Categories
AI

Stable Diffusion Tutorial – Nvidia GPU Installation

Like most other internet-connected people, I have seen the increase in AI-generated content in recent months. ChatGPT is fun to use and I’m sure there are plenty of useful use cases for it but I’m not sure I have the imagination required to use it to it’s full potential. The AI art fad of a couple months ago was cool too. In the back of my mind, I kept thinking “where will AI take us in the next couple years”. I still don’t know the answer to that. The only “art” I am good at is pottery (thanks to high-school pottery class – I took 4 semesters of it and had a great time doing so, whole different story). But now I’m able to generate my own AI art thanks to a guide I found the other day on /g/. I am re-writing it here with screenshots and a bit more detail to try and make it more accessible to general users.

NOTE: You need a decent/recent Nvidia GPU to follow this guide. I have a RTX 2080 Super with 8GB of VRAM. There are low-memory workarounds but I haven’t tested them yet. An absolute limit is 2GB VRAM, and a GTX 7xx (Maxwell architecture) or newer GPU.

Stable Diffusion Tutorial Contents

  1. Installing Python 3.10
  2. Installing Git (the source control system)
  3. Clone the Automatic1111 web UI (this is the front-end for using the various models)
  4. Download models
  5. Adjust memory limits & enable listening outside of localhost
  6. First run
  7. Launching the web UI
  8. Generating Stable Diffusion images

Video version of this install guide

Coming soon. I always do the written guide first, then record based off the written guide. Hopefully by end of day (mountain time) Feb 24.

1 – Installing Python 3.10

This is relatively straight-forward. To check your Python version, go to a command line and enter

python --version

If you already have Python 3.10.x installed (as seen in the screenshot below), you’re good to go (minor version doesn’t matter).

Python 3.10 installed for Stable Diffusion

If not, go to the Python 3 download page and select the most recent 3.10 version. As of writing, the most recent is 3.10.10. Download the x64 installer and install. Ensure the “add python.exe to PATH” checkbox is checked. Adding python.exe to PATH means it can be called with only python at a command prompt instead of the full path, which is something like c:/users/whatever/somedirectory/moredirectories/3.10.10/python.exe.

Installing python and adding python.exe to PATH

2 – Installing Git (the source control system)

This is easier than Python – just install it – https://git-scm.com/downloads. Check for presence and version with git –version:

git installed and ready to go for Stable Diffusion

3 – Clone the Automatic1111 web UI (this is the front-end for using the various models)

With Git, clone means to download a copy of the code repository. When you clone a repo, a new directory is created in whatever directory the command is run in. Meaning that if you navigate to your desktop, and run git clone xyz, you will have a new folder on your desktop named xyz with the contents of the repository. To keep things simple, I am going to create a folder for all my Stable Diffusion stuff in the C:/ root named sd and then clone into that folder.

Open a command prompt and enter

cd c:\

Next create the sd folder and enter it:

mkdir sd
cd sd

Now clone the repository while in your sd folder:

git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui

After the git clone completes, there will be a new directory called ‘stable-diffusion-webui’:

stable-diffusion-webui cloned and ready to download models

4 – Download models

“Models” are what actually generate the content based on provided prompts. Generally, you will want to use pre-trained models. Luckily, there are many ready to use. Training your own model is far beyond the scope of this basic installation tutorial. Training your own models generally also requires huge amounts of time crunching numbers on very powerful GPUs.

As of writing, Stable Diffusion 1.5 (SD 1.5) is the recommended model. It can be downloaded (note: this is a 7.5GB file) from huggingface here.

Take the downloaded file, and place it in the stable-diffusion-webui/models/Stable-diffusion directory and rename it to model.ckpt (it can be named anything you want but the web UI automatically attempts to load a model named ‘model.ckpt’ upon start). If you’re following along with the same directory structure as me, this file will end up at C:\sd\stable-diffusion-webui\models\Stable-diffusion\model.ckpt.

Another popular model is Deliberate. It can be downloaded (4.2GB) here. Put it in the same folder as the other model. No need to rename the 2nd (and other) models.

After downloading both models, the directory should look like this:

Stable Diffusion 1.5 (SD 1.5) and Deliberate_v11 models ready for use

5 – Adjust memory limits & enable listening outside of localhost (command line arguments)

Inside the main stable-diffusion-webui directory live a number of launcher files and helper files. Find webui-user.bat and edit it (.bat files can be right-clicked -> edit).

Add –medvram (two dashes) after the equals sign of COMMANDLINE_ARGS. If you also want the UI to listen on all IP addresses instead of just localhost (don’t do this unless you know what that means), also add –listen.

webui-user.bat after edits

@echo off

set PYTHON=
set GIT=
set VENV_DIR=
set COMMANDLINE_ARGS=--listen --medvram

call webui.bat

6 – First run

The UI tool (developed by automatic1111) will automatically download a variety of requirements upon first launch. It will take a few minutes to complete. Double-click the webui-user.bat file we just edited. It calls a few .bat files and eventually launches a Python file. The .bat files are essentially glue to stick a bunch of stuff together for the main file.

The very first thing it does is creates a Python venv (virtual environment) to keep the Stable Diffusion packages separate from your other Python packages. Then it pip installs a bunch of packages related to cuda/pytorch/numpy/etc so Python can interact with your GPU.

webui-user.bat using pip to install necessary python packages like cuda

After everything is installed and ready to go, you will see a line that says: Running on local URL: http://127.0.0.1:7860. That means the Python web server UI is running on your own computer on port 7860 (if you added –listen to the launch args, it will show 0.0.0.0:7860, which means it is listening on all IP addresses and can be accessed by external machinse).

stable-diffusion-webui launched and ready to load

7 – Launching the web UI

With the web UI server running, it can be accessed via browser on the same computer running the Python at http://127.0.0.1:7860. That link should work for you if you click it.

