A library of Python modules for processing and analysing outputs from various simulations of the BIOPERIANT ocean model developed by Dr. Nicolette Chang from SOCCO. BIOPERIANT12 is a mesoscale-resolving ocean model whose simulations results in a series of experiments to address science questions formulated by the Southern Ocean Carbon – Climate Observatory (SOCCO), a research program within the Holistic Climate Change division, Smart Places, of the Council for Scientific and Industrial Research (CSIR)."
Important: This Python project is still at its early development stage and is being populated as new processing and analysis tools are developed.
The package is currently loaded internally for preprocessing the BIOPERIANT12 model outputs from SOCCO data server hosted by lengau cluster, at the Centre for High-Performance Computing (CHPC).
We have a series of tutorials on Jupyter notebooks in the notebooks folder. We recommend reading them in the following order to see a typical workflow. First install the main required packages as follows: pip install -r requirements.txt.
- Import the BIOPERIANT12 module:
import bioperiant_main as mbp- Assign the data loader attributes and call it:
suffix_endwith = "diadT.nc"
year_start = 1991
year_end = 2010
time_step = "5-daily"
bp12_diadT = mbp.BP12DataLoader(suffix_endwith=suffix_endwith,
year_start=year_start,
year_end=year_end,
time_step=time_step)- Load the data corresponding to the assigned attrbutes:
xds_diadT = bp12_diadT.load()- Assign the data loader attributes and call it:
ocean_mask_path = "../ocean_mask.nc" # A netCDF file (.nc format)
bp12_processor = mbp.BP12DataProcessor(path_to_ocean_mask=ocean_mask_path)- Pre-process the variables of interest:
var_names = [var1, var2, ...] # Variables of interest
xds_diadT = bp12_processor.process(xds_params=xds_diadT,
var_names=var_names)