Python XPlane

Coding a wing leveler autopilot in X-Plane with Python


Continuing from the first post, where we hooked up X-Plane to our Python code, we will build a wing leveler today. The first post was just about making sure we could get data into and out of X-Plane. Today will add a few features to our XPlane autopilot written in Python.

  • A control loop timer (we will be targeting a loop frequency of 10 Hz, or 10 updates per second)
  • Additional data feeds from X-Plane
  • Two PID controllers, one for roll, one for pitch
  • Some debugging output to keep track of what the PIDs are doing

The full code will be at the end of this post.

Video Link

Python Tutorial: code a wing leveler in X-Plane using PID loops


  1. Control loop timer/limiter
  2. Obtaining current pitch/roll values from X-Plane
  3. Initializing the PID controllers
  4. Feeding the PID controllers within the control loop
  5. Controlling the aircraft with the new PID output
  6. Monitoring the control loops via debug prints

1 – Control loop timer/limiter

Since we will be targeting a 10 Hz update rate, we need to develop a method to ensure the loop does not run more frequent than once every 100 milliseconds. We do not want the loop running uninhibited, because that will result in variable loop execution times and we like to keep those things constant. It could potentially execute thousands of times per second, which is entirely unnecessary. Most control loop algorithms run in the 10-100 Hz range (10-100 times per second). For reference, my RC plane running ArduPlane on Pixhawk uses 50 Hz as a standard update frequency.

To accomplish this task, we need to set up some timing variables.

First of all, add an import statment for datetime and timedelta:

from datetime import datetime, timedelta

Next, define the timing variables:

update_interval = 0.100 # this value is in seconds. 1/0.100 = 10 which is the update interval in Hz

# start is set to the time the line of code is executed, which is essential when the program started
start =

# last_update needs to be set to start for the first execution of the loop to successfully run.
# alternatively, we could've set start to something in the past.
last_update = start

# last update needs to be defined as a global within the monitor() function:
def monitor():
	global last_update

That handles the variables. Now we need to limit the loop execution. To do so requires wrapping all of the loop code into a new if statement that evaluates the time and only executes if the current time is 100 milliseconds greater than the last loop execution:

# loop is the "while True:" statement
while True:
	# this if statement is evaluated with every loop execution
	if ( > last_update + timedelta(milliseconds = update_interval * 1000)):
		# when the if statement evaluates to true, the first thing we'll do is set the last update to the current time so the next iteration fires at the correct time
		last_update =

		# rest of the loop code goes here

2 – Obtaining current roll/pitch values from X-Plane

This task is pretty straightforward. In the first post, the code included obtaining the position with posi = client.getPOSI(). There are 7 elements returned with that method, and two of them are roll and pitch.

Put the below after the .getPOSI() call.

current_roll = posi[4]
current_pitch = posi[3]

3 – Initializing the PID controllers

First you need to get the PID control file from the Ivmech (Ivmech Mechatronics Ltd.) GitHub page. Direct link file here. Put the file in the same working directory as everything else then import it with import PID at the top with the rest of the imports.

Then we can initialize the control instances. PID controllers are magic. But they do need to be set with smart values for the initial run. The default values with the PID library are P=0.2, I=0, D=0. This essentially means make the output be 20% of the error between the setpoint and the current value. For example, if the aircraft has a roll of 10 degrees to the left (-10), and the P=0.2, the output from the PID controller will be -2.

When setting PID values, it is almost always a good idea to start small and work your way up. If your gains are too high, you could get giant oscillations and other undesirable behaviors. In the YouTube video, I talk about the various types of PID controllers. They will basically always have a P (proportional) set. Many will also have an I (integral) value set, but not many use the D term (derivative).

