pencil.ism_dyn.get_masks

Derive auxilliary data and other diagnostics from var.h5 file and save to new h5 file

uses:

compute ‘data’ arrays of size [nz,ny,nx] as required store ‘time’ of snapshot compute ‘masks’ for example by temperature phase compute summary statistics ‘stats’ compute ‘structure’ functions as required

Functions

thermal_decomposition(ss, pars[, unit_key, ent_cut])

call signature:

derive_masks(sim_path, src, dst[, data_key, par, ...])

Module Contents

pencil.ism_dyn.get_masks.thermal_decomposition(ss, pars, unit_key='unit_entropy', ent_cut=[2320000000.0])

call signature:

thermal_decomposition(ss, pars, unit=’unit_entropy’, ent_cut=[2.32e9,])

Keyword Arguments:
  • ss – dataset used for masks, default ‘ss’, alternate e.g.’tt’

  • pars – Param() object required for units rescaling

  • unit_key – label of physical units in pars to apply to code values

  • ent_cut – list of boundary mask values, default see thesis http://hdl.handle.net/10443/1755 Figure 5.10 may have multiple boundaries

pencil.ism_dyn.get_masks.derive_masks(sim_path, src, dst, data_key='data/ss', par=[], comm=None, overwrite=False, rank=0, size=1, nghost=3, status='a', chunksize=1000.0, quiet=True, nmin=32, ent_cuts=[2320000000.0], mask_keys=['hot'], unit_key='unit_entropy')