diff --git a/2024_config_D3_Demo.pdf b/2024_config_D3_Demo.pdf
deleted file mode 100644
index 9a5e13b..0000000
Binary files a/2024_config_D3_Demo.pdf and /dev/null differ
diff --git a/data/measurements/dummy_file b/data/measurements/dummy_file
deleted file mode 100644
index c81b66a..0000000
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diff --git a/data/measurements/measurements.json b/data/measurements/measurements.json
deleted file mode 100644
index a5dcda9..0000000
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-{"unit": "pv319_p", "value": 10.984, "time": 1717757868.581}
-{"unit": "pv319_q", "value": 0.134, "time": 1717757868.581}
-{"unit": "pv330_p", "value": 15.868, "time": 1717757868.38}
-{"unit": "pv330_q", "value": -0.2, "time": 1717757868.738}
-{"unit": "battery_p", "value": -0.221, "time": 1717757868.412}
-{"unit": "battery_q", "value": 0.036, "time": 1717757868.412}
-{"unit": "b2b_p", "value": -0.91, "time": 1717757867.994}
-{"unit": "b2b_q", "value": 3.523, "time": 1717757867.994}
-{"unit": "gaia_p", "value": 3.838, "time": 1717757868.77}
-{"unit": "gaia_q", "value": -8.304, "time": 1717757868.77}
-{"unit": "pcc_p", "value": -29.671, "time": 1717757869.705}
-{"unit": "pcc_q", "value": 4.368, "time": 1717757869.705}
-{"unit": "dumpload_p", "value": 0.0, "time": 1717757869.84}
-{"unit": "dumpload_q", "value": 0.0, "time": 1717757869.84}
-{"unit": "pv319_p", "value": 11.002, "time": 1717757869.82}
-{"unit": "pv319_q", "value": 0.131, "time": 1717757869.82}
-{"unit": "pv330_p", "value": 15.908, "time": 1717757869.56}
-{"unit": "pv330_q", "value": -0.192, "time": 1717757869.56}
-{"unit": "battery_p", "value": -0.219, "time": 1717757869.677}
-{"unit": "battery_q", "value": 0.036, "time": 1717757869.677}
-{"unit": "b2b_p", "value": -0.908, "time": 1717757869.846}
-{"unit": "b2b_q", "value": 3.505, "time": 1717757869.846}
-{"unit": "gaia_p", "value": 3.549, "time": 1717757869.87}
-{"unit": "gaia_q", "value": -8.192, "time": 1717757869.87}
-{"unit": "pcc_p", "value": -29.287, "time": 1717757870.795}
-{"unit": "pcc_q", "value": 4.26, "time": 1717757870.795}
-{"unit": "dumpload_p", "value": 0.0, "time": 1717757870.91}
-{"unit": "dumpload_q", "value": 0.0, "time": 1717757870.91}
-{"unit": "pv319_p", "value": 11.022, "time": 1717757870.985}
-{"unit": "pv319_q", "value": 0.129, "time": 1717757870.985}
-{"unit": "pv330_p", "value": 15.933, "time": 1717757870.88}
-{"unit": "pv330_q", "value": -0.202, "time": 1717757870.88}
-{"unit": "battery_p", "value": -0.22, "time": 1717757870.892}
-{"unit": "battery_q", "value": 0.036, "time": 1717757870.892}
-{"unit": "b2b_p", "value": -0.908, "time": 1717757870.97}
-{"unit": "b2b_q", "value": 3.507, "time": 1717757870.97}
-{"unit": "gaia_p", "value": 3.909, "time": 1717757871.053}
-{"unit": "gaia_q", "value": -8.314, "time": 1717757871.053}
-{"unit": "pcc_p", "value": -29.725, "time": 1717757872.045}
-{"unit": "pcc_q", "value": 4.375, "time": 1717757872.045}
-{"unit": "dumpload_p", "value": 0.0, "time": 1717757872.0}
-{"unit": "dumpload_q", "value": 0.0, "time": 1717757872.0}
-{"unit": "pv319_p", "value": 11.022, "time": 1717757871.733}
-{"unit": "pv319_q", "value": 0.127, "time": 1717757872.136}
-{"unit": "pv330_p", "value": 15.978, "time": 1717757871.992}
-{"unit": "pv330_q", "value": -0.203, "time": 1717757871.992}
-{"unit": "battery_p", "value": -0.22, "time": 1717757872.102}
-{"unit": "battery_q", "value": 0.036, "time": 1717757872.102}
-{"unit": "b2b_p", "value": -0.908, "time": 1717757872.185}
-{"unit": "b2b_q", "value": 3.514, "time": 1717757872.185}
-{"unit": "gaia_p", "value": 3.595, "time": 1717757872.173}
