LC_POM

Simulate polarized optical microscopy(POM) image from director field.

Step 1: create director profile on a regular grid

1. Route 1:
   • Generate [ regular-gird director file ] by ansatz-generate.py
2. Route 2:
   • Read the director information from .vtk file and write a [ .csv ] file by Paraview.
   • Convert the [ .csv ] file to the [ original director file ] by director-to-Q.py
   • Interpolate [ original director file ] to [ regular-grid director file ] by mesh-to-grid.py
3. Route 3:
   • Output [ original director file (Q.out) ] file directly from simulation.
   • Interpolate [ original director file ] to [ regular-grid director file ] by mesh-to-grid.py
4. Route 4 (not recommended)
   • Read the director information from .vtk file and convert to [ original director file ] by Read_vtk.py. (This function is not robust)
   • Interpolate [ original director file ] to [ regular-grid director file ] by mesh-to-grid.py

Read_vtk.py (not recommended)

This function has limited applicability to parse a .vtk file directly to a coordinate file.

• input: .vtk file
• output: Frame-#-Coordinates.txt and Frame-#-Directors.txt

director-to-Q.py

The interpolation of director field from .vtk outputs work better for Q tensor than director field. This file transforms director to Q tensor assuming that Q is locally uniaxial.

mesh-to-grid.py

This function uses interpolation to create director field on a regular grid ready to be read by pom-image.py. This file also takes care of rotation by specifiying the Eurler angles. input: The Q tensor profile in the folder Original_Director_Field in .csv format (output from Paraview) or .out file where the first block (n_nodes lines) are coordinates and the second block (n_node lines) are the five independent Q tensor entries (q11, q12, q13, q22, q23) output: [interpolated-directors].txt such as "radial-16p75-rot-x0y0z0-interpolated-directors.txt"

[ regular-grid director file ] fname = "interpolated-directors.txt" X = np.loadtxt(fname,dtype = np.float32);

  # the header two lines
  [Nx, Ny, Nz] = np.asarray (X[0, :3], dtype = np.int32) 
  [dx, dy, dz] = X[0, 3:] 
  [x_min, x_max, y_min, y_max, z_min, z_max] = X[1] 
  # the actual data 
  rr = X[2:,:3]; nn = X[2:,3:] 

Step 2: calculate the POM images

pom-image.py

Computes the polarized image with various options. The script will try to load parameters from params.py. If it doesn't exist, user will be prompted to enter parameters manually.

• Input mode:
  1. single image
  2. batch processing (specified by file names)
  3. batch processing (specified by frames)
• Polarizer angle: 0-180 degrees
• Color mode:
  1. Single wave length
  2. Simplified color
  3. Full spectrum color