How to Install and Uninstall python3-amp Package on Kali Linux
Last updated: November 22,2024
1. Install "python3-amp" package
This guide covers the steps necessary to install python3-amp on Kali Linux
$
sudo apt update
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$
sudo apt install
python3-amp
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2. Uninstall "python3-amp" package
Please follow the instructions below to uninstall python3-amp on Kali Linux:
$
sudo apt remove
python3-amp
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$
sudo apt autoclean && sudo apt autoremove
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3. Information about the python3-amp package on Kali Linux
Package: python3-amp
Source: amp (0.6.1-1)
Version: 0.6.1-1+b9
Installed-Size: 3337
Maintainer: Debian Science Maintainers
Architecture: amd64
Depends: python3-ase (>= 3.14.0~), python3-scipy, python3 (<< 3.13), python3 (>= 3.11~), python3-matplotlib, python3-numpy (>= 1:1.22.0), python3-numpy-abi9, python3-pexpect, python3-zmq, python3:any (>= 3.5~), libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgfortran5 (>= 10)
Size: 276408
SHA256: 737b64581a50e4d700ff49e9af95d974ad139b8624a9f01ac8e94f67e0f3dd70
SHA1: 3caf8f099bfa03ac2a0ce6dd8288a6214437f6a8
MD5sum: c3b826402fb5158c2904202ffcd512c5
Description: Atomistic Machine-learning Package (python 3)
Amp is an open-source package designed to easily bring machine-learning to
atomistic calculations. This project is being developed at Brown University in
the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi,
and is released under the GNU General Public License. Amp allows for the
modular representation of the potential energy surface, allowing the user to
specify or create descriptor and regression methods.
.
Amp is designed to integrate closely with the Atomic Simulation Environment
(ASE). As such, the interface is in pure python, although several
compute-heavy parts of the underlying code also have fortran versions to
accelerate the calculations. The close integration with ASE means that any
calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
and Gaussian ─ can easily be used as the parent method.
.
This package provides the python 3 modules.
Description-md5:
Homepage: https://bitbucket.org/andrewpeterson/amp
Section: science
Priority: optional
Filename: pool/main/a/amp/python3-amp_0.6.1-1+b9_amd64.deb
Source: amp (0.6.1-1)
Version: 0.6.1-1+b9
Installed-Size: 3337
Maintainer: Debian Science Maintainers
Architecture: amd64
Depends: python3-ase (>= 3.14.0~), python3-scipy, python3 (<< 3.13), python3 (>= 3.11~), python3-matplotlib, python3-numpy (>= 1:1.22.0), python3-numpy-abi9, python3-pexpect, python3-zmq, python3:any (>= 3.5~), libc6 (>= 2.29), libgcc-s1 (>= 4.0), libgfortran5 (>= 10)
Size: 276408
SHA256: 737b64581a50e4d700ff49e9af95d974ad139b8624a9f01ac8e94f67e0f3dd70
SHA1: 3caf8f099bfa03ac2a0ce6dd8288a6214437f6a8
MD5sum: c3b826402fb5158c2904202ffcd512c5
Description: Atomistic Machine-learning Package (python 3)
Amp is an open-source package designed to easily bring machine-learning to
atomistic calculations. This project is being developed at Brown University in
the School of Engineering, primarily by Andrew Peterson and Alireza Khorshidi,
and is released under the GNU General Public License. Amp allows for the
modular representation of the potential energy surface, allowing the user to
specify or create descriptor and regression methods.
.
Amp is designed to integrate closely with the Atomic Simulation Environment
(ASE). As such, the interface is in pure python, although several
compute-heavy parts of the underlying code also have fortran versions to
accelerate the calculations. The close integration with ASE means that any
calculator that works with ASE ─ including EMT, GPAW, DACAPO, VASP, NWChem,
and Gaussian ─ can easily be used as the parent method.
.
This package provides the python 3 modules.
Description-md5:
Homepage: https://bitbucket.org/andrewpeterson/amp
Section: science
Priority: optional
Filename: pool/main/a/amp/python3-amp_0.6.1-1+b9_amd64.deb