3. Install BPS from source (hybrid)

This path is for developers. You install the Python processors from this repository in editable mode, and pull only the native binary packages from the Aresys bundle.

Note

A fully source-based install is not possible because the native binaries (bps-l1_binaries, bps-stack_binaries, libl1framing.so) are not in the public repository. They are delivered via the Aresys bundle.

3.1 Create the environment

cd ~/bps-work/BPS
conda create -n bps-dev python=3.12
conda activate bps-dev

3.2 Install Python processors in editable mode

The Python source for every processor sits at the repo root under bps-common/, bps-l1_processor/, etc. Install them in dependency order:

pip install -e bps-common
pip install -e bps-transcoder
pip install -e bps-l1_pre_processor
pip install -e bps-l1_core_processor
pip install -e bps-l1_framing_processor
pip install -e bps-l1_processor
pip install -e bps-stack_pre_processor
pip install -e bps-stack_coreg_processor
pip install -e bps-stack_cal_processor
pip install -e bps-stack_processor
pip install -e bps-l2a_processor
pip install -e bps-l2b_agb_processor
pip install -e bps-l2b_fh_processor
pip install -e bps-l2b_fd_processor

Any Python edit you make under bps-l1_processor/bps/l1_processor/... is picked up immediately, with no rebuild step.

3.3 Install the native binary packages from the bundle

The bps-l1_binaries and bps-stack_binaries packages are not in this repository (the source is not public). Extract them from the Aresys bundle and install via conda:

tar -xzf ~/bps-work/SW/bps-bundle-v4.4.3.tar.gz -C ~/bps-work/SW/BPS_V443/
conda install -c "file://$HOME/bps-work/SW/BPS_V443/bundle/bps_conda_channel" \
    bps-l1_binaries bps-stack_binaries

3.4 Verify

which bps_l1_processor   # should resolve to your editable install
which libl1framing       # comes from bps-l1_binaries
bps_l1_processor --help

If installation fails

Clean up the partially created environment before retrying:

conda env list
conda env remove --name bps-dev
conda env list

The most common failure causes are insufficient RAM (the conda solver crashes when resolving all packages at once) or a missing Aresys dependency. See ModuleNotFoundError: arepytools.