Skip to main content

Companion library to the ndfes C++ program for analyzing umbrella sampling

Project description

The configuration is performed with cmake. You will need cmake (version 3.12 or later installed). The installation of the python components is performed with pip.

To install cmake and update your version of pip, you can run:

USERBASE=$(python3 -m site --user-base)
USERSITE=$(python3 -m site --user-site)
export PATH="${USERBASE}/bin:${PATH}"
export PYTHONPATH="${USERSITE}:${PYTHONPATH}"
python3 -m pip install pip --upgrade --user
python3 -m pip install cmake --upgrade --user

You should consider adding the above export commands to your ${HOME}/.bashrc and then source ~/.bashrc.

If, for whatever reason, pip is unavailable on your system, you can install it using the directions here: https://pip.pypa.io/en/stable/installation/

To install ndfes:

cd build
bash ./run_cmake.sh
make install VERBOSE=1 -j4
cd ../local
export PATH="${PWD}/bin:${PATH}"
export PYTHONPATH="${PWD}/lib/python3.XX/site-packages:${PYTHONPATH}"

where python3.XX should be replaced by the appropriate python version.

ndfes has dependencies on blas/lapack and nlopt. The cmake configuration will check if these libraries are available. If they are not available, it will automatically download them from github and install them.

The blas/lapack libraries are installed from the openblas package.

https://github.com/OpenMathLib/OpenBLAS

The nlopt software is used to perform nonlinear optimizations.

https://github.com/stevengj/nlopt

These libraries could also be installed globally using your system’s package manager. For example, on Fedora, you could install them with:

sudo dnf install NLopt-devel openblas

See the contents of build/run_cmake.sh to see how you can adjust the compilers, compiler flags, python interpreter, and installation directory.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

ndfes-3.5.6-py2.py3-none-musllinux_1_2_x86_64.whl (24.1 MB view details)

Uploaded Python 2Python 3musllinux: musl 1.2+ x86-64

ndfes-3.5.6-py2.py3-none-musllinux_1_2_i686.whl (22.1 MB view details)

Uploaded Python 2Python 3musllinux: musl 1.2+ i686

ndfes-3.5.6-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (21.7 MB view details)

Uploaded Python 2Python 3manylinux: glibc 2.17+ x86-64

ndfes-3.5.6-py2.py3-none-manylinux_2_17_i686.manylinux2014_i686.whl (18.5 MB view details)

Uploaded Python 2Python 3manylinux: glibc 2.17+ i686

File details

Details for the file ndfes-3.5.6-py2.py3-none-musllinux_1_2_x86_64.whl.

File metadata

File hashes

Hashes for ndfes-3.5.6-py2.py3-none-musllinux_1_2_x86_64.whl
Algorithm Hash digest
SHA256 2dfa94fe1835cd32df1edc068300169f75e4a52c2ad899e366b865eef9eb97cd
MD5 116b44bc5ca0fde20df094e9359d4214
BLAKE2b-256 0a50a1605520ee5d9d6aad6498eb9329fdc33bc6d799ffd3dc4266f0ffa3f4e6

See more details on using hashes here.

File details

Details for the file ndfes-3.5.6-py2.py3-none-musllinux_1_2_i686.whl.

File metadata

  • Download URL: ndfes-3.5.6-py2.py3-none-musllinux_1_2_i686.whl
  • Upload date:
  • Size: 22.1 MB
  • Tags: Python 2, Python 3, musllinux: musl 1.2+ i686
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.11

File hashes

Hashes for ndfes-3.5.6-py2.py3-none-musllinux_1_2_i686.whl
Algorithm Hash digest
SHA256 440e1d20fe576214524e54f3e8e967655fa214f48df316539ed37583aaa16c4e
MD5 948d13ce3a19e91eaa046a14ce39ca2c
BLAKE2b-256 7b9825c1a0e5247086b85cd27aff419bc0e871cb533a8e905f81034369ae1797

See more details on using hashes here.

File details

Details for the file ndfes-3.5.6-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for ndfes-3.5.6-py2.py3-none-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6efda90d2f6c40f5e6e8814e6d46eb0ba1e22ec062bd2c8b49166c8833e86cc2
MD5 076eed75f44a158814ae43ad8f5c6152
BLAKE2b-256 f661713eae08b4dc7d4560457f7d3bf20b09ac767a48865767ad894542b847ce

See more details on using hashes here.

File details

Details for the file ndfes-3.5.6-py2.py3-none-manylinux_2_17_i686.manylinux2014_i686.whl.

File metadata

File hashes

Hashes for ndfes-3.5.6-py2.py3-none-manylinux_2_17_i686.manylinux2014_i686.whl
Algorithm Hash digest
SHA256 f1abde6998dba670b52105853ad0de3b6dc3c44e36d40236f4cc2a42803a53bf
MD5 d53751a39c218dfff0cb06ed66f2b0c4
BLAKE2b-256 ade248d68382c3b19f6f3030520d31b4863a2c23a36cf0bdf8c5d78c65cffd59

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page