porespy.metrics

This submodule contains functions for determining key metrics about an image. Typically these are applied to an image after applying a filter, but a few functions can be applied directly to the binary image.

Functions

bond_number(im, delta_rho, g, sigma, voxel_size[, ...])	Computes the Bond number for an image
boxcount(im[, bins])	Calculates the fractal dimension of an image using the tiled box counting
chord_counts(im)	Find the length of each chord in the supplied image
chord_length_distribution(im[, bins, log, voxel_size, ...])	Determines the distribution of chord lengths in an image containing chords.
find_h(saturation[, position, srange])	Given a saturation profile, compute the height between given bounds
find_porosity_threshold(im[, axis, conn])	Finds the porosity of the image at the percolation threshold
find_porosity_threshold(im[, axis, conn])	Finds the porosity of the image at the percolation threshold
is_percolating(im[, axis, inlets, outlets, conn])	Determines if a percolating path exists across the domain (in the specified
lineal_path_distribution(im[, bins, voxel_size, log])	Determines the probability that a point lies within a certain distance
mesh_surface_area([mesh, verts, faces])	Calculate the surface area of a meshed region
mesh_volume(region[, voxel_size])	Compute the volume of a single region by meshing it
pc_map_to_pc_curve(pc, im[, seq, mode, pc_min, ...])	Converts a pc map into a capillary pressure curve
percolating_porosity(im[, axis, inlets, outlets, conn])	Finds volume fraction of void space which belongs to percolating paths
percolating_porosity(im[, axis, inlets, outlets, conn])	Finds volume fraction of void space which belongs to percolating paths
phase_fraction(im[, normed])	Calculate the fraction of each phase in an image
phase_fraction(im[, normed])	Calculate the fraction of each phase in an image
pore_size_distribution(im[, bins, log, voxel_size])	Calculate a pore-size distribution based on the image produced by the
pore_size_distribution(im[, bins, log, voxel_size])	Calculate a pore-size distribution based on the image produced by the
porosity(im[, mask, fill_closed, fill_surface])	Calculates the porosity of an image assuming 1's are void space and 0's
porosity(im[, mask, fill_closed, fill_surface])	Calculates the porosity of an image assuming 1's are void space and 0's
porosity_by_type(im[, conn])	Computes different types of porosity in an image including total, closed, and
porosity_profile(im[, axis, span, mode])	Computes the porosity profile along the specified axis
porosity_profile(im[, axis, span, mode])	Computes the porosity profile along the specified axis
prop_to_image(regionprops, shape, prop)	Create an image with each region colored according the specified prop,
props_to_DataFrame(regionprops)	Create a pandas DataFrame containing all the scalar metrics for each
radial_density_distribution(dt[, bins, log, voxel_size])	Computes radial density function by analyzing the histogram of voxel
radial_density_distribution(dt[, bins, log, voxel_size])	Computes radial density function by analyzing the histogram of voxel
region_interface_areas(regions, areas[, voxel_size, strel])	Calculate the interfacial area between all pairs of adjecent regions
region_surface_areas(regions[, voxel_size, strel])	Extract the surface area of each region in a labeled image.
region_volumes(regions[, method, voxel_size])	Compute volume of each labelled region in an image
regionprops_3D(im)	Calculates various metrics for each labeled region in a 3D image.
rev_porosity(im[, n, slices])	Calculates the porosity for a many subdomains of diffrent sizes, suitable for
rev_tortuosity(im[, n, axis, slices, dask_on])	Calculates the tortuosity for a range of subdomain sizes suitable for an REV plot
satn_profile(satn[, s, im, axis, span, mode])	Computes a saturation profile from an image of fluid invasion
satn_profile(satn[, s, im, axis, span, mode])	Computes a saturation profile from an image of fluid invasion
two_point_correlation(im[, voxel_size, bins])	Calculate the two-point correlation function using Fourier transforms
two_point_correlation(im[, voxel_size, bins])	Calculate the two-point correlation function using Fourier transforms