pyVisOS is designed to be simple to use and easy to understand. The primary goal is to provide an in-memory representation of the HDF5 output generated by PIC code OSIRIS. It also provides some functions to automate MPI parallelism.

H5Data is a subclass of numpy ndarray with following additional attributes:

• name and timestamp: inferred from the filename. These two also serve as default output filename.
• run_attrs: a dictionary inferred from the root directory of the HDF5 output. It provides information about the simulation.
• data_attrs: a dictionary inferred from the dataset directory. It provides information about the quantity. One quantity for each dataset for the grid type output.
• axes: a list inferred from the /AXIS directory. It provides information about the axes. It is always consistent with the data array.

A common workflow for data analysis would look like this:

1. load a few quantities into memory
2. distribute the workload across MPI processes
3. data analysis
4. (optionally) gather results from each MPI process and some more analysis (but without redistributing the workload)

Step 3 and 4 are the actual work we should be focusing on and pyVisOS provides a wrapper to automate step 1 and 2 (and partially, step 4).

independent_timeframe_pptm: can be used if the analysis can be done in a way that data at different timestamps are independent. see pynting_flux_example.py for details.

The codes are tested under python 3.5