Session 8: Postprocessing

Getting started

Scientific simulation is worthless unless the results of those simulations can be analyzed and understood. Unfortunately, most classes on scientific computing focus almost exclusively on how to create the methods for performing the simulations, with little time (if any) dedicated to the analysis of those results. While one session of a workshop is insufficient to fully rectify this situation, we’ll try to get get you started down the right path, by first focusing on how to input simulation data into more interactive computing environments, and then how to postprocess and visualize that data. While there are many available interactive computing environments that can be used for these purposes, we’ll focus on two of the most popular options, Matlab and Python.

To perform this tutorial session, we’ll need to set up our environment to use Python 2.7.x,

$ module load python/2.7.8
$ module load MATLAB

Retrieve the set of files for this session either through clicking here or by copying the relevant files at the command line:

$ cp ~dreynolds/ManeFrame_tutorial/session8.tgz .

Unzip this file, enter the resulting directory, and build the executable with make.

Run the executable at the command-line.

$ ./advection.exe

You should see a set of output, ending with lines similar to:

writing output file 24, step = 2399, t = 0.48
writing output file 25, step = 2499, t = 0.5
total runtime = 3.1000000000000000e-01

List the files in the subdirectory; you should see a new set of files with the names u_sol.###.txt. These files contain solution data from the simulation that you just ran, which models the propagation of an acoustic wave over a periodic, two-dimensional surface, using a coarse \(50 \times 50\) spatial grid.

In the following sections, we will work on importing these data files into either a Matlab or Python environment, and then performing some simple data analysis. For the remainder of this session, both Matlab and Python will be presented, though you may choose to specialize in only your preferred interactive environment.

Importing/exporting data

Before we can understand how to load data into Matlab or Python, we must understand how it was written from the program. Here is the C++ function used to output the two-dimensional data array u:

// Daniel R. Reynolds
// SMU HPC Workshop
// 20 May 2013

// Inclusions
#include <stdio.h>
#include <string.h>
#include "advection.h"

// Writes current solution to disk
int output(double *u, double t, int nx, int ny, int noutput) {

  // set output file name
  char outname[100];
  sprintf(outname, "u_sol.%03i.txt", noutput);

  // open output file
  FILE *FID = fopen(outname,"w");
  if (FID == NULL) {
    fprintf(stderr, "output: error opening output file %s\n", outname);
    return 1;

  // output the solution values
  for (int j=0; j<ny; j++)
    for (int i=0; i<nx; i++)
      fprintf(FID, "%.16e\n",u[idx(i,j,nx)]);

  // write current solution time and close the data set
  fprintf(FID, "%.16e\n", t);

  // now output a metadata file, containing general run information
  FID = fopen("u_sol_meta.txt","w");
  fprintf(FID, "%i\n", nx);
  fprintf(FID, "%i\n", ny);
  fprintf(FID, "%i\n", noutput);

  return 0;
} // end output

A few contextual notes about this code to better understand what is happening (we’ll discuss in greater detail during class):

  • u holds a two-dimensional array of size nx by ny, stored in a one-dimensional index space of length nx*ny. The mapping between the 2D physical space and 1D index space is handled by the idx() macro, defined in advection.h, of the form

    // simple macro to map a 2D index to a 1D address space
    #define idx(i,j,nx)  ((j)*(nx)+(i))
  • This function is called once every output time; these outputs are indexed by the integer noutput, and correspond to the solution at the physical time t.

  • At each output time, this routine writes two files:

    • The first file is the solution file (u_sol.###.txt), that holds the 2D data array, printed as one long array with the \(x\) coordinate the faster index. In this same file, after u is stored, the physical time of the output, t is also stored.
    • The second file is a metadata file (u_sol_meta.txt), that contains the problem size and the total number of outputs that have been written so far in the simulation.

We will first build Matlab and Python functions that can read in the metadata file. First. let’s view the contents of the metadata file:

$ cat u_sol_meta.txt

Here the first “50” corresponds to nx, the second “50” corresponds to ny, and the “25” corresponds to the total number of solutions that have been output (i.e. the final value for noutput).

