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question: prep file for answers

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DBras 2024-06-10 11:33:35 +02:00
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demo_plotter.py
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@ -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)