Going with the “less is more” truism with PID controllers, I started with a P value of 0.1, and an I value of 0.01 (P/10). The I term (integral) is meant to take care of accumulated errors (which are usually long term errors). An example is your car’s cruise control going up a hill. If your cruise control is set to 65 mph, it will hold 65 no problem on flat roads. If you start going up a hill, the controller will slowly apply more throttle. With an I of 0, your car would never get to 65. It would probably stay around 62 or so. With the integrator going, the error will accumulate and boost the output (throttle) to get you back up to your desired 65. In the linked YouTube video, I show what happens when the I value is set to 0 and why it is necessary to correct long-term errors.

These values (P=0.1, I=0.01, D=0) turned out to work perfectly.

Place the following before the monitor function:

# defining the initial PID values
P = 0.1 # PID library default = 0.2
I = P/10 # default = 0
D = 0 # default = 0

# initializing both PID controllers
roll_PID = PID.PID(P, I, D)
pitch_PID = PID.PID(P, I, D)

# setting the desired values
# roll = 0 means wings level
# pitch = 2 means slightly nose up, which is required for level flight
desired_roll = 0
desired_pitch = 2

# setting the PID set points with our desired values
roll_PID.SetPoint = desired_roll
pitch_PID.SetPoint = desired_pitch

4 – Updating the PID controllers within the control loop

With the PIDs created, they will need to be updated with the new, current pitch and roll values with every loop execution.

Place the following after current_roll and current_pitch are assigned:


5 – Controlling the aircraft with the new PID output

Updating the PID controller instances will generate new outputs. We can use those outputs to set the control surfaces. You can place these two lines directly below the update lines:

new_ail_ctrl = roll_PID.output
new_ele_ctrl = pitch_PID.output

So we have new control surface values – now we need to actually move the control surfaces. This is accomplished by sending an array of 4 floats with .sendCTRL():

# ele, ail, rud, thr. -998 means don't set/change
ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998]

6 – Monitoring the control loops via debug prints

The last bit to tie a bunch of this together is printing out values with every loop execution to ensure things are heading the right direction. We will turn these into graphs in the next post or two.

We will be concatenating strings because it’s easy and we aren’t working with enough strings for it to be a performance problem.

In newer Python versions (3.6+), placing ‘f’ before the quotes in a print statement (f””) means the string is interpolated. This means you can basically put code in the print statement, which makes creating print statements much easier and cleaner.

The first line will print out the current roll and pitch value (below). We are using current_roll and current_pitch interpolated. The colon, then blank space with 0.3f is a string formatter. It rounds the value to 3 decimal places and leaves space for a negative. It results in things being lined up quite nicely.

output = f"current values --    roll: {current_roll: 0.3f},  pitch: {current_pitch: 0.3f}"

The next code statement will add a new line to the previous output, and also add the PID outputs for reference:

output = output + "\n" + f"PID outputs    --    roll: {roll_PID.output: 0.3f},  pitch: {pitch_PID.output: 0.3f}"

The final line will just add another new line to keep each loop execution’s print statements grouped together:

output = output + "\n"

Finally, we print the output with print(output), which will look like this:

loop start - 2021-10-19 14:24:26.208945
current values --    roll:  0.000,  pitch:  1.994
PID outputs    --    roll: -0.000,  pitch:  0.053

Full code of

import sys
import xpc
import PID
from datetime import datetime, timedelta

update_interval = 0.100 # seconds
start =
last_update = start

# defining the initial PID values
P = 0.1 # PID library default = 0.2
I = P/10 # default = 0
D = 0 # default = 0

# initializing both PID controllers
roll_PID = PID.PID(P, I, D)
pitch_PID = PID.PID(P, I, D)

# setting the desired values
# roll = 0 means wings level
# pitch = 2 means slightly nose up, which is required for level flight
desired_roll = 0
desired_pitch = 2

