CLN: Consolidate numba facilities

pandas/core/util/numba_.py

@@ -0,0 +1,58 @@
+"""Common utilities for Numba operations"""
+import types
+from typing import Callable, Dict, Optional
+
+import numpy as np
+
+from pandas.compat._optional import import_optional_dependency
+
+
+def check_kwargs_and_nopython(
+    kwargs: Optional[Dict] = None, nopython: Optional[bool] = None
+):
+    if kwargs and nopython:
+        raise ValueError(
+            "numba does not support kwargs with nopython=True: "
+            "https://github.com/numba/numba/issues/2916"
+        )
+
+
+def get_jit_arguments(engine_kwargs: Optional[Dict[str, bool]] = None):
+    """
+    Return arguments to pass to numba.JIT, falling back on pandas default JIT settings.
+    """
+    if engine_kwargs is None:
+        engine_kwargs = {}
+
+    nopython = engine_kwargs.get("nopython", True)
+    nogil = engine_kwargs.get("nogil", False)
+    parallel = engine_kwargs.get("parallel", False)
+    return nopython, nogil, parallel
+
+
+def jit_user_function(func: Callable, nopython: bool, nogil: bool, parallel: bool):
+    """
+    JIT the user's function given the configurable arguments.
+    """
+    numba = import_optional_dependency("numba")
+
+    if isinstance(func, numba.targets.registry.CPUDispatcher):
+        # Don't jit a user passed jitted function
+        numba_func = func
+    else:
+
+        @numba.generated_jit(nopython=nopython, nogil=nogil, parallel=parallel)
+        def numba_func(data, *_args):
+            if getattr(np, func.__name__, False) is func or isinstance(
+                func, types.BuiltinFunctionType
+            ):
+                jf = func
+            else:
+                jf = numba.jit(func, nopython=nopython, nogil=nogil)
+
+            def impl(data, *_args):
+                return jf(data, *_args)
+
+            return impl
+
+    return numba_func

pandas/core/window/numba_.py

@@ -1,66 +1,60 @@
-import types
 from typing import Any, Callable, Dict, Optional, Tuple
 
 import numpy as np
 
 from pandas._typing import Scalar
 from pandas.compat._optional import import_optional_dependency
 
+from pandas.core.util.numba_ import (
+    check_kwargs_and_nopython,
+    get_jit_arguments,
+    jit_user_function,
+)
 
-def make_rolling_apply(
-    func: Callable[..., Scalar],
+
+def generate_numba_apply_func(
     args: Tuple,
-    nogil: bool,
-    parallel: bool,
-    nopython: bool,
+    kwargs: Dict[str, Any],
+    func: Callable[..., Scalar],
+    engine_kwargs: Optional[Dict[str, bool]],
 ):
     """
-    Creates a JITted rolling apply function with a JITted version of
-    the user's function.
+    Generate a numba jitted apply function specified by values from engine_kwargs.
+
+    1. jit the user's function
+    2. Return a rolling apply function with the jitted function inline
+
+    Configurations specified in engine_kwargs apply to both the user's
+    function _AND_ the rolling apply function.
 
     Parameters
     ----------
-    func : function
-        function to be applied to each window and will be JITed
     args : tuple
         *args to be passed into the function
-    nogil : bool
-        nogil parameter from engine_kwargs for numba.jit
-    parallel : bool
-        parallel parameter from engine_kwargs for numba.jit
-    nopython : bool
-        nopython parameter from engine_kwargs for numba.jit
+    kwargs : dict
+        **kwargs to be passed into the function
+    func : function
+        function to be applied to each window and will be JITed
+    engine_kwargs : dict
+        dictionary of arguments to be passed into numba.jit
 
     Returns
     -------
     Numba function
     """
+    nopython, nogil, parallel = get_jit_arguments(engine_kwargs)
+
+    check_kwargs_and_nopython(kwargs, nopython)
+
+    numba_func = jit_user_function(func, nopython, nogil, parallel)
+
     numba = import_optional_dependency("numba")
 
     if parallel:
         loop_range = numba.prange
     else:
         loop_range = range
 
-    if isinstance(func, numba.targets.registry.CPUDispatcher):
-        # Don't jit a user passed jitted function
-        numba_func = func
-    else:
-
-        @numba.generated_jit(nopython=nopython, nogil=nogil, parallel=parallel)
-        def numba_func(window, *_args):
-            if getattr(np, func.__name__, False) is func or isinstance(
-                func, types.BuiltinFunctionType
-            ):
-                jf = func
-            else:
-                jf = numba.jit(func, nopython=nopython, nogil=nogil)
-
-            def impl(window, *_args):
-                return jf(window, *_args)
-
-            return impl
-
     @numba.jit(nopython=nopython, nogil=nogil, parallel=parallel)
     def roll_apply(
         values: np.ndarray, begin: np.ndarray, end: np.ndarray, minimum_periods: int,
@@ -78,49 +72,3 @@ def roll_apply(
         return result
 
     return roll_apply
-
-
-def generate_numba_apply_func(
-    args: Tuple,
-    kwargs: Dict[str, Any],
-    func: Callable[..., Scalar],
-    engine_kwargs: Optional[Dict[str, bool]],
-):
-    """
-    Generate a numba jitted apply function specified by values from engine_kwargs.
-
-    1. jit the user's function
-    2. Return a rolling apply function with the jitted function inline
-
-    Configurations specified in engine_kwargs apply to both the user's
-    function _AND_ the rolling apply function.
-
-    Parameters
-    ----------
-    args : tuple
-        *args to be passed into the function
-    kwargs : dict
-        **kwargs to be passed into the function
-    func : function
-        function to be applied to each window and will be JITed
-    engine_kwargs : dict
-        dictionary of arguments to be passed into numba.jit
-
-    Returns
-    -------
-    Numba function
-    """
-    if engine_kwargs is None:
-        engine_kwargs = {}
-
-    nopython = engine_kwargs.get("nopython", True)
-    nogil = engine_kwargs.get("nogil", False)
-    parallel = engine_kwargs.get("parallel", False)
-
-    if kwargs and nopython:
-        raise ValueError(
-            "numba does not support kwargs with nopython=True: "
-            "https://github.com/numba/numba/issues/2916"
-        )
-
-    return make_rolling_apply(func, args, nogil, parallel, nopython)