Note that if the Python process closes for whatever reason (you close the command window, your computer reboots, etc), you need to double-click webui-user.bat to relaunch it and it needs to be running any time you want to access the web UI.

Automatic1111 stable diffusion web UI up and running

As seen in the screenshot, there are a ton of parameters/settings. I’ll highlight a few in the next section

8 – Generating Stable Diffusion images

This is the tricky part. The prompts make or break your generation. I am still learning. The prompt is where you enter what you want to see. Negative prompt is where you enter what you don’t want to see.

Let’s start simple, with cat in the prompt. Then click generate. A very reasonable-looking cat should soon appear (typically takes a couple seconds per image):

AI-generated cat with stable diffusion 1.5 with default settings

To highlight a few of the settings/sliders:

  • Stable diffusion checkpoint – model selector. Note that it’ll take a bit to load a new model (the multi-GB files need to be read in their entirety and ingested).
  • Prompt – what you want to see
  • Negative prompt – what you don’t want to see
  • Sampling method – various methods to sample new points
  • Sampling steps – how many iterations to use for image generation for a single image
  • Width – width of image to generate (in pixels). NOTE, you need a very powerful GPU with a ton of VRAM to go much higher than the default 512
  • Height – height of image to generate (in pixels). Same warning applies as width
  • Batch count – how many images to include in a batch generation
  • Batch size – haven’t used yet, presumably used to specify how many batches to generate
  • CFG Scale – this slider tells the models how specific they need to be for the prompt. Higher is more specific. Really high values (>12ish) start to get a bit abstract. Probably want to be in the range of 3-10 for this slider.
  • Seed – random number generator seed. -1 means use a new seed for every image.

Some thoughts on prompt/negative prompt

From my ~24 hours using this tool, it is very clear that prompt/negative prompts are what make or break your generation. I think that your ability as a pre-AI artist would come in handy here. I am no artist so I have a hard time putting what I want to see into words. Take example prompt: valley, fairytale treehouse village covered, matte painting, highly detailed, dynamic lighting, cinematic, realism, realistic, photo real, sunset, detailed, high contrast, denoised, centered. I would’ve said “fairytale treehouse” and stopped at that. Compare the two prompts below with the more detailed prompt directly below and the basic “fairytale treehouse” prompt after that:

AI-generated “fairytale treehouse” via stable diffusion. Prompt: valley, fairytale treehouse village covered, matte painting, highly detailed, dynamic lighting, cinematic, realism, realistic, photo real, sunset, detailed, high contrast, denoised, centered
AI-generated “fairytale treehouse” via stable diffusion. Prompt: fairytale treehouse

One of these looks perfectly in place for a fantasy story. The other you could very possibly see in person in a nearby forest.

Both positive and negative can get very long very quickly. Many of the AI-generated artifacts present over the last month or two can be eliminated with negative prompt entries.

Example negative prompt: ugly, deformed, malformed, lowres, mutant, mutated, disfigured, compressed, noise, artifacts, dithering, simple, watermark, text, font, signage, collage, pixel

I will not pretend to know what works well vs not. Google is your friend here. I believe that “prompt engineering” will be very important in AI’s future. Google is your friend here.

Conclusion

AI-generated content is here. It will not be going away. Even if it is outlawed, the code is out there. AI will be a huge part of our future, regardless of if you want it or not. As the saying goes – pandora’s box is now open.

I figured it was worth trying. The guide this is based off made it relatively easy for me (but I do have above-average computer skill), and I wanted to make it even easier. Hopefully you found this ‘how to set up stable diffusion’ guide easy to use as well. Please let me know in the comments section if you have any questions/comments/feedback – I check at least daily!

Resources

Huge shout out to whoever wrote the guide (“all anons”) at https://rentry.org/voldy. That is essentially where this entire guide came from.

Categories
Home Assistant Home Automation Python

Ultra efficient “air conditioner” (fans controlled with Home Assistant and Python) using cold outside air

Just getting this up as a draft now for a Reddit user.

In short, this Python script reads the temperatures of two different sensors (outside from an The Ambient Weather WS-2902C weather station and in our master bedroom with a Govee Bluetooth Thermometer), the temperature set point for a generic thermostat entity, does some logic, and turns a switch on or off, all with the Home Assistant API. The switch control two basic box fans that are set to blow air into our bedroom from outside. It runs every X minutes (currently set to 5). This method works great if nighttime temperatures drop below 70F before bedtime. We like the bedroom temp at 66F, so unless it gets below 70F by around 9PM, it probably won’t cool enough for us to be comfortable enough to fall asleep. My wife wakes up at 5:45am, me at 6:30am, pending what our 22 month old daughter thinks of that schedule, so we typically aim to be asleep by 10pm.

Today, 2022-05-14, was the day I got out the window AC. It will be in place for the rest of the summer.

Requirements:

  • A working Home Assistant installation (mine is Python venv install in a Ubuntu VM)
  • A bearer token authorization code for a/your Home Assistant user
  • A working MQTT installation
  • A switch controllable by Home Assistant
  • 1-2 box fans plugged into said switch controlled by Home Assistant

Here is what the Home Assistant control screen looks like. The buttons should be self explanatory. The generic thermostat entity doesn’t need to be on/active for this to work. It uses the set point for control purposes (set to 67.0F in the screenshot).

Home Assistant control screen for ultra efficient air conditioner system

I believe there is currently a logic bug with max cool not respecting the delta_temp variable. Other than that it works perfect. Below is a screenshot of the last 7 days showing the room cooling off nicely to the setpoint of 66F on nights 1-3 and 67F on nights 4-7. Switching a control device on and off every so often is a version of a bang-bang controller. If you look closely, you will notice that each cycle on and off results in a greater temperature drop, which is due to the colder outside air being blown in for the same duration regardless of delta T.

