Repository: heavyai/rbc (language: python)

Issue:
Document UD[T]F functions input types
All NumPy (and other) functions require that the inputs follow specific types. These should be documented and easily accessible. Ideally IDE should pick up type hints (possible??), and it should be in the doc of all functions.


Discussion / hints:

Fix patch:
diff --git a/doc/api.rst b/doc/api.rst
index ed7d4e5c..311d6ca2 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -26,6 +26,19 @@ Top-level functions
     utils
 
 
+Array API
+=========
+
+.. autosummary::
+    :toctree: generated/
+
+    stdlib.datatypes
+    stdlib.constants
+    stdlib.creation_functions
+    stdlib.elementwise_functions
+    stdlib.statistical_functions
+
+
 Externals
 =========
 
diff --git a/notebooks/rbc-omnisci-black-scholes.ipynb b/notebooks/rbc-omnisci-black-scholes.ipynb
index 92756416..fafbaaf0 100644
--- a/notebooks/rbc-omnisci-black-scholes.ipynb
+++ b/notebooks/rbc-omnisci-black-scholes.ipynb
@@ -470,7 +470,7 @@
    "source": [
     "import numba\n",
     "import math\n",
-    "from rbc import omnisci_backend as np"
+    "from rbc.stdlib import array_api as np"
    ]
   },
   {
@@ -572,7 +572,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -673,7 +673,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/rbc/omnisci_backend/__init__.py b/rbc/omnisci_backend/__init__.py
index 7d86d3a0..afc8e168 100644
--- a/rbc/omnisci_backend/__init__.py
+++ b/rbc/omnisci_backend/__init__.py
@@ -1,5 +1,3 @@
-from .numpy_funcs import *  # noqa: F401, F403
-from .numpy_ufuncs import *  # noqa: F401, F403
 from .omnisci_array import *  # noqa: F401, F403
 from .omnisci_column import *  # noqa: F401, F403
 from .omnisci_bytes import *  # noqa: F401, F403
@@ -13,4 +11,7 @@
 import rbc.omnisci_backend.npyimpl as np  # noqa: F401
 import rbc.omnisci_backend.python_operators as operators  # noqa: F401
 
