2. Install BPS from the bundle#

This is the recommended path for scientific users who want a working BPS without modifying processor code. Installs all 14 processors at once from the Aresys-distributed conda bundle.

Download the BPS bundle#

  1. Go to service.aresys.it/downloads

  2. Request access if you do not have credentials yet (free, account-based)

  3. Download the bundle matching the version you want, e.g. bps-bundle-v4.4.3.tar.gz

  4. Place it in ~/bps-work/SW/:

    mv ~/Downloads/bps-bundle-v4.4.3.tar.gz ~/bps-work/SW/
mkdir -p ~/bps-work/SW/BPS_V443
tar -xzf ~/bps-work/SW/bps-bundle-v4.4.3.tar.gz -C ~/bps-work/SW/BPS_V443

Run the install notebook#

Open notebooks/0_BPS_installation.ipynb in JupyterLab, VS Code, or any notebook front-end.

Set the BPS version at the top of cell 0:

BPS_VERSION = "4.4.3"   # match your tarball

If your working directory is not /home/jovyan/, also edit the path constants near the top of cell 0 to point at your local layout (~/bps-work/SW/, ~/bps-work/BPS/docs/tutorials/run-bps-locally/).

Run all cells. The notebook:

  1. Creates a conda environment (e.g. BPS_443) with Python 3.12

  2. Builds a local conda channel from the bundle packages

  3. Installs all BPS processors (bps-l1_processor, bps-l1_framing_processor, bps-stack_processor, bps-l2a_processor, …)

  4. Patches set_environment.bash with the correct paths

  5. Adds a shell alias to ~/.bashrc (or ~/.zshrc):

    alias bps443='source ~/bps-work/BPS/docs/tutorials/run-bps-locally/CONFIGURATION_FILE/set_environment.bash'

  6. Verifies the installation by running --help on every processor

Verify#

After install completes, open a fresh terminal and check:

bps443
which bps_l1_processor
bps_l1_processor --help

If installation fails

A failed install often leaves a partially created conda environment, which causes subsequent retries to fail. Clean up before retrying:

conda env list                       # 1. confirm the broken env exists
conda env remove --name BPS_443      # 2. remove it
conda env list                       # 3. confirm it is gone

Then re-run the install steps. If it fails again at the same point, the most likely causes are insufficient RAM (the conda solver crashes when resolving all packages at once) or a corrupted bundle.