Master bedroom temperature with Home Assistant controlled fans blowing in cold air from outside. Setpoint was 66F for evening of 5/7-5/9 (first 3 nights) and 67F for the rest (next 4 nights).
Zoomed in view of the evening of 5/9 to the morning of 5/10. The temperature drops quickly to the setpoint of 66F and does not go much above. Not sure what the spike is right after 23:00. The outside temp starts at 65F for this same timeframe, dropping to 60 at 21:00 and 55 at 22:00, so a great night to use cold outside air for cooling (thus the “ultra efficient AC”.
import json
import datetime
import time
from dateutil import parser
from requests import get, post
import paho.mqtt.client as mqttClient
import logging

logging.basicConfig(
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.DEBUG)

loggers_to_set_to_warning = ['urllib3.connectionpool']
for l in loggers_to_set_to_warning:
    logging.getLogger(l).setLevel(logging.WARNING)

delta_temp = 3.0
mqtt_host = "mqtt.example.com"
mqtt_port = 1883

base_url = "http://ha.example.come:8123/"
states_url = base_url + "api/states/"
switch_url = base_url + "api/services/switch"
bearer_token = "ey...Vw"
full_bearer_token = "Bearer " + bearer_token
request_headers = {
    "Authorization": full_bearer_token,
    "content-type": "application/json"
}
endpoints = ["climate.masterbedfancooling",
             "sensor.real_outside_temp"]
fan_switch_entity_id = "switch.fan_switch"
states = {}
climate_topic = "climate/fan_control_state"
current_state = "off"
last_state = None
desired_seconds_to_sleep = 300
max_cool_outdoor_temp_limit = 66
desired_temp = None
outside_temp = None
current_temp = None
next_fan_action_time = datetime.datetime.now()


def set_fan_switch_state(state):
    new_fan_state = None
    if state == "on":
        new_fan_state = "on"
    elif state == "off":
        new_fan_state = "off"
    else:
        logging.warn("requested fan state unknown")
        return

    entity_info = {"entity_id": fan_switch_entity_id}
    full_url = switch_url + "/turn_" + new_fan_state
    response = post(full_url, headers=request_headers,
                    data=json.dumps(entity_info))
    if response.status_code != 200:
        logging.error(
            f"attempted to set fan state to {new_fan_state} but encountered error with status code: {response.status_code}")
    else:
        logging.info(f"successfully set fan state to {new_fan_state}")


def set_state_from_mqtt_message(message):
    global current_state, next_fan_action_time
    if message == "max_cool":
        current_state = "max_cool"
    elif message == "normal_cool":
        current_state = "normal_cool"
    elif message == "off":
        current_state = "off"
    elif message == "on":
        current_state = "on"
    else:
        logging.error(f"unable to determine state, setting to off")
        current_state = "off"
    logging.info(f"current_state set to: {current_state}")
    next_fan_action_time = datetime.datetime.now()


def connect_mqtt():
    # Set Connecting Client ID
    client = mqttClient.Client("python_window_fan_control")
    #client.username_pw_set(username, password)
    client.on_connect = on_connect
    client.connect(mqtt_host, mqtt_port)
    return client


def on_connect(client, userdata, flags, rc):
    if rc == 0:
        logging.info("Connected to MQTT Broker!")
    else:
        logging.info("Failed to connect, return code %d\n", rc)


def on_message(client, userdata, msg):
    logging.info(f"Received `{msg.payload.decode()}` from `{msg.topic}` topic")
    set_state_from_mqtt_message(msg.payload.decode())


def on_subscribe(client, userdata, mid, granted_qos):
    logging.info(f"subscribed to topic")


def set_fan_state(state):
    if state == "on":
        logging.info(f"setting fan state to on")
        set_fan_switch_state("on")
    elif state == "off":
        logging.info(f"setting fan state to off")
        set_fan_switch_state("off")


def get_and_set_temperatures():
    global desired_temp, current_temp, outside_temp
    logging.debug("executing loop")

    for endpoint in endpoints:
        full_url = states_url + endpoint
        response = get(full_url, headers=request_headers)
        parsed_json = json.loads(response.text)
        entity = parsed_json['entity_id']
        hvac_action = ""
        if endpoint == 'climate.masterbedfancooling':
            desired_temp = float(
                parsed_json['attributes']['temperature'])
            current_temp = float(
                parsed_json['attributes']['current_temperature'])
            hvac_action = parsed_json['attributes']['hvac_action']
        elif endpoint == 'sensor.real_outside_temp':
            outside_temp = float(parsed_json['state'])
        last_updated = parser.parse(parsed_json['last_updated'])
    logging.info(
        f"temps: current={current_temp}, desired={desired_temp}, outside={outside_temp}")
    if desired_temp == None or current_temp == None or outside_temp == None:
        logging.error(
            "one or more temps invalid, turning off switch and breaking execution")
        set_fan_state("off")


client = mqttClient.Client("window-fan-client")
client.on_connect = on_connect
client.on_message = on_message
client.on_subscribe = on_subscribe
client.connect(mqtt_host, mqtt_port)
client.subscribe(climate_topic)
client.loop_start()

while(True):
    # logging.info("loop")
    if datetime.datetime.now() > next_fan_action_time:
        logging.info("fan action time")
        if current_state == "off":
            new_fan_state = "off"
            logging.info(
                f"current_state is {current_state}, turning fan {new_fan_state}")
            set_fan_state("off")
        else:
            get_and_set_temperatures()

