Postprocess CE fix

.github/workflows/python-package.yml

@@ -27,9 +27,10 @@ jobs:
       - name: Coveralls
         run: |
           pip install coveralls
-          coveralls --service=github
+          coveralls --service=github-actions
         env:
           GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
+          COVERALLS_REPO_TOKEN: ${{ secrets.COVERALLS_REPO_TOKEN }}
       - name: Test example notebooks
         if: ${{ runner.os == 'Linux' }}
         run: |

battdat/postprocess/integral.py

@@ -135,8 +135,8 @@ class StateOfCharge(RawDataEnhancer):
     Output dataframe has 3 new columns:
         - ``cycled_charge``: Amount of observed charge cycled since the beginning of the cycle, in A-hr
         - ``cycled_energy``: Amount of observed energy cycled since the beginning of the cycle, in W-hr
-        - ``CE_charge``: Amount of charge in the battery relative to the beginning of the cycle, accounting for Coulombic
-            Efficiency, in A-hr
+        - ``CE_adjusted_charge``: Amount of charge in the battery relative to the beginning of the cycle, accounting for
+            Coulombic Efficiency (CE), in A-hr
     """
     def __init__(self, coulombic_efficiency: float = 1.0):
         """
@@ -157,7 +157,7 @@ def coulombic_efficiency(self, value: float):
 
     @property
     def column_names(self) -> List[str]:
-        return ['cycled_charge', 'cycled_energy', 'CE_charge']
+        return ['cycled_charge', 'cycled_energy', 'CE_adjusted_charge']
 
     def _get_CE_adjusted_curr(self, current: np.ndarray) -> np.ndarray:
         """Adjust the current based on the coulombic efficiency
@@ -168,14 +168,13 @@ def _get_CE_adjusted_curr(self, current: np.ndarray) -> np.ndarray:
         Returns:
             Adjusted current array in A
         """
-        adjusted_current = current.copy()
-        adjusted_current[current > 0] *= self.coulombic_efficiency
-        return adjusted_current
+        adjusted_current = np.where(current > 0, self.coulombic_efficiency * current, current)
+        return adjusted_current.flatten()
 
     def enhance(self, data: pd.DataFrame):
         # Add columns for the capacity and energy
         for c in self.column_names:
-            data[c] = np.nan
+            data.loc[:, (c,)] = np.nan
 
         # Compute the capacity and energy for each cycle
         ordered_copy = data.reset_index()  # Ensure a sequential ordering from 0
@@ -193,5 +192,5 @@ def enhance(self, data: pd.DataFrame):
 
             # Store them in the raw data
             data.loc[cycle_subset['index'], 'cycled_charge'] = capacity_change / 3600  # To A-hr
-            data.loc[cycle_subset['index'], 'CE_charge'] = ce_charge / 3600  # To A-hr
+            data.loc[cycle_subset['index'], 'CE_adjusted_charge'] = ce_charge / 3600  # To A-hr
             data.loc[cycle_subset['index'], 'cycled_energy'] = energy_change / 3600  # To W-hr

battdat/postprocess/tagging.py

@@ -37,7 +37,7 @@ def column_names(self) -> List[str]:
 
     def enhance(self, df: pd.DataFrame):
         # Insert a new column into the dataframe, starting with everything marked as other
-        df['method'] = ControlMethod.other
+        df.loc[:, ('method',)] = ControlMethod.other
 
         # array of indexes
         cycles = df.groupby(["cycle_number", "step_index"])
@@ -148,7 +148,7 @@ def column_names(self) -> List[str]:
 
     def enhance(self, data: pd.DataFrame) -> None:
         logger.debug('Adding states')
-        data['state'] = data.apply(_determine_state, axis=1, args=(self.rest_curr_threshold,))
+        data.loc[:, ('state',)] = data.apply(_determine_state, axis=1, args=(self.rest_curr_threshold,))
 
 
 class AddSteps(RawDataEnhancer):
@@ -192,7 +192,7 @@ def _determine_steps(df: DataFrame, column: str, output_col: str):
 
     # The step number is equal to the number of changes observed previously in a batch
     #  Step 1: Compute the changes since the beginning of file
-    df[output_col] = change.cumsum()
+    df.loc[:, (output_col,)] = change.cumsum()
 
     # Step 2: Adjust so that each cycle starts with step 0
     for _, cycle in df.groupby("cycle_number"):