Source code for porespy.networks.__snow__

from porespy.networks import regions_to_network, add_boundary_regions
from porespy.networks import _net_dict
from porespy.networks import label_boundary_cells
from import pad_faces
from porespy.filters import snow_partitioning
from import make_contiguous
from porespy.metrics import region_surface_areas, region_interface_areas
import scipy as sp

[docs]def snow(im, voxel_size=1, boundary_faces=['top', 'bottom', 'left', 'right', 'front', 'back'], marching_cubes_area=False): r""" Analyzes an image that has been partitioned into void and solid regions and extracts the void and solid phase geometry as well as network connectivity. Parameters ---------- im : ND-array Binary image in the Boolean form with True’s as void phase and False’s as solid phase. voxel_size : scalar The resolution of the image, expressed as the length of one side of a voxel, so the volume of a voxel would be **voxel_size**-cubed. The default is 1, which is useful when overlaying the PNM on the original image since the scale of the image is alway 1 unit lenth per voxel. boundary_faces : list of strings Boundary faces labels are provided to assign hypothetical boundary nodes having zero resistance to transport process. For cubical geometry, the user can choose ‘left’, ‘right’, ‘top’, ‘bottom’, ‘front’ and ‘back’ face labels to assign boundary nodes. If no label is assigned then all six faces will be selected as boundary nodes automatically which can be trimmed later on based on user requirements. marching_cubes_area : bool If ``True`` then the surface area and interfacial area between regions will be using the marching cube algorithm. This is a more accurate representation of area in extracted network, but is quite slow, so it is ``False`` by default. The default method simply counts voxels so does not correctly account for the voxelated nature of the images. Returns ------- A dictionary containing the void phase size data, as well as the network topological information. The dictionary names use the OpenPNM convention (i.e. 'pore.coords', 'throat.conns') so it may be converted directly to an OpenPNM network object using the ``update`` command. * ``net``: A dictionary containing all the void and solid phase size data, as well as the network topological information. The dictionary names use the OpenPNM convention (i.e. 'pore.coords', 'throat.conns') so it may be converted directly to an OpenPNM network object using the ``update`` command. * ``im``: The binary image of the void space * ``dt``: The combined distance transform of the image * ``regions``: The void and solid space partitioned into pores and solids phases using a marker based watershed with the peaks found by the SNOW Algorithm. """ # ------------------------------------------------------------------------- # SNOW void phase regions = snow_partitioning(im=im, return_all=True) im = dt = regions.dt regions = regions.regions b_num = sp.amax(regions) # ------------------------------------------------------------------------- # Boundary Conditions regions = add_boundary_regions(regions=regions, faces=boundary_faces) # ------------------------------------------------------------------------- # Padding distance transform and image to extract geometrical properties dt = pad_faces(im=dt, faces=boundary_faces) im = pad_faces(im=im, faces=boundary_faces) regions = regions*im regions = make_contiguous(regions) # ------------------------------------------------------------------------- # Extract void and throat information from image net = regions_to_network(im=regions, dt=dt, voxel_size=voxel_size) # ------------------------------------------------------------------------- # Extract marching cube surface area and interfacial area of regions if marching_cubes_area: areas = region_surface_areas(regions=regions) interface_area = region_interface_areas(regions=regions, areas=areas, voxel_size=voxel_size) net['pore.surface_area'] = areas * voxel_size**2 net['throat.area'] = interface_area.area # ------------------------------------------------------------------------- # Find void to void connections of boundary and internal voids boundary_labels = net['pore.label'] > b_num loc1 = net['throat.conns'][:, 0] < b_num loc2 = net['throat.conns'][:, 1] >= b_num pore_labels = net['pore.label'] <= b_num loc3 = net['throat.conns'][:, 0] < b_num loc4 = net['throat.conns'][:, 1] < b_num net['pore.boundary'] = boundary_labels net['throat.boundary'] = loc1 * loc2 net['pore.internal'] = pore_labels net['throat.internal'] = loc3 * loc4 # ------------------------------------------------------------------------- # label boundary cells net = label_boundary_cells(network=net, boundary_faces=boundary_faces) # ------------------------------------------------------------------------- # assign out values to dummy dict temp = _net_dict(net) = im.copy() temp.dt = dt temp.regions = regions return temp