        if current_state == "max_cool":
            logging.info(
                f"current_state is {current_state}, call for max cooling")
            if outside_temp < max_cool_outdoor_temp_limit:
                logging.info(
                    f"able to max_cool with outside temp: {outside_temp}, lower than {max_cool_outdoor_temp_limit} ")
                set_fan_state("on")
            elif outside_temp < (current_temp - delta_temp):
                logging.info(
                    f"unable to max cool, but still can cool with outside: {outside_temp} and inside: {current_temp}")
                set_fan_state("on")
            else:
                logging.info("unable to cool at all, turning fan off")
                set_fan_state("off")
        elif current_state == "normal_cool":
            logging.info(
                f"current_state is {current_state}")
            if (current_temp > desired_temp):
                logging.info(
                    f"call for cooling. current: {current_temp}, desired: {desired_temp}")
                if (current_temp > (outside_temp - delta_temp)):
                    logging.info("can cool, turning fan on")
                    set_fan_state("on")
                else:
                    logging.info("can't cool, turning fan off")
                    set_fan_state("off")
            else:
                logging.info("no need for cooling, turning fans off")
                set_fan_state("off")
        elif current_state == "on":
            new_fan_state = "on"
            logging.info(
                f"current_state is {current_state}, turning fan {new_fan_state}")
            set_fan_state("on")

        last_state = current_state
        next_fan_action_time = datetime.datetime.now() \
            + datetime.timedelta(seconds=desired_seconds_to_sleep)
        logging.info(f"next fan action in {desired_seconds_to_sleep} seconds")
        logging.info("---------loop end-------------------")
    else:
        #logging.debug("not fan action time yet, sleeping 1s")
        time.sleep(0.25)
    # actual_seconds_to_sleep = desired_seconds_to_sleep - datetime.datetime.now().minute % desired_seconds_to_sleep
    # seconds_to_sleep = actual_minutes_to_sleep * 60.0
    # logging.info(f"sleeping {desired_seconds_to_sleep}s")
    # time.sleep(desired_seconds_to_sleep)
Categories
Home Assistant Home Automation

Using the Govee Bluetooth Thermometer with Home Assistant (Python and MQTT)

Introduction

Like many other Home Automation enthusiasts, I have been on the lookout for a cheap thermometer/hygrometer that has either WiFi or Bluetooth connectivity. I think I found the answer in the $12 USD Govee Bluetooth Digital Thermometer and Hygrometer. The fact that the device broadcasts the current temperature and humidity every 2 seconds via low energy Bluetooth (BLE) is the metaphorical icing on the cake. Here is a pic of the unit in our garage:

Govee bluetooth thermometer and hygrometer showing 56*F/24% in a garage

Specifications

This is a pretty basic device. It has a screen that shows the current temperature, humidity, and min/max values. It sends the current readings (along with battery health) every 2 seconds via low-energy bluetooth (BLE). The description on Amazon says it has a “Swiss-made smart hygrometer sensor”. Dunno if I believe that but for $12 it is good enough. The temperature is accurate to +/- 0.54F and humidity is +/- 3% RH. If you use the app, it is apparently possible to read the last 20 days or 2 years of data from the device (I haven’t used the app at all).

Enough with the boring stuff. Let’s get it connected to Home Assistant.

Reading the Govee bluetooth advertisements with Python

I found some sample code on tchen’s GitHub page (link) to help get me going in the right direction.

Without further ado, here is the code I’m using to read the data and publish via MQTT:

observe.py (updated 2022-01-04 with some logging improvements. default logging level is now WARNING, which disables printing every advertisement)

# basic govee bluetooth data reading code for model https://amzn.to/3z14BIi
# written/modified by Austin of austinsnerdythings.com 2021-12-27
# original source: https://gist.github.com/tchen/65d6b29a20dd1ef01b210538143c0bf4
import logging
import json
from time import sleep
from basic_mqtt import basic_mqtt
from bleson import get_provider, Observer

logging.basicConfig(
	format='%(asctime)s.%(msecs)03d - %(name)s - %(levelname)s - %(message)s',
	datefmt='%Y-%m-%d %H:%M:%S',
	level=logging.WARNING)

# I did write all the mqtt stuff. I kept it in a separate class
mqtt = basic_mqtt()
mqtt.connect()

# writing code on my windows computer, committing, pushing, and then 
# pulling the new code on the Raspberry Pi is a tedious "debug" process.
# there are quite a few errors that spit out from bleson, which as far
# as I can tell, isn't super polished. if we set the debug level to
# critical, most of those disappear.
logging.getLogger("bleson").setLevel(logging.CRITICAL)

# basic celsius to fahrenheit function
def c2f(val):
    return round(32 + 9*val/5, 2)

last = {}

# I didn't write this, but it takes the raw BT data and spits out the data of interest
def temp_hum(values, battery, address):
    global last
    #########################
    #
    # there is a fix for temperatures below freezing here - 
    # https://github.com/joshgordon/govee_ble_to_mqtt/blob/master/observe.py
    # I will adjust post code afternoon of 2022-11-28
    #
    #########################
    values = int.from_bytes(values, 'big')
    if address not in last or last[address] != values:
        last[address] = values
        temp = float(values / 10000)
        hum = float((values % 1000) / 10)
        # this print looks like this:
        # 2021-12-27T11:22:17.040469 BDAddress('A4:C1:38:9F:1B:A9') Temp: 45.91 F  Humidity: 25.8 %  Battery: 100 %
        logging.info(f"decoded values: {address} Temp: {c2f(temp)} F  Humidity: {hum} %  Battery: {battery}")
        # this code originally just printed the data, but we need it to publish to mqtt.
        # added the return values to be used elsewhere
        return c2f(temp), hum, battery

def on_advertisement(advertisement):
    #print(advertisement)
    mfg_data = advertisement.mfg_data