-{"unit": "gaia_q", "value": -8.206, "time": 1717757872.173}
-{"unit": "pcc_p", "value": -29.546, "time": 1717757873.125}
-{"unit": "pcc_q", "value": 4.299, "time": 1717757873.125}
-{"unit": "dumpload_p", "value": 0.0, "time": 1717757873.245}
-{"unit": "dumpload_q", "value": 0.0, "time": 1717757873.245}
-{"unit": "pv319_p", "value": 11.038, "time": 1717757873.22}
-{"unit": "pv319_q", "value": 0.127, "time": 1717757873.22}
-{"unit": "pv330_p", "value": 16.0, "time": 1717757873.277}
-{"unit": "pv330_q", "value": -0.203, "time": 1717757873.277}
-{"unit": "battery_p", "value": -0.221, "time": 1717757872.917}
-{"unit": "battery_q", "value": 0.034, "time": 1717757872.917}
-{"unit": "b2b_p", "value": -0.908, "time": 1717757873.298}
-{"unit": "b2b_q", "value": 3.501, "time": 1717757873.298}
-{"unit": "gaia_p", "value": 3.593, "time": 1717757873.269}
-{"unit": "gaia_q", "value": -8.215, "time": 1717757873.269}
diff --git a/data/setpoints/dummy_file b/data/setpoints/dummy_file
deleted file mode 100644
index c81b66a..0000000
--- a/data/setpoints/dummy_file
+++ /dev/null
@@ -1 +0,0 @@
-Makes folder visible for git.
\ No newline at end of file
diff --git a/demo_datalogger.py b/demo_datalogger.py
deleted file mode 100644
index 0d1e1e0..0000000
--- a/demo_datalogger.py
+++ /dev/null
@@ -1,47 +0,0 @@
-import syslab
-from json import dump
-from time import sleep, time
-
-# Defines location and name of the measurements file
-LOG_FILE = f'data/measurements/measurements_{time():.00f}.json'
-print(f"Logging to file {LOG_FILE}")
-
-# Set up a connection to the switchboards
-sb3192 = syslab.SwitchBoard('319-2')
-sb3193 = syslab.SwitchBoard('319-3')
-sb33012 = syslab.SwitchBoard("330-12")
-sb1172 = syslab.SwitchBoard('117-2')
-
-# Convenience function to
-def take_measurements():
-    measurements = {
-        "pcc_p": sb3192.getActivePower('Grid'),
-        "pcc_q": sb3192.getReactivePower('Grid'),
-        "pv319_p": sb3192.getActivePower('PV'),
-        "pv319_q": sb3192.getReactivePower('PV'),
-        "dumpload_p": sb3192.getActivePower('Dumpload'),
-        "dumpload_q": sb3192.getReactivePower('Dumpload'),
-        "gaia_p": sb33012.getActivePower('Gaia'),
-        "gaia_q": sb33012.getReactivePower('Gaia'),
-        "pv330_p": sb33012.getActivePower('PV_1'),
-        "pv330_q": sb33012.getReactivePower('PV_1'),
-        "b2b_p": sb3193.getActivePower('ABB_Sec'),
-        "b2b_q": sb3193.getReactivePower('ABB_Sec'),
-        "battery_p": sb1172.getActivePower('Battery'),
-        "battery_q": sb1172.getReactivePower('Battery'),
-    }
-    return [{'unit': k, 'value': meas.value, 'time': meas.timestampMicros/1e6} for k, meas in measurements.items()]
-
-
-while True:
-    measurement = take_measurements()
-
-    # Open the output file in "append" mode which adds lines to the end
-    with open(LOG_FILE, 'a') as file:
-        for m in measurement:
-            # Convert the dictionary m to a json string and put it
-            # in the file.
-            dump(m, file)
-            # Write a newline for each measurement to make loading easier
-            file.write('\n')
-    sleep(1)
diff --git a/demo_plotter.py b/demo_plotter.py
deleted file mode 100644
index 372f2fb..0000000
--- a/demo_plotter.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import pandas as pd
-import json
-import matplotlib.pyplot as plt
-import os
-from datetime import timedelta
-
-## Read the measurements data file ##
-DATA_MEAS_DIR = 'data\measurements'
-# Always plot latest datafile - replace [-1] with another index if you want to plot a specific file.
