Live Plot of Data in Matplotlib - Time Series Visualization with Time on the x-axis

Question

Live Plot of Data in Matplotlib - Time Series Visualization with Time on the x-axis

Is it possible to use time as x-axis values for a live matplotlib graph? If so, how should it be implemented? Despite trying various methods, I keep encountering errors. Below is my current code:


update_label(label):

    def getvoltage():

        f=open("VoltageReadings.txt", "a+")
        readings = [0]*100
        maxsample = 100
        counter = 0

        while (counter < maxsample):

            reading = adc.read_adc(0, gain=GAIN)
            readings.append(reading)
            counter += 1

        avg = sum(readings)/100
        voltage = (avg * 0.1259)/100
        time = str(datetime.datetime.now().time())
        f.write("%.2f," % (voltage) + time + "\r\n")
        readings.clear()

        label.config(text=str('Voltage: {0:.2f}'.format(voltage)))
        label.after(1000, getvoltage)
    getvoltage()

def animate(i):
    pullData = open("VoltageReadings.txt","r").read()
    dataList = pullData.split('\n')
    xList=[]
    yList=[]
    for eachLine in dataList:
        if len(eachLine) > 1:
            y, x = eachLine.split(',')
            xList.append(float(x)))
            yList.append(float(y))
            a.clear()
    a.plot(xList,yList)

Even after trying the above method, I am receiving an error message that states

ValueError: could not convert string to float: '17:21:55'

I have attempted to convert the string into a float but have been unsuccessful.

Your help and guidance would be greatly appreciated, thank you :)

python python-3.x matplotlib tkinter

Answer 1

Answer №1

If you're looking to work with dates in Python, I recommend using the datetime library. To begin, you can utilize the datetime.strptime command to read your dates. For example, by setting a reference date using Julian date like

date=datetime.strptime('17:21:55','%H:%M:%S')

. Another approach is to set your starting point as a date0 and then define your date as

date=datetime.strptime('01-01-2000 17:21:55','%d-%m-%Y %H%H:%M:%S')

. By calculating the differences between your current date and the reference date in seconds for each line in your file using a loop, you can store these differences in a list element (referred to as Diff_list). Finally, converting the timestamp values from Diff_List to UTC format using

T_plot= [dtm.datetime.utcfromtimestamp(i) for i in Diff_List]

, you can visualize the dates on the x-axis with plt.plot(T_plot,values).

An alternative option is to make use of the pandas library. Firstly, define your date parsing based on the type of dates in your file like

parser=pd.datetime.strptime(date, '%Y-%m-%d %H:%M:%S')

. Then proceed to read your file with the necessary configurations:

tmp = pd.read_csv(your_file, parse_dates={'datetime': ['date', 'time']},                          date_parser=parser, comment='#',delim_whitespace=True,names=['date', 'time', 'Values'])

data = tmp.set_index(tmp['datetime']).drop('datetime', axis=1)

If you only need to represent hours HH:MM:SS instead of the complete date, you can adjust these lines accordingly. Remember, indexing will not be sequential but based on the dates themselves. Therefore, when plotting, ensure to call import matplotlib.pyplot as plt and then plt.plot(data.index,data.Values) to display your data.

Answer 2

If you're looking to work with dates in Python, I recommend using the datetime library. To begin, you can utilize the datetime.strptime command to read your dates. For example, by setting a reference date using Julian date like

date=datetime.strptime('17:21:55','%H:%M:%S')

. Another approach is to set your starting point as a date0 and then define your date as

date=datetime.strptime('01-01-2000 17:21:55','%d-%m-%Y %H%H:%M:%S')

. By calculating the differences between your current date and the reference date in seconds for each line in your file using a loop, you can store these differences in a list element (referred to as Diff_list). Finally, converting the timestamp values from Diff_List to UTC format using

T_plot= [dtm.datetime.utcfromtimestamp(i) for i in Diff_List]

, you can visualize the dates on the x-axis with plt.plot(T_plot,values).

