Move non-standardized functions (nan functions, pad, map_overlap) to separate package (#690)

Author: tomwhite

cubed/__init__.py

@@ -13,12 +13,12 @@
     defaults=[{"spec": {"allowed_mem": "2GB", "reserved_mem": "100MB"}}],
 )
 
+from .array.nan_functions import nanmean, nansum
+from .array.overlap import map_overlap
+from .array.pad import pad
 from .core.array import compute, measure_reserved_mem, visualize
 from .core.gufunc import apply_gufunc
 from .core.ops import from_array, from_zarr, map_blocks, rechunk, store, to_zarr
-from .nan_functions import nanmean, nansum
-from .overlap import map_overlap
-from .pad import pad
 from .runtime.types import Callback, TaskEndEvent
 from .spec import Spec
 

cubed/array/__init__.py

@@ -50,9 +50,13 @@ def _nannumel(x, **kwargs):
     return nxp.sum(~(nxp.isnan(x)), **kwargs)
 
 
-def nansum(x, /, *, axis=None, dtype=None, keepdims=False, split_every=None, device=None):
+def nansum(
+    x, /, *, axis=None, dtype=None, keepdims=False, split_every=None, device=None
+):
     """Return the sum of array elements over a given axis treating NaNs as zero."""
-    dtype = _upcast_integral_dtypes(x, dtype, allowed_dtypes=("numeric",), fname="nansum", device=device)
+    dtype = _upcast_integral_dtypes(
+        x, dtype, allowed_dtypes=("numeric",), fname="nansum", device=device
+    )
     return reduction(
         x,
         nxp.nansum,