-MEAS_LOG_FILE = sorted(os.listdir(DATA_MEAS_DIR))[-1]
-
-# Store each dictionary of the measurements json in a list
-with open(os.path.join(DATA_MEAS_DIR, MEAS_LOG_FILE)) as f:
-    meas_data = [json.loads(line) for line in f]
-
-# Use setpoint logger (only necessary for part two of the exercise "collecting fresh data")
-use_setpoint_log = False
-
-
-## Read the setpoints data file ##
-if use_setpoint_log:
-    DATA_SP_DIR = 'data\setpoints'
-    # Always plot latest datafile
-    SP_LOG_FILE = sorted(os.listdir(DATA_SP_DIR))[-1]
-
-    # Store each dictionary of the setpoints json in a list
-    with open(os.path.join(DATA_SP_DIR, SP_LOG_FILE)) as f:
-        sp_data = [json.loads(line) for line in f]
-
-    # Merge measurements and setpoints in one list
-    data = meas_data + sp_data
-
-else:
-    data = meas_data
-
-
-# Construct a dataframe and pivot it to obtain a dataframe with a column per unit, and a row per timestamp.
-df = pd.DataFrame.from_records(data)
-df_pivot = df.pivot_table(values='value', columns='unit', index='time')
-
-
-# Plot the data. Note, that the data will mostly not be plotted with lines.
-plt.ion()  # Turn interactive mode on
-plt.figure()
-ax1 = plt.subplot(211)  # Make two separate figures
-ax2 = plt.subplot(212)
-df_pivot[[c for c in df_pivot.columns if "_p" in c]].plot(marker='.', ax=ax1, linewidth=3)
-df_pivot[[c for c in df_pivot.columns if "_q" in c]].plot(marker='.', ax=ax2, linewidth=3)
-plt.show(block=True)
-
-
-## TODO Q1: Your code here
-
-
-
-
-
-## TODO Q2:
-# Convert time column (index) of df_pivot to datetime
-# TODO Your code here
-# Hint1: You can use pandas to_numeric() to prepare the index for pandas to_datetime function
-# Hint2: Remember to define the unit within pandas to_datetime function
-
-# Resample the data
-# TODO Your code here
-
-
-# Interpolate the measurements
-# TODO Your code here
-# Hint: For part two of the exercise ("collecting fresh data") the nan rows after a setpoint
-# in the recorded step function should be filled with the value of the setpoint until the row of the next setpoint is reached
-# You can use the df.fillna(method="ffill") function for that purpose. However, the measurements should still be interpolated!
-
-
-
-# Plot the resampled data
-# TODO Your code here
-
-
-
-## TODO Q3: Your code here
-
-
-
-
-## TODO Q4: Your code here
-
-
-
-## Part two: "Collecting fresh data"
-
-# Hint 1: You can build up on the "read_and_plot_data.py" from day 2
-# Hint 2: Yoy may want to store your response metric functions from day 2 in the "util.py" and import all of them with
-# "from util import *"
-
-if use_setpoint_log:
-
-    # Add a column to df_pivot containing the reference/target signal
-    # TODO your code here
-
-    # Loop over all steps and extract T_1, T_2 and the step size
-    results = {}
-
-    for idx in range(0, len(sp_data)-1):
-        label = f"Step_{sp_data[idx]['value']}kW"
-
-        # Extract T_1 and T_2 from the setpoint JSON
-        # TODO your code here
-
-
-        # Change timestamp format
-        T_1 = pd.to_datetime(pd.to_numeric(T_1), unit="s").round("0.1S")
-        T_2 = pd.to_datetime(pd.to_numeric(T_2), unit="s").round("0.1S")
-
-        # To ensure we are not considering values of the next load step
-        T_2 = T_2 - timedelta(seconds=0.2)
-
-
-        # define measured output y and target setpoint r
-        # TODO your code here
-
-        # Derive step direction from the setpoint data
-        if ...:  # TODO your code here
-            Positive_step = True
-        else:
-            Positive_step = False
-
-        # Collect response metrics results
-        results[label] = {
-            # TODO your code here
-        }
-
-    pd.DataFrame.from_dict(results).plot(kind='bar')
-    plt.title("Metrics")
-    plt.tight_layout()
-    plt.savefig('data/test_metrics'+MEAS_LOG_FILE[-10:]+'.png')
-    plt.show(block=True)
-
-