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Command

fio --name=write_test --filename=/media/usb/foo --size=1G --time_based --runtime=30 --rw=write --direct=1 --bs=4M --iodepth=1 --numjobs=1

Results

Device	Technology	FS	Write (MB/s) RPI5	Write (MB/s) x86
Platinet PMFPPD256	USB 3.2 SSD	FAT32	309	409

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from pymodbus.client import ModbusTcpClient
import struct
import os
# Define device connection
client = ModbusTcpClient('127.0.0.1', port=502)  # Change IP and port as needed

# Connect to the device
while client.connect():
  
    num_registers = 12

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cmake_minimum_required(VERSION 3.10)
project(WGS)


add_executable(main main.cpp wgs84_do_puwg92.cc)

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cmake_minimum_required(VERSION 3.10)
project(HelloCeres)

# Find Ceres.
find_package(Ceres REQUIRED)

# Add executable.
add_executable(hello_ceres hello_ceres.cpp)

# Link Ceres to the target.

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import os
import rosbag2_py
import numpy as np
import struct
from sensor_msgs.msg import PointCloud2
from sensor_msgs_py import point_cloud2
from rclpy.serialization import deserialize_message

def read_bag_convert_to_csv(bag_file, topic, output_csv):
    # Open ROS 2 bag

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Install libs:

sudo apt-get install python3-rpi.gpio

Then run:

python3 test_gpio_output.py
python3 test_gpio_input.py
``

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{
	"type": "CFG-ITEM",
	"items": [
		{
			"value": 9600,
			"layer": 0,
			"action": "Set",
			"valueType": "U4",
			"requestId": "1882a918-6321-4b5f-aa26-ac4a1f98fa68",
			"key": "0x40520001"

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from pathlib import Path
import gpxpy
import gpxpy.gpx

from rosbags.highlevel import AnyReader
import sys

bag = sys.argv[1]
gpx = sys.argv[2]

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#include <Atom/RPI.Public/AuxGeom/AuxGeomFeatureProcessorInterface.h>
#include <Atom/RPI.Public/Scene.h>

void MyComponent::Activate()
{
    auto* entityScene = AZ::RPI::Scene::GetSceneForEntityId(GetEntityId());
    m_drawQueue = AZ::RPI::AuxGeomFeatureProcessorInterface::GetDrawQueueForScene(entityScene);

    AZ::TickBus::Handler::BusConnect();
    TwistNotificationBus::Handler::BusConnect(GetEntityId());

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#!/usr/bin/env python
# --------------------------------------------------------------------
# Simple sum of squared differences (SSD) stereo-matching using Numpy
# --------------------------------------------------------------------

# Copyright (c) 2016 David Christian
# Licensed under the MIT License
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches