Version 0.1.0

  • First alpha version of postopus.

  • Supports reading of “cube”, “xsf”, “vtk”, “x=0”, “y=0”, “z=0”, “x=0,y=0”, “x=0,z=0”, “y=0,z=0”, “ncdf”, and “nc” field octopus output files.

  • The field data is stored in the dictionary run.systemname.calculationmode.fieldname for ScalarFields and run.systemname.calculationmode.fieldname(.dimension) for VectorFields. It can be retrieved by using any of the get() methods. Depending on the situation, we can use: get(), iget(), get_converged(), or get_all(). These methods will return an xarray object, also with units support (as strings).

  • Comprehensive tutorials on how to use the xarray objects for analyzing data and visualizing it, also in combination with other libraries like holoviews or xrft.

  • Supports the reading of many table-like and unstructured files without extension stored in td.general and static folders. The data is stored in the dictionary run.systemname.calculationmode.filename. The return type will be either a pandas.DataFrame or a string depending on the file type. We do not need to use any of the get() methods for files without extensions.

Version 0.2.0

  • Octopus test data is now created on the fly during each CI pipeline. The invoke task

generatePytestData was created for this purpose. Needed inp files are stored in the tests/data folder. We are no longer downloading the test data from the test data repo. All the inp files were changed to generate lighter test data and different values. All the tests were changed accordingly. This makes it easy to combine different octopus versions with different python versions. Supported in the pipelines now: py 3.8 - 3.10, octopus: 12.0 - 12.2. Almost all tutorials generate the data on the fly now.

  • Now we are able to read vtk vector fields within static/ and output_iter/ correctly.

  • Added nestedRun objects. This is documented and tested.

  • Added Application Examples chapter to documentation.