CLN: Move rolling helper functions to where they are used

pandas/core/window/common.py

@@ -5,10 +5,8 @@
 
 import numpy as np
 
-from pandas.core.dtypes.common import is_integer
 from pandas.core.dtypes.generic import ABCDataFrame, ABCSeries
 
-import pandas.core.common as com
 from pandas.core.generic import _shared_docs
 from pandas.core.groupby.base import GroupByMixin
 from pandas.core.indexes.api import MultiIndex
@@ -224,75 +222,6 @@ def dataframe_from_int_dict(data, frame_template):
         return _flex_binary_moment(arg2, arg1, f)
 
 
-def _get_center_of_mass(comass, span, halflife, alpha):
-    valid_count = com.count_not_none(comass, span, halflife, alpha)
-    if valid_count > 1:
-        raise ValueError("comass, span, halflife, and alpha are mutually exclusive")
-
-    # Convert to center of mass; domain checks ensure 0 < alpha <= 1
-    if comass is not None:
-        if comass < 0:
-            raise ValueError("comass must satisfy: comass >= 0")
-    elif span is not None:
-        if span < 1:
-            raise ValueError("span must satisfy: span >= 1")
-        comass = (span - 1) / 2.0
-    elif halflife is not None:
-        if halflife <= 0:
-            raise ValueError("halflife must satisfy: halflife > 0")
-        decay = 1 - np.exp(np.log(0.5) / halflife)
-        comass = 1 / decay - 1
-    elif alpha is not None:
-        if alpha <= 0 or alpha > 1:
-            raise ValueError("alpha must satisfy: 0 < alpha <= 1")
-        comass = (1.0 - alpha) / alpha
-    else:
-        raise ValueError("Must pass one of comass, span, halflife, or alpha")
-
-    return float(comass)
-
-
-def calculate_center_offset(window):
-    if not is_integer(window):
-        window = len(window)
-    return int((window - 1) / 2.0)
-
-
-def calculate_min_periods(
-    window: int,
-    min_periods: Optional[int],
-    num_values: int,
-    required_min_periods: int,
-    floor: int,
-) -> int:
-    """
-    Calculates final minimum periods value for rolling aggregations.
-
-    Parameters
-    ----------
-    window : passed window value
-    min_periods : passed min periods value
-    num_values : total number of values
-    required_min_periods : required min periods per aggregation function
-    floor : required min periods per aggregation function
-
-    Returns
-    -------
-    min_periods : int
-    """
-    if min_periods is None:
-        min_periods = window
-    else:
-        min_periods = max(required_min_periods, min_periods)
-    if min_periods > window:
-        raise ValueError(f"min_periods {min_periods} must be <= window {window}")
-    elif min_periods > num_values:
-        min_periods = num_values + 1
-    elif min_periods < 0:
-        raise ValueError("min_periods must be >= 0")
-    return max(min_periods, floor)
-
-
 def zsqrt(x):
     with np.errstate(all="ignore"):
         result = np.sqrt(x)
@@ -317,12 +246,3 @@ def prep_binary(arg1, arg2):
     Y = arg2 + 0 * arg1
 
     return X, Y
-
-
-def get_weighted_roll_func(cfunc: Callable) -> Callable:
-    def func(arg, window, min_periods=None):
-        if min_periods is None:
-            min_periods = len(window)
-        return cfunc(arg, window, min_periods)
-
-    return func

pandas/core/window/ewm.py

@@ -9,12 +9,8 @@
 from pandas.core.dtypes.generic import ABCDataFrame
 
 from pandas.core.base import DataError
-from pandas.core.window.common import (
-    _doc_template,
-    _get_center_of_mass,
-    _shared_docs,
-    zsqrt,
-)
+import pandas.core.common as com
+from pandas.core.window.common import _doc_template, _shared_docs, zsqrt
 from pandas.core.window.rolling import _flex_binary_moment, _Rolling
 
 _bias_template = """
@@ -27,6 +23,34 @@
 """
 
