2017-12-22 (2dd269eb12e86dff1711715031eec19679e996e5)

Running the tests as:

 ./pyro.py compressible_fv4 acoustic_pulse inputs.acoustic_pulse.512 vis.dovis=0
 ./pyro.py compressible_fv4 acoustic_pulse inputs.acoustic_pulse.256 vis.dovis=0
 ./pyro.py compressible_fv4 acoustic_pulse inputs.acoustic_pulse vis.dovis=0
 ./pyro.py compressible_fv4 acoustic_pulse inputs.acoustic_pulse.64 vis.dovis=0

Analysis as:

./convergence.py acoustic_pulse_512_0641.h5 acoustic_pulse_256_0320.h5
inf/L2 norm of density:  (7.282642333628075e-08, 1.929593132662066e-08)

./convergence.py acoustic_pulse_256_0320.h5 acoustic_pulse_0160.h5
inf/L2 norm of density:  (1.3214920793203078e-06, 3.0769036841664511e-07)

./convergence.py acoustic_pulse_0160.h5 acoustic_pulse_64_0080.h5
inf/L2 norm of density:  (1.7923237683126203e-05, 4.8255402486336417e-06)


Note that the numbers for the first 2 sets match the McCorquodale and
Collella table 2 to 3 significant digits.

We see:

                L-inf                         L2
 64 to 128    1.7923237683126203e-05     4.8255402486336417e-06
   rate              3.76                       3.97
128 to 258    1.3214920793203078e-06     3.0769036841664511e-07
   rate              4.18                       4.00
256 to 512    7.282642333628075e-08      1.929593132662066e-08