+# initialize the array api
+from rbc.stdlib import array_api  # noqa: F401
+
 __all__ = [s for s in dir() if not s.startswith('_')]
diff --git a/rbc/omnisci_backend/numpy_funcs.py b/rbc/omnisci_backend/numpy_funcs.py
deleted file mode 100644
index 6d00fb70..00000000
--- a/rbc/omnisci_backend/numpy_funcs.py
+++ /dev/null
@@ -1,314 +0,0 @@
-import numpy as np
-from rbc.externals.stdio import printf
-from rbc import typesystem
-from .omnisci_array import Array, ArrayPointer
-from numba import njit
-from numba.core import extending, types, errors
-from numba.np import numpy_support
-
-__all__ = [
-    'full', 'full_like', 'empty_like', 'empty', 'zeros', 'zeros_like',
-    'ones', 'ones_like', 'array', 'max', 'min', 'sum', 'prod',
-    'mean', 'cumsum'
-]
-
-
-def expose_and_overload(func, name=None):
-    if name is None:
-        name = func.__name__
-
-    s = f'def {name}(*args, **kwargs): pass'
-    exec(s, globals())
-
-    fn = globals()[name]
-    decorate = extending.overload(fn)
-
-    def wrapper(overload_func):
-        return decorate(overload_func)
-
-    return wrapper
-
-
-@expose_and_overload(np.full)
-def omnisci_np_full(shape, fill_value, dtype=None):
-
-    # XXX: dtype should be infered from fill_value
-    if dtype is None:
-        nb_dtype = types.double
-    else:
-        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-    def impl(shape, fill_value, dtype=None):
-        a = Array(shape, nb_dtype)
-        a.fill(nb_dtype(fill_value))
-        return a
-    return impl
-
-
-@expose_and_overload(np.full_like)
-def omnisci_np_full_like(a, fill_value, dtype=None):
-    if isinstance(a, ArrayPointer):
-        if dtype is None:
-            nb_dtype = a.eltype
-        else:
-            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-        def impl(a, fill_value, dtype=None):
-            sz = len(a)
-            other = Array(sz, nb_dtype)
-            other.fill(nb_dtype(fill_value))
-            return other
-        return impl
-
-
-@expose_and_overload(np.empty_like)
-def omnisci_np_empty_like(a, dtype=None):
-    if isinstance(a, ArrayPointer):
-        if dtype is None:
-            nb_dtype = a.eltype
-        else:
-            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-        def impl(a, dtype=None):
-            other = Array(0, nb_dtype)
-            other.set_null()
-            return other
-        return impl
-
-
-@expose_and_overload(np.empty)
-def omnisci_np_empty(shape, dtype=None):
-
-    if dtype is None:
-        nb_dtype = types.double
-    else:
-        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-    def impl(shape, dtype=None):
-        arr = Array(shape, nb_dtype)
-        arr.set_null()
-        return arr
-    return impl
-
-
-@expose_and_overload(np.zeros)
-def omnisci_np_zeros(shape, dtype=None):
-
-    if dtype is None:
-        nb_dtype = types.double
-    else:
-        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-    fill_value = False if isinstance(nb_dtype, types.Boolean) else 0
-
-    def impl(shape, dtype=None):
-        return full(shape, fill_value, nb_dtype)  # noqa: F821
-    return impl
-
-
-@expose_and_overload(np.zeros_like)
-def omnisci_np_zeros_like(a, dtype=None):
-    if isinstance(a, ArrayPointer):
-        if dtype is None:
-            nb_dtype = a.eltype
-        else:
-            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-        fill_value = False if isinstance(nb_dtype, types.Boolean) else 0
-
-        def impl(a, dtype=None):
-            return full_like(a, fill_value, nb_dtype)  # noqa: F821
-        return impl
-
-
-@expose_and_overload(np.ones)
-def omnisci_np_ones(shape, dtype=None):
-
-    if dtype is None:
-        nb_dtype = types.double
-    else:
-        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
-
-    fill_value = True if isinstance(nb_dtype, types.Boolean) else 1
-
-    def impl(shape, dtype=None):
-        return full(shape, fill_value, nb_dtype)  # noqa: F821
-    return impl
-
-
-@expose_and_overload(np.ones_like)
-def omnisci_np_ones_like(a, dtype=None):
-    if isinstance(a, ArrayPointer):
-        if dtype is None:
-            nb_dtype = a.eltype
-        else:
-            nb_dtype = dtype
-
-        fill_value = True if isinstance(nb_dtype, types.Boolean) else 1
-
-        def impl(a, dtype=None):
-            return full_like(a, fill_value, nb_dtype)  # noqa: F821
-        return impl
-
-
-@expose_and_overload(np.array)
-def omnisci_np_array(a, dtype=None):
-
-    @njit
-    def omnisci_array_non_empty_copy(a, nb_dtype):
-        """Implement this here rather than inside "impl".
-        LLVM DCE pass removes everything if we implement stuff inside "impl"
-        """
-        other = Array(len(a), nb_dtype)
-        for i in range(len(a)):
-            other[i] = a[i]
-        return other
-
-    if isinstance(a, ArrayPointer):
-        if dtype is None:
-            nb_dtype = a.eltype
-        else:
-            nb_dtype = dtype
-
-        def impl(a, dtype=None):
-            if a.is_null():
-                return empty_like(a)  # noqa: F821
-            else:
-                return omnisci_array_non_empty_copy(a, nb_dtype)
-        return impl
-
-
-def get_type_limits(eltype):
-    np_dtype = numpy_support.as_dtype(eltype)
-    if isinstance(eltype, types.Integer):
-        return np.iinfo(np_dtype)
-    elif isinstance(eltype, types.Float):
-        return np.finfo(np_dtype)
-    else:
-        msg = 'Type {} not supported'.format(eltype)
-        raise errors.TypingError(msg)
-
-
-@extending.overload_method(ArrayPointer, 'fill')
-def omnisci_array_fill(x, v):
-    if isinstance(x, ArrayPointer):
-        def impl(x, v):
-            for i in range(len(x)):
-                x[i] = v
-        return impl
-
-
-@extending.overload(max)
-@expose_and_overload(np.max, name='max')
-@extending.overload_method(ArrayPointer, 'max')
-def omnisci_array_max(x, initial=None):
-    if isinstance(x, ArrayPointer):
-        # the array api standard says this is implementation specific
-        limits = get_type_limits(x.eltype)
-        t = typesystem.Type.fromobject(x.eltype)
-        if t.is_float:
-            min_value = limits.min
-        elif t.is_int or t.is_uint:
-            min_value = 0 if t.is_uint else limits.min + 1
-        else:
-            raise TypeError(f'Unsupported type {t}')
-
-        def impl(x, initial=None):
-            if len(x) <= 0:
-                printf("omnisci_array_max: cannot find max of zero-sized array")  # noqa: E501
-                return min_value
-            if initial is not None:
-                m = initial
-            else:
-                m = x[0]
-            for i in range(len(x)):
-                v = x[i]
-                if v > m:
-                    m = v
-            return m
-        return impl
-
-
-@extending.overload(min)
-@expose_and_overload(np.min, name='min')
-@extending.overload_method(ArrayPointer, 'min')
-def omnisci_array_min(x, initial=None):
-    if isinstance(x, ArrayPointer):
-        max_value = get_type_limits(x.eltype).max
-
-        def impl(x, initial=None):
-            if len(x) <= 0:
-                printf("omnisci_array_min: cannot find min of zero-sized array")  # noqa: E501
-                return max_value
-            if initial is not None:
-                m = initial
-            else:
-                m = x[0]
-            for i in range(len(x)):
-                v = x[i]
-                if v < m:
-                    m = v
-            return m
-        return impl
-
-
-@extending.overload(sum)
-@expose_and_overload(np.sum)
-@extending.overload_method(ArrayPointer, 'sum')
-def omnisci_np_sum(a, initial=None):
-    if isinstance(a, ArrayPointer):
-        def impl(a, initial=None):
-            if initial is not None:
-                s = initial
-            else:
-                s = 0
-            n = len(a)
-            for i in range(n):
-                s += a[i]
-            return s
-        return impl
-
-
-@expose_and_overload(np.prod)
-@extending.overload_method(ArrayPointer, 'prod')
-def omnisci_np_prod(a, initial=None):
-    if isinstance(a, ArrayPointer):
-        def impl(a, initial=None):
-            if initial is not None:
-                s = initial
-            else:
-                s = 1
-            n = len(a)
-            for i in range(n):
-                s *= a[i]
-            return s
-        return impl
-
-
-@expose_and_overload(np.mean)
-@extending.overload_method(ArrayPointer, 'mean')
-def omnisci_array_mean(x):
-    zero_value = np.nan
-
-    if isinstance(x, ArrayPointer):
-        def impl(x):
-            if len(x) == 0:
-                printf("Mean of empty array")
-                return zero_value
-            return sum(x) / len(x)
-        return impl
-
-
-@expose_and_overload(np.cumsum)
-def omnisci_np_cumsum(a):
-    if isinstance(a, ArrayPointer):
-        eltype = a.eltype
-
-        def impl(a):
-            sz = len(a)
-            out = Array(sz, eltype)
-            out[0] = a[0]
-            for i in range(sz):
-                out[i] = out[i-1] + a[i]
-            return out
-        return impl
diff --git a/rbc/omnisci_backend/numpy_ufuncs.py b/rbc/omnisci_backend/numpy_ufuncs.py
deleted file mode 100644
index 0f077238..00000000
--- a/rbc/omnisci_backend/numpy_ufuncs.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import numpy as np
-from .omnisci_array import Array, ArrayPointer
-from .. import typesystem
-from numba.core import extending, types
-
-
-__all__ = [
-    'add', 'subtract', 'multiply', 'divide', 'logaddexp', 'logaddexp2',
-    'true_divide', 'floor_divide', 'power', 'remainder', 'mod',
-    'fmod', 'gcd', 'lcm', 'bitwise_and', 'bitwise_or', 'bitwise_xor',
-    'bitwise_not', 'left_shift', 'right_shift', 'arctan2', 'hypot',
-    'greater', 'greater_equal', 'less', 'less_equal', 'not_equal',
-    'equal', 'logical_and', 'logical_or', 'logical_xor', 'maximum',
-    'minimum', 'fmax', 'fmin', 'nextafter', 'ldexp', 'negative',
-    'positive', 'absolute', 'fabs', 'rint', 'sign', 'absolute', 'conj',
-    'conjugate', 'exp', 'exp2', 'log', 'log2', 'log10', 'expm1',
-    'log1p', 'sqrt', 'square', 'reciprocal', 'invert', 'sin', 'cos',
-    'tan', 'arcsin', 'arccos', 'arctan', 'sinh', 'cosh', 'tanh',
-    'arcsinh', 'arccosh', 'arctanh', 'degrees', 'radians', 'deg2rad',
-    'rad2deg', 'logical_not', 'isfinite', 'isinf', 'isnan', 'fabs',
-    'floor', 'ceil', 'trunc', 'signbit', 'copysign', 'spacing',
-    'heaviside'
-]
-
-
-def determine_dtype(a, dtype):
-    if isinstance(a, ArrayPointer):
-        return a.eltype if dtype is None else dtype
-    else:
-        return a if dtype is None else dtype
-
-
-def determine_input_type(argty):
-    if isinstance(argty, ArrayPointer):
-        return determine_input_type(argty.eltype)
-
-    if argty == typesystem.boolean8:
-        return bool
-    else:
-        return argty
-
-
-def overload_elementwise_binary_ufunc(ufunc, name=None, dtype=None):
-    """
-    Wrapper for binary ufuncs that returns an array
-    """
-    if name is None:
-        name = ufunc.__name__
-    globals()[name] = ufunc
-
-    def binary_ufunc_impl(a, b):
-        typA = determine_input_type(a)
-        typB = determine_input_type(b)
-
-        # XXX: raise error if len(a) != len(b)
-        @extending.register_jitable(_nrt=False)
-        def binary_impl(a, b, nb_dtype):
-            sz = len(a)
-            x = Array(sz, nb_dtype)
-            for i in range(sz):
-                cast_a = typA(a[i])
-                cast_b = typB(b[i])
-                x[i] = nb_dtype(ufunc(cast_a, cast_b))
-            return x
-
-        @extending.register_jitable(_nrt=False)
-        def broadcast(e, sz, dtype):
-            b = Array(sz, dtype)
-            b.fill(e)
-            return b
-
-        if isinstance(a, ArrayPointer) and isinstance(b, ArrayPointer):
-            nb_dtype = determine_dtype(a, dtype)
-
-            def impl(a, b):
-                return binary_impl(a, b, nb_dtype)
-            return impl
-        elif isinstance(a, ArrayPointer):
-            nb_dtype = determine_dtype(a, dtype)
-            other_dtype = b
-
-            def impl(a, b):
-                b = broadcast(b, len(a), other_dtype)
-                return binary_impl(a, b, nb_dtype)
-            return impl
-        elif isinstance(b, ArrayPointer):
-            nb_dtype = determine_dtype(b, dtype)
-            other_dtype = a
-
-            def impl(a, b):
-                a = broadcast(a, len(b), other_dtype)
-                return binary_impl(a, b, nb_dtype)
-            return impl
-        else:
-            nb_dtype = determine_dtype(a, dtype)
-
-            def impl(a, b):
-                cast_a = typA(a)
-                cast_b = typB(b)
-                return nb_dtype(ufunc(cast_a, cast_b))
-            return impl
-
-    decorate = extending.overload(ufunc)
-
-    def wrapper(overload_func):
-        return decorate(binary_ufunc_impl)
-
-    return wrapper
-
-
-# math functions
-@overload_elementwise_binary_ufunc(np.add)
-@overload_elementwise_binary_ufunc(np.subtract)
-@overload_elementwise_binary_ufunc(np.multiply)
-@overload_elementwise_binary_ufunc(np.divide, name='divide')
-@overload_elementwise_binary_ufunc(np.logaddexp)
-@overload_elementwise_binary_ufunc(np.logaddexp2)
-@overload_elementwise_binary_ufunc(np.true_divide)
