Created PyLammps documentation
Based on material presented during MD Workshop at Temple University in August 2016.
This commit is contained in:
Richard Berger
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python/examples/pylammps/interface_usage.ipynb
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517
python/examples/pylammps/interface_usage.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Using LAMMPS with iPython and Jupyter"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"LAMMPS can be run interactively using iPython easily. This tutorial shows how to set this up."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Installation"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"1. Download the latest version of LAMMPS into a folder (we will calls this `$LAMMPS_DIR` from now on)\n",
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"2. Compile LAMMPS as a shared library and enable exceptions and PNG support\n",
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" ```bash\n",
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" cd $LAMMPS_DIR/src\n",
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" python Make.py -m mpi -png -s exceptions -a file\n",
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" make mode=shlib auto\n",
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" ```\n",
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"\n",
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"3. Create a python virtualenv\n",
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" ```bash\n",
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" virtualenv testing\n",
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" source testing/bin/activate\n",
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" ```\n",
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"\n",
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"4. Inside the virtualenv install the lammps package\n",
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" ```\n",
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" (testing) cd $LAMMPS_DIR/python\n",
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" (testing) python install.py\n",
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" (testing) cd # move to your working directory\n",
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" ```\n",
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"\n",
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"5. Install jupyter and ipython in the virtualenv\n",
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" ```bash\n",
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" (testing) pip install ipython jupyter\n",
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" ```\n",
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"\n",
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"6. Run jupyter notebook\n",
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" ```bash\n",
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" (testing) jupyter notebook\n",
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" ```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from lammps import IPyLammps"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L = IPyLammps()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# 3d Lennard-Jones melt\n",
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"\n",
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"L.units(\"lj\")\n",
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"L.atom_style(\"atomic\")\n",
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"L.atom_modify(\"map array\")\n",
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"\n",
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"L.lattice(\"fcc\", 0.8442)\n",
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"L.region(\"box block\", 0, 4, 0, 4, 0, 4)\n",
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"L.create_box(1, \"box\")\n",
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"L.create_atoms(1, \"box\")\n",
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"L.mass(1, 1.0)\n",
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"\n",
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"L.velocity(\"all create\", 1.44, 87287, \"loop geom\")\n",
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"\n",
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"L.pair_style(\"lj/cut\", 2.5)\n",
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"L.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
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"\n",
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"L.neighbor(0.3, \"bin\")\n",
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"L.neigh_modify(\"delay 0 every 20 check no\")\n",
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"\n",
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"L.fix(\"1 all nve\")\n",
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"\n",
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"L.variable(\"fx atom fx\")\n",
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"\n",
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"L.run(10)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.image(zoom=1)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Queries about LAMMPS simulation"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.system"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.system.natoms"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.communication"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.fixes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.computes"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.dumps"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.groups"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Working with LAMMPS Variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variable(\"a index 2\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variable(\"t equal temp\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"\n",
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"if sys.version_info < (3, 0):\n",
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" # In Python 2 'print' is a restricted keyword, which is why you have to use the lmp_print function instead.\n",
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" x = float(L.lmp_print('\"${a}\"'))\n",
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"else:\n",
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" # In Python 3 the print function can be redefined.\n",
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" # x = float(L.print('\"${a}\"')\")\n",
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" \n",
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" # To avoid a syntax error in Python 2 executions of this notebook, this line is packed into an eval statement\n",
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" x = float(eval(\"L.print('\\\"${a}\\\"')\"))\n",
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"x"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables['t'].value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.eval(\"v_t/2.0\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variable(\"b index a b c\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables['b'].value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.eval(\"v_b\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables['b'].definition"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variable(\"i loop 10\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables['i'].value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.next(\"i\")\n",
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"L.variables['i'].value"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.eval(\"ke\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Accessing Atom data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"[x for x in dir(L.atoms[0]) if not x.startswith('__')]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0].position"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0].id"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0].velocity"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0].force"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.atoms[0].type"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.variables['fx'].value"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Accessing thermo data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0].thermo"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.runs[0].thermo.Temp"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Saving session to as LAMMPS input file"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"L.write_script(\"in.output\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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