 
+def get_center_of_mass(comass, span, halflife, alpha) -> float:
+    valid_count = com.count_not_none(comass, span, halflife, alpha)
+    if valid_count > 1:
+        raise ValueError("comass, span, halflife, and alpha are mutually exclusive")
+
+    # Convert to center of mass; domain checks ensure 0 < alpha <= 1
+    if comass is not None:
+        if comass < 0:
+            raise ValueError("comass must satisfy: comass >= 0")
+    elif span is not None:
+        if span < 1:
+            raise ValueError("span must satisfy: span >= 1")
+        comass = (span - 1) / 2.0
+    elif halflife is not None:
+        if halflife <= 0:
+            raise ValueError("halflife must satisfy: halflife > 0")
+        decay = 1 - np.exp(np.log(0.5) / halflife)
+        comass = 1 / decay - 1
+    elif alpha is not None:
+        if alpha <= 0 or alpha > 1:
+            raise ValueError("alpha must satisfy: 0 < alpha <= 1")
+        comass = (1.0 - alpha) / alpha
+    else:
+        raise ValueError("Must pass one of comass, span, halflife, or alpha")
+
+    return float(comass)
+
+
 class EWM(_Rolling):
     r"""
     Provide exponential weighted (EW) functions.
@@ -144,7 +168,7 @@ def __init__(
         axis=0,
     ):
         self.obj = obj
-        self.com = _get_center_of_mass(com, span, halflife, alpha)
+        self.com = get_center_of_mass(com, span, halflife, alpha)
         self.min_periods = min_periods
         self.adjust = adjust
         self.ignore_na = ignore_na

pandas/core/window/rolling.py

@@ -44,9 +44,6 @@
     _doc_template,
     _flex_binary_moment,
     _shared_docs,
-    calculate_center_offset,
-    calculate_min_periods,
-    get_weighted_roll_func,
     zsqrt,
 )
 from pandas.core.window.indexers import (
@@ -59,6 +56,81 @@
 from pandas.tseries.offsets import DateOffset
 
 
+def calculate_center_offset(window) -> int:
+    """
+    Calculate an offset necessary to have the window label to be centered.
+
+    Parameters
+    ----------
+    window: ndarray or int
+        window weights or window
+
+    Returns
+    -------
+    int
+    """
+    if not is_integer(window):
+        window = len(window)
+    return int((window - 1) / 2.0)
+
+
+def calculate_min_periods(
+    window: int,
+    min_periods: Optional[int],
+    num_values: int,
+    required_min_periods: int,
+    floor: int,
+) -> int:
+    """
+    Calculate final minimum periods value for rolling aggregations.
+
+    Parameters
+    ----------
+    window : passed window value
+    min_periods : passed min periods value
+    num_values : total number of values
+    required_min_periods : required min periods per aggregation function
+    floor : required min periods per aggregation function
+
+    Returns
+    -------
+    min_periods : int
+    """
+    if min_periods is None:
+        min_periods = window
+    else:
+        min_periods = max(required_min_periods, min_periods)
+    if min_periods > window:
+        raise ValueError(f"min_periods {min_periods} must be <= window {window}")
+    elif min_periods > num_values:
+        min_periods = num_values + 1
+    elif min_periods < 0:
+        raise ValueError("min_periods must be >= 0")
+    return max(min_periods, floor)
+
+
+def get_weighted_roll_func(cfunc: Callable) -> Callable:
+    """
+    Wrap weighted rolling cython function with min periods argument.
+
+    Parameters
+    ----------
+    cfunc : function
+        Cython weighted rolling function
+
+    Returns
+    -------
+    function
+    """
+
+    def func(arg, window, min_periods=None):
+        if min_periods is None:
+            min_periods = len(window)
+        return cfunc(arg, window, min_periods)
+
+    return func
+
+
 class _Window(PandasObject, ShallowMixin, SelectionMixin):
     _attributes: List[str] = [
         "window",