-@overload_elementwise_binary_ufunc(np.floor_divide)
-@overload_elementwise_binary_ufunc(np.power)
-# @overload_elementwise_binary_ufunc(np.float_power)  # not supported by numba
-@overload_elementwise_binary_ufunc(np.remainder)
-@overload_elementwise_binary_ufunc(np.mod, name='mod')
-@overload_elementwise_binary_ufunc(np.fmod)
-# @overload_elementwise_binary_ufunc(np.divmod) # not supported by numba
-@overload_elementwise_binary_ufunc(np.gcd)
-@overload_elementwise_binary_ufunc(np.lcm)
-# Bit-twiddling functions
-@overload_elementwise_binary_ufunc(np.bitwise_and)
-@overload_elementwise_binary_ufunc(np.bitwise_or)
-@overload_elementwise_binary_ufunc(np.bitwise_xor)
-@overload_elementwise_binary_ufunc(np.bitwise_not, name='bitwise_not')
-@overload_elementwise_binary_ufunc(np.left_shift)
-@overload_elementwise_binary_ufunc(np.right_shift)
-# trigonometric functions
-@overload_elementwise_binary_ufunc(np.arctan2)
-@overload_elementwise_binary_ufunc(np.hypot)
-# Comparison functions
-@overload_elementwise_binary_ufunc(np.greater, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.greater_equal, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.less, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.less_equal, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.not_equal, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.equal, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.logical_and, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.logical_or, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.logical_xor, dtype=typesystem.boolean8)
-@overload_elementwise_binary_ufunc(np.maximum)
-@overload_elementwise_binary_ufunc(np.minimum)
-@overload_elementwise_binary_ufunc(np.fmax)
-@overload_elementwise_binary_ufunc(np.fmin)
-# Floating functions
-@overload_elementwise_binary_ufunc(np.nextafter)
-@overload_elementwise_binary_ufunc(np.ldexp)
-def dummy_binary_ufunc(a, b):
-    pass
-
-
-def overload_elementwise_unary_ufunc(ufunc, name=None, dtype=None):
-    """
-    Wrapper for unary ufuncs that returns an array
-    """
-    if name is None:
-        name = ufunc.__name__
-    globals()[name] = ufunc
-
-    def unary_elementwise_ufunc_impl(a):
-        nb_dtype = determine_dtype(a, dtype)
-        typ = determine_input_type(a)
-
-        if isinstance(a, ArrayPointer):
-            def impl(a):
-                sz = len(a)
-                x = Array(sz, nb_dtype)
-                for i in range(sz):
-                    # Convert the value to type "typ"
-                    cast = typ(a[i])
-                    x[i] = nb_dtype(ufunc(cast))
-                return x
-            return impl
-        else:
-            def impl(a):
-                # Convert the value to type typ
-                cast = typ(a)
-                return nb_dtype(ufunc(cast))
-            return impl
-
-    decorate = extending.overload(ufunc)
-
-    def wrapper(overload_func):
-        return decorate(unary_elementwise_ufunc_impl)
-
-    return wrapper
-
-
-# Math operations
-@overload_elementwise_unary_ufunc(np.negative)
-@overload_elementwise_unary_ufunc(np.positive)
-@overload_elementwise_unary_ufunc(np.absolute)
-@overload_elementwise_unary_ufunc(np.fabs)
-@overload_elementwise_unary_ufunc(np.rint)
-@overload_elementwise_unary_ufunc(np.sign)
-@overload_elementwise_unary_ufunc(np.absolute)
-@overload_elementwise_unary_ufunc(np.conj, name='conj')
-@overload_elementwise_unary_ufunc(np.conjugate)
-@overload_elementwise_unary_ufunc(np.exp)
-@overload_elementwise_unary_ufunc(np.exp2)
-@overload_elementwise_unary_ufunc(np.log)
-@overload_elementwise_unary_ufunc(np.log2)
-@overload_elementwise_unary_ufunc(np.log10)
-@overload_elementwise_unary_ufunc(np.expm1)
-@overload_elementwise_unary_ufunc(np.log1p)
-@overload_elementwise_unary_ufunc(np.sqrt)
-@overload_elementwise_unary_ufunc(np.square)
-# @overload_elementwise_unary_ufunc(np.cbrt)  # not supported by numba
-@overload_elementwise_unary_ufunc(np.reciprocal)
-# Bit-twiddling functions
-@overload_elementwise_unary_ufunc(np.invert)
-# trigonometric functions
-@overload_elementwise_unary_ufunc(np.sin)
-@overload_elementwise_unary_ufunc(np.cos)
-@overload_elementwise_unary_ufunc(np.tan)
-@overload_elementwise_unary_ufunc(np.arcsin)
-@overload_elementwise_unary_ufunc(np.arccos)
-@overload_elementwise_unary_ufunc(np.arctan)
-@overload_elementwise_unary_ufunc(np.sinh)
-@overload_elementwise_unary_ufunc(np.cosh)
-@overload_elementwise_unary_ufunc(np.tanh)
-@overload_elementwise_unary_ufunc(np.arcsinh)
-@overload_elementwise_unary_ufunc(np.arccosh)
-@overload_elementwise_unary_ufunc(np.arctanh)
-@overload_elementwise_unary_ufunc(np.degrees)
-@overload_elementwise_unary_ufunc(np.radians)
-@overload_elementwise_unary_ufunc(np.deg2rad)
-@overload_elementwise_unary_ufunc(np.rad2deg)
-# Comparison functions
-@overload_elementwise_unary_ufunc(np.logical_not, dtype=typesystem.boolean8)
-# Floating functions
-@overload_elementwise_unary_ufunc(np.isfinite, dtype=typesystem.boolean8)
-@overload_elementwise_unary_ufunc(np.isinf, dtype=typesystem.boolean8)
-@overload_elementwise_unary_ufunc(np.isnan, dtype=typesystem.boolean8)
-@overload_elementwise_unary_ufunc(np.fabs, dtype=types.double)
-@overload_elementwise_unary_ufunc(np.floor, dtype=types.double)
-@overload_elementwise_unary_ufunc(np.ceil, dtype=types.double)
-@overload_elementwise_unary_ufunc(np.trunc, dtype=types.double)
-# not supported?
-# @overload_elementwise_unary_ufunc(np.isnat, dtype=types.int8)
-# issue 152:
-@overload_elementwise_unary_ufunc(np.signbit, dtype=typesystem.boolean8)
-@overload_elementwise_unary_ufunc(np.copysign)
-@overload_elementwise_unary_ufunc(np.spacing, dtype=types.double)
-def dummy_unary_ufunc(a):
-    pass
-
-
-def heaviside(x1, x2):
-    pass
-
-
-@extending.overload(heaviside)
-def impl_np_heaviside(x1, x2):
-    nb_dtype = types.double
-    typA = determine_input_type(x1)
-    typB = determine_input_type(x2)
-    if isinstance(x1, ArrayPointer):
-        def impl(x1, x2):
-            sz = len(x1)
-            r = Array(sz, nb_dtype)
-            for i in range(sz):
-                r[i] = heaviside(x1[i], x2)
-            return r
-        return impl
-    else:
-        def impl(x1, x2):
-            if typA(x1) < 0:
-                return nb_dtype(0)
-            elif typA(x1) == 0:
-                return nb_dtype(typB(x2))
-            else:
-                return nb_dtype(1)
-        return impl
diff --git a/rbc/stdlib/__init__.py b/rbc/stdlib/__init__.py
index e69de29b..2be91e9f 100644
--- a/rbc/stdlib/__init__.py
+++ b/rbc/stdlib/__init__.py
@@ -0,0 +1,203 @@
+import functools
+from enum import Enum
+from numba.core import extending
+from rbc.omnisci_backend import Array, ArrayPointer
+from rbc import typesystem
+
+
+ARRAY_API_ADDRESS = ("https://data-apis.org/array-api/latest/API_specification"
+                     "/generated/signatures.{0}.{1}.html"
+                     "#signatures.{0}.{1}")
+NUMPY_API_ADDRESS = ("https://numpy.org/doc/stable/reference/generated/numpy.{0}.html")
+ADDRESS = ARRAY_API_ADDRESS
+
+
+class API(Enum):
+    NUMPY_API = 0
+    ARRAY_API = 1
+
+
+def determine_dtype(a, dtype):
+    if isinstance(a, ArrayPointer):
+        return a.eltype if dtype is None else dtype
+    else:
+        return a if dtype is None else dtype
+
+
+def determine_input_type(argty):
+    if isinstance(argty, ArrayPointer):
+        return determine_input_type(argty.eltype)
+
+    if argty == typesystem.boolean8:
+        return bool
+    else:
+        return argty
+
+
+class Expose:
+    def __init__(self, globals, module_name):
+        self._globals = globals
+        self.module_name = module_name
+
+    def create_function(self, func_name):
+        s = f'def {func_name}(*args, **kwargs): pass'
+        exec(s, self._globals)
+        fn = self._globals.get(func_name)
+        return fn
+
+    def format_docstring(self, ov_func, func_name, api):
+        original_docstring = ov_func.__doc__
+        if api == API.NUMPY_API:
+            # Numpy
+            link = (
+                f"`NumPy '{func_name}' "
+                f"doc <{NUMPY_API_ADDRESS.format(func_name)}>`_")
+        else:
+            # Array API
+            link = (
+                f"`Array-API '{func_name}' "
+                f"doc <{ARRAY_API_ADDRESS.format(self.module_name, func_name)}>`_")
+
+        if original_docstring is not None:
+            new_doctring = f"{original_docstring}\n\n{link}"
+        else:
+            new_doctring = link
+        return new_doctring
+
+    def implements(self, func_name, api=API.ARRAY_API):
+        fn = self.create_function(func_name)
+        decorate = extending.overload(fn)
+
+        def wrapper(overload_func):
+            overload_func.__doc__ = self.format_docstring(overload_func, func_name, api)
+            functools.update_wrapper(fn, overload_func)
+            return decorate(overload_func)
+
+        return wrapper
+
+    def not_implemented(self, func_name):
+        s = f'def {func_name}(*args, **kwargs): pass'
+        exec(s, self._globals)
+
+        fn = self._globals.get(func_name)
+
+        def wraps(func):
+            func.__doc__ = "❌ Not implemented"
+            functools.update_wrapper(fn, func)
+            return func
+        return wraps
+
+
+class BinaryUfuncExpose(Expose):
+
+    def implements(self, ufunc, ufunc_name=None, dtype=None, api=API.ARRAY_API):
+        """
+        Wrapper for binary ufuncs that returns an array
+        """
+        if ufunc_name is None:
+            ufunc_name = ufunc.__name__
+
+        def binary_ufunc_impl(a, b):
+            typA = determine_input_type(a)
+            typB = determine_input_type(b)
+
+            # XXX: raise error if len(a) != len(b)
+            @extending.register_jitable(_nrt=False)
+            def binary_impl(a, b, nb_dtype):
+                sz = len(a)
+                x = Array(sz, nb_dtype)
+                for i in range(sz):
+                    cast_a = typA(a[i])
+                    cast_b = typB(b[i])
+                    x[i] = nb_dtype(ufunc(cast_a, cast_b))
+                return x
+
+            @extending.register_jitable(_nrt=False)
+            def broadcast(e, sz, dtype):
+                b = Array(sz, dtype)
+                b.fill(e)
+                return b
+
+            if isinstance(a, ArrayPointer) and isinstance(b, ArrayPointer):
+                nb_dtype = determine_dtype(a, dtype)
+
+                def impl(a, b):
+                    return binary_impl(a, b, nb_dtype)
+                return impl
+            elif isinstance(a, ArrayPointer):
+                nb_dtype = determine_dtype(a, dtype)
+                other_dtype = b
+
+                def impl(a, b):
+                    b = broadcast(b, len(a), other_dtype)
+                    return binary_impl(a, b, nb_dtype)
+                return impl
+            elif isinstance(b, ArrayPointer):
+                nb_dtype = determine_dtype(b, dtype)
+                other_dtype = a
+
+                def impl(a, b):
+                    a = broadcast(a, len(b), other_dtype)
+                    return binary_impl(a, b, nb_dtype)
+                return impl
+            else:
+                nb_dtype = determine_dtype(a, dtype)
+
+                def impl(a, b):
+                    cast_a = typA(a)
+                    cast_b = typB(b)
+                    return nb_dtype(ufunc(cast_a, cast_b))
+                return impl
+
+        fn = self.create_function(ufunc_name)
+
+        def wrapper(overload_func):
+            overload_func.__doc__ = self.format_docstring(overload_func, ufunc_name, api)
+            functools.update_wrapper(fn, overload_func)
+
+            decorate = extending.overload(fn)
+            return decorate(binary_ufunc_impl)
+
+        return wrapper
+
+
+class UnaryUfuncExpose(BinaryUfuncExpose):
+
+    def implements(self, ufunc, ufunc_name=None, dtype=None, api=API.ARRAY_API):
+        """
+        Wrapper for unary ufuncs that returns an array
+        """
+        if ufunc_name is None:
+            ufunc_name = ufunc.__name__
+
+        def unary_ufunc_impl(a):
+            nb_dtype = determine_dtype(a, dtype)
+            typ = determine_input_type(a)
+
+            if isinstance(a, ArrayPointer):
+                def impl(a):
+                    sz = len(a)
+                    x = Array(sz, nb_dtype)
+                    for i in range(sz):
+                        # Convert the value to type "typ"
+                        cast = typ(a[i])
+                        x[i] = nb_dtype(ufunc(cast))
+                    return x
+                return impl
+            else:
+                def impl(a):
+                    # Convert the value to type typ
+                    cast = typ(a)
+                    return nb_dtype(ufunc(cast))
+                return impl
+
+        fn = self.create_function(ufunc_name)
+
+        def wrapper(overload_func):
+            overload_func.__doc__ = self.format_docstring(overload_func, ufunc_name, api)
+            functools.update_wrapper(fn, overload_func)
+
+            decorate = extending.overload(fn)
+            return decorate(unary_ufunc_impl)
+
+        return wrapper
diff --git a/rbc/stdlib/array_api.py b/rbc/stdlib/array_api.py
index 0590f8dc..498de8a0 100644
--- a/rbc/stdlib/array_api.py
+++ b/rbc/stdlib/array_api.py
@@ -2,36 +2,10 @@
 Array API for rbc.
 """
 
