<jupyter_start><jupyter_text>QuickstartOnce installation is complete you can start running Sarkas. This quickstart guide will walk you througha simple example in order to check that everything is running smoothly.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_cgs_pp.yaml) and this notebook at [notebook](https://raw.githubusercontent.com/murillo-group/sarkas/master/docs/documentation/Tutorial_NB/Quickstart.ipynb)--- SimulationIn Jupyter notebook you can run the following commands<jupyter_code># Import the usual libraries
%pylab
%matplotlib inline
import os

plt.style.use('MSUstyle')

# Import sarkas
from sarkas.processes import Simulation, PostProcess

# Create the file path to the YAML input file
input_file_name = os.path.join('input_files', 'yocp_cgs_pp.yaml')<jupyter_output>Using matplotlib backend: Qt5Agg
Populating the interactive namespace from numpy and matplotlib<jupyter_text>The above commands imported the required libraries and define the file path to our input file. Let's now run the simulation<jupyter_code># Initialize the Simulation class
sim = Simulation(input_file_name)
# Setup the simulation's parameters
sim.setup(read_yaml=True)
# Run the simulation
sim.run()<jupyter_output>[38;2;0;129;131m




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An open-source pure-python molecular dynamics suite for non-ideal plasmas.



* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
                                   Simulation                                   
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

Job ID:  yocp_cgs_pp
Job directory:  Simulations/yocp_cgs_pp

Equilibration dumps directory: 
 Simulations/yocp_cgs_pp/Simulation/Equilibration/dumps
Production dumps directory: 
 Simulations/yocp_cgs_pp/Simulation/Production/dumps

Equilibration Thermodynamics file: 
 Simulations/yocp_cgs_pp/Simulation/Equilibration/EquilibrationEnergy_yocp_cgs_pp.csv
Production Thermodynamics file: 
 Simulations/yocp_c[...]<jupyter_text>PostprocessingNow that our simulation is complete we need to check if the simulation was physically sound. Run the following three lines will initialize the `PostProcess` class and calculate the observables defined in the input file.It will also produce a plot of the Temperature and Total Energy of the Production phase.<jupyter_code># Initialize the Postprocessing class
postproc = PostProcess(input_file_name)
# Read the simulation's parameters and assign attributes
postproc.setup(read_yaml=True)
# Calculate observables
postproc.run()<jupyter_output>* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *
                                 Postprocessing                                 
* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *

Job ID:  yocp_cgs_pp
Job directory:  Simulations/yocp_cgs_pp
PostProcessing directory: 
 Simulations/yocp_cgs_pp/PostProcessing

Equilibration dumps directory:  Simulations/yocp_cgs_pp/Simulation/Equilibration/dumps
Production dumps directory: 
 Simulations/yocp_cgs_pp/Simulation/Production/dumps

Equilibration Thermodynamics file: 
 Simulations/yocp_cgs_pp/Simulation/Equilibration/EquilibrationEnergy_yocp_cgs_pp.csv
Production Thermodynamics file: 
 Simulations/yocp_cgs_pp/Simulation/Production/ProductionEnergy_yocp_cgs_pp.csv


==================== Radial Distribution Function ==================== 

Data saved in: 
 Simulations/yocp_cgs_pp/PostProcessing/RadialDistributionFunction/Production/RadialDistri[...]<jupyter_text>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%.--- ObservablesThe 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 $a_{\rm ws}$.<jupyter_code># Initialize the Pair Distribution Function class
postproc.rdf.plot(scaling=postproc.parameters.a_ws, xlabel = r'$r/a_{\rm ws}$')<jupyter_output><empty_output>
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