Due to this file’s simple structure, we we only need to read three numbers in a single column and store them appropriately. The relevant Matlab code is in the file load_info.m, and relies on the built-in Matlab function load:

function [nx,ny,nt] = load_info()
% Usage: [nx,ny,nt] = load_info()
% Outputs: nx,ny are the grid size, and nt is the total number of
% time steps that have been output to disk.
% Daniel R. Reynolds
% SMU HPC Workshop
% 20 May 2013

% input general problem information
load u_sol_meta.txt;   % reads values from disk, storing in a vector
nx = u_sol_meta(1);    % unpack vector to name each output
ny = u_sol_meta(2);
nt = u_sol_meta(3);

% end of function

The corresponding Python code is in the file, which similarly relies on the built-in Numpy function loadtxt:

# Defines the function load_info().
# Daniel R. Reynolds
# SMU HPC Workshop
# 20 May 2013

# import requisite modules
import numpy as np

def load_info():
    """Returns the mesh size and total number of output times
       from the input file 'u_sol_meta.txt'.  Has calling syntax:
          nx,ny,nt = load_info(). """

    # reads integer values from disk, storing in a vector
    data = np.loadtxt("u_sol_meta.txt", dtype=int)
    return data     # return entire vector

# end of file

In both of these scripts, the data in the file u_sol_meta.txt is input and converted to a one-dimensional array of numbers. In the Matlab code we name these and return each separately. In the Python code we merely return the array, leaving unpacking and naming to the calling routine.


In the R package for interactive statistical data analysis, the corresponding command to Matlab’s load and Python/Numpy’s loadtxt is the R function read.table, e.g.

> read.table("u_sol_meta.txt")
1 50
2 50
3 25

However, since I do not know how to use R all of the following examples will only be in Matlab or Python. Of course, if you are more familiar with R, you are welcome to attempt the remainder of this session with that instead of Matlab or Python.

Now that we’ve seen a simple approach for loading an array into Matlab and Python, we can move on to functions for reading the larger u_sol.###.txt files. As with the above functions, since the data is output in a single (but very long) column of numbers, we may use load or loadtxt to input the data. Once this data has been read in, however, we will then split it into the solution component, u, and the current time, t. Since u holds a two-dimensional array, but is stored in a flattened one-dimensional format, we can use reshape (the same command in both Matlab and Python) to convert it from the one-dimensional to the two-dimensional representation.

First, the Matlab code, load_data_2d.m:

function [t,u] = load_data_2d(tstep)
% Usage: [t,u] = load_data_2d(tstep)
% Input: tstep is an integer denoting which time step output to load
% Outputs: t is the physical time, and u is the 2D array containing
% the result at the requested time step
% Daniel R. Reynolds
% SMU HPC Workshop
% 20 May 2013

% input general problem information
[nx,ny,nt] = load_info();

% ensure that tstep is allowable
if (tstep < 0 || tstep > nt)
   error('load_data_2d error: illegal tstep')

% set filename string and load as a long 1-dimensional array
infile = sprintf('u_sol.%03i.txt',tstep);
data = load(infile);

% separate data array from current time, and reshape data into 2D
u1D = data(1:end-1);
t = data(end);
u = reshape(u1D, [nx, ny]);


and here is the corresponding Python code,

# Defines the function load_data_2d().
# Daniel R. Reynolds
# SMU HPC Workshop
# 20 May 2013

# import requisite modules
import numpy as np
from load_info import load_info

def load_data_2d(tstep):
    """Returns the solution over the mesh for a given time snapshot.
       Has calling syntax:
          t,u = load_data_2d(tstep)
       Input: tstep is an integer denoting which time step output to load.
       Outputs: t is the physical time, and u is the 2D array containing
                the result at the requested time step."""