-__all__ = [
-    'Array',
-    'bool',
-    'int8',
-    'int16',
-    'int32',
-    'int64',
-    'uint8',
-    'uint16',
-    'uint32',
-    'uint64',
-    'float32',
-    'float64'
-]
+from .datatypes import *  # noqa: F401, F403
+from .constants import *  # noqa: F401, F403
+from .creation_functions import *  # noqa: F401, F403
+from .elementwise_functions import *  # noqa: F401, F403
+from .statistical_functions import *  # noqa: F401, F403
 
-# NOTE: currently the code lives in rbc.omnisci_backend, but eventually we
-# should move it here and leave rbc.omnisci_backend.Array only for backwards
-# compatibility
-from rbc.omnisci_backend import Array
-
-# array API data types
-from numba.types import (
-    boolean as bool,
-    int8,
-    int16,
-    int32,
-    int64,
-    uint8,
-    uint16,
-    uint32,
-    uint64,
-    float32,
-    float64)
+__all__ = [s for s in dir() if not s.startswith('_')]
diff --git a/rbc/stdlib/constants.py b/rbc/stdlib/constants.py
new file mode 100644
index 00000000..8473411d
--- /dev/null
+++ b/rbc/stdlib/constants.py
@@ -0,0 +1,17 @@
+"""
+https://data-apis.org/array-api/latest/API_specification/constants.html
+"""
+import numpy as np
+
+
+__all__ = [
+    'e', 'inf', 'nan', 'pi'
+]
+
+
+# it doesn't seem to be possible to document constants with autosummary
+# https://github.com/sphinx-doc/sphinx/issues/6794
+e = np.e
+inf = np.inf
+nan = np.nan
+pi = np.pi
diff --git a/rbc/stdlib/creation_functions.py b/rbc/stdlib/creation_functions.py
new file mode 100644
index 00000000..4dbaed0b
--- /dev/null
+++ b/rbc/stdlib/creation_functions.py
@@ -0,0 +1,286 @@
+"""
+Array API specification for creation functions.
+
+https://data-apis.org/array-api/latest/API_specification/creation_functions.html
+"""
+
+from rbc import typesystem
+from rbc.omnisci_backend.omnisci_array import Array, ArrayPointer
+from rbc.stdlib import Expose
+from numba import njit
+from numba.core import extending, types
+
+__all__ = [
+    'full', 'full_like', 'empty_like', 'empty', 'zeros', 'zeros_like',
+    'ones', 'ones_like', 'array', 'cumsum'
+]
+
+
+expose = Expose(globals(), 'creation_functions')
+
+
+@expose.not_implemented('arange')
+def arange(start, stop=None, step=1, dtype=None, device=None):
+    """
+    Return evenly spaced values within a given interval.
+    """
+    pass
+
+
+@expose.not_implemented('asarray')
+def asarray(obj, dtype=None, device=None, copy=None):
+    """
+    Convert the input to an array.
+    """
+    pass
+
+
+@expose.not_implemented('eye')
+def eye(n_rows, n_cols=None, k=0, dtype=None, device=None):
+    """
+    Return a 2-D array with ones on the diagonal and zeros elsewhere.
+    """
+    pass
+
+
+@expose.not_implemented('from_dlpack')
+def from_dlpack(x):
+    """
+    """
+    pass
+
+
+@expose.not_implemented('linspace')
+def linspace(start, stop, num, dtype=None, device=None, endpoint=True):
+    """
+    Return evenly spaced numbers over a specified interval.
+    """
+    pass
+
+
+@expose.not_implemented('meshgrid')
+def meshgrid(*arrays, indexing='xy'):
+    """
+    Return coordinate matrices from coordinate vectors.
+    """
+    pass
+
+
+@expose.not_implemented('tril')
+def tril(x, k=0):
+    """
+    Lower triangle of an array.
+    """
+    pass
+
+
+@expose.not_implemented('triu')
+def triu(x, k=0):
+    """
+    Upper triangle of an array.
+    """
+    pass
+
+
+@expose.implements('full')
+def _omnisci_np_full(shape, fill_value, dtype=None):
+    """
+    Return a new array of given shape and type, filled with fill_value.
+    """
+
+    # XXX: dtype should be infered from fill_value
+    if dtype is None:
+        nb_dtype = types.double
+    else:
+        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+    def impl(shape, fill_value, dtype=None):
+        a = Array(shape, nb_dtype)
+        a.fill(nb_dtype(fill_value))
+        return a
+    return impl
+
+
+@expose.implements('full_like')
+def _omnisci_np_full_like(a, fill_value, dtype=None):
+    """
+    Return a full array with the same shape and type as a given array.
+    """
+    if isinstance(a, ArrayPointer):
+        if dtype is None:
+            nb_dtype = a.eltype
+        else:
+            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+        def impl(a, fill_value, dtype=None):
+            sz = len(a)
+            other = Array(sz, nb_dtype)
+            other.fill(nb_dtype(fill_value))
+            return other
+        return impl
+
+
+@expose.implements('empty_like')
+def _omnisci_np_empty_like(a, dtype=None):
+    """
+    Return a new array with the same shape and type as a given array.
+    """
+    if isinstance(a, ArrayPointer):
+        if dtype is None:
+            nb_dtype = a.eltype
+        else:
+            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+        def impl(a, dtype=None):
+            return empty(len(a), nb_dtype)  # noqa: F821
+        return impl
+
+
+@expose.implements('empty')
+def _omnisci_np_empty(shape, dtype=None):
+    """
+    Return a new array of given shape and type, without initializing entries.
+    """
+    if dtype is None:
+        nb_dtype = types.double
+    else:
+        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+    def impl(shape, dtype=None):
+        arr = Array(shape, nb_dtype)
+        for i in range(shape):
+            arr.set_null(i)
+        return arr
+    return impl
+
+
+@expose.implements('zeros')
+def _omnisci_np_zeros(shape, dtype=None):
+    """
+    Return a new array of given shape and type, filled with zeros.
+    """
+
+    if dtype is None:
+        nb_dtype = types.double
+    else:
+        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+    fill_value = False if isinstance(nb_dtype, types.Boolean) else 0
+
+    def impl(shape, dtype=None):
+        return full(shape, fill_value, nb_dtype)  # noqa: F821
+    return impl
+
+
+@expose.implements('zeros_like')
+def _omnisci_np_zeros_like(a, dtype=None):
+    """
+    Return an array of zeros with the same shape and type as a given array.
+    """
+    if isinstance(a, ArrayPointer):
+        if dtype is None:
+            nb_dtype = a.eltype
+        else:
+            nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+        fill_value = False if isinstance(nb_dtype, types.Boolean) else 0
+
+        def impl(a, dtype=None):
+            return full_like(a, fill_value, nb_dtype)  # noqa: F821
+        return impl
+
+
+@expose.implements('ones')
+def _omnisci_np_ones(shape, dtype=None):
+    """
+    Return a new array of given shape and type, filled with ones.
+    """
+
+    if dtype is None:
+        nb_dtype = types.double
+    else:
+        nb_dtype = typesystem.Type.fromobject(dtype).tonumba()
+
+    fill_value = True if isinstance(nb_dtype, types.Boolean) else 1
+
+    def impl(shape, dtype=None):
+        return full(shape, fill_value, nb_dtype)  # noqa: F821
+    return impl
+
+
+@expose.implements('ones_like')
+def _omnisci_np_ones_like(a, dtype=None):
+    """
+    Return an array of ones with the same shape and type as a given array.
+    """
+    if isinstance(a, ArrayPointer):
+        if dtype is None:
+            nb_dtype = a.eltype
+        else:
+            nb_dtype = dtype
+
+        fill_value = True if isinstance(nb_dtype, types.Boolean) else 1
+
+        def impl(a, dtype=None):
+            return full_like(a, fill_value, nb_dtype)  # noqa: F821
+        return impl
+
+
+@expose.implements('array')
+def _omnisci_np_array(a, dtype=None):
+    """
+    Create an array.
+    """
+
+    @njit
+    def _omnisci_array_non_empty_copy(a, nb_dtype):
+        """Implement this here rather than inside "impl".
+        LLVM DCE pass removes everything if we implement stuff inside "impl"
+        """
+        other = Array(len(a), nb_dtype)
+        for i in range(len(a)):
+            other[i] = a[i]
+        return other
+
+    if isinstance(a, ArrayPointer):
+        if dtype is None:
+            nb_dtype = a.eltype
+        else:
+            nb_dtype = dtype
+
+        def impl(a, dtype=None):
+            if a.is_null():
+                return empty_like(a)  # noqa: F821
+            else:
+                return _omnisci_array_non_empty_copy(a, nb_dtype)
+        return impl
+
+
+@extending.overload_method(ArrayPointer, 'fill')
+def _omnisci_array_fill(x, v):
+    """
+    Fill the array with a scalar value.
+    """
+    if isinstance(x, ArrayPointer):
+        def impl(x, v):
+            for i in range(len(x)):
+                x[i] = v
+        return impl
+
+
+@expose.implements('cumsum')
+def _omnisci_np_cumsum(a):
+    """
+    Return the cumulative sum of the elements along a given axis.
+    """
+    if isinstance(a, ArrayPointer):
+        eltype = a.eltype
+
+        def impl(a):
+            sz = len(a)
+            out = Array(sz, eltype)
+            out[0] = a[0]
+            for i in range(sz):
+                out[i] = out[i-1] + a[i]
+            return out
+        return impl
diff --git a/rbc/stdlib/datatypes.py b/rbc/stdlib/datatypes.py
new file mode 100644
index 00000000..5a1197f0
--- /dev/null
+++ b/rbc/stdlib/datatypes.py
@@ -0,0 +1,37 @@
+"""
+https://data-apis.org/array-api/latest/API_specification/data_types.html
+"""
+
+__all__ = [
+    'Array',
+    'bool',
+    'int8',
+    'int16',
+    'int32',
+    'int64',
+    'uint8',
+    'uint16',
+    'uint32',
+    'uint64',
+    'float32',
+    'float64'
+]
+
+# NOTE: currently the code lives in rbc.omnisci_backend, but eventually we
+# should move it here and leave rbc.omnisci_backend.Array only for backwards
+# compatibility
+from rbc.omnisci_backend import Array
+
+# array API data types
+from numba.types import (
+    boolean as bool,
+    int8,
+    int16,
+    int32,
+    int64,
+    uint8,
+    uint16,
+    uint32,
+    uint64,
+    float32,
+    float64)
diff --git a/rbc/stdlib/elementwise_functions.py b/rbc/stdlib/elementwise_functions.py
new file mode 100644
index 00000000..c5b01fce
--- /dev/null
+++ b/rbc/stdlib/elementwise_functions.py
@@ -0,0 +1,834 @@
+"""
+Array API specification for element-wise functions.
+
+https://data-apis.org/array- api/latest/API_specification/elementwise_functions.html.
+"""
+
+from rbc.stdlib import Expose, BinaryUfuncExpose, UnaryUfuncExpose, API, determine_input_type
+import numpy as np
+from rbc import typesystem
+from rbc.omnisci_backend import ArrayPointer, Array
+from numba.core import types
+
+
+__all__ = [
+    'add', 'subtract', 'multiply', 'divide', 'logaddexp', 'logaddexp2',
+    'true_divide', 'floor_divide', 'pow', 'remainder', 'mod',
+    'fmod', 'gcd', 'lcm', 'bitwise_and', 'bitwise_or', 'bitwise_xor',
+    'bitwise_not', 'atan2', 'hypot',
+    'greater', 'greater_equal', 'less', 'less_equal', 'not_equal',
+    'equal', 'logical_and', 'logical_or', 'logical_xor', 'maximum',
+    'minimum', 'fmax', 'fmin', 'nextafter', 'ldexp', 'negative',
+    'positive', 'rint', 'sign', 'abs', 'conj',
+    'conjugate', 'exp', 'exp2', 'log', 'log2', 'log10', 'expm1',
+    'log1p', 'sqrt', 'square', 'reciprocal', 'bitwise_not', 'sin', 'cos',
+    'tan', 'asin', 'acos', 'atan', 'sinh', 'cosh', 'tanh',
+    'asinh', 'acosh', 'atanh', 'degrees', 'radians', 'deg2rad',
+    'rad2deg', 'logical_not', 'isfinite', 'isinf', 'isnan', 'fabs',
+    'floor', 'ceil', 'trunc', 'signbit', 'copysign', 'spacing',
+    'heaviside', 'bitwise_left_shift', 'bitwise_right_shift',
+    'round',
+    # numpy specifics
+    'power', 'arctan2', 'left_shift', 'right_shift', 'absolute',
+    'invert', 'arcsin', 'arctan', 'arccos', 'arcsinh', 'arccosh', 'arctanh'
+]
+
+
+expose = Expose(globals(), 'elementwise_functions')
+binary_expose = BinaryUfuncExpose(globals(), 'elementwise_functions')
+unary_expose = UnaryUfuncExpose(globals(), 'elementwise_functions')
+
+
+# math functions
+@binary_expose.implements(np.add)
+def _omnisci_add(x1, x2):
+    """
+    Calculates the sum for each element x1_i of the input array x1 with the respective element
+    x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.subtract)
+def _omnisci_ufunc_subtract(x1, x2):
+    """
+    Calculates the difference for each element x1_i of the input array x1 with the respective
+    element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.multiply)
+def _omnisci_ufunc_multiply(x1, x2):
+    """
+    Calculates the product for each element x1_i of the input array x1 with the respective element
+    x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.divide, ufunc_name='divide')
