snow2

snow2(phases, phase_alias=None, boundary_width=3, accuracy='standard', voxel_size=1, parallelization={})[source]

Applies the SNOW algorithm to each phase indicated in phases.

This function is a combination of snow 1, snow_dual 2, snow_n 3, and snow_parallel 4 from previous versions.

Parameters
  • phases (ND-image) – An image indicating the phase(s) of interest. A watershed is produced for each integer value in phases (except 0’s). These are then combined into a single image and one network is extracted using regions_to_network.

  • phase_alias (dict) – A mapping between integer values in phases and phase name, used to add labels to the network. For instance, asssuming a two-phase image, {1: 'void', 2: 'solid'} will result in the labels 'pore.void' and 'pore.solid', as well as 'throat.solid_void', 'throat.solid_solid', and 'throat.void_void'. If not provided, aliases are assumed to be {1: 'phase1', 2: 'phase2, ...}. Phase labels can also be applied afterward using label_phases.

  • boundary_width (depends) – Number of voxels to add to the beginning and end of each axis. This argument is handled the same as pad_width in the np.pad function. A scalar adds the same amount to the beginning and end of each axis. [A, B] adds A to the beginning of each axis and B to the ends. [[A, B], ..., [C, D]] adds A to the beginning and B to the end of the first axis, and so on. The default is to add 3 voxels on both ends of all axes. For each boundary width that is not 0, a label will automatically be applied indicating which end of which axis (i.e. 'xmin' and 'xmax').

  • accuracy (string) –

    Controls how accurately certain properties are calculated during the analysis of regions in the regions_to_network function. Options are:

    ’standard’ (default)

    Computes the surface areas and perimeters by simply counting voxels. This is much faster but does not properly account for the rough voxelated nature of the surfaces.

    ’high’

    Computes surface areas using the marching cube method, and perimeters using the fast marching method. These are substantially slower but better account for the voxelated nature of the images.

  • voxel_size (scalar (default = 1)) – 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.

  • parallelization (dict) – The arguments for controlling the parallization of the watershed function are rolled into this dictionary, otherwise the function signature would become too complex. Refer to the docstring of snow_partitioning_parallel for complete details. If no values are provided then the defaults for that function are used. To disable parallelization pass parallel=None, which will invoke the standard snow_partitioning.

Returns

network – A named-tuple is returned with the padded phases image, the watershed segmentated regions, and the network dictionary.

Return type

dict or named-tuple

References

1

Gostick JT. Versatile and efficient pore network extraction method using marker-based watershed segmentation. Phys. Rev. E. 96, 023307 (2017)

2

Khan ZA, Tranter TG, Agnaou M, Elkamel A, and Gostick JT, Dual network extraction algorithm to investigate multiple transport processes in porous materials: Image-based modeling of pore and grain- scale processes. Computers and Chemical Engineering. 123(6), 64-77 (2019)

3

Khan ZA, García-Salaberri PA, Heenan T, Jervis R, Shearing P, Brett D, Elkamel A, Gostick JT, Probing the structure-performance relationship of lithium-ion battery cathodes using pore-networks extracted from three-phase tomograms. Journal of the Electrochemical Society. 167(4), 040528 (2020)

4

Khan ZA, Elkamel A, Gostick JT, Efficient extraction of pore networks from massive tomograms via geometric domain decomposition. Advances in Water Resources. 145(Nov), 103734 (2020)