This collection of Python scripts makes use of the excellent Python-OBD library. Even though the Python-OBD library is exceedingly easy to use, I hope these scripts simplify the process of automotive troubleshooting. The last thing I want to do when I'm having car trouble is read matplotlib documentation.
The Python-OBD library, thus these scripts, are designed to work with any ELM 327 OBD-II device. I used the OBD Link EX purchased from Amazon (ASIN: B081VQVD3F). Out of the box its USB cable was loose at the OBD-II end. It worked fine after disassembling and repinning the JST connector inside. 🙄
> pip install obd
> pip install matplotlib
> pip install pandas
Codes.py returns a list of DTCs (diagnostic trouble codes) and other parameter values as a snapshot. Adjust to taste.
> python codes.py
--- codes.py --------------------------------
GET_DTC: [('P2188', 'System Too Rich at Idle')]
---------------------------------------------
FREEZE_DTC: None
---------------------------------------------
MAF: None
---------------------------------------------
INTAKE_PRESSURE: 31 kilopascal
---------------------------------------------
BAROMETRIC_PRESSURE: 96 kilopascal
---------------------------------------------
AMBIANT_AIR_TEMP: 28 degC
---------------------------------------------
ENGINE_LOAD: 25.098039215686274 percent
---------------------------------------------
FUEL_STATUS: ('Closed loop, using oxygen sensor feedback to determine fuel mix', '')
Clear.py clears DTCs from a vehicle, but before doing so it returns a list of DTCs and prompts the user for input to confirm that those codes should be cleared.
> python clear.py
--- clear.py --------------------------------
CODES FOUND: [('P2188', 'System Too Rich at Idle')]
---------------------------------------------
Clear diagnostic trouble code(s)? (yes/no) yes
~~~ CODES CLEARED ~~~
---------------------------------------------
Log.py scans your vehicle for supported OBD-II commands and outputs their values in CSV format at a rate of about 2 Hz. This rate can be increased, but I found it to be sufficient for my needs. In an effort to keep this script simple it relies on standard output redirection (>) for writing data to a file. -not the most robust data collection technique, but for shade tree mechanics it should be sufficient. Run this script then let your vehicle idle or go for a drive. Use ctrl + c to interrupt its execution. Use chart.py and comp.py to review the collected data.
> python log.py > data.csv
Chart.py is useful for those times when you'd like to plot one parameter only or compare one parameter to another with a common x-axis.
x_axis = 'TIME (UTC)'
y_axis = 'RPM (revolutions per minute)'
y_axis_alt = 'INTAKE_PRESSURE (kilopascal)'
> python chart.py
Comp.py compares one parameter across two different datasets. For example, say you wanted to compare intake pressure values before and after a vehicle's manifold air pressure sensor was replaced with respect to engine RPM. -accommodates an optional secondary y-axis; intake temperature shown.
data_1 = 'log_before_MAP.csv'
data_2 = 'log_after_MAP.csv'
plot_name = 'test.png'
x_axis = 'RPM (revolutions_per_minute)' # often 'TIME (UTC)'
y_axis_1 = 'INTAKE_PRESSURE (kilopascal)'
y_axis_2 = 'INTAKE_PRESSURE (kilopascal)'
y_axis_alt = False
> python comp.py