    # there are lots of BLE advertisements flying around. only look at ones that have mfg_data
    if mfg_data is not None:
        #print(advertisement)
        # there are a few Govee models and the data is in different positions depending on which
        # the unit of interest isn't either of these hardcoded values, so they are skipped
        if advertisement.name == 'GVH5177_9835':
            address = advertisement.address
            temp_hum(mfg_data[4:7], mfg_data[7], address)
        elif advertisement.name == 'GVH5075_391D':
            address = advertisement.address
            temp_hum(mfg_data[3:6], mfg_data[6], address)
        elif advertisement.name != None:
            # this is where all of the advertisements for our unit of interest will be processed
            address = advertisement.address
            if 'GVH' in advertisement.name:
                #print(advertisement)
                temp_f, hum, battery = temp_hum(mfg_data[3:6], mfg_data[6], address)

                if temp_f > 180.0 or temp_f < -30.0:
                    return
                # as far as I can tell bleson doesn't have a string representation of the MAC address
                # address is of type BDAddress(). str(address) is BDAddress('A4:C1:38:9F:1B:A9')
                # substring 11:-2 is just the MAC address
                mac_addr = str(address)[11:-2]

                # construct dict with relevant info
                msg = {'temp_f':temp_f,
                        'hum':hum,
                        'batt':battery}
                
                # looks like this:
                # msg data: {'temp_f': 45.73, 'hum': 25.5, 'batt': 100}
                logging.info(f"MQTT msg data: {msg}")

                # turn into JSON for publishing
                json_string = json.dumps(msg, indent=4)

                # publish to topic separated by MAC address
                mqtt.publish(f"govee/{mac_addr}", json_string)

# base stuff from the original gist
logging.warning(f"initializing bluetooth")
adapter = get_provider().get_adapter()
observer = Observer(adapter)
observer.on_advertising_data = on_advertisement

logging.warning(f"starting observer")
observer.start()
logging.warning(f"listening for events and publishing to MQTT")
while True:
    # unsure about this loop and how much of a delay works
    sleep(1)
observer.stop()

And for the MQTT helper class (mqtt_helper.py):

# basic MQTT helper class. really needed to write one of these to simplify basic MQTT operations
# written/modified by Austin of austinsnerdythings.com 2021-12-27
# original source: https://gist.github.com/fisherds/f302b253cf7a11c2a0d814acd424b9bb
# filename is basic_mqtt.py
from paho.mqtt import client as mqtt_client
import logging
import datetime
logging.basicConfig(
	format='%(asctime)s.%(msecs)03d - %(name)s - %(levelname)s - %(message)s',
	datefmt='%Y-%m-%d %H:%M:%S',
	level=logging.INFO)


mqtt_host = "mqtt.home.fluffnet.net"
test_topic = "mqtt_test_topic"

# this is really not polished. it was a stream of consciousness project to pound
# something out to do basic MQTT publish stuff in a reusable fashion.
class basic_mqtt:
	def __init__(self):
		self.client = mqtt_client.Client()
		self.subscription_topic_name = None
		self.publish_topic_name = None
		self.callback = None
		self.host = mqtt_host
	
	def connect(self):
		self.client.on_connect = self.on_connect
		self.client.on_subscribe = self.on_subscribe
		self.client.on_message = self.on_message
		logging.info(f"connecting to MQTT broker at {mqtt_host}")
		self.client.connect(host=mqtt_host,keepalive=30)
		self.client.loop_start()

	def on_connect(self, client, userdata, flags, rc):
		print("Connected with result code "+str(rc))

		# Subscribing in on_connect() means that if we lose the connection and
		# reconnect then subscriptions will be renewed.
		#self.client.subscribe("$SYS/#")

	def on_message(self, client, userdata, msg):
		print(f"got message of topic {msg.topic} with payload {msg.payload}")

	def on_subscribe(self, client, userdata, mid, granted_qos):
		print("Subscribed: " + str(mid) + " " + str(granted_qos))

	def publish(self, topic, msg):
		self.client.publish(topic=topic, payload=msg)

	def subscribe_to_test_topic(self, topic=test_topic):
		self.client.subscribe(topic)

	def send_test_message(self, topic=test_topic):

		self.publish(topic=topic, msg=f"test message from python script at {datetime.datetime.now()}")

	def disconnect(self):
		self.client.disconnect()

	def loop(self):
		self.client.loop_forever()

if __name__ == "__main__":
	logging.info("running MQTT test")
	mqtt_helper = basic_mqtt()
	mqtt_helper.connect()
	mqtt_helper.subscribe_to_test_topic()
	mqtt_helper.send_test_message()
	mqtt_helper.disconnect()

Results

Running this script on a Raspberry Pi 3 shows the advertisements coming in as expected. The updates are very quick compared to the usual 16 second update interval for my Acurite stuff.

screenshot of Python code running to receive BLE advertisements from Govee Bluetooth Thermometer
screenshot of Python code running to receive BLE advertisements from Govee Bluetooth Thermometer

And running mosquitto_sub with the right arguments (mosquitto_sub -h mqtt -v -t “govee/#”) shows the MQTT messages are being published as expected:

mosquitto_sub showing our published MQTT messages with temperature/humidity data from the Govee sensor

Getting Govee MQTT data into Home Assistant

Lastly, we need to add a MQTT sensor to get the data importing into Home Assistant:

- platform: mqtt
  state_topic: "govee/A4:C1:38:9F:1B:A9"
  value_template: "{{ value_json.temp_f }}"
  name: "garage temp"
  unit_of_measurement: "F"

And from there you can do whatever you want with the collected data!

Home Assistant displaying data from Govee Bluetooth temperature and humidity sensor

Conclusion

This was a relatively quick post and code development. I really hate the cycle of developing on my Bluetooth-less Windows computer, committing the code, pushing to Git, pulling on the Pi, and running to “debug”. Thus, the code isn’t as good as it can be. I probably did 20-25 iterations before calling it good enough.

Regardless, I think this $12 Govee Bluetooth Thermometer and Hygrometer is a great little tool for collecting data around the house. You don’t need an SDR to get Acurite beacons, and you don’t need to spend a lot either. You just need a way to receive BLE advertisements (basically any Bluetooth-capable device can do this). There is even a 2 pack of just the sensors that I just discovered on Amazon for $24 – 2 pack of Govee bluetooth thermometer and hygrometer. I know there are other Bluetooth devices but they’re quite a bit more expensive.

Disclosure: Some of the links on this post are Amazon affiliate links. This means that, at zero cost to you, I will earn an affiliate commission if you click through the link and finalize a purchase.

Also, at least for me, these are available with same day shipping on Amazon. Scratch that instant gratification itch.

Same day shipping available on Amazon near Denver for the Govee sensors
Categories
Python XPlane

Coding a pitch/roll/altitude autopilot in X-Plane with Python

Introduction

Sorry it has taken me so long to write this post! The last post on the blog was October 19th – almost 6 weeks ago. Life happens. We have a 15 month old running around and she is a handful!

Anyways, back to the next topic – coding a pitch/roll (2 axis) autopilot in X-Plane with Python with altitude and heading hold. Today we will be adding the following:

  • Real-time graphing for 6 parameters
  • Additional method to grab data out of X-Plane
  • A normalize function to limit outputs to reasonable values
  • Altitude preselect and hold function

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

Video Link

coming soon

Contents

  1. Adding PyQtGraph
  2. Developing a normalize function
  3. Initializing the data structures to hold the graph values
  4. Defining the PyQtGraph window and parameters
  5. Getting more data points out of X-Plane
  6. Feeding the graph data structures with data
  7. Adding altitude preselect and hold

1 – Adding PyQtGraph

Pip is my preferred tool to manage Python packages. It is easy and works well. I initially tried graphing with MatPlotLib but it took 200-300ms to update, which really dragged down the control loop to the point of being unusable. Instead, we will be using PyQtGraph. Install it with Pip:

pip install pyqtgraph

2 – Developing a normalize function

This task is pretty straightforward. There are a couple places where we want to pass values that need to be within a certain range. The first example is to the client.sendCTRL() method to set the control surfaces in X-Plane. The documentation states values are expected to be from -1 to 1. I have got some really weird results sending values outside that range (specifically for throttle, if you send something like 4, you can end up with 400% throttle which is wayyy more than the engines can actually output).

# this function takes an input and either passes it through or adjusts
# the input to fit within the specified max/min values
def normalize(value, min=-1, max=1):
	# if value = 700, and max = 20, return 20
	# if value = -200, and min = -20, return -20
	if (value > max):
		return max
	elif (value < min):
		return min
	else:
		return value

3 – Initializing the graphing data structures

We need a couple of arrays to store the data for our graphs. We need (desired data to plot) + 1 arrays. The +1 is the x-axis, which will just store values like 0,1,2,3,etc. The others will be the y-values. We haven’t added the altitude stuff yet, so you can add them but they won’t be used yet.

x_axis_counters = [] #0, 1, 2, 3, etc. just basic x-axis values used for plotting
roll_history = []
pitch_history = []
#altitude_history = []
roll_setpoint_history = []
pitch_setpoint_history = []
#altitude_setpoint_history = []
plot_array_max_length = 100 # how many data points to hold in our arrays and graph
i = 1 # initialize x_axis_counter

4 – Defining the PyQtGraph window and parameters

Working with PyQtGraph more or less means we’ll be working with a full blown GUI (just stripped down).

# first the base app needs to be instantiated
app = pg.mkQApp("python xplane autopilot monitor")

# now the window itself is defined and sized
win = pg.GraphicsLayoutWidget(show=True)
win.resize(1000,600) #pixels
win.setWindowTitle("XPlane autopilot system control")

# we have 3 subplots
p1 = win.addPlot(title="roll",row=0,col=0)
p2 = win.addPlot(title="pitch",row=1,col=0)
p3 = win.addPlot(title="altitude", row=2, col=0)

# show the y grid lines to make it easier to interpret the graphs
p1.showGrid(y=True)
p2.showGrid(y=True)
p3.showGrid(y=True)

5 – Getting more data points out of X-Plane

The initial .getPOSI() method that came in the example has worked well for us so far. But at this point we need more data that isn’t available in the .getPOSI() method. We will be utilizing a different method called .getDREFs() which is short for ‘get data references’. We will need to construct a list of data references we want to retrieve, pass that list to the method, and then parse the output. It is more granular than .getPOSI(). I haven’t evaluated the performance but I don’t think it is a problem.

The DREFs we want are for indicated airspeed, magnetic heading (.getPOSI() has true heading, not magnetic), an indicator to show if we are on the ground or not, and height as understood by the flight model. Thus, we can define our DREFs as follows:

DREFs = ["sim/cockpit2/gauges/indicators/airspeed_kts_pilot",
		"sim/cockpit2/gauges/indicators/heading_electric_deg_mag_pilot",
		"sim/flightmodel/failures/onground_any",
		"sim/flightmodel/misc/h_ind"]

And we can get the data with client.getDREFs(DREFs). The returned object is a 2d array. We need to parse out our values of interest. The full data gathering code looks like this:

posi = client.getPOSI();
ctrl = client.getCTRL();
multi_DREFs = client.getDREFs(DREFs)

current_roll = posi[4]
current_pitch = posi[3]
current_hdg = multi_DREFs[1][0]
current_altitude = multi_DREFs[3][0]
current_asi = multi_DREFs[0][0]
onground = multi_DREFs[2][0]

With those data points, we have everything we need to start plotting the state of our aircraft and monitoring for PID tuning.

