import numpy as np
from skimage import measure
from pumapy.utilities.generic_checks import check_ws_cutoff
[docs]def identify_porespace(workspace, solid_cutoff):
""" Identify the porespace
:param workspace: domain
:type workspace: Workspace
:param solid_cutoff: specify the solid range to discard from pores identification
:type solid_cutoff: tuple(int, int)
:return: porespace marked as: 0 solid, 1 largest pore (likely open porosity), >1 other pores
:rtype: ndarray
"""
# error check
check_ws_cutoff(workspace, solid_cutoff)
ws = workspace.copy()
ws.binarize_range(solid_cutoff)
pore_labels = measure.label(ws.matrix, background=1)
unique_pore_ids, unique_id_counts = np.unique(pore_labels[pore_labels != 0], return_counts=True)
sorted_unique_pore_ids = unique_pore_ids[np.argsort(unique_id_counts)[::-1]]
keyarray = np.arange(np.max(pore_labels) + 1)
keyarray[sorted_unique_pore_ids] = unique_pore_ids
return keyarray[pore_labels]
[docs]def fill_closed_pores(workspace, solid_cutoff, fill_value, return_pores=False):
""" Identify the porespace and fill closed porosity
:param workspace: domain
:type workspace: Workspace
:param solid_cutoff: specify the solid range to discard from pores identification
:type solid_cutoff: tuple(int, int)
:param fill_value: value to fill closed porosity with
:type fill_value: int
:param return_pores: specifies whether to return identified pores
:type return_pores: bool, optional
:return: filled workspace with IDs set as: 0 for void, 1 for solid, fill_value for
added filler material. In addition, if return_pores==True, then it also returns
the porespace marked as: 0 solid, 1 largest pore (likely open porosity), >1 other pores
:rtype: Workspace
"""
pores = identify_porespace(workspace, solid_cutoff)
if isinstance(pores, bool):
return False
ws = workspace.copy()
ws.binarize_range(solid_cutoff)
ws[np.where(pores > 1)] = np.uint16(fill_value)
if return_pores:
return ws, pores
else:
return ws