changed functions for Average and MaximalCalibrationError to split ar…

…ray in arrays of two when both eyes

eye2bids/edf2bids.py

@@ -123,13 +123,13 @@ def _extract_CalibrationType(df: pd.DataFrame) -> list[int]:
 
 
 def _extract_CalibrationCount(df: pd.DataFrame) -> int:
-    if _extract_RecordedEye == "Both":
+    if _extract_RecordedEye(df) == "Both":
         return len(_calibrations(df)) // 2
     return len(_calibrations(df))
 
 
 def _get_calibration_positions(df: pd.DataFrame) -> list[int]:
-    if _extract_RecordedEye == "Both":
+    if _extract_RecordedEye(df) == "Both":
         return (
         np.array(df[df[2] == "VALIDATE"][8].str.split(",", expand=True))
         .astype(int)
@@ -196,12 +196,16 @@ def _has_validation(df: pd.DataFrame) -> bool:
 def _extract_MaximalCalibrationError(df: pd.DataFrame) -> list[float]:
     if not _has_validation(df):
         return []
+    if _extract_RecordedEye(df) == "Both" and _extract_CalibrationCount(df) > 1:
+        return np.array_split((np.array(_validations(df)[[11]]).astype(float).tolist()), _extract_CalibrationCount(df))
     return np.array(_validations(df)[[11]]).astype(float).tolist()
 
 
 def _extract_AverageCalibrationError(df: pd.DataFrame) -> list[float]:
     if not _has_validation(df):
         return []
+    if _extract_RecordedEye(df) == "Both" and _extract_CalibrationCount(df) > 1:
+        return np.array_split((np.array(_validations(df)[[9]]).astype(float).tolist()), _extract_CalibrationCount(df))
     return np.array(_validations(df)[[9]]).astype(float).tolist()