#!/usr/bin/env python
# coding: utf-8

# # Einstein Toolkit Tutorial 

# This tutorial is a smaller version of the <a href="https://einsteintoolkit.org/documentation/new-user-tutorial">Einstein Toolkit tutorial</a>. The tutorial in this page let's you download and install only the Einstein Toolkit modules ("thorns") that are needed in order to run a simple simulation. This tutorial can run on a laptop and should require less than one hour in total.

# First we need to download some packages needed to compile the Einstein Toolkit.

# If you use a <b>Mac</b>, you should first install MacPorts https://www.macports.org/install.php.
# And then you can install the packages you need by executing these commands in a terminal:<br>
# <code>sudo port install subversion mercurial wget git</code><br>
# <code>sudo port install pkgconfig gcc7 openmpi fftw-3 gsl jpeg zlib hdf5 +fortran +gfortran openssl py-matplotlib</code>
# 
# If you use <b>Ubuntu</b>, you should install the packages you need using these commands in a terminal:<br>
# <code>sudo apt-get install subversion git mercurial wget python python-matplotlib curl</code><br>
# <code>sudo apt-get install build-essential perl gfortran g++ libmpich-dev</code><br>
# <code>sudo apt-get install libfftw3-dev libgsl-dev libatlas-base-dev libjpeg-dev libssl-dev</code><br>
# <code>sudo apt-get install libhdf5-dev hdf5-tools libnuma-dev libltdl-dev libhwloc-dev zlib1g-dev</code>

# Now we create the directory where the tutorial material will be located

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get_ipython().system('mkdir Einstein_Toolkit_CINECA')


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get_ipython().run_line_magic('cd', 'Einstein_Toolkit_CINECA')


# In this directory we now download the files used for this tutorial

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get_ipython().system('wget http://brunogiacomazzo.org/ETK_Tutorial_CINECA_2018/einsteintoolkit_CINECA.th')


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get_ipython().system('wget http://brunogiacomazzo.org/ETK_Tutorial_CINECA_2018/tov_ETK_2018_CINECA.par')


# We now create the directory where the simulation data will be stored

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get_ipython().system('mkdir simulations')


# Now we download the components of the Einstein Toolkit needed to run this tutorial

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get_ipython().system('curl -kLO https://raw.githubusercontent.com/gridaphobe/CRL/ET_2018_09/GetComponents')


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get_ipython().system('chmod a+x GetComponents')


# Note: the following command sometimes may fail with an error similar to this:
# "Server SSL certificate verification failed: issuer is not trusted". <br>
# In this case, open a terminal and type:<br>
# <code>svn co <link_to_the_failed_repository> </code><br>
# where <link_to_the_failed_repository> should be replaced with the address that is printed by the SSL certificate error.<br>
# At this point svn will ask if you want to accept the certificate. Type <code>p</code> to accept the certificate permanently and then rerun the GetComponents command below.

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get_ipython().system('./GetComponents einsteintoolkit_CINECA.th')


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get_ipython().run_line_magic('cd', 'Cactus')


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get_ipython().system('cp ../tov_ETK_2018_CINECA.par ./par')


# Now we set up simfactory

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get_ipython().system('./simfactory/bin/sim setup-silent')


# Now we compile the Einstein Toolkit (this may take ~30 minutes or more)

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#uncomment the line that refers to your operating system
#!./simfactory/bin/sim build --machine=osx-macports --thornlist=thornlists/einsteintoolkit_CINECA.th
#!./simfactory/bin/sim build --machine=ubuntu --thornlist=thornlists/einsteintoolkit_CINECA.th


# And now we are ready to run a simple simulation. In the example we use 2 MPI processes.

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get_ipython().system('./simfactory/bin/sim create-run tov_test --procs 2 --num-threads 1 --parfile=./par/tov_ETK_2018_CINECA.par --basedir=../simulations/')


# Once the simulation has finished we can go in the output directory and have a look at the results.

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get_ipython().run_line_magic('cd', '../simulations/tov_test/')


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get_ipython().run_line_magic('cd', 'output-0000/tov_ETK_2018_CINECA/')


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get_ipython().system('ls')


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# This cell enables inline plotting in the notebook
get_ipython().run_line_magic('matplotlib', 'inline')

import matplotlib
import numpy as np
import matplotlib.pyplot as plt


# Let's have a look at the maximum of the rest-mass density

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lin_data = np.genfromtxt("hydrobase-rho.maximum.asc")


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lin_data


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plt.plot(lin_data[:,1],lin_data[:,2])