implement boolean ops for function.Array

This patch implements boolean not, and, or, all and any for `function.Array`.

nutils/function.py

@@ -3045,6 +3045,56 @@ def maximum(a: IntoArray, b: IntoArray) -> Array:
             raise ValueError('Complex numbers have no total order.')
         return _Wrapper.broadcasted_arrays(evaluable.Maximum, a, b)
 
+    @implements(numpy.logical_and)
+    @implements(numpy.bitwise_and)
+    def logical_and(a: IntoArray, b: IntoArray) -> Array:
+        a, b = map(Array.cast, (a, b))
+        if a.dtype != bool or b.dtype != bool:
+            return NotImplemented
+        return _Wrapper.broadcasted_arrays(evaluable.multiply, a, b)
+
+    @implements(numpy.logical_or)
+    @implements(numpy.bitwise_or)
+    def logical_or(a: IntoArray, b: IntoArray) -> Array:
+        a, b = map(Array.cast, (a, b))
+        if a.dtype != bool or b.dtype != bool:
+            return NotImplemented
+        return _Wrapper.broadcasted_arrays(evaluable.add, a, b)
+
+    @implements(numpy.logical_not)
+    @implements(numpy.invert)
+    def logical_not(a: IntoArray) -> Array:
+        a = Array.cast(a)
+        if a.dtype != bool:
+            return NotImplemented
+        return _Wrapper.broadcasted_arrays(evaluable.LogicalNot, a, force_dtype=bool)
+
+    @implements(numpy.all)
+    def all(a: IntoArray, axis = None) -> Array:
+        a = Array.cast(a)
+        if a.dtype != bool:
+            return NotImplemented
+        if axis is None:
+            a = numpy.ravel(a)
+        elif isinstance(axis, int):
+            a = _Transpose.to_end(a, axis)
+        else:
+            return NotImplemented
+        return _Wrapper(evaluable.Product, a, shape=a.shape[:-1], dtype=bool)
+
+    @implements(numpy.any)
+    def any(a: IntoArray, axis = None) -> Array:
+        a = Array.cast(a)
+        if a.dtype != bool:
+            return NotImplemented
+        if axis is None:
+            a = numpy.ravel(a)
+        elif isinstance(axis, int):
+            a = _Transpose.to_end(a, axis)
+        else:
+            return NotImplemented
+        return _Wrapper(evaluable.Sum, a, shape=a.shape[:-1], dtype=bool)
+
     @implements(numpy.sum)
     def sum(arg: IntoArray, axis: Optional[Union[int, Sequence[int]]] = None) -> Array:
         arg = Array.cast(arg)

tests/test_function.py

@@ -323,6 +323,17 @@ def _check(name, op, n_op, *args):
 _check('max', lambda a, b: numpy.maximum(a, function.Array.cast(b)), numpy.maximum, ANY(4, 1), ANY(1, 4))
 _check('heaviside', function.heaviside, lambda u: numpy.heaviside(u, .5), ANY(4, 4))
 
+_check('logical_and', lambda a, b: numpy.logical_and(a, function.Array.cast(b)), numpy.logical_and, numpy.array([False, True, True])[:,None], numpy.array([True, False])[None,:])
+_check('bitwise_and-bool', lambda a, b: numpy.bitwise_and(a, function.Array.cast(b)), numpy.bitwise_and, numpy.array([False, True, True])[:,None], numpy.array([True, False])[None,:])
+_check('logical_or', lambda a, b: numpy.logical_or(a, function.Array.cast(b)), numpy.logical_or, numpy.array([False, True, True])[:,None], numpy.array([True, False])[None,:])
+_check('bitwise_or-bool', lambda a, b: numpy.bitwise_or(a, function.Array.cast(b)), numpy.bitwise_or, numpy.array([False, True, True])[:,None], numpy.array([True, False])[None,:])
+_check('logical_not', lambda a: numpy.logical_not(function.Array.cast(a)), numpy.logical_not, numpy.array([[False, True], [True, False], [True, True]]))
+_check('invert-bool', lambda a: numpy.invert(function.Array.cast(a)), numpy.invert, numpy.array([[False, True], [True, False], [True, True]]))
+_check('all-bool-all-axes', lambda a: numpy.all(function.Array.cast(a)), numpy.all, numpy.array([[False, True], [True, True]]))
+_check('all-bool-single-axis', lambda a: numpy.all(function.Array.cast(a), axis=0), lambda a: numpy.all(a, axis=0), numpy.array([[False, True], [True, True]]))
+_check('any-bool-all-axes', lambda a: numpy.any(function.Array.cast(a)), numpy.any, numpy.array([[False, True], [True, True]]))
+_check('any-bool-single-axis', lambda a: numpy.any(function.Array.cast(a), axis=0), lambda a: numpy.any(a, axis=0), numpy.array([[False, True], [False, False]]))
+
 ## TODO: opposite
 ## TODO: mean
 ## TODO: jump