This is the code and data for a course on uncertainty, calibration and traceability held on Romania at CETAL in fall 2019.
The structure and content is as follows:
- Schedule and Presentations: Includes the course schedule and the presentation that are explaining the tasks that are then implemented in the Matlab code.
- ExampleData: Data used by the provided Matlab code
- Matlab Code: Example Matlab code, covering the example and exercises of this training school
Matlab function details, data and relevant presentations follow below.
Presentations:
| File name | Comment |
|---|---|
| Read the spectrometer data and determine statistics.pdf |
Data files:
| File name | Comment |
|---|---|
| 10000fL_15ms.xlsx | |
| 1000fL_15ms.xlsx | |
| 100fL_15ms.xlsx | |
| 5fL_15ms.xlsx | |
| Dark15ms.xlsx |
Matlab code:
| File name | Comment |
|---|---|
| ReadSpectrometerDataMeanStd.m | note: the pathnames need adjusting to your local machine! |
Presentations:
| File name | Comment |
|---|---|
| Intro to Monte Carlo and Application to RAD CAL.pdf | |
| Radiometric Calibration Coefficient Determination.pdf |
Data files: (see also last slide of presentation):
| File name | Comment |
|---|---|
| L_Sphere.mat | Input file for the code: radiance levels of the integrating sphere |
| STD_DN.mat | Input file for the code: noise of the DN measurements, given as standard deviation |
| DN_L_CAL.mat | Input file for the code: DN levels of the instrument as exposed to integrating sphere at different light levels |
| uL.mat | Input file for the code: uncertainty of the radiance calibration of the sphere (given at confidence interval of k=2) |
| u_rad_coeffs.mat | Output of Monte Carlo run: uncertainties of gain, offset and uncertainty due to gain and offset correlation. This file is eventually generated by the code itself. |
Matlab code:
| File name | Comment |
|---|---|
| MC_Introduction.m | Code to produce plots shown in the intro to Monte Carlo presentation |
| RAD_CAL_with_Linear_Fit_and_uncertainty_estimation_with_Monte_Carlo.m | Main script. Note: set the run_sim = true on line 408 to run MC (This can take very long! You may want initially to choose a lower number of realisations by e.g. setting N = 10 on line 282). Set to false once you have them calculated. |
| print_jpeg.m print_pdf.m | Functions to export figure to JPEG or PDF |
| progressbar.m, gui_active.m | Functions for progress bar used to show progress during monte carlo run |
| get_realisations_gauss_dist.m | Function to create realisations |
Presentations:
| File name | Comment |
|---|---|
| RTM_and_uncertainty propagation_Session_1_RTM.pdf | |
| RTM_and_uncertainty propagation_Session_2_RTM.pdf | |
| RTM_and_uncertainty propagation_Ex_1.pdf | |
| RTM_and_uncertainty propagation_Ex_2.pdf |
Data files:
| File name | Comment |
|---|---|
| Ex1_TableLeafParam.csv | |
| Ex2_BOAirradiance.csv | |
| Ex2_TableLAI.csv | |
| Ex2_TableLeafParam.csv | |
| Soil.csv |
Matlab code:
| File name | Comment |
|---|---|
| RTM_and_uncertainty propagation_Ex_1_solved.m | note: you need to download PROSPECT-D model separately |
| normrnd_truncated.m | |
| RTM_and_uncertainty propagation_Ex_2_solved.m | note: you need to download PROSPECT-D model separately. |