vbmc 0.0.0
CPU version of Voxel-based Monte Carlo simulation
License: MIT License

vbmc
========================
vbmc is under development.

This project uses voxel-based Monte Carlo simulations to calculate light transport in diffuse media.

INSTALLATION
==============

::

 $ pip install vbmc


USAGE
============

.. code:: python

  from vbmc import vbmc
  from vbmc import angularyResolved,spatiallyResolved
  import numpy as np
  import matplotlib.pyplot as plt


  # This is sample code for vbmc.
  #
  # vbmc can compute diffuse light transport
  # for any voxel model. The voxel model is
  # a 3-dimensional array, and integer voxel
  # values from 0 to 255 are allowed. The voxel
  # values must match the array number of the
  # optical properties, and the optical properties
  # of the model can be set with the set_params
  # function.
  #
  # This sample file shows an example calculation
  # for a multilayered medium with the following
  # optical properties.
  #
  # | Layer | µ_a [/cm] |  µ_s [/cm] |  g  |   n   |   d   |
  # |   1   |    1.     |     100    | 0.9 |  1.37 |  0.1  |
  # |   2   |    1.     |     10     | 0.  |  1.37 |  0.1  |
  # |   3   |    1.     |     10     | 0.7 |  1.37 |  0.2  |
  #
  # Here, the refractive index of air is assumed to be 1.
  # and the medium size in the x-y direction is assumed
  # to be semi-infinite.　The photon number was set to 1e6.
  #
  # Please refer to the "docs/mcml_result_multilayer_rdr_tdr.csv"
  # or the following paper for the MCML results of the calculations.
  #
  # Reference
  # Wang and Jacques,"Monte Carlo Modeling of Light Transport
  # in Multi-layered Tissues in Standard C Lihong",(1992)
  # URL: https://omlc.org/software/mc/mcml/MCman.pdf

  nPh = 1e6
  model = vbmc(nPh = nPh)
  params = {
          'n':[1.37,1.37,1.37],
          'n_air':1.,
          'ma':[1,1,2],
          'ms':[100,10,10],
          'g':[0.9,0.,0.7],
          'end_point':False,
          'voxel_space':0.1,
  }
  voxel_model = np.zeros((1001,1001,4))
  voxel_model[:,:,1] = 1
  voxel_model[:,:,2:] = 2

  # Voxel model setting
  model.set_model(voxel_model)
  # optical properties setting
  model.set_params(**params)
  # model build
  model.build()

  # start vbmc calculation
  model.start()

  # get_result()
  #
  # The result is in dictionary format with keys "p", "v",
  # and "w", where p is a position vector, v is a direction
  # vector, and w is a photon weight.
  #
  # The shape of p and v is (number of photons, 3), and the
  # one-dimensional elements of the array are the xyz
  # components of the vector. For example, p[:,0] is the x
  # component of p, p[:,1] is the y component of p, and p[:,2]
  # is the z component of p.
  # On the other hand, the shape of w is a one-dimensional
  # array of (number of photons).
  res = model.get_result()

  # Separation of diffuse reflection and transmission
  # For a medium with a semi-infinite size in the xy direction,
  # the direction vector in the final result implies reflection
  # if it is in the negative z direction and transmission if it
  # is in the positive z direction.

  Rd_index = np.where(res['v'][:,2] < 0)[0] # Index of diffuse reflected photons
  Td_index = np.where(res['v'][:,2] > 0)[0] # Index of diffuse transmitted photons

  ##### View Results #####
  #
  # Spatially resolved diffuse reflectance and transmittance
  #
  dr = 0.005
  nn = 100
  r,Rd_r = spatiallyResolved(
      res['p'][Rd_index],res['w'][Rd_index],nPh,nn,dr
  )
  r,Td_r = spatiallyResolved(
      res['p'][Td_index],res['w'][Td_index],nPh,nn,dr
  )

  plt.plot(r,Td_r,'-', c = 'k')
  plt.yscale('log')
  plt.title('Spatially resolved diffuse transmittance')
  plt.xlabel('r [cm]')
  plt.ylabel('$T_d(r)$ $[cm^{-2}]$')
  plt.show()

  plt.plot(r,Rd_r,'-', c = 'k')
  plt.yscale('log')
  plt.title('Spatially resolved diffuse reflectance')
  plt.xlabel('r [cm]')
  plt.ylabel('$R_d(r)$ $[cm^{-2}]$')
  plt.show()

  #
  # Angulary resolved diffuse reflectance and transmittance
  #
  nn = 30
  alpha,Rd_a = angularyResolved(
      res['v'][Rd_index],res['w'][Rd_index],nPh,nn
  )
  alpha,Td_a = angularyResolved(
      res['v'][Td_index],res['w'][Td_index],nPh,nn
  )

  plt.plot(alpha,Rd_a,'.',c = 'k')
  plt.title('Angulary resolved diffuse reflectance')
  plt.xlabel('Exit angle α [rad]')
  plt.ylabel('$R_d(α)$ $[sr^{-1}]$')
  plt.show()

  plt.plot(alpha,Td_a,'.', c = 'k')
  plt.title('Angulary resolved diffuse transmittance')
  plt.xlabel('Exit angle α [rad]')
  plt.ylabel('$T_d(α)$ $[sr^{-1}]$')
  plt.show()

NOTE
============

Reference

`K.Miura et.al., "Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation", JBO Vo.27(5),(2022) <https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9116466/>`_.