6 – Feeding the real-time graphs with data

Next up is actually adding data to be plotted. There are two scenarios to consider when adding data to the arrays: 1) the arrays have not yet reached the limit we set earlier (100 points), and 2) they have. Case 1 is easy. We just append the current values to the arrays:

x_axis_counters.append(i)
roll_history.append(current_roll)
roll_setpoint_history.append(desired_roll)
pitch_history.append(current_pitch)
pitch_setpoint_history.append(pitch_PID.SetPoint)
altitude_history.append(current_altitude)
altitude_setpoint_history.append(desired_altitude)

The above code will work perfectly fine if you want the arrays to grow infinitely large over time. Ain’t nobody got time for that so we need to check how long the arrays are and delete data. We’ll check the length of the x-axis array as a proxy for all the others and use that to determine what to do. Typing this code that looks very similar over and over again means it’s probably time to abstract it into classes or something else. The more you type something over and over again, the larger indication you have that you need to so something about it. But for now we’ll leave it like this for ease of reading and comprehension.

# if we reach our data limit set point, evict old data and add new.
# this helps keep the graph clean and prevents it from growing infinitely
if(len(x_axis_counters) > plot_array_max_length):
	x_axis_counters.pop(0)
	roll_history.pop(0)
	roll_setpoint_history.pop(0)
	pitch_history.pop(0)
	pitch_setpoint_history.pop(0)
	altitude_history.pop(0)
	altitude_setpoint_history.pop(0)

	x_axis_counters.append(i)
	roll_history.append(current_roll)
	roll_setpoint_history.append(desired_roll)
	pitch_history.append(current_pitch)
	pitch_setpoint_history.append(pitch_PID.SetPoint)
	altitude_history.append(current_altitude)
	altitude_setpoint_history.append(desired_altitude)
# else, just add new. we are not yet at limit.
else:
	x_axis_counters.append(i)
	roll_history.append(current_roll)
	roll_setpoint_history.append(desired_roll)
	pitch_history.append(current_pitch)
	pitch_setpoint_history.append(pitch_PID.SetPoint)
	altitude_history.append(current_altitude)
	altitude_setpoint_history.append(desired_altitude)
i = i + 1

You will notice that there are quite a few entries for altitude. We haven’t done anything with that yet so just set desired_altitude to an integer somewhere in the code so it doesn’t error out.

To complete the graphing portion, we need to actually plot the data. The clear=true in the below lines clears out the plot so we’re not replotting on top of the old data. We also need to process events to actually draw the graph:

# process events means draw the graphs
pg.QtGui.QApplication.processEvents()

# arguments are x values, y values, options
# pen is a different line in the plot
p1.plot(x_axis_counters, roll_history, pen=0, clear=True)
p1.plot(x_axis_counters, roll_setpoint_history, pen=1)

p2.plot(x_axis_counters, pitch_history, pen=0,clear=True)
p2.plot(x_axis_counters, pitch_setpoint_history, pen=1)

p3.plot(x_axis_counters, altitude_history, pen=0,clear=True)
p3.plot(x_axis_counters, altitude_setpoint_history, pen=1)

You can now run the code to see your graph populating with data!

PyQtGraph plotting the aircraft’s roll/pitch and desired roll/pitch

7 – Adding altitude autopilot (preselect and hold)

Ok so now that we have eye candy with the real-time graphs, we can make our autopilot do something useful: go to a selected altitude and hold it.

We already have the roll and pitch PIDs functioning as desired. How do we couple the pitch PID to get to the desired altitude? One cannot directly control altitude. Altitude is controlled via a combination of pitch and airspeed (and time).

We will call the coupled PIDs an inner loop (pitch) and an outer loop (altitude). The outer loop runs and its output will feed the input of the inner loop. The altitude PID will be fed a desired altitude and current altitude. The output will then mostly be the error (desired altitude – current altitude) multiplied by our P setting. Of course I and D will have a say in the output but by and large it will be some proportion of the error.

Let’s start with defining the altitude PID and desired altitude:

altitude_PID = PID.PID(P, I, D)
desired_altitude = 8000
altitude_PID.SetPoint = desired_altitude

With those defined, we now move to the main loop. The outer loop needs to be updated first. From there, we will normalize the output from the altitude PID and use that to set the pitch PID. The pitch PID will also be normalized to keep values in a reasonable range:

# update outer loops first
altitude_PID.update(current_altitude)

# if alt=12000, setpoint = 10000, the error is 2000. if P=0.1, output will be 2000*0.1=200
pitch_PID.SetPoint = normalize(altitude_PID.output, min=-15, max=10)

# update PIDs
roll_PID.update(current_roll)
pitch_PID.update(current_pitch)

# update control outputs
new_ail_ctrl = normalize(roll_PID.output)
new_ele_ctrl = normalize(pitch_PID.output)

Now we just need to send those new control surface commands and we’ll be controlling the plane!

