Examples and demo

Demo for impatients

PAINTER.jl contains a demo file painterdemo.jl with an OIFITS folder in the default installation folder. To run the demo type:

using PAINTER
painterdemo()

painterdemo() run a simation with data generated with ASPRO with AMBER configuration and a gray object. The demo includes warm start, save and load of structures, a custom plot function (require PyPLot), ...

painterdemo("gravity") run simulation with data from the beauty contest 2016 (http://www.opticalinterferometry.com/beauty2016). Data will be downloaded in current folder and contains gravity simulation. In this case PAINTER uses the phases of the complexe visibities and the closure phases for the phases estimation. The demo includes save and load of structures, a custom plot function (require PyPLot), ...

painterdemo("bc04") run simulation with data from the beauty contest 2004. Data are monochromatic. The demo includes save and load of structures, a custom plot function (require PyPLot), ...

User parameters and single execution for painterdemo()

• The folowing parameters are set by the user:

  path        = '../OifitsFolder'
  FOV         = 0.01
  indwvl      = 1:30
  nx          = 64
  eps1        = 1e-4
  eps2        = 1e-4
  rho_y       = 10
  alpha       = 1e4
  beta        = 1e5
  rho_spat    = 4
  rho_ps      = rho_spat
  lambda_spat = 1e-5
  rho_spec    = 1/2
  lambda_spec = 1e-5
  dptype      = "sliding" # type of differential phases
  aff         = true      # plot is enabled
  nbitermax   = 100
  

  PAINTER.jl will extract OIFITS informations from all files in the folder ../OifitsFolder and will restrict the analysis to the first 29 wavelengths.

• The initial estimate is the default. ADMM is enabled by default and will run the algorithm for 100 iterations.

• The support constraint is defined by a disk:

  mask3D = mask(nx, int(nx/2 -3), choice="disk")
  

• Other parameters take the default values.

PAINTER.jl is then executed:

OIDATA, PDATA = painter(Folder=Folder, nbitermax=nbitermax, nx=nx, lambda_spat=lambda_spat=lambda_spat, lambda_spec=lambda_spec, rho_y= rho_y, rho_spat= rho_spat, rho_spec= rho_spec, rho_ps= rho_ps, alpha= alpha, beta=beta, eps1=eps1, eps2=eps2, FOV= FOV, indwvl=indwvl)

Algorithm warm start

PDATA contains all variables and array modified during iterations, including the Lagrange multipliers. This allows a warm start of the ADMM algorithm. This is useful for example when the iterations have been stopped by nbitermax but the algorithm has not yet converged.

In this example the user wants 1000 additional iterations with disabled plots:

nbitermax += 1000
aff = false
OIDATA, PDATA_new = painter(OIDATA,PDATA, nbitermax, aff, PlotFct = Plotfunction)

PDATA_new is used to store the new auxiliary variables.

Outer iterations mode

It is possible to save the estimates (or other variables) at each iteration using single iterations in a loop:

for n = 1:10
  nbitermax += 1
  OIDATA, PDATA = painter(OIDATA, PDATA, nbitermax, aff)
  saveX[n] = PDATA.x
  saveW[n] = PDATA.w
end

Note that this is a very time consuming process.

User defined plot function

It is possible to plot or to print some informations on available data during iterations. If PyPlot.jl is installed, painter will execute each CountPlot iterations the function defined by the variable PlotFct. This user defined function must respect the input arguments of painterplotfct:

Plotfunction(PDATA::PAINTER_Data, OIDATA::PAINTER_Input)

For example, to plot at each iteration the sum over all wavelengths of an estimated polychromatic object, projected on a support constraint:

using PyPlot

function Plotfunction(PDATA::PAINTER_Data,OIDATA::PAINTER_Input)
        x = PDATA.x
        s = (PDATA.w.>0.0)
        im2show = squeeze(sum(x.*s,3),3)
        imshow(im2show)
end

OIDATA,PDATA = painter(..., PlotFct = Plotfunction)