Closes #3855: refactor boolReductionMsg

ServerModules.cfg

@@ -26,6 +26,7 @@ OperatorMsg
 ParquetMsg
 RandMsg
 ReductionMsg
+ReductionMsgFunctions
 RegistrationMsg
 SegmentedMsg
 SequenceMsg

arkouda/array_api/statistical_functions.py

@@ -50,11 +50,7 @@ def max(
 
     from arkouda import max as ak_max
 
-    arr = Array._new(ak_max(x._array, axis=axis))
-    if keepdims or axis is None or x.ndim == 1:
-        return arr
-    else:
-        return squeeze(arr, axis)
+    return Array._new(ak_max(x._array, axis=axis, keepdims=keepdims))
 
 
 # this is a temporary fix to get mean working with XArray
@@ -138,11 +134,7 @@ def min(
 
     from arkouda import min as ak_min
 
-    arr = Array._new(ak_min(x._array, axis=axis))
-    if keepdims or axis is None or x.ndim == 1:
-        return arr
-    else:
-        return squeeze(arr, axis)
+    return Array._new(ak_min(x._array, axis=axis, keepdims=keepdims))
 
 
 def prod(
@@ -180,11 +172,7 @@ def prod(
 
     from arkouda import prod as ak_prod
 
-    arr = Array._new(ak_prod(x_op, axis=axis))
-    if keepdims or axis is None or x.ndim == 1:
-        return arr
-    else:
-        return squeeze(arr, axis)
+    return Array._new(ak_prod(x_op, axis=axis, keepdims=keepdims))
 
 
 # Not working with XArray yet, pending a fix for:
@@ -277,11 +265,7 @@ def sum(
 
     from arkouda import sum as ak_sum
 
-    arr = Array._new(ak_sum(x_op, axis=axis))
-    if keepdims or axis is None or x.ndim == 1:
-        return arr
-    else:
-        return squeeze(arr, axis)
+    return Array._new(ak_sum(x_op, axis=axis, keepdims=keepdims))
 
 
 # Not working with XArray yet, pending a fix for:

arkouda/categorical.py

@@ -26,6 +26,7 @@
 from arkouda.numpy import cast as akcast
 from arkouda.numpy import where
 from arkouda.numpy.dtypes import bool_ as akbool
+from arkouda.numpy.dtypes import bool_scalars
 from arkouda.numpy.dtypes import dtype as akdtype
 from arkouda.numpy.dtypes import int64 as akint64
 from arkouda.numpy.dtypes import int_scalars, resolve_scalar_dtype, str_, str_scalars
@@ -290,7 +291,7 @@ def standardize_categories(cls, arrays, NAvalue="N/A"):
             new_categories = concatenate((new_categories, array([NAvalue])))
         return [arr.set_categories(new_categories, NAvalue=NAvalue) for arr in arrays]
 
-    def equals(self, other) -> bool:
+    def equals(self, other) -> bool_scalars:
         """
         Whether Categoricals are the same size and all entries are equal.
 
@@ -320,9 +321,11 @@ def equals(self, other) -> bool:
             if other.size != self.size:
                 return False
             else:
-                return akall(self == other)
-        else:
-            return False
+                result = akall(self == other)
+                if isinstance(result, (bool, np.bool_)):
+                    return result
+
+        return False
 
     def set_categories(self, new_categories, NAvalue=None):
         """

arkouda/index.py

@@ -4,6 +4,7 @@
 import json
 from typing import TYPE_CHECKING, List, Optional, Tuple, Union
 
+import numpy as np
 import pandas as pd
 from numpy import array as ndarray
 from numpy import dtype as npdtype
@@ -13,6 +14,7 @@
 from arkouda.groupbyclass import GroupBy, unique
 from arkouda.numpy import cast as akcast
 from arkouda.numpy.dtypes import bool_ as akbool
+from arkouda.numpy.dtypes import bool_scalars
 from arkouda.numpy.dtypes import float64 as akfloat64
 from arkouda.numpy.dtypes import int64 as akint64
 from arkouda.pdarrayclass import RegistrationError, pdarray
@@ -290,7 +292,7 @@ def from_return_msg(cls, rep_msg):
 
         return cls.factory(idx) if len(idx) > 1 else cls.factory(idx[0])
 
-    def equals(self, other: Index) -> bool:
+    def equals(self, other: Index) -> bool_scalars:
         """
         Whether Indexes are the same size, and all entries are equal.
 
@@ -351,7 +353,10 @@ def equals(self, other: Index) -> bool:
 
             return True
         else:
-            return akall(self == other)
+            result = akall(self == other)
+            if isinstance(result, (bool, np.bool_)):
+                return result
+        return False
 
     def memory_usage(self, unit="B"):
         """

arkouda/numpy/_numeric.py

@@ -8,7 +8,7 @@
 from typeguard import typechecked
 
 from arkouda.client import generic_msg
-from arkouda.dtypes import str_ as akstr_
+from arkouda.numpy.dtypes import str_ as akstr_
 from arkouda.groupbyclass import GroupBy, groupable
 from arkouda.numpy.dtypes import DTypes, bigint
 from arkouda.numpy.dtypes import bool_ as ak_bool

arkouda/numpy/dtypes/dtypes.py

@@ -56,6 +56,7 @@
     "int8",
     "intTypes",
     "int_scalars",
+    "isSupportedBool",
     "isSupportedFloat",
     "isSupportedInt",
     "isSupportedNumber",
@@ -245,6 +246,8 @@ def __repr__(self) -> str:
         return self.value
 
 
+ARKOUDA_SUPPORTED_BOOLS = (bool, np.bool_)
+
 ARKOUDA_SUPPORTED_INTS = (
     int,
     np.int8,
@@ -313,6 +316,10 @@ def isSupportedNumber(num):
     return isinstance(num, ARKOUDA_SUPPORTED_NUMBERS)
 
 
+def isSupportedBool(num):
+    return isinstance(num, ARKOUDA_SUPPORTED_BOOLS)
+
+
 def resolve_scalar_dtype(val: object) -> str:
     """
     Try to infer what dtype arkouda_server should treat val as.