Training session to set up MESH for the Bow River at Calgary catchment

Launching MyBinder Session:

Library requirements

General

Certain libraries and binary executables are necessary to run the workflows in this repository. Below necessary libraries for general usage are mentioned:

1. CDO (Climate Data Operators >=v2.2.1),
2. ecCodes (>=v2.25.0),
3. Expat XML parser (>=v2.4.1),
4. GDAL (>=3.5.1),
5. GEOS (>=3.10.2),
6. HDF5 (>=1.10.6),
7. JasPer (>=2.0.16),
8. libaec (>=1.0.6),
9. libfabric (>=1.10.1),
10. libffi (>=3.3),
11. libgeotiff (>=1.7.1),
12. librttopo (>=1.1.0),
13. libspatialindex (>=1.8.5),
14. libspatilite (>=5.0.1),
15. netcdf-fortran (>=4.5.2),
16. netcdf (>=4.7.4),
17. postgresql (>=12.4),
18. proj (>=9.0.1),
19. python (>=3.10.2),
20. sqlite (>=3.38.5),
21. udunits (>=2.2.28)

Each of the above libraries and binaries may need further dependencies. It is up to the user to assure all requirements are satisfied. Most GNU/Linux distributions should be able to offer all the libraries above through their remote package repositories. If not, it is recommended to compile and store them for future reference. In the relevant session, these packages are pre-installed and available for testing.

University of Calgary's ARC HPC

Fortunately, all the above requirements are available on ARC. You may load the modules with the following command:

# first activating Ucalgary's Computational Hydrology's customized moduled system
. /work/comphyd_lab/local/modules/spack/2024v5/lmod-init-bash
module unuse $MODULEPATH
module use /work/comphyd_lab/local/modules/spack/2024v5/modules/linux-rocky8-x86_64/Core/
# activating modules needed for this workshop
module load \
  gcc/14.2.0 openssh/9.8p1 python-venv/1.0 py-xarray/2024.7.0 \
  py-traitlets/5.14.3 py-asttokens/2.4.0 htop/3.3.0 slurm/24.11.0-1 \
  antlr/2.7.7 py-cdo/1.5.6 py-comm/0.1.4 py-executing/1.2.0 \
  glibc/2.28 ucx/1.17.0 gsl/2.7.1 r-rcpp/1.0.12 py-debugpy/1.6.7 \
  py-pure-eval/0.2.2 gcc-runtime/14.2.0 openmpi/4.1.6 nco/5.2.4 \
  r-lattice/0.21-8 py-decorator/5.1.1 py-stack-data/0.6.2 tree/2.1.0 \
  libunistring/1.2 py-numpy/1.26.4 r-sp/1.6-0 py-parso/0.8.3 \
  py-typing-extensions/4.8.0 lz4/1.9.4 libidn2/2.3.7 gdal/3.9.2 \
  r-terra/1.8-10 py-jedi/0.18.2 py-ipython/8.28.0 snappy/1.1.10 \
  nghttp2/1.62.0 geos/3.12.2 r-raster/3.6-20 py-contourpy/1.0.7 \
  py-platformdirs/3.10.0 c-blosc/1.21.5 curl/8.7.1 which/2.21 \
  r-mass/7.3-59 py-cycler/0.11.0 py-jupyter-core/5.3.0 libaec/1.0.6 \
  libjpeg-turbo/3.0.3 r/4.4.1 r-class/7.3-21 py-fonttools/4.39.4 \
  py-pycparser/2.21 hdf5/1.14.3 libtiff/4.6.0 qt/5.15.14 \
  r-proxy/0.4-27 py-kiwisolver/1.4.5 py-cffi/1.16.0 netcdf-c/4.9.2 \
  sqlite/3.46.0 py-mpi4py/4.0.0 r-e1071/1.7-13 py-pillow/10.4.0 \
  py-greenlet/2.0.2 netcdf-fortran/4.6.1 proj/9.4.1 py-six/1.16.0 \
  r-kernsmooth/2.23-20 py-pyparsing/3.1.2 py-zope-event/4.6 \
  openjpeg/2.3.1 fftw/3.3.10 py-python-dateutil/2.8.2 \
  r-classint/0.4-9 py-pyqt5-sip/12.13.0 py-zope-interface/5.4.0 \
  eccodes/2.34.0 libmd/1.0.4 py-pytz/2023.3 r-dbi/1.1.3 \
  py-pyqt5/5.15.9 py-gevent/23.7.0 openssl/3.3.1 libbsd/0.12.2 \
  py-tzdata/2023.3 r-magrittr/2.0.3 py-matplotlib/3.9.2 \
  py-pyzmq/25.0.2 libevent/2.1.12 expat/2.6.2 py-pandas/2.2.3 \
  r-wk/0.7.2 py-matplotlib-inline/0.1.6 py-tornado/6.3.3 \
  numactl/2.0.14 udunits/2.2.28 py-certifi/2023.7.22 r-s2/1.1.2 \
  py-ptyprocess/0.7.0 py-jupyter-client/8.2.0 opa-psm2/11.2.230 \
  util-linux-uuid/2.40.2 py-cftime/1.0.3.4 r-units/0.8-1 \
  py-pexpect/4.8.0 py-nest-asyncio/1.6.0 krb5/1.21.2 cdo/2.4.3 \
  py-netcdf4/1.6.5 r-sf/1.0-12 py-wcwidth/0.2.7 py-psutil/5.9.5 \
  libedit/3.1-20230828 libffi/3.4.6 py-packaging/23.1 \
  r-exactextractr/0.10.0 py-prompt-toolkit/3.0.43 py-ipykernel/6.29.5 \
  libxcrypt/4.4.35 python/3.11.7 py-setuptools/69.2.0 py-cython/3.0.10 \
  py-pygments/2.18.0;

DRAC Fir HPC (at the Simon Fraser University)

The necessary modules may be loaded with the following command:

module load CCconfig StdEnv/2023 gcc/12.3 \
  ucc/1.2.0 r/4.4.0 hdf5/1.14.2 jasper/4.0.0 \
  libgeotiff/1.7.1 postgresql/16.0 gentoo/2023 openmpi/4.1.5 \
  rstudio-server/4.4 netcdf/4.9.2 libaec/1.0.6 hdf/4.2.16 \
  gdal/3.9.1 aocl-blas/5.1 python/3.11.5 fftw/3.3.10 \
  eccodes/2.31.0 boost/1.82.0 rust/1.85.0 mii/1.1.2 aocl-lapack/5.1 \
  ipykernel/2023b proj/9.2.0 netcdf-fortran/4.6.1 eigen/3.4.0 \
  mpi4py/4.0.3 flexiblas/3.3.1 ipython-kernel/2023b cdo/2.2.2 \
  qt/5.15.11 arpack-ng/3.9.1 code-server/4.101.2 \
  jupyterlab-apps/1.0 antlr/2.7.7 freexl/2.0.0 armadillo/12.6.4 \
  hwloc/2.9.1 calibre/8.6.0 scipy-stack/2023b libdap/3.20.11 \
  geos/3.12.0 cfitsio/4.3.0 ucx/1.14.1 libreqda/1.0.1 arrow/14.0.1 \
  gsl/2.7 librttopo/1.1.0 brunsli/0.1 libfabric/1.18.0 flexiblascore/.3.3.1 \
  openrefine/3.9.3 nco/5.1.7 libspatialite/5.1.0 qhull/2020.2 pmix/4.2.4 \
  java/17.0.6 yaxt/0.10.0 udunits/2.2.28 libspatialindex/1.9.3 \
  lerc/4.0.0;

Saving Module Collections

It is recommended to save all load modules as a list to be able to restore them whenever needed. Using the LMOD features, you may save them with:

module save scimods # you can change "scimods" to anything!

And, you may restore the list with:

module restore scimods

Note

Please note that these libraries are necessary for the Python environment to run smoothly (see below).

Python requirements

General

The following list of Python packages are required to run much of the workflows in this repository. The requirements.txt file describes the packages necessary to run the workflows.

To download this repository on the $HOME directory:

git clone https://github.com/kasra-keshavarz/community-modelling-workflow-training.git $HOME/github-repos/community-workflows

You may create Python virtual environments (after assuring all the modules are loaded) on HPCs, to isolate the environment to execute the workflows. On HPCs, typically, it is recommended to use your $HOME directory, so a path like the following is recommended:

python -m venv $HOME/virtual-envs/scienv

After creating the virtual environment, you can activate the environment with:

source $HOME/virtual-envs/scienv/bin/activate

And your shell prompt, should look like this:

(scienv) foo@bar: ~$ # this is how your HPC will look

After the activation of the virtual environment, you may install any Python package within the environment. To install those we need for the modelling workflows:

pip install -r $HOME/github-repos/community-workflows/0-prerequisites/requirements_%HPC%.txt

Important

Please replace %HPC% with either arc or fir, depending on which HPC you are using.

Once the scienv is ready, you may add the virtual environment to the Jupyter Lab as a kernel using the following command:

python -m ipykernel install --name "scienv" --user

Once added as a kernel, you should your virtual environment within your Jupyter sessions.

Last edited: September 17th, 2025