{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Quickstart\n", "\n", "Once installation is complete you can start running Sarkas. This quickstart guide will walk you through\n", "a simple example in order to check that everything is running smoothly.\n", "\n", "The YAML input file can be found at [input_file](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/documentation/Tutorial_NB/input_files/yocp_quickstart.yaml) and this notebook at [notebook](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/documentation/Tutorial_NB/Quickstart.ipynb)\n", "\n", "\n", "---\n", "## Simulation\n", "\n", "In Jupyter notebook you can run the following commands" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: Qt5Agg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "# Import the usual libraries\n", "%pylab\n", "%matplotlib inline\n", "import os\n", "\n", "plt.style.use('MSUstyle')\n", "\n", "# Import sarkas\n", "from sarkas.processes import Simulation, PostProcess\n", "\n", "# Create the file path to the YAML input file\n", "input_file_name = os.path.join('input_files', 'yocp_quickstart.yaml')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The above commands imported the required libraries and define the file path to our input file. \n", "\n", "Let's now run the simulation" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[38;2;24;69;49m\n", "\n", "\n", "\n", "\n", " _______ __ \n", "| __|.---.-.----.| |--.---.-.-----.\n", "|__ || _ | _|| <| _ |__ --|\n", "|_______||___._|__| |__|__|___._|_____|\n", " \n", "\n", "\u001b[0m\n", "An open-source pure-python molecular dynamics suite for non-ideal plasmas.\n", "\n", "\n", "\n", "\n", "\n", "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n", " Simulation \n", "* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *\n", "\n", "Job ID: yocp_quickstart\n", "Job directory: Simulations/yocp_quickstart\n", "\n", "Equilibration dumps directory: \n", " Simulations/yocp_quickstart/Simulation/Equilibration/dumps\n", "Production dumps directory: \n", " Simulations/yocp_quickstart/Simulation/Production/dumps\n", "\n", "Equilibration Thermodynamics file: \n", " Simulations/yocp_quickstart/Simulation/Equilibration/EquilibrationEnergy_yocp_quickstart.csv\n", "Production Thermodynamics file: \n", " Simulations/yocp_quickstart/Simulation/Production/ProductionEnergy_yocp_quickstart.csv\n", "\n", "PARTICLES:\n", "Total No. of particles = 1000\n", "No. of species = 1\n", "Species ID: 0\n", "\tName: C\n", "\tNo. of particles = 1000 \n", "\tNumber density = 1.136669e+23 [N/cc]\n", "\tAtomic weight = 12.0105 [a.u.]\n", "\tMass = 2.008900e-23 [g]\n", "\tMass density = 2.283455e+00 [g/cc]\n", "\tCharge number/ionization degree = 1.9766 \n", "\tCharge = 9.494010e-10 [esu]\n", "\tTemperature = 5.000000e+03 [K] = 4.308667e-01 [eV]\n", "\tDebye Length = 7.322426e-10 [1/cm^3]\n", "\tPlasma Frequency = 2.531585e+14 [rad/s]\n", "\n", "SIMULATION AND INITIAL PARTICLE BOX:\n", "Units: cgs\n", "Wigner-Seitz radius = 1.280636e-08 [cm]\n", "No. of non-zero box dimensions = 3\n", "Box side along x axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Box side along y axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Box side along z axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Box Volume = 8.797633e-21 [cm^3]\n", "Initial particle box side along x axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Initial particle box side along y axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Initial particle box side along z axis = 1.611992e+01 a_ws = 2.064375e-07 [cm]\n", "Initial particle box Volume = 8.797633e-21 [cm^3]\n", "Boundary conditions: periodic\n", "\n", "ELECTRON PROPERTIES:\n", "Number density: n_e = 2.246739e+23 [N/cc]\n", "Wigner-Seitz radius: a_e = 1.020437e-08 [cm]\n", "Temperature: T_e = 5.000000e+03 [K] = 4.308667e-01 [eV]\n", "de Broglie wavelength: lambda_deB = 1.054132e-07 [cm]\n", "Thomas-Fermi length: lambda_TF = 4.702909e-09 [cm]\n", "Fermi wave number: k_F = 1.880722e+08 [1/cm]\n", "Fermi Energy: E_F = 1.347634e+01 [eV]\n", "Relativistic parameter: x_F = 7.262582e-03 --> E_F = 1.347617e+01 [eV]\n", "Degeneracy parameter: Theta = 3.197207e-02 \n", "Coupling: r_s = 1.928347, Gamma_e = 32.750858\n", "Warm Dense Matter Parameter: W = 3.8973e-03\n", "Chemical potential: mu = 3.1251e+01 k_B T_e = 9.9916e-01 E_F\n", "\n", "POTENTIAL: yukawa\n", "electron temperature = 5.0000e+03 [K] = 4.3087e-01 [eV]\n", "kappa = 2.7231\n", "Gamma_eff = 101.96\n", "\n", "ALGORITHM: pp\n", "rcut = 4.6852 a_ws = 6.000000e-08 [cm]\n", "No. of PP cells per dimension = 3, 3, 3\n", "No. of particles in PP loop = 662\n", "No. of PP neighbors per particle = 102\n", "Tot Force Error = 5.818612e-06\n", "\n", "THERMOSTAT: \n", "Type: berendsen\n", "First thermostating timestep, i.e. relaxation_timestep = 300\n", "Berendsen parameter tau: 2.000 [timesteps]\n", "Berendsen relaxation rate: 0.500 [1/timesteps] \n", "Thermostating temperatures: \n", "Species ID 0: T_eq = 5.000000e+03 [K] = 4.308667e-01 [eV]\n", "\n", "INTEGRATOR: \n", "Type: verlet\n", "Time step = 5.000000e-17 [s]\n", "Total plasma frequency = 2.531585e+14 [rad/s]\n", "w_p dt = 0.0127 ~ 1/79\n", "\n", "Equilibration: \n", "No. of equilibration steps = 10000 \n", "Total equilibration time = 5.0000e-13 [s] ~ 126 w_p T_eq \n", "snapshot interval step = 10 \n", "snapshot interval time = 5.0000e-16 [s] = 0.1266 w_p T_snap\n", "Total number of snapshots = 1000 \n", "\n", "Production: \n", "No. of production steps = 20000 \n", "Total production time = 1.0000e-12 [s] ~ 253 w_p T_prod \n", "snapshot interval step = 10 \n", "snapshot interval time = 5.0000e-16 [s] = 0.1266 w_p T_snap\n", "Total number of snapshots = 2000 \n", "\n", "\n", "------------------------ Initialization Times ------------------------ \n", "\n", "\n", "Potential Initialization Time: 1 sec 95 msec 165 usec 515 nsec\n", "\n", "Particles Initialization Time: 0 sec 0 msec 354 usec 373 nsec\n", "\n", "Total Simulation Initialization Time: 1 sec 95 msec 165 usec 515 nsec\n", "\n", "\n", "--------------------------- Equilibration ---------------------------- \n", "\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "dd87a37175684343b8da3ee4ccaf6b75", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0/10000 [00:00" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize the Postprocessing class\n", "postproc = PostProcess(input_file_name)\n", "# Read the simulation's parameters and assign attributes\n", "postproc.setup(read_yaml=True)\n", "# Calculate observables\n", "postproc.run()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You will notice that both the energy and temperature oscillates wildly. This is fine as long as the percentage deviations, in the top plots, are small. You should have a temperature deviations between -2% to ~ 4-5% while energy deviations between -2% and 1%.\n", "\n", "---\n", "### Observables\n", "\n", "The most common observable is the radial distribution function. This was calculated by `postproc.run()`, here we plot it by rescaling the x axis by the Wigner-Seitz radius $a_{\\rm ws}$." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize the Pair Distribution Function class\n", "ax = postproc.rdf.plot(\n", " scaling=postproc.parameters.a_ws,\n", " y = [(\"C-C RDF\", \"Mean\")],\n", " xlabel = r'$r/a_{\\rm ws}$',\n", " ylabel = r'$g(r)$'\n", ")\n", "ax.legend([\"C-C RDF\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Things to check in here are: \n", "\n", "* Does $g(r)$ go to 1 for large $r$ values ?\n", "* Is there a peak at $r \\sim ~1.5 a$ ?\n", "* Is the height of this peak about ~ 1.4?\n", "\n", "If the answer to all these question is yes than the simulation was successfull." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.10" } }, "nbformat": 4, "nbformat_minor": 4 }