shepherd-core 2024.9.1

Description:

shepherdcore 2024.9.1

Core Library




Main Documentation: https://orgua.github.io/shepherd
Source Code: https://github.com/orgua/shepherd-datalib
Main Project: https://github.com/orgua/shepherd

shepherd-core is designed as a library and bundles data-models and file-access-routines for the shepherd-testbed, that are used by several codebases.
For postprocessing shepherds .h5-files usage of shepherd_data is recommended.
Features

read and write shepherds hdf5-files
create, read, write and convert experiments for the testbed

all required data-models are included


simulate the virtual source, including virtual harvesters (and virtual converter as a whole)
connect and query the testbed via a webclient (TestbedClient in alpha-stage)

offline usage defaults to static demo-fixtures loaded from yaml-files in the model-directories


work with target-firmwares

embed, modify, verify, convert
Note: working with ELF-files requires external dependencies, see Installation-Chapter


decode waveforms (gpio-state & timestamp) to UART
create an inventory (for deployed versions of software, hardware)

See official documentation or example scripts for more details and usage. Most functionality is showcased in both. The extra-directory holds data-generators relevant for the testbed. Notably is a trafficbench-experiment that's used to derive the link-matrix of the testbed-nodes.
Config-Models in Detail
These pydantic data-models are used throughout all shepherd interfaces. Users can create an experiment, include their own content and feed it to the testbed.

orchestration /data-models with focus on remote shepherd-testbed
classes of sub-models

/base: base-classes, configuration and -functionality for all models
/testbed: meta-data representation of all testbed-components
/content: reusable user-defined meta-data for fw, h5 and vsrc-definitions
/experiment: configuration-models including sub-systems
/task: digestible configs for shepherd-herd or -sheep
behavior controlled by ShpModel and content-model


a basic database is available as fixtures through a tb_client

fixtures selectable by name & ID
fixtures support inheritance


the models support

auto-completion with neutral / sensible values
complex and custom datatypes (i.e. PositiveInt, lists-checks on length)
checking of inputs and type-casting
generate their own schema (for web-forms)
pre-validation
store to & load from yaml with typecheck through wrapper
documentation


experiment-definition is designed securely

types are limited in size (str)
exposes no internal paths


experiments can be transformed to task-sets (TestbedTasks.from_xp())

Compatibility



OS
PyVersion
Comment




Ubuntu
3.8 - 3.13



Windows
3.8 - 3.13
no support for elf and hex-conversions yet


MacOS
3.8 - 3.13
hex-conversion missing



Notes:

hex-conversion needs a working and accessible objcopy
elf-supports needs

shepherd-core[elf] installs pwntools-elf-only
most elf-features also still utilize hex-conversion



Installation
The Library is available via PyPI and can be installed with
pip install shepherd-core -U

# or for the full experience (includes core)
pip install shepherd-data -U

For bleeding-edge-features or dev-work it is possible to install directly from GitHub-Sources (here dev-branch):
pip install git+https://github.com/orgua/shepherd-datalib.git@dev#subdirectory=shepherd_core -U
# and on sheep with newer debian
sudo pip install git+https://github.com/orgua/shepherd-datalib.git@dev#subdirectory=shepherd_core -U --break-system-packages

If you are working with .elf-files (embedding into experiments) you make "objcopy" accessible to python. In Ubuntu, you can either install build-essential or binutils-$ARCH with arch being msp430 or arm-none-eabi for the nRF52.
sudo apt install build-essential

For more advanced work with .elf-files (modify value of symbols / target-ID) you should install
pip install shepherd-core[elf]

and also make sure the prereqs for the pwntools are met.
For creating an inventory of the host-system you should install
pip install shepherd-core[inventory]

Unittests
To run the testbench, follow these steps:

Navigate your host-shell into the package-folder and
install dependencies
run the testbench (~ 320 tests):

cd shepherd-datalib/shepherd_core
pip3 install ./[tests]
pytest