Ghost feather v1

Ghost Feather

[!NOTE] Please bear in mind that the workspace was primarily created for enjoyment and educational endeavors.

The MIT License (MIT)

Copyright (c) 2011-2024 The Bootstrap Authors

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Compilation process

[!NOTE] The current compilation process will be changed

~ arm-none-eabi-gcc -g -c -mfloat-abi=hard -mfpu=fpv4-sp-d16 -mcpu=cortex-m7 -mthumb \
                          -I memory -I bootloader \
                          startup/stm32f722xx/stm32722xx_startup.c -o build/stm32f722xx_startup.o

~ arm-none-eabi-gcc -g -c -mfloat-abi=hard -mfpu=fpv4-sp-d16 -mcpu=cortex-m7 -mthumb \
                          -I libopencm3/include -I memory -DSTM32F7 \
                          bootloader/stm32f722xx_first_bootloader.c -o build/stm32f722xx_first_bootloader.o

~ arm-none-eabi-as -g vectortable/stm32f722xx/stm32f722xx_vectortable.S -o build/stm32f722xx_vectortable.o

~ arm-none-eabi-gcc -g -nostartfiles -nostdlib -mfloat-abi=hard -mfpu=fpv4-sp-d16 -mcpu=cortex-m7 -mthumb \
                       build/stm32f722xx_first_bootloader.o build/stm32f722xx_startup.o build/stm32f722xx_vectortable.o -o build/stm32f722xx_first_bootloader.elf \
                       -T linkerscript/stm32f722xx/stm32f722xx_bootloader.ld -L linkerscript/stm32f722xx -L libopencm3/lib -lopencm3_stm32f7

CMake generators

Unix Makefiles

~ cmake -S . -B build -DCMAKE_TOOLCHAIN_FILE=arm-none-eabi-toolchain.cmake
~ cd build
~ cmake --build . --config Debug

Ninja Multi-Config

~ cmake -G "Ninja Multi-Config" -S . -B build -DCMAKE_TOOLCHAIN_FILE=arm-none-eabi-toolchain.cmake
~ cd build
~ cmake --build . --config Debug

CMake Presets

~ cmake --preset list
~ cmake --preset <build-type>

Build shell script

~ ./build.sh <build-type>

How to program

For now, CMake generates two elf files. Each of these files needs to be uploaded to the device.

Newlib

To use newlib with libopencm3 it is necessary to recomplie the libc to be able to use hard floating point numbers which use on-chip FPU. The second option is to use soft floating point numbers but the speed will be slower.

Marco Paland printf implementation

The Marco Paland printf implementation is a lightweight, standalone alternative designed specifically to address these limitations. This implementation offers a small memory footprint, portability, and independence from the full C standard library. It is widely used in embedded projects where developers require formatted output but cannot afford the code size and dependencies of a complete libc implementation.

DFU script

Description

The script enables firmware update over USART. It allows users to upload binary firmware files to microcontroller-based devices with minimal effort. The script handles communication setup, data formatting, and transmission to ensure reliable updates.

Usage

~ python3 dfu_usart.py <binary> <port> <baudrate>

• <binary>: The firmware to be uploaded.
• <port>: The name of the serial port connected to the target device (e.g., /dev/tty.usbmodem).
• <baudrate>: The communication speed in bits per second (e.g., 9600, 115200).

SOCAT tool

Usage

~ socat -d -d PTY,raw,echo=0 PTY,raw,echo=0
~ stty -f /dev/ttysxxx 115200
~ screen /dev/ttysxxx 115200

Unit tests

Description

The unit tests verify the functionality, reliability, and error handling of embedded system controllers using Google Test (GTest).

They cover various scenarios, including:

• Core functionality validation.
• Boundary condition testing.
• Error handling and fault tolerance.
• Performance and efficiency checks.

Usage

~ cmake -G "Ninja" -S . -B build -DPROJECT_ROOT_DIR=$(pwd)/../..
~ cmake --build build
~ cd build && ctest

Conda

What is Conda?

Conda is an open-source package manager and environment manager used for installing, running, and updating software packages and their dependencies. It is widely used in data science, machine learning, and scientific computing because it simplifies managing complex software environments.

Unlike pip, which only installs Python packages, Conda can manage packages written in multiple languages (such as Python, R, and C libraries) and handles system-level dependencies as well.

Conda is distributed with platforms such as:

• Anaconda – a large distribution that includes many preinstalled scientific packages.
• Miniconda – a lightweight version that installs only Conda and Python.

Why Use Conda?

Conda helps solve common development problems:

• Dependency management – automatically installs required libraries.
• Environment isolation – different projects can use different package versions.
• Cross-language support – works with Python, R, and other tools.
• Reproducibility – environments can be exported and recreated.

Example: one project may require Python 3.9 while another requires Python 3.11. Conda allows both to coexist without conflicts.

Creating an Environment

A Conda environment is an isolated workspace containing its own Python version and installed packages.

Create a new environment:

conda create -n myenv python=3.10

Here:

• -n myenv specifies the environment name
• python=3.10 installs a specific Python version

Activating an Environment

Before using an environment, it must be activated.

conda activate myenv

After activation, all installed packages and Python commands will come from this environment.

To deactivate:

conda deactivate

Installing Packages

Packages can be installed directly into the active environment.

conda install numpy pandas matplotlib

Conda will automatically resolve and install required dependencies.

Listing Environments

To see all available environments:

conda env list

or:

conda info --envs

Removing an Environment

If an environment is no longer needed, it can be deleted:

conda remove -n myenv --all

Exporting an Environment

To share or reproduce an environment:

conda env export > environment.yml

This file can later be used to recreate the same environment.

Recreating an Environment

conda env create -f environment.yml

This installs all packages and dependencies exactly as defined in the exported file.

EASTER EGG!

CBR 929 broke. We have to repair it. I will do todays job tomorrow.