From 2a6fc39dc057be19b80ae0f72facdef92e429349 Mon Sep 17 00:00:00 2001
From: DBras <danielbrasholt@gmail.com>
Date: Mon, 10 Jun 2024 11:33:35 +0200
Subject: [PATCH] question: prep file for answers

---
 demo_plotter.py | 90 -------------------------------------------------
 1 file changed, 90 deletions(-)

diff --git a/demo_plotter.py b/demo_plotter.py
index bc23c1f..b876c8e 100755
--- a/demo_plotter.py
+++ b/demo_plotter.py
@@ -48,93 +48,3 @@ 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)
-
-