140 lines
4.0 KiB
Python
140 lines
4.0 KiB
Python
import pandas as pd
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import json
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import matplotlib.pyplot as plt
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import os
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from datetime import timedelta
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## Read the measurements data file ##
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DATA_MEAS_DIR = 'data\measurements'
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# Always plot latest datafile - replace [-1] with another index if you want to plot a specific file.
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MEAS_LOG_FILE = sorted(os.listdir(DATA_MEAS_DIR))[-1]
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# Store each dictionary of the measurements json in a list
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with open(os.path.join(DATA_MEAS_DIR, MEAS_LOG_FILE)) as f:
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meas_data = [json.loads(line) for line in f]
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# Use setpoint logger (only necessary for part two of the exercise "collecting fresh data")
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use_setpoint_log = False
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## Read the setpoints data file ##
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if use_setpoint_log:
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DATA_SP_DIR = 'data\setpoints'
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# Always plot latest datafile
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SP_LOG_FILE = sorted(os.listdir(DATA_SP_DIR))[-1]
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# Store each dictionary of the setpoints json in a list
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with open(os.path.join(DATA_SP_DIR, SP_LOG_FILE)) as f:
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sp_data = [json.loads(line) for line in f]
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# Merge measurements and setpoints in one list
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data = meas_data + sp_data
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else:
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data = meas_data
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# Construct a dataframe and pivot it to obtain a dataframe with a column per unit, and a row per timestamp.
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df = pd.DataFrame.from_records(data)
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df_pivot = df.pivot_table(values='value', columns='unit', index='time')
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# Plot the data. Note, that the data will mostly not be plotted with lines.
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plt.ion() # Turn interactive mode on
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plt.figure()
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ax1 = plt.subplot(211) # Make two separate figures
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ax2 = plt.subplot(212)
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df_pivot[[c for c in df_pivot.columns if "_p" in c]].plot(marker='.', ax=ax1, linewidth=3)
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df_pivot[[c for c in df_pivot.columns if "_q" in c]].plot(marker='.', ax=ax2, linewidth=3)
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plt.show(block=True)
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## TODO Q1: Your code here
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## TODO Q2:
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# Convert time column (index) of df_pivot to datetime
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# TODO Your code here
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# Hint1: You can use pandas to_numeric() to prepare the index for pandas to_datetime function
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# Hint2: Remember to define the unit within pandas to_datetime function
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# Resample the data
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# TODO Your code here
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# Interpolate the measurements
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# TODO Your code here
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# Hint: For part two of the exercise ("collecting fresh data") the nan rows after a setpoint
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# in the recorded step function should be filled with the value of the setpoint until the row of the next setpoint is reached
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# You can use the df.fillna(method="ffill") function for that purpose. However, the measurements should still be interpolated!
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# Plot the resampled data
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# TODO Your code here
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## TODO Q3: Your code here
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## TODO Q4: Your code here
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## Part two: "Collecting fresh data"
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# Hint 1: You can build up on the "read_and_plot_data.py" from day 2
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# Hint 2: Yoy may want to store your response metric functions from day 2 in the "util.py" and import all of them with
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# "from util import *"
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if use_setpoint_log:
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# Add a column to df_pivot containing the reference/target signal
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# TODO your code here
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# Loop over all steps and extract T_1, T_2 and the step size
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results = {}
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for idx in range(0, len(sp_data)-1):
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label = f"Step_{sp_data[idx]['value']}kW"
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# Extract T_1 and T_2 from the setpoint JSON
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# TODO your code here
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# Change timestamp format
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T_1 = pd.to_datetime(pd.to_numeric(T_1), unit="s").round("0.1S")
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T_2 = pd.to_datetime(pd.to_numeric(T_2), unit="s").round("0.1S")
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# To ensure we are not considering values of the next load step
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T_2 = T_2 - timedelta(seconds=0.2)
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# define measured output y and target setpoint r
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# TODO your code here
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# Derive step direction from the setpoint data
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if ...: # TODO your code here
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Positive_step = True
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else:
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Positive_step = False
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# Collect response metrics results
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results[label] = {
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# TODO your code here
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}
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pd.DataFrame.from_dict(results).plot(kind='bar')
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plt.title("Metrics")
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plt.tight_layout()
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plt.savefig('data/test_metrics'+MEAS_LOG_FILE[-10:]+'.png')
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plt.show(block=True)
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