    right_data = dpnp.take_along_axis(a, right[..., None], axis=-1)
+    res = dpnp.where(
+        valid_counts[..., None] > 0, (left_data + right_data) / 2.0, dpnp.nan
+    )
 
-            if left == right:
-                res[idx] = data[left]
-            else:
-                res[idx] = (data[left] + data[right]) / 2.0
-        else:
-            warnings.warn(
-                "All-NaN slice encountered", RuntimeWarning, stacklevel=6
-            )
-            res[idx] = dpnp.nan
+    if mask.all(axis=-1).any():
+        warnings.warn("All-NaN slice encountered", RuntimeWarning, stacklevel=6)
 
-    return res
+    return dpnp.squeeze(res)
 
 
 def _flatten_array_along_axes(a, axes_to_flatten, overwrite_input):
@@ -232,7 +224,8 @@ def dpnp_median(
 
     if ignore_nan:
         # sorting puts NaNs at the end
-        res = _calc_nanmedian(a_sorted, axis=axis, out=out)
+        assert axis == -1
+        res = _calc_nanmedian(a_sorted, out=out)
     else:
         # We can't pass keepdims and use it in dpnp.mean and dpnp.any
         # because of the reshape hack that might have been used in