fixed an layer assigment error, where a ladder from layer one was assigned as layer two

rootable/rootable.py

@@ -72,7 +72,7 @@ class Rootable:
                                    420])
 
         # the layer and ladder arrays, for finding them from sensor id
-        self.panelLayer = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
+        self.panelLayer = np.array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])
         self.panelLadder = np.array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8, 8, 9, 9, 10, 10, 12, 12, 13, 13, 14, 14, 15, 15, 16, 16, 17, 17, 18, 18, 19, 19, 20, 20, 21, 21])
 
         # all transpormaations are stored in a dict, with the sensor id as a keyword
@@ -143,6 +143,9 @@ class Rootable:
                           [[-2.35 , -1.725, -1.725, -2.35 , -2.35 ], [ 0.345,  1.4  ,  1.4  ,  0.345,  0.345], [-4.28, -4.28, 8.08, 8.08, -4.28]],      # 18
                           [[-1.85 , -0.78 , -0.78 , -1.85 , -1.85 ], [ 1.48 ,  2.1  ,  2.1  ,  1.48 ,  1.48 ], [-4.28, -4.28, 8.08, 8.08, -4.28]]]      # 19
 
+        # parameter for checking if coordinates have been loaded
+        self.gotCoordinates = False
+
     def __getitem__(self, index: str | int | ArrayLike) -> np.ndarray | dict:
         """
         this makes the class subscriptable, one can retrieve one coloumn by using
@@ -264,7 +267,6 @@ class Rootable:
             for column in columns:
                 self.data.pop(column)
 
-
     def loadData(self, file: str, events: int = None, selection: str = None) -> None:
         """
         Reads the file off of the hard drive; it automatically creates event numbers.
@@ -370,7 +372,8 @@ class Rootable:
         # Combine the results from all chunks
         self.data['cluster'] = np.concatenate(results).astype('int')
 
-    def _getMatrices(self, indexChunks: ArrayLike, uCellIDs: ArrayLike, vCellIDs: ArrayLike, cellCharges: ArrayLike, clusterDigits: ArrayLike, matrixSize: tuple = (9, 9)) -> np.ndarray:
+    @staticmethod
+    def _getMatrices(indexChunks: ArrayLike, uCellIDs: ArrayLike, vCellIDs: ArrayLike, cellCharges: ArrayLike, clusterDigits: ArrayLike, matrixSize: tuple = (9, 9)) -> np.ndarray:
         """
         this takes the ragged/jagged digit arrays and converts them into 9x9 matrices
         it's a rather slow process because of all the looping
@@ -401,9 +404,13 @@ class Rootable:
         """
         converting the uv coordinates, together with sensor ids, into xyz coordinates
         """
+        # checking if cluster parameters have been loaded
         if self.gotClusters is False:
             self.getClusters()
 
+        # setting a bool for checking if coordinates were calculated
+        self.gotCoordinates = True
+
         indexChunnks = np.array_split(range(len(self.data['sensorID'])), 4)
 
         with ThreadPoolExecutor(max_workers=None) as executor:
@@ -445,6 +452,29 @@ class Rootable:
 
         return xArr, yArr, zArr
 
+    def getSphericals(self) -> None:
+        """
+        Calculate spherical coordinates for each cluster.
+        """
+        # Checking if coordinates have been loaded
+        if self.gotClusters is False:
+            self.getCoordinates()
+        
+        xSquare = np.square(self.data['xPosition'])
+        ySquare = np.square(self.data['yPosition'])
+        zSquare = np.square(self.data['zPosition'])
+        
+        # Avoid division by zero by replacing zeros with a small number
+        r = np.sqrt(xSquare + ySquare + zSquare)
+        rSafe = np.where(r == 0, 1e-10, r)
+        
+        theta = np.arccos(self.data['zPosition'] / rSafe)
+        phi = np.arctan2(self.data['yPosition'], self.data['xPosition'])
+        
+        self.data['rPosition'] = r
+        self.data['thetaPosition'] = theta
+        self.data['phiPosition'] = phi
+
     def getLayers(self) -> None:
         """
         looks up the corresponding layers and ladders for every cluster
@@ -516,7 +546,8 @@ class Rootable:
         self.data['pdg'] = self._flatten(pdgList)
         self.data['clsNumber'] = self._flatten(clusterNumbersList)
 
-    def _findMissing(self, lst: list, length: int) -> list:
+    @staticmethod
+    def _findMissing(lst: list, length: int) -> list:
         """
         a private method for finding missing elements in mc data arrays
         """
@@ -541,7 +572,8 @@ class Rootable:
                 fillIndex += 1
         return testList
 
-    def _getMCData(self, toClusters: ArrayLike, pdgs: ArrayLike, xMom: ArrayLike, yMom: ArrayLike, zMom: ArrayLike) -> tuple[np.ndarray]:
+    @staticmethod
+    def _getMCData(toClusters: ArrayLike, pdgs: ArrayLike, xMom: ArrayLike, yMom: ArrayLike, zMom: ArrayLike) -> tuple[np.ndarray]:
         """
         after filling and reorganizing MC data arrays one can finally collect the
         actual MC data, where there's data missing I will with zeros