Working with output

Utilities

Several simply utilities exist to operate on output files

• compare.py: this script takes two plot files and compares them zone-by-zone and reports the differences. This is useful for testing, to see if code changes affect the solution. Many problems have stored benchmarks in their solver’s tests directory. For example, to compare the current results for the incompressible shear problem to the stored benchmark, we would do:

  ./compare.py shear_128_0216.pyro incompressible/tests/shear_128_0216.pyro
  

  Differences on the order of machine precision may arise because of optimizations and compiler differences across platforms. Students should familiarize themselves with the details of how computers store numbers (floating point). An excellent read is What every computer scientist should know about floating-point arithmetic by D. Goldberg.

• plot.py: this script uses the solver’s dovis() routine to plot an output file. For example, to plot the data in the file shear_128_0216.pyro from the incompressible shear problem, you would do:

  ./plot.py -o image.png shear_128_0216.pyro
  

  where the -o option allows you to specify the output file name.

Reading and plotting manually

pyro output data can be read using the util.io_pyro.read method. The following sequence (done in a python session) reads in stored data (from the compressible Sedov problem) and plots data falling on a line in the x direction through the y-center of the domain. The return value of read is a Simulation object.

import matplotlib.pyplot as plt
import pyro.util.io_pyro as io

sim = io.read("sedov_unsplit_0290.h5")
dens = sim.cc_data.get_var("density")

fig, ax = plt.subplots()
ax.plot(dens.g.x, dens[:,dens.g.qy//2])
ax.grid()

Note

This includes the ghost cells, by default, seen as the small regions of zeros on the left and right. The total number of cells, including ghost cells in the y-direction is qy, which is why we use that in our slice.

If we wanted to exclude the ghost cells, then we could use the .v() method on the density array to exclude the ghost cells, and then manually index g.x to just include the valid part of the domain:

ax.plot(dens.g.x[g.ilo:g.ihi+1], dens.v()[:, dens.g.ny//2])

Note

In this case, we are using ny since that is the width of the domain excluding ghost cells.