+def _omnisci_ufunc_divide(x1, x2):
+    """
+    Calculates the division for each element x1_i of the input array x1 with the respective
+    element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.logaddexp)
+def _omnisci_ufunc_logaddexp(x1, x2):
+    """
+    Calculates the logarithm of the sum of exponentiations ``log(exp(x1) + exp(x2))`` for each
+    element x1_i of the input array x1 with the respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.copysign, api=API.NUMPY_API)
+def _omnisci_ufunc_copysign(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.logaddexp2, api=API.NUMPY_API)
+def _omnisci_ufunc_logaddexp2(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.true_divide, api=API.NUMPY_API)
+def _omnisci_ufunc_true_divide(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.floor_divide)
+def _omnisci_ufunc_floor_divide(x1, x2):
+    """
+    Rounds the result of dividing each element x1_i of the input array x1 by the respective
+    element x2_i of the input array x2 to the greatest (i.e., closest to +infinity) integer-value
+    number that is not greater than the division result.
+    """
+    pass
+
+
+@binary_expose.implements(np.power, ufunc_name='power', api=API.NUMPY_API)
+def _omnisci_ufunc_power(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.power, ufunc_name='pow')
+def _omnisci_ufunc_pow(x1, x2):
+    """
+    Calculates an implementation-dependent approximation of exponentiation by raising each element
+    x1_i (the base) of the input array x1 to the power of x2_i (the exponent), where x2_i is the
+    corresponding element of the input array x2.
+    """
+    pass
+
+
+@binary_expose.not_implemented('float_power')  # not supported by Numba
+def _omnisci_ufunc_float_power(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.remainder)
+def _omnisci_ufunc_remainder(x1, x2):
+    """
+    Returns the remainder of division for each element x1_i of the input array x1 and the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.mod, ufunc_name='mod', api=API.NUMPY_API)
+def _omnisci_ufunc_mod(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.fmod, api=API.NUMPY_API)
+def _omnisci_ufunc_fmod(x1, x2):
+    pass
+
+
+@binary_expose.not_implemented('divmod')  # not supported by Numba
+def _omnisci_ufunc_divmod(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.gcd, api=API.NUMPY_API)
+def _omnisci_ufunc_gcd(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.lcm, api=API.NUMPY_API)
+def _omnisci_ufunc_lcm(x1, x2):
+    pass
+
+
+# Bit-twiddling functions
+@binary_expose.implements(np.bitwise_and)
+def _omnisci_ufunc_bitwise_and(x1, x2):
+    """
+    Computes the bitwise AND of the underlying binary representation of each element x1_iof the
+    input array x1 with the respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.bitwise_or)
+def _omnisci_ufunc_bitwise_or(x1, x2):
+    """
+    Computes the bitwise OR of the underlying binary representation of each element x1_i of the
+    input array x1 with the respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.bitwise_xor)
+def _omnisci_ufunc_bitwise_xor(x1, x2):
+    """
+    Computes the bitwise XOR of the underlying binary representation of each element x1_i of the
+    input array x1 with the respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.bitwise_not, ufunc_name='bitwise_not')
+def _omnisci_ufunc_bitwise_not(x1, x2):
+    """
+    Computes the bitwise NOR of the underlying binary representation of each element x1_i of the
+    input array x1 with the respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.left_shift, api=API.NUMPY_API)
+def _omnisci_ufunc_left_shift(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.left_shift, ufunc_name='bitwise_left_shift')
+def _omnisci_ufunc_bitwise_left_shift(x1, x2):
+    """
+    Shifts the bits of each element x1_i of the input array x1 to the left by appending x2_i
+    (i.e., the respective element in the input array x2) zeros to the right of x1_i.
+    """
+    pass
+
+
+@binary_expose.implements(np.right_shift, api=API.NUMPY_API)
+def _omnisci_ufunc_right_shift(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.right_shift, ufunc_name='bitwise_right_shift')
+def _omnisci_ufunc_bitwise_right_shift(x1, x2):
+    """
+    Shifts the bits of each element x1_i of the input array x1 to the right by appending x2_i
+    (i.e., the respective element in the input array x2) zeros to the right of x1_i.
+    """
+    pass
+
+
+# trigonometric functions
+@binary_expose.implements(np.arctan2, api=API.NUMPY_API)
+def _omnisci_ufunc_arctan2(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.arctan2, ufunc_name='atan2')
+def _omnisci_ufunc_atan2(x1, x2):
+    """
+    Calculates an implementation-dependent approximation of the inverse tangent of the quotient
+    x1/x2, having domain [-infinity, +infinity] x ``[-infinity, +infinity]`` (where the x notation
+    denotes the set of ordered pairs of elements (x1_i, x2_i)) and codomain [-π, +π], for each
+    pair of elements (x1_i, x2_i) of the input arrays x1 and x2, respectively.
+    """
+    pass
+
+
+@binary_expose.implements(np.hypot, api=API.NUMPY_API)
+def _omnisci_ufunc_hypot(x1, x2):
+    pass
+
+
+# Comparison functions
+@binary_expose.implements(np.greater, dtype=typesystem.boolean8)
+def _omnisci_ufunc_greater(x1, x2):
+    """
+    Computes the truth value of x1_i > x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.greater_equal, dtype=typesystem.boolean8)
+def _omnisci_ufunc_greater_equal(x1, x2):
+    """
+    Computes the truth value of x1_i >= x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.less, dtype=typesystem.boolean8)
+def _omnisci_ufunc_less(x1, x2):
+    """
+    Computes the truth value of x1_i < x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.less_equal, dtype=typesystem.boolean8)
+def _omnisci_ufunc_less_equal(x1, x2):
+    """
+    Computes the truth value of x1_i <= x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.not_equal, dtype=typesystem.boolean8)
+def _omnisci_ufunc_not_equal(x1, x2):
+    """
+    Computes the truth value of x1_i != x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.equal, dtype=typesystem.boolean8)
+def _omnisci_ufunc_equal(x1, x2):
+    """
+    Computes the truth value of x1_i == x2_i for each element x1_i of the input array x1 with the
+    respective element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.logical_and, dtype=typesystem.boolean8)
+def _omnisci_ufunc_logical_and(x1, x2):
+    """
+    Computes the logical AND for each element x1_i of the input array x1 with the respective
+    element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.logical_or, dtype=typesystem.boolean8)
+def _omnisci_ufunc_logical_or(x1, x2):
+    """
+    Computes the logical OR for each element x1_i of the input array x1 with the respective
+    element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.logical_xor, dtype=typesystem.boolean8)
+def _omnisci_ufunc_logical_xor(x1, x2):
+    """
+    "Computes the logical XOR for each element x1_i of the input array x1 with the respective
+    element x2_i of the input array x2.
+    """
+    pass
+
+
+@binary_expose.implements(np.maximum, api=API.NUMPY_API)
+def _omnisci_ufunc_maximum(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.minimum, api=API.NUMPY_API)
+def _omnisci_ufunc_minimum(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.fmax, api=API.NUMPY_API)
+def _omnisci_ufunc_fmax(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.fmin, api=API.NUMPY_API)
+def _omnisci_ufunc_fmin(x1, x2):
+    pass
+
+
+# Floating functions
+@binary_expose.implements(np.nextafter, api=API.NUMPY_API)
+def _omnisci_ufunc_nextafter(x1, x2):
+    pass
+
+
+@binary_expose.implements(np.ldexp, api=API.NUMPY_API)
+def _omnisci_ufunc_ldexp(x1, x2):
+    pass
+
+
+##################################################################
+
+
+@unary_expose.implements(np.around, ufunc_name='round')
+def _omnisci_ufunc_round(a):
+    """
+    Rounds each element x_i of the input array x to the nearest integer-valued number.
+    """
+    pass
+
+
+@unary_expose.implements(np.negative)
+def _omnisci_ufunc_negative(a):
+    """Computes the numerical negative of each element x_i (i.e., y_i = -x_i) of the
+    input array x."""
+    pass
+
+
+@unary_expose.implements(np.positive)
+def _omnisci_ufunc_positive(a):
+    """Computes the numerical positive of each element x_i (i.e., y_i = +x_i) of the
+    input array x."""
+    pass
+
+
+@unary_expose.implements(np.absolute, api=API.NUMPY_API)
+def _omnisci_ufunc_absolute(a):
+    pass
+
+
+@unary_expose.implements(np.absolute, ufunc_name='abs')
+def _omnisci_ufunc_abs(a):
+    """
+    Calculates the absolute value for each element x_i of the input array x (i.e., the element-
+    wise result has the same magnitude as the respective element in x but has positive sign).
+    """
+    pass
+
+
+@unary_expose.implements(np.rint, api=API.NUMPY_API)
+def _omnisci_ufunc_rint(a):
+    pass
+
+
+@unary_expose.implements(np.sign)
+def _omnisci_ufunc_sign(a):
+    """
+    Returns an indication of the sign of a number for each element x_i of the input array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.conj, ufunc_name='conj', api=API.NUMPY_API)
+def _omnisci_ufunc_conj(a):
+    pass
+
+
+@unary_expose.implements(np.conjugate, api=API.NUMPY_API)
+def _omnisci_ufunc_conjugate(a):
+    pass
+
+
+@unary_expose.implements(np.exp)
+def _omnisci_ufunc_exp(a):
+    """Calculates an implementation-dependent approximation to the exponential function, having
+    domain.
+
+    [-infinity, +infinity] and codomain [+0, +infinity], for each
+    element x_i of the input array x (e raised to the power of x_i,
+    where e is the base of the natural logarithm).
+
+    """
+    pass
+
+
+@unary_expose.implements(np.exp2, api=API.NUMPY_API)
+def _omnisci_ufunc_exp2(a):
+    pass
+
+
+@unary_expose.implements(np.log)
+def _omnisci_ufunc_log(a):
+    """
+    Calculates an implementation-dependent approximation to the natural (base e) logarithm, having
+    domain [0, +infinity] and codomain [-infinity, +infinity], for each element x_i of the input
+    array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.log2)
+def _omnisci_ufunc_log2(a):
+    """Calculates an implementation-dependent approximation to the base 2 logarithm, having
+    domain.
+
+    [0,
+
+    +infinity] and codomain [-infinity, +infinity], for each element x_i
+    of the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.log10)
+def _omnisci_ufunc_log10(a):
+    """Calculates an implementation-dependent approximation to the base 10 logarithm, having
+    domain.
+
+    [0,
+
+    +infinity] and codomain [-infinity, +infinity], for each element x_i
+    of the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.expm1)
+def _omnisci_ufunc_expm1(a):
+    """Calculates an implementation-dependent approximation to exp(x)-1, having domain [-infinity,
+
+    +infinity] and codomain [-1, +infinity], for each element x_i of the
+    input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.log1p)
+def _omnisci_ufunc_log1p(a):
+    """
+    Calculates an implementation-dependent approximation to log(1+x), where log refers to the
+    natural (base e) logarithm, having domain [-1, +infinity] and codomain [-infinity, +infinity],
+    for each element x_i of the input array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.sqrt)
+def _omnisci_ufunc_sqrt(a):
+    """
+    Calculates the square root, having domain [0, +infinity] and codomain [0, +infinity], for each
+    element x_i of the input array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.square)
+def _omnisci_ufunc_square(a):
+    """Squares (x_i * x_i) each element x_i of the input array x."""
+    pass
+
+
+# @unary_expose.implements(np.cbrt)  # not supported by numba
+@unary_expose.not_implemented('cbrt')
+def _omnisci_ufunc_cbrt(a):
+    pass
+
+
+@unary_expose.implements(np.reciprocal, api=API.NUMPY_API)
+def _omnisci_ufunc_reciprocal(a):
+    pass
+
+
+# Bit-twiddling functions
+@unary_expose.implements(np.invert, api=API.NUMPY_API)
+def _omnisci_ufunc_invert(a):
+    pass
+
+
+@unary_expose.implements(np.invert, ufunc_name='bitwise_invert')
+def _omnisci_ufunc_bitwise_invert(a):
+    """
+    Inverts (flips) each bit for each element x_i of the input array x.
+    """
+    pass
+
+
+# trigonometric functions
+@unary_expose.implements(np.sin)
