using info() from core.py

src/apouey_index.py

@@ -1,5 +1,6 @@
 import numpy as np
-import typing
+from typing import Sequence
+from src.core import info, IND_TYPES
 
 ALPHA_LOOKUP_TABLE = {
     3: round(np.log(2) / np.log(3), 2),
@@ -13,41 +14,37 @@
 }
 
 
-def get_apouey_index(categories: int, responses: typing.List[int]) -> float:
-    if categories < 3 or categories > 10:
-        raise AttributeError(
+def get_apouey_index(categories: IND_TYPES, responses: Sequence) -> float:
+    number_of_categories = len(categories)
+    if number_of_categories < 3 or number_of_categories > 10:
+        raise ValueError(
             "Category out of range. Please use category within range 3-10."
         )
-    resp_cumulative_proportions = _get_cumulative_proportions(
-        _get_proportions(categories, responses)
-    )
+
+    alpha = ALPHA_LOOKUP_TABLE[number_of_categories]
+    proportions_info = info(responses, categories)
+
     sum_fc_alpha = 0
-    for category in range(1, categories - 1):
-        fc = resp_cumulative_proportions[category]
-        sum_fc_alpha += abs(fc - 0.5) ** ALPHA_LOOKUP_TABLE[categories]
-
-    result = 1 - (
-            (2 ** ALPHA_LOOKUP_TABLE[categories]) / (categories - 1)
-    ) * sum_fc_alpha
-    return round(result, 2)
-
-
-def _get_proportions(categories: int, responses: typing.List[int]) -> dict:
-    proportions_dict = {}
-    responses_num = len(responses)
-    for category in range(1, categories + 1):
-        proportions_dict[category] = round(
-            responses.count(category) / responses_num, 2)
-
-    return proportions_dict
-
-
-def _get_cumulative_proportions(proportions_dict: dict) -> dict:
-    cumulative_proportions_dict = {}
-    proportions_list = list(proportions_dict.values())
-    for key, value in proportions_dict.items():
-        if key == 3:
-            cumulative_proportions_dict[key] = value
-        cumulative_proportions_dict[key] = round(
-            sum(proportions_list[:key]), 2)
-    return cumulative_proportions_dict
+
+    for category in range(1, len(categories) - 1):
+        fc = proportions_info['cumulative'][category]/100
+        sum_fc_alpha += abs(fc - 0.5) ** alpha
+
+    apouey_index = 1 - (2 ** alpha)/(len(categories) - 1) * sum_fc_alpha
+    return apouey_index
+
+
+if __name__ == "__main__":
+    INDICATORS = [1, 2, 3, 4, 5]
+
+    DS = []
+    DS.extend(10 * [1])
+    DS.extend(10 * [2])
+    DS.extend(10 * [3])
+    DS.extend(5 * [4])
+    DS.extend(15 * [5])
+    RESULT = info(DS, INDICATORS)
+
+    print(RESULT)
+
+    print(get_apouey_index(INDICATORS, DS))