    # load the parallelism information
    nx,ny,nt = load_info()

    # check that tstep is allowed
    if (tstep < 0 or tstep > nt):
        print 'load_data_2d error: illegal tstep!'

    # determine data file name and load as a long 1-dimensional array
    infile = 'u_sol.' + repr(tstep).zfill(3) + '.txt'
    data = np.loadtxt(infile, dtype=np.double)

    # separate data array from current time and reshape data into 2D
    u1D = data[:len(data)-1]
    t = data[-1];
    u = np.reshape(u1D, (nx,ny), order='F')

    return [t,u]

How these work:

  • These routines take as input an integer, tstep, that corresponds to the desired time step output file (the ### in the file name).
  • They then call the corresponding load_info function to find out the two-dimensional domain size and the total number of time steps written to disk, and perform a quick check to see whether tstep is an allowable time step index.
    • Matlab: The function namespace for Matlab corresponds to all ”.m” files in the current folder, followed by all built-in functions. So as long as both of the scripts load_info.m and load_data_2d.m are in the same folder, the load_data_2d function can call the load_info function automatically.
    • Python: Since Python protects the namespace by default, any non-built-in Python functions from other files must be loaded before they may be executed. As a result, must import the load_info function from the file before it may be used (note: the ”.py” extension for the ```` file is assumed, and should not be added to the “from” portion of the ``import`` command).
  • The routine then combines the time step index into a string that represents the correct file name (e.g. u_sol.006.txt), and calls the relevant load or loadtxt routine to input the data.
  • The routine then splits the data into the one-dimensional version of u (called u1D) and t, before reshaping u1D into a two-dimensional version of the solution, before returning the values.


In the Python version, we must specify that the data is ordered in “Fortran” style, i.e. that the first index is the fastest (as opposed to “C” style, where the first index is the slowest). Fortran ordering is the default in Matlab, whereas C ordering is the default in Python. This output was written in “Fortran” style, so we use that here.

These data input routines can be used by Matlab or Python scripts to first read in the data, before either performing analysis or plotting.

A few general comments on the above approach:

  • By storing the values as raw text, these files are larger than necessary. In this example the files are not too large (~58 KB each), but in more realistic simulations it would be preferred to store data in a more compressed format. Two approaches for this are to:
    1. Zip each file after it is written to disk, through using library routines (e.g. libz, libzip, libgzip), and the uncompress them when reading. If the file is compressed with gzip, Numpy’s loadtxt routine will automatically unzip as it reads.
    2. Write the data to disk in binary format.
  • Performance-wise, it is best to write out data in the order in which it is stored in memory during the simulation. In this example, the data is stored with the x index being the fastest, hence the “Fortran” ordering of the data file.

High-quality alternatives to such manual I/O approaches abound. Two popular I/O libraries in high-performance computing are HDF5 and netCDF. Both of these libraries have the following benefits over doing things manually:

  • Natively output in binary format for smaller file sizes.
  • Allow you to output descriptive information in addition to just the data (e.g. units of each field, version of the code).
  • Allow you to output multiple items to the same file (e.g. density, momentum, energy).
  • Support parallel computing, allowing many MPI tasks to write to the same file.
  • Professional visualization utilities typically have readers built-in for these file types.
  • Have data input utilities in both Matlab and Python:
  • Last but not least: someone else writes and debugs the code, allowing you to focus on your work instead of spending your time fiddling with I/O.


We will now use the above data input routines to do some post-processing of these simulated results. For this example, we’ll create surface plots of the field u, one for each time step, and write them to the disk. Of course, once the data is available in our preferred scripting environment (Matlab, Python, etc.), we can easily perform additional data analysis, as will be included in the hands-on exercise at the end of this session.

As we did earlier, we’ll first show the code and then go through the steps. You may focus on your preferred computing environment, since both scripts are functionally equivalent.

