Skip to content

coordination_api.rst

Latest commit

 

History

History
76 lines (51 loc) · 3.61 KB

coordination_api.rst

File metadata and controls

76 lines (51 loc) · 3.61 KB

coordination

The coordination module contains functions related to extracting pore coordination numbers.

coordination_nums_3D()

.. autofunction:: pore2chip.coordination.coordination_nums_3D

Functionality:

  1. Check for OpenPNM network:
    • If pn is not None (i.e., an OpenPNM network is provided), the function extracts the coordination numbers directly from the network using op.models.network.coordination_number(pn).
    • This bypasses the image processing step and leverages the existing information in the network.
  2. Image processing (if no network provided):
    • If pn is None, the function assumes img_list contains 3D image data as NumPy arrays.
    • It utilizes the porespy library to process the images:
    • ps.networks.snow2(img_list, voxel_size=1): This function likely performs segmentation and network extraction from the images.
      • voxel_size=1 is assumed to be a unit voxel size (adjust if needed).
    • The processed network is then converted to an OpenPNM network using op.io.network_from_porespy(snow_output.network).
  3. Coordination number calculation:
    • Regardless of the input (image or network), the function calculates the coordination number of each pore using:
    • op.models.network.coordination_number(pn2): This function calculates the coordination number for each pore in the OpenPNM network pn2.
  4. Return coordination numbers:
    • The function returns the calculated coordination numbers (coordination_nums) as a list.
  • This function assumes the porespy and openpnm libraries are installed and imported.
  • The specific implementation of ps.networks.snow2 might require further investigation depending on the exact functionality of the PoresPy library.

coordination_nums_2D()

.. autofunction:: pore2chip.coordination.coordination_nums_2D

Functionality:

  1. Iterate through image slices:
    • The function loops through each image in img_list using a for loop.
  2. Process individual image:
    • Inside the loop, for each image slice (img_list[k]), it extracts a 2D portion using slicing (img_list[k, :, :]).
    • ps.networks.snow2(img_slice, voxel_size=1) likely performs segmentation and network extraction specifically for the 2D slice.
    • voxel_size=1 is assumed to be a unit voxel size (adjust if needed).
    • The processed network information is stored in snow_output.network.
    • An OpenPNM network object (pn) is created by converting the PoresPy network using op.io.network_from_porespy(snow_output.network).
  3. Coordination number calculation:
    • The coordination number for each pore in the current image slice's network (pn) is calculated using op.models.network.coordination_number(pn).
    • This function from OpenPNM provides the coordination numbers as a list.
  4. Accumulate coordination numbers:
    • The calculated coordination numbers for the current slice (temp_coordination) are appended to the coordination_nums_2D list using np.concatenate.
    • This accumulates coordination numbers from all processed slices into a single list.
  5. Return results:
    • After iterating through all images, the function returns the final coordination_nums_2D list containing coordination numbers for all pores across the entire 2D image series.
  • This function assumes the porespy and openpnm libraries are installed and imported.
  • The specific implementation of ps.networks.snow2 might require further investigation depending on the exact functionality of the PoresPy library.

Note

This project is under active development.