An alternative option is to make use of the pandas library. Firstly, define your date parsing based on the type of dates in your file like

parser=pd.datetime.strptime(date, '%Y-%m-%d %H:%M:%S')

. Then proceed to read your file with the necessary configurations:

tmp = pd.read_csv(your_file, parse_dates={'datetime': ['date', 'time']},                          date_parser=parser, comment='#',delim_whitespace=True,names=['date', 'time', 'Values'])

data = tmp.set_index(tmp['datetime']).drop('datetime', axis=1)

If you only need to represent hours HH:MM:SS instead of the complete date, you can adjust these lines accordingly. Remember, indexing will not be sequential but based on the dates themselves. Therefore, when plotting, ensure to call import matplotlib.pyplot as plt and then plt.plot(data.index,data.Values) to display your data.

Answer 3

Answer №2

If you're looking to visualize data from various sources at the same time, consider using the polt Python package that I have personally developed for this specific purpose. The polt package leverages matplotlib to generate multi-source data displays.

To get started, create a script called adc_read.py that fetches readings from your ADC and prints them:

import random, sys, time

def read_adc():
    """
    Add your code here to read voltage values from your ADC
    """
    # Simulate measurement delay/sampling interval
    time.sleep(0.001)
    # Simulate reading a voltage between 0 and 5V
    return random.uniform(0, 5)


while True:
    # Collect 100 readings
    adc_readings = tuple(read_adc() for i in range(100))
    # Calculate average
    adc_average = sum(adc_readings) / len(adc_readings)
    # Output the average
    print(adc_average)
    sys.stdout.flush()

This script will output results like:

python3 adc_read.py
# Output
2.3187490696344444
2.40019412977279
2.3702603804716555
2.3793495215651435
2.5596985467604703
2.5433401603774413
2.6048815735614004
2.350392397280291
2.4372325168231948
2.5618046803145647
...

You can then pipe this output into polt for live data visualization:

python3 adc_read.py | polt live

https://i.stack.imgur.com/jjA9K.png

To add labels, include metadata:

python3 adc_read.py | \
    polt \
        add-source -c- -o name=ADC \
        add-filter -f metadata -o set-quantity=voltage -o set-unit='V' \
        live

https://i.stack.imgur.com/uIreo.png

The polt documentation offers insights into further customization options.

Answer 4

If you're looking to visualize data from various sources at the same time, consider using the polt Python package that I have personally developed for this specific purpose. The polt package leverages matplotlib to generate multi-source data displays.

To get started, create a script called adc_read.py that fetches readings from your ADC and prints them:

import random, sys, time

def read_adc():
    """
    Add your code here to read voltage values from your ADC
    """
    # Simulate measurement delay/sampling interval
    time.sleep(0.001)
    # Simulate reading a voltage between 0 and 5V
    return random.uniform(0, 5)


while True:
    # Collect 100 readings
    adc_readings = tuple(read_adc() for i in range(100))
    # Calculate average
    adc_average = sum(adc_readings) / len(adc_readings)
    # Output the average
    print(adc_average)
    sys.stdout.flush()

This script will output results like:

python3 adc_read.py
# Output
2.3187490696344444
2.40019412977279
2.3702603804716555
2.3793495215651435
2.5596985467604703
2.5433401603774413
2.6048815735614004
2.350392397280291
2.4372325168231948
2.5618046803145647
...

You can then pipe this output into polt for live data visualization:

python3 adc_read.py | polt live

https://i.stack.imgur.com/jjA9K.png

To add labels, include metadata:

python3 adc_read.py | \
    polt \
        add-source -c- -o name=ADC \
        add-filter -f metadata -o set-quantity=voltage -o set-unit='V' \
        live

https://i.stack.imgur.com/uIreo.png

The polt documentation offers insights into further customization options.

Live Plot of Data in Matplotlib - Time Series Visualization with Time on the x-axis

Answer №1

Answer №2

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