pipe.add_transform(r) # Our real solution should also be changed, so we need to use same trick again. mod.solution = real_solution pipe.forward() real_solution = mod.solution mod.plot2d() mod.solution = None # The rescaling is successful. # If you want to switch to previous 20x30 cells case just use pipe.backward(). Note that you will lost # Now let's find the solution. In order to do so we need to create solver. solver = Solver() # We can easily track different statistics. Let's track residual norm and Chi^2 statistics. # When the calculations are over you can plot the results or find statistical value at each step # in "data" attribute of each object, e.g. in solver.statistics[0].data solver.statistics = [statistics.RN(), statistics.RMS()] # RMS need to know real solution in order to perform calculations. solver.real_solution = real_solution # Finally, let's choose method, we would like to use in order to find the solution. # We start with maximum likelihood method. solver.iterator = ml.ML() # Let's do 50 steps and see resulted image and statistics. steps = 50 solver.solve(mod, steps=steps) mod.plot2d() solver.plot_statistics() # Now let's change to algebraic reconstruction technique. solver.iterator = algebraic.ART() # We can also add some constraints. This is important in the case of limited date reconstruction.
# Next step is to simulate each detector signal. det_signal = signal.get_signal(real_solution, det) mod.detector_signal = det_signal # Now let's create solver which will use ART with step = 0.1 in order to find the solution. solver = solver.Solver() solver.iterator = algebraic.ART() solver.iterator.alpha = 0.1 # When you are using ART, often a good idea is to suggest that all values are positive. c1 = tomomak.constraints.basic.Positive() solver.constraints = [c1] # Finally we want some quantitative parameters in order to evaluate reconstruction results. # Let's count residual mean square and residual norm. solver.statistics = [statistics.RMS(), statistics.RN()] # In order to get RMS real solution should be defined. solver.real_solution = real_solution # Ok, it's time to solve. Let's do 1000 iterations and see the results. steps = 1000 solver.solve(mod, steps) mod.plot2d() # There are some artifacts but generally picture looks pretty good. # Now let's plot the statistics. solver.plot_statistics() # You can see that both RMS and RN quickly fall during several first steps # and than begin to slowly reach zero. !!!reach # You can check yourself that after more iterations # for the naked eye calculated solution will look exactly like the real one. Just uncomment two following lines. # solver.solve(mod, 30000)