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https://github.com/yurlungur/cactus_scripts

Scripts I've used with the Einstein Toolkit
https://github.com/yurlungur/cactus_scripts

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Scripts I've used with the Einstein Toolkit

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README 

        
VISUALIZATION SCRIPTS FOR APPLES-WITH-APPLES TESTS

======================================================================



Authors: Jonah Miller ([email protected])

	 Frederico Guercilena

         Ian Hinder

	 Erik Schnetter



Time-stamp: <2014-02-28 15:15:47 (jonah)>



This is a suite of python scripts and mathematica notebooks to plot

the gaugewave, shifted gaugewave, and robust stability

Apples-with-Apples tests. Each file has documentation in a comment at

the top. This readme file exists to give the user a brief overview of

the suite's capabilities.



The scripts serve the following purposes:



extract_tensor_data.py:

                       --- This is not really a script. Rather, it's a library

		           of methods used to convert Cactus ASCII output into

			   Python data types. At the moment, it can

			   only extract one-dimensional data for symmetric

			   three-tensors projected along the x-axis.



extract_scalar_data.py:

                       --- Exactly the same as extract_tensor_data.py,

		           but for scalar information.



plot_gaugewave.py:

                       --- The final library in this set of scripts,

		           plot_gaugewave.py defines the methods to actually

			   plot a gaugewave solution. It also defines methods

			   to plot a convergence test if given an analytic

			   solution.

			   If you want to change parameters like the amplitude

			   or period of the gaugewave, you need to change

			   the constants at the top of this library for

			   the plot to come out right.



plot_gaugewave_*.py:

		       --- These little scripts are small wrappers of

		           plot_gaugewave.py that provide an analytic solution

			   to plot against. The plot the xx-component of the

			   metric or extrinsic curvature at a given time t

			   as function of space. One and 3d solutions available.

			   Available scripts:

			   --- plot_gaugewave_kxx.py

			   --- plot_gaugewave_gxx.py

			   --- plot_gaugewave_3d_gxx.py

			   Example call:

			   python2 /path/to/plot_gaugewave_kxx.py 0 rho2.curv.x.asc rho4.curv.x.asc

			   more documentation available in the mathematica notebook:

			   --- 3d_gaugewave_analysis.nb



plot_shifted_gaugewwave_*.py:

                       --- Same as plot_gaugewave_*.py, but for the shifted

		       	   gaugewave. More documentation available in the

			   following mathematica notebooks:

			   --- Shifted Gauge Wave Solution and Convergence.nb

			   --- 3d_shifted_gaugewave_analysis.nb

			   Available scripts:

			   --- plot_shifted_gaugewave_kxx.py

			   --- plot_shifted_gaugewave_3d_kxx.py



richardson_extrapolation.py:

                       --- Performs a richardson extrapolation on a set of

		       	   solutions to solve for the order

		           of convergence. Plots a convergence

			   test and outputs an ascii file with the true

			   solution. More documentation is available in

			   the pdf compiled from:

			   --- richardson_extrapolation.py

			   Example call:

			   python2 richardson_extrapolation.py time res1.asc res2.asc res3.asc



plot_robust_stability_at_time.py:

                       --- The robust stability test simulates a noisy Minkowski

		       	   space, so the off-diagonal components of the metric

			   should be zero. This means that a good approximation

			   of the error is the (x,y)-component of the metric. This

			   plots that component at a given time t as a function of

			   space projected along the x-axis. Useful for getting

			   a good idea of the simulations.

			   Example call:

			   python2 plot_robust_stability_at_time.py 0 rho2.metric.x.asc rho4.metric.x.asc



plot_robust_stability_evolution.py:

                       --- The robust stability test simulates a noisy Minkowski

		       	   space, so the off-diagonal components of the metric

			   should be zero. This means that a good approximation

			   of the total error is the L2-norm of the

			   (x,y)-component of the metric. This plots that for any

			   number of output files.

			   Example call:

			   python2 plot_robust_stability_evolution.py *.metric.norm2.asc