Merge pull request #31 from ONSdigital/395-add-kwargs-low-level-fun

Add kwargs to arguments

src/apply_imputation_link.py

@@ -11,6 +11,7 @@ def create_and_merge_imputation_values(
     auxiliary,
     construction_link,
     imputation_types=("c", "fir", "bir", "fic"),
+    **kwargs
 ):
     """
     Loop through different imputation types and merge the results according
@@ -44,6 +45,8 @@ def create_and_merge_imputation_values(
         tuple. If 'fic' is selected 'c' must also be selected and proceed 'fic'.
         For 'fic' to produce the correct result, the C marker must be in the first
         period for a given reference.
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
 
     Returns
     -------

src/calculate_imputation_link.py

@@ -10,6 +10,7 @@ def calculate_imputation_link(
     target_variable: str,
     predictive_variable: str,
     link_col: str,
+    **kwargs
 ) -> pd.DataFrame:
     """
     Calculate link between target_variable and predictive_variable by strata,
@@ -32,7 +33,8 @@ def calculate_imputation_link(
         Column name of the predicted target variable.
     link_col : str
         Name to use for the new column containing imputation link
-
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
     Returns
     -------
     df : pd.DataFrame

src/construction_matches.py

@@ -1,7 +1,7 @@
 import pandas as pd
 
 
-def flag_construction_matches(dataframe, target, period, auxiliary):
+def flag_construction_matches(dataframe, target, period, auxiliary, **kwargs):
     """
     Add flag to indicate whether the record has non-null target, period and
     auxiliary variables, and is therefore valid to use when calculating
@@ -16,7 +16,8 @@ def flag_construction_matches(dataframe, target, period, auxiliary):
         name of column containing time period
     auxiliary : string
         name of column containing auxiliary information
-
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
     Returns
     -------
     pandas.DataFrame

src/cumulative_imputation_links.py

@@ -10,6 +10,7 @@ def get_cumulative_links(
     period,
     imputation_link,
     time_difference=1,
+    **kwargs
 ):
     """
     Create cumulative imputation links for multiple consecutive periods
@@ -33,6 +34,8 @@ def get_cumulative_links(
         column name containing imputation links
     time_difference : int
         time difference between predictive and target period in months
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func
 
     Returns
     -------

src/flag_and_count_matched_pairs.py

@@ -3,7 +3,14 @@
 
 
 def flag_matched_pair(
-    df, forward_or_backward, target, period, reference, strata, time_difference=1
+    df,
+    forward_or_backward,
+    target,
+    period,
+    reference,
+    strata,
+    time_difference=1,
+    **kwargs
 ):
     """
     function to flag matched pairs using the shift method
@@ -24,6 +31,8 @@ def flag_matched_pair(
         column name containing strata information (sic)
     time_difference: int
         lookup distance for matched pairs
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
 
     Returns
     -------
@@ -54,7 +63,7 @@ def flag_matched_pair(
     return df
 
 
-def count_matches(df, flag, period, strata):
+def count_matches(df, flag, period, strata, **kwargs):
     """
     function to flag matched pairs using the shift method
 
@@ -70,6 +79,8 @@ def count_matches(df, flag, period, strata):
         column name containing time period
     strata : str
         column name containing strata information (sic)
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
 
     Returns
     -------

src/imputation_flags.py

@@ -9,6 +9,7 @@ def create_impute_flags(
     strata: str,
     auxiliary: str,
     predictive_auxiliary: str,
+    **kwargs
 ):
 
     """
@@ -36,6 +37,8 @@ def create_impute_flags(
     predictive_auxiliary: str
         Column name containing predictive auxiliary data, this is created,
         by flag_matched_pair_merge function.
+    kwargs : mapping, optional
+        A dictionary of keyword arguments passed into func.
 
     Returns
     -------