# setting the PID set points with our desired values
roll_PID.SetPoint = desired_roll
pitch_PID.SetPoint = desired_pitch

def monitor():
	global last_update
	with xpc.XPlaneConnect() as client:
		while True:
			if ( > last_update + timedelta(milliseconds = update_interval * 1000)):
				last_update =
				print(f"loop start - {}")

				posi = client.getPOSI();
				ctrl = client.getCTRL();

				current_roll = posi[4]
				current_pitch = posi[3]


				new_ail_ctrl = roll_PID.output
				new_ele_ctrl = pitch_PID.output

				ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998] # ele, ail, rud, thr. -998 means don't change

				output = f"current values --    roll: {current_roll: 0.3f},  pitch: {current_pitch: 0.3f}"
				output = output + "\n" + f"PID outputs    --    roll: {roll_PID.output: 0.3f},  pitch: {pitch_PID.output: 0.3f}"
				output = output + "\n"

if __name__ == "__main__":

Using the autopilot / Conclusion

To use the autopilot, fire up XPlane, hop in a small-ish plane (gross weight less than 10k lb), take off, climb 1000′, then execute the code. Your plane should level off within a second or two in both axis.

Here is a screenshot of the output after running for a few seconds:

Screenshot showing current pitch and roll values along with their respective PID outputs

The linked YouTube video shows the aircraft snapping to the desired pitch and roll angles very quickly from a diving turn. The plane is righted to within 0.5 degrees of the setpoints within 2 seconds.

A note about directly setting the control surfaces

If you are familiar with PIDs and/or other parts of what I’ve discussed here, you’ll realize that we could be setting large values for the control surfaces (i.e. greater than 1 or less than -1). We will address that next post with a normalization function. It will quickly become a problem when a pitch of 100+ is commanded for altitude hold. I have found that XPlane will allow throttle values of more than 100% (I’ve seen as high as ~430%) if sent huge throttle values.

Python XPlane

Creating an autopilot in X-Plane using Python – part 1


Today’s post will take us in a slightly different direction than the last few. Today’s post will be about hooking up some Python code to the X-Plane flight simulator to enable development of an autopilot using PID (proportional-integral-derivative) controllers. I’ve been a fan of flight simulators for quite some time (I distinctly remember getting Microsoft Flight Simulator 98 for my birthday when I was like 8 or 9) but have only recently started working with interfacing them to code. X-Plane is a well-known flight simulator developed by another Austin – Austin Meyer. It is regarded as having one of the best flight models and has tons of options for getting data into/out of the simulator. More than one FAA-certified simulator setups are running X-Plane as the primary driver software.

I got started thinking about writing some code for X-Plane while playing another game, Factorio. I drive a little plane or car in the game to get around my base and I just added a plug-in that “snaps” the vehicle to a heading, which makes it easier to go in straight lines. I thought – “hmm how hard could this be to duplicate in a flight sim?”. So here we are.

This post will get X-Plane hooked up to Python. The real programming will start with the next post.

Video Link


  1. Download and install X-Plane (I used X-Plane 10 because it uses less resources than X-Plane 11 and we don’t need the graphics/scenery to look super pretty to do coding. It also loads faster.)
  2. Download and install NASA’s XPlaneConnect X-Plane plug-in to X-Plane
  3. Verify the XPlaneConnect plug-in is active in X-Plane
  4. Download sample code from XPlaneConnect’s GitHub page
  5. Run the sample script to verify data is being transmitted from X-Plane via UDP to the XPlaneConnect code

1 – Download and install X-Plane 10 or X-Plane 11

I’ll leave this one up to you. X-Plane 10 is hard to find these days I just discovered. X-Plane 11 is available on Steam for $59.99 as of writing. I just tested and the plug-in/code works fine on X-Plane 11 (but the flight models are definitely different and will need different PID values). My screenshots might jump between the two versions but the content/message will be the same.

2 – Download and install NASA’s XPlaneConnect plug-in

NASA (yes, that NASA, the National Aeronautics and Space Administration) has wrote a bunch of code to interface with X-Plane. They have adapters for C, C++, Java, Matlab, and Python. They work with X-Plane 9, 10, and 11.