+def _omnisci_ufunc_sin(a):
+    """Calculates an implementation-dependent approximation to the sine, having domain (-infinity,
+
+    +infinity) and codomain [-1, +1], for each element x_i of the input
+    array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.cos)
+def _omnisci_ufunc_cos(a):
+    """Calculates an implementation-dependent approximation to the cosine, having domain
+    (-infinity,
+
+    +infinity) and codomain [-1, +1], for each element x_i of the input
+    array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.tan)
+def _omnisci_ufunc_tan(a):
+    """Calculates an implementation-dependent approximation to the tangent, having domain
+    (-infinity,
+
+    +infinity) and codomain (-infinity, +infinity), for each element x_i
+    of the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.arcsin, api=API.NUMPY_API)
+def _omnisci_ufunc_arcsin(a):
+    pass
+
+
+@unary_expose.implements(np.arcsin, ufunc_name='asin')
+def _omnisci_ufunc_asin(a):
+    """
+    Calculates an implementation-dependent approximation of the principal value of the inverse
+    sine, having domain [-1, +1] and codomain [-π/2, +π/2] for each element x_i of the input array
+    x.
+    """
+    pass
+
+
+@unary_expose.implements(np.arccos, api=API.NUMPY_API)
+def _omnisci_ufunc_arccos(a):
+    pass
+
+
+@unary_expose.implements(np.arccos, ufunc_name='acos')
+def _omnisci_ufunc_acos(a):
+    """
+    Calculates an implementation-dependent approximation of the principal value of the inverse
+    cosine, having domain [-1, +1] and codomain [+0, +π], for each element x_i of the input array
+    x.
+    """
+    pass
+
+
+@unary_expose.implements(np.arctan, api=API.NUMPY_API)
+def _omnisci_ufunc_arctan(a):
+    pass
+
+
+@unary_expose.implements(np.arctan, ufunc_name='atan')
+def _omnisci_ufunc_atan(a):
+    """
+    Calculates an implementation-dependent approximation of the principal value of the inverse
+    tangent, having domain [-infinity, +infinity] and codomain [-π/2, +π/2], for each element x_i
+    of the input array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.sinh)
+def _omnisci_ufunc_sinh(a):
+    """Calculates an implementation-dependent approximation to the hyperbolic sine, having domain.
+
+    [-infinity, +infinity] and codomain [-infinity, +infinity], for each
+    element x_i of the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.cosh)
+def _omnisci_ufunc_cosh(a):
+    """Calculates an implementation-dependent approximation to the hyperbolic cosine, having
+    domain.
+
+    [-infinity, +infinity] and codomain [-infinity, +infinity], for each
+    element x_i in the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.tanh)
+def _omnisci_ufunc_tanh(a):
+    """Calculates an implementation-dependent approximation to the hyperbolic tangent, having
+    domain.
+
+    [-infinity, +infinity] and codomain [-1, +1], for each element x_i
+    of the input array x.
+
+    """
+    pass
+
+
+@unary_expose.implements(np.arcsinh, api=API.NUMPY_API)
+def _omnisci_ufunc_arcsinh(a):
+    pass
+
+
+@unary_expose.implements(np.arcsinh, ufunc_name='asinh')
+def _omnisci_ufunc_asinh(a):
+    """
+    Calculates an implementation-dependent approximation to the inverse hyperbolic sine, having
+    domain [-infinity, +infinity] and codomain [-infinity, +infinity], for each element x_i in the
+    input array x.
+    """
+    pass
+
+
+@unary_expose.implements(np.arccosh, api=API.NUMPY_API)
+def _omnisci_ufunc_arccosh(a):
+    pass
+
+
+@unary_expose.implements(np.arccosh, ufunc_name='acosh')
+def _omnisci_ufunc_acosh(a):
+    """
+    Calculates an implementation-dependent approximation to the inverse hyperbolic cosine, having
+    domain [+1, +infinity] and codomain [+0, +infinity], for each element x_i of the input array
+    x.
+    """
+    pass
+
+
+@unary_expose.implements(np.arctanh, api=API.NUMPY_API)
+def _omnisci_ufunc_arctanh(a):
+    pass
+
+
+@unary_expose.implements(np.arctanh, ufunc_name='atanh')
+def _omnisci_ufunc_atanh(a):
+    """
+    Calculates an implementation-dependent approximation to the inverse hyperbolic tangent, having
+    domain [-1, +1] and codomain [-infinity, +infinity], for each element x_i of the input array
+    x.
+    """
+    pass
+
+
+@unary_expose.implements(np.degrees, api=API.NUMPY_API)
+def _omnisci_ufunc_degrees(a):
+    pass
+
+
+@unary_expose.implements(np.radians, api=API.NUMPY_API)
+def _omnisci_ufunc_radians(a):
+    pass
+
+
+@unary_expose.implements(np.deg2rad, api=API.NUMPY_API)
+def _omnisci_ufunc_deg2rad(a):
+    pass
+
+
+@unary_expose.implements(np.rad2deg, api=API.NUMPY_API)
+def _omnisci_ufunc_rad2deg(a):
+    pass
+
+
+# Comparison functions
+@unary_expose.implements(np.logical_not, dtype=typesystem.boolean8)
+def _omnisci_ufunc_logical_not(a):
+    """
+    Computes the logical NOT for each element x_i of the input array x.
+    """
+    pass
+
+
+# Floating functions
+@unary_expose.implements(np.isfinite, dtype=typesystem.boolean8)
+def _omnisci_ufunc_isfinite(a):
+    """
+    Tests each element x_i of the input array x to determine if finite (i.e., not NaN and not
+    equal to positive or negative infinity).
+    """
+    pass
+
+
+@unary_expose.implements(np.isinf, dtype=typesystem.boolean8)
+def _omnisci_ufunc_isinf(a):
+    """
+    Tests each element x_i of the input array x to determine if equal to positive or negative
+    infinity.
+    """
+    pass
+
+
+@unary_expose.implements(np.isnan, dtype=typesystem.boolean8)
+def _omnisci_ufunc_isnan(a):
+    """
+    Tests each element x_i of the input array x to determine whether the element is NaN.
+    """
+    pass
+
+
+@unary_expose.implements(np.fabs, dtype=types.double, api=API.NUMPY_API)
+def _omnisci_ufunc_fabs(a):
+    pass
+
+
+@unary_expose.implements(np.floor, dtype=types.double)
+def _omnisci_ufunc_floor(a):
+    """
+    Rounds each element x_i of the input array x to the greatest (i.e., closest to +infinity)
+    integer-valued number that is not greater than x_i.
+    """
+    pass
+
+
+@unary_expose.implements(np.ceil, dtype=types.double)
+def _omnisci_ufunc_ceil(a):
+    """
+    Rounds each element x_i of the input array x to the smallest (i.e., closest to -infinity)
+    integer-valued number that is not less than x_i.
+    """
+    pass
+
+
+@unary_expose.implements(np.trunc, dtype=types.double)
+def _omnisci_ufunc_trunc(a):
+    """
+    Rounds each element x_i of the input array x to the integer-valued number that is closest to
+    but no greater than x_i.
+    """
+    pass
+
+
+# not supported?
+# @unary_expose.implements(np.isnat, dtype=types.int8)
+@unary_expose.not_implemented('isnat')
+def _omnisci_ufunc_isnat(a):
+    pass
+
+
+# issue 152:
+@unary_expose.implements(np.signbit, dtype=typesystem.boolean8, api=API.NUMPY_API)
+def _omnisci_ufunc_signbit(a):
+    pass
+
+
+@unary_expose.implements(np.spacing, dtype=types.double, api=API.NUMPY_API)
+def _omnisci_ufunc_spacing(a):
+    pass
+
+
+@expose.implements('heaviside', api=API.NUMPY_API)
+def _impl_heaviside(x1, x2):
+    nb_dtype = types.double
+    typA = determine_input_type(x1)
+    typB = determine_input_type(x2)
+    if isinstance(x1, ArrayPointer):
+        def impl(x1, x2):
+            sz = len(x1)
+            r = Array(sz, nb_dtype)
+            for i in range(sz):
+                r[i] = heaviside(x1[i], x2)  # noqa: F821
+            return r
+        return impl
+    else:
+        def impl(x1, x2):
+            if typA(x1) < 0:
+                return nb_dtype(0)
+            elif typA(x1) == 0:
+                return nb_dtype(typB(x2))
+            else:
+                return nb_dtype(1)
+        return impl
diff --git a/rbc/stdlib/statistical_functions.py b/rbc/stdlib/statistical_functions.py
new file mode 100644
index 00000000..c6d58d17
--- /dev/null
+++ b/rbc/stdlib/statistical_functions.py
@@ -0,0 +1,151 @@
+"""
+Array API specification for statistical functions.
+
+https://data-apis.org/array-api/latest/API_specification/statistical_functions.html
+"""
+from rbc.externals.stdio import printf
+from rbc import typesystem
+from rbc.omnisci_backend import ArrayPointer
+from rbc.stdlib import Expose
+from numba.core import extending, types, errors
+from numba.np import numpy_support
+import numpy as np
+
+
+__all__ = [
+    'min', 'max', 'mean', 'prod', 'sum'
+]
+
+
+expose = Expose(globals(), 'statistical_functions')
+
+
+def _get_type_limits(eltype):
+    np_dtype = numpy_support.as_dtype(eltype)
+    if isinstance(eltype, types.Integer):
+        return np.iinfo(np_dtype)
+    elif isinstance(eltype, types.Float):
+        return np.finfo(np_dtype)
+    else:
+        msg = 'Type {} not supported'.format(eltype)
+        raise errors.TypingError(msg)
+
+
+@extending.overload(max)
+@expose.implements('max')
+@extending.overload_method(ArrayPointer, 'max')
+def _omnisci_array_max(x):
+    """
+    Calculates the maximum value of the input array x
+    """
+    if isinstance(x, ArrayPointer):
+        # the array api standard says this is implementation specific
+        limits = _get_type_limits(x.eltype)
+        t = typesystem.Type.fromobject(x.eltype)
+        if t.is_float:
+            min_value = limits.min
+        elif t.is_int or t.is_uint:
+            min_value = 0 if t.is_uint else limits.min + 1
+        else:
+            raise TypeError(f'Unsupported type {t}')
+
+        def impl(x):
+            if len(x) <= 0:
+                printf("omnisci_array_max: cannot find max of zero-sized array")  # noqa: E501
+                return min_value
+            m = x[0]
+            for i in range(len(x)):
+                v = x[i]
+                if v > m:
+                    m = v
+            return m
+        return impl
+
+
+@extending.overload(min)
+@expose.implements('min')
+@extending.overload_method(ArrayPointer, 'min')
+def _omnisci_array_min(x):
+    """
+    Calculates the minimum value of the input array x.
+    """
+    if isinstance(x, ArrayPointer):
+        max_value = _get_type_limits(x.eltype).max
+
+        def impl(x):
+            if len(x) <= 0:
+                printf("omnisci_array_min: cannot find min of zero-sized array")  # noqa: E501
+                return max_value
+            m = x[0]
+            for i in range(len(x)):
+                v = x[i]
+                if v < m:
+                    m = v
+            return m
+        return impl
+
+
+@extending.overload(sum)
+@expose.implements('sum')
+@extending.overload_method(ArrayPointer, 'sum')
+def _omnisci_np_sum(a):
+    """
+    Calculates the sum of the input array x.
+    """
+    if isinstance(a, ArrayPointer):
+        def impl(a):
+            s = 0
+            n = len(a)
+            for i in range(n):
+                s += a[i]
+            return s
+        return impl
+
+
+@expose.implements('prod')
+@extending.overload_method(ArrayPointer, 'prod')
+def _omnisci_np_prod(a):
+    """
+    Calculates the product of input array x elements.
+    """
+    if isinstance(a, ArrayPointer):
+        def impl(a):
+            s = 1
+            n = len(a)
+            for i in range(n):
+                s *= a[i]
+            return s
+        return impl
+
+
+@expose.implements('mean')
+@extending.overload_method(ArrayPointer, 'mean')
+def _omnisci_array_mean(x):
+    """
+    Calculates the arithmetic mean of the input array x.
+    """
+    zero_value = np.nan
+
+    if isinstance(x, ArrayPointer):
+        def impl(x):
+            if len(x) == 0:
+                printf("Mean of empty array")
+                return zero_value
+            return sum(x) / len(x)
+        return impl
+
+
+@expose.not_implemented('std')
+def _omnisci_array_std(x, axis=None, correction=0.0, keepdims=False):
+    """
+    Calculates the standard deviation of the input array x.
+    """
+    pass
+
+
+@expose.not_implemented('var')
+def _omnisci_array_var(x, axis=None, correction=0.0, keepdims=False):
+    """
+    Calculates the variance of the input array x.
+    """
+    pass
diff --git a/setup.cfg b/setup.cfg
index 5c6f51e6..9d3e0fd4 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -15,6 +15,9 @@ parentdir_prefix =
 per-file-ignores =
     rbc/omnisci_backend/numpy_ufuncs.py: F822 # undefined name in __all__
     rbc/omnisci_backend/numpy_funcs.py: F822
+    rbc/stdlib/creation_functions.py: F822
+    rbc/stdlib/elementwise_functions.py: F822
+    rbc/stdlib/statistical_functions.py: F822
 exclude = 
     versioneer.py
     .eggs