First the Matlab code, plot_solution.m:

% Plotting script for 2D acoustic wave propagation example
% simulation.  This script inputs the file u_sol_meta.txt to determine
% simulation information (grid size and total number of time steps).
% It then calls load_data_2d() to read the solution data from each
% time step, plotting the results (and saving them to disk).
% Daniel R. Reynolds
% SMU HPC Workshop
% 20 May 2013

% input general problem information
[nx,ny,nt] = load_info();

% loop over time steps
for tstep = 0:nt

   % load time step data
   [t,u] = load_data_2d(tstep);

   % plot current solution (and save to disk)
   xvals = linspace(0,1,nx);
   yvals = linspace(0,1,ny);
   h = surf(yvals,xvals,u);
   shading flat
   view([50 44])
   axis([0, 1, 0, 1, -1, 1])
   xlabel('x','FontSize',14), ylabel('y','FontSize',14)
   title(sprintf('u(x,y) at t = %g, mesh = %ix%i',t,nx,ny),'FontSize',14)
   pfile = sprintf('u_surf.%03i.png',tstep);

   %disp('pausing: hit enter to continue')

and then the Python code,

# Plotting script for 2D acoustic wave propagation example
# simulation.  This script calls load_info() to determine
# simulation information (grid size and total number of time steps).
# It then calls load_data_2d() to read the solution data from each
# time step, plotting the results (and saving them to disk).
# Daniel R. Reynolds
# SMU HPC Workshop
# 20 May 2013

# import the requisite modules
from pylab import *
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import matplotlib.pyplot as plt
from load_info import load_info
from load_data_2d import load_data_2d

# input general problem information
nx,ny,nt = load_info()

# iterate over time steps
for tstep in range(nt+1):

    # input solution at this time
    t,u = load_data_2d(tstep)

    # set string constants for output plots, current time, mesh size
    pname = 'u_surf.' + repr(tstep).zfill(3) + '.png'
    tstr = repr(round(t,4))
    nxstr = repr(nx)
    nystr = repr(ny)

    # set x and y meshgrid objects
    xspan = np.linspace(0.0, 1.0, nx)
    yspan = np.linspace(0.0, 1.0, ny)
    X,Y = np.meshgrid(xspan,yspan)

    # plot current solution as a surface, and save to disk
    fig = plt.figure(1)
    ax = fig.add_subplot(111, projection='3d')
    ax.plot_surface(X, Y, u, rstride=1, cstride=1, cmap=cm.jet,
                    linewidth=0, antialiased=True, shade=True)
    title('u(x,y) at t = ' + tstr + ', mesh = ' + nxstr + 'x' + nystr)

    #raw_input('pausing: hit enter to continue')


# end of script

How these work:

  • These first call load_info to determine the simulation grid size and total number of time steps that have been output to disk.
  • These then loop over each time step, and:
    • Call load_data_2d to read the simulation time and solution array.
    • Create arrays for the \(x\) and \(y\) coordinates of each solution data point.
    • Plot u at that time step as a 2D surface plot, setting the plot labels and title appropriately.
    • Save the plot to disk in files of the form u_surf.###.png.
    • (Commented out) Pause the loop until the user hits “enter”.

Run this code as usual, using either Matlab,

$ matlab -r plot_solution

or Python,

$ python ./

You should then see a set of .png images in the directory:

$ ls
Makefile          plot_solution.m   u_sol.012.txt  u_sol_meta.txt  u_surf.013.png
advection.cpp  u_sol.013.txt  u_surf.000.png  u_surf.014.png
advection.exe     u_sol.000.txt     u_sol.014.txt  u_surf.001.png  u_surf.015.png
advection.h       u_sol.001.txt     u_sol.015.txt  u_surf.002.png  u_surf.016.png
density.txt       u_sol.002.txt     u_sol.016.txt  u_surf.003.png  u_surf.017.png
initialize.cpp    u_sol.003.txt     u_sol.017.txt  u_surf.004.png  u_surf.018.png
input.txt         u_sol.004.txt     u_sol.018.txt  u_surf.005.png  u_surf.019.png
load_data_2d.m    u_sol.005.txt     u_sol.019.txt  u_surf.006.png  u_surf.020.png   u_sol.006.txt     u_sol.020.txt  u_surf.007.png  u_surf.021.png
load_data_2d.pyc  u_sol.007.txt     u_sol.021.txt  u_surf.008.png  u_surf.022.png
load_info.m       u_sol.008.txt     u_sol.022.txt  u_surf.009.png  u_surf.023.png      u_sol.009.txt     u_sol.023.txt  u_surf.010.png  u_surf.024.png
load_info.pyc     u_sol.010.txt     u_sol.024.txt  u_surf.011.png  u_surf.025.png
output.cpp        u_sol.011.txt     u_sol.025.txt  u_surf.012.png

You can view these plots on ManeFrame with the command, e.g.

$ display u_surf.009.png

Alternately, you can open them all and cycle through them by right-clicking and selecting “Next”:

$ display u_surf.*.png

Advanced visualization

A few difficulties with using either Matlab or Python for data visualization include:

  • Difficulty dealing with three-dimensional plotting: while slices and projections are simple, 3D data sets require much more interactive visualization, including isocontour surface plots, moving slices, rotating, etc.
  • Difficulty dealing with data output from parallel simulations: you need to read in each processor’s data file and glue them together manually, and such in-core processing is impossible when the data sets grow too large.

As a result, there are a variety of high-quality visualization packages that are designed for interactive 3D visualization, as discussed below. None of these are installed on ManeFrame at present, though all are freely-available and open-source, so if you need/want one you should make a request to the ManeFrame system administrators.


Mayavi is a Python plotting package designed primarily for interactive 3D visualization. See:


VisIt is an open source visualization package being developed at Lawrence Livermore National Laboratory. It is designed for large-scale visualization problems (i.e. large data sets, rendered in parallel). VisIt has a GUI interface, as well as a Python interface for scripting. See:


Like VisIt, ParaView is another open source package for large-scale visualization developed at the U.S. Department of Energy National Labs. It also has both a GUI interface and a Python interface for scripting. See:


In the set of files for this session, you will find one additional file that you have not yet used, density.txt. This is a snapshot of a three-dimensional cosmological density field at a redshift of approximately \(z = 9\). Unlike the previous example, this file contains only the data field itself, with no auxiliary metadata. Like the previous example, this data is stored in a single column, with \(x\) being the fastest index and \(z\) the slowest. The three-dimensional grid is uniform in each direction, (i.e. it has size \(N\times N\times N\)) so the total number of lines in the file should equal \(N^3\).

Create a Matlab or Python script that accomplishes the following tasks:

  1. Determine the maximum density over the domain, and where it occurs.
  2. Determine the minimum density over the domain, and where it occurs.
  3. Determine the average density over the domain.
  4. Generate the following two-dimensional plots, and save each to disk:
    • Slice through the center of the domain parallel to the \(xy\) plane.
    • Slice through the center of the domain parallel to the \(xz\) plane.
    • Slice through the center of the domain parallel to the \(yz\) plane.
    • Plot a projection of the density onto the \(xy\) plane (i.e. add all entries in the \(z\) direction to collapse the 3D set to 2D).
    • Plot a projection of the density onto the \(xz\) plane.
    • Plot a projection of the density onto the \(yz\) plane.


  • If you plot the \(log\) of the density, you will get more interesting pictures. In both Matlab and Python/Numpy, this is easily computed using whole-array operations, e.g. logd = log(d).
  • Both Matlab and Python allow array slicing to extract a plane from a 3D data set, e.g.
    • Matlab: dslice = squeeze(d(:,:,2)) – here, the squeeze command may be used to eliminate the now-trivial 3rd dimension that has length 1.
    • Python/Numpy: dslice = d[:][:][2] or even dslice = d[:,:,2] (both forms of syntax are equivalent for Numpy arrays).
  • Both Matlab and Python/Numpy have a sum command that will add all values of a multi-dimensional array along a specified dimension. Read their documentation to see how this works (it will help with the average value and with the projection plots).
  • Both Matlab and Python/Numpy have max and min commands that can be applied to array-valued data. Read their documentation to see how this works (it will help with the maximum and minimum values).