data/setpoints/setpoints_1718013923.json

@@ -1,4 +0,0 @@
-{"unit": "dumpload_sp", "value": 0.0, "time": 1718013928.6948164}
-{"unit": "dumpload_sp", "value": 20.0, "time": 1718013959.6977577}
-{"unit": "dumpload_sp", "value": 10.0, "time": 1718013989.6989655}
-{"unit": "dumpload_sp", "value": 0.0, "time": 1718014018.716735}

demo_datalogger.py

@@ -1,4 +1,3 @@
-#!./venv/bin/python3
 import syslab
 from json import dump
 from time import sleep, time

demo_plotter.py

@@ -1,13 +1,11 @@
-#!./venv/bin/python3
 import pandas as pd
-import numpy as np
 import json
 import matplotlib.pyplot as plt
 import os
 from datetime import timedelta
 
 ## Read the measurements data file ##
-DATA_MEAS_DIR = 'data/measurements'
+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]
 
@@ -21,7 +19,7 @@ use_setpoint_log = False
 
 ## Read the setpoints data file ##
 if use_setpoint_log:
-    DATA_SP_DIR = 'data/setpoints'
+    DATA_SP_DIR = 'data\setpoints'
     # Always plot latest datafile
     SP_LOG_FILE = sorted(os.listdir(DATA_SP_DIR))[-1]
 
@@ -35,39 +33,107 @@ if use_setpoint_log:
 else:
     data = meas_data
 
-################################################################################
-################## 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.
 
 # 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['time'] = pd.to_datetime(df['time'], unit='s')
 df_pivot = df.pivot_table(values='value', columns='unit', index='time')
-df_resampled = df_pivot.resample('0.1s').mean()
+
-df_resampled.interpolate(method='linear', inplace=True)
-df_resampled = pd.DataFrame(df_resampled)
 
 # Plot the data. Note, that the data will mostly not be plotted with lines.
 plt.ion()  # Turn interactive mode on
 plt.figure()
-ax1, ax2 = plt.subplot(211), plt.subplot(212)
+ax1 = plt.subplot(211)  # Make two separate figures
-df_resampled[[c for c in df_resampled.columns if '_p' in c]].plot(marker='.', ax=ax1, linewidth=3)
+ax2 = plt.subplot(212)
-ax2.plot(df_resampled['pcc_p'][SETPOINT_TS:WINDOW], marker='.', linewidth=3, label='pcc_p')
+df_pivot[[c for c in df_pivot.columns if "_p" in c]].plot(marker='.', ax=ax1, linewidth=3)
-ax2.plot(df_resampled['dumpload_p'][SETPOINT_TS:WINDOW], marker='.', linewidth=3, label='dumpload')
+df_pivot[[c for c in df_pivot.columns if "_q" in c]].plot(marker='.', ax=ax2, linewidth=3)
-plt.legend()
-
-# print(overshoot(df_resampled['pcc_p'][SETPOINT_TS:WINDOW], SETPOINT_TS, WINDOW))
-
 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)
+
+

plotter.py

@@ -1,28 +0,0 @@
-#!./venv/bin/python3
-import pandas as pd
-import json
-import matplotlib.pyplot as plt
-import os
-
-DATA_MEAS_DIR = 'data/measurements'
-SPECIFIC_FILE = ''
-MEAS_LOG_FILE = sorted(os.listdir(DATA_MEAS_DIR))[-1] if not SPECIFIC_FILE else SPECIFIC_FILE
-
-with open(os.path.join(DATA_MEAS_DIR, MEAS_LOG_FILE)) as f:
-    meas_data = [json.loads(line) for line in f]
-data = meas_data
-
-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.interpolate(method='linear', inplace=True)
-df_resampled = pd.DataFrame(df_resampled)
-
-# Plot the data. Note, that the data will mostly not be plotted with lines.
-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[[c for c in df_resampled.columns if '_q' in c]].plot(marker='.', ax=ax2, linewidth=3)
-plt.show(block=True)