Encoder. Function to manually shift stacking events.

cfatools/processor/encoder.py

@@ -146,3 +146,51 @@ def smooth_unblocking_events(unblocking_events_df: pd.DataFrame, arrival_df: pd.
         # Reset the positions, during the event
         arrival_df.loc[event["start_datetime"]:event["end_datetime"], "position"] = positions
     return arrival_df
+
+
+def shift_stacking_event(encoder_df: pd.DataFrame,
+                         old_peak_start: pd.Timestamp, old_peak_end: pd.Timestamp, shift: pd.Timedelta) -> pd.DataFrame:
+    """Time-shift a "stacking" move-up event
+
+    Notes
+    -----
+    This function should be used wisely, as it modifies the base data. Only for specific human-verified hacks to fix
+    clearly identified problems.
+
+    Parameters
+    ----------
+    encoder_df: pd.DataFrame
+        Encoder dataframe
+    old_peak_start: pd.Timestamp
+        Beginning of the stacking event to be shifted (index of the last value *before* the upwards movement).
+    old_peak_end: pd.Timestamp
+        End of the stacking event to be shifted (index of the first value *after* the downwards movement).
+    shift: pd.Timedelta
+        Duration of the shift.
+
+    Returns
+    -------
+    pd.DataFrame
+        Same as input's `encoder_df`, with a stacking move-up event shifted.
+    """
+    # Determine the boundaries of the new peak's position
+    new_peak_start = encoder_df.iloc[encoder_df.index.get_loc(old_peak_start + shift, method='nearest')].name
+    new_peak_end = encoder_df.iloc[encoder_df.index.get_loc(old_peak_end + shift, method='nearest')].name
+
+    # Determine the average slope of the curve, after the original peak
+    step_mm_per_sec = (encoder_df.loc[old_peak_end, "position"] - encoder_df.loc[new_peak_end, "position"]) / (
+                old_peak_end - new_peak_end).total_seconds()
+
+    # Replace data between old-peak-start and new-peak-start with a linear interpolation based on the slope found above.
+    prev_position = encoder_df.loc[old_peak_start, "position"]
+    prev_datetime = old_peak_start
+    for index, line in encoder_df[old_peak_start:new_peak_start].iterrows():
+        position = prev_position + step_mm_per_sec * (index - prev_datetime).total_seconds()
+        encoder_df.loc[index, "position"] = position
+        encoder_df.loc[index, "speed"] = step_mm_per_sec
+
+    # Create a new peak
+    encoder_df.loc[new_peak_start:new_peak_end, "position"] = 1310.000
+    encoder_df.loc[new_peak_start:new_peak_end, "speed"] = 0.0
+
+    return encoder_df
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