  1. Download the latest version from the XPlaneConnect GitHub releases page, 1.3 RC6 as of writing
  2. Open the .zip and place the contents in the [X-Plane directory]/Resources/plugins folder. There are few other folders already present in this directory. Mine looked like this after adding the XPlaneConnect folder:
Screenshot of X-Plane 10 plugins directory with XPlaneConnect folder added
Screenshot of X-Plane 11 plugins directory with XPlaneConnect folder added

3 – Verify XPlaneConnect is active in X-Plane

Now we’ll load up X-Plane and check the plug-ins to verify XPlaneConnect is enabled. Go to the top menu and select Plugins -> Plugin Admin. You should see X-Plane Connect checked in the enabled column:

Screenshot showing XPlaneConnect plug-in active in X-Plane 11
Screenshot showing XPlaneConnect plug-in active in X-Plane 10

4 – Download sample code from XPlaneConnect’s GitHub page

From the Python3 portion of the GitHub code, download and and stick them in your working directory (doesn’t matter where). For me, I just downloaded the entire git structure so the code is at C:\Users\Austin\source\repos\XPlaneConnect\Python3\src:

Screenshot showing and in my working directory

5 – Run sample code to verify data is making it from X-Plane to our code

With X-Plane running with a plane on a runway (or anywhere really), go ahead and run! I will be using Visual Studio Code to program this XPlane Python autopilot stuff so that’s where I’ll run it from.

You will start seeing lines scroll by very fast with 6 pieces of information – latitude, longitude, elevation (in meters), and the control deflections for aileron, elevator, and rudder (normalized from -1 to 1, with 0 being centered). In the below screenshot, we see a lat/lon of 39.915, -105.128, with an elevation of 1719m. First one to tell me in the comments what runway that is wins internet points!

Screenshot showing Visual Studio Code running in front of X-Plane 10 and the output scrolling by.


In this post, we have successfully downloaded the XPlaneConnect plug-in, and demonstrated that it can successfully interface with Python code in both X-Plane 10 and X-Plane 11.

Next up is to start controlling the plane with a basic wing leveler. As of writing this post, I have the following completely functional:

  • Pitch / roll hold at reasonable angles (-25 to 25)
  • Altitude set and hold
  • Heading set and hold
  • Airspeed set and hold
  • Navigate directly to a lat/lon point

See you at the next post! Next post – Coding a wing leveler autopilot in X-Plane with Python

Offgrid Solar Uncategorized

Sending MPP inverter data to MQTT and InfluxDB with Python

Catching up

Hey there, welcome back to Austin’s Nerdy Things. It’s been a while since my last post – life happens. I haven’t lost sight of the blog. Just needed to do some other things and finish up some projects before documenting stuff.


If you recall, my DIY hybrid solar setup with battery backup is up and running. I wrote about it here – DIY solar with battery backup – up and running!

The MPP LV2424 inverter I’m using puts out a lot of data. Some of it is quite useful, some of it not so much. Regardless, I am capturing all of it in my InfluxDB database with Python. This allows me to chart it in Grafana, which I use for basic monitoring stuff around the house. This post will document getting data from the MPP inverter to Grafana.

Jumping ahead

Final product first. This is a screenshot showing my complete (work-in-progress) Grafana dashboard for my DIY hybrid solar setup.

screenshot showing Grafana dashboard which contains various charts and graphs of MPP solar inverter voltages and datat
Solar dashboard as seen in Grafana pulling data from InfluxDB

The screenshot shows the last 6 hours worth of data from my system. It was a cloudy and then stormy afternoon/evening here in Denver (we got 0.89 inches of rain since midnight as of typing this post!), so the solar production wasn’t great. The panels are as follows:

  • Temperatures – showing temperatures from 3 sources: the MPP LV2424 inverter heat sink, and both BMS temperature probes (one is on the BMS board itself, the other is a probe on the battery bank). The inverter has at least 2 temperature readings, maybe 3. They all basically show the same thing.
  • Solar input – shows solar voltage as seen by the solar charge controller as well as solar power (in watts) going through the charge controller.
  • Cell voltages – the voltage reading of each of my 8 battery bank cells as reported by the BMS. The green graph also shows the delta between max and min cells. They are still pretty balanced in the flat part of the discharge curve.
  • Total power – a mashup of what the inverter is putting out, what the solar is putting in, the difference between the two, and what the BMS is reporting. I’m still trying to figure out all the nuances here. There is definitely a discrepancy between what the inverter is putting out and what the solar is putting in when the batteries are fully charged. I believe the difference is the power required to keep the transformer energized (typically ranges from 30-60W).
  • DC voltages – as reported by the inverter and the BMS. The inverter is accurate to 0.1V, the BMS goes to 0.01V.
  • AC voltages – shows the input voltage from the grid and the output voltage from the inverter. These will match unless the inverter is disconnected from the grid.
  • Data table – miscellaneous information from the inverter that isn’t graphable
  • Output load – how much output I’m using compared to the inverter’s limit
  • Total Generation – how much total energy has been captured by the inverter/solar panels. This is limited because I’m not back feeding the grid.

Getting data out of the MPP Solar LV2424 inverter with Python to MQTT

I am using two cables to plug the inverter into a computer. The first is the serial cable that came with the inverter. The second is a simple USB to RS232 serial adapter plugged into a Dell Micro 3070.

The computer is running Proxmox, which is a virtual machine hypervisor. That doesn’t matter for this post, we can ignore it. Just pretend the USB cable is plugged directly into a computer running Ubuntu 20.04 Linux.

The main bit of software I’m using is published on GitHub by jblance under the name ‘mpp-solar’ – This is a utility written in Python to communicate with MPP inverters.

There was a good bit of fun trying to figure out exactly what command I needed to run to get data out of the inverter as evidenced by the history command:

Trying to figure out the usage of the Python mpp-solar utility

In the end, what worked for me to get the data is the following. I believe the protocol (-P) will be different for different MPP Solar inverters:

sudo mpp-solar -p /dev/ttyUSB0 -b 2400 -P PI18 --getstatus

And the results are below. The grid voltage reported is a bit low because the battery started charging from the grid a few minutes before this was run.

[email protected]:~/mpp-solar-python$ sudo mpp-solar -p /dev/ttyUSB0 -b 2400 -P PI18 --getstatus
Command: ET - Total Generated Energy query
Parameter                       Value           Unit
working_mode                    Hybrid mode(Line mode, Grid mode)
grid_voltage                    111.2           V
grid_frequency                  59.9            Hz
ac_output_voltage               111.2           V
ac_output_frequency             59.9            Hz
ac_output_apparent_power        155             VA
ac_output_active_power          139             W
output_load_percent             5               %
battery_voltage                 27.1            V
battery_voltage_from_scc        0.0             V
battery_voltage_from_scc2       0.0             V
battery_discharge_current       0               A
battery_charging_current        19              A
battery_capacity                76              %
inverter_heat_sink_temperature  30              °C
mppt1_charger_temperature       0               °C
mppt2_charger_temperature       0               °C
pv1_input_power                 0               W
pv2_input_power                 0               W
pv1_input_voltage               0.0             V
pv2_input_voltage               0.0             V
setting_value_configuration_state       Something changed
mppt1_charger_status            abnormal
mppt2_charger_status            abnormal
load_connection                 connect
battery_power_direction         charge
dc/ac_power_direction           AC-DC
line_power_direction            input
local_parallel_id               0
total_generated_energy          91190           Wh

And to get the same data right into MQTT I am using the following:

sudo mpp-solar -p /dev/ttyUSB0 -P PI18 --getstatus -o mqtt -q mqtt --mqtttopic mpp

The above command is being run as a cron job once a minute. The default baud rate for the inverter is 2400 bps (yes, bits per second), which is super slow so a full poll takes ~6 seconds. Kind of annoying in 2021 but not a huge problem. The cron entry for the command is this:

# this program feeds a systemd service to convert the outputted mqtt to influx points
* * * * * /usr/local/bin/mpp-solar -p /dev/ttyUSB0 -P PI18 --getstatus -o mqtt -q mqtt --mqtttopic mpp

So with that we have MPP inverter data going to MQTT.