Test patch:
diff --git a/.github/workflows/rbc_test.yml b/.github/workflows/rbc_test.yml
index 5e05e741..205491fe 100644
--- a/.github/workflows/rbc_test.yml
+++ b/.github/workflows/rbc_test.yml
@@ -105,7 +105,7 @@ jobs:
     runs-on: ${{ matrix.os }}
     timeout-minutes: 20
     strategy:
-      fail-fast: true
+      fail-fast: false
       matrix:
         os: [ubuntu-latest]
         python-version: ['3.9', '3.8', '3.7']
diff --git a/rbc/tests/__init__.py b/rbc/tests/__init__.py
index 7bd4c461..2ef36a5a 100644
--- a/rbc/tests/__init__.py
+++ b/rbc/tests/__init__.py
@@ -34,7 +34,7 @@ def sql_execute(query):
 
 def omnisci_fixture(caller_globals, minimal_version=(0, 0),
                     suffices=['', '10', 'null', 'array', 'arraynull'],
-                    load_columnar=True, debug=False):
+                    load_columnar=True, load_test_data=True, debug=False):
     """Usage from a rbc/tests/test_xyz.py file:
 
     .. code-block:: python
@@ -166,6 +166,10 @@ def require_version(version, message=None, label=None):
     config = rbc_omnisci.get_client_config(debug=debug)
     m = rbc_omnisci.RemoteOmnisci(**config)
 
+    if not load_test_data:
+        yield m
+        return
+
     sqltypes = ['FLOAT', 'DOUBLE', 'TINYINT', 'SMALLINT', 'INT', 'BIGINT',
                 'BOOLEAN']
     arrsqltypes = [t + '[]' for t in sqltypes]
diff --git a/rbc/tests/test_omnisci_array.py b/rbc/tests/test_omnisci_array.py
index 69647116..84782921 100644
--- a/rbc/tests/test_omnisci_array.py
+++ b/rbc/tests/test_omnisci_array.py
@@ -2,6 +2,7 @@
 from collections import defaultdict
 from rbc.omnisci_backend import Array
 from rbc.errors import OmnisciServerError
+from rbc.stdlib import array_api
 from numba import types as nb_types
 import pytest
 
@@ -276,13 +277,12 @@ def test_array_constructor_noreturn(omnisci):
     omnisci.reset()
 
     from rbc.omnisci_backend import Array
-    from numba import types
 
     @omnisci('float64(int32)')
     def array_noreturn(size):
-        a = Array(size, types.float64)
-        b = Array(size, types.float64)
-        c = Array(size, types.float64)
+        a = Array(size, nb_types.float64)
+        b = Array(size, nb_types.float64)
+        c = Array(size, nb_types.float64)
         for i in range(size):
             a[i] = b[i] = c[i] = i + 3.0
         s = 0.0
@@ -300,14 +300,13 @@ def test_array_constructor_return(omnisci):
     omnisci.reset()
 
     from rbc.omnisci_backend import Array
-    from numba import types
     from rbc.externals.stdio import printf
 
     @omnisci('float64[](int32)')
     def array_return(size):
         printf("entering array_return(%i)\n", size)
-        a = Array(size, types.float64)
-        b = Array(size, types.float64)
+        a = Array(size, nb_types.float64)
+        b = Array(size, nb_types.float64)
         for i in range(size):
             a[i] = float(i)
             b[i] = float(size - i - 1)
@@ -330,11 +329,10 @@ def test_array_constructor_len(omnisci):
     omnisci.reset()
 
     from rbc.omnisci_backend import Array
-    from numba import types
 
     @omnisci('int64(int32)')
     def array_len(size):
-        a = Array(size, types.float64)
+        a = Array(size, nb_types.float64)
         return len(a)
 
     query = 'select array_len(30)'
@@ -388,18 +386,15 @@ def array_is_null(size):
 def test_issue197(omnisci, typ, col, suffix):
     omnisci.reset()
 
-    import rbc.omnisci_backend as np
-    from numba import types
-
     cast = dict(
-        trunc=types.int64,
-        sext=types.int8,
-        zext=types.uint8,
-        fptrunc=types.float64,
-        fpext=types.float32)[suffix]
+        trunc=nb_types.int64,
+        sext=nb_types.int8,
+        zext=nb_types.uint8,
+        fptrunc=nb_types.float64,
+        fpext=nb_types.float32)[suffix]
 
     def fn_issue197(x):
-        y = np.zeros_like(x)
+        y = array_api.zeros_like(x)
         for i in range(len(x)):
             y[i] = cast(x[i] + 3)
         return y
@@ -421,11 +416,9 @@ def fn_issue197(x):
 def test_issue197_bool(omnisci):
     omnisci.reset()
 
-    import rbc.omnisci_backend as np
-
     @omnisci('bool[](bool[])')
     def fn_issue197_bool(x):
-        y = np.zeros_like(x)
+        y = array_api.zeros_like(x)
         for i in range(len(x)):
             y[i] = bool(x[i])
         return y
diff --git a/rbc/tests/test_omnisci_array_functions.py b/rbc/tests/test_omnisci_array_functions.py
index a784d8ee..82e2c65f 100644
--- a/rbc/tests/test_omnisci_array_functions.py
+++ b/rbc/tests/test_omnisci_array_functions.py
@@ -1,6 +1,6 @@
 import pytest
 import numpy as np
-import rbc.omnisci_backend as omni
+from rbc.stdlib import array_api
 from numba.core import types
 
