# Data elements in discussion¶

## Extensions: Storage of time-dependent data¶

### Time-dependent data with constant increments¶

A time-dependent data group with step and time having constant increments, i.e., if the sampling occurs at constant rate and the equations of motion are integrated at a fixed time step, may use the following structure:

<data_group>
\-- step
|    +-- (offset)
\-- time
|    +-- (offset)
|    +-- (unit)
\-- value [variable][...]
+-- (unit)

step
A compact dataset that holds the increment of the integration step between two successive sampling events, stored as a scalar of integer type.
time
A compact dataset that holds the increment of the physical time between two successive sampling events as a real-valued scalar.
value
A dataset that holds the data of the time series. It follows the same specifications as for a general time-dependent data group.

The datasets step and time may possess an optional attribute offset specifying the absolute step and time corresponding to the sample at index 0. If the attribute is absent, the respective offset equals to 0.

### Time-averaged data¶

Time-averaged data are stored for some applications, for example the potential energy is computed every 200 simulation steps but only the average of 50 such computations is stored (every ${10}^{4}$ steps). Additional statistical information along with the mean value is stored by extending the triple value, step, time:

The structure of such a data group is :

data_group
\-- value [var][...]
+-- (unit)
\-- error [var][...]
\-- count [var]
\-- step [var]
\-- time [var]
+-- (unit)

• The value dataset is as before, but stores the arithmetic mean of the data sampled since the last output to this group.
• The error dataset stores the statistical error of the mean value, given by $\sqrt{{\sigma }^{2}/\left(N-1\right)}$ with the variance ${\sigma }^{2}$ and the number of sampled data points $N$. The error is 0 in case of $N=1$. The dimension of the dataset must agree with those of value and its (optional) unit is inferred from value.
• The count dataset is of integer type and stores the number $N$ of sampled data points used. The dimension of the dataset is variable and must agree with the first dimension of value.

Note that the statistical variance and the standard deviation are easily obtained from combining the datasets error and count and need not to be stored explicitly.

## Further suggestions¶

• Simulation box information

Some information on the simulation box geometry could be included. For now, the box size is included in the observables group. Symmetry groups could be included in the future.

• Topology

There is the need to store topology for rigid bodies, elastic networks or proteins. The topology may be a connectivity table, contain bond lengths, ...

• Scalar and vector fields

May be used to store coarse grained or cell-based physical quantities.

• The “density” dataset has dimensions [variable][Nx][Ny][Nz] where the variable dimension allows to accumulate steps, and Nx, Ny and Nz are the number of data points in each dimension. This dataset possesses the attributes “x0” and “dx”, both of dimension “D” (the dimensionality of the system). “x0” stores the center of the 0-th cell (the [0,0,0] cell) and “dx” stores the cell spacing. The notation from “x” to “z” is given as an example and other ranks can be given for other dimensionalities.

• The “velocity_field” dataset has dimensions [variable][Nx][Ny][Nz][D] where “D” is the dimensionality of the system. It stores a cell-baed velocity field. The same remark as for the “x”, “y” and “z” variables as for the “density” dataset applies.

• Tracking history of authors and creator programs

It would be desirable to track authors and creator programs. This could be achieved by replacing the respective attributes in /hm5d by datasets of variable dimension. The object tracking of these datasets may then be matched (approximately) with the creation/modification times of other datasets.

• Parallel issues

Although not a specification in itself, one advantage of using HDF5 is the Parallel-HDF5 extension for MPI environments. File written by parallel programs should be identical to programs written by serial programs.

An issue remains however: as particles move in space, they may belong to varying CPUs. A proposition to this problem is to send all particles, as a copy, to their original CPU and to write them from there using collective IO calls. Particles for which the ordering is not important (for instance solvent particles that may be required for checkpointing only) could be written from their actual CPU without recreating the original order.