307 lines
8.0 KiB
Plaintext
307 lines
8.0 KiB
Plaintext
{
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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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"<div style=\"text-align: center\"><a href=\"index.ipynb\">LAMMPS Python Tutorials</a></div>"
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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 1: Using LAMMPS with Python"
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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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"Author: [Richard Berger](mailto:richard.berger@outlook.com)"
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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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"The LAMMPS Python package enables calling the LAMMPS C library API."
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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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"## Prerequisites\n",
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"\n",
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"Before running this example, make sure your Python environment can find the LAMMPS shared library (`liblammps.so`) and the LAMMPS Python package is installed. If you followed the [README](README.md) in this folder, this should already be the case. You can also find more information about how to compile LAMMPS and install the LAMMPS Python package in the [LAMMPS manual](https://docs.lammps.org/Python_install.html). There is also a dedicated [LAMMPS Python HowTo](https://docs.lammps.org/Howto_python.html)."
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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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"## Creating a new simulation\n",
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"\n",
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"Once the LAMMPS shared library and the LAMMPS Python package are installed, you can create a new LAMMMPS instance in your Python interpreter as follows:"
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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 lammps\n",
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"L = lammps()"
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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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"With the `lammps` class you can write LAMMPS simulations similar to the input script language. Take the following LAMMPS input script:\n",
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"\n",
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"```lammps\n",
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"# 3d Lennard-Jones melt\n",
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"\n",
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"units lj\n",
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"atom_style atomic\n",
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"\n",
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"lattice fcc 0.8442\n",
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"region box block 0 4 0 4 0 4\n",
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"create_box 1 box\n",
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"create_atoms 1 box\n",
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"mass 1 1.0\n",
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"\n",
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"velocity all create 1.44 87287 loop geom\n",
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"\n",
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"pair_style lj/cut 2.5\n",
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"pair_coeff 1 1 1.0 1.0 2.5\n",
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"\n",
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"neighbor 0.3 bin\n",
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"neigh_modify delay 0 every 20 check no\n",
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"\n",
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"fix 1 all nve\n",
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"\n",
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"thermo 50\n",
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"```\n",
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"The equivalent can be written in Python:"
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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.cmd.units(\"lj\")\n",
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"L.cmd.atom_style(\"atomic\")\n",
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"\n",
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"L.cmd.lattice(\"fcc\", 0.8442)\n",
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"L.cmd.region(\"box\", \"block\", 0, 4, 0, 4, 0, 4)\n",
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"L.cmd.create_box(1, \"box\")\n",
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"L.cmd.create_atoms(1, \"box\")\n",
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"L.cmd.mass(1, 1.0)\n",
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"\n",
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"L.cmd.velocity(\"all\", \"create\", 1.44, 87287, \"loop geom\")\n",
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"\n",
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"L.cmd.pair_style(\"lj/cut\", 2.5)\n",
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"L.cmd.pair_coeff(1, 1, 1.0, 1.0, 2.5)\n",
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"\n",
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"L.cmd.neighbor(0.3, \"bin\")\n",
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"L.cmd.neigh_modify(\"delay\", 0, \"every\", 20, \"check no\")\n",
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"\n",
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"L.cmd.fix(\"1\", \"all\", \"nve\")\n",
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"\n",
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"L.cmd.thermo(50)"
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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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"Some LAMMPS commands will produce output that will be visible in the notebook. However, due to buffering, it might not be shown right away. Use the `flush_buffers` method to see all the output that has been written so far."
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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.flush_buffers()"
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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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"An alternative to this is to enable auto flushing after each command by setting `cmd.auto_flush` to `True`. Each command will then call `flush_buffers()` automatically."
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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.cmd.auto_flush = True\n",
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"L.cmd.info(\"system\")"
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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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"In many cases the LAMMPS output will become excessive, which is why you may want to suppress it. For this purpose we provide a IPython extension in the `lammps.ipython` package. To load the extension, add a code cell with the following content:"
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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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"%load_ext lammps.ipython"
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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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"Once the extension is loaded you have access to the `%%capture_lammps_output` magic. In its simplest form it can be used to supress LAMMPS 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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"%%capture_lammps_output\n",
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"L.cmd.info(\"system\")"
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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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"You can also use the same `%%capture_lammps_output` magic to store the output in a variable by providing a variable name:"
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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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"%%capture_lammps_output out\n",
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"L.cmd.info(\"system\")"
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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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"In this case we are storing the output in a `out` variable. Note the output is only available after the cell has been executed, not within the same cell."
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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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"print(out)"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Visualizing the initial state\n",
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"\n",
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"The `lammps` class also has an `ipython` attribute which provides some basic visualization capabilities in IPython Jupyter notebooks. E.g., you can visualize the current simulation state with the [image](https://docs.lammps.org/Python_module.html#lammps.ipython_wrapper.image) command. Here we use it to create an image of the initial state of the 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.ipython.image()"
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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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"## Running simulations\n",
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"\n",
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"Use the `run` command to start the simulation. It will print the output of the 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.cmd.run(250)"
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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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"Visualizing the system will now show us how the atoms have moved."
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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.ipython.image(zoom=1.0)"
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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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"## Conclusion\n",
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"This covered the basics of creating an instance of LAMMPS from Python, passing commands to LAMMPS and potentially supressing or capturing its output, and visualizing the system. In the [following tutorial](thermo.ipynb) we will look at how to process thermodynamic output from LAMMPS.\n",
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"\n",
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"<div style=\"text-align:right\"><a href=\"thermo.ipynb\">Next</a>"
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]
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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 (ipykernel)",
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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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"version": "3.12.7"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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