cbsPlot Files

1. Install MCR installer (Depending on your 32 or 64 bit windows) if matlab is not already installed. 
Note: To download MCR installer go to http://in.mathworks.com/products/compiler/mcr/ and select 2012b. Or go to https://sourceforge.net/projects/cbsplot/files/?source=navbar
2. Run cbsPlot.exe


How to use?
The application helps us to find out the mean free path of the light passing through a Random medium in a single click. It plots the normalized experimental intensity plot of CBS Intensity vs radians. And then search for the best normalized analytic intensity function (Ref:1) which mathches the experimental plot and gives us the mean free path value (lt) of fitted function. User can see graphically, how the analytic function is approximating to the experimental result. Program stops automatically and give the mean free path value (lt), once the value of fwhm's of both experimental and theoretical become same. It is suggested to start with a high 'Step' value at first and then reduce the 'Step' value gradually in successive runs to get more accurate 'lt' value. 

Functions of various callback buttons and input text editors:

Choose file: CBSPLOT can choose a .csv file which contains the CBS experimental data from anywhere in the user’s desktop. 
Load Data: The entire data in the .csv file can be loaded to the application engine for further analysis. It takes a while, less than 7-8 seconds (for a computer of 4GB Ram, Intel core i3, Clock speed: 2.53 GHz.), to load data. The real time display, at the bottom of the file options will show the details about the current operation executing in CBSPLOT.
Data Range: This range should be set in alphanumeric numbers according to the CBS data, under consideration. These can easily be entered by looking at the .csv file and noting down the cell addresses corresponding to the top-left initial cell and bottom-right final cell which constitutes the entire CBS data.
Pixel width: Pixel width of CCD has to be entered in micrometers in this text editor.
Distance: This parameter corresponds to the distance between the lens (used to focus CBS rays on CCD) and CCD, in mm.
Wavelength: The wavelength of the laser in micrometers has to be entered in this text editor.
Initial mfp: This parameter corresponds to the initial photon mean free path, taken by the CBSPLOT for fitting the experimental data with the analytical CBS function. The program will plot the theoretical model for each mfp (photon mean free path) starting from ‘initial mfp’ value. 
Step: CBSPLOT will plot the analytical CBS function for values of mean free paths starting from ‘initial mfp’ in steps of the value which user entered in the text editor 'Step'.
Plot now: This callback function plots the color-map, according to the intensity distribution captured by CCD, as well as the normalized intensity distribution along the line seen in the color map. The plotting method is described in implementation and architecture section. The normalized intensity and color-map are formed on the first and second axes in the GUI respectively. 
Remove noise: Generally, noise in CBS data are very high intensity points compared to the acquired signal and are formed due to various reasons such as thermal noise, shot noise etc. This can easily be identified after plotting the data using plotting method, and can be eliminated either automatically or manually. This ‘Remove Noise’ panel has a set of callback functions optimized for removing noise from the plotted data either automatically or manually. The ‘Auto Removal’ callback function will only be enabled, once the user prefers to remove some noise data points from the normalized intensity plot. This callback button considers the highest intensity peak in the current plot as the noise data and replace it with the average of the neighbouring intensity points. After the removal of noise data it plots the intensity distributions again according to plotting method. Manual noise data removal called ‘Delete & Modify’ allows user to select more than one data point manually using mouse pointer and replace them with average of neighbouring intensity points. 
Fit my data: Experimental data is fitted with analytical CBS expression and corresponding photon mean free path is shown in the GUI. Fitting will start automatically by taking the ‘Initial mfp’ value and then continue searching for the best fit by successively taking mean free path values in steps of ‘Step’. Data fitting made more intuitive by animating the analytic CBS data plot for various mean free path values and finally stops animation when full width at half maximum of experimental and analytical become same. It has to be noted that fitting is not based on least square algorithm.
Apart from the above listed options, there are other functions dedicated for saving figures and exporting the data plotted. Fitting the same experimental data with other input parameters are also allowed by removing the current fit using ‘Remove fit’ callback button. The callback button ‘Wipe all’ will reset everything the user has done and start a fresh session.

Ref 1: Optical coherent backscattering by random media: an experimental study. P.E. Wolf et al., J. Phys. 
	France 49 (1988) 63-75.

How does it work?
1. Program needs an excel file with a 'matrix' of intensity values corresponding to each pixel of CCD. This matrix of cells represent the intensity distribution on the 2D plane of CCD. In the sample_data.csv, we have 1200 pixels along the y axis (column) and 1600 pixels along the x axis (row).
2. The program search for the row which includes the maximum intensity value. And then get the entire row values (Intensity). This is our required experimental intensity to plot.
3. X axis (Row) pixel values are converted into radians according to:(pixel number)*pixel width (along x-axis) /distance.
4. Appropriate shifting will be done to get the peak intensity at 0 radian.
5. Then plot the normalized experimental intensity vs radian.
6. Then search for the CBS analytic function which suits the experimental plot (in terms of their FWHMs).
7. Finally the program throws the best fit mean free path value with an error.
Acknowledgements: Thanks to Photonics Lab  :)
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