 
@@ -56,56 +56,68 @@ def row_value(row, col, colname):
 
 
 def np_ones(sz):
-    return omni.ones(sz, types.int32)
+    return array_api.ones(sz, types.int32)
 
 
 def np_ones_dtype(sz):
-    return omni.ones(sz)
+    return array_api.ones(sz)
 
 
 def np_ones_like_dtype(i4):
-    return omni.ones_like(i4, dtype=types.double)
+    return array_api.ones_like(i4, dtype=types.double)
 
 
 def np_ones_like(i4):
-    return omni.ones_like(i4)
+    return array_api.ones_like(i4)
+
+
+def np_empty(sz):
+    return array_api.empty(sz, np.int32)
+
+
+def np_empty_dtype(sz):
+    return array_api.empty(sz)
+
+
+def np_empty_like(i4):
+    return array_api.empty_like(i4)
 
 
 def np_zeros(sz):
-    return omni.zeros(sz, np.int32)
+    return array_api.zeros(sz, np.int32)
 
 
 def np_zeros_dtype(sz):
-    return omni.zeros(sz)
+    return array_api.zeros(sz)
 
 
 def np_zeros_like(i4):
-    return omni.zeros_like(i4)
+    return array_api.zeros_like(i4)
 
 
 def np_zeros_like_dtype(i4):
-    return omni.zeros_like(i4, dtype=types.double)
+    return array_api.zeros_like(i4, dtype=types.double)
 
 
 def np_full(sz, fill_value):
-    return omni.full(sz, fill_value, types.double)
+    return array_api.full(sz, fill_value, dtype=types.double)
 
 
 def np_full_dtype(sz, fill_value):
-    return omni.full(sz, fill_value)
+    return array_api.full(sz, fill_value)
 
 
 def np_full_like(i1, fill_value):
-    return omni.full_like(i1, fill_value)
+    return array_api.full_like(i1, fill_value)
 
 
 def np_full_like_dtype(i1, fill_value):
-    return omni.full_like(i1, fill_value, dtype=types.double)
+    return array_api.full_like(i1, fill_value, dtype=types.double)
 
 
 def np_cumsum(sz):
-    a = omni.ones(sz)
-    return omni.cumsum(a)
+    a = array_api.ones(sz)
+    return array_api.cumsum(a)
 
 
 array_methods = [
@@ -121,6 +133,9 @@ def np_cumsum(sz):
     ('zeros_like', 'int32[](int32[])', ('i4',), np.zeros(6, dtype='i')),
     ('zeros_like_dtype', 'double[](int32[])', ('i4',), np.zeros(6, dtype='q')),
     ('zeros_dtype', 'double[](int64)', (5,), np.zeros(5)),
+    ('empty', 'int32[](int64)', (5,), np.empty(5, dtype=np.int32)),
+    ('empty_like', 'int32[](int32[])', ('i4',), np.empty(6, dtype='i')),
+    ('empty_dtype', 'double[](int64)', (5,), np.empty(5)),
     ('cumsum', 'double[](int32)', (5,), np.arange(1, 6)),
 ]
 
@@ -139,7 +154,10 @@ def test_array_methods(omnisci, method, signature, args, expected):
     _, result = omnisci.sql_execute(query)
     out = list(result)[0]
 
-    assert np.array_equal(expected, out[0]), 'np_' + method
+    if 'empty' in method:
+        assert out == ([None] * len(expected),)
+    else:
+        assert np.array_equal(expected, out[0]), 'np_' + method
 
 
 @pytest.mark.parametrize('col', ('i4', 'i8', 'f4'))
@@ -148,7 +166,7 @@ def test_dtype(omnisci, col):
 
     @omnisci('T[](T[])', T=['int32', 'int64', 'float32'], devices=['cpu'])
     def zeros_like(x):
-        z = omni.zeros(len(x), x.dtype)
+        z = array_api.zeros(len(x), x.dtype)
         return z
 
     query = f'select zeros_like({col}) from {omnisci.table_name} limit 1;'
diff --git a/rbc/tests/test_omnisci_array_math.py b/rbc/tests/test_omnisci_array_math.py
index 2fb86e45..554ce5f9 100644
--- a/rbc/tests/test_omnisci_array_math.py
+++ b/rbc/tests/test_omnisci_array_math.py
@@ -1,6 +1,7 @@
 import pytest
 import numpy as np
 import rbc.omnisci_backend as omni  # noqa: F401
+from rbc.stdlib import array_api
 
 
 rbc_omnisci = pytest.importorskip('rbc.omniscidb')
@@ -113,7 +114,7 @@ def is_number(s):
 def test_omnisci_array_binary_math(omnisci, method, signature, columns):
     omnisci.reset()
 
-    s = f'def np_{method}(a, b): return omni.{method}(a, b)'
+    s = f'def np_{method}(a, b): return array_api.{method}(a, b)'
     exec(s, globals())
 
     omnisci(signature)(eval('np_{}'.format(method)))
@@ -175,7 +176,7 @@ def test_omnisci_array_binary_math(omnisci, method, signature, columns):
 def test_omnisci_array_binary_math_scalar(omnisci, method, signature, args):
     omnisci.reset()
 
-    s = f'def np_{method}(a, b): return omni.{method}(a, b)'
+    s = f'def np_{method}(a, b): return array_api.{method}(a, b)'
     exec(s, globals())
 
     omnisci(signature)(eval('np_{}'.format(method)))
@@ -258,7 +259,7 @@ def test_omnisci_array_binary_math_scalar(omnisci, method, signature, args):
 def test_omnisci_array_unary_math_fns(omnisci, method, signature, column):
     omnisci.reset()
 
-    s = f'def np_{method}(a): return omni.{method}(a)'
+    s = f'def np_{method}(a): return array_api.{method}(a)'
     exec(s, globals())
 
     omnisci(signature)(eval('np_{}'.format(method)))
@@ -282,7 +283,7 @@ def test_heaviside(omnisci):
 
     @omnisci('double[](int64[], int64)')
     def heaviside(x1, x2):
-        return omni.heaviside(x1, x2)
+        return array_api.heaviside(x1, x2)
 
     query = f'select i8, heaviside(i8, 1) from {omnisci.table_name}'
     _, result = omnisci.sql_execute(query)
diff --git a/rbc/tests/test_omnisci_array_methods.py b/rbc/tests/test_omnisci_array_methods.py
index ddedc4bf..ff7b60d8 100644
--- a/rbc/tests/test_omnisci_array_methods.py
+++ b/rbc/tests/test_omnisci_array_methods.py
@@ -28,7 +28,6 @@ def omnisci():
     ('max_empty_int64', (0, ), np.iinfo(np.int64).min + 1),
     ('max_empty_float32', (0, ), np.finfo(np.float32).min),
     ('max_empty_float64', (0, ), np.finfo(np.float64).min),
-    ('max_initial', (5, 4.0, 30.0), 30.0),
     ('mean', (5, 2), 2.0),
     ('mean', (5, 2.0), 2.0),
     ('mean_empty_int8', (0, ), np.nan),
@@ -44,11 +43,8 @@ def omnisci():
     ('min_empty_int64', (0, ), np.iinfo(np.int64).max),
     ('min_empty_float32', (0, ), np.finfo(np.float32).max),
     ('min_empty_float64', (0, ), np.finfo(np.float64).max),
-    ('min_initial', (5, 4.0, -3.0), -3.0),
     ('sum', (5, 2.0), 10.0),
-    ('sum_initial', (5, 2.0, 2.0), 12.0),
     ('prod', (5, 3.0), 243.0),
-    ('prod_initial', (5, 3.0, 2), 486.0),
 ]
 
 
@@ -79,12 +75,6 @@ def ndarray_max(size, v):
             else:
                 omnisci(f'{retty}(int32)')(fn)
 
-    @omnisci('double(int64, double, double)')
-    def ndarray_max_initial(size, v, initial):
-        a = Array(size, 'double')
-        a.fill(v)
-        return a.max(initial=initial)
-
     @omnisci('double(int64, double)')
     def ndarray_mean(size, v):
         a = Array(size, 'double')
@@ -97,36 +87,18 @@ def ndarray_min(size, v):
         a.fill(v)
         return a.min()
 
-    @omnisci('double(int64, double, double)')
-    def ndarray_min_initial(size, v, initial):
-        a = Array(size, 'double')
-        a.fill(v)
-        return a.min(initial=initial)
-
     @omnisci('double(int64, double)')
     def ndarray_sum(size, v):
         a = Array(size, 'double')
         a.fill(v)
         return a.sum()
 
-    @omnisci('double(int64, double, double)')
-    def ndarray_sum_initial(size, v, initial):
-        a = Array(size, 'double')
-        a.fill(v)
-        return a.sum(initial=initial)
-
     @omnisci('double(int64, double)')
     def ndarray_prod(size, v):
         a = Array(size, 'double')
         a.fill(v)
         return a.prod()
 
-    @omnisci('double(int64, double, double)')
-    def ndarray_prod_initial(size, v, initial):
-        a = Array(size, 'double')
-        a.fill(v)
-        return a.prod(initial=initial)
-
 
 @pytest.mark.parametrize("method, args, expected", ndarray_methods,
                          ids=[item[0] for item in ndarray_methods])
diff --git a/rbc/tests/test_omnisci_constants.py b/rbc/tests/test_omnisci_constants.py
new file mode 100644
index 00000000..bf5bf0e0
--- /dev/null
+++ b/rbc/tests/test_omnisci_constants.py
@@ -0,0 +1,40 @@
+import pytest
+import numpy as np
+from rbc.tests import omnisci_fixture
+from rbc.stdlib import array_api
+from collections import OrderedDict
+
+
+@pytest.fixture(scope='module')
+def omnisci():
+
+    for o in omnisci_fixture(globals(), load_test_data=False):
+        define(o)
+        yield o
+
+
+def define(omnisci):
+    @omnisci('float64(int32)')
+    def get_constant(typ):
+        if typ == 0:
+            return array_api.e
+        if typ == 1:
+            return array_api.inf
+        if typ == 2:
+            return array_api.nan
+        return array_api.pi
+
+
+constants_map = OrderedDict(e=np.e, inf=np.inf, nan=np.nan, pi=np.pi)
+
+
+@pytest.mark.parametrize('C', constants_map)
+def test_constants(omnisci, C):
+    idx = list(constants_map.keys()).index(C)
+    _, result = omnisci.sql_execute(f'select get_constant({idx});')
+
+    expected = constants_map[C]
+    if np.isnan(expected):
+        assert np.isnan(list(result)[0])
+    else:
+        assert list(result) == [(expected,)]
diff --git a/rbc/tests/test_omnisci_math.py b/rbc/tests/test_omnisci_math.py
index 8cd2365e..a31c37dc 100644
--- a/rbc/tests/test_omnisci_math.py
+++ b/rbc/tests/test_omnisci_math.py
@@ -1,11 +1,11 @@
 import math
 import pytest
 import sys
-import rbc.omnisci_backend as omni  # noqa: F401
+import numpy as np
+import numba as nb
+import rbc.omniscidb as rbc_omnisci
+from rbc.stdlib import array_api
 
-rbc_omnisci = pytest.importorskip('rbc.omniscidb')
-np = pytest.importorskip('numpy')
-nb = pytest.importorskip('numba')
 available_version, reason = rbc_omnisci.is_available()
 pytestmark = pytest.mark.skipif(not available_version, reason=reason)
 
@@ -334,10 +334,10 @@ def test_numpy_function(omnisci, device, nb_version, fn_name, signature, np_func
     if isinstance(np_func, np.ufunc):
         # numba does not support jitting ufunc-s directly
         if arity == 1:
-            fn = eval(f'lambda x: omni.{np_func.__name__}(x)', dict(omni=omni))
+            fn = eval(f'lambda x: array_api.{np_func.__name__}(x)', dict(array_api=array_api))
         elif arity == 2:
-            fn = eval(f'lambda x, y: omni.{np_func.__name__}(x, y)',
-                      dict(omni=omni))
+            fn = eval(f'lambda x, y: array_api.{np_func.__name__}(x, y)',
+                      dict(array_api=array_api))
         else:
             raise NotImplementedError((signature, arity))
     else: