SIMUnet installation guide

The installation process for SIMUnet is very similar to the one for NNPDF, more details can be found on their website. The only difference is that you need to clone the SIMUnet repository instead of the NNPDF one.

Linux

The following instructions have been tested on a Linux system.

Dependencies installation

The dependencies need by to be installed via conda:

conda create -n simunet
conda activate simunet
conda install --only-deps nnpdf=4.0.5
conda install gxx_linux-64
conda install pkg-config swig cmake
conda install sysroot_linux-64=2.17

Code compilation

The SIMUnet code can be downloaded from GitHub:

mkdir simunet_git
cd simunet_git
git clone https://github.com/HEP-PBSP/SIMUnet.git

The code can then be compiled and installed with the following commands:

simunet_git$ cd simunet_release
mkdir conda-bld
cd conda-bld
cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
make
make install

The SIMUnet code, in addition to the regular files that are needed in the NNPDF methodology to produce theoretical predictions, requires K-factors to account for the effect of SMEFT operators. These K-factors are implemented in simu_fac files, which exists for each dataset in the SIMUnet methodology. For a given dataset, the simu_fac file includes the SM theory prediction, and the SMEFT theory prediction at LO and/or NLO, if applicable. These K-factors are hosted in the NNPDF theory_270 folder, which will be automatically downloaded when required by the user’s runcard.

M1/M2/M3 MacOS

The following instructions have been tested on MacOS systems with arm processors like M1, M2, and M3.

Dependencies installation

In order to install SIMUnet on arm64 MacOS machines using conda, we must create an environment which is able to accomodate a C++ compiler, e.g. clangxx_osx-64 as we will see later. We must create a conda environment setting the CONDA_SUBDIR=osx-64 since we want a x86_64 environment, moreover, we will use the following yml file to build the environment with the right dependencies:

name: simunet
channels:
    - anaconda
    - https://packages.nnpdf.science/public
    - defaults
    - conda-forge
dependencies:
    - _tflow_select=2.2.0=eigen
    - abseil-cpp=20220623.0=hddbf539_6
    - absl-py=1.4.0=py310hecd8cb5_0
    - accessible-pygments=0.0.4=pyhd8ed1ab_0
    - aiohttp=3.8.3=py310h6c40b1e_0
    - aiosignal=1.2.0=pyhd3eb1b0_0
    - alabaster=0.7.12=pyhd3eb1b0_0
    - anyio=3.5.0=py310hecd8cb5_0
    - apfel=3.0.6.3=h8d2ef1a_9
    - appdirs=1.4.4=pyhd3eb1b0_0
    - appnope=0.1.2=py310hecd8cb5_1001
    - argon2-cffi=20.1.0=py310hca72f7f_1
    - asttokens=2.0.5=pyhd3eb1b0_0
    - astunparse=1.6.3=py_0
    - async-timeout=4.0.2=py310hecd8cb5_0
    - babel=2.11.0=py310hecd8cb5_0
    - backcall=0.2.0=pyhd3eb1b0_0
    - banana-hep=0.6.8=pyhd8ed1ab_0
    - beautifulsoup4=4.12.2=py310hecd8cb5_0
    - blas=1.0=openblas
    - bleach=4.1.0=pyhd3eb1b0_0
    - blessings=1.7=py310hecd8cb5_1002
    - blinker=1.4=py310hecd8cb5_0
    - bokeh=2.4.3=py310hecd8cb5_0
    - bottleneck=1.3.5=py310h4e76f89_0
    - brotli=1.0.9=hca72f7f_7
    - brotli-bin=1.0.9=hca72f7f_7
    - brotlipy=0.7.0=py310hca72f7f_1002
    - bzip2=1.0.8=h1de35cc_0
    - c-ares=1.19.0=h6c40b1e_0
    - ca-certificates=2023.08.22=hecd8cb5_0
    - cachetools=4.2.2=pyhd3eb1b0_0
    - cctools=949.0.1=h9abeeb2_25
    - cctools_osx-64=949.0.1=hc7db93f_25
    - certifi=2023.11.17=py310hecd8cb5_0
    - cffi=1.15.1=py310h6c40b1e_3
    - chardet=4.0.0=py310hecd8cb5_1003
    - charset-normalizer=2.0.4=pyhd3eb1b0_0
    - clang=14.0.6=hecd8cb5_1
    - clang-14=14.0.6=default_hd95374b_1
    - clang_osx-64=14.0.6=hb1e4b1b_0
    - clangxx=14.0.6=default_hd95374b_1
    - clangxx_osx-64=14.0.6=hd8b9576_0
    - click=8.0.4=py310hecd8cb5_0
    - cloudpickle=2.2.1=py310hecd8cb5_0
    - cmake=3.22.1=hbfa4a85_0
    - colorama=0.4.6=py310hecd8cb5_0
    - comm=0.1.2=py310hecd8cb5_0
    - commonmark=0.9.1=pyhd3eb1b0_0
    - compiler-rt=14.0.6=hda8b6b8_0
    - compiler-rt_osx-64=14.0.6=h8d5cb93_0
    - conda=22.11.1=py310h2ec42d9_1
    - conda-build=3.27.0=py310hecd8cb5_0
    - conda-index=0.3.0=py310hecd8cb5_0
    - conda-package-handling=2.2.0=py310hecd8cb5_0
    - conda-package-streaming=0.9.0=py310hecd8cb5_0
    - contourpy=1.0.5=py310haf03e11_0
    - cryptography=39.0.1=py310hf6deb26_2
    - curio=1.4=pyhd3eb1b0_0
    - cycler=0.11.0=pyhd3eb1b0_0
    - cytoolz=0.12.0=py310hca72f7f_0
    - dask=2023.4.1=py310hecd8cb5_1
    - dask-core=2023.4.1=py310hecd8cb5_0
    - dataclasses=0.8=pyh6d0b6a4_7
    - debugpy=1.6.7=py310hcec6c5f_0
    - decorator=5.1.1=pyhd3eb1b0_0
    - defusedxml=0.7.1=pyhd3eb1b0_0
    - distributed=2023.4.1=py310hecd8cb5_1
    - docutils=0.18.1=py310hecd8cb5_3
    - eko=0.13.5=pyhd8ed1ab_0
    - exceptiongroup=1.2.0=py310hecd8cb5_0
    - executing=0.8.3=pyhd3eb1b0_0
    - expat=2.4.9=he9d5cce_0
    - fiatlux=0.1.2=py310h0eb4f65_0
    - filelock=3.9.0=py310hecd8cb5_0
    - flatbuffers=22.12.06=hf0c8a7f_2
    - fonttools=4.25.0=pyhd3eb1b0_0
    - freetype=2.12.1=hd8bbffd_0
    - frozenlist=1.3.3=py310h6c40b1e_0
    - fsspec=2023.9.2=py310hecd8cb5_0
    - future=0.18.3=py310hecd8cb5_0
    - gast=0.4.0=pyhd3eb1b0_0
    - giflib=5.2.1=h6c40b1e_3
    - google-auth=2.6.0=pyhd3eb1b0_0
    - google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
    - google-pasta=0.2.0=pyhd3eb1b0_0
    - greenlet=2.0.1=py310hcec6c5f_0
    - grpc-cpp=1.51.1=h88f4db0_1
    - grpcio=1.51.1=py310hdfcfac3_1
    - gsl=2.7.1=hdbe807d_1
    - h5py=3.7.0=py310h6c517f8_0
    - hdf5=1.10.6=h10fe05b_1
    - heapdict=1.0.1=pyhd3eb1b0_0
    - hyperopt=0.2.7=pyhd8ed1ab_0
    - icu=70.1=h96cf925_0
    - idna=3.4=py310hecd8cb5_0
    - imagesize=1.4.1=py310hecd8cb5_0
    - importlib-metadata=6.0.0=py310hecd8cb5_0
    - importlib_metadata=6.0.0=hd3eb1b0_0
    - ipykernel=6.25.0=py310h20db666_0
    - ipython=8.12.0=py310hecd8cb5_0
    - ipython_genutils=0.2.0=pyhd3eb1b0_1
    - ipywidgets=8.0.4=py310hecd8cb5_0
    - jedi=0.18.1=py310hecd8cb5_1
    - jinja2=3.1.2=py310hecd8cb5_0
    - jpeg=9e=h6c40b1e_1
    - jsonschema=4.17.3=py310hecd8cb5_0
    - jupyter=1.0.0=pyhd8ed1ab_10
    - jupyter_client=8.6.0=py310hecd8cb5_0
    - jupyter_console=6.6.3=py310hecd8cb5_0
    - jupyter_core=5.5.0=py310hecd8cb5_0
    - jupyter_server=1.23.4=py310hecd8cb5_0
    - jupyterlab_pygments=0.2.2=py310hecd8cb5_0
    - jupyterlab_widgets=3.0.9=py310hecd8cb5_0
    - keras=2.11.0=py310_0
    - keras-preprocessing=1.1.2=pyhd3eb1b0_0
    - kiwisolver=1.4.4=py310hcec6c5f_0
    - krb5=1.20.1=h428f121_1
    - latexcodec=2.0.1=pyh9f0ad1d_0
    - lcms2=2.12=hf1fd2bf_0
    - ld64=530=h20443b4_25
    - ld64_osx-64=530=h70f3046_25
    - ldid=2.1.5=hc58f1be_3
    - lerc=3.0=he9d5cce_0
    - lhapdf=6.5.0=py310ha23aa8a_1
    - libabseil=20220623.0=cxx17_h844d122_6
    - libarchive=3.4.2=ha0e9c3a_2
    - libbrotlicommon=1.0.9=hca72f7f_7
    - libbrotlidec=1.0.9=hca72f7f_7
    - libbrotlienc=1.0.9=hca72f7f_7
    - libclang-cpp14=14.0.6=default_hd95374b_1
    - libcurl=8.1.1=hf20ceda_1
    - libcxx=14.0.6=h9765a3e_0
    - libdeflate=1.17=hb664fd8_0
    - libedit=3.1.20221030=h6c40b1e_0
    - libev=4.33=h9ed2024_1
    - libffi=3.4.4=hecd8cb5_0
    - libgfortran=5.0.0=11_3_0_hecd8cb5_28
    - libgfortran5=11.3.0=h9dfd629_28
    - libgrpc=1.51.1=h1ddfa78_1
    - libiconv=1.16=hca72f7f_2
    - liblief=0.12.3=hcec6c5f_0
    - libllvm14=14.0.6=h91fad77_3
    - libnghttp2=1.52.0=h9beae6a_1
    - libopenblas=0.3.21=h54e7dc3_0
    - libpng=1.6.39=h6c40b1e_0
    - libprotobuf=3.21.12=hbc0c0cd_0
    - libsodium=1.0.18=h1de35cc_0
    - libsqlite=3.42.0=h58db7d2_0
    - libssh2=1.10.0=h04015c4_2
    - libtiff=4.5.0=hcec6c5f_2
    - libuv=1.44.2=h6c40b1e_0
    - libwebp=1.2.4=hf6ce154_1
    - libwebp-base=1.2.4=h6c40b1e_1
    - libxml2=2.9.14=hea49891_4
    - libzlib=1.2.13=h8a1eda9_5
    - llvm-openmp=14.0.6=h0dcd299_0
    - llvm-tools=14.0.6=he0576d7_3
    - llvmlite=0.40.0=py310hfff2838_0
    - locket=1.0.0=py310hecd8cb5_0
    - lz4=4.3.2=py310h6c40b1e_0
    - lz4-c=1.9.4=hcec6c5f_0
    - markdown=3.4.1=py310hecd8cb5_0
    - markupsafe=2.1.1=py310hca72f7f_0
    - matplotlib=3.7.1=py310hecd8cb5_1
    - matplotlib-base=3.7.1=py310ha533b9c_1
    - matplotlib-inline=0.1.6=py310hecd8cb5_0
    - mistune=2.0.4=py310hecd8cb5_0
    - more-itertools=8.12.0=pyhd3eb1b0_0
    - msgpack-python=1.0.3=py310haf03e11_0
    - multidict=6.0.2=py310hca72f7f_0
    - munkres=1.1.4=py_0
    - mypy_extensions=1.0.0=py310hecd8cb5_0
    - nbclassic=1.0.0=py310hecd8cb5_0
    - nbclient=0.8.0=py310hecd8cb5_0
    - nbconvert=7.10.0=py310hecd8cb5_0
    - nbformat=5.9.2=py310hecd8cb5_0
    - ncurses=6.4=hcec6c5f_0
    - nest-asyncio=1.5.6=py310hecd8cb5_0
    - networkx=2.8.4=py310hecd8cb5_1
    - notebook=6.5.4=py310hecd8cb5_0
    - notebook-shim=0.2.3=py310hecd8cb5_0
    - numba=0.57.0=py310h3ea8b11_0
    - numexpr=2.8.4=py310he50c29a_1
    - numpy=1.24.3=py310he50c29a_0
    - numpy-base=1.24.3=py310h992e150_0
    - oauthlib=3.2.2=py310hecd8cb5_0
    - openssl=3.0.12=hca72f7f_0
    - opt_einsum=3.3.0=pyhd3eb1b0_1
    - packaging=23.0=py310hecd8cb5_0
    - pandas=1.5.3=py310h3ea8b11_0
    - pandoc=2.12=hecd8cb5_3
    - pandocfilters=1.5.0=pyhd3eb1b0_0
    - parso=0.8.3=pyhd3eb1b0_0
    - partd=1.4.1=py310hecd8cb5_0
    - patch=2.7.6=h1de35cc_1001
    - pcre=8.45=h23ab428_0
    - pendulum=2.1.2=pyhd3eb1b0_1
    - pexpect=4.8.0=pyhd3eb1b0_3
    - pickleshare=0.7.5=pyhd3eb1b0_1003
    - pillow=9.4.0=py310hcec6c5f_0
    - pineappl=0.6.0=py310h3461e44_0
    - pip=23.1.2=py310hecd8cb5_0
    - pkg-config=0.29.2=h3efe00b_8
    - pkginfo=1.9.6=py310hecd8cb5_0
    - platformdirs=3.10.0=py310hecd8cb5_0
    - pluggy=1.0.0=py310hecd8cb5_1
    - pooch=1.4.0=pyhd3eb1b0_0
    - prometheus_client=0.14.1=py310hecd8cb5_0
    - prompt-toolkit=3.0.36=py310hecd8cb5_0
    - prompt_toolkit=3.0.36=hd3eb1b0_0
    - protobuf=4.21.12=py310h7a76584_0
    - psutil=5.9.0=py310hca72f7f_0
    - ptyprocess=0.7.0=pyhd3eb1b0_2
    - pure_eval=0.2.2=pyhd3eb1b0_0
    - py-lief=0.12.3=py310hcec6c5f_0
    - py4j=0.10.9.3=py310hecd8cb5_0
    - pyasn1=0.4.8=pyhd3eb1b0_0
    - pyasn1-modules=0.2.8=py_0
    - pybtex=0.24.0=pyhd8ed1ab_2
    - pybtex-docutils=1.0.2=py310h2ec42d9_2
    - pycosat=0.6.6=py310h6c40b1e_0
    - pycparser=2.21=pyhd3eb1b0_0
    - pydata-sphinx-theme=0.13.3=pyhd8ed1ab_0
    - pygments=2.15.1=py310hecd8cb5_1
    - pyjwt=2.4.0=py310hecd8cb5_0
    - pyopenssl=23.0.0=py310hecd8cb5_0
    - pyparsing=3.0.9=py310hecd8cb5_0
    - pyrsistent=0.18.0=py310hca72f7f_0
    - pysocks=1.7.1=py310hecd8cb5_0
    - python=3.10.9=he7542f4_0_cpython
    - python-dateutil=2.8.2=pyhd3eb1b0_0
    - python-fastjsonschema=2.16.2=py310hecd8cb5_0
    - python-flatbuffers=2.0=pyhd3eb1b0_0
    - python-libarchive-c=2.9=pyhd3eb1b0_1
    - python-lmdb=1.4.1=py310hcec6c5f_0
    - python_abi=3.10=2_cp310
    - pytz=2022.7=py310hecd8cb5_0
    - pytzdata=2020.1=pyhd3eb1b0_0
    - pyyaml=6.0=py310h6c40b1e_1
    - pyzmq=25.1.0=py310hcec6c5f_0
    - qtconsole-base=5.5.1=pyha770c72_0
    - qtpy=2.4.1=py310hecd8cb5_0
    - re2=2023.02.01=hf0c8a7f_0
    - readline=8.2=hca72f7f_0
    - recommonmark=0.6.0=pyhd3eb1b0_0
    - reportengine=0.30.28=py_0
    - requests=2.29.0=py310hecd8cb5_0
    - requests-oauthlib=1.3.0=py_0
    - rhash=1.4.3=h04015c4_0
    - rich=12.5.1=py310hecd8cb5_0
    - rsa=4.7.2=pyhd3eb1b0_1
    - ruamel.yaml=0.17.21=py310hca72f7f_0
    - ruamel.yaml.clib=0.2.7=py310h6729b98_2
    - ruamel_yaml=0.15.100=py310hca72f7f_0
    - scipy=1.10.1=py310ha516a68_1
    - seaborn=0.12.2=py310hecd8cb5_0
    - send2trash=1.8.2=py310hecd8cb5_0
    - setuptools=67.8.0=py310hecd8cb5_0
    - six=1.16.0=pyhd3eb1b0_1
    - snappy=1.1.9=he9d5cce_0
    - sniffio=1.2.0=py310hecd8cb5_1
    - snowballstemmer=2.2.0=pyhd3eb1b0_0
    - sortedcontainers=2.4.0=pyhd3eb1b0_0
    - soupsieve=2.4=py310hecd8cb5_0
    - sphinx=5.0.2=py310hecd8cb5_0
    - sphinx-book-theme=1.0.1=pyhd8ed1ab_0
    - sphinx_rtd_theme=1.1.1=py310hecd8cb5_0
    - sphinxcontrib-applehelp=1.0.2=pyhd3eb1b0_0
    - sphinxcontrib-bibtex=2.5.0=pyhd8ed1ab_0
    - sphinxcontrib-devhelp=1.0.2=pyhd3eb1b0_0
    - sphinxcontrib-htmlhelp=2.0.0=pyhd3eb1b0_0
    - sphinxcontrib-jsmath=1.0.1=pyhd3eb1b0_0
    - sphinxcontrib-qthelp=1.0.3=pyhd3eb1b0_0
    - sphinxcontrib-serializinghtml=1.1.5=pyhd3eb1b0_0
    - sqlalchemy=1.4.39=py310hca72f7f_0
    - sqlite=3.41.2=h6c40b1e_0
    - stack_data=0.2.0=pyhd3eb1b0_0
    - swig=4.0.2=he9d5cce_4
    - tapi=1000.10.8=ha1b3eb9_0
    - tbb=2021.8.0=ha357a0b_0
    - tblib=1.7.0=pyhd3eb1b0_0
    - tensorboard=2.11.0=py310_0
    - tensorboard-data-server=0.6.1=py310h7242b5c_0
    - tensorboard-plugin-wit=1.6.0=py_0
    - tensorflow=2.11.0=cpu_py310h22f808f_0
    - tensorflow-base=2.11.0=cpu_py310h760b059_0
    - tensorflow-estimator=2.11.0=cpu_py310h5e669bb_0
    - termcolor=2.1.0=py310hecd8cb5_0
    - terminado=0.17.1=py310hecd8cb5_0
    - tinycss2=1.2.1=py310hecd8cb5_0
    - tk=8.6.12=h5d9f67b_0
    - tomli=2.0.1=py310hecd8cb5_0
    - toolz=0.12.0=py310hecd8cb5_0
    - tornado=6.2=py310hca72f7f_0
    - tqdm=4.65.0=py310h20db666_0
    - traitlets=5.7.1=py310hecd8cb5_0
    - typing_extensions=4.6.3=py310hecd8cb5_0
    - tzdata=2023c=h04d1e81_0
    - urllib3=1.26.16=py310hecd8cb5_0
    - validobj=1.0=pyhd8ed1ab_0
    - wcwidth=0.2.5=pyhd3eb1b0_0
    - webencodings=0.5.1=py310hecd8cb5_1
    - websocket-client=0.58.0=py310hecd8cb5_4
    - werkzeug=2.2.3=py310hecd8cb5_0
    - wheel=0.35.1=pyhd3eb1b0_0
    - widgetsnbextension=4.0.5=py310hecd8cb5_0
    - wrapt=1.14.1=py310hca72f7f_0
    - xz=5.2.10=h6c40b1e_1
    - yaml=0.2.5=haf1e3a3_0
    - yaml-cpp=0.7.0=he9d5cce_1
    - yarl=1.8.1=py310hca72f7f_0
    - zeromq=4.3.4=h23ab428_0
    - zict=3.0.0=py310hecd8cb5_0
    - zipp=3.11.0=py310hecd8cb5_0
    - zlib=1.2.13=h8a1eda9_5
    - zstandard=0.19.0=py310h6c40b1e_0
    - zstd=1.5.5=hc035e20_0
    - pip:
        - attrs==23.2.0
        - black==24.4.0
        - hypothesis==6.100.1
        - pathspec==0.12.1
prefix: <path_to_root_conda_directory>/envs/simunet

where <path_to_root_conda_directory> can be obtained using the following command line when the (base) environment is activated, in particular, we will obtain the absolute path to the (base) environment, e.g. the miniconda3 folder if we use it.

echo $CONDA_PREFIX

Then, as we anticipated, we must set the CONDA_SUBDIR=osx-64, create the environment from the yml file, and activate it

CONDA_SUBDIR=osx-64 conda env create -f simunet.yml
conda activate simunet

Another important step is installing a previous version of the MacOSX Software Development Kit (SDK), we can download the chosen version from this link, we suggest MacOSX10.9.sdk.tar.xz, which has been tested.

curl -L -O https://github.com/phracker/MacOSX-SDKs/releases/download/11.3/MacOSX10.9.sdk.tar.xz

once we have downloaded the tar.xz file we must untar it into the (base) environment folder

tar xfz MacOSX10.9.sdk.tar.xz -C <path_to_root_conda_directory>

where <path_to_root_conda_directory> is the same we obtained before. Then we must run the following command line to set the right path for the SDK that will be used during the installation of the SIMUnet C++ code

export CONDA_BUILD_SYSROOT=<path_to_root_conda_directory>/MacOSX10.9.sdk

Code compilation

The SIMUnet code can be downloaded from GitHub:

mkdir simunet_git
cd simunet_git
git clone https://github.com/HEP-PBSP/SIMUnet.git

The code can then be compiled and installed with the following commands, first we have to move into the folder downloaded using git, then we must create and place ourselves in the build folder:

cd SIMUnet
mkdir conda-bld
cd conda-bld

Finally, we can complete the installation with the following three steps:

cmake .. -DCMAKE_INSTALL_PREFIX=$CONDA_PREFIX
make
make install

Note that if your CPU has more than one core, which is the case for M1/M2/M3 Macs, the option -j4 can be used to speed up the make command.

Moreover, the make install command will raise some non-stopping errors, which do invalidated the installation.

The SIMUnet code, in addition to the regular files that are needed in the NNPDF methodology to produce theoretical predictions, requires K-factors to account for the effect of SMEFT operators. These K-factors are implemented in simu_fac files, which exists for each dataset in the SIMUnet methodology. For a given dataset, the simu_fac file includes the SM theory prediction, and the SMEFT theory prediction at LO and/or NLO, if applicable. These K-factors are hosted in the NNPDF theory_270 folder, which will be automatically downloaded when required by the user’s runcard.