Foreword

• pizza is a high-performance numerical code for quasi-geostrophic and non-rotating convection in a 2-D annulus geometry. pizza solves for the Navier-Stokes equation including Coriolis force coupled with a temperature equation and/or an equation for the chemical composition under the Boussinesq approximation.

• pizza uses either a Chebyshev collocation or a sparse Chebyshev formulation in the radial direction and a Fourier decomposition in the azimuthal direction. Several Implicit-Explicit (IMEX) time-steppers are implemented including multistep time schemes (Crank-Nicolson-Adams Bashforth, Semi-implicit Backward Difference) as well as IMEX Runge-Kutta schemes.

• pizza is written in Fortran and designed to be used on supercomputing clusters. It requires CMake, an MPI library, FFTW and LAPACK to be compiled and executed. Postprocessing functions written in python (requiring matplotlib and scipy) are also provided to allow a useful data analysis.

• pizza is a free software. It can be used, modified and redistributed under the terms of the GNU GPL v3 licence.

Quickly start using pizza

1) In order to check out the code, use the command

$ git clone https://github.com/magic-sph/pizza.git

or via SSH (it requires a public key):

$ git clone ssh://git@github.com/magic-sph/pizza.git

2) Set up your compiler and compile the code

Create a directory where the sources will be built

$ mkdir build
$ cd build

Set up your compilers

$ export FC=mpiifort

or

$ export FC=mpif90

Compile and produce the executable

$ cmake ..
$ make -j

The executable pizza.exe has been produced!

3) Go to the samples directory and check that everything is fine

$ cd pizza/samples
$ ./pizza_wizard.py --nranks 4 --mpicmd mpiexec

If everything is correctly set, all auto-tests should pass!

4) You're ready for a production run

$ mkdir run
$ cp path_to/pizza/build/pizza.exe .
$ cp path_to/pizza/samples/RotE1e3EkPump/input.nml .

Then change the input namelist to the setup you want and run the code:

$ mpiexec -n 4 ./pizza.exe input.nml

5) Data visualisation and postprocessing

a) Set-up your PYTHON environment (ipython, scipy and matplotlib are needed)

b) Add pizza to your PYTHONPATH

$ export PYTHONPATH=$PYTHONPATH:path_to/pizza/python

c) You can now import the python classes:

python> from pizza import *

and use them to read time series, graphic files, movies, ...

python> ts = PizzaTs(field='e_kin', all=True)
python> f = PizzaFields(verbose=True)
python> ...