diff --git a/demo_plotter.py b/demo_plotter.py
index d8b419d..fd9bd3d 100755
--- a/demo_plotter.py
+++ b/demo_plotter.py
@@ -1,5 +1,6 @@
 #!./venv/bin/python3
 import pandas as pd
+import numpy as np
 import json
 import matplotlib.pyplot as plt
 import os
@@ -38,6 +39,15 @@ else:
 ################## Part 2 ######################################################
 ################################################################################
 
+def overshoot(df, T1, T2):
+    yT1, yT2 = df[T1], df[T2]
+    over = 1 / (yT1 - yT2) * np.max(yT2 - df)
+    return over
+
+SETPOINT_UNIX = 1718013959.6977577
+SETPOINT_TS = pd.to_datetime(SETPOINT_UNIX, unit='s')
+WINDOW = pd.to_datetime(SETPOINT_UNIX+25, unit='s')
+
 ## The controller is reasonably fast at reacting to changes; the sum of in and
 ## out is at zero roughly 5-10 seconds after a change.
 
@@ -45,7 +55,7 @@ else:
 df = pd.DataFrame.from_records(data)
 df['time'] = pd.to_datetime(df['time'], unit='s')
 df_pivot = df.pivot_table(values='value', columns='unit', index='time')
-df_resampled = df_pivot.resample('s').mean()
+df_resampled = df_pivot.resample('0.1s').mean()
 df_resampled.interpolate(method='linear', inplace=True)
 df_resampled = pd.DataFrame(df_resampled)
 
@@ -54,5 +64,10 @@ plt.ion()  # Turn interactive mode on
 plt.figure()
 ax1, ax2 = plt.subplot(211), plt.subplot(212)
 df_resampled[[c for c in df_resampled.columns if '_p' in c]].plot(marker='.', ax=ax1, linewidth=3)
-df_resampled['pcc_p'].plot(marker='.', ax=ax2, linewidth=3)
+ax2.plot(df_resampled['pcc_p'][SETPOINT_TS:WINDOW], marker='.', linewidth=3, label='pcc_p')
+ax2.plot(df_resampled['dumpload_p'][SETPOINT_TS:WINDOW], marker='.', linewidth=3, label='dumpload')
+plt.legend()
+
+# print(overshoot(df_resampled['pcc_p'][SETPOINT_TS:WINDOW], SETPOINT_TS, WINDOW))
+
 plt.show(block=True)