pofff.jobs.data module

Script to write the benchmark data

pofff.jobs.data.compute_m_c(dig, dil)

Normalized total variation of the concentration field within Box C

Args:

dig (dict): Global dictionary

dil (dict): Local dictionary

Returns:

dil (dict): Modified local dictionary

pofff.jobs.data.create_from_summary(dig, dil)

Use the summary arrays for the sparse data interpolation

Args:

dig (dict): Global dictionary

dil (dict): Local dictionary

Returns:

dil (dict): Modified local dictionary

pofff.jobs.data.dense_data(dig)

Generate the dense data within the benchmark format

Args:

dig (dict): Global dictionary

Returns:

None

pofff.jobs.data.generate_arrays(dig, dil, names, t_n)

Arrays for the dense data

Args:

dig (dict): Global dictionary

dil (dict): Local dictionary

names (list): Strings with the quantities for the spatial maps

t_n (int): Index for the number of restart file

Returns:

dil (dict): Modified local dictionary

pofff.jobs.data.main()

Postprocessing to generate the benchmark data

pofff.jobs.data.map_to_report_grid(dil, names)

Map the simulation grid to the reporting grid

Args:

dil (dict): Local dictionary

names (list): Strings with the quantities for the spatial maps

Returns:

dil (dict): Modified local dictionary

pofff.jobs.data.read_opm(dig)

Read the simulation files using OPM

Args:

dig (dict): Global dictionary

Returns:

dig (dict): Modified global dictionary

pofff.jobs.data.sparse_data(dig)

Generate the sparse data within the benchmark format

Args:

dig (dict): Global dictionary

Returns:

None

pofff.jobs.data.write_dense_data(dig, dil, n)

Map the quantities to the cells

Args:

dig (dict): Global dictionary

dil (dict): Local dictionary

n (int): Number of csv file

Returns:

None

pofff.jobs.data.write_sparse_data(dig, dil)

Write the sparse data

Args:

dig (dict): Global dictionary

dil (dict): Local dictionary

Returns:

None