Outputting the control deflections should basically be the last part of the loop. We’ll put it right before the debug output:

# sending actual control values to XPlane
ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998] # ele, ail, rud, thr. -998 means don't change
client.sendCTRL(ctrl)

Full code of pitch_roll_autopilot_with_graphing.py

import sys
import xpc
import PID
from datetime import datetime, timedelta
import pyqtgraph as pg
from pyqtgraph.Qt import QtCore, QtGui
import time

def normalize(value, min=-1, max=1):
	# if value = 700, and max = 20, return 20
	# if value = -200, and min = -20, return -20
	if (value > max):
		return max
	elif (value < min):
		return min
	else:
		return value

update_interval = 0.050 # seconds, 0.05 = 20 Hz
start = datetime.now()
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 PID controllers
roll_PID = PID.PID(P, I, D)
pitch_PID = PID.PID(P, I, D)
altitude_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
desired_altitude = 8000

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

x_axis_counters = [] #0, 1, 2, 3, etc. just basic x-axis values used for plotting
roll_history = []
pitch_history = []
altitude_history = []
roll_setpoint_history = []
pitch_setpoint_history = []
altitude_setpoint_history = []
plot_array_max_length = 300 # how many data points to hold in our arrays and graph
i = 1 # initialize x_axis_counter

# first the base app needs to be instantiated
app = pg.mkQApp("python xplane autopilot monitor")

# now the window itself is defined and sized
win = pg.GraphicsLayoutWidget(show=True)
win.resize(1000,600) #pixels
win.setWindowTitle("XPlane autopilot system control")

# we have 3 subplots
p1 = win.addPlot(title="roll",row=0,col=0)
p2 = win.addPlot(title="pitch",row=1,col=0)
p3 = win.addPlot(title="altitude", row=2, col=0)

# show the y grid lines to make it easier to interpret the graphs
p1.showGrid(y=True)
p2.showGrid(y=True)
p3.showGrid(y=True)

DREFs = ["sim/cockpit2/gauges/indicators/airspeed_kts_pilot",
		"sim/cockpit2/gauges/indicators/heading_electric_deg_mag_pilot",
		"sim/flightmodel/failures/onground_any",
		"sim/flightmodel/misc/h_ind"]

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

				posi = client.getPOSI();
				ctrl = client.getCTRL();
				multi_DREFs = client.getDREFs(DREFs)

				current_roll = posi[4]
				current_pitch = posi[3]
				current_hdg = multi_DREFs[1][0]
				current_altitude = multi_DREFs[3][0]
				current_asi = multi_DREFs[0][0]
				onground = multi_DREFs[2][0]

				# update the display
				pg.QtGui.QApplication.processEvents()

				# update outer loops first
				altitude_PID.update(current_altitude)

				# if alt=12000, setpoint = 10000, the error is 2000. if P=0.1, output will be 2000*0.1=200
				pitch_PID.SetPoint = normalize(altitude_PID.output, min=-15, max=10)

				# update PIDs
				roll_PID.update(current_roll)
				pitch_PID.update(current_pitch)

				# update control outputs
				new_ail_ctrl = normalize(roll_PID.output)
				new_ele_ctrl = normalize(pitch_PID.output)

				# if we reach our data limit set point, evict old data and add new.
				# this helps keep the graph clean and prevents it from growing infinitely
				if(len(x_axis_counters) > plot_array_max_length):
					x_axis_counters.pop(0)
					roll_history.pop(0)
					roll_setpoint_history.pop(0)
					pitch_history.pop(0)
					pitch_setpoint_history.pop(0)
					altitude_history.pop(0)
					altitude_setpoint_history.pop(0)

					x_axis_counters.append(i)
					roll_history.append(current_roll)
					roll_setpoint_history.append(desired_roll)
					pitch_history.append(current_pitch)
					pitch_setpoint_history.append(pitch_PID.SetPoint)
					altitude_history.append(0)
					altitude_setpoint_history.append(desired_altitude)
				# else, just add new. we are not yet at limit.
				else:
					x_axis_counters.append(i)
					roll_history.append(current_roll)
					roll_setpoint_history.append(desired_roll)
					pitch_history.append(current_pitch)
					pitch_setpoint_history.append(pitch_PID.SetPoint)
					altitude_history.append(0)
					altitude_setpoint_history.append(desired_altitude)
				i = i + 1

				p1.plot(x_axis_counters, roll_history, pen=0, clear=True)
				p1.plot(x_axis_counters, roll_setpoint_history, pen=1)

				p2.plot(x_axis_counters, pitch_history, pen=0,clear=True)
				p2.plot(x_axis_counters, pitch_setpoint_history, pen=1)

				p3.plot(x_axis_counters, altitude_history, pen=0,clear=True)
				p3.plot(x_axis_counters, altitude_setpoint_history, pen=1)

				# sending actual control values to XPlane
				ctrl = [new_ele_ctrl, new_ail_ctrl, 0.0, -998] # ele, ail, rud, thr. -998 means don't change
				client.sendCTRL(ctrl)

				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"
				print(output)

if __name__ == "__main__":
	monitor()

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 bring roll to 0 pretty quick and start the climb/descent to the desired altitude.

X-Plane Python autopilot leveling off at 8000' with pitch/roll/altitude graphed in real-time
X-Plane Python autopilot leveling off at 8000′ with pitch/roll/altitude graphed in real-time
Categories
Python XPlane

Coding a wing leveler autopilot in X-Plane with Python

Introduction

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

Contents

  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 = datetime.now()

# 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 (datetime.now() > 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 = datetime.now()

		# 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 monitorExample.py 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 PID.py 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:

roll_PID.update(current_roll)
pitch_PID.update(current_pitch)

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]
client.sendCTRL(ctrl)

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 pitch_roll_autopilot.py

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

update_interval = 0.100 # seconds
start = datetime.now()
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 (datetime.now() > last_update + timedelta(milliseconds = update_interval * 1000)):
				last_update = datetime.now()
				print(f"loop start - {datetime.now()}")

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

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

				roll_PID.update(current_roll)
				pitch_PID.update(current_pitch)

				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
				client.sendCTRL(ctrl)

				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"
				print(output)

if __name__ == "__main__":
	monitor()

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.