Putting MPP data into InfluxDB from MQTT

Here we need another script written in… take a guess… Python! This Python basically just opens a connection to a MQTT broker and transmits any updates to InfluxDB. The full script is a bit more complicated and I actually stripped a lot out because my MQTT topic names didn’t fit the template the original author used. I have started using this framework in other places to do the MQTT to InfluxDB translation. I like things going to the intermediate MQTT so they can be picked up for easy viewing in Home Assistant. Original code from The original code seems like it was built to be more robust than what I’m using it for (read: I have no idea what half of it does) but it worked for my purposes.

You’ll need a simple text file with your InfluxDB password and then reference it in the arguments. If your password is ‘password’, the only contents of the file should be ‘password’. I added the isFloat() function to basically make sure that strings weren’t getting added to the numeric tables in InfluxDB. I honestly find the structure/layout of storing stuff in Influx quite confusing so I’m sure there’s a better way to do this.

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# originally found at/modified from

# - forwards IoT sensor data from MQTT to InfluxDB
# Copyright (C) 2016 Michael Haas <[email protected]>
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software Foundation,
# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301  USA

import argparse
import paho.mqtt.client as mqtt
from influxdb import InfluxDBClient
import json
import re
import logging
import sys
import requests.exceptions

class MessageStore(object):

        def store_msg(self, node_name, measurement_name, value):
                raise NotImplementedError()

class InfluxStore(MessageStore):

        logger = logging.getLogger("forwarder.InfluxStore")

        def __init__(self, host, port, username, password_file, database):
                password = open(password_file).read().strip()
                self.influx_client = InfluxDBClient(
                        host=host, port=port, username=username, password=password, database=database)

        def store_msg(self, database, sensor, value):
                influx_msg = {
                        'measurement': database,
                        'tags': {'sensor': sensor},
                        'fields': {'value' : value}
                self.logger.debug("Writing InfluxDB point: %s", influx_msg)
                except requests.exceptions.ConnectionError as e:

class MessageSource(object):

        def register_store(self, store):
                if not hasattr(self, '_stores'):
                        self._stores = []

        def stores(self):
                # return copy
                return list(self._stores)

def isFloat(str_val):
    return True
  except ValueError:
    return False

def convertToFloat(str_val):
    if isFloat(str_val):
        fl_result = float(str_val)
        return fl_result
        return str_val

class MQTTSource(MessageSource):

        logger = logging.getLogger("forwarder.MQTTSource")

        def __init__(self, host, port, node_names, stringify_values_for_measurements):
       = host
                self.port = port
                self.node_names = node_names
                self.stringify = stringify_values_for_measurements

        def _setup_handlers(self):
                self.client = mqtt.Client()

                def on_connect(client, userdata, flags, rc):
              "Connected with result code  %s", rc)
                        # subscribe to /node_name/wildcard
                        #for node_name in self.node_names:
                        # topic = "{node_name}/#".format(node_name=node_name)
                        topic = "get_status/status/#"
              "Subscribing to topic %s", topic)

                def on_message(client, userdata, msg):
                        self.logger.debug("Received MQTT message for topic %s with payload %s", msg.topic, msg.payload)
                        list_of_topics = msg.topic.split('/')
                        measurement = list_of_topics[2]
                        if list_of_topics[len(list_of_topics)-1] == 'unit':
                                value = None
                                value = msg.payload
                                decoded_value = value.decode('UTF-8')
                                if isFloat(decoded_value):
                                        str_value = convertToFloat(decoded_value)
                                        for store in self.stores:
                                        for store in self.stores:

                self.client.on_connect = on_connect
                self.client.on_message = on_message

        def start(self):
                print(f"starting mqtt on host: {} and port: {self.port}")
                self.client.connect(, self.port)
                # Blocking call that processes network traffic, dispatches callbacks and
                # handles reconnecting.
                # Other loop*() functions are available that give a threaded interface and a
                # manual interface.

