225 lines
4.5 KiB
Plaintext
225 lines
4.5 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 3: Working with Per-Atom Data"
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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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"2D circle of particles inside of box with LJ walls"
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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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"## Setup 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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"from lammps import lammps"
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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 = lammps()\n",
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"cmd = L.cmd"
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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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"# 2d circle of particles inside a box with LJ walls\n",
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"import math\n",
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"\n",
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"b = 0\n",
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"x = 50\n",
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"y = 20\n",
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"d = 20\n",
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"\n",
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"# careful not to slam into wall too hard\n",
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"\n",
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"v = 0.3\n",
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"w = 0.08\n",
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" \n",
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"cmd.units(\"lj\")\n",
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"cmd.dimension(2)\n",
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"cmd.atom_style(\"bond\")\n",
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"cmd.boundary(\"f f p\")\n",
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"\n",
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"cmd.lattice(\"hex\", 0.85)\n",
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"cmd.region(\"box\", \"block\", 0, x, 0, y, -0.5, 0.5)\n",
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"cmd.create_box(1, \"box\", \"bond/types\", 1, \"extra/bond/per/atom\", 6)\n",
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"cmd.region(\"circle\", \"sphere\", d/2.0+1.0, d/2.0/math.sqrt(3.0)+1, 0.0, d/2.0)\n",
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"cmd.create_atoms(1, \"region\", \"circle\")\n",
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"cmd.mass(1, 1.0)\n",
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"\n",
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"cmd.velocity(\"all create 0.5 87287 loop geom\")\n",
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"cmd.velocity(\"all set\", v, w, 0, \"sum yes\")\n",
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"\n",
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"cmd.pair_style(\"lj/cut\", 2.5)\n",
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"cmd.pair_coeff(1, 1, 10.0, 1.0, 2.5)\n",
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"\n",
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"cmd.bond_style(\"harmonic\")\n",
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"cmd.bond_coeff(1, 10.0, 1.2)\n",
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"\n",
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"cmd.create_bonds(\"many\", \"all\", \"all\", 1, 1.0, 1.5)\n",
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"\n",
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"cmd.neighbor(0.3, \"bin\")\n",
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"cmd.neigh_modify(\"delay\", 0, \"every\", 1, \"check yes\")\n",
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"\n",
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"cmd.fix(1, \"all\", \"nve\")\n",
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"\n",
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"cmd.fix(2, \"all wall/lj93 xlo 0.0 1 1 2.5 xhi\", x, \"1 1 2.5\")\n",
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"cmd.fix(3, \"all wall/lj93 ylo 0.0 1 1 2.5 yhi\", y, \"1 1 2.5\")"
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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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"## Visualize initial state"
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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=1.8)"
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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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"## Run simulation and visualize new state"
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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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"cmd.thermo_style(\"custom step temp epair press\")\n",
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"cmd.thermo(100)\n",
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"output = cmd.run(40000)\n",
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"L.ipython.image(zoom=1.8)"
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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.numpy.extract_atom(\"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.numpy.extract_atom(\"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.numpy.extract_atom(\"v\")"
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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.numpy.extract_atom(\"f\")"
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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.numpy.extract_atom(\"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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}
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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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