refactor: using LoopStructural BoundingBox

FoldOptLib/input/input_data_processor.py

@@ -1,19 +1,14 @@
 import pandas as pd
 from typing import List, Optional, Dict
-import numpy as np
+import numpy
 from .input_data_checker import CheckInputData
-from ..helper._helper import *
 from ..helper.utils import *
-
-
-def _normalise_gradient(gradient: np.ndarray) -> np.ndarray:
-    """Normalise gradient vectors."""
-    return gradient / np.linalg.norm(gradient, axis=1)[:, None]
+from LoopStructural import BoundingBox
 
 
 class InputDataProcessor(CheckInputData):
 
-    def __init__(self, data: pd.DataFrame, bounding_box: np.ndarray,
+    def __init__(self, data: pd.DataFrame, bounding_box: BoundingBox,
                  geological_knowledge: Dict = None) -> None:
         """
         Constructs all the necessary attributes for the InputDataProcessor object.
@@ -27,15 +22,13 @@ def __init__(self, data: pd.DataFrame, bounding_box: np.ndarray,
         geological_knowledge : Dict, optional
             geological knowledge dictionary.
         """
-        super().__init__(data, bounding_box, geological_knowledge)  # Assuming parent class requires this
+        super().__init__(data, bounding_box, geological_knowledge)
         self.data = data
         self.bounding_box = bounding_box
         self.knowledge_constraints = geological_knowledge
 
     def process_data(self):
-        check_data = CheckInputData(self.data, self.bounding_box, self.knowledge_constraints)
-        check_data.check_input_data()  # check the input data is valid
-
+        self.check_input_data()
         if 'strike' in self.data.columns and 'dip' in self.data.columns:
             strike = self.data['strike'].to_numpy()
             dip = self.data['dip'].to_numpy()
@@ -45,7 +38,12 @@ def process_data(self):
         else:
             return None
 
-        gradient = _normalise_gradient(gradient)
+        gradient = InputDataProcessor.normalise(gradient)
         self.data['gx'], self.data['gy'], self.data['gz'] = gradient[:, 0], gradient[:, 1], gradient[:, 2]
 
         return self.data
+
+    @staticmethod
+    def normalise(gradient: numpy.ndarray) -> numpy.ndarray:
+        """Normalise vectors."""
+        return gradient / numpy.linalg.norm(gradient, axis=1)[:, None]