def main():
        parser = argparse.ArgumentParser(
                description='MQTT to InfluxDB bridge for IOT data.')
        parser.add_argument('--mqtt-host', default="mqtt", help='MQTT host')
        parser.add_argument('--mqtt-port', default=1883, help='MQTT port')
        parser.add_argument('--influx-host', default="dashboard", help='InfluxDB host')
        parser.add_argument('--influx-port', default=8086, help='InfluxDB port')
        parser.add_argument('--influx-user', default="power", help='InfluxDB username')
        parser.add_argument('--influx-pass', default="<I have a password here, unclear if the pass-file takes precedence>", help='InfluxDB password')
        parser.add_argument('--influx-pass-file', default="/home/austin/mpp-solar-python/pass.file", help='InfluxDB password file')
        parser.add_argument('--influx-db', default="power", help='InfluxDB database')
        parser.add_argument('--node-name', default='get_status', help='Sensor node name', action="append")
        parser.add_argument('--stringify-values-for-measurements', required=False,      help='Force str() on measurements of the given name', action="append")
        parser.add_argument('--verbose', help='Enable verbose output to stdout', default=False, action='store_true')
        args = parser.parse_args()

        if args.verbose:
                logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
                logging.basicConfig(stream=sys.stdout, level=logging.WARNING)

        print("creating influxstore")
        store = InfluxStore(host=args.influx_host, port=args.influx_port, username=args.influx_user, password_file=args.influx_pass_file, database=args.influx_db)
        print("creating mqttsource")
        source = MQTTSource(host=args.mqtt_host,
                                                port=args.mqtt_port, node_names=args.node_name,
        print("registering store")

if __name__ == '__main__':

Running the MQTT to InfluxDB script as a system daemon

Next up, we need to run the MQTT to InfluxDB Python script as a daemon so it starts with the machine and runs in the background. If you haven’t noticed by now, this is the standard pattern for most of the stuff I do – either a cron job or daemon to get data and another daemon to put it where I want it. Sometimes they’re the same.

[email protected]:~$ cat /etc/systemd/system/mpp-solar.service
Description=MPP inverter data - MQTT to influx

# data feeds this script from a root cronjob running every 60s
ExecStart=/usr/bin/python3 /home/austin/mpp-solar-python/


Then activate it:

[email protected]:~$ sudo systemctl daemon-reload
[email protected]:~$ sudo systemctl enable mpp-solar.service
[email protected]:~$ sudo systemctl start mpp-solar.service
[email protected]:~$ sudo systemctl status mpp-solar.service
● mpp-solar.service - MPP inverter data - MQTT to influx
     Loaded: loaded (/etc/systemd/system/mpp-solar.service; enabled; vendor preset: enabled)
     Active: active (running) since Sat 2021-07-31 02:21:32 UTC; 1 day 13h ago
   Main PID: 462825 (python3)
      Tasks: 1 (limit: 1072)
     Memory: 19.2M
     CGroup: /system.slice/mpp-solar.service
             └─462825 /usr/bin/python3 /home/austin/mpp-solar-python/

Jul 31 02:21:32 mpp-linux systemd[1]: Started MPP inverter data - MQTT to influx.

All done

With that, you should now have data flowing into your InfluxDB instance from your MPP inverter via this Python script. This is exactly what I’m using for my LV2424 but it should work with others like the PIP LV2424 MSD, PIP-4048MS, IPS stuff, LV5048, and probably a lot of others.

Next Steps

Next post will cover designing the Grafana dashboard to show this data.