The functions called in the compute.py are documented in the API reference section. However, this section aims to elucidate the order and reasoning behinds the various steps when setting up a case, allocating memory and passing information from the Python layer to the Fortran layer.

Each of the following code blocks assume that the dnami Python library has been imported as dn.

## Creating the tree

The first dnami library function called in the compute.py is:

dtree = dn.create_tree()


This creates a dictionary of dictionaries each containing information about different aspects of the computation henceforth referred to as the ‘tree’. Many dictionaries are initially empty but some contain information derived for the pseudo-code translation and compilation steps such as a list of the solved and stored variables or the number of halo points (determined by the largest finite-difference or filter stencil). For example, inspection of the src/dnami.py source shows an equation-related dictionary being populated with the list of solved variables:

for v in varsolved:
dtree['eqns']['qvec']['solved'].append([v,varname[v]])


where varsolved was imported from information generated from the equations.py file. In the case of the 1D Euler equations solved in the quickstart guide, this list would contain rho, u, et which are the density, the velocity and the total energy. After this tree creating step, the information about the computational parameters have to be given to the tree e.g. the grid size, the MPI domain decomposition, etc. For instance, the number of x-direction grid points nxgb can be set in the relevant section of the tree as:

dtree['grid']['size']['nxgb'] = nxgb


Note that this is the global number of points, not the processor specific number. The split across nxpr processors in the x-direction can be specified as:

dtree['mpi']['split']['nxpr'] = nxpr


## Initialising the Message Passing Interface

If the total number of processors is not one, then the next step involves setting up the Message Passing Interface via:

dtree = dn.start_mpi(dtree)


With the information supplied in the previous section, the appropriate number of MPI processes are setup with the communicators for exchanging information between neighboring subdomains and specific ones for the boundary conditions if required. Currently, dNami uses MPI4PY. For more details the reader is referred to the MPI4PY documentation. The user can then obtain splitting-derived information such as the local number of points in the x-direction from the updated tree:

nx = dtree['mpi']['dMpi'].nx


Note that dtree['mpi']['dMpi'] is a class.

## Creating the grid and writing to file

The next step involved creating the computational grid based on the number of grid points and length in each spatial direction. The following lines create the grid and then output it to a file:

dtree = dn.create_grid(dtree)
dn.dnami_io.write_grid(dtree)


By default, the grid is written to out/axes.bin. It contains the information about the grid in each spatial direction. For each given direction, a different distribution of points is adopted if said direction is periodic or not. Fig. 13 illustrates the distribution of points along a domain of size $$L$$ for both cases. If the direction is non-periodic, then the first (and last) point is located on the boundary. If the direction is periodic then the grid starts $$\Delta l/2$$ into the domain and ends $$\Delta l/2$$ from the end of the domain where $$\Delta l /2$$ is the grid spacing in $$x$$, $$y$$ or $$z$$. Fig. 13 Distribution of grid points along a spatial direction with periodic and non-periodic boundary conditions. The black dots represent the location of the grid points. The same number of points is used for each case.

## Allocating memory

In this step, the memory used by each subprocess for the run parameters and the data (i.e. the solved and stored variables) is allocated:

dtree = dn.allocate(dtree)


Three main elements are allocated: a set of integer parameter (e.g. number of halo points, number of grid points, number of variables etc) which are used for memory reference purpose in the Fortran layer, a set of float parameters (e.g. grid spacing, time step, run constants, etc) and the data used and/or output during the run (see the difference between solved, stored and static variables). Views on these allocated memory regions are created so that the user can fill it (e.g. with the initial conditions) or perform operations with it (e.g. output the min/max of a given field). These views are added to the tree. The user can then create an alias to the views; for example, referring again to the 1D Euler case:

rho = dtree['eqns']['qvec']['views']['rho'] # density view
u   = dtree['eqns']['qvec']['views']['u']   # velocity view
et  = dtree['eqns']['qvec']['views']['et']  # total energy view


The user can then set the initial velocity field to zero:

u[:] = np.float64(0.)


To be clear, this operation does not create a new numpy array, it zeros the portion of the already-allocated memory that corresponds to the velocity variable.

## Passing information to the Fortran layer

A set of aliases for the three aforementioned arrays are created:

intparam,fltparam,data = (dtree['libs']['fort']['integers'],
dtree['libs']['fort']['floats'],
dtree['libs']['fort']['data'])


These memory references are then passed to the Fortran layer when calling the functions compiled with f2py e.g. when advancing the solution in time during the sub-RK steps:

dn.dnamiF.time_march(intparam,fltparam,data)


The integer parameters (which are organised in a set pre-defined order) are used to read and modify the correct portion of the memory corresponding to data.

## Computing stored variables

If the user chooses to create stored variables in the equations.py then these quantities can be computed by invoking the appropriate function in the compute.py. When specifying the stored variable in the equations.py, the user can choose whether the variable is ‘static’ or not. This distinction can be used to differentiate between fields that need to be calculated every time step (e.g. if the pressure is calculated and stored then used to update the RHS) or at regular intervals during the computation (e.g. an output of the vorticity field). To compute the value, the following code block is used:

if 'qstored' in dtree['eqns']['qvec']['views'].keys():
dn.dnamiF.stored(intparam,fltparam,data,m)


where $$m=0$$ will compute the non-static variables and $$m=1$$ will compute the static variables. In the case that one of the variables is used to update the RHS, then this call should be made at every sub-RK step before the call to the time_march() funtion.

## Restarting a simulation

dNami offers a built-in function to restart a simulation from a previous output. During a simulation, if a user chooses to output ‘restart’ files, using the dnami_io.write_restart() function, these files can be fed back to a compute.py to fill the allocated memory and continue the simulation from that point. To do so, instead of filling the allocated memory with initial condition, the user can use the following code block:

dn.dnami_io.read_restart(dtree)
ni = dtree['num']['tint']['itn']
ti = dtree['eqns']['time']


By default, the dnami_io.read_restart() function looks for a restart.bin file in the same folder as the compute.py and reads the information it contains (a different path can be provided with additional arguments, see API reference). It also updates information in the tree such as the timestep number and time (the above code block shows this information being extracted from the tree).

When the case is not fully periodic, the dnami_io.read_restart() will also look for a restartshell_i1, restartshell_imax, restartshell_j1, … depending on which direction(s) is (are) not periodic. These files contain the information outside of the core domain i.e. in the halo points and